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Recruitment and Selection Process Using Artificial ...
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Recruitment and Selection Process Using Artificial Intelligence: How Do Candidates React?
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https://www.mdpi.com
|
[
"Ligeiro",
"Dias",
"Moreira",
"Nuno Ligeiro",
"Ivo Dias",
"Ana Moreira"
] |
by N Ligeiro · 2024 · Cited by 5 — This study aimed to study the association between organizational attractiveness, intrinsic motivation, perceived novelty, trust in the process, and the ...
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This study also aimed to determine whether, due to the use of artificial intelligence in a recruitment and selection process, attractiveness, associated intrinsic rewards, perceived novelty, and trust positively influence a candidate’s intention to get involved and complete the process.
Thus, the main objective of this study was to find out what the jobseeker’s perceptions are of a process of this type and what influence this has on the process itself, an idea that materialized in our starting question:
From here, we embarked on a novel and significant piece of research, in which we realized that, although this is a topic that has been in vogue and widely discussed in recent years, there are few studies that focus on how candidates perceive this type of process and how it relates to effective attraction, involvement, and completion in R&S processes with the help of AI technology.
In all the studies on this subject, there is always one element that is at the centre of the process, and without it, it would make no sense: the candidate.
These changes bring with them the need to understand candidates’ perceptions when placed in a scenario where all or part of a decision that influences their future in an organization is now made by technologies based on or supported by AI ( Al-Alawi et al. 2021 ). Some studies have identified the use of AI in recruitment and selection (R&S) processes as an asset ( Pan et al. 2022 ).
How technology, with artificial intelligence (AI), has swept through companies and HR departments has triggered a significant change in the processes and decision-making that directly affect people and, in the specific case of this dissertation, job applicants.
We live in one of the most technologically relevant eras of modern times. Human Resources (HR) and its management must be open to the constant developments and changes occurring in Portugal and worldwide.
However, this literature review does not necessarily confirm that candidates will trust an R&S process because it contains AI technologies, but rather the likelihood that this increase in trust will lead to greater involvement and therefore a higher rate of completion of the R&S process. So, we arrive at our fourth hypothesis:
Because the name of this technology (AI) contains the word “intelligence,” which tends to be seen as an exclusively human characteristic, it seems somewhat reasonable to expect that in an R&S process, there will be people who can trust this technology associated with the process and others who are reluctant to use it.
In line with this reasoning, Xu et al. 2020 ) carried out a study in the banking sector to see whether customers preferred to be served by a human operator or by an autonomous AI system and concluded that, for what were considered more basic tasks, customers preferred to use AI because it is faster, more dynamic, and offers a wider range of solutions to solve the problem. On the other hand, when faced with tasks that were becoming more complex and individualized, customers preferred solving their problem through a human.
In the opposite direction, Logg et al. 2018 ), confronted with all the literature against and in favor of logical algorithms, which give rise to AI “thinking”, carried out a study in which they put groups of participants in a position to choose between a human and an algorithm when advising on a highly important decision. Surprisingly, they found results that indicated there was no aversion on the part of people to “machine” decisions, just as this study indicates that, over time, people tend to follow the algorithm’s recommendation, even when faced with alternative advice from highly credentialled human experts.
Another relevant study was carried out by Prahl and van Swol 2017 ) on recent university graduates in the USA who, when faced with a surgical situation and having to decide the point of view of operating theatre management, did not show a preferential choice for counselling between a human expert and an AI. This opinion was then tested in another variable, where participants were told that the AI had already made a mistake in the past in an identical situation. The research postulated that, with this small change, the participants radically changed their opinion, totally in favor of the human side.
This study aims to directly analyze candidates’ trust in an R&S process in which AI is used. However, it has the challenge of entering a relatively new reality where data on whether candidates trust this type of process are limited and often held by the companies that build the AI tools ( Avelar et al. 2021 Nawaz 2020 ).
Michael Gerlich is one of the authors who has studied trust in artificial intelligence. In one of his studies, he investigated trust in artificial intelligence compared with human beings ( Gerlich 2024 ), concluding that there is a preference for artificial intelligence, mainly due to its impartiality and accuracy in contrast to low human reliability. In another study, whose participants were academics from the United States, Gerlich 2023 ) concluded that trust, breadth of application, and perceived vulnerability influence public opinion, which can regard artificial intelligence as a blessing or a disgrace for humanity.
As can be seen in the studies of some authors who have been cross-referencing the themes that govern this study ( Chapman and Webster 2003 van Esch et al. 2021 ), trust as a reaction factor is not emphasized in most of the research in the area of R&S using technology, although in other areas it is considered preponderant.
For Pringle et al. 2016 ), the use of AI in its more general scope, in the face of a society thirsty for novelty, brings with it an incalculable number of consequences. Some will jeopardize the process, others will improve it considerably, because innovation, and this novelty in particular, can make some people trust the process and others anxious ( Nikolaou et al. 2019 ).
So far, all the hypotheses have centered on the importance of novelty as a positive influence on the constructs that lead candidates to engage in and complete an R&S process.
Several studies in recent years have attempted to associate the factor of technological novelty and relate it directly to the use of technology. According to van Esch and Black 2019 ), the more the user of a given technology anticipates using a new one, the more likely they are to be interested and involved. Other studies corroborated this theory ( Wells et al. 2010 ), which analyzed the risk of users using or not using something new versus an old method. The following hypothesis is therefore formulated:
Some other facts discovered during the research have to do with whether novelty is a source of direct attractiveness. A classic theory ( Deci and Ryan 1985 ) classified novelty as a particular type of intrinsic motivation, although more recent studies ( Jeno et al. 2019 van Esch and Black 2019 ) have informed us that engaging in activities that are novel to individuals can generate feelings of enthusiasm, creativity, and innovation. These studies have proven that an activity perceived as novel can be a powerful source of attraction and motivation.
This feeling of being able to be in an innovative and creative environment will increase intrinsic motivation, so an R&S process that uses AI technology should be perceived as innovative, rewarding, and likely to make candidates engage and complete the process. So, we arrive at our second hypothesis:
For Venkatesh 2000 ), the anticipation of using a particular technology, considered new to users, was intrinsically motivating, so individuals’ perceptions of that technology increased the likelihood of using it. From the perspective of Martín-Núñez et al. 2023 ), the perceived learning of artificial intelligence is related to intrinsic motivation. Another study by Fidan and Gencel 2022 ) concluded that students who can interact with chatbots based on artificial intelligence have higher levels of intrinsic motivation than those who do not.
Intrinsic motivation can be defined as the impulse that drives us to perform an activity ( Ryan and Deci 2000 ), in which it can be considered that the person has so much fun that the activity (work) in itself gives rise to a feeling of personal fulfilment and empowerment, increasing levels of trust in the organization, combined with a feeling of greater independence that promotes creativity, regardless of whether or not the results are expected ( Black and van Esch 2019 2021 ).
In the specific case of AI, its widespread use in organizational platforms, assessment centers, and companies specializing in R&S is still relatively recent ( Delecraz et al. 2022 van Esch et al. 2021 ) and, as we are dealing with sophisticated, innovative, and disruptive constructs, it is reasonable to expect that candidates, when they recognize its use, will subsequently have the same perception as the organizations involved.
Recent studies, such as van Esch and Black 2019 )’s, tell us that the factors that influence the success of an R&S process, especially with new generations, are directly related to the engagement that is created and the use of technology that is seen as innovative ( Ehrhart and Ziegert 2005 ).
Some articles ( Langer et al. 2017 Meijerink and Keegan 2016 ) show that there is a relationship between the use of technology in the broad sense and an organization being perceived as more attractive by candidates. Pramod and Bharathi 2016 ) already studied the relationship between organizations that used social media to promote themselves and postulated that this made them more attractive to potential employee candidates.
Therefore, when an organization uses AI in its R&S processes, this can give the candidate the perception that they are dealing with an innovative organization ( Nikolaou et al. 2019 ) that is open to new realities.
Thus, candidates perceive the use or not of innovative technologies, such as AI and its direct or indirect applications ( van Esch et al. 2019 ), to build an image of what will perhaps be the organizational nature of the company where they intend to make their contribution.
Some of the studies carried out on this subject ( Black and van Esch 2019 van Esch et al. 2021 ) corroborate the idea that candidates analyze and perceive not only the recruitment processes themselves, independently of the organizations, but also draw from them an objective perception of what the organization is versus what it demonstrates in terms of applicability.
Therefore, in this crucial research, we embarked on a journey to analyze, by means of a quantitative methodology, based on broad theoretical foundations, the possibility of candidates being attracted to processes, registering, engaging, and completing them. We aim to pinpoint factors that would be perceived by candidates and trigger reactions, which will lead us to draw conclusions that are materialized in the following points. Even if the R&S process with the help of AI is more efficient and effective than its predecessor, nothing guarantees that its real value is not limited by those perceptions that triggered reaction x or y to the process ( van Esch and Mente 2018 ).
From another perspective, Zha and Wu 2014 ) concluded that in digital marketing, adverts and other forms of advertising that appear to us automatically daily when we use our smartphones, and which are controlled by AI-enabled tools, are widely considered intrusive and ignored by consumers. Hence, the brands with which they are associated tend to be less considered ( Miles and McCamey 2018 ).
Although there has been little research on the subject, specifically on the relationship between the process and the perceptions that give rise to candidates’ reactions to this type of process, the evidence that people are increasingly using technological means is strong. Souza and Cunha 2019 ) carried out a systematic literature review in which they postulated that the use of social networks could be very close to being considered a new addiction, with the main influence on young teenagers. In a few years, this generation will represent the greatest mass of workers at the start of their careers and will be confronted almost exclusively with R&S processes of the type we have mentioned.
Screening and testing processes for potential candidates are now carried out online, and the option of interviewing/hiring employees is taken autonomously. If they are not the first choice, these autonomous platforms place the aligned profiles in massive databases, in a queuing logic, where, in the event of a need, all that is required is to ask the platform for a replacement, which quickly acts as a second line option for the market ( Garg et al. 2021 ).
In a study by Lee 2011 ), he created one of the first models to explain the benefits of integrating electronic recruitment. The researcher explained that, due to economic, social, technological, and cultural changes, modern organizations should strategically focus on managing technological recruitment, either because of the brutal reduction in process time or because of its return on investment (ROI). A six to one differential (ROI) had previously been pointed out in a study by Buckley et al. 2004 ). This research indicated that for every USD 1 spent on recruitment technology, we would get a return of USD 6.
Some researchers ( Chernov and Chernova 2019 Jain et al. 2021 ) have discussed the idea that in a globalized world where change is constant, laws, organizations, technology, social trends, and diversity act as challenges that can alter candidate perceptions, forcing R&S to change.
Traditional methods of searching for candidates, such as mass media adverts and print advertising, are in apparent decline ( Garg et al. 2021 ), and online recruitment sources, known as e-recruiting, have taken center stage.
Various authors have studied the acceptance of artificial intelligence, including Gerlich 2024 ). This author concluded that the preference for using artificial intelligence is mainly due to its reliability compared with human reliability.
This evolutionary phenomenon has been driven by the rapid evolution of AI for social, strategic, and financial reasons ( Jain et al. 2021 ). From a broader point of view, this evolution is part of what we might call the fourth industrial revolution, with new concepts such as big data, robotics, the Internet of Things, algorithms, and digital platforms ( Correio et al. 2021 ).
Years later, Black and van Esch 2021 ) confirmed that this trend had materialized, and that AI applied to R&S intensifies the competitiveness of companies, bringing talent to them quickly and, above all, reaching passive candidates, those who are not actively looking for work but are available to be recruited if they can be reached.
First, Alan Turing published an article entitled Computing Machinery and Intelligence ( Turing 1950 ). After that, research into this subject never stopped, although the initial ecstasy was halted by the overly optimistic prospects of the time, giving rise to what experts call the AI winter ( Smith et al. 2006 ) until the mid-1970s. That winter was to end in the 1990s when data storage and processing capacity grew exponentially, so autonomous machines began to carry out tasks that had previously been considered quite complex ( OECD 2019 ).
According to the Organization for Economic Co-operation and Development ( OECD 2019 ), there is a broad consensus that the 1956 Dartmouth Summer Research Project may have been the birthplace of AI, as at this event, John McCarthy, Alan Newell, Arthur Samuel, Herbert Simon, and Marvin Minsky conceptualized the principles of AI.
As no one individual can exclusively be identified as the inventor of AI, we must highlight those who are the concept’s forerunners and who have brought us to the state of the art where the term is today.
The association between the variables under study was analyzed using Pearson’s correlations. To test the effect of sociodemographic variables on the variables under study, parametric t -student tests for independent samples and One-Way ANOVA were used. Path analysis was used to test the hypotheses formulated in this study, carrying out multi-group analyses according to age group.
The items’ sensitivity was tested by calculating the median, minimum, maximum, asymmetry, and kurtosis. The items’ medians should not touch any of the extremes; they should have responses at all points, and the absolute values of skewness and kurtosis should be below 3 and 7, respectively ( Kline 1998 ).
Next, internal consistency was tested by calculating Cronbach’s Alpha, the value of which should vary between “0” and “1” and should not be negative ( Hill and Hill 2002 ). In organizational studies, Cronbach’s Alpha greater than 0.70 was considered an appropriate value ( Bryman and Cramer 2003 ).
Once the data had been collected on Google Forms, it was imported into SPSS Statistics software 29 (SPSS; IBM Corp 2021). The first step was to test the metric qualities of the instruments used in this study. To validate the scale, exploratory factor analysis was first carried out, the aim of which is to discover and analyze the structure of a set of interrelated variables to construct a measurement scale for (intrinsic) factors that, in some way, control the original variables ( Marôco 2021 ). The KMO (Keiser-Meyer-Olkin) value should be greater than 0.70 and the Bartlett’s test should be significant, thus indicating that the data come from a normal multivariate population ( Pestana and Gageiro 2003 ). Two confirmatory factor analyses were then carried out using AMOS Graphics software 29 (SPSS; IBM Corp 2021), with one and five factors, to confirm the factor structure of this instrument ( Arbuckle 2008 ). The maximum likelihood estimation method and the covariance matrix were used ( Russell 2002 ). The recommendations set out by Hu and Bentler 1999 ) were followed, combining the six fit indices: chi-square ratio/degrees of freedom (χ/gl); Tucker–Lewis Index (TLI); Goodness-of-fit Index (GFI); Comparative, et al. (CFI); Root Mean Square Error of Approximation (RMSEA); and Root Mean Square Residual (RMSR). For chi-square, a value ≤ 5 was considered acceptable. For CFI, TLI, and GFI, values above 0.90 were considered acceptable. For RMSEA, values below 0.08 were acceptable ( Wright and Bonett 2002 ). Finally, concerning RMSR, the lower the value, the better the fit ( Hu and Bentler 1999 ). To establish good construct validity, good convergent validity, and good discriminant validity and to check the risks associated with common variance methods, it was essential to obtain a good fit for the measurement model ( Podsakoff et al. 2003 ).
As for the sensitivity of the items, all of them had responses at all points, none of them had the median leaning against one of the extremes, and their absolute values of asymmetry and kurtosis were below 3 and 7, respectively, which indicates that they did not grossly violate normality ( Kline 1998 ).
A five-factor confirmatory factor analysis was then carried out. The fit indices obtained were adequate (χ 2 /gl = 2.06; GFI = 0.91; CFI = 0.97; TLI = 0.96; RMSEA = 0.059; SRMR = 0.136), confirming the five factors’ existence. Composite reliability varied between 0.88 (innovation) and 0.94 (trust), which indicates that all the dimensions had good composite reliability. Good convergent validity values were also obtained for all the dimensions, with AVE values ranging from 0.64 (innovation) to 0.89 (trust). The AVE square root values were higher than the correlation values between the respective factors, which indicates the existence of discriminant validity. As for internal consistency, Cronbach’s alpha was 0.90 for the application process using AI, 0.92 for organizational attractiveness, 0.93 for intrinsic motivation to apply, 0.89 for innovation, and 0.94 for trust in the process.
An exploratory factor analysis was initially carried out to test validity. A KMO of 0.91 was obtained, and Bartlett’s test proved to be significant (< 0.001), which indicates that the data come from a multivariate population ( Pestana and Gageiro 2003 ). This instrument comprises five dimensions that explain 79.78 per cent of its total variability.
To measure the variables under study, we used the instrument developed by van Esch et al. 2021 ), adapted for this study since the “anxiety” dimension was not used. This instrument is composed of six subscales: application process, organizational attractiveness, intrinsic motivation, innovation, confidence in the process, and anxiety. The anxiety subscale was not used in this study, as mentioned above. All the items in this instrument were classified on a Likert scale (from 1 “Strongly Disagree” to 7 “Strongly Agree”). The five subscales are made up as follows: the application process is made up of 5 items (e.g., “how likely am I to contact a company to find out more about a job vacancy if I know that artificial intelligence is used in the recruitment and selection process?”); organizational attractiveness is made up of 3 items (e.g., “I feel inspired by organizations that use new technologies such as artificial intelligence”); intrinsic motivation is made up of 5 items (e.g., “applying for a job using artificial intelligence would give me valuable feelings of personal fulfilment”); innovation is made up of 4 items (e.g., “using artificial intelligence platforms to apply for jobs offers me new experiences”); and trust in the process is made up of two items (e.g., “using artificial intelligence to apply for a job seems safe to me”).
The sample for this study consisted of 299 participants aged between 19 and 70 (M = 44.44; SD = 11.92). The age of participants was transformed into age groups, and four groups were formed: up to 35 years old, 35 to 45 years old, 45 to 54 years old, and over 54 years old. Of these participants, 171 (57.2%) were female and 128 (42.8%) males. In terms of educational qualifications, 55 (18.4%) had a 12th-grade degree or less, 143 (47.8%) had a bachelor’s degree, and 101 (33.8%) had a master’s degree or higher. When asked if they had ever been in a recruitment and selection process in which AI-containing technologies were used, 53 (17.7%) said they did not know, 218 (72.9%) said no, and 28 (9.4%) said yes.
The questionnaire was posted on the Google Forms platform and circulated on social and professional networks, Facebook, LinkedIn, WhatsApp, and email. At the beginning of the questionnaire, all the information about the purpose of the study was given and it was stated that the confidentiality of the answers would be guaranteed. Participants were also informed that their data would never be known, as it would only be processed as a whole. After reading the informed consent form, the participants had to answer a question about their willingness to participate in the study. If they agreed to participate in the study, they were directed to the questionnaire in the next section. If they did not agree to participate, they were directed to the end of the form. In addition to the scale used in this study, the questionnaire consisted of three sociodemographic questions (age, gender, and academic qualifications) and a question asking whether the individuals had a clear idea of having gone through a similar process in their personal experience. The data were collected between January and March 2023.
This study was aimed at Portuguese citizens of working age. The participants were selected through a non-probabilistic process of convenience, which, according to Marôco and Bispo 2003 ), means that some of the participants were selected purely accidentally, thus facilitating data collection.
Finally, about Hypothesis 4 (“In a recruitment and selection process using artificial intelligence, trust in the process positively and significantly affects the intention to apply and complete the process.”), the results show that, for participants aged between 35 and 44, trust in the process has a positive and significant effect on the intention to apply to, engage in, and reach the end of the R&S process with AI-enabled technology (β = 0.443,= 0.015) ( Table 3 ). The model explains 13.8 per cent of the variability in intention to apply to, engage in, and reach the end of the R&S process with AI-enabled technology.
As for Hypothesis 3 (“In a recruitment and selection process using artificial intelligence, the perception of novelty positively and significantly affects the intention to apply and complete the process.”), the results show that, for participants aged between 45 and 54, innovation has a positive and significant effect on the intention to apply to, engage in, and reach the end of the R&S process with AI-enabled technology (β = 0.332,= 0.030) ( Table 3 ). The model explains 41.1% of the variability in intention to apply to, engage in, and reach the end of the R&S process with AI-enabled technology.
In turn, concerning Hypothesis 2 (“In a recruitment and selection process using artificial intelligence, intrinsic motivation has a positive and significant effect on the intention to apply and complete the process.”), the results show that intrinsic motivation does not significantly affect the intention to apply to, engage in, and complete the R&S process with AI-enabled technology ( Table 3 ).
About Hypothesis 1 (“In a recruitment and selection process using artificial intelligence, attractiveness positively and significantly affects the intention to apply and complete the process.”), the results show that, for participants aged between 45 and 54, organizational attractiveness has a positive and significant effect on the intention to apply to, engage in, and reach the end of the R&S process with AI-enabled technology (β = 0.345,= 0.010) ( Table 3 ). The model explains 41.1 per cent of the variability in intention to apply to, engage in, and reach the end of the R&S process with AI-enabled technology.
After analyzing and describing the results above regarding the evaluation of metric qualities, the relationships between variables, and the impact of sociodemographic variables on the variables under study, we then proceeded to study the research hypotheses that had been identified. Path analysis was used to test the hypotheses formulated in this study, carrying out multi-group analyses according to age group.
The higher the perception of organizational attractiveness, intrinsic motivation to apply, innovation, and trust in the process, the greater the intention to apply to, get involved in, and finish the R&S process with AI-enabled technology.
On the other hand, those aged between 45 and 54 have the lowest perception of the R&S process with AI-enabled technology, organizational attractiveness, intrinsic motivation to apply, and trust in the process. The participants with the highest perception of the AI-enabled R&S process are those aged between 35 and 45.
As shown in Figure 5 , the participants’ age has already been transformed into the age ranges explained above. In the over-54 age group, there is a greater perception of organizational attractiveness, intrinsic motivation to apply, innovation, and trust in the process.
Participants with a 12th-grade degree or less have a higher perception of intrinsic motivation to apply and trust in the process. Participants with a bachelor’s degree have a higher perception of innovation but a lower perception of the R&S process with AI-enabled technology.
Regarding academic qualifications, as Figure 4 shows, participants with a master’s degree or higher have a higher perception of the R&S process with AI-enabled technology and organizational attractiveness but a lower perception of intrinsic motivation to apply, innovation, and trust in the process.
Next, we tested the effect of sociodemographic variables on the variables under study, using the parametric Student’s t -test for independent samples and One-Way ANOVA after testing the assumptions of normality and homogeneity of variances.
These results indicate that the participants in this study have a high intention to apply to, get involved in, and reach the end of the R&S process with AI-enabled technology, a high perception of organizational attractiveness, innovation, and trust in the process. An intrinsic motivation to apply does not differ significantly from the centre point of the scale (4).
According to Table 1 , the variables themselves, the intention to apply to, get involved in, and finish the AI-enabled R&S process, organizational attractiveness, perceived innovation, and trust in the process are significantly above the central point (4) of the scale.
To understand the position of the answers given by the participants in this research, descriptive statistics were carried out on the variables under study.
5. Discussion
This study aimed to investigate the association between organizational attractiveness, intrinsic motivation, perceived novelty, trust in the process, and the intention to apply for, participate in, and complete an artificial intelligence recruitment and selection process.
Next, we will discuss the research hypotheses. As expected, Hypothesis 1 was confirmed since, for participants aged between 45 and 54, attractiveness positively and significantly affects the intention to apply for and complete the recruitment and selection process using artificial intelligence. The results indicated that organizational attractiveness is crucial in determining candidates’ intention to engage in the R&S process with AI. The more candidates perceive the organization as attractive due to the adoption of this technology, the greater their intention to apply to, get involved in, and complete the process. These results are in line with the literature, which states that the factors that influence the success of a recruitment and selection process, especially for the younger generations, are directly related to the engagement that is created using technology since this use of technology is seen as innovative ( Ehrhart and Ziegert 2005 van Esch and Black 2019 ).
Secondly, and as expected, Hypothesis 2 did not confirm that intrinsic motivation positively and significantly affects the intention to apply and complete the recruitment process using artificial intelligence. Intrinsic motivation did not prove to have a substantial impact on candidates’ intentions. These results run counter to the literature. According to Venkatesh 2000 ), the anticipation of using a particular technology that users consider new is intrinsically motivating, and the greater the anticipated intrinsic motivation, the more likely participants are to engage in the associated process ( Deci and Ryan 2016 ). This may be because intrinsic motivation is strongly correlated with the other independent variables despite the fact that there were no multicollinearity problems. Thirdly, and as expected, Hypothesis 3 confirmed that, for participants aged between 45 and 54, in a recruitment process using artificial intelligence, innovation has a positive and significant effect on the intention and completion of the process. Innovation also proved to be a crucial factor in candidates’ intentions. This suggests that the perceived novelty associated with AI-enabled technology plays a more prominent role in candidate motivation in contexts where this perception is more pronounced. These results are also in line with what the literature tells us. In the view of van Esch and Black 2019 ), when the user of a particular technology foresees the possibility of using a new technology, they are likely to become interested in it and get involved. In this sense, candidates can perceive the use of AI in the recruitment and selection process as a novelty ( Soares et al. 2020 ).
Finally, and as expected, Hypothesis 4 confirmed that in a recruitment process using artificial intelligence, for participants aged between 35 and 44, trust in the process has a positive and significant effect on the intention and conclusion of the process. Trust in the process positively and significantly impacted candidates’ intentions, suggesting that it plays a vital role in candidates’ decisions. These results are in line with some studies and against other studies carried out previously. In a study carried out in the banking sector by Xu et al. 2020 ), which aimed to study whether customers preferred to be served by a human operator or an autonomous AI system, they concluded that when the tasks were more basic, customers preferred to use AI because it was faster, more dynamic, and offered a wider range of solutions for solving the problem. However, when the task was more complex and individualized, they preferred their problem to be solved by a human being.
It should be noted that contrary to expectations, for younger participants aged up to 35, organizational attractiveness, intrinsic motivation, innovation, and trust in the process did not significantly affect the intention to apply to, engage in, and reach the end of the R&S process with AI-enabled technology.
Despite the many reports in the national and international media on the subject, which indicate the exponential growth of this type of technology, most of the participants in this study say they have never taken part in a process of this type or do not know whether they have.
5.1. Limitations and Future Studies This study has some limitations. The first limitation is the data collection process, which was non-probabilistic, intentional, and of the snowball type. Another limitation is that a closed-ended questionnaire was used, which may have biased the participants’ responses. The fact that it was a cross-sectional study can be considered another limitation since no causal relationships can be established. Finally, another limitation was the low percentage of participants who revealed they had participated in a recruitment and selection process using artificial intelligence. This research opens a path for organizations to develop their perceptions and apply what this theoretical framework indicates in practice. The way forward will be to build real scenarios where organizations can understand candidates’ real perceptions, their reactions based on these perceptions, and, most importantly, the reliability factor that requires experience; only after use will people’s opinions normally reach the next stage. In the future, other academic or organizational researchers can improve the development of the research framework by applying experimental studies, where the idea is to compare groups with actual experience in these processes with others who only know traditional R&S. In the future, with an expanded theoretical framework and after many practical tests, we may be able to arrive at a framework of perceptions that trigger reactions in people when confronted with R&S processes with AI-enabled technology and that are distributed across various cultural and personal factors. Thus, “intelligent” technology may become closer to us and understanding us, with all the good and bad that this has to offer.
| 2024-07-14T00:00:00 |
2024/07/14
|
https://www.mdpi.com/2076-3387/14/7/155
|
[
{
"date": "2023/03/01",
"position": 39,
"query": "artificial intelligence hiring"
}
] |
Is Character.AI hiring? Seriously. I totally want to work there ...
|
The heart of the internet
|
https://www.reddit.com
|
[] |
I wish they had remote jobs. I'm very good at customer service (IE being yelled at by Karens) and this is one of the only companies I feel that might actually ...
|
I seriously have spent so much time on this site because I adore it! With how much the site seems to have exploded, surely they would be willing to hire people? Either way, here I am! HIRE ME, I'M HELPFUL! Who do I contact and make a pitch to ask for a job?!
| 2023-03-01T00:00:00 |
https://www.reddit.com/r/CharacterAI/comments/11slqwa/is_characterai_hiring_seriously_i_totally_want_to/
|
[
{
"date": "2023/03/01",
"position": 50,
"query": "artificial intelligence hiring"
}
] |
|
The Impact of Artificial Intelligence on the Human Resource ...
|
The Impact of Artificial Intelligence on the Human Resource Industry and the Process of Recruitment and Selection
|
https://link.springer.com
|
[
"Aamer",
"Amal Khalifa Al",
"The Gulf Downstream Association",
"Hamdan",
"Ahlia University",
"Abusaq",
"Jeddah College Of Engineering",
"University Of Business",
"Awali",
"Kingdom Of Bahrain"
] |
by AKA Aamer · 2022 · Cited by 7 — Hence, hiring skilled employees who are experienced and efficient in achieving the job objectives is a crucial part of any organization's recruitment strategy.
|
In the contemporary world, artificial intelligence (AI) is an industry that continues to transform human lives and has a profound impact on business in almost all spheres. Organizations are searching for bright, dynamic, and potential employees to remain competitive in this digital age. Managing the digital world and developing the business environment will require organizations to employ a suitable individual with an effective recruitment strategy. Hence, hiring skilled employees who are experienced and efficient in achieving the job objectives is a crucial part of any organization's recruitment strategy. Artificial Intelligence has a key role to play in recruiting, its ultimate goal is to allow computers to carry out the same work as normally performed by humans. Artificial intelligence functions and reacts as if it were a human leading with incredible speed and accuracy. This study aims at analyzing how Artificial Intelligence (AI) impacts the Human Resource Industry. The study sheds light on the way artificial intelligence is used during the recruiting and selection process. The study finds that artificial intelligence technology capabilities can significantly improve the Human Resource and recruitment process with potential benefits such as increased productivity, reduced costs, improved accuracy, reduced workload, and improved candidate experience.
| 2023-07-14T00:00:00 |
2023/07/14
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https://link.springer.com/chapter/10.1007/978-3-031-26953-0_57
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[
{
"date": "2023/03/01",
"position": 51,
"query": "artificial intelligence hiring"
}
] |
How Algorithm-Based Decision Aids Influence Recruiters ...
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Artificial Intelligence (AI) in Employee Selection: How Algorithm-Based Decision Aids Influence Recruiters’ Decision-Making in Resume Screening
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https://search.proquest.com
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[
"Chen"
] |
by D Chen · 2022 · Cited by 7 — Data Engineer) and algorithm-based selection information. Results showed that younger managers, managers with AI use experience and more recent hiring ...
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Access to the complete full text
This is a short preview of the document. Your library or institution may give you access to the complete full text for this document in ProQuest.
Alternatively, you can purchase a copy of the complete full text for this document directly from ProQuest using the option below:
| 2023-03-01T00:00:00 |
https://search.proquest.com/openview/f5389772b984132419b4168d49f012bf/1?pq-origsite=gscholar&cbl=18750&diss=y
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[
{
"date": "2023/03/01",
"position": 66,
"query": "artificial intelligence hiring"
}
] |
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the impact of Artificial Intelligence on the hiring process
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Towards a digital enterprise: the impact of Artificial Intelligence on the hiring process
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https://journal.lu.lv
|
[
"Karim Amzile",
"Mohamed Beraich",
"Imane Amouri",
"Cheklekbire"
] |
by K AMZILE · 2022 · Cited by 10 — However, our results show that Artificial Intelligence techniques can provide a better decision support tool for recruiters while minimising the cost and time ...
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Abstract
In this paper, we proposed a decision support tool for recruiters to improve their hiring decisions of suitable candidates for such a vacancy post. For this purpose, we proposed the use of the Artificial Neural Network (ANN) method from Artificial Intelligence (AI), thus we used real data from a semi-public recruitment agency in Morocco. However, for the adopted methodology, we used the process opted by the methods and techniques related to Data Mining.
As a result, after completing the modelling process, we were able to obtain a model capable of predicting the decision to accept or reject such a candidate for such a vacancy. However, we obtained a model with an accuracy of 99% as well as with a very low error rate.
However, our results show that Artificial Intelligence techniques can provide a better decision support tool for recruiters while minimising the cost and time of processing applications and maximising the accuracy of the decisions made.
| 2023-03-01T00:00:00 |
https://journal.lu.lv/JISIB/article/view/2350
|
[
{
"date": "2023/03/01",
"position": 84,
"query": "artificial intelligence hiring"
}
] |
|
A Brief History of AI in Education - Dr Philippa Hardman
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A Brief History of AI in Education
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https://drphilippahardman.substack.com
|
[
"Dr Philippa Hardman"
] |
Artificial Intelligence in Education (AIED) has been around for over 60 years. We've been building AI-powered education technologies to design and deliver ...
|
Amid all of the recent excitement / confusion / terror around the use of ChatGPT in education, we could be forgiven for thinking that the use of AI in education is uncharted territory.
In fact, Artificial Intelligence in Education (AIED) has been around for over 60 years.
We’ve been building AI-powered education technologies to design and deliver learning experiences for the same amount of time as we have explored space and researched nuclear physics and DNA.
PLATO: Ed-tech with Ai Capability, launched in 1963
In this week’s newsletter, I’m going to give you a whistle-stop tour of the history of AIED and make some predictions about what the role of the educator will look like in a post-AI world.
Let’s go! 🚀
What is AI?
First things first, let’s get back to basics. Artificial intelligence (AI) is a branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.
Think of AI as a process where humans teach computers how to do things which normally require human intelligence - things like recognising images, understanding speech, responding to instructions or making decisions. They do this by training machines to identify and reproduce structural patterns.
So, for example, back in the ‘60s computer scientists taught a computer to be able to predict if an image contained a cat or a dog by feeding the machine images of cats and dogs and training the machine to understand the differential characteristics of each “item”.
Machine Learning in Action
AI in Education
The same Machine Learning technology and expertise that enabled computer scientists to train computers to understand the differential characteristics of cats and dogs has been used - and continues to be used - to design and deliver education for over 60 years.
Here are three of the most compelling use cases of AIED and some predictions on where they will go next.
1.Automated Essay Grading
Machine learning algorithms can be used to automatically score essays and written assignments, based on factors such as grammar, organisation, and coherence. This can save teachers time and provide students with more immediate feedback.
Tools like Gradescope and E-rater use Machine Learning algorithms to analyze essays and provide scores based on various characteristics such as use of vocabulary, referencing standards and techniques, sentence structure, overall organization etc.
Gradescope by Turnitin
Phil’s Prediction: These tools are becoming increasingly powerful and will quickly become capable of providing more nuanced feedback on more nuanced measures like levels of critical thinking, analysis skills and originality of thought. There is perhaps a not too distant future where a large part of the educator’s role is focused less on the admin of grading and more on the art and science of teaching, learning and research.
2.Personalised Learning Paths
One of the most well known and robust examples in this area is Carnegie Mellon University’s tool, OpenSimon.
OpenSimon
OpenSimon analyses student data and provides real-time personalised feedback based both student performance (e.g. how well they perform on a knowledge check) and student behaviour (e.g. how long they spend on certain activities).
This data is then used to train an algorithm, which learns to predict which learning activities or interventions are most effective for each individual student.
Phil’s Prediction: Again, these tools are becoming increasingly powerful and will quickly become capable not just of predicting outcomes and recommending interventions but generating those interventions (for example, individualised content and activities) on the fly, in response to data. There is perhaps a not too distant future where a large part of the educator’s role is dedicated to a combination of data analysis and student coaching.
3.Intelligent Tutoring Systems
Finally one of the most interesting (but also for any educators like me perhaps scary!) examples of AIED tools are so-called “intelligent tutoring systems” or, if you like, teacher bots.
Like personalised learning pathways, intelligent Tutoring Systems learn atterns and adapt to the learning needs of individual students based on their performance and behaviour.
Unlike personalised learning pathways, intelligent Tutoring Systems are delivered through more “humanised”, dialogue-based interface and can provide personalised feedback and support that is both academic and social-emotional.
The most famous and successful examples of these sorts of technologies is Jill Watson - an intelligent tutor developed by Georgia Tech University that uses Machine Learning to adapt to the learning needs of individual students.
When a student interacts with Jill Watson, the system collects data on their performance, such as how well they do on quizzes or how quickly they complete assignments. This data is then used to train a Reinforcement Learning algorithm, which learns to predict which learning tasks or interventions are most effective for each individual student.
One key benefit of intelligent tutoring systems like Jill Watson is that they can provide personalised support and feedback to each individual student, even in large classes where it may be difficult for the instructor to provide individual attention to each student.
An additional benefit is that intelligent tutoring systems can be trained to provide both academic and pastoral support. Computer Scientists at Georgia Tech have created two versions of Jill: one which is expert in domain expertise and academic support, and a “social agent” which is more expert in providing more social emotional support and building community by connecting students to one another.
The Jill Watson Intelligent Tutoring System @ Georgia Tech Uni
Phil’s Prediction: Georgia Tech have recently created “Agent Smith” which will enable any academic to create their own version of Jill based on their own source materials. There is perhaps a not too distant future where a large part of the educator’s role is to understand and be able to build and validate machine learning algorithms to optimise their teaching impact at scale.
That’s all folks! If you liked this, please share it with your network and let me know by liking the post and tagging me on LinkedIn.
Comments are open for questions, criticisms, challenges etc - it’s always great to hear from you.
Phil 👋
Share
| 2023-03-01T00:00:00 |
https://drphilippahardman.substack.com/p/a-brief-history-of-ai-in-education
|
[
{
"date": "2023/03/01",
"position": 7,
"query": "artificial intelligence education"
}
] |
|
Influence of artificial intelligence in education on ...
|
Influence of artificial intelligence in education on adolescents’ social adaptability: The mediatory role of social support
|
https://journals.plos.org
|
[
"Tinghong Lai",
"Center For Brain",
"Cognitive Science",
"School Of Education",
"Guangzhou University",
"Guangzhou",
"P.R. China",
"Gannan Medical University",
"Chuyin Xie",
"Minhua Ruan"
] |
by T Lai · 2023 · Cited by 60 — Results showed that AIEd has a negative impact on adolescents' social adaptability, and is significantly negatively correlated with social adaptability and ...
|
Artificial intelligence (AI) is widely used in the field of education at present, but people know little about its possible impacts, especially on the physical and mental development of the educated. It is important to explore the possible impacts of the application of artificial intelligence in education (AIEd) in order to avoid the possible adverse effects. Prior research has focused on theory to the exclusion of the psychological impact of AIEd, and the empirical research was relatively lacking. This study aimed to identify the influence of AIEd on adolescents’ social adaptability via social support. A total of 1332 students were recruited using random sampling from 13 Artificial Intelligence Curriculum Reform Experimental Schools in Guangzhou, Southern China, completed the survey. There were 342 primary school students (Mean age = 10.6), 351 junior high school students (Mean age = 13.1), and 639 senior high school students (Mean age = 15.8). Results showed that AIEd has a negative impact on adolescents’ social adaptability, and is significantly negatively correlated with social adaptability and family support, but there is no significant correlation with school support. AIEd could not only affect social adaptability directly, but also could affected it through the family support.
Copyright: © 2023 Lai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Introduction
The application of artificial intelligence in education (AIEd) is a new trend in educational innovation and development. Particularly after the outbreak of COVID-19 in 2020, large-scale online teaching has become a big test of how artificial intelligence (AI) technology might enable education. Some researchers believe that AIEd brings more opportunities than threats [1, 2]. For example, Intelligent Tutoring System (ITS) has been found to be more effective than traditional teaching tools. An intelligent learning environment created based on a network tutoring system has been found to have a positive impact on the durability of education [3]. However, some researchers believe that there are potential risks of AIEd. For example, using intelligent technology to collect learners’ data may cause safety and ethical problems due to data leakage [4]. Some researchers are even worried that AIEd may deviate from the purpose of education, and become a potential educational risk behavior due to the bias of its designers and executants.
Adolescents are the principal recipients of AIEd, and they are in a critical period in which they are very easily affected by the external environment [5, 6]. However, previous studies have only discussed the influence of AIEd on adolescent at the theoretical level, the lack of research into emotion and influencing factors research has always been a prominent problem in AIEd [7]. So, it is important to pay attention to the impact of AIEd on adolescents’ physical and mental development.
Application of artificial intelligence in education The application of artificial intelligence in education (AIEd) can be understood as integrating artificial intelligence (AI) technology into the scenes of education. At present, a number of key AI technologies, including machine learning, knowledge mapping, and natural language processing are gradually being applied in education. In general, there are five typical ways in which AIEd is applied: an intelligent education environment, intelligent learning process support, intelligent educational evaluation, intelligent teacher assistance, and intelligent educational management and services [8]. In this study, AIEd refers to the universal application of AI technology in education, i.e., the new technologies used to improve teaching methods and enhance learning efficiency, expand teaching time-space environment, and improve teaching management and services. These can also be referred to as VR teaching, online learning, flat panel teaching, etc. Research to date has shown there are three main forms of AIEd being used in the Artificial Intelligence Curriculum Reform Experimental Schools in Guangzhou. One is the form of curriculum teaching, such as information technology courses, general technology courses, flat panel teaching, intelligent reading, etc. The second takes the form of interest classes, such as programming courses, assembling robots, etc. The third involves mass organization, such as 3D printing, Lego plug-ins, teaching boxes, etc.
Application of artificial intelligence in education and social adaptability Interpersonal relationships are important to one’s social adaptability. Specifically, a good interpersonal relationship is conducive to social adaptability; otherwise, it is unfavorable [9]. According to the theory of social presence and the theory of social cue reduction which are based on cue filtering orientation, media communication is more prone than face-to-face communication to weaken the ability and expectation of individual to establish social interaction due to the lack of important nonverbal and situational cues, such as those involving vision, hearing and touch [10]. Although non-face-to-face online social contact produces less social pressure and lower social anxiety than real face-to-face social contact, most young people with social anxiety further escape from real social contact after obtaining social support through online [11], which is disadvantageous to their social adaptability. Studies have shown that the application of artificial intelligence technology is conducive to individual development. For example, children who interact with robots show a high degree of creativity, promoting the development of their social ability [12], and wearable machines can enhance the expression ability of adolescents with autism spectrum disorders [13]. However, some studies have shown that the application of artificial intelligence technology is disadvantageous to individual development. For example, frequent use of intelligent electronic devices has a negative impact on adolescents’ interpersonal relationships [14] and social adaptability [15], and the elderly who are cared for by robot partners feel more lonely and emotionally indifferent [16]. The application of artificial intelligence in education (AIEd) is based on computers and other media technologies, making it inseparable from the use of intelligent devices such as the Internet and electronic equipment. Education is a kind of social activity, and interaction and cooperation are the core of the teaching process. However, AIEd makes machines become the intermediary connecting students and teachers, which changes the interpersonal relationship of teaching from human-human to human-machine-human. The changed space-time relationship of teaching leads to a decrease in real teacher-student interpersonal interaction, and the students’ sense of social presence is weakened. AIEd has great situational difference from conventional teaching and lacks of sufficient nonverbal clues, situational clues and other important information, which is disadvantageous to adolescents’ social adaptability.
| 2023-03-01T00:00:00 |
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283170
|
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{
"date": "2023/03/01",
"position": 11,
"query": "artificial intelligence education"
}
] |
|
Influence of artificial intelligence in education on ...
|
Influence of artificial intelligence in education on adolescents' social adaptability: The mediatory role of social support
|
https://pubmed.ncbi.nlm.nih.gov
|
[
"Lai T Xie C Ruan M Wang Z Lu H Fu S",
"Xie C",
"Ruan M",
"Lin P",
"Wang Z",
"Lai T",
"Xie Y",
"Fu S",
"Lu H.",
"Et Al."
] |
by T Lai · 2023 · Cited by 60 — Results showed that AIEd has a negative impact on adolescents' social adaptability, and is significantly negatively correlated with social adaptability and ...
|
Artificial intelligence (AI) is widely used in the field of education at present, but people know little about its possible impacts, especially on the physical and mental development of the educated. It is important to explore the possible impacts of the application of artificial intelligence in education (AIEd) in order to avoid the possible adverse effects. Prior research has focused on theory to the exclusion of the psychological impact of AIEd, and the empirical research was relatively lacking. This study aimed to identify the influence of AIEd on adolescents' social adaptability via social support. A total of 1332 students were recruited using random sampling from 13 Artificial Intelligence Curriculum Reform Experimental Schools in Guangzhou, Southern China, completed the survey. There were 342 primary school students (Meanage = 10.6), 351 junior high school students (Meanage = 13.1), and 639 senior high school students (Meanage = 15.8). Results showed that AIEd has a negative impact on adolescents' social adaptability, and is significantly negatively correlated with social adaptability and family support, but there is no significant correlation with school support. AIEd could not only affect social adaptability directly, but also could affected it through the family support.
| 2023-03-01T00:00:00 |
https://pubmed.ncbi.nlm.nih.gov/36930593/
|
[
{
"date": "2023/03/01",
"position": 19,
"query": "artificial intelligence education"
}
] |
|
Artificial intelligence: the new frontier of education in ...
|
Artificial intelligence: the new frontier of education in the 21st century
|
https://www.linkedin.com
|
[
"Jobs For The Future",
"Jff",
"Chris Chiancone",
"Edtech Hub",
"Assaad Dib",
"Championing Next-Generation Teaching",
"Learning Methods In The Digitalised Ai-Infused World",
"Dean Of Quality",
"Standards",
"Programme Lead"
] |
In the 21st century, AI is expected to play a vital role in revolutionizing the way we learn, teach, and access educational resources.
|
Artificial intelligence (AI) has already begun to transform various industries, including education. In the 21st century, AI is expected to play a vital role in revolutionizing the way we learn, teach, and access educational resources. With the development of AI-based technologies, the education sector is experiencing a new frontier that has the potential to enhance the learning experience for students and empower educators to provide more personalized and effective teaching.
In this article, you will get a quick idea about Artificial Intelligence in Education Today and how it is being used to improve the teaching and learning process.
Artificial Intelligence
Before delving into the use of Artificial Intelligence in Current Education, it is important to understand what this technology consists of. Artificial intelligence is a set of techniques that allow machines to learn and make decisions autonomously, without human intervention.
That is, it is a branch of computer science that focuses on the creation of systems that mimic human intelligence.
Artificial intelligence has different applications in everyday life, such as recommendation systems in social networks or chatbots in customer service.
In the field of education, Artificial Intelligence in Education is being used to improve the quality of learning.
Artificial Intelligence in Education Today
AI is a broad term that refers to computer systems that can perform tasks that usually require human intelligence, such as recognizing patterns, understanding natural language, and making decisions based on data. In education, AI technologies can be used to develop intelligent tutoring systems, adaptive learning systems, and educational chatbots, among other things. Here are some of the ways AI is transforming education:
Personalization of learning
Students Evaluation
Enhanced Teaching
Improved Accessibility
Identification of learning problems
Personalization of Learning
One of the biggest advantages of artificial intelligence in education is that it can help personalize learning. Each student has a different pace of learning and often schools and teachers do not have the capacity to meet the individual needs of each one of them.
Artificial intelligence can help teachers personalize learning for each student. Adaptive learning systems use Artificial Intelligence in Education to analyze student learning patterns and adapt the content and difficulty of the tasks to the needs of each one of them.
Learning personalization can help students progress at their own pace, which can improve learning effectiveness.
Additionally, personalization of learning can also help teachers identify areas of student difficulty and adapt content and teaching strategies to address those difficulties.
Students Evaluation
Assessment is one of the most important aspects of education. It is the way in which teachers measure the progress of students and determine their level of understanding of a particular topic. However, traditional assessment can be limited and not always accurately reflect students’ knowledge.
Artificial Intelligence in Education can be used to assess students in a more accurate and objective way.
Furthermore, AI in Education can assess students in a more detailed way than traditional exams, which are often based on multiple-choice questions or essays. The AI can use a variety of evaluation methods, such as natural language analysis, speech recognition, analysis of data etc. to assess students’ knowledge and understanding.
This can be especially helpful in online education, where students often don’t have the opportunity to interact with a teacher face-to-face. Automated assessment systems can provide instant feedback and help students identify areas where they need to improve.
Enhanced Teaching
AI can also help educators become more effective in their teaching by providing them with real-time feedback on student performance. AI-based tools can monitor and analyze student interactions with learning materials, identifying areas where students struggle, and provide suggestions on how to improve their teaching.
For example, AI-based chatbots can interact with students, answer their questions, and provide feedback on their performance. This allows teachers to focus on higher-order tasks, such as designing engaging learning experiences and developing innovative teaching strategies.
AI can provide real-time feedback to educators and suggest improvements by analyzing student interactions with learning materials, while AI-based chatbots can answer questions and provide feedback to students, enabling teachers to focus on higher-level tasks.
Improved Accessibility
AI can also improve access to education for learners who face barriers due to disabilities or geographical location. For instance, AI-powered transcription and captioning tools can make learning materials more accessible to students with hearing impairments. Similarly, AI-based translation tools can help students who speak different languages access educational content in their native language.
Additionally, AI-based virtual and augmented reality tools can provide immersive learning experiences that can transport students to various parts of the world or simulate real-world scenarios that are difficult to replicate in the classroom.
AI can increase access to education for learners with disabilities or in remote locations through transcription and captioning tools and translation tools, as well as provide immersive learning experiences using virtual and augmented reality.
Identification of learning problems
Another way that Artificial Intelligence in Education can be used in education is to identify learning problems. Teachers often do not have the ability to monitor the progress of each individual student and may not be aware of the difficulties a student is facing until it is too late.
Artificial intelligence can be used to monitor student progress and detect learning problems at an early stage.
Adaptive learning systems can analyze student learning patterns and provide real-time feedback on their performance.
This can help teachers identify areas in which students are struggling and tailor their teaching to help students overcome difficulties.
In addition, artificial intelligence can also be used to detect learning problems that may not be obvious to teachers.
Data analytics systems can analyze large amounts of information to identify patterns and trends that may be affecting student learning.
For example, they can detect patterns of absenteeism or lack of participation that may indicate motivation problems or personal problems that are affecting learning.
Challenges of Adopting AI in Education
The implementation of artificial intelligence (AI) in education is not without challenges. Here are some of the most significant challenges that educators and institutions face when adopting AI-based systems and tools in the classroom:
Limited access to resources: Implementing AI in education requires significant investments in infrastructure, hardware, software, and skilled personnel, which can be a significant challenge for institutions with limited resources. Data privacy and security concerns: AI relies on large amounts of data to learn and improve, which can raise concerns about data privacy and security. Institutions need to ensure that student data is kept secure and used ethically. Bias and fairness issues: AI algorithms are only as good as the data they are trained on, which can result in biases and fairness issues. Institutions need to ensure that AI-based systems and tools are developed and used ethically and fairly. Resistance to change: The adoption of AI in education requires a significant shift in the traditional teaching and learning practices, which can be met with resistance from educators and students. Lack of awareness and understanding: Many educators and students may not be familiar with AI technology, its benefits, and its limitations, which can hinder its adoption and effectiveness. Maintenance and scalability issues: AI-based systems and tools require regular maintenance and updates, which can be a significant challenge for institutions with limited resources. Additionally, scaling up AI-based systems and tools can be challenging and costly. Ethical considerations: The use of AI in education raises ethical questions around issues such as transparency, accountability, and responsibility. Institutions need to ensure that the ethical implications of AI-based systems and tools are carefully considered and addressed.
Finally, it is important to note that artificial intelligence cannot completely replace teachers. Technology can help improve the teaching and learning process, but teachers remain essential in the educational process.
Conclusions
In conclusion, while artificial intelligence (AI) has the potential to transform education by improving student learning outcomes, increasing access to education, and providing real-time feedback to educators, its implementation is not without challenges. Institutions need to carefully consider these challenges and develop strategies to address them to ensure the effective and ethical use of AI in education. This includes investing in resources and infrastructure, addressing data privacy and security concerns, ensuring fairness and mitigating biases, promoting awareness and understanding of AI technology, and addressing ethical considerations. By addressing these challenges, institutions can harness the power of AI to create innovative teaching and learning experiences that can improve student outcomes and prepare them for the demands of the 21st-century workforce.
— — — — — — — — — — — — — — — — — — — -
| 2023-03-01T00:00:00 |
https://www.linkedin.com/pulse/artificial-intelligence-new-frontier-education-21st-century-iqbal
|
[
{
"date": "2023/03/01",
"position": 25,
"query": "artificial intelligence education"
}
] |
|
Artificial Intelligence in Education: A Reading Guide ...
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Artificial Intelligence in Education: A Reading Guide Focused on Promoting Equity and Accountability in AI – CIRCLS
|
https://circls.org
|
[
"Pati Ruiz"
] |
I consider the work to promote equity and accountability in AI to be the most important and I created this list to focus on those issues.
|
by Pati Ruiz
As a former Spanish and computer science teacher, I think a lot about emerging technologies and how they apply in learning contexts. Recently, I have been focused on artificial intelligence (AI) and how it affects students, their families, and communities. I am particularly interested in the consequences (intended and unanticipated) of these emerging AI technologies on students who experience exclusion, specifically Latinx, Black, Indigenous, and students with disabilities.
Working with the Center for Integrative Research on Computing and Learning Sciences (CIRCLS), I have seen work ranging from Intelligent Tutors and systems designed to adapt and personalize learning, including some that are developing pedagogical agents and robots. I’ve also seen work that seeks to minimize bias and promote equity in AI, projects using computer vision, natural language processing and speech technologies. Of all of these efforts, I consider the work to promote equity and accountability in AI to be the most important and I created this list to focus on those issues. You can also check out this glossary of AI terms written specifically for educators.
First published: March 2020. Last updated: March 2024
Educator CIRCLS posts are licensed under a Creative Commons Attribution 4.0 International License. If you use content from this site, please cite the post and consider adding: “Used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).”
Suggested citation format: [Authors] ([Year]). [Title]. Educator CIRCLS Blog. Retrieved from [URL]
| 2023-03-01T00:00:00 |
https://circls.org/educatorcircls/ai-in-education/ai-in-ed-reading-guide
|
[
{
"date": "2023/03/01",
"position": 28,
"query": "artificial intelligence education"
}
] |
|
Artificial Intelligence (AI) in Higher Education
|
DePaul University, Chicago
|
https://resources.depaul.edu
|
[] |
At DePaul, we're encouraged to learn more about emerging AI technologies and help students to learn to use them responsibly.
|
Teaching Commons > Teaching Guides > Technology > Artificial Intelligence
Main Content
What is Artificial Intelligence?
Artificial intelligence (AI) encompasses a variety of computer-based tools that source existing data to solve a problem, such as search tools that rely on algorithms to find information or language translation tools (McCarthy, 2007). Generative artificial intelligence refers to a suite of tools that source existing data to create new artifacts in response to user prompts (Goodfellow et al., 2020). For example, ChatGPT is a large language model (LLM) and conversational generative AI that is built on many existing texts. In response to prompts from users, ChatGPT generates text that mimics the writing of humans through a process of statistical correlation. The Modern Language Association and Conference on College Composition and Communication's working paper explains how LLMs and ChatGPT work in greater detail.
Generative AI is an emergent and rapidly evolving space. New tools, or updates to existing tools, are released frequently, but a few examples are helpful in framing out what these tools do:
| 2023-03-01T00:00:00 |
https://resources.depaul.edu/teaching-commons/teaching-guides/technology/artificial-intelligence/Pages/default.aspx
|
[
{
"date": "2023/03/01",
"position": 30,
"query": "artificial intelligence education"
}
] |
|
Stakeholder Perspectives on the Ethics of AI in Distance- ...
|
Stakeholder Perspectives on the Ethics of AI in Distance-Based Higher Education
|
https://www.irrodl.org
|
[
"Wayne Holmes",
"Ucl Knowledge Lab",
"University College London",
"Francisco Iniesto",
"The Open University",
"Stamatina Anastopoulou",
"University Of Leicester",
"Jesus G. Boticario",
"Universidad Nacional De Educación A Distancia"
] |
by W Holmes · 2023 · Cited by 34 — His research takes a critical studies perspective to the teaching and application of Artificial Intelligence in educational contexts (AI&ED), and their ethical, ...
|
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), 42. https://doi.org/10.1186/s41239-020-00218-x
Bell, G., Gould, M., Martin, B., McLennan, A., & O’Brien, E. (2021). Do more data equal more truth? Toward a cybernetic approach to data. Australian Journal of Social Issues, 56(2), 213–222. https://doi.org/10.1002/ajs4.168
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). https://doi.org/10.1145/3442188.3445922
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| 2023-03-01T00:00:00 |
https://www.irrodl.org/index.php/irrodl/article/view/6089
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[
{
"date": "2023/03/01",
"position": 47,
"query": "artificial intelligence education"
}
] |
|
Do you think AI will fundamentally change the education ...
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The heart of the internet
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https://www.reddit.com
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[] |
I believe very soon you will be able to plug your entire textbook into an AI model and essentially “talk” to the textbook. Unlimited personal tutoring.
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I think the more we go forward, the more obvious will be that our education system is outdated. But, in the other hand, I realise if I rely only on AI to complete task, I do not fundamentally understand them. Therefore a generation that doesn’t have fundamental knowledge on life I think is going ti be too reliable on artificial intelligence. So what do you think will the future educational system be like?
| 2023-03-01T00:00:00 |
https://www.reddit.com/r/singularity/comments/127yg8x/do_you_think_ai_will_fundamentally_change_the/
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[
{
"date": "2023/03/01",
"position": 49,
"query": "artificial intelligence education"
}
] |
|
Study on AI in Education Policies
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Abstract View
|
https://library.iated.org
|
[
"V. Slavov",
"Y. Yan"
] |
by V Slavov · 2023 · Cited by 4 — This study proposes a model, with students in the center, to describe how AIinED can be implemented with relevant stakeholders and needed resources at the ...
|
STUDY ON AI IN EDUCATION POLICIES
1 Technical University of Sofia, Faculty of Automatics (BULGARIA)
2 Western Carolina University (UNITED STATES)
About this paper:
Publication year: 2023
Pages: 673-680
ISBN: 978-84-09-49026-4
ISSN: 2340-1079
doi: Appears in: INTED2023 Proceedings Publication year: 2023Pages: 673-680ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0227 Conference name: 17th International Technology, Education and Development Conference
Dates: 6-8 March, 2023
Location: Valencia, Spain
| 2023-03-01T00:00:00 |
https://library.iated.org/view/SLAVOV2023STU
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[
{
"date": "2023/03/01",
"position": 51,
"query": "artificial intelligence education"
}
] |
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AI is uncovering the very true nature of flawed school ...
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The heart of the internet
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https://www.reddit.com
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[] |
It isn't just our school systems. AI is the crucible that will force the archaic and nonsensical systems that have grown so pervasive, to their breaking point.
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I am out of school and I can say that we will finally see a revolution if this AI thing really stays here.
Homework, useless essays, all the brute force work that should be done with teachers AND alone, and not during free time, will hopefully be obliterated by the impossibility to keep up with AI generated content and detection.
How much time before they realize that this will be unstoppable and we have to rethink the way we teach... I don't really know, but thinking this was just a breath of fresh air, wanted to share.
| 2023-03-01T00:00:00 |
https://www.reddit.com/r/artificial/comments/11h1pqh/ai_is_uncovering_the_very_true_nature_of_flawed/
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[
{
"date": "2023/03/01",
"position": 61,
"query": "artificial intelligence education"
}
] |
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Examining Science Education in ChatGPT: An Exploratory ...
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Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
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https://link.springer.com
|
[
"Cooper",
"Grant.Cooper Curtin.Edu.Au",
"Curtin University",
"Bentley",
"Wa",
"Grant Cooper",
"Search Author On",
"Author Information",
"Corresponding Author",
"Correspondence To"
] |
by G Cooper · 2023 · Cited by 1207 — The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) H.
|
RQ1: How Did ChatGPT Answer Questions Related to Science Education?
I asked ChatGPT a series of questions broadly related to science education. It is worth acknowledging that its capacity to emulate human-like responses is nothing short of extraordinary. Broadly speaking, the ChatGPT response commonly aligned with key research themes in the literature. As discussed, the AI output in both prompts 1 and 3 highlighted strong synergies between the ChatGPT response and key themes in the research. Despite this, a major criticism of its current design is the absence of evidence to support its output. As it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed, without a proper grounding in evidence or presented with sufficient qualifications. The response to prompt 2 highlights the problematic absence of evidence, where the AI stated it was important to aim for a balance between teacher-centred and student-centred pedagogies. As stated earlier, where is the recognition of context? Where is the evidence base? Even when ChatGPT is prompted to provide references, it has been reported that… “it continually provides false and misleading references. To make matters worse, it will often provide correct references to papers that do exist and mix these in with incorrect references and references to non-existent papers… The question is, when does it give good answers and when does it give garbage answers?” (Buchanan, 2023, para. 1–3). Science educators, who prioritise evidence-based explanations in their own teaching, may find the current design of ChatGPT problematic.
Beyond its narrow framing of truth, its output is based on… “argumentum ad populum-it considers to be true what is repeated the most” (Darics & Poppel, 2023, para. 4). As discussed, the model is generally skewed towards content that reflects Western perspectives and people. Whose voices are silenced by the algorithm? Who is the author, and what is their bias? These are critical questions for educators, as well as students, to think carefully about.
Although this paper does not extensively probe the ethical implications related to ChatGPT, it may be valuable to discuss the matters listed below with students who are exploring its use or AI in general. One consideration is the potential environmental impact of AI platforms. Although the information is not readily available on ChatGPT, machine learning models require substantial processing power, and data centres hosting cloud networks must be effectively cooled (Boudreau, 2023; Wu et al, 2022). It was also reported that Kenyan workers were paid about $2 per hour to work as content moderators for systems associated with the creation of ChatGPT, sifting through disturbing content like sexual abuse, hate speech, and violence (Perrigo, 2023). The use of large language models such as ChatGPT also raises questions about the potential for copyright infringement when generated text resembles or copies existing content (Karim, 2023). Apart from these broader concerns, there are other considerations for how students use AI. For instance, is it okay for students to reference ChatGPT verbatim in an assessment? Given my previous discussion about its lack of evidence, I have instructed students in my classes not to. Students may first generate essay text in ChatGPT and subsequently insert key references mentioned in class. I do not know how I feel about this, it does not quite sit well with me. Instead of allowing research to drive the argument, it seems more like an essay hack. Matching an AI-generated narrative with research to legitimise it.
I am interested in the potential of ChatGPT to be used as (1) a learning scaffold for learning new concepts (before supporting students to engage with more traditional stimuli, such as an academic journal or textbook) and its potential to (2) help students who are not strong writers. For instance, I have modelled prompts students can enter to get a broad overview of a concept (e.g. ChatGPT prompt-Imagine I am an undergrad student, make some bullet points about [phenomena]). I suspect that for students who find it difficult to write, it will be helpful in overcoming writer’s block. To demonstrate to students the advantages of using ChatGPT to improve their writing, again, I have modelled prompts (e.g. ChatGPT prompt-rewrite: [paste your text here]). It is important for stakeholders to carefully consider how AI impacts the design of, and completion of, assessments and pre-service teacher programmes more broadly. Prioritising student’s critical thinking, critiquing ethical issues related to the use of AI systems, modelling its responsible use, and being clear about expectations for its use in assessments seems like a good place to start a broader conversation. Beyond large language models, educators need to consider generative AI more broadly (e.g. image, audio, video etc.). The ability to think critically as an educator is now more important than ever, an essential element of a science teacher’s toolkit. In an age of social media echo chambers, climate change scepticism, and uncertainty about sources of evidence and “truth(s)”, the emergence of generative AI introduces further complexity.
RQ2: What Are Some Ways Educators Could Utilise ChatGPT in Their Science Pedagogy?
In this part of the study, I was interested in exploring how educators might draw on its use of ChatGPT. Its output to prompts 4, 5, and 6 illustrates ways ChatGPT can be helpful to generate ideas when designing science units, rubrics, and quizzes. I was particularly impressed by its capacity to generate a science unit underpinned by the 5Es model, even if some of the output seemed a little generic and in need of further refinement. The output embedded the science topic (renewable and non-renewable energy sources) within a pedagogical framework (the 5Es). Again, however, educators need to critically evaluate any resources and adapt it to their specific context. Teacher’s expertise, experience, and understanding of their students remain key to making sound pedagogical decisions. AI does not replace the expertise of the science teacher (yet).
RQ3: How Has ChatGPT Been Utilised in This Study, and What Are My Reflections About its Use as a Research Tool?
As part of my research exploration, I was interested in using ChatGPT as a research tool in the present study. It has been reported that some scientists are already…. “using chatbots as research assistants- to help organize their thinking, generate feedback on their work, assist with writing code and summarize research literature” (Nature, 2023, p. 612). The large bulk of its use in this research was assistance with editing. There were sentences that I asked ChatGPT to rewrite (ChatGPT prompt-rewrite: [paste sentence]) at different stages of the paper to help with phrasing, flow, and word choice. Researchers who tend to write excessively long or complicated sentences could use ChatGPT to clarify their message. Certain sentences, however, had better phrasing prior to being entered into ChatGPT, while others were improved after a rewrite by the AI. When composing this paper, I kept a browser window open, experimenting with the possibilities of making my research narrative clearer.
There is presently a debate among journal editors, researchers, and publishers regarding the role of such AI tools in published literature and whether it is acceptable to attribute authorship to the bot (Stokel-Walker, 2023). Nature, along with all Springer Nature journals, has formulated two key principles to their existing guidelines for authors in response to the rise of ChatGPT. The first principle is that no large language model will be recognised as an accredited author because attribution carries accountability for the work, which AI tools cannot take such responsibility (Nature, 2023). “If ChatGPT deserves authorship, Microsoft Word deserves it, too, for providing us with the platform to organise and write documents more efficiently…. Excel, R, or Python deserve to be co-authors for calculating statistics or analysing data for a quantitative scientific publication” (Karim, 2023, para. 5). The second principle added to Nature’s author guidelines is that researchers need to disclose their use of large language models in the methods or acknowledgements sections (Nature, 2023). Similar to how journals require statements about data availability or ethical research, authors may soon have the option to disclose their use of large language models or AI during the journal submission process. I suspect formatting guidelines about the use of ChatGPT input and output will become clearer soon as well. The addition of AI to the research process commonly means new rules and processes for investigators. Ultimately, transparency and clearer guidelines about the use of AI platforms in research are essential for advancing scientific knowledge.
| 2023-06-14T00:00:00 |
2023/06/14
|
https://link.springer.com/article/10.1007/s10956-023-10039-y
|
[
{
"date": "2023/03/01",
"position": 73,
"query": "artificial intelligence education"
}
] |
Artificial Intelligence in Education — AIED | by Abbas Wahab
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Artificial Intelligence in Education — AIED
|
https://medium.com
|
[
"Abbas Wahab"
] |
The use of AI in education by building numerous quality intelligent learning platforms to enhance both teaching and learning practices.
|
Artificial Intelligence in Education — AIED Abbas Wahab 4 min read · Mar 14, 2023 -- 1 Listen Share
AI in Education — AIED
Artificial Intelligence is mounting to capture its space in every field of life like education, health, business and many others. Researchers are innovating in different fields by using AI tools and techniques. The use of AI in education by building numerous quality intelligent learning platforms to enhance both teaching and learning practices. AI in education (AIED) is the latest trend of technological practice in education thou it is not an easy way for many educators to adopt due to multiple problems like lacking of adequate skills and knowledge. It is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningful teaching and learning in higher education (Zawacki-Richter et al., 2019).
To reflect and implement our core concepts and learning indicators, it is a dire need to elaborately design teaching and learning resources to promote and design courses based on task and project-based learning (Song et al., n.d.). Courses need to be designed to teach in schools to foster four core literacies according to the national curriculum, namely information awareness, computational thinking, digital learning and innovation, and information society responsibility. This will enhance students’ concepts about AI and ML and will be getting inspired them to practice self-inquiry through project-based learning and explore answers in multiple fields of study.
The upcoming generation is all about technology and it is very important to teach them the latest technologies. With the importance of teaching the latest technologies, it also has difficulty in teaching. Due to a lack of trained teaching staff and logical prior knowledge. Schools are also not well equipped to tackle the issue of providing quality resources i.e., LEGOS Kits and much more for the actual implementation of learned concepts. In terms of AI, it is a dire need to teach programming concepts, algorithms, and data structure. There should be a stepwise curriculum which will first be focusing on basic programming concepts and problem-solving and it should be followed by implementing those concepts in AI.
Many researchers have claimed that viable applications of AI in society often have a positive social impact by increasing the availability and accessibility of information, a result of more efficient search tools and language-translation tools, provision of better information communication services, enhanced transportation systems, and modified healthcare and education. Current debates have been focusing on AI in education, particularly how the AI ecosystem might be yoked to progress global education given the need to move to teach and learn online because of the COVID-19 pandemic(Nemorin et al., 2023).
Many educators terms against AI & ML due to the reason of reducing human efforts and creativity. This cannot be denied but AI itself is very creative. A learner will learn multiple skills working with AI. Skills like mathematics, logic, statistics, problem-solving, strategy adoption, data structure, programming, data science, machine learning, and many more.
Application of AI in education
Adaptive Learning: “Used to teach students basic and advanced skills by assessing their present skill level and creating a guided instructional experience that helps them become proficient.”
Assistive Technology: AI can help special needs students access a more equitable education, for example by “reading passages to a visually impaired student.”
Early Childhood Education: “AI is currently being used to power interactive games that teach children basic academic skills and more.”
Data and Learning Analytics: “AI is currently being used by teachers and education administrators to analyse and interpret data,” enabling them to make better-informed decisions.
Scheduling: Helping administrators schedule courses and individuals to manage their daily, weekly, monthly or yearly schedules.
Facilities Management: AI is effective at “monitoring the status of power, Wi-Fi and water services; alerting the facilities management workers when problems arise.”
School Management: AI is currently being used to manage entire schools, powering student records systems, transportation, IT, maintenance, scheduling, budgeting, etc.
Writing: Not only does Lynch assert that AI is already at work helping students improve their writing skills, he confesses, “I am currently using a grammar and usage app to help me write this article.”
Intelligent tutoring
Tutoring programs or intelligent tutoring systems (ITS) based on artificial intelligence are equipped to handle personalized feedback and instructions for one-on-one teaching.
Gamification
AI-powered gamification can make learning more engaging and enjoyable by using game elements such as points, badges, and leaderboards. Gamification can motivate students to learn and help them to develop important skills such as problem-solving and critical thinking.
References
Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2023). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38–51. https://doi.org/10.1080/17439884.2022.2095568
Song, J., Yu, J., Yan, L., Zhang, L., Liu, B., Zhang, Y., & Lu, Y. (n.d.). Develop AI Teaching and Learning Resources for Compulsory Education in China.
Zawacki-richter, O., Marín, V. I., & Bond, M. (2019). Systematic review of research on artificial intelligence applications in higher education — where are the educators?
https://onlinedegrees.sandiego.edu/artificial-intelligence-education/
https://www.westagilelabs.com/blog/8-applications-of-artificial-intelligence
| 2023-03-14T00:00:00 |
2023/03/14
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https://medium.com/@abbasw555/artificial-intelligence-in-education-aied-812bae2c5009
|
[
{
"date": "2023/03/01",
"position": 74,
"query": "artificial intelligence education"
}
] |
Generative artificial intelligence (AI) powered ...
|
Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift
|
https://www.asianjde.com
|
[
"Aras Bozkurt",
"Anadolu University"
] |
by A Bozkurt · 2023 · Cited by 342 — This paper discusses the prospects of generative AI in utilizing language and its potential role as a conversational agent within the educational realm.
|
Generative AI, specifically ChatGPT, represents a significant technological advancement in natural language processing (NLP) large language models (LLM) with far-reaching implications in many dimensions of our lives, including education. This paper discusses the prospects of generative AI in utilizing language and its potential role as a conversational agent within the educational realm. Emulating the most advanced human technology, language, generative AI’s success relies on understanding and generating human-like text. However, its comprehension is solely based on patterns and structures it learns from its training data. With the advent of AI-driven conversational agents, prompt engineering emerges as a vital form of digital literacy. The convergence of general and educational technologies necessitates preparedness for a future dominated by AI. This paper highlights the importance of vigilance and prudence in harnessing the potential of generative AI technologies, emphasizing the responsibility of humans, as creators, in mitigating any potential mishaps. In conclusion, this paper suggests that preparedness for a future dominated by AI is essential, as generative AI technologies have the potential to profoundly impact teaching and learning methods, and necessitate new ways of thinking.
| 2023-03-01T00:00:00 |
https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/718
|
[
{
"date": "2023/03/01",
"position": 77,
"query": "artificial intelligence education"
}
] |
|
Artificial Intelligence in Education: Education Book Chapter
|
Artificial Intelligence in Education
|
https://www.igi-global.com
|
[
"Rania Abdalla Abdulmunem",
"El Aqsa University",
"Abdulmunem",
"Rania Abdalla"
] |
by RA Abdulmunem · 2023 · Cited by 23 — Artificial intelligence (AI) has emerged as one of the key drivers of growth and innovation in numerous fields, including the professional academic sector. In ...
|
Chapter Preview
Artificial Intelligence in Education
Artificial intelligence (AI) has emerged as one of the key drivers of growth and innovation in numerous fields, including the professional academic sector. In recent years, the integration of (AI) in education has provided a way to tackle some of the most challenging issues faced by educators. The pandemic caused by Corona has brought a significant change in the process of education, now incorporating virtual learning as an integral part of the academic experience. This chapter reviews the importance, components, applications, usage, and systems of (AI), as well as its future in professional academic contexts.
As (AI) now playing an increasingly vital role in our daily lives, it's no surprise that educational institutions are vying to develop more AI-driven skills to keep pace with its ongoing development. Despite being a theoretical and practical concept for several decades, the application of (AI) has remained limited in most domains. Nevertheless, recent advancements in both (AI) and computing have propelled the integration of AI-enhanced applications across various industries, as well as an increasing number of consumer products. The prevalence of (AI) platforms and services that are geared towards improving educational outcomes and streamlining administrative responsibilities is steadily rising, with a growing number of educational institutions and workplaces embracing these innovative technologies (Remian, 2019).
(AI) is based on two principles: the first is data representation, which refers to how data or problems are represented in computers so that the computer can process them. The second principle is the research and thinking process itself, where the computer explores the available options and evaluates them based on the criteria set for it, or it derives the optimal solution on its own.
| 2023-07-14T00:00:00 |
2023/07/14
|
https://www.igi-global.com/chapter/artificial-intelligence-in-education/320547
|
[
{
"date": "2023/03/01",
"position": 82,
"query": "artificial intelligence education"
}
] |
AI in Education: Let's Talk About Policy, Awareness, and ...
|
AI in Education: Let’s Talk About Policy, Awareness, and Challenges for Educators
|
https://medium.com
|
[
"Alfonso Mendoza Jr."
] |
In this blog post, we'll explore AI's role in education, why we need policies and awareness, and the challenges we might face.
|
AI in Education: Let’s Talk About Policy, Awareness, and Challenges for Educators Alfonso Mendoza Jr. 3 min read · Mar 28, 2023 -- Share
Inspired by Episode 187 of My EdTech Life with special guest Logan Greenhaw
Education is a huge part of our lives, and with technology moving forward, artificial intelligence (AI) is starting to play a big role in learning. As educators, it’s important to understand what AI means for education and be aware of the challenges it brings, like privacy issues and ethical concerns. In this blog post, we’ll explore AI’s role in education, why we need policies and awareness, and the challenges we might face. My goal is to create a safe and responsible environment for AI in education and get more people talking about it.
Created in Cava
AI’s Growing Role in Education
Our world is becoming more and more tech-focused, and AI is popping up in all sorts of industries, including education. AI can make learning more efficient and effective, especially in today’s digital age. But like any new technology, there are potential downsides and concerns that we, as education professionals, need to address.
The Importance of Talking About AI and Creating Policies for the Classroom
As AI gets more popular in education, educators and policymakers must have conversations about how it can be used in schools. We must create policies that give us…
| 2023-04-11T00:00:00 |
2023/04/11
|
https://medium.com/educreation/ai-in-education-lets-talk-about-policy-awareness-and-challenges-for-educators-8e1e56bd5bc4
|
[
{
"date": "2023/03/01",
"position": 90,
"query": "artificial intelligence education"
}
] |
Exploring the Intersection of Artificial Intelligence and Journalism:
|
Exploring the Intersection of Artificial Intelligence and Journalism The Emergence of a New Journalistic Paradigm
|
https://www.routledge.com
|
[] |
This book studies the role of Artificial Intelligence (AI) in journalism. It traces the origin, growth and development of the media and communication industry ...
|
“Technology has always been a disruptor of the status-quo and creator of new paradigms. News-media has experienced this change from the beginning. The latest entrant in the long list of disruptive technologies is AI, which is changing the media ecology with a great speed. New forms of journalism are being created. New questions related to journalistic ethics are emerging in the light of intersection of AI and journalism. This book, one of the pioneers in this domain, discusses these issues and attempts to answer some of the emerging questions. It is useful for understanding what AI is doing and can do in the field of journalism and other social studies and how one should effectively engage with it.”
Professor Mrinal Chatterjee, Regional Director, Indian Institute of Mass Communication, Odisha, India
“Generative Artificial Intelligence is very easy to use, which has led to its rapid uptake and current widespread use. With it, however, come a series of very important considerations. As automatic text generation becomes increasingly sophisticated and nearly indistinguishable from an author’s creation, questions of ethics, copyright, truthfulness of information, and provability of statements come to the forefront. This book provides an intriguing set of possibilities, and a novel journey into uncharted territory that is both compelling and realistic. It is an enjoyable, thought provoking read.”
Professor Luigi Benedicenti, Dean, Faculty of Computer Science, University of New Brunswick, Canada
| 2023-03-01T00:00:00 |
https://www.routledge.com/Exploring-the-Intersection-of-Artificial-Intelligence-and-Journalism-The-Emergence-of-a-New-Journalistic-Paradigm/Biswal-Kulkarni/p/book/9781032716893?srsltid=AfmBOopjP_cCTssvvp5U_xXOLliruVHbXt7uO5TeujKcQ2L61QXOW9M4
|
[
{
"date": "2023/03/01",
"position": 50,
"query": "artificial intelligence journalism"
}
] |
|
What It Means to Do Journalism in the Age of AI: Journalist Views on ...
|
What It Means to Do Journalism in the Age of AI: Journalist Views on Safety, Technology and Government
|
https://cnti.org
|
[] |
We surveyed more than 430 journalists from more than 60 countries about government, technology, online harassment and what it means to be a journalist these ...
|
By Jay Barchas-Lichtenstein, Amy Mitchell, Emily Wright, Celeste LeCompte, Samuel Jens and Nicholas Beed
OVERVIEW
Over the last few decades, the widespread adoption and use of new communications technology have rapidly reshaped global information ecosystems. For journalists and news organizations, these changes have both empowered their work and presented new challenges. For the public, these technological and social changes have provided many more options, disrupting how news outlets have historically interacted with them. Concurrently, governments around the world are increasingly impinging on press freedom, weaponizing the law against journalists, while questions about revenue models and digital content valuation remain unsettled.
Social media platforms specifically have offered new opportunities to meet the public where they are, but have also put journalists on the receiving end of continuous legal threats and harassment.
Most recently, the convenience and expediency of AI tools can help individuals and teams produce more content — but these tools are resource-intensive, have shown potential for inaccuracy, and the opacity of their algorithms can leave journalists unsure about how their own work is being repurposed.
Surveys are a snapshot of what people think at a particular moment in time. We surveyed more than 430 journalists from more than 60 countries about government, technology, online harassment and what it means to be a journalist these days between October 14, 2024 and December 1, 2024. As with all CNTI research, this report was prepared by the research and professional staff of CNTI. Here are some highlights of what we learned:
Journalists see a lot of value in their field, but they are unsure that value is being communicated well, leading to public confusion: U.S. journalists don’t think the public can identify what journalism is — or what it isn’t. About one-in-four U.S. journalists (24%) think the public can distinguish journalism from other kinds of news and information. Meanwhile, about half of Mexican journalists (48%) think the public can make a distinction, as do 70% of Nigerian journalists. And while professional training and institutions matter to journalists’ self-conception, most of them agree that people who are not journalists can produce journalism. (Read this section of the report.)
Half of the journalists surveyed (50%) have experienced direct government overreach in the last year. This may be why strong majorities (more than three-quarters) say it is inappropriate for the government to define journalism or journalists, and about half say their government exerts too much control over journalism. (Read this section of the report.)
Journalists believe technology is improving their work, but they are hesitant about AI: Two-thirds say that technology in general — and social media in particular — are having a positive effect on their work, although only one-third say the same about AI’s effect on the information landscape. Journalists in the Global South are more positive overall. (Read this section of the report.)
Serious risks are wide-spread: one-in-three face them somewhat often or more. All the same, preparation varies: Changing passwords and updating hardware and software on devices is done frequently — but journalists don’t necessarily communicate with sources through the most secure platforms. About 40% said they do both once every few months on average, and 30% or less say they did so once every few years or when the device stops working. And journalists in the Global North do both of these more often than their colleagues elsewhere. Meanwhile, 15% of journalists say that they use encrypted peer-to-peer messaging as their primary means of communication with sources. Strong majorities do feel comfortable discussing safety with colleagues and managers, including government censorship and personal experiences of abuse. (Read this section of the report.)
Finally, as a way to connect the dots across the different issue areas asked about, we asked an overarching question about seven current issues facing many news organizations today. The results provide valuable insight into journalists’ sense of field — and newsroom-wide priorities — which may not match newsroom leaders’ perspective. According to respondents, the long-standing issue of audience engagement continues to get the most attention inside news organizations with revenue streams and misinformation coming next. At the bottom: Online abuse.
This report addresses the information environment with a focus on four of the most pressing issue areas today, each of which deserves more research and conversation: definitions of news and journalism, relationships between news organizations and the government, technology and AI, and security and safety. While the report parses out these areas, there is a great deal of overlap, addressed across sections.
Continue reading:
Read CNTI’s companion report based on surveys with representative publics in four countries.
| 2023-03-01T00:00:00 |
https://cnti.org/2024-journalist-survey/
|
[
{
"date": "2023/03/01",
"position": 55,
"query": "artificial intelligence journalism"
},
{
"date": "2023/06/01",
"position": 58,
"query": "artificial intelligence journalism"
},
{
"date": "2023/07/01",
"position": 54,
"query": "artificial intelligence journalism"
},
{
"date": "2023/10/01",
"position": 54,
"query": "artificial intelligence journalism"
},
{
"date": "2024/02/01",
"position": 46,
"query": "artificial intelligence journalism"
},
{
"date": "2024/05/01",
"position": 55,
"query": "artificial intelligence journalism"
},
{
"date": "2024/06/01",
"position": 54,
"query": "artificial intelligence journalism"
},
{
"date": "2024/09/01",
"position": 55,
"query": "artificial intelligence journalism"
},
{
"date": "2025/01/01",
"position": 57,
"query": "artificial intelligence journalism"
}
] |
|
The Impact of Artificial Intelligence on Media, Journalists, and ...
|
The Impact of Artificial Intelligence on Media, Journalists, and Audiences
|
https://www.frontiersin.org
|
[] |
In the context of journalism, for example, the applications of content automation are commonly associated with the use of algorithmic processes that convert ...
|
The media have been undergoing a digital transformation for several decades; one that has had profound effects on all their work processes. The emergence of artificial intelligence (AI) and, more recently, generative AI, has given a significant boost to content automation; a process already experimented with for over half a century. In the context of journalism, for example, the applications of content automation are commonly associated with the use of algorithmic processes that convert data into narrative texts and news, with limited or no human intervention beyond the initial programming phase (Carlson, 2015). Unlike previous experiences, the implementation of AI in the media will have a more global and widespread impact on the creation of content, thereby fueling the debate about the future of journalists, audiences, and the media themselves.
AI is a dual-use technology; as such, its impact on the media, journalists, and audiences is complex and multifaceted. On the one hand, it has the potential to eventually turn algorithms into proper content-generating agents, thus assuming a role that goes far beyond the one it has played until now as a mediating agent of human communication (Guzman and Lewis, 2020). Its capabilities can also be key in the optimization of production processes, the improvement of the media’s economy, and the creation of new business models, such as personalized content.
On the other hand, the implementation of AI in the media can create new vulnerabilities, multiply information disorders (Brennen et al., 2018; Brundage, 2018; Karnouskos, 2020), as well as generate challenges relating to transparency. Furthermore, it also risks becoming the back door to increasing the media’s dependence on the large technological platforms.
By the same token, the potential for AI to transform the work of journalists is enormous: routine tasks can be fully automated, leaving them with more time to return to the essence of their profession. Yet such advantages are accompanied by the fear of staff redundancies as well as the questioning of their social status.
Finally, while audiences may perceive the emergence of AI tools as an opportunity to enrich the information they access, they may also experience new threats: the rise of new divides caused by the employment of old/biased data banks or design errors, the invasion of their privacy, damage to pluralism, and political polarization.
The goal of this Research Topic is to respond to these challenges, risks, and opportunities in the light of the growing implementation of AI in the field of communication. Potential themes of interest include, but are not limited to, the following:
• the analysis of automated information content (credibility, quality of information, ease of reading, etc.)
• new journalistic formats
• the relationship between platforms and media in an increasingly technological environment
• the impact of AI on the privacy and transparency of media content
• regulatory and legal developments on AI
• the ethical repercussions of the use of automated content
• new professional profiles and new tasks
• changes in the training of journalists and communicators
• the impact of AI on the employment and working conditions of journalists
• the reception of automated content by audiences and its social effects (polarization, pluralism, etc.)
• new digital divides (gender, race, social class, etc.).
Image credit: Martina Stiftinger
| 2023-03-01T00:00:00 |
https://www.frontiersin.org/research-topics/60151/the-impact-of-artificial-intelligence-on-media-journalists-and-audiencesundefined
|
[
{
"date": "2023/03/01",
"position": 71,
"query": "artificial intelligence journalism"
},
{
"date": "2023/06/01",
"position": 70,
"query": "artificial intelligence journalism"
},
{
"date": "2024/02/01",
"position": 80,
"query": "artificial intelligence journalism"
},
{
"date": "2024/05/01",
"position": 71,
"query": "artificial intelligence journalism"
},
{
"date": "2024/09/01",
"position": 71,
"query": "artificial intelligence journalism"
},
{
"date": "2025/01/01",
"position": 69,
"query": "artificial intelligence journalism"
}
] |
|
The media industry is now facing a huge wave of artificial ...
|
The media industry is now facing a huge wave of artificial intelligence (AI) innovation. This is cer..
|
https://www.mk.co.kr
|
[
"Hwang Soon-Min"
] |
For example, WSJ used AI to expand investigative journalism when TikTok's algorithms tracked users and analyzed financial disclosure data from ...
|
The media industry is now facing a huge wave of artificial intelligence (AI) innovation. This is certainly a crisis and an opportunity. Some jobs may be reduced as AI replaces repetitive and simple tasks, but on the contrary, reporters and creators can focus on more creative and analytical tasks. At the same time, clear threats such as a decline in readers' credibility and willingness to pay for AI-made articles, the spread of low-quality content, and job losses are also approaching.
It is expected that the distribution (news platform) ecosystem of news will also change significantly with AI. This is because AI services such as ChatGPT are the trigger for portal reorganization. In addition, there is a remarkable trend among young people in their teens and 20s to use YouTube and TikTok, not Naver and Google, which are traditional search platforms.
The message delivered to the Korean media by Artem Fishman, Dow Jones Chief Technology Officer (CTO), an AI technology innovator, is by no means light. Major media around the world are already preparing for a new era. CTO Fishman's advice is to make the most of the efficiency and data analysis capabilities provided by AI, but to further strengthen the essence of journalism that only humans can do, such as fact verification, interpretation, and in-depth coverage. How are major media companies such as the Wall Street Journal (WSJ) introducing AI into newsrooms. I asked Fishman CTO.
-In the process of introducing AI into the newsroom, how is Dow Jones balancing work automation with traditional journalism methods.
▷Our clear goal is to support newsrooms that provide high-quality content, and to leverage AI to enhance storytelling. For example, WSJ used AI to expand investigative journalism when TikTok's algorithms tracked users and analyzed financial disclosure data from 12,000 federal officials. There are other areas where AI can be utilized, such as business. For example, AI can use structured data to quickly report on stock market announcements, earnings reports, and economic data. This allows journalists to prioritize more important articles such as exclusive articles, descriptors, and analyses.
-Cooperation between technical experts and journalists is also likely to be important.
The ▷ technical team is working with colleagues with readers as their top priority.
-Can AI improve the reader's experience.
▷In fact, WSJ continues to test AI-based functions such as article summary and personalization to increase participation and provide readers with a reader-first experience. The important point is that while AI tools can support newsrooms, journalists must apply editorial judgment and rigor to reporting.
[Reporter Hwang Soon Min]
| 2025-07-06T00:00:00 |
2025/07/06
|
https://www.mk.co.kr/en/it/11360770
|
[
{
"date": "2023/03/01",
"position": 83,
"query": "artificial intelligence journalism"
}
] |
10 Best AI Graphic Design Tools (July 2025) - Unite.AI
|
10 Best AI Graphic Design Tools (July 2025)
|
https://www.unite.ai
|
[
"Alex Mcfarland"
] |
10 Best AI Graphic Design Tools (July 2025) · 1. Designs.ai · 2. Adobe Sensei · 3. Fronty · 4. AutoDraw · 5. Khroma · 6. Let's Enhance · 7.
|
The digital design realm is witnessing an upheaval, thanks to the unprecedented influence of artificial intelligence (AI). AI graphic design tools are restructuring the way artists and designers express their creativity, enabling them to craft more unique designs in significantly less time. Let's navigate through the top 10 AI graphic design tools that are pushing the boundaries of your creative potential.
Designs.ai is a complete AI-assisted design toolkit that transforms the perception of what an AI graphic design tool can accomplish. From a standout logo, a persuasive video, to an effective social media advertisement, Designs.ai arms you with every tool you might need. Its unique strength lies in its machine learning abilities, which optimize the design process by studying your likes and offering a range of tailor-made design solutions.
Designs.ai offers more than a varied toolkit; it ensures an efficient and personalized design journey. Whether your project involves branding or video production, its varied suite can cater to every creative requirement. With an easy-to-use platform, Designs.ai encourages creativity and originality irrespective of your design background.
Top features of Designs.ai:
Extensive toolkit for varied design requirements.
Machine learning algorithms that adjust to personal design tastes.
A vast collection of fonts, colors, and graphics.
Visit Designs.ai →
Adobe Sensei exemplifies how AI can enhance efficiency in design. By leveraging AI and machine learning, Sensei automates routine tasks and encourages innovative design solutions. This AI helper, embedded effortlessly within Adobe's suite of design tools, is a priceless resource for professional graphic designers and creatives.
Sensei boosts creativity by taking care of mundane tasks, thereby allowing designers to focus on their art. Integrated into the well-known Adobe suite, Sensei merges robust AI capabilities with familiar design tools, forming a comprehensive package for any designer.
Prominent features of Adobe Sensei:
AI automation of recurring tasks.
A wide range of functionalities to boost creativity.
Smooth integration with Adobe's suite of design tools.
Visit Adobe Sensei →
Fronty stands at the intersection of design and development, symbolizing the potential of AI in both domains. This AI graphic design tool simplifies the web design process by turning image designs into code, morphing a simple picture into a functional website with a few clicks.
Fronty's utility goes beyond transforming designs into code. By generating custom HTML, CSS, and React code, Fronty offers versatility for both web designers and developers. Moreover, it notably reduces the time from concept to live site, making it an essential tool for web development.
Standout features of Fronty:
Transforms image designs into operational websites.
Produces custom HTML, CSS, and React code.
Accelerates the web development process.
Visit Fronty →
AutoDraw, created by Google, showcases how accessible AI design tools can be. It effortlessly converts your rough sketches into refined illustrations. Its machine learning algorithm predicts what you're attempting to draw and presents a selection of polished sketches to choose from.
AutoDraw revolutionizes quick sketching and ideation by integrating AI. Its intuitive interface combined with machine learning makes it a suitable tool for everyone, from doodling enthusiasts to professional designers. Whether you're working on a complex design project or just sketching for fun, AutoDraw's predictive drawings enhance your creative journey.
Key features of AutoDraw:
Converts rough sketches into polished illustrations.
Uses machine learning to predict and improve drawings.
Perfect for quick sketching and ideation.
Visit AutoDraw →
Khroma is an AI color tool that plays a significant role in the design process, particularly when it comes to color selection and consistency. Based on your aesthetic preferences, Khroma generates personalized color palettes, offering you infinite options that align with your style.
Khroma transcends the role of a basic color tool by understanding your color preferences and delivering customized palettes. It makes finding the right color combination easier and ensures consistency in your designs. Whether you're looking for color inspiration or aiming for uniformity across your projects, Khroma is an excellent choice.
Top features of Khroma:
Creates color combinations based on user preferences.
Ideal for maintaining color consistency across designs.
Great tool for color inspiration.
Visit Khroma →
Let's Enhance is an AI-driven graphic design tool that improves the quality of your images without losing detail. It is especially useful for enlarging low-resolution images or restoring the quality of old photos. Using neural networks, it can add in details that were not initially present, delivering a high-quality image from even the most pixelated sources.
Let's Enhance stands as a testament to the power of AI in image editing and restoration. It revitalizes low-quality images and ensures they meet the highest resolution requirements. Whether you're looking to enhance image quality for print or digital media, Let's Enhance offers an effective solution.
Main features of Let's Enhance:
Upscales images without compromising quality.
Uses neural networks to fill in missing details.
Ideal for preparing low-res images for print.
Visit Let's Enhance →
Jasper.ai, an AI writing assistant, is designed to create compelling text to supplement your designs. Whether you need product descriptions, ad copy, or blog posts, Jasper.ai can generate engaging content that resonates with your audience.
While Jasper.ai is not a traditional graphic design tool, it is crucial for designers who want to weave captivating narratives around their visuals. It simplifies content creation and ensures that your design's message is as powerful as the design itself. For creative professionals who recognize the impact of well-written words, Jasper.ai is an essential tool.
Leading features of Jasper.ai:
Generates compelling text for various purposes.
Streamlines the content creation process.
Perfect for creating narratives that complement designs.
Read Review →
Visit Jasper →
8. Alpaca
Alpaca is an innovative tool that demonstrates the potential of AI in 3D modeling. It takes your 2D design and, with the power of machine learning, transforms it into a 3D model. Alpaca interprets the depth and perspective of your design, rendering a three-dimensional model that provides a more realistic view of your project.
Alpaca enables product designers and architects to animate their 2D sketches. By rendering three-dimensional models from flat designs, it provides a more comprehensive visualization of the project. Alpaca is an ideal tool for those who want to visualize their designs in 3D.
Notable features of Alpaca:
Transforms 2D designs into 3D models.
Interprets depth and perspective for accurate 3D rendering.
Suitable for product designers and architects.
Visit Alpaca →
A Beginner's Guide to Uizard (Full Walkthrough)
Watch this video on YouTube
Uizard, encapsulating the potential of AI in streamlining the design process, is a remarkable tool. It digitizes hand-drawn ideas into usable digital design files, acting as an efficient pathway for app developers and designers.
Uizard not only speeds up the prototyping process, but it also retains the personal touch of hand-drawn designs. By digitizing sketches, it enables creative concepts to swiftly transition from the sketchbook to the digital screen, proving to be a powerful resource.
Notable features of Uizard:
Transforms sketches into digital UI designs.
Accelerates prototyping and iteration.
Offers exportable designs in multiple formats.
Visit Uizard →
Nvidia Canvas showcases the power of AI to morph simple brush strokes into photorealistic images. Using a technique called GAN (Generative Adversarial Network), Nvidia Canvas translates your sketches into stunning landscapes and intricate scenes.
Nvidia Canvas empowers anyone to create stunning, photorealistic images. Whether you're a digital artist or just dabbling in design, this tool can transform your simple sketches into masterpieces. It is a perfect example of how AI can democratize the creative process.
Key features of Nvidia Canvas:
Transforms brush strokes into photorealistic images.
Uses GAN to interpret and improve sketches.
Perfect for creating detailed, stunning designs.
Visit Canvas →
Summary
This list signifies the increasing prevalence of AI in the graphic design world. These AI-infused tools enhance creativity, streamline design processes, and empower users to produce more unique designs in significantly less time. Whether you're a seasoned professional or a design enthusiast, these tools can assist you in realizing your creative potential.
| 2023-05-22T00:00:00 |
2023/05/22
|
https://www.unite.ai/10-best-ai-graphic-design-tools/
|
[
{
"date": "2023/03/01",
"position": 91,
"query": "artificial intelligence graphic design"
},
{
"date": "2025/07/01",
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"date": "2025/07/01",
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"date": "2025/07/01",
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"date": "2025/07/01",
"position": 99,
"query": "AI graphic design"
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"date": "2025/07/01",
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{
"date": "2025/07/01",
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"query": "AI graphic design"
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{
"date": "2025/07/01",
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{
"date": "2025/07/01",
"position": 86,
"query": "artificial intelligence graphic design"
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{
"date": "2025/07/01",
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"query": "artificial intelligence graphic design"
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{
"date": "2025/07/01",
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{
"date": "2025/07/01",
"position": 85,
"query": "AI graphic design"
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{
"date": "2025/07/01",
"position": 86,
"query": "AI graphic design"
},
{
"date": "2025/07/01",
"position": 80,
"query": "artificial intelligence graphic design"
},
{
"date": "2025/07/01",
"position": 80,
"query": "artificial intelligence graphic design"
},
{
"date": "2025/07/01",
"position": 100,
"query": "AI graphic design"
},
{
"date": "2025/07/01",
"position": 78,
"query": "artificial intelligence graphic design"
},
{
"date": "2025/07/01",
"position": 78,
"query": "artificial intelligence graphic design"
},
{
"date": "2025/07/01",
"position": 99,
"query": "AI graphic design"
},
{
"date": "2025/07/01",
"position": 99,
"query": "AI graphic design"
}
] |
Can artificial intelligence, under human supervision ...
|
Can artificial intelligence, under human supervision, dismiss me?
|
https://bloglaboral.garrigues.com
|
[
"Pablo Salguero"
] |
In short, artificial intelligence will be able to take significant decisions regarding employment, provided that, before it is implemented, it can be proven ...
|
The proposal for a European Directive on improving working conditions in platform work requires that, in intensely digitalized environments, there is always human control. This means that the decision regarding a job cannot be left exclusively in the hands of artificial intelligence.
Elon Musk, together with other collaborators, created in 2015 a language model called Chat GPT. Visually, it operates like a chatbot similar to others with which we already interact as part of the customer service of the most prominent companies. However, there is a fundamental difference between it and those chatbots. It is capable of providing responses that can hardly be distinguished, at certain levels, from those that would be given by a human worker. Its secret is that it can draw upon databases to locate the origin of the information requested and is capable of combining resources to generate responses. In other words, it really does some research work, similar to that which could be carried out by any human.
The fact that it is a free resource, readily accessible by any user, together with its huge potential, has caused in recent weeks a burst of uses, which seems to question the future feasibility of jobs based on tasks requiring documentation and analysis of data. A million-dollar offer, launched in the United States, to whoever is prepared to allow the legal arguments to be used at a trial to be dictated by this artificial intelligence, has even gone viral.
We cannot deny that, in the coming years, this type of technologies will pose a huge challenge for employment relations and will probably do so more quickly than was envisaged due to their huge creative capacity.
Perhaps for this reason, the proposal for a Directive of the European Parliament and of the Council, on improving the working conditions in platform work has been published very recently. One of the objectives of this legislation is to ensure that, in intensely digitalized environments, there will always be human control and a minimum of human contact. Thus, employees would be protected from the adverse effects on their employment contract of automated decisions. In fact, it is sought to establish a kind of human “quota”, to ensure that the employer will always have a sufficient number of persons with high quality training, whose function is to monitor decisions automatically taken regarding the workforce, with an impact on employment. Human control is thus proposed as the ultimate guarantee mechanism and must have the capacity to annul and overrule automated decisions. It must also be capable of promptly providing an explanation regarding any decision adopted that significantly affects employment.
In conclusion, it is desired that a job cannot be affected solely by the decision of artificial intelligence, but rather there must be at least one person in the company’s structure that has supervised the decision adopted. And if it has been erroneous in the supervisor’s opinion, he can overrule it, thereby preventing damage to employment.
This legislation seems to embark on the path of a new list of minimum guarantees in employment conditions, by which it is sought to provide “human” security mechanisms that defend employees from artificial intelligence. Thus, the employer is discreetly placed in the background, since he is authorized to delegate certain decisions to artificial intelligence, although not with full freedom since he must have ultimate control.
In short, artificial intelligence will be able to take significant decisions regarding employment, provided that, before it is implemented, it can be proven that the decision has been supervised by a human.
Pablo Salguero
Garrigues Employment & Labor Law Department
| 2023-03-02T00:00:00 |
2023/03/02
|
https://bloglaboral.garrigues.com/en/can-artificial-intelligence-under-human-supervision-dismiss-me
|
[
{
"date": "2023/03/02",
"position": 68,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/02",
"position": 68,
"query": "artificial intelligence workers"
}
] |
AI could make more work for us, instead of simplifying our ...
|
AI could make more work for us, instead of simplifying our lives
|
https://theconversation.com
|
[
"Barbara Ribeiro"
] |
There's a common perception that artificial intelligence (AI) will help streamline our work. There are even fears that it could wipe out the need for some ...
|
There’s a common perception that artificial intelligence (AI) will help streamline our work. There are even fears that it could wipe out the need for some jobs altogether.
But in a study of science laboratories I carried out with three colleagues at the University of Manchester, the introduction of automated processes that aim to simplify work — and free people’s time — can also make that work more complex, generating new tasks that many workers might perceive as mundane.
In the study, published in Research Policy, we looked at the work of scientists in a field called synthetic biology, or synbio for short. Synbio is concerned with redesigning organisms to have new abilities. It is involved in growing meat in the lab, in new ways of producing fertilisers and in the discovery of new drugs.
Synbio experiments rely on advanced, robotic platforms to repetitively move a large number of samples. They also use machine learning to analyse the results of large-scale experiments.
These, in turn, generate large amounts of digital data. This process is known as “digitalisation”, where digital technologies are used to transform traditional methods and ways of working.
Some of the key objectives of automating and digitalising scientific processes are to scale up the science that can be done while saving researchers time to focus on what they would consider more “valuable” work.
Paradoxical result
However, in our study, scientists were not released from repetitive, manual or boring tasks as one might expect. Instead, the use of robotic platforms amplified and diversified the kinds of tasks researchers had to perform. There are several reasons for this.
Among them is the fact that the number of hypotheses (the scientific term for a testable explanation for some observed phenomenon) and experiments that needed to be performed increased. With automated methods, the possibilities are amplified.
Scientists said it allowed them to evaluate a greater number of hypotheses, along with the number of ways that scientists could make subtle changes to the experimental set-up. This had the effect of boosting the volume of data that needed checking, standardising and sharing.
Also, robots needed to be “trained” in performing experiments previously carried out manually. Humans, too, needed to develop new skills for preparing, repairing, and supervising robots. This was done to ensure there were no errors in the scientific process.
Scientific work is often judged on output such as peer-reviewed publications and grants. However, the time taken to clean, troubleshoot and supervise automated systems competes with the tasks traditionally rewarded in science. These less valued tasks may also be largely invisible — particularly because managers are the ones who would be unaware of mundane work due to not spending as much time in the lab.
The synbio scientists carrying out these responsibilities were not better paid or more autonomous than their managers. They also assessed their own workload as being higher than those above them in the job hierarchy.
Wider lessons
It’s possible these lessons might apply to other areas of work too. ChatGPT is an AI-powered chatbot that “learns” from information available on the web. When prompted by questions from online users, the chatbot offers answers that appear well-crafted and convincing.
According to Time magazine, in order for ChatGPT to avoid returning answers that were racist, sexist or offensive in other ways, workers in Kenya were hired to filter toxic content delivered by the bot.
There are many often invisible work practices needed for the development and maintenance of digital infrastructure. This phenomenon could be described as a “digitalisation paradox”. It challenges the assumption that everyone involved or affected by digitalisation becomes more productive or has more free time when parts of their workflow are automated.
Concerns over a decline in productivity are a key motivation behind organisational and political efforts to automate and digitalise everyday work. But we should not take promises of gains in productivity at face value.
Instead, we should challenge the ways we measure productivity by considering the invisible types of tasks humans can accomplish, beyond the more visible work that is usually rewarded.
We also need to consider how to design and manage these processes so that technology can more positively add to human capabilities.
| 2023-03-02T00:00:00 |
2023/03/02
|
https://theconversation.com/ai-could-make-more-work-for-us-instead-of-simplifying-our-lives-199554
|
[
{
"date": "2023/03/02",
"position": 80,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/02",
"position": 16,
"query": "artificial intelligence workers"
}
] |
AI in HR: Benefits, Challenges, Best Practices, Trends
|
AI in HR: Benefits, Challenges, Best Practices, Trends
|
https://blog.darwinbox.com
|
[
"Chaitanya Peddi",
"Co-Founder Of Darwinbox",
"Dhrishni Thakuria"
] |
Widespread use of AI in recruitment: AI in HR statistics reveal the technology's importance in recruitment, along with a few caveats. AI will free up a ...
|
Artificial Intelligence (AI) in HR refers to the use of cognitive technologies to mimic human decision-making and action at key stages of the employee lifecycle, like recruitment, engagement, performance management, time & attendance, and HR analytics. Learn about the benefits of AI in HR and how you can unlock the potential of AI for HR service delivery.
The adoption of AI in HR is growing dramatically. 82% of HR professionals believe their teams will incorporate more AI tools into workflows over the next few years. As this and other similar AI in HR statistics illustrate, companies need to be prepared to deploy and tap into the power of AI as part of their HR practices. What does this entail? What does artificial intelligence mean for HR, and what are its pros and cons?
Read on for all the insight you need.
What is AI in HR ?
Artificial intelligence (AI) is a subfield of computer science that focuses on the development of intelligent machines that function and respond in a manner that is similar to human HR executives. And, machine learning (ML) is a subset of artificial intelligence that focuses on how computer systems read and learn from data. Rather than depending on a human to write a program to carry out a task, ML can identify trends and generate predictions that could assist AI in HR.
Another way AI in HR works is through digital assistants. AI-powered digital assistants are built using ML algorithms to comprehend natural language and the purpose of an employee's inquiry, respond accordingly, and offer intelligent direction to complete essential actions.
AI comprises all the ways in which computer programs make intelligent judgments. Its integration within HR processes will improve organizational performance, since AI applications can help evaluate, anticipate, and analyze, thus improving HR teams' decision-making.
This spans nearly every area of HR operations, from recruitment to HR analytics.
Learn more: The Future of AI in HR: How HR Tech is Evolving
The Use of AI in HR
AI opens the doorway to better HR process efficiency in a number of different ways.
Essentially, the incorporation of artificial intelligence reimagines how you interact with your workforce and your capacity to evaluate data, foresee situations, and take necessary action. This is particularly important in the field of recruitment and analytics.
Eventually, artificial intelligence will be able to manage all low-value HR functions, including benefits administration, employee concerns regarding regulations and protocols, and the administration of leave applications. HR practitioners will then have more time to concentrate on high-value activities and achieve organizational change.
Managers can devote more time to mentoring, providing constructive criticism, inspiring and empowering employees, and fostering strong workplace connections.
Let us now explore the key uses of AI in HR, in more detail.
The role of AI in HR and recruitment
One of the major applications of AI in HR is the process of talent acquisition. AI can minimize the effort and time necessary to complete routine tasks and improve the efficiency and effectiveness of the recruiting process. It allows the HR department to concentrate on sourcing, general administration, recruitment promotions and branding, and other relevant areas.
Notably, AI helps develop a predictive and efficacious recruiting framework by bringing intelligence to evaluate a candidate's suitability and talent mapping, predicting patterns in the offer-to-join percentage, and updating the database of all applicants across the numerous levels/aspects of hiring. This helps in improved and faster decision-making.
Organizations can also use AI to scan applicant resumes,fill out digital forms, and perform other tasks. This makes it easier for applicants to complete their paperwork, thus providing them with a better and even personalized employee experience. After the recruiting stage, AI also assists in the development and implementation of effective onboarding procedures and forecasting the employee’s level of engagement with the organization.
This allows the HR team to save effort and time — and build a recruitment process powered by data.
The role of AI in HR analytics
The future of AI in HR analytics is more scalable, effective, and precise. .
AI can enhance an organization's capacity to use HR analytics and large data sources to uncover business insights. Big data will also be examined by artificial intelligence to discover the activity patterns of an organization's workforce. This might help you forecast the sentiments of employees and take action as necessary.
This is just one example of AI in HR analytics. Organizations will be able to use the power that artificial intelligence brings, in sorting through the massive human resources databases in order to tackle business challenges.
If an organization has trouble retaining its best employees, these next-generation analytics will reveal why and how to fix it. If sales in certain stores are lower compared to other locations, AI-led analytics will pinpoint the problem's origins in employee engagement, enabling the company to modify its management strategies. The possibilities are endless.
Learn More: 23 Crucial People Analytics Metrics HR Leaders Must Track
The uses of AI in HR for Employee engagement
Employee engagement is the evaluation and analysis of employee attitudes on a daily basis. The use of AI-powered chatbots may allow both the employee and HR experts to continue the engagement dialogue at regular intervals. These chatbots offer a genuine, human-like, always-available communications platform that engages users in personalized interactions.
These discussions are then examined and used to address the employee's individual issues, aspirations, and objectives. However, it does not stop there.
The action taken following the chat is equally important as seeking feedback. AI-supported chatbots enable HR managers grasp sentiment shifts so that they may solve possible bottlenecks via actions and let workers feel that their opinion matters. Ultimately, this increases employee engagement and decreases turnover.
Learn More: Top 11 Employee Engagement Tools for HR Leaders
The uses of AI in HR for Learning and development (L&D)
Using AI, you can reimagine learning and development, and talent management as a whole, ensuring that your programs are adaptable, personalized, and include employee-specific material.
AI enables L&D executives to develop individualized learning paths that reflect employee roles, skill sets, future objectives, and other pertinent aspects. It can offer learning opportunities based on the requirements of specific workers and provide immediate solutions to basic inquiries that employees have.
In addition, AI can develop learner profiles by merging diverse data sources, a task that would need weeks to complete manually. It can quickly and automatically identify the skills that your employees are missing.
Learn more: How More Retail created a talent management & learning ecosystem for 12,000+ future leaders
Applications of AI in HR Tech
Today, AI is used in HR to make products more powerful, intuitive and differentiated. Here are a few real-world examples of AI in HR tech that you can use at your organization:
Employee lifecycle optimization : AI and machine learning features can be embedded across the employee journey . For example, Darwinbox’s employee lifecycle management platform uses cognitive technologies to help HR manage the entire employee lifecycle, from hiring to exit interviews. Using AI helps make smarter, data-driven decisions across employee lifecycle journey. Considering the vast amounts of data generated, the AI-driven systems will only get smarter and more precise over time.
employee lifecycle management platform AI voice bots : Chatbots are now extremely popular, but AI in HR can power even voice-enabled bots that work as smoothly as consumer programs such as Siri or Alexa. Voice bots can be used with FAQs and helpdesks, replacing a text-based UI with text as well as voice support.
Facial recognition system : Another example of AI in HR is the use of facial recognition technology in an attendance management system . Darwinbox, for example, applies this technology to allow employees to clock in from any location, using geotagging and geofencing along with facial recognition using AI to ensure security and compliance.
Prediction engines : AI in HR analytics can reveal hidden patterns in organizational data using a predictive analytics engine . For example, AI-driven predictive analytics can be used to analyze employee behavior and other parameters to drive the correct action across the employee lifecycle. The AI will automatically nudge employees and managers to take corrective action.
Document scanning : Optical character recognition (OCR) can help scan employee and HR documents without the need for manual effort. For instance, you can simplify expense management by capturing expenses on the go with AI-powered bill scanning software . This also applies to the verification of candidate/new employee ID proof and documents.
Candidate selection and ranking : AI makes it possible to evaluate resumes faster using multi-format resume parsing and stack ranking. You can even program it to utilize an exhaustive skill taxonomy using. The system will rank candidates automatically based on the best-fit analysis.
The Benefits of Using AI in HR
Using artificial intelligence to automate human resource management tasks, make HR decisions, and analyze HR data can unlock the following benefits:
Stay ahead of the curve
Today, staying ahead of competition is a tough proposition. You require a combination of smart, data-driven strategy, and powerful tools and processes. To increase your chances of success in a market where thousands of businesses compete for a few people, you must be aware of the strategies you employ to engage, attract, and recruit the right and the best) talent. To achieve this, you need powerful AI models to crunch HR data and perform people analytics.
More efficient use of resources
In HR, artificial intelligence will allow you to simultaneously save both money and time. It can significantly improve operations and increase employee productivity continuously. There are already dozens of AI-based HR technologies on the marketto help enhance the way you interact and oversee your workforce and applicants.
Better assess soft skills that traditional tests cannot capture
AI technology can assist hiring managers in evaluating nontechnical qualities such as diligence, collaboration, and attention to detail, and do so without bias This is done by using AI technologies for psychometric evaluation or assessing a candidate's reaction to tasks such as a simulated customer contact or sales presentation to determine how well they employ inductive reasoning, logic, or mental agility. These are skills traditionally tested by human recruiters (difficult to scale), as they cannot be quantified through technical tests.
Personalize skill development
AI in HR can enable organizations to personalize the employment experience for every individual. It can be used to evaluate an employee’s current skills, match them with requirements, andoffer specialized programs to get employees up to speed. It could also be used to comb through performance indicators to determine whether individuals need more training and to aid in discovering potential career pathways. With the proper AI technologies, you can guarantee that every worker improves more quickly and enjoys an optimal employee experience.
Eliminate manual work and simplify work
For Since AI technology is accessible 24 hours a day, seven days a week, and can remove human error from routine work, it makes life simpler for employees and supervisors.instance, you may automate the leaverequest system in your organization so employees don’t have to contact their HR business partners or managers to discuss taking time off. . This frees managers’ and HR leaders’ time, allowing them to focus on other crucial tasks that can’t be automated.
Improve candidate experiences
The HR department is often overburdened with multiple responsibilities, resulting in slower response times and poor applicant engagement and experience. This is especially true for small and medium-sized businesses. AI-based tools such aschatbots and virtual assistants will be of great help here. By giving candidates easy access to information, you might be able to leave a good impression with them about your company, which could lead to their to choose your organization over the others..
Increase speed and accuracy
One of the greatest benefits of AI in HR is that it can get work done faster than it can get done if done manually. The HR department will be able to manage more data, for instance, and employees will have fewer tasks to handle. Additionally, AI can help reduce human error. AI automates tasks such as data input – such as autofill forms. With this, employees will not have to repeatedly copy, paste, or reenter identical or repetitive data and introduce errors inadvertently.
Strengthen the employer brand
In a tight talent market, it is essential for every company to build a strong, attractive brand image to attract qualified candidates. Using AI-based technologies during recruiting, onboarding, and even benefits administration may have a significant influence on a candidate's perception of the organization. It not only presents your organization as creative, but also demonstrates that you value employee experience and satisfaction as much as job performance.
Enhance mental health in the age of remote work
One of the applications of AI technology – sentiment analysis – can predict how an employee feels. It could predict or gauge an employee's level of engagement, fatigue, depression, and anxiety, enabling early and individualized intervention so the employee’s mental health can be taken care of. These are insights that one would otherwise only be able to gather in person, a big challenge in today’s remote/hybrid workplaces. .
Provide personalized employee experiences
HR technology will be able to collate, organize, and analyze employee data, and provide people managers with the information and insight they might need to provide each employee with unique, personalized experiences. For instance, AI can be used to make note of patterns of employee behavior and enable the organization to work with the employee accordingly.
Learn more: Highlights from Darwinbox’s HR ChangeMakers Club Session: Digital Foundation For A Future-Ready Workforce
The Challenges of AI in HR
While using AI in HR can make life a lot easier and efficient for organizations, they must also be wary of the challenges surrounding AI in HR. Some areas of concern include:
Risk of perpetuating human bias
AI is only as effective as the people who create it and the data it is trained on. Unfortunately, we live in a society plagued with inequities, so using current data to educate and train AI software and systems might actually result in the replication of pre-existing biases. Even if the creators of AI technologies have the right intentions to keep things neutral and not allow biases to seep in, growing research suggests that the usage of AI-based HR technologies may eventually end up perpetuating discrimination, unless tested rigorously.
Data privacy risks surrounding AI in HR analytics
There are privacy problems and challenges associated with the use of artificial intelligence, particularly in the field of HR analytics. Based on the sector you operate in, the use of AI might impede your compliance with privacy regulations. Companies employing AI in the workplace must carefully evaluate the collection, processing, and retention of data.
Logical errors due to a lack of contextualized cognitive reasoning
AI technologies can reduce human error, but what the AI-driven system processes might be based on fundamentally erroneous ideas. For instance, technology that monitors facial expressions and conversational tonality assumes that these variables would provide pertinent insights into a candidate's eligibility for a position. However, these qualities may be exclusive to a person's culture or individual environment and may lead to bias against individuals who do not conform to a predetermined, even arbitrary, standard.
Incorrect results due to poor coding/programming
Machines aren’t always the the best option for conducting analyses. For example, while using AI for recruiting, with features such as stack ranking, programming errors may lead to misunderstanding of data or inclusion of the incorrect factors while ranking candidates. HR professionals must be wary of entrusting vital reports to AI exclusively.
Limitations in the language analysis model
Sometimes, AI used for recruiting might be too reliant on specific keywords. When the software analyzes the piles of submissions, it searches for terms and phrases that assist in determining the most qualified applicants for the position. Those who understand how AI operates, however, may simply outwit and deceive it by employing these keywords in their forms and making themselves seem qualified for tasks for which they are actually not qualified.
Inability to spot high-potential outliers
This is a common challenge when using AI in HR, particularly for recruitment. HR professionals may end up ignoring candidates that may not meet all of an organization's stringent recruiting criteria but might still be outliers who can contribute significantly to a business. These qualities and their potential might not always come through in a resume and if organizations depend entirely on AI-driven tools, they might miss out on such candidates because the AI technologies used to scan resumes wouldn’t pick them out as potential candidates.
Although it's a good idea to employ AI, the HR department should keep a strict eye on the process in order to identify candidates with specific qualities. Set a time for both you and the team to individually evaluate each applicant, if required.
Issues around AI ethics
Ethical responsibility is crucial for HR departments today, particularly when it comes to employing minorities, responding to sexual assaults, and addressing other such concerns. AI lacks the finer, humane aspects of dealing with employees, particularly in cases of layoffs.
For instance, AI-based tools may be able to look purely at data ad recommend which employees a company should lay off, but HR teams must consider whether or not these solutions are appropriate for the organization.
Skill gaps and other implementation hurdles
In order to integrate AI into the HR department, your team must acquire skills required to handle AI-based tools and software. Implementing AI requires time, money and resources, and this must be factored in while deciding to upgrade your software and processes. In addition, businesses require a certain level of operational and technological maturity to be able to plug AI-based tools into their existing processes. To manage this, a company will have to work with an HR technology provider who will be able to guide them and help them integrate AI into their workflows smoothly and effectively.
AI in HR Statistics : Trends and Insights to Guide Your Strategy
Statistics always add weight to any idea. Here are eight AI in HR statistics that illustrate the benefits and challenges we just discussed, and also give us an insight into the future of AI in HR.
Growing preference for AI-generated recommendations: According to a survey by Oracle/Future Workplace, the majority of HR professionals welcome the use of AI in HR processes. According to the poll , 64% of respondents stated they would prefer a robot above their supervisor or manager for advice.
Widespread use of AI in recruitment: AI in HR statistics reveal the technology’s importance in recruitment, along with a few caveats. AI will free up a recruiter's time (44%), provide important insights throughout the recruiting process (41%), and simplify the recruiter's tasks (39%). However, respondents to the study also feel that incorporating artificial intelligence in the recruiting process might result in the overlooking of exceptional and unconventional talent (35%).
Growing investment in AI in HR and Diversity, Equity, and Inclusion (DEI): 92% of HR professionals want to increase their AI usage for talent acquisition and management, new employee training and onboarding, and payroll processing. In addition to assisting with talent acquisition and administration, 95% of HR leaders use AI for DEI initiatives, the study reveals.
Expansion of AI-based HR tools: SHRM research shows that one-quarter of organizations studied expect to begin using or expanding their usage of automation or AI in recruiting and hiring over the next five years, while one-fifth of organizations plan to begin using or expand their use of these technologies for performance management.
Large companies are leading AI adoption in HR: The majority of small and medium-sized businesses do not use AI or automation to assist in HR-related tasks. SHRM also discovered that just 16% of organizations with less than 100 employees use automation or AI, compared to 42% of businesses with 5,000 or more employees.
Organizations see multiple tangible benefits of AI in HR : According to a poll, around 68% of organizations surveyed feel that the incorporation of AI in HR improves overall work accuracy. 72% of respondents agreed that adopting AI in HR processes increases productivity and helps to save time since it can automate numerous HR-related activities and improve the system's performance via chatbots.
Businesses are focusing on three core areas for the use of AI: All HR processes are ripe for AI implementation. However, the majority of businesses are focusing their AI efforts on three areas: HR operations (40% of businesses), talent acquisition (38%), and employee engagement monitoring (38%), according to Gartner .
AI in HR analytics driving industry growth: 53% of companies in the global HR technology sector are now eager to employ AI to discover patterns and trends in massive data sets. According to SkyQuest, this makes it a helpful instrument for detecting and resolving organizational challenges linked to performance evaluation, interviewing, and employee retention. With the AI revolution, the HR tech industry will reach $35.68 billion by 2028, SkyQuest’s research suggests.
Apprehension about the future: The bulk of job searchers (80%) think that AI will play a significant role in future application processes. However, the majority of candidates (67%) also feel unprepared for this change. Only 35% of respondents believe their organization is mature enough and prepared for the implementation of AI technologies, research suggests.
Learn more: Top 11 Employee Engagement Tools for 2023
Best Practices to Remember
To unlock the full benefits of AI in HR and overcome its challenges, organizations need to follow seven best practices:
Provide the right training data sets to the AI program
Understanding the data is extremely important when using AI applications, since the data fed into the AI tool to train it might cause inconsistencies. If the management team in charge of the application is unable to distinguish between genuine and questionable data, it is the responsibility of the HR team to identify the right data and feed it to the system for better use of the tool. It’s also important to constantly update the data so the results from the AI system is accurate and up to date.
Test the AI-based HR solution for bias before going live
It is critical to test the AI system for bias before it is implemented in an organization. The implementing team could run multiple tests to identifying data set defects that may be uncovered by testing for biases. For instance, a specific demographic variable, such as gender, might be omitted while training the AI system, and then and explicitly included in the results This could be an indication that there’s systemic bais that requires fixing.
Train and empower managers to work with AI in HR
The capacity to learn and adapt is among the most powerful aspects of AI and ML. The best companies enable team members and senior leaders to provide feedback and challenge AI findings, and iterate until the solution meets the specific requirements of each team.
Keep tabs on the latest AI technology developments
Since artificial intelligence is a developing field, it is essential to continuously monitor and evaluate the tools and technologies in place to guarantee they are delivering the desired outcomes. To ensure the effective implementation of AI in HR, it is essential to coordinate with the IT department to ensure that the system is always fully integrated and updated.
Ensure that your AI-based HR tech solution is the right fit for you
Before incorporating AI technology into HR processes, it is essential to identify your objectives and determine how AI may assist you in achieving them. There are several AI-based HR solutions in the market, but it is essential to choose the one that best fits your organization's needs and objectives. HR professionals must be taught how to apply AI technologies and comprehend the data they're dealing with in order to maximize its potential. So, choose a solutioning partner that can help you with AI readiness and training.
Learn More: Is the Metaverse Workplace the Future of Work?
Conclusion
The future of AI in HR is definitely promising, with intelligent technologies altering the HR ecosystem. Internal processes and other HR operations, including data acquisition, recruitment, hiring, evaluation, mentoring, and talent management, can be significantly enhanced by the use of AI in HR.
Successful AI deployment in HR is sure to boost your company's overall efficiency and performance by attracting more talented, experienced, and qualified recruits and assisting existing employees in their professional growth. You simply need to plan the implementation journey alongside an experienced, AI-first HR tech partner.
Darwinbox can help you incorporate AI-driven programs and tools in your HRMS. Schedule a demo today to learn more!
| 2023-03-02T00:00:00 |
https://blog.darwinbox.com/ai-in-hr-trends-benefits-best-practices
|
[
{
"date": "2023/03/02",
"position": 86,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/02",
"position": 86,
"query": "artificial intelligence workers"
},
{
"date": "2023/03/02",
"position": 99,
"query": "artificial intelligence hiring"
}
] |
|
Automation and the Future of the African American Workforce
|
Automation and the Future of the African American Workforce
|
https://www.cogentinfo.com
|
[] |
Across the United States, companies are working toward automating repetitive jobs by employing algorithms that can execute administrative tasks, ...
|
Across the United States, companies are working toward automating repetitive jobs by employing algorithms that can execute administrative tasks, drones that can deliver goods, and robots that can streamline manufacturing. The effect of automation is widespread—grocery stores are installing self-checkout kiosks and truck drivers are gradually ceding ground to autonomous driving technology.
While driverless long-haul trucking may still be some distance away from becoming an everyday reality, at a micro level, people in support-related roles like administrative support, helpers, operatives, and laborers are directly impacted.
Such a scenario leaves African Americans particularly vulnerable since they are more likely to be employed in jobs that are at risk of being automated within the next two decades and those that cannot be performed remotely. Automation trends are also widening the traditional, well-documented racial wealth gap between African Americans and white families in the United States. By attempting to transition the general US workforce to automation, existing disparities in income, wealth, and opportunities are likely to become much worse.
Why are African Americans Vulnerable?
Even though diversity and inclusion are sources of competitive advantage, representation of the African American workforce follows a trend opposite to that of the general population in the United States.
According to the U.S. Bureau of Labor Statistics,
African Americans represent only 13% of the labor workforce compared to 77% of Whites. This is a clear indication of the existing disparity and discrimination.
African Americans hold only 34% of management, professional, and related occupations as compared to 43% of Whites.
There are ongoing efforts to improve diversity and inclusion in the workplace. Nevertheless, disparities still exist and progress has been slow, especially in certain industries.
While a majority of the general population is employed in directive roles, African Americans are overwhelmingly employed in support roles. This alone worsens racial wealth inequity because the average wage for support roles is $32,000 compared to the average wage for directive roles, which is $69,000.
In addition, automation is expected to reduce approximately 53% of the time spent on support functions. Thus, African Americans are particularly susceptible to the changing market.
Historically, African Americans have faced barriers to accessing educational and job opportunities . This limits their ability to adapt to the changes brought about by automation.
. This limits their ability to adapt to the changes brought about by automation. They are constantly overrepresented in certain low-skilled and low-paying occupations. Examples: manufacturing work, support jobs, and personal care and service. Meanwhile, they are underrepresented in high-skilled and high-paying occupations. Examples: technology, engineering, and management.
Examples: manufacturing work, support jobs, and personal care and service. Meanwhile, they are underrepresented in high-skilled and high-paying occupations. Examples: technology, engineering, and management. Even if they are looking to transition to a high-paying job, their current qualifications are insufficient . They are not paid enough to access the education they need to move to a high-paying job.
. They are not paid enough to access the education they need to move to a high-paying job. Additionally, African Americans face a persistent wage gap compared to their white counterparts. The median weekly earnings of African American workers are approximately 82% of those of White workers.
One way to practically address these issues is by ensuring that African Americans have the opportunity to develop new skills and access the education needed to succeed in the new landscape. This may include investments in education and job training programs, as well as efforts to increase access to opportunities in high-paying fields like technology and engineering.
Benefits of a Diverse Workforce
Retraining African American workers in five occupation categories (Office & Administrative Support, Production, Food Preparation and Serving-Related, Sales & Related, and Transportation and Moving Materials) would reduce almost 60% of the risk of job displacement.
The benefits of a diverse workforce are manifold:
Organizations can benefit from the diverse viewpoints that their African American employees can bring in, breeding innovation and thereby increasing their reach in the global market.
By improving opportunities for African Americans, the economy becomes more innovative and resilient due to the diverse workforce. It can provide a range of perspectives and ideas for addressing challenges and promoting growth.
Increased competitiveness in the job market and improved earnings will lead to increased consumer spending, thereby improving the economy at large.
Opportunities to Retrain
The private sector, public sector, and social sector are required to collaborate to enable African Americans to receive retraining opportunities that will enable them to join strategic and value-add job roles.
Some ways in which retraining opportunities can be promoted are:
Double down on Diversity and Inclusion programs
Diversity in the workplace has repeatedly proven to be effective. It fosters innovation, breeds creativity and introduces new perspectives that can efficiently solve business challenges. It also helps minority groups reach out to a diverse customer segment, thereby improving its market appeal and competitiveness. Corporations already have diversity and inclusion programs. However, Black executives still find it difficult to navigate the corporate world due to poor representation.
According to a review by The Washington Post of 50 companies that took the pledge to address racial inequality, Black employees constitute only a "strikingly small fraction" of top executives. Further, companies that have hired diversity chiefs to improve Black representation often don’t feel empowered to make the right decisions. It has almost become a necessity to make it a corporate responsibility and promote diversity in leadership positions.
Investment in Education and Job Training
Providing education and job training programs to help minority workers acquire in-demand skills can close the skill gap and open a new world of opportunities for them. This can mitigate the effects of automation on minority communities and help them stay competitive in the changing work landscape.
Further, job training programs can even help African Americans transition to new industries and careers by equipping them with the skills and knowledge necessary to make the switch. This may include programs that focus on:
Digital skills that have a high demand in the market such as data analytics, coding, and digital marketing. For instance, Etsy provides three-month scholarships to African American women to upskill them in programming skills. To date, the company has increased the number of women in its engineering team by 500% through its training programs.
Technical skills such as robotics, artificial intelligence, and machine learning since they are in high demand as automation becomes more widespread.
Soft skills such as communication, problem-solving, and collaboration. These are becoming increasingly pivotal as jobs become more reliant on technology and automation.
Entrepreneurship skills such as innovation, risk-taking, and financial management. This will help create self-employment opportunities and businesses in the face of automation.
Schemes to promote education in high-demand fields among African Americans:
Low-income African American workers or underemployed workers can be provided educational aids to improve accessibility to community colleges and be introduced to new educational programs to help them get job-ready. The U.S. government offers a range of schemes to help African Americans in the changing workplace landscape, including workforce development programs, vocational and technical training programs, apprenticeships, financial assistance programs, and affirmative action programs. For example, The Kansas Advanced Manufacturing Program offers employer-driven training courses in advanced-manufacturing industries by working with public workforce agencies, industry groups, and colleges.
Sensitizing employees
There may be unintentional microaggressions and discrimination in the workplace. Therefore, regularly sensitizing employees and consciously promoting inclusivity can be game-changing. This can be done through coaching and workshops that enable employees to work in diverse environments without offending others or suffering discrimination.
Improve the accessibility to growth:
Career training can be achieved through mentorship programs, internships, tie-ups with good recruitment agencies and outreach to African American communities. Existing successful employees/leaders in African American communities can pave the way for more such individuals to come to the forefront. They are aware of the problems faced by individuals in the community. They have also successfully tackled them. Hence, their mentorship and contribution to the community can help a great deal to shatter the glass ceiling.
Conclusion
Automation and technological advancements bring many benefits to the workplace. Yet, the effect it has on increasing the disparity in wealth, income, and opportunities between African American workers and White workers cannot be overlooked. Organizations and leaders need to actively intervene to help them thrive through these changes. This is the perfect time to rethink DEI strategies.
Helping African Americans navigate the impacts of automation has several economic benefits. For organizations, it is, increased productivity, decreased inequality, and improved competitiveness. Addressing disparities and promoting an equitable workplace supports building a stronger and more resilient economy for all.
Cogent Staffing can help businesses build a diverse, inclusive, and advanced workforce to meet their evolving requirements. Click here to read more about how Cogent Staffing can help with retraining needs to sensitize professionals to racial inequities and enable a commitment to DEI initiatives.
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| 2023-03-02T00:00:00 |
https://www.cogentinfo.com/resources/automation-and-the-future-of-the-african-american-workforce
|
[
{
"date": "2023/03/02",
"position": 56,
"query": "automation job displacement"
},
{
"date": "2023/03/02",
"position": 23,
"query": "job automation statistics"
}
] |
|
The Rise of ChatGPT and the Future of White-Collar Jobs
|
The Rise of ChatGPT and the Future of White-Collar Jobs
|
https://library.stratfor.com
|
[
"Senior Global Analyst At Rane"
] |
High Frictional Unemployment: A 2020 study by Acemoglu and Restrepo found that since 1987, industries that increased automation were no longer replacing jobs ...
|
Since its release on Nov. 30, 2022, an artificial intelligence (AI) chatbot called ChatGPT has taken the world by storm with its ability to provide in-depth answers to a variety of questions and seemingly hold meaningful conversations. ChatGPT's resounding success has pushed U.S. software and internet services company Microsoft to expand investment in the technology's developer, U.S. AI research and deployment company OpenAI, as well as integrate the chatbot with its Bing search engine. Since then, U.S. technology company Google, Chinese internet company Baidu and U.S. social media platform Facebook have accelerated their own plans to develop chatbots and other generative AI tools, lest they be left behind. ...
| 2023-03-02T00:00:00 |
https://library.stratfor.com/article/rise-chatgpt-and-future-white-collar-jobs
|
[
{
"date": "2023/03/02",
"position": 65,
"query": "automation job displacement"
},
{
"date": "2023/03/02",
"position": 48,
"query": "ChatGPT employment impact"
},
{
"date": "2023/03/02",
"position": 48,
"query": "ChatGPT employment impact"
}
] |
|
Invalidity insurance (AI)
|
Invalidity insurance (AI)
|
https://www.avscvci.ch
|
[] |
Unemployment insurance (AC) · Accident insurance (AI) and occupational ... Within this overall rate, AVS is 8.7%, AI is 1.4%, and APG is 0.50%. In ...
|
Invalidity insurance (AI)
Invalidity insurance (AI)
CONTRIBUTIONS As an employer, you are responsible for the payment of social contributions in respect of all your employees (as from 1st January following their 17th birthday) and in respect of any self-employed persons working for you who have not been declared to a social security institution. The obligation terminates when your employees reach retirement age and cease all gainful employment. For administrative purposes, the AVS/AI/APG contributions are grouped together at an overall rate of 10.60%, of which half (5.30%) is contributed by the employee and half by you. Within this overall rate, AVS is 8.7%, AI is 1.4%, and APG is 0.50%. In addition, there is a contribution to the Compensation Fund’s administrative costs (between 0.02% and 0.175% of the payroll), calculated on the basis of reported annual salaries.
PENSIONS In principle, all insured persons who as a result of an injury are partially or completely unable to continue in gainful employment are entitled to a disability pension. Before applying for a pension, together with the AI Office you should examine measures for early detection and preventive action. As a rule, the AI pays a daily allowance to insured persons over 18 years of age who have an incapacity for work equal to or greater than 50%, and who undergo rehabilitation. The insured person is entitled to an allowance if the disability causes work incapacity of at least 40%, on average, for one year. The allowance is paid at the earliest six months after the insured person has applied for a pension. To apply for a pension or request advice on behalf of the employee concerned, you should contact the AI office.
| 2023-03-02T00:00:00 |
https://www.avscvci.ch/en/employer/invalidity-insurance-ai.html
|
[
{
"date": "2023/03/02",
"position": 57,
"query": "AI unemployment rate"
}
] |
|
Machine learning is galloping, but somebody still needs to ...
|
Machine learning is galloping, but somebody still needs to manage the data and feed the machine
|
https://www.workingnation.com
|
[
"Victoria Lim"
] |
We want to reduce cost as a barrier and increase teacher capacity.” AI and Machine Learning: A Catalyst for Career Advancement. A 2021 study shows fewer Black ...
|
Let’s start with a definition from Merriam-Webster.
machine learning (noun)
: the process by which a computer is able to improve its own performance (as in analyzing image files) by continuously incorporating new data into an existing statistical model
Machine learning is the idea that computers can use artificial intelligence (AI) to learn from vast amounts of data without being programmed directly. They will learn and adapt.
Neither AI nor machine learning (ML) are new, but the advancements are galloping. ChatGPT is just the latest example!
It’s hard to say just how sophisticated and complex this new technology will ultimately become over the next few years and what the big impact will be on the workforce, but it is clear some jobs will be lost while many more will be transformed.
A report from the World Economic Forum forecasts continued advances in technology will eliminate 85 million jobs by 2025, but more than 97 million new jobs will be created to help support the industry. That’s 12 million more jobs being created than those being destroyed.
By the end of the decade one million of those jobs are expected to be in AI and ML, says Mike Miller, general manager of artificial intelligence devices for Amazon Web Services.
For now, however, there are not enough people working in the field and AWS is giving away some of its most innovate teachings for free to select learning institutes to develop and upskill the workforce of the present, and the future.
Teaching the Teachers: Free Learning and Curriculum
AWS is offering the free programs to U.S. community colleges, Historically Black Colleges and Universities (HBCUs), and Minority Serving Institutions (MSIs).
The new AWS Machine Learning University educator enablement program teaches artificial intelligence and machine learning courses to professors and instructors so they can incorporate the learnings into their classes. It includes an in-person bootcamp scheduled during the evening or weekends so it doesn’t disrupt teaching schedules.
Mike Miller, general manager AI devices, AWS
The curriculum consists of online modules and learning resources, as well as access to AWS resources to provide hands-on experience with AWS, AI, and machine learning concepts – all for free, in the cloud.
Lecture slides, coding exercises, exams, and instructor handbooks can be used for college level, for-credit courses.
“AWS and Amazon have a goal to educate people around the world. Whether developers or students or professional learners, and machine learning being such a fast moving and key technology to future innovation, they are going to generate products and services that assist businesses and also help sustainability around the world,” explains Miller.
“We saw elite institutions are able to spend two-to-five times more per student than less resourced colleges and universities,” he adds. “Unfortunately, that’s where underrepresented students are concentrated and it deepens gaps in opportunities. We want to reduce cost as a barrier and increase teacher capacity.”
AI and Machine Learning: A Catalyst for Career Advancement
A 2021 study shows fewer Black and Latino students are earning engineering degrees, which is considered the most popular pathway to AI and machine learning careers.
The catalyst for the educator program was a request by G. Raymond Brown, Ph.D., program coordinator for artificial intelligence at Houston Community College (HCC) who was already using some of the free materials AWS offers in his courses.
G. Raymond Brown, program coordinator for artificial intelligence, Houston Community College
HCC is the first in Texas to offer an associate’s degree in AI. Brown says he realized the urgent need in the workforce – current and future – and collaborates with AWS to help educators at not just HCC, but other schools.
“The idea is to provide a workforce at scale,” Brown says. ”We don’t think the universities and undergraduate programs can generate enough people to spread AI as rapidly as it wants to be spread throughout the economy.”
He adds, “Our intent has been to design a model that will allow thousands of students from 1,500 community colleges across the country to meet that workforce need. Every large corporation we speak to about how they might use AI say they need thousands of people. They can’t hire at this point.”
HCC just received approval to start a bachelor’s degree program in AI and robotics, further emphasizing the need and support for the educator enablement program.
Expanding Educational Opportunities
The pilot bootcamp finished in January, and Laney College dean of math and sciences Angel Fuentes was part of it. Fuentes started the first-ever associate degree in AI and machine learning in the nation while at Gilbert Community College; it launched a week before HCC’s.
Angel Fuentes, dean of math and sciences, Laney College
“Education is always changing. I’ve been teaching for a long time, since 2006. If I taught today the way I taught in ‘06 I’d be worst professor in the world,” Fuentes says. “I saw (the enablement program) as a way to establish and really grow more with this community and to see what’s the latest and greatest, get ideas with others, share ideas and have a network that we can count on, that can help me, and me help them.”
Fuentes says being surrounded by Bay area tech companies brings a growing demand for AI and ML knowledge.
Laney College will offer an associate degree and certificates in both, in the fall. The enablement program is also opening up his mind to possibilities of how AI and ML can be used ethically to do good.
“In my head I envision a program we’re starting in the fall, with student-led projects towards social justice,” he says.
“How to enable AI and ML, incorporating, for example, my participation with the Hispanic American Association of Community Colleges, to find food deserts? How do we use AI and ML to bring food to low-income communities, especially?”
‘Changing lives in ways we still can’t really understand’
In addition to the developing bachelor degree, HCC is planning certifications and stackable credentials. Brown says their programs attract recent high school graduates, working adults, even those with graduate degrees in other disciplines. They see the associate degree as a way to pivot their careers. He says it’s typical to have companies and professional organizations reach out to HCC once they hear about the program.
“(AI and ML) will be more a general scope in our economy than electricity is proven to be. That’s a huge thing to say, but it really appears to be the case,” predicts Brown.
“It will be more generally useful even than our main power source and it’ll change the way people live in ways that we still can’t really understand or imagine yet just like electricity did, or introduction of automobiles did, telephonic communication of all types. All of those grow with AI applications,” he says.
Cultivating the ‘best minds from all backgrounds’
While the curriculum is from AWS, it’s designed to help with entry level roles in AI and ML in any industry and any company; it’s not specific to Amazon careers.
By the end of 2023, the educator enablement program expects to have 330 educators through its bootcamps largely from community colleges, MSIs, and HBCUs.
“What we see is the demand of these skills and talent is strong. If you look at the computer science graduation rate in U.S., there’s 54,000 graduates, which is a dominant pathway. But the AI workforce is expected to need one million (workers) by end of decade,” Miller says.
“We want to make sure the best minds from all backgrounds are entering the fields. Innovation can come from anywhere. It’s important for us to democratize AI and ML knowledge and enabling those career related opportunities.”
WorkingNation editor-in-chief Ramona Schindelheim contributed to this article.
| 2023-03-02T00:00:00 |
2023/03/02
|
https://www.workingnation.com/machine-learning-is-galloping-but-somebody-still-needs-to-manage-the-data-and-feed-the-machine/
|
[
{
"date": "2023/03/02",
"position": 92,
"query": "AI unemployment rate"
},
{
"date": "2023/03/02",
"position": 65,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/02",
"position": 6,
"query": "machine learning workforce"
}
] |
Top 10 manufacturing jobs and who's hiring
|
Top 10 manufacturing jobs and who’s hiring
|
https://joinhandshake.com
|
[
"Ben Nemeroff"
] |
... automated manufacturing systems and develop new production software. It can help you land jobs like robotics engineer and data analyst. Quality management ...
|
Alright, you just graduated, but what do you do now? If you want to work in a massive, global industry with almost limitless potential for career growth, maybe you should try manufacturing.
Looking to start your career but don’t know where to begin? Don’t worry — we have you covered. We’ll tell you about the best manufacturing career opportunities in areas like quality control, supply chain logistics, and production management.
Then, we’ll give you the job description for each and provide a list of companies looking for skilled manufacturing workers to fill part- and full-time jobs through Handshake.
Should you pursue a career in manufacturing?
Even though the U.S. Bureau of Labor Statistics (BLS) expects production jobs to decrease by about 2% from 2021 to 2031, it also projects that around 1 million manufacturing jobs will become free due to people retiring each year, so there will be plenty of open positions.
Most manufacturing jobs require technical skills and attention to detail. Some higher-level manufacturing operations jobs also require leadership, analytical abilities, and problem-solving skills. And since technology is always evolving, manufacturing technicians must be able to adapt quickly to new tools and processes.
Best degrees to land manufacturing jobs
The degree plans that can get you into a manufacturing career are diverse. Here are some of the best ones:
Engineering. This degree can teach you how to engineer new materials, products, and processes to help you land jobs like materials or manufacturing engineer.
This degree can teach you how to engineer new materials, products, and processes to help you land jobs like materials or manufacturing engineer. Operations management. This degree will teach leadership and organization skills to help you direct production. It can help you land jobs like production supervisor and supply chain manager.
This degree will teach leadership and organization skills to help you direct production. It can help you land jobs like production supervisor and supply chain manager. Computer science and information technology (IT). This degree can give you the technical skills to manage automated manufacturing systems and develop new production software. It can help you land jobs like robotics engineer and data analyst.
This degree can give you the technical skills to manage automated manufacturing systems and develop new production software. It can help you land jobs like robotics engineer and data analyst. Quality management. This degree will teach you how to inspect and test manufacturing processes to ensure they meet quality standards. It can help you land jobs like industrial engineer and quality control specialist.
This degree will teach you how to inspect and test manufacturing processes to ensure they meet quality standards. It can help you land jobs like industrial engineer and quality control specialist. Industrial design. This degree will teach you to improve current products and create new ones with sustainability and the end user’s convenience in mind. It can help you land jobs like industrial designer and user experience (UX) designer.
Industries where you can build a manufacturing career
It’s difficult to find an industry that doesn’t need manufacturing. Here are some of the top sectors where Handshake can help you find manufacturing jobs:
Health care. Hospitals and caretakers need a steady supply of pharmaceuticals and medical devices. Careers include medical device manufacturing engineer and pharmaceutical production technician.
Hospitals and caretakers need a steady supply of pharmaceuticals and medical devices. Careers include medical device manufacturing engineer and pharmaceutical production technician. Technology. Computers and electronics need smart chips and a lot of other components. Careers include electronic manufacturing engineer and electronic quality control analyst.
Computers and electronics need smart chips and a lot of other components. Careers include electronic manufacturing engineer and electronic quality control analyst. Retail. Stores need clothes, home goods, and other products to sell. Careers include garment production manager and product development engineer.
Stores need clothes, home goods, and other products to sell. Careers include garment production manager and product development engineer. Power. Manufacturers build materials to harness fossil fuels and renewable forms of energy, like solar power. Careers include oil and gas production technician and renewable energy materials engineer.
Manufacturers build materials to harness fossil fuels and renewable forms of energy, like solar power. Careers include oil and gas production technician and renewable energy materials engineer. Aerospace . All types of aircraft need safe, reliable parts. Careers include composite materials technician and aerospace manufacturing engineer.
All types of aircraft need safe, reliable parts. Careers include composite materials technician and aerospace manufacturing engineer. Motor vehicles. Car and motorcycle manufacturers are always building safer, more efficient vehicle parts. Careers include manufacturing manager and quality control analyst.
Scroll to the bottom of this article to find a bigger list of companies hiring manufacturing professionals through Handshake.
Top 10 entry-level jobs in manufacturing
Here are some of the top entry-level jobs in the manufacturing industry you can get.
1. Manufacturing engineer
Manufacturing engineers work with other departments to find and fix obstacles in production, so factories can run more efficiently. They also help businesses make the best use of resources.
Median salary: $95,300 a year
Qualifications:
Bachelor’s degree in mechanical or industrial engineering or a related field
Mechanical engineers need a license from an ABET-accredited program
Skills:
Communication and collaboration skills
The ability to use computer-aided design (CAD) tools
Problem-solving abilities
Analysis skills
Attention to detail
Independence
Adaptability
2. Production worker
Production workers operate on assembly lines in factories. They’re the ones who actually build products. They’re also responsible for inspection and shipping.
Median salary: $36,230 a year
Qualifications:
High school diploma or equivalent
A certified production technician course can be substituted
Skills:
Ability to listen and follow instructions
Attention to detail
Endurance and strength
Time management
Communication
Problem-solving
3. Machine operator
Machine operators use manufacturing devices in assembly and product transportation. They also make sure machines work properly and produce quality products.
Median salary: $38,380 a year
Qualifications:
High school diploma or equivalent
Some businesses require prior machine operating experience or technical training
Skills:
Endurance and strength
Ability to follow technical instructions
Technical machine operating skills
Organizational skills
Ability to work with team members
4. Quality control technician
Quality control technicians inspect and test products and machinery. Products must be free of defects and meet quality standards before reaching customers. Quality control technicians use calipers, micrometers, spectrometers, and other tools to take measurements, run tests, and document defects.
Median salary: $49,522 a year
Qualifications:
High school diploma or equivalent
Some businesses may require an associate or bachelor’s degree in a relevant field
Some businesses require former experience
Skills:
Problem-solving
Attention to detail
Communication and collaboration
Flexibility
Basic documenting and computer skills
5. Process technician
Process technicians manage the production process in a facility. They watch for errors in equipment and troubleshoot problems. They work in rotating schedules to make sure production always runs smoothly.
Median salary: $62,001 a year
Qualifications:
High school diploma or equivalent
Some companies prefer an associate or bachelor’s degree in manufacturing, engineering, or a related field
Skills:
Technical skills
Troubleshooting
Problem-solving
Attention to detail
Interpersonal skills
Teamwork
Ability to perform under pressure
Ability to use CAD tools and other software
6. Supply chain coordinator
Supply chain coordinators manage materials. They check inventory levels and correspond with vendors to ensure all supplies get delivered and end up where they need to go.
Median salary: $52,241 a year
Qualifications:
Bachelor’s degree in supply chain management or a related field
Certified Supply Chain Professional (CSCP) or similar certification
Skills:
Strong organizational skills
Analytical skills
Clear communication skills
Ability to make quick decisions
Ability to use software tools, like Microsoft Excel
Attention to detail
7. Maintenance technician
Maintenance technicians keep manufacturing machines working smoothly and safely. They examine equipment to see what could go wrong and perform preventative repairs. When machines malfunction, they troubleshoot and fix them.
Median salary: $59,380 a year
Qualifications:
High school diploma or equivalent
A technical certificate or maintenance degree
Skills:
Ability to understand technical manuals
Basic computer skills
Problem-solving
Communication skills
Adaptability
Troubleshooting skills
8. Inventory control specialist
Inventory control specialists track and order stock, keep performance records on shipping, receiving, and storage transactions, and make suggestions to improve inventory management. They also research and solve inventory disparities.
Median salary: $37,870 a year
Qualifications:
High school diploma or equivalent
Some businesses may require a degree in supply chain management or a related field
Skills:
Excellent organizational skills
Attention to detail
Communication and collaboration skills
Teamwork
Ability to use inventory management software
9. Industrial engineer
Industrial engineers use automation and analysis tools and engineering software to design ways to improve manufacturing processes, maximize efficiency, and save money. They work with other departments to better understand how production operates.
Median salary: $95,300 a year
Qualifications:
Bachelor’s degree in industrial engineering or a related field
Skills:
Excellent mathematical abilities
Ability to use statistical analysis and process mapping
Analytical skills
Attention to detail
Organizational skills
Project management
Ability to use engineering software like CAD
10. CNC operator
Computer numerical control (CNC) operators study blueprints and use precision measuring tools to program machines to make parts with specific dimensions. CNC operators must adjust, calibrate, and test equipment to ensure it produces parts with the right specifications.
Median salary: $46,640 a year
Qualifications:
High school diploma or equivalent
Understanding of CNC machines
CNC certifications can be helpful
Skills:
Ability to understand and use schematics
Technical ability to operate and adjust machines
Attention to detail
Ability to use precision measuring tools
Basic computer skills
Who’s hiring in manufacturing on Handshake?
Start your career in manufacturing with help from Handshake
If you’ve dreamed of being a machinist, or working with machines, and you want to work hard in a manufacturing environment with huge opportunities for advancement, a career in manufacturing might be just what you need.
Handshake helps job seekers move their job search along. We’ll give you job alerts and access to job posts from top companies looking to hire talented people for great manufacturing jobs. Create your profile and get started today.
Sources:
| 2023-03-02T00:00:00 |
https://joinhandshake.com/blog/students/manufacturing-jobs/
|
[
{
"date": "2023/03/02",
"position": 91,
"query": "job automation statistics"
}
] |
|
The Impact of Artificial Intelligence on the Job Market
|
The Impact of Artificial Intelligence on the Job Market
|
https://corpessentials.com
|
[] |
Another concern is that AI will create a skills gap, where workers who lack the necessary skills to work with AI technologies will be left behind. As ...
|
Artificial intelligence (AI) is a rapidly advancing field that is changing the way we live, work, and interact with the world around us. From self-driving cars to personalized healthcare, AI is transforming nearly every industry and sector. However, with these advancements come concerns about the impact of AI on the job market. Many people worry that AI will replace human workers and lead to widespread unemployment. In this article, we will explore the impact of AI on the job market and what it means for the future of work.
The Benefits of AI in the Workplace
Before we dive into the potential negative impacts of AI on the job market, it’s important to acknowledge the many benefits of AI in the workplace. AI can automate repetitive and time-consuming tasks, freeing up employees to focus on higher-level tasks that require creativity, critical thinking, and emotional intelligence. This can lead to increased job satisfaction and productivity, as well as higher-quality work.
AI can also improve efficiency and accuracy in industries such as healthcare, finance, and manufacturing. For example, AI-powered diagnostic tools can help doctors make more accurate diagnoses, while AI algorithms can help financial institutions detect fraud and identify investment opportunities. In manufacturing, AI can optimize production processes, reduce waste, and improve product quality.
Additionally, AI has the potential to create entirely new industries and job opportunities. As AI technologies continue to advance, new roles will emerge that require specialized skills in areas such as data science, machine learning, and natural language processing. These roles will be essential in developing, implementing, and maintaining AI systems, as well as analyzing and interpreting the data generated by these systems.
The Potential Negative Impact of AI on the Job Market
Despite the many benefits of AI in the workplace, there are concerns about the potential negative impact on the job market. One of the biggest concerns is that AI will replace human workers, particularly in industries that rely heavily on manual labor or routine tasks.
For example, in the manufacturing industry, robots and other forms of automation have already replaced many jobs that were previously done by humans. Similarly, in the retail industry, self-checkout machines and online shopping platforms have reduced the need for human cashiers and sales associates. As AI technologies continue to advance, it’s likely that more jobs will be automated, leading to job losses and unemployment.
Another concern is that AI will create a skills gap, where workers who lack the necessary skills to work with AI technologies will be left behind. As mentioned earlier, AI has the potential to create new job opportunities, but these jobs will require specialized skills that many workers may not possess. This could lead to a situation where there are a large number of open jobs in AI-related fields, but a shortage of qualified candidates to fill them.
Finally, there are concerns about the ethical implications of using AI in the workplace. For example, there are concerns about bias in AI algorithms, which could lead to discrimination against certain groups of people. Additionally, there are concerns about the impact of AI on privacy, as AI systems are able to collect and analyze large amounts of personal data.
Mitigating the Negative Impact of AI on the Job Market
While there are certainly concerns about the negative impact of AI on the job market, there are also steps that can be taken to mitigate these impacts.
Investing in Education and Training Programs
One of the most important steps in mitigating the negative impact of AI on the job market is to invest in education and training programs that prepare workers for the jobs of the future. This could include programs that teach skills in data science, machine learning, and other AI-related fields. By investing in education and training programs, we can ensure that workers have the skills they need to succeed in the jobs of the future.
Governments, businesses, and educational institutions can work together to create these programs and make them accessible to everyone. This can help to close the skills gap and ensure that workers are prepared for the changes that are coming to the job market.
Creating New Jobs
Another approach to mitigating the negative impact of AI on the job market is to focus on creating new jobs that are less likely to be automated. For example, jobs that require human interaction, creativity, and emotional intelligence are less likely to be replaced by AI. This could include roles in healthcare, education, and the arts.
In addition to creating new jobs, it’s important to invest in industries that are less likely to be automated. This could include industries such as renewable energy, sustainable agriculture, and advanced manufacturing. By investing in these industries, we can create jobs that are less likely to be automated and more likely to provide long-term stability for workers.
Encouraging Lifelong Learning
As AI technologies continue to advance, it’s important for workers to be able to adapt and learn new skills throughout their careers. Encouraging lifelong learning can help to ensure that workers are able to stay up-to-date with the latest technologies and trends in their industries.
Businesses can provide opportunities for employees to learn new skills and advance in their careers. This could include on-the-job training, mentorship programs, and tuition reimbursement for continuing education. Governments can also provide incentives for businesses to invest in lifelong learning programs.
Addressing the Ethical Implications of AI
Finally, it’s important to address the ethical implications of AI in the workplace. This includes concerns about bias in AI algorithms, which could lead to discrimination against certain groups of people. Additionally, there are concerns about the impact of AI on privacy, as AI systems are able to collect and analyze large amounts of personal data.
To address these concerns, it’s important to develop ethical guidelines and standards for the use of AI in the workplace. These guidelines should be developed in collaboration with experts in the field, as well as representatives from labor unions, civil society organizations, and other stakeholders.
While there are certainly concerns about the negative impact of AI on the job market, there are steps that can be taken to mitigate these impacts. By investing in education and training programs, creating new jobs, encouraging lifelong learning, and addressing the ethical implications of AI, we can ensure that the benefits of AI are realized while minimizing its negative impact on the job market. It’s important for businesses, governments, and other stakeholders to work together to create a future of work that is equitable, sustainable, and inclusive.
| 2023-03-02T00:00:00 |
2023/03/02
|
https://corpessentials.com/2023/03/02/the-impact-of-artificial-intelligence-on-the-job-market/
|
[
{
"date": "2023/03/02",
"position": 13,
"query": "AI skills gap"
},
{
"date": "2023/03/02",
"position": 64,
"query": "AI labor market trends"
}
] |
Skills Intelligence: The Future Hiring Metric
|
Skills Intelligence: The Future Hiring Metric
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https://www.imocha.io
|
[] |
Increasing need for upskilling and reskilling: With the rapid pace of technological changes, job candidates need to upskill or reskill to keep up with the ...
|
And this sort of agility and resilience can only be achieved through ‘Skills Intelligence.’ Before we go any further, if you wish to know more about what is skills intelligence, visit here!
The concept of skills intelligence is not new but has gained increased attention in recent years as the nature of work has evolved and technological changes have accelerated.
Earlier, skills intelligence was perceived as a concept emphasizing the importance of identifying and developing top talent within an organization to gain a competitive advantage. But as times have changed, it has evolved to encompass broader skills and competencies beyond top talent.
Today, around 59% of HR professionals believe that skills intelligence is a critical organizational priority. (LinkedIn's 2021 Workplace Learning Report)
How Skills Intelligence can be the future hiring metric?
Skills intelligence has the potential to become a significant hiring metric in the future for several reasons:
Increased focus on skills-based hiring: Many companies are shifting from traditional resume-based hiring to skills-based hiring. This means that they are looking for candidates who have the specific skills required for the job, rather than just looking at their education or work history. Automation of recruitment processes: With the rise of artificial intelligence and machine learning, recruitment processes are becoming increasingly automated. This means that skills intelligence platforms can be integrated into recruitment software to help filter and rank candidates based on their skills. Increasing need for upskilling and reskilling: With the rapid pace of technological changes, job candidates need to upskill or reskill to keep up with the changing demands of the job market. Skills intelligence can help employers identify the skills gaps in their candidate pool and hire only job-fit candidates.
Struggling to cope up with the ever-changing skill demands? Try iMocha's Skill Intelligence for free! Book a demo
Why are some organizations still resisting the adoption of skills intelligence and a skill-first mindset?
The reason can be one of the following:
Implementing a skills intelligence strategy often requires significant changes to an organization's culture, processes, and systems. This can be met with resistance from employees who may be hesitant to embrace new ways of working or to invest time and effort in upskilling. Developing and implementing a skills intelligence strategy requires resources, including time, money, and expertise. Organizations with limited resources may struggle to invest in skills intelligence initiatives. Skills intelligence is closely tied to technology, and organizations must stay updated with the latest tools and platforms to implement a skills intelligence strategy effectively. Most organizations still face challenges identifying the most important skills for success in a given role or industry. Many organizations struggle with data quality issues, making it difficult to understand their workforce's skills and skill requirements clearly.
Is data quality hindering your decision making? With iMocha's Skill Intelligence make data-driven decisions using detailed and real-time reports! Book a demo
Amongst the reasons listed above, those associated with the ‘fear of change’ can only be tackled through practical thinking. For the rest, ‘skills intelligence platforms’ can be the answer.
How can iMocha help you hire better candidates?
iMocha is a comprehensive skill intelligence platform that combines advanced technologies, such as AI, ML, and data analytics, to identify and assess job role-specific skills and collect and evaluate quality data. With iMocha, you can leverage the following benefits:
Identification of the right skills: iMocha uses various assessment tools and techniques to help you identify your team’s strengths and weaknesses in terms of skills/skills gaps. It helps you understand where your workforce excels and what new skills are needed from the new hire. Mapping skills to career opportunities Meeting DEI goals: iMocha prioritizes skills over gender, color, race, or any bias-inducing factors. It also removes bias from the assessment process and helps you choose candidates only on the basis of merit. Testing of soft skills: One of the most valuable features of iMocha is that it helps you assess the language proficiency of candidates. Unlike the conventional method of soft skill testing (such as verbal interviews and resume screening), iMocha uses AI-EnglishPro to accurately asses business communication proficiency across all communication aspects and deliver accurate results and reports. Developing skills: iMocha’s skill intelligence platform offers training and development programs to help you simplify talent onboarding and project-specific training. These programs can be customized to your specific needs and also be used for upskilling/reskilling the existing workforce.
Conclusion
Skills intelligence is rapidly evolving and can potentially transform how we hire and manage employees.
Using skills intelligence platforms like iMocha can help individuals and organizations identify their strengths and weaknesses, map their skills to career opportunities, and develop the skills they need to succeed in a rapidly changing job market. As employers increasingly prioritize skills-based hiring and upskilling/reskilling, skills intelligence will likely become an increasingly important metric for measuring job candidates’ performance and potential and a key driver of success for individuals and organizations.
Let us know how you think skills intelligence will affect the way we hire candidates in the future, by reaching out to us at [email protected].
| 2023-03-02T00:00:00 |
https://www.imocha.io/guides/skills-intelligence-the-future-hiring-metric
|
[
{
"date": "2023/03/02",
"position": 43,
"query": "AI skills gap"
},
{
"date": "2023/03/02",
"position": 96,
"query": "artificial intelligence hiring"
}
] |
|
Upskilling and Reskilling for Future-Proof Workforce
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Upskilling and Reskilling for Future-Proof Workforce
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https://www.dbs.com
|
[] |
... (AI/ML), cybersecurity, cloud, blockchain and Internet of Things (IoT) ... By investing in their employees, businesses can close the skills development gap ...
|
In today’s fast-paced and constantly evolving business landscape, it's essential for employees to have the skills necessary to adapt to change and stay ahead of the curve.
To enable our employees to do so, upskilling and reskilling has been a key focus for the bank.
For instance, in 2020, DBS announced we would train over 8,600 employees across the bank in emerging areas with technological advancement such as design thinking, data and analytics, artificial intelligence, machine learning and agile practices. This was part of our range of initiatives to embrace the future of work amid the changes brought about by the pandemic, so as to bolster our future-ready workforce.
As we embarked on our digital transformation journey in 2014, we invested heavily to upskill our employees so that no one would be left behind. We were the first bank in 2015 to incorporate hackathons into our talent development programme, where we had employees partner start-ups to create prototype mobile apps, exposing them to fintech culture, agile methodology and more.
In 2017, DBS also announced a SGD 20 million investment over five years to equip 10,000 Singapore-based employees with skills in digital banking and emerging technologies.
In 2021, we also launched the DBS Future Tech Academy, an in-house digital training institute to keep its close to 5,000-strong technology workforce updated with skills in six areas: Site Reliability Engineering (SRE), Artificial Intelligence and Machine Learning (AI/ML), cybersecurity, cloud, blockchain and Internet of Things (IoT).
Here are other ways DBS Bank is committed to stay ahead by continuing to provide upskilling and reskilling opportunities for our employees.
Upskilling vs. Reskilling
Upskilling is the process of enhancing an individual's existing skill set, while reskilling involves learning new skills entirely. Both processes play a crucial role in ensuring that the workforce is prepared for the future. In fact, The World Economic Forum predicts that by 2025, 50% of all employees will need reskilling.
How the workforce benefits from upskilling and reskilling
When it comes to reskilling and upskilling programmes, we believe the learning experience should be enjoyable and engaging. DBS' training programmes are designed to provide employees with a hands-on, interactive learning experience that allows them to fully immerse themselves in the material. This approach to learning not only helps employees learn new skills, but also helps to build their confidence and motivation. This ultimately benefits the long term careers of our people.
One such example was the DBS x AWS DeepRacer League, in collaboration with Amazon Web Services (AWS) back in 2020, where some 3,000 employees learnt Artificial Intelligence and Machine Learning skills through a series of hands-on online tutorials.
Employees then put their skills to the test by programming their own autonomous model race car in a virtual racing environment.
For Ray Goh, Executive Director of Technology & Operations at DBS, this programme was his stepping stone to the global stage. In the AWS DeepRacer League F1 ProAm Event in May 2020, he emerged as the global champion, even surpassing McLaren driver Daniel Ricciardo.
“I’m delighted to be able to make Singapore and DBS proud, and more importantly, to inspire my colleagues to join me in developing a deeper level of mastery in Artificial Intelligence and Machine Learning, which are both important skills for the bank and the digital economy,” he said.
Where to begin?
So, where should businesses begin in their efforts to upskill and reskill their employees?
First, it's important to assess the skill sets of individual employees and determine what areas they need to improve in. This will help to determine the best upskilling strategies for each individual. For example, some employees may need to develop technical skills in areas such as artificial intelligence or digital transformation, while others may benefit from leadership and communication training.
We offer a range of upskilling programmes to help employees bridge the skills gap. From leadership development to technical training, the bank provides employees with the tools they need to advance their careers and stay ahead of the curve.
One such initiative is “iGrow”, a personalised career companion for every DBS employee. It uses machine learning and artificial intelligence to help identify future career paths, and the skills required to reach these goals.
It can also identify suitable roles employees can move into as part of DBS’ internal mobility programmes.
Stay prepared for the future
Reskilling employees is also a crucial aspect of the learning experience. As the business world continues to evolve, and technological advancements are made, employees must be equipped with the latest skills and knowledge to keep up. DBS provides reskilling programmes as well as supports our employees who are keen to transition into new roles or industries.
Paremswari Rajandiran, a bank teller for 21 years, had always dreamed of becoming a Wealth Planning Manager. When her three daughters were old enough and busy in school, in the name of learning new skills, she enrolled in a part-time diploma in Business Accounting at Nanyang Polytechnic, with the full support of her manager.
For three years, her learning experience meant juggled work and night classes. When she graduated, she approached her manager to inquire about changing roles.
To become a WPM, she still had to sit for six additional tests, as required by the Monetary Authority of Singapore (MAS). DBS fully funded the course, and as of March 2022, she’s now a full-fledged WPM.
“In my new role as a WPM, I’m glad that my decades of experience as a teller have come in handy – I am comfortable interacting with customers on a one-to-one basis, and meeting their every banking need.
At the same time, the role has challenged me to be more proactive, such as calling our customers from leads or setting up appointments. Previously, customers came to me, but now I have to run and chase after them!
Despite the steep learning curve, I've decided to take every day as an opportunity to grow. This means learning to be okay with failing, and not tiring from trying over and over again.”
That was truly a benefit of reskilling. Thanks to the bank’s initiatives, today, over one-third of all job vacancies in the bank are filled by internal candidates. Our employees also continue to remain highly engaged, with our ranking increasing from the 87th percentile in 2020 to the 91st percentile in the 2021 Kincentric My Voice Survey.
Helping businesses stay ahead
Our reskilling initiatives aren’t just limited to bank employees - DBS has also partnered with SkillsFuture Singapore to launch the DBS SME Skills Booster Programme to help SMEs improve productivity and efficiency by creating a learning and development plan that is aligned with their long term business aspirations.
The benefits of reskilling and upskilling go beyond just individual employee development. By investing in their employees, businesses can close the skills development gap and stay ahead of the curve in the ever-changing world of work. In addition, a well-trained workforce can lead to increased productivity, better performance, and a more positive work environment.
| 2023-03-02T00:00:00 |
https://www.dbs.com/media/upskilling-reskilling/upskilling-and-reskilling-for-a-future-proof-workforce.page
|
[
{
"date": "2023/03/02",
"position": 84,
"query": "AI skills gap"
},
{
"date": "2023/03/02",
"position": 45,
"query": "machine learning workforce"
}
] |
|
Critical Legal Issues Facing AI and Machine Learning ...
|
Critical Legal Issues Facing AI and Machine Learning Companies
|
https://www.traverselegal.com
|
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These laws may require a company to obtain specific permissions before deploying an AI system; they may impose restrictions on how an AI system can be used or ...
|
I am an AI attorney representing AI companies. You are wondering what legal issues AI companies might face. I have developed a list of critical legal issues that all AI companies should consider before launching service-based software. Watch the video below, or read the article that follows.
https://youtu.be/RZwK3IwD8LI
GET IN TOUCH Contact an AI Lawyer Learn How We Can Help Your AI Copmany Reduce Legal Risks. In the video above titled “Critical Legal Issues Facing AI and Machine Learning Companies,” our AI attorneys outline the key legal considerations that AI companies must navigate before launching service-based software. These include securing intellectual property rights, understanding liability and responsibility, addressing potential biases and discrimination, and complying with complex regulations and standards. From copyright infringement lawsuits to ethical dilemmas and regulatory compliance, the video provides a comprehensive overview of the legal landscape that shapes the AI and machine learning industry, emphasizing the need for transparency, accountability, and ethical development. Here is s summary of the topics covered in this video.
I. Intellectual Property and Data Protection
Consideration should be given to securing intellectual property rights such as patents, trademarks, copyrights, or trade secrets for any AI algorithm or software developed. Companies’ data set to build their platform might be copyright protected. We are already seeing copying infringement lawsuits against AI companies based on the data they have included in their learning models.
Companies must also be aware of data protection laws and ensure compliance with data privacy regulations, including the GDPR or CCPA, especially where personal data is being processed. An AI usage policy must address security and privacy issues specific to your organization.
II. Liability and Responsibility
AI systems can cause harm, and therefore companies should consider the potential risks involved, including any harm that may result from an artificial intelligence system’s failure or misuse. Companies must consider legal responsibility for any harm or damage caused by their AI software, and insurance policies should be implemented to cover any potential liability. Every company which uses AI faces legal and liability risks that can be minimized with an AI use policy that evolves as your AI systems and processes evolve.
III. Bias and Discrimination
AI systems can perpetuate biases and discrimination if not developed unbiasedly.
The AI industry is already facing several ethical issues, including bias in the development of algorithms, privacy concerns, the potential for abuse by malicious actors and states, and transparency around how AI systems make decisions.
To address these issues, companies should take steps to understand and mitigate their biases during development. They should also consider what data they use for training purposes and whether ethical standards have collected it. Finally, companies must ensure clear policies around how their AI systems use personal information and what privacy protections are required by law.
IV. Regulation and Compliance
AI companies must consider the regulatory landscape and ensure compliance with all relevant regulations and standards, including industry-specific regulations and standards such as those governing medical devices or financial services.
Companies must ensure their AI systems are transparent, explainable, and accountable, especially when making decisions affecting individuals or groups.
The development of artificial intelligence systems can be constrained by legal requirements that apply to an AI system’s design, development, deployment, and operation. These laws may require a company to obtain specific permissions before deploying an AI system; they may impose restrictions on how an AI system can be used or require companies to take steps to protect individuals’ privacy or other rights.
V. AI Service Website Agreements
In the evolving field of AI-as-a-service, legal considerations take center stage, particularly when it comes to drafting AI-specific website agreements. Tailoring terms of service and privacy agreements to the unique characteristics and challenges of AI is not just a legal necessity but a strategic imperative. These agreements must reflect the dynamic nature of AI, addressing specific concerns such as data usage, algorithm transparency, potential biases, liability, and privacy protections. By crafting AI-specific agreements, artificial intelligence and machine learning service and platform companies not only ensure legal compliance and legal risk reduction, but also build trust and transparency with users.
You Need an AI-Specific Considerations in Terms of Service: For AI-as-a-service companies, drafting tailored terms of service is crucial to define the boundaries of the relationship between the provider and the user. Unlike traditional software, AI systems are dynamic and ever-changing, leading to changes in functionality and behavior. An AI-specific agreement must address unique aspects such as data usage, algorithm transparency, potential biases, and liability for AI’s autonomous decisions. By clearly outlining these terms, companies can mitigate legal risks, ensure compliance with regulations, and build trust with users, all of which are vital for the sustainable growth of the business. AI Companies Need AI-Centric Privacy Agreements and Data Protection Policies: Privacy agreements are equally vital for AI-as-a-service companies, as AI systems often rely on vast amounts of data, including personal and sensitive information. Drafting a robust privacy agreement ensures that the company’s data collection, processing, and sharing practices are transparent and align with legal requirements such as GDPR or CCPA. It also helps define the rights and responsibilities related to data ownership, access, and security. Establishing clear privacy policies is essential for AI-as-a-service companies.
Together, these AI-specific agreements form the legal foundation of the relationship between AI-as-a-service companies and their users, addressing the unique challenges posed by AI technology and ensuring a transparent, responsible, and legally compliant operation.
A Word About AI Governance and AI Use Policies
An AI usage policy is an essential document that guides how artificial intelligence (AI) technology is to be used within an organization. This policy outlines the rules, responsibilities, and ethical guidelines to ensure that AI is used to align with the organization’s values, legal obligations, and business goals. An AI usage policy is step one for every organization whose employees use AI or want to develop AI solutions. Every company needs an AI use policy for its corporate governance and fiduciary obligations.
An AI usage policy is not merely a regulatory compliance document but a roadmap for the responsible and strategic use of AI within an organization. Regardless of its size or industry, every company that is engaged in or planning to engage in AI-related activities must have a robust AI usage policy. Such a policy protects the organization’s legal interests, fosters innovation, ensures ethical conduct, and helps fulfill corporate governance and fiduciary obligations.
By providing clarity and direction, an AI usage policy empowers organizations to leverage AI’s immense potential while managing the associated risks and responsibilities.
Visit: Necessary UAV Contracts for Professional Drone Services and Businesses
| 2023-03-02T00:00:00 |
2023/03/02
|
https://www.traverselegal.com/blog/legal-issues-ai/
|
[
{
"date": "2023/03/02",
"position": 90,
"query": "AI regulation employment"
}
] |
A Road Map for Dealing With Government's Workforce Crisis
|
A Road Map for Dealing With Government’s Workforce Crisis
|
https://www.governing.com
|
[
"Robert J. Lavigna Is Senior Fellow-Public Sector For The Ultimate Kronos Group."
] |
Every day, more than 12 million dedicated public servants in federal, state and local government provide critical services to the American public.
|
Every day, more than 12 million dedicated public servants in federal, state and local government provide critical services to the American public. Most Americans take these services, overwhelmingly provided effectively and efficiently, for granted.However, public-sector employee turnover and hiring challenges have put government’s ability to deliver essential services at risk. Government must meet these challenges, and there are solutions that, taken together, can have a powerful impact. The stakes couldn’t be higher because when government fails, people can die. That may seem like hyperbole, but history tells us otherwise.Consider the government response to Hurricane Katrina, which former NBC broadcaster Brian Williams characterized as “government mismanagement” and “a complete breakdown in coordination” across all levels of government. We’ll never know how many of the nearly 1,400 deaths attributed to the hurricane might have been prevented by a more robust, better-staffed government response.While that was almost 18 years ago, the U.S. Government Accountability Office testified to Congress last year that staffing shortages at the Federal Emergency Management Agency continue to interfere with the agency’s ability “to help people before, during and after disasters, the number of which are increasing.”Public administration scholar Paul C. Light, in a Brookings Institute analysis, identified “a cascade of failures” of the federal government and concluded that “persistent understaffing” was a leading cause. That was in 2014, well before the so-called “Great Resignation” exacerbated the government workforce crisis that had been building for years.In state and local government, consider the clean-water emergency in Jackson, Miss. The city’s water crisis, wrote , was a “systemic failure at every level of government” caused in part by a shortage of city Class A water operators — highly trained workers who run the plant. Those who were on the job were working punishing overtime hours. This problem is not limited to Jackson. According to the Center for American Progress , there were 10,000 fewer water and wastewater treatment plant operators on the job in 2021 than in 2019.In law enforcement, Rand Corporation researchers wrote , “police recruiting — and staffing in general — is in a prolonged crisis” that is “real, persistent and worsening.”State-run correctional institutions are also struggling. According to one report , the prison workforce across the nation is down nearly 20 percent, and most have left the job in the past two years — a staffing shortage that “threatens officers, inmates and the public’s safety.”And in public transit, a 2022 American Public Transit Association report chronicled a national shortage of bus and rail operators and mechanics that has forced transit agencies to reduce service.These are not isolated examples. According to the U.S. Bureau of Labor Statistics , there were 575,000 job openings in state and local government in December 2022, compared to only 195,000 hires. While the private sector has recovered the jobs lost during the pandemic, state and local government had 450,000 fewer employees in January 2023 than in February 2020. And a MissionSquare Research Institute survey revealed that 52 percent of state and local government employees were considering leaving their jobs.While these aggregate numbers are alarming, the true story of the workforce crisis is the impact on critical government services, such as law enforcement, emergency services, public transit and public works. Problems attracting and retaining talent in government affect people where they live, literally.What solutions can ensure that the public sector has the talent it needs to continue to deliver for the American public? There isn’t a single answer. It’s an all-of-the-above situation, but here are some suggestions for starters:Government should not have to compete with the fast-food industry for talent. That may be an extreme example, but if pay is too low, increase it to be competitive. Also calculate and publicize the total value of compensation to include the monetary value of benefits, which remain competitive with the private sector.Managers want employees who can work collaboratively to solve problems, function successfully in teams and communicate well. Rather than imposing arbitrary minimum qualifications on job applicants, such as a degree or specific years of experience (unless required by law), hire based on whether the candidate can excel in the job. Apply tools and techniques that truly assess job candidates’ skills and experience. The result will be a more diverse, robust and qualified talent pipeline.One agency’s hiring process was once described as “hiring the best of the desperate”— those hardy souls with few options willing to fight their way through a slow and convoluted process. I recently heard a government HR director proudly proclaim that his agency had reduced time-to-hire to 170 days. And thisa big reduction. Government needs to do better, including aggressively marketing the value of public service, to be competitive for the best talent.People want to work where diversity, equity, inclusion and belonging (DEIB) are embraced and championed from the top down. In recruiting, let prospective employees know that DEIB is integral to the organization and its leaders, and to everyone’s success. The result, according to Great Place To Work , a global authority on workplace culture, will be employees who are 5.4 times more likely to want to stay with their organization for a long time, as well as larger, more diverse and more talented applicant pools.Talented people are theorganization’s competitive advantage — but only when they’re having a great employee experience. Engaged employees are proud of what they do and feel the organization values them. They believe in their organization’s mission, are more productive and deliver better customer service.Building engagement requires a positive employee experience that includes every aspect of the employment life cycle — recruiting and hiring, onboarding, supervision, training and development, recognition, performance management, pay and benefits, equity and inclusion, and employee well-being. Organizations that create a positive employee experience are five times more likely to engage and retain employees Creating a positive experience also means meeting employees’ demands for workplace flexibility. Even when employees can’t work remotely, employers need to provide as much scheduling flexibility as possible.You can’t manage what you don’t measure. However, not all government organizations have the tools or the capacity to collect and analyze data to measure their effectiveness in attracting, developing, engaging and retaining talent. That’s a barrier that has to be overcome.The most comprehensive way to understand and measure the employee experience is to survey employees. Asking employees how they feel about the workplace and their work experience enables the employer to understand the level of engagement in the workforce — and how to boost it. Guessing or relying on anecdotes or social events no longer work in an era of intense competition for talent.It's imperative for governments at all levels to up their game in that competition. Public servants have played a critical role in making America one of history’s great civilizations. But the work of America is not finished. To fully achieve its potential, our nation must have a strong and high-performing public sector. The stakes are far too high to settle for any other outcome.GoverningGoverning
| 2023-03-02T00:00:00 |
2023/03/02
|
https://www.governing.com/work/a-road-map-for-dealing-with-governments-workforce-crisis
|
[
{
"date": "2023/03/02",
"position": 26,
"query": "government AI workforce policy"
}
] |
Transforming citizen services - Chatbots for Government
|
Chatbots for Government transforming citizen services
|
https://sentione.com
|
[
"Agnieszka Uba",
"Somehow Ended Up In The Tech World",
"Keep Trying To Market It To The Less Savvy Individuals. Over The Course Of The Career Developed Big Brands",
"Challenger Brands",
"Youtube Brand",
"Now On A Mission To Shape Product Communication Of Sentione So More People Can Benefit Ai.",
"Mathilda Hartnell",
"Ula Rydiger",
"Katarzyna Piocha"
] |
... government's agencies, news, press releases, workforce, and policies. Leverage AI to Transform Your Government Support Services. SentiOne can automate over ...
|
What Are Chatbots?
“hi, I’m Phil, your virtual assistant. What can I help you with today?”
It seems like every other website you visit these days has a chatbot popping up with a similar message. The term “chatbot” has increasingly become part of common language, but what is a chatbot really? Despite the human names often given them, a chatbot is, at it’s core, software designed to interpret messages from humans in order to provide support or solve problems.
There’s a few layers to that. Chatbots need to interpret language, to derive meaning from a string of text characters or spoken syllables. Chatbots use natural language processing capabilities to interpret the incoming messages. Chatbots also need to associate those incoming questions or requests with responses that are relevant and helpful. This can be achieved through rule-based routines, but for more complex topics an additional layer of artificial intelligence is used to deliver an appropriate response.
In this post we’ll take a look at how chatbots can help city, state and federal government agencies more effectively service their constituents.
The Many Use Cases for Chatbots in Government Service
While chatbots have their roots in the private sector, public officials are growing increasingly aware of the many beneficial use cases of government chatbots. The 2020 Center for Digital Government Surveys found significant activity and interest around chatbots at all levels. Among city and county governments, roughly 2/3rds responded that they are either using chatbots currently or plan to within the next 12-18 months. Among state-level respondents, this figure rose to a full 100%.
Chatbots can help respond to public needs by offering fast and effective support of common requests or information needs. This is most evident in areas where the government has exclusive access to information that is useful to a large group of people. Within the government sector, that can include:
Public health information, such as COVID-related questions
Tax information, like filing deadlines or forms
Unemployment insurance details and application procedures
Transit, including routes and scheduling
Immigration, including application status updates
Permits, for business and individuals
City events, such as festivals
Improving Citizen Support – The Benefits of Chatbots for Government Services
Governments, on a limited budget, are tasked with providing a tremendous range of service capabilities. Unlike private businesses which have the luxury of focusing their service attention on a particular group of customers, governments are tasked with serving every adult within their jurisdiction.
The highest-impact benefit for government chatbots is improving response times for common service tasks. Chatbots are a great solution for the scenario of a person with a simple, account-specific inquiry who has to wait in a lengthy phone support queue. There are numerous examples (several cited below) of government agencies dramatically reducing response times through chatbot support. Chatbot responses are fast and consistent, and available 24/7.
There are many additional benefits governments can gain from employing chatbots, including:
Reduced staffing and training costs : Chatbots can dramatically reduce call center staffing costs.
: Chatbots can dramatically reduce call center staffing costs. Improved employee morale and retention : Freeing employees from repetitive service tasks allows them to focus on more complex customer needs.
: Freeing employees from repetitive service tasks allows them to focus on more complex customer needs. Multichannel access : An ongoing challenge for government agencies is reaching their audience. Chatbots easily integrate with multiple digital channels, allowing governments to share important information in whatever channel the public is using. That can include the web, text, social media, messenging apps, or voice assistants.
: An ongoing challenge for government agencies is reaching their audience. Chatbots easily integrate with multiple digital channels, allowing governments to share important information in whatever channel the public is using. That can include the web, text, social media, messenging apps, or voice assistants. Multilanguage support : Chatbots can potentially speak several languages, helping reduce accessibility barriers.
: Chatbots can potentially speak several languages, helping reduce accessibility barriers. Logging and data mining: On the back-end, chatbots offer integrated logging and tracking of service tickets, allowing government teams to analyze trends and patterns, and further optimize the chatbot service for the public’s most pressing needs.
Cons or Considerations for Using Chatbots in Government
When introducing new technology, there are always some areas to tread carefully. For government teams considering chatbots, bear these factors in mind:
Chatbots are limited by available data : People love epic technology fails – and in the past few years the media has delighted in high-profile chatbots that provide ludicrous or embarrassing answers. It’s important to remember that these mishaps generally involve chatbots proposing to answer anything, often based on data fed from millions of online conversations covering 1,000s of topics. The reality is the familiar “garbage in, garbage out” analogy. In situations with a clear business purpose, chatbots won’t veer off into abstract territory.
: People love epic technology fails – and in the past few years the media has delighted in high-profile chatbots that provide ludicrous or embarrassing answers. It’s important to remember that these mishaps generally involve chatbots proposing to answer anything, often based on data fed from millions of online conversations covering 1,000s of topics. The reality is the familiar “garbage in, garbage out” analogy. In situations with a clear business purpose, chatbots won’t veer off into abstract territory. Chatbots aren’t human : Chatbots can’t be expected to deliver the warm and fuzzy impression associated with a helpful and spontaneous human who is helping you out over the phone. They are not emotive, personal or spontaneous. While chatbots can be incredibly efficient at task-based support, government agencies that deal with sensitive or emotional issues (e.g. social services) should have a process for addressing the unique sensitivities of their audience.
: Chatbots can’t be expected to deliver the warm and fuzzy impression associated with a helpful and spontaneous human who is helping you out over the phone. They are not emotive, personal or spontaneous. While chatbots can be incredibly efficient at task-based support, government agencies that deal with sensitive or emotional issues (e.g. social services) should have a process for addressing the unique sensitivities of their audience. Government integrations can be complex: While some of the examples below were developed and launched in less than a week, government teams do need to consider whether the chatbot will collect any personally identifiable information, integration with other government systems, and whether it needs a FedRAMP authorization.
Serving Citizens – Government Chatbot Examples
City Government Chatbots
There are many examples of chatbots in local government helping inform and administer vital city services. Here are some examples:
San Francisco’s Office of Contract Administration designed their chatbot, PAIGE, to offer IT procurement support for employees. The designers estimate that PAIGE can answer about 80% of the questions asked about procurement procedures.
Kansas City’s chatbot is available on the City’s website and Facebook Messenger. The chatbot handles services such as permits, reporting maintenance issues, or paying utility bills. Requests can be submitted directly through the chatbot interface.
Looking further internationally, Dubai’s Electricity & Water Authority launched a chatbot called Rammas. It speaks both English and Arabic, and can be accessed via the web, iOS, Android, Amazon Alexa, Facebook, and a physical robot. Rammas has led to a reduction in customer response time by 97%, processing over 6.8 million enquiries since its launch in May, 2017.
The government agency Travel for London launched their Travelbot on Facebook in 2017. The chatbot provides support for bus arrivals, route status, service updates, and maps. Travelbot has had over one million conversations with over 150,000 people.
Government Chatbots used by County Governments
King County, Washington used a chatbot during the height of the pandemic to help residents screen for coronavirus-like symptoms. By filtering asymptomatic symptoms from symptomatic people, the county estimated that the chatbot saved 35% of the time nurses would have spent assisting those without symptoms.
Placer County, California has a bot called Ask Placer that can answer more than 375 questions.
Cabarrus County in North Carolina launched a chatbot integrated with their content management system to make a wide variety of documents accessible to their residents.
Government Chatbots used by State Governments
The National Association of State Chief Information Officers (NASCIO) lists 35 US States already using chatbots or virtual assistants to improve citizen services.
The Texas Workforce Commission created a chatbot, Larry, to handle frequently asked questions. Larry, which took just four days to develop, has since handled 4.8 million questions from 1.2 million Texans. The State of Minnesota also launched a chatbot dedicated to common questions. It’s estimated to have saved 1,700 hours of staff time.
Connecticut used a COVID chatbot that logged 40,000 interactions over just four months in 2021. The State estimates that it did the work of four full-time employees during that span.
Tax and driver licensing are often a key service area handled by government chatbots. In Missouri, DORA answered 100,000 resident questions about taxes, driver’s licenses and motor vehicles in the three months after it launched in November 2019. North Carolina’s myNCDMV chatbot allows residents to register new vehicles, renew registration and driver’s licenses, order license plates, register to vote, and update their address. The State estimates that the new digital workflows have generated a 25% increase in online revenue collection and a 40% shorter wait at DMV offices. In Mississippi, the Missi chatbot helps with public services info like taxation, health services, public transport, elderly care centers, social gatherings, tourism hotspots, family services, and job opportunities.
Government Chatbots used by Federal Government Agencies
USA.gov built AskTSA (on Twitter and Facebook) to answer questions from people on their way to board a plane. Queries that would have included a 1.5-hour wait over the phone can be answered almost instantly.
The Department of Home Land Security launched EMMA to help with immigration, green cards, passports and other services. Available in Spanish or English and accessed by text or voice commands, EMMA handles over one million interactions a month.
The Government of Singapore’s Ministry of Communications and Information (MCI) launched a chatbot through Facebook Messenger. It allows citizens to easily locate information about the government’s agencies, news, press releases, workforce, and policies.
Leverage AI to Transform Your Government Support Services
SentiOne can automate over 40% of repetitive tasks or customer requests. SentiOne’s AI bots allow governments to:
Deploy both on-premises and in the cloud
Integrate with several digital channels, CRM, and call centers
Design sophisticated conversation models using a drag-and-drop interface – no code required!
Achieve 94% intent accuracy recognition
Analyze metrics such as average handling time, resolution time and more
Harness the power of AI to offer improved service to your constituents. Book a demo to see SentiOne’s AI bots in action.
| 2023-03-02T00:00:00 |
2023/03/02
|
https://sentione.com/blog/chatbots-for-government
|
[
{
"date": "2023/03/02",
"position": 82,
"query": "government AI workforce policy"
}
] |
Why Invest in Factory Automation in the Workplace?
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The Future of Automation in the Workplace
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https://www.tm-robot.com
|
[] |
... machine learning, and artificial intelligence. They use the technology to automate their processes, reduce their labor costs, increase their efficiency, and ...
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Companies today are pressured to produce quality products every time while making sure to maintain their efficiency. With the growing demand, you might also feel like you need to deliver goods quickly without sacrificing the quality of your outputs.
Achieving this consistently without the help of technology is challenging, if not impossible, for your workforce. Speed and accuracy are not something you can easily train your staff to accomplish.
To address the growing demand for better and faster processes, more and more companies are looking to maximize the future of automation in the workplace. Businesses are investing in different automation systems to improve productivity in the workplace and manage the increasing workload.
What is Factory Automation?
Factory automation refers to the process of using software or hardware to complete repetitive and predictable tasks in the workplace without needing a human’s manual intervention. This allows you to speed up your operations and increase your employees’ productivity and efficiency.
Businesses all over the world take advantage of factory automation in the workplace by using cloud-based tools and applications, machine learning, and artificial intelligence. They use the technology to automate their processes, reduce their labor costs, increase their efficiency, and optimize their workflows.
Benefits of Automation in the Workplace
Higher Productivity and Performance
Dedicating your workforce to mundane and repetitive tasks that require the least amount of skill and effort might just cause your employees to lose interest in their jobs. Aside from the risk of your team members quitting, you’re wasting your resources by having your employees do tasks that a computer can complete in a much shorter amount of time.
Automation can help your staff focus on more complex tasks while freeing them from the more mundane ones. This allows them to stay productive and focused, allowing your company to reach its goals in a much shorter amount of time.
Lower Operating Costs
Although you might think the opposite, automation actually helps you lower your operating costs significantly. This is possible because the technology greatly reduces your need for manual labor.
By investing in factory automation in the workplace, you can have robots that can do the work of three to five people. So, not only does the technology allow you to boost your productivity, but it also helps you save money on labor costs.
Improved Safety
The risks that come with manual labor can be eliminated when you implement factory automation in the workplace. By automating hazardous processes, your team is no longer exposed to the risks and dangers of their job.
Instead, they can use automated processes and machines to perform tasks safely and quickly. This can help lower workplace accidents, resulting in an improved working environment for everyone.
Fewer Human Errors
One of the greatest benefits of automation in the workplace is the reduction of human errors. When processes are automated, there is a lower chance for mistakes to occur, which allows your company to maintain the quality of its products and services.
With automated systems, you can also be sure that all processes are following standard protocols. As a result, you can be confident that the output of your processes will always meet the highest quality standards.
Reduced Lead Times
Investing in factory automation in the workplace helps you keep your processes in-house without sacrificing your process control and lead times. This is possible because automated systems help you manage your operations more efficiently and reduce the time it takes to complete tasks.
Choosing to automate your processes is also much better than outsourcing them to an overseas company. It will give you more control over the output, allowing you to ensure that the quality is up to standard.
Consistency
Producing consistent outputs will be much easier for your team if you invest in factory automation in the workplace. Automated systems can be programmed to replicate a certain process without any deviations. This will allow your team to produce consistent products and services with ease.
In turn, your customers will be able to get the same quality and experience every time they use your products or services. This will likely lead to higher customer satisfaction ratings and more loyal customers.
Less Environmental Footprint
Automation helps reduce your company’s environmental footprint. This is possible because automated systems optimize your operations, which leads to a decrease in energy consumption and waste. These automated systems can also help you reduce your environmental footprint by taking up less space.
Improve Your Operations With Factory Automation in the Workplace
Automation is becoming an increasingly important part of modern workplaces, and it can provide a range of benefits. As more companies get a clearer view of the future of automation in the workplace, you can expect more businesses to invest in factory automation solutions.
One of the automation systems you can invest in is the AI Cobot. This AI-powered collaborative robot can help you automate repetitive, mundane tasks in your workplace and allow you to enjoy the benefits listed above.
If you would like to learn more about AI Cobots and their benefits, don’t hesitate to reach out to us.
Techman Robot’s AI cobots are equipped with advanced Al and machine learning capabilities that enable the cobots to increase their efficiency over time. These cobots are known for their built-in smart vision system, providing ease of use and versatility, ideal for a wide range of applications, including material handling, AOI inspection, and palletizing.
| 2023-02-28T00:00:00 |
2023/02/28
|
https://www.tm-robot.com/en/future-of-automation-in-the-workplace/
|
[
{
"date": "2023/03/02",
"position": 24,
"query": "machine learning workforce"
}
] |
New Database—Labor and Employment: The American ...
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New Database—Labor and Employment: The American Worker
|
https://home.heinonline.org
|
[
"Heinonline Blogger",
"More Posts",
"See Posts",
"Cammie Clayton",
"Lauren Mattiuzzo"
] |
View global features, AI tools, research aids, and more. Knowledge Base ... From 1866, when the first national labor union was formed, to today, when ...
|
Explore the plight and successes of America’s working class with HeinOnline’s newest editorialized collection of more than 10,000 titles that illuminate the history of labor conditions and employment law in the United States. Labor and Employment: The American Worker is an extensive collection that allows users to explore all of the cases, legislation, scholarly articles, and much more related to the workplace in the United States. And the best part is we’ve made it easy for you to add to your subscription—this collection is available for an affordable, one-time payment. Check out the video below and keep reading to learn more about this hard-working database.
About the Database
From 1866, when the first national labor union was formed, to today, when employees still struggle with poor wages, lack of benefits, and high degrees of job dissatisfaction in the wake of the Covid-19 pandemic, the American workplace has always been rife with conflict between employees and employers. With this newest database, users can explore legislative histories, Supreme Court case briefs, accounts of historical labor riots, current reports on working conditions, and so much more in order to illuminate the rich history and current landscape of America’s workforce.
Get a unique perspective with our chart of landmark court cases related to labor and employment law, including links to the full text, synopsis, and explanations of each case’s significance in both labor jurisprudence and history. From state court cases that impacted the entire country, to decisions made in the U.S. Supreme Court, this database covers cases from 1842 to 2018.
In addition to over 10,000 subject-coded titles, our editors have carefully curated hundreds of expertly written scholarly articles on topics like employment protections, labor contracts, collective bargaining, and more, all ranging from 1904 to present and with new articles added each month.
For those looking to further their research, our editors have even selected over 200 highly recommended titles. While full text of these titles is not available in the database, each title is linked to its WorldCat entry to help users locate the book in a nearby library.
Featured Content
Landmark Cases Unique to this database is a chart of 24 landmark federal and state court cases related to labor and employment law, each including date and a brief synopsis and linking directly to the original full-text decision. Books Browse thousands of books ranging in date from the early 1900s to present day. Find accounts of the Haymarket Riot and other famous labor uprisings, treatises decrying communism, histories of unions, and more.
Government Documents HeinOnline editors have hand-selected committee prints and congressional hearings that provide insight into the federal government’s stances on the state of labor and employment, as well as nearly 3,000 relevant Congressional Research Service and Government Accountability Office reports. Legislation & Supreme Court Briefs This collection includes nearly 200 legislative histories that illustrate the progression of essential labor-related laws, as well as relevant sections of the CFR on labor and employment (CFR Title 20: Employees Benefits, CFR Title 29: Labor and US Code Title 29: Labor) and more than 300 Supreme Court briefs.
Scholarly Articles & Bibliography Researchers can peruse hundreds of articles selected on employment discrimination, wrongful termination, collective bargaining, how the New Deal shaped labor law, right-to-work laws, labor disputes in major league sports, and more. Additionally, our editors have created a bibliography of other relevant books, including links to WorldCat entries. Serials & Periodicals Browse serials and periodicals dedicated to labor and employment matters, including Annual Report of the National Labor Relations Board, Decisions of the Federal Labor Relations Authority, Employee Rights and Employment Policy Journal, and Occupational Outlook Handbook.
Includes Extensive Content on Rail Working Conditions
In light of the environmental and public health disaster in East Palestine, OH, stemming from the recent derailment of a Norfolk Southern freight train carrying hazardous materials, researchers will find a variety of current and historical content in this database regarding rail workers and railroad working conditions over the past century. Example titles include:
Subject-Coded Content for Simplified Searching
To help users navigate the content spanning this extensive database, HeinOnline editors have created 18 new subjects:
u003cstrongu003eCaptive Laboru003c/strongu003e Slave labor formed an integral part of America’s early economy and labor force. Today, incarcerated workers repair roads, fight wildfires, manufacture furniture, license plates, and more, often with paltry or no pay and without the workplace protections given to their non-incarcerated peers. u003cstrongu003eEmployment Benefitsu003c/strongu003e Workers expect to receive more than just a paycheck for their hard work and time, in the form of paid time off, employer-sponsored healthcare, paid sick time, and more. Learn about common benefits and the inequality experienced between workers in receiving these benefits. u003cstrongu003eEqual Employmentu003c/strongu003e This subject presents titles on creating a more equitable workplace for women, minorities, the elderly, and persons with disabilities, and the workplace challenges faced by the marginalized. u003cstrongu003eImmigrant Workers u0026amp; Agricultureu003c/strongu003e Foreign-born workers make up approximately 17% of the total workforce in the United States and are more likely to be employed in agriculture, construction, and service industries. Learn about the vital role immigrant workers play in the economy. u003cstrongu003eLabor Disputes u0026amp; Strikesu003c/strongu003e Under this subject, users can find titles on specific strikes and work stoppages from history as well as titles dealing with these subjects generally. u003cstrongu003eLabor Laws u0026amp; Legislationu003c/strongu003e Users can find titles relating to specific labor laws that have been passed and proposed laws. These laws can affect hiring, firing, benefits, workplace protections, and more. u003cstrongu003eLabor Marketu003c/strongu003e The “labor market” refers to supply and demand for labor. It includes people looking for jobs as well as the type of employee that employers want. u003cstrongu003eLabor Unionsu003c/strongu003e Labor unions are groups of employees who work together to protect and advance their common interests, such as higher wages, benefits, and safer working conditions. Learn about specific unions as well as how the work of unions help advance all workers. u003cstrongu003eMediation u0026amp; Labor Relationsu003c/strongu003e This subject covers the relationship between employees and employers, the relationship between employees, and how a workplace functions. u003cstrongu003eMinimum Wageu003c/strongu003e The federal minimum wage was created in 1938 by the Fair Labor Standard Act. Learn about the law that established it, arguments over what the federal minimum wage should be, and efforts to raise the minimum wage with this subject. u003cstrongu003eOccupational Illness u0026amp; Dangersu003c/strongu003e When workers are injured on the job, they have certain legal rights. Certain professions are also more inherently dangerous than others, exposing workers to conditions that can result in life-threatening diseases. Learn how injured workers are compensated. u003cstrongu003eOvertime Payu003c/strongu003e Today, employees covered under the Fair Labor Standard Act (passed in 1938) must receive overtime pay for hours worked over 40 in a workweek at a rate of time and one-half their regular pay rate. Learn about overtime pay with this subject. u003cstrongu003ePensions u0026amp; Retirementu003c/strongu003e Pensions, or funds to care for a person when they retire from working, are as old as antiquity. In the United States, public pensions were offered to Revolutionary War and Civil War veterans and their survivors. Modern employer pension plans boomed in popularity during and after World War II, with defined benefit plans becoming the more popular retirement plan in the 1980s. Invest in how workers prepare to live after their working years with this subject. u003cstrongu003ePublic Sector Employeesu003c/strongu003e Public sector employees work for the government, either federal, state, or local. It is taxpayer funded and service driven. Examples of public sector employees include public school teachers, firefighters, police officers, public transportation workers, and government workers. Approximately 14% of the U.S. workforce is employed in the public sector, or 20 million people. u003cstrongu003eTrade u0026amp; Economyu003c/strongu003e What goods the United States trades with and imports from other countries drives the manufacturing base of the economy—and in turn affects workers and their wages. In turn, a healthy economy can support workers with good jobs and high wages. Learn about how trade and the broader economy impacts all workers with this subject. u003cstrongu003eUnemployment Compensationu003c/strongu003e Eligible workers who are out of a job are entitled to benefits that vary from state to state. The Covid-19 pandemic saw unprecedented numbers of workers eligible for historical unemployment assistance. Learn about the history and current state of these benefits, as well as what wider unemployment trends may signal about the wider workforce. u003cstrongu003eWagesu003c/strongu003e Workers trade their time for a paycheck. Learn about how wages are paid, calculated, and have changed over time. u003cstrongu003eWorkplace Protectionsu003c/strongu003e Various laws exist to make every workplace safe and welcoming for all. These protections include safeguarding workers’ physical and emotional well-being, insuring they are safe from injury and harassment.
Labor and Employment LibGuide
This database’s dedicated LibGuide provides researchers with tips for browsing and searching the variety of content within this database, as well as additional resources, subject scope notes, and much more!
Research Won’t Feel Like Work with This Database
And it’s easy to add it to your institution’s HeinOnline subscription, too! Contact our Sales and Marketing team today to receive a quote for a one-time payment today.
| 2023-03-02T00:00:00 |
2023/03/02
|
https://home.heinonline.org/blog/2023/03/new-database-labor-and-employment-the-american-worker/
|
[
{
"date": "2023/03/02",
"position": 98,
"query": "AI labor union"
}
] |
Alliance for Artificial Intelligence in Healthcare
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AAIH
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https://www.theaaih.org
|
[] |
The global advocacy organization dedicated to the discovery, development and delivery of better solutions to improve patient lives.
|
Active Projects
In 2023, AAIH is focusing on two primary practice areas: 1) the Regulatory Working Group is developing a comprehensive framework for digitizing IND, and 2) the Data Working Group is addressing core challenges in data accessibility through a real-world data registry.
Click below for information on how to get involved with these or spearhead initiatives in other interest areas.
| 2023-03-02T00:00:00 |
https://www.theaaih.org/
|
[
{
"date": "2023/03/02",
"position": 10,
"query": "AI healthcare"
},
{
"date": "2024/03/01",
"position": 28,
"query": "artificial intelligence healthcare"
}
] |
|
An Executive's Definitive Guide to Artificial Intelligence
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An Executive's Definitive Guide to Artificial Intelligence
|
https://www.rapidops.com
|
[
"Saptarshi Das",
"Years Of Expertise In Content Marketing",
"Seo",
"Serp Research. Creates Informative",
"Engaging Content To Achieve Marketing Goals. Empathetic Approach",
"Deep Understanding Of Target Audience Needs. Expert In Seo Optimization For Maximum Visibility. Your Ideal Content Marketing Strategist."
] |
Artificial Intelligence has proven its ability to enhance organizational growth, yet many executives are uncertain where and how they should start utilizing it.
|
Artificial Intelligence is having a significant impact on industries worldwide today. Artificial Intelligence has proven its ability to enhance organizational growth, yet many executives are uncertain where and how they should start utilizing it. Moreover, they fear investing in unfitting technology may be damaging rather than advantageous for their business.
Software is redefining the world, and AI is the accelerant! - Jayesh Mori, CEO, Rapidops Inc.
It's no secret, though, that a business's path to disruption in any market takes a little risk and entails its own set of challenges along the way. So, the question for executives teetering on whether to commit to advanced technologies like AI is, "what would you choose between risking your competitive edge in the market or completely losing it?"
The global artificial intelligence (AI) software market is forecast to increase in the coming years, reaching around 126 billion U.S. dollars by 2025. – Statista
AI is no longer a figment of the imagination, but rather it has become a vital component for businesses looking to scale operations and stay ahead of the competition.
Market research revealed that the artificial intelligence market is expected to reach USD 190 Billion by 2025, growing at a CAGR (Compound Annual Growth Rate) of 36% between 2018 and 2025.
If you haven't yet thought about implementing artificial intelligence-powered applications and solutions into your business strategy, your competition probably already has.
To ensure you stay ahead of the curve, we've compiled a definitive artificial intelligence guide that gives business leaders all the information they need to know when planning or implementing AI-powered solutions into their business processes.
What are AI, ML, and Deep Learning?
Let's start this definitive artificial intelligence guide by defining the technologies we will explore in this article for a better understanding.
1. Artificial Intelligence
AI is the ability of machines to perform tasks that were once only possible for humans, such as analyzing data, making decisions, and solving problems.
AI is being applied in various industries to improve business processes, such as chatbots in customer service, voice assistants in retail, quality inspections in manufacturing, and personalized e-commerce.
For example, imagine a manufacturing company that wants to improve its quality control process. By using AI-powered inspection systems, the company can quickly and accurately detect defects in its products.
This reduces the number of faulty products being shipped to customers, increasing customer satisfaction and reducing the cost of returns.
2. Machine Learning
Machine learning is a type of Artificial Intelligence (AI) that enables systems to improve performance by learning from data. It aims to teach computers to identify patterns in data and make decisions with minimal human intervention.
ML can be a valuable business tool to streamline operations, reduce costs, and drive growth.
For example, consider a retail company that uses machine learning to analyze customer behavior and predict product demand.
The company can use machine learning to analyze historical sales data to identify patterns and make accurate demand predictions. This allows the company to adjust its inventory levels in real time, reducing the amount of surplus or lost sales.
3. Deep Learning
Deep Learning is a type of Artificial Intelligence (AI) that uses complex algorithms called artificial neural networks to solve problems such as image and speech recognition, natural language processing, and decision-making.
DL is a powerful tool for businesses, allowing them to automate complex processes, make better-informed decisions, and increase profitability.
For example, imagine a retail company that wants to improve its customer targeting for advertising. Using Deep Learning, the company can analyze customer data such as purchase history, browsing behavior, and demographic information to identify patterns and preferences. With this information, the company can create more effective, personalized advertising campaigns with higher conversion rates and increased sales.
Disclaimer: Results from AI implementation will vary based on factors like the business area of operations, automated processes, and market conditions. This example is based on hypothetical scenarios and should not be taken as a guarantee of specific outcomes.
Why is artificial intelligence critical for your business?
Business owners always try to find technologies that help them stay ahead of the competition, and understanding the potential of AI is one crucial step. Tech giants have already harnessed AI for growth, while traditional industries are still exploring its potential.
AI and ML can automate tasks and processes and provide a solution to the limitations of human Intelligence. With each successful iteration, the AI system becomes more intelligent and efficient. Leverage AI to streamline operations, increase efficiency, and scale to meet market demands.
When AI is deployed with machine learning, businesses can rapidly turn massive volumes of data into actionable insight, resulting in company revenue growth and cost savings.
Top reasons why AI/ML is critical for every growth-driven business
AI can improve analytical functions, such as collecting, analyzing, and identifying patterns in large datasets.
AI systems can become specialists in fastening any process that requires quick decision-making.
AI can increase organizational efficiencies by suggesting optimum resource utilization.
It can detect irregular patterns, inform the likelihood of an error, alert the monitoring, and help in better cost-saving.
AI-enabled and data analytics-backed security systems can warn businesses about suspicious or fraudulent activity in real-time.
As explained above, you won't want to miss out on the game-changing benefits of AI. With AI, you can automate essential processes, gain valuable insights, and stay ahead of the competition. Leverage the power of AI to optimize your process efficiency, save on miscellaneous costs, and deliver outstanding customer experiences.
For example, a retail company can use AI-powered demand forecasting to optimize inventory management, reducing stockouts and overstocking, leading to cost savings and increased customer satisfaction. By leveraging AI, the company can analyze past sales data, weather patterns, economic trends, and other factors to accurately predict future demand, enabling it to make more informed decisions about how much inventory to stock and when to restock.
Disclaimer: Results from AI implementation will vary based on factors like the business area of operations, automated processes, and market conditions. This example is based on hypothetical scenarios and should not be taken as a guarantee of specific outcomes.
Read more on how startups and enterprises can leverage AI for growth.
Unlocking the power of AI: An overview of how it works
Artificial intelligence (AI) systems can analyze vast amounts of data and identify patterns, characteristics, and insights to make intelligent decisions. AI is centered around data analysis, machine learning, and iterative improvement.
For example, a chatbot can learn to respond to customer inquiries by analyzing large amounts of text data and continuously improving its responses based on customer feedback. Similarly, an image recognition tool can learn to accurately identify objects in images by analyzing millions of examples and adjusting its algorithms accordingly.
Disclaimer: Results from AI implementation will vary based on factors like the business area of operations, automated processes, and market conditions. This example is based on hypothetical scenarios and should not be taken as a guarantee of specific outcomes.
Effective development and deployment of AI systems will require specialized hardware and software and proficiency in programming languages such as Python, R, and Java to build the necessary algorithmic code.
The three key components of AI: A closer look
The process of artificial Intelligence (AI) is driven by three key components that work together to deliver intelligent outcomes:
1. Learning processes
Gathering data and transforming it into useful information is the foundation of AI.
The learning process involves collecting and processing large amounts of data, such as customer interactions or images, and then organizing this data into a format that can be analyzed.
This process is critical for training AI algorithms and ensuring they can effectively perform their intended tasks.
2. Reasoning processes
Another important aspect of AI is selecting the best algorithms to achieve desired results.
The reasoning process involves evaluating different algorithms and determining the best suited for a given task.
For example, a decision-making AI system may consider multiple algorithms and choose the best results based on the analyzed data.
3. Self-correction processes
Fine-tuning algorithms to deliver accurate results is the final component of AI.
The self-correction process involves continuously monitoring and adjusting the algorithms used by AI systems to ensure they provide the most accurate results possible.
This process is crucial for improving the performance of AI systems over time and addressing any issues that may arise.
How to get started with developing your artificial intelligence solution?
Here are a few critical steps while creating your artificial intelligence solution.
Step 1 – Identify the problem
It's critical to focus on the pain point and establish a value proposition that you will gain from your AI solution before beginning development. So, start by identifying the business problem you are trying to solve with the AI solution.
Step 2 – Quality check your data
Now that you've identified the problem, it's time to choose the appropriate data sources that will be crucial for training the AI model. High-quality data is vital as it helps curb the time spent developing the AI model.
Next, you must clean and process the data before using it to train the AI model. Data cleansing software plays a vital role in quality improvement by removing mistakes and omissions to enhance data quality.
Step 3 – Opt for the right platform
Aside from the data necessary to train your AI model, you must select the right platform for your needs. You have two options available, cloud-based and in-house frameworks.
Selecting the appropriate framework for artificial intelligence solution development is critical to achieving top-notch results. The decision between these two frameworks can be daunting as both provide advantages.
Cloud frameworks
AI cloud frameworks provide a comprehensive platform for developers and data scientists to efficiently construct, launch and manage machine learning models in the cloud.
By providing access to essential services such as storage, data processing, training models, and deployments all within one computing setting - it has become much more straightforward for companies to develop ML solutions across multiple industries.
1. Accessibility
Cloud frameworks are typically more accessible than in-house alternatives, with developers able to access them from anywhere with an internet connection.
2. Scalability
Cloud frameworks can be easily scaled up or down as required, allowing businesses to handle spikes in demand without investing in new infrastructure.
3. Flexibility
Cloud frameworks are highly flexible and can be integrated with other cloud services, making it easier for businesses to build and deploy AI solutions.
4. Cost-effectiveness
Cloud frameworks offer a more cost-effective solution for businesses, as they do not require a significant capital investment in infrastructure.
Examples of cloud frameworks used in AI solution development
1. Amazon Web Services (AWS)
AWS offers many cloud services, including AI and machine learning tools. This allows businesses to leverage AI's power without investing in expensive hardware and infrastructure.
With AWS, businesses can access a wealth of data, compute resources, and cutting-edge AI algorithms. This helps them build and deploy AI applications that are scalable, flexible, and cost-effective.
2. Google Cloud AI Platform
Google Cloud AI Platform provides businesses with a comprehensive suite of AI and machine learning tools hosted on the cloud. This makes it easier for them to develop, test, and deploy AI applications without worrying about infrastructure and maintenance.
The platform provides access to powerful data analytics tools, machine learning algorithms, and deep learning frameworks, allowing businesses to harness the power of AI to make more informed decisions.
3. Microsoft Azure AI
Microsoft Azure AI is a cloud-based platform with various AI services, including machine learning and natural language processing. It offers businesses a wealth of tools and resources and helps them build and deploy AI applications quickly and easily.
4. IBM Cloud
IBM Cloud is a comprehensive platform that provides businesses access to powerful data analytics tools, machine learning algorithms, and deep learning frameworks, helping businesses unlock new business insights and opportunities.
The platform also provides businesses the resources and support they need to stay ahead of the curve in AI and machine learning.
In-house frameworks
An in-house AI framework is a custom-built platform developed by your internal team to address your specific needs for developing and deploying machine learning models. This framework gives your business complete control over the software and allows you to customize it to meet your requirements.
Larger companies with significant resources and expertise in machine learning and software development often develop in-house frameworks. These companies have unique business needs, data sets, and technology stacks that require specialized tools and approaches to create value.
Developing an in-house AI framework is a significant investment of time, money, and resources, but it can offer several benefits, including customization and greater security.
1. Customization
In-house frameworks allow businesses to design AI solutions that align with their unique needs and requirements.
2. Data ownership and control
Having control over the data used in AI solutions is essential to ensure the security and reliability of the solution. This control includes the ability to govern who has access to the data, ensure its accuracy, and maintain its integrity.
By having control over the data, businesses can reduce the risk of data breaches, unauthorized access, and other security threats that could compromise the success of their AI solution.
3. Data privacy
In-house frameworks offer greater privacy than cloud-based solutions, as businesses completely control how their data is stored and processed.
This allows them to ensure that sensitive information is protected and not disclosed to unauthorized parties, reducing the risk of data breaches, cyber-attacks, and other security threats.
Examples of in-house frameworks used in AI solution development
1. TensorFlow
TensorFlow is a widely used open-source machine learning and deep learning software library. It provides a comprehensive platform for building, training, and deploying AI models, making it a popular choice for businesses looking to develop their own AI solutions in-house.
2. PyTorch
PyTorch is an open-source machine learning library that offers a flexible platform for building and training AI models. It is designed to be user-friendly and easy to use, making it an excellent choice for businesses looking to develop AI solutions in-house without needing to rely on specialized technical knowledge.
3. Caffe
Caffe is a deep learning framework that provides a platform for building, training and deploying deep learning models. It is optimized for speed and efficiency, making it an excellent choice for businesses looking to build AI solutions that require high performance and rapid processing.
4. Torch
Torch is an open-source machine learning library that provides a platform for building and training AI models. It is designed to be highly modular and flexible, allowing businesses to integrate it easily into their existing software development processes and workflows.
The choice between cloud-based and in-house frameworks depends on a business's needs.
Cloud-based frameworks offer accessibility, scalability, flexibility, and cost-effectiveness, while in-house frameworks offer customization, control over data, and privacy.
Businesses must carefully consider their needs and requirements when selecting the proper framework for their AI solution. Or let the experts decide on the right technology stack!
Step 4 – Select a programming language
Make an informed decision based on your objectives and demands. Several different programming languages include C++, Java, Python, and R. The latter two languages are more widely used since they provide many tools, such as extensive ML libraries.
Python is famous because it has the most uncomplicated syntax that a non-programmer can comprehend.
C++ boasts high performance and efficiency, making it ideal for game AI.
Java is simple to debug, user-friendly, and may be used on virtually any computer. It also works well with search engine algorithms and for large-scale projects.
R is a statistical programming language that's designed for predictive analysis. As a result, it's primarily utilized in data science.
Step 5 – Create algorithms
When instructing the computer on what to do, you must also decide how to accomplish it. That's where computer algorithms come into play.
Algorithms are mathematical instructions used in computers. You'll need prediction or classification machine learning algorithms to allow the AI model to learn from the data.
Moving forward, you must train the algorithm using the gathered data. Optimizing the algorithm during training is preferable to creating an AI model with high accuracy.
However, to improve the accuracy of your model, you'll need additional data.
The critical step is model accuracy. As a result, you must establish a minimum acceptable threshold to assess model precision.
Step 6 – Deploy, monitor, and optimize
Finally, it's time to put your plan into action. Monitoring can validate that your AI solution is viable and self-sufficient.
It's crucial to check in on the performance of your AI models and ML algorithms after they've been deployed.
Also, don't forget to keep an eye on things and keep optimizing them for better and faster viability. Doing so will only make artificial Intelligence more beneficial for your organization.
Constantly monitoring and assessing the critical aspects of your AI solutions is essential for optimal performance that yields a maximum return on your investments.
Critical aspects to monitor and assess for optimal AI solution performance
1. Data quality
Ensure accurate, up-to-date, and relevant input data. Monitor data drift and bias over time to improve accuracy.
2. Model performance
Track key metrics such as accuracy, precision, recall, and F1 score. Monitor for changes in these metrics over time.
3. Computing resources
Monitor the utilization of computing resources such as CPU, GPU, and memory to ensure efficiency and cost savings.
4. User feedback
Collect feedback to identify issues or areas for improvement.
5. Security
Monitor for potential security threats, including data breaches, hacks, and attacks on the system.
What are the benefits of Artificial Intelligence?
Global corporate investment in artificial intelligence rose from $12.8 billion in 2015 to $93.5 billion in 2021 at a CAGR of 39.4% – Statista
Artificial Intelligence will not be an answer for every problem; it is a set of real-world capabilities that unlocks profit growth and cost savings for an organization. AI has enormous potential, and its applications in business sectors and processes will allow organizations to:
Convert larger data sets into analysis
Identify concepts and patterns in data better than rules-based systems
Enable human-to-machine conversation
In approximately 60% of occupations, at least 30% of constituent activities are technically automatable by adapting currently proven AI technologies. - McKinsey Global Institute
The most significant benefit of implementing artificial Intelligence in your business process is that it can work for you 24/7/365! And while it shouldn't replace or belittle the importance of human intelligence, there are many benefits to incorporating ai to take care of essential business operations.
1. Solve complex problems
Machine and deep learning algorithms help AI solve various complex problems. For example, multiple businesses can apply AI to detect fraud, create personalized customer interactions, provide weather forecasting, or even offer an advanced medical diagnosis.
Using machine learning and graph technologies, PayPal can form connections within its data to identify and prevent fraud effectively. PayPal has designed an innovative, evolved machine learning system called PayPal Fraud Protection to guard against payment fraud.
The company claims this system is so powerful because it draws from a vast pool of data generated by its 2-Sided Network, the two sides being each transaction conducted on the platform.
After comprehensively considering various tree-based algorithms and complex models such as Random Forest and neural networks, PayPal decided to employ the Gradient Boosting Machine (GBM) model for production.
Subsequent testing of GBM revealed that it was the most successful choice providing optimal results throughout. With such reliable protection in place, merchants can trust that their payments are secure with PayPal.
2. Automate repetitive processes
AI can be credited for its seamless automation of your business process. Automation enables businesses to get rid of repetitive and tedious tasks. It can also eliminate human errors or reduce the threat to workers' lives in the manufacturing industry.
3. Contribute to world economic growth
AI could contribute up to $15.7 trillion to the global economy in 2030. Of this, $6.6 trillion is likely from increased productivity and $9.1 trillion from consumption. Significant contributors to this exponential growth are the automation of routine tasks and the development of intelligent bots that can seamlessly carry out human-only tasks.
4. Optimize marketing strategies
AI-based solutions can also help you optimize your marketing efforts and reduce marketing costs by providing the most effective marketing tool and techniques for your business while eliminating unlikely consumers from the list of potential customers.
We are not belittling the importance of human Intelligence here. If an organization wants to excel in its relevant market, it must find a way to balance both. AI can optimize marketing strategies by analyzing vast amounts of data to generate insights for smarter decisions.
For example, an e-commerce business can use AI to analyze historical data on customer behavior, purchase patterns, and ad performance. This generates insights into which products are popular, which audiences convert, and what ad messaging resonates.
AI can identify patterns in the data, making it easier to adjust the strategy. Optimizing its advertising strategy can reduce wasted ad spend, increase conversions, and drive more revenue.
AI can also track and analyze the impact of advertising campaigns in real time, making it easier to adjust the strategy accordingly.
Disclaimer: Results from AI implementation will vary based on factors like the business area of operations, automated processes, and market conditions. This example is based on hypothetical scenarios and should not be taken as a guarantee of specific outcomes.
What are types of artificial Intelligence?
Type 1: Reactive machines
Reactive machines are the most basic form of artificial Intelligence. They must react in a predefined manner to circumstances, and they can't learn or change their behavior.
These AI systems have no memory and are focused on a single activity. Deep Blue, an IBM chess program that can identify objects on the chessboard and make predictions, defeated Garry Kasparov in the 1990s is an excellent example of this.
Type 2: Limited memory
Memory storage capacity is a finite resource. Artificial intelligence systems with restricted memory can retain and utilize past experiences for a limited time, using them to guide future actions. Some of the decision-making capabilities in self-driving cars are structured this way.
Type 3: Theory of mind
The phrase "theory of mind" is a psychological term. In this context, it refers to the capacity of AI to comprehend emotions. This form of AI can infer human intentions and anticipate actions, a vital ability for AI systems to join human teams as essential members.
According to a recent Google engineer's announcement, Google has developed sentient AI. LaMDA is an advanced AI chatbot that Google claims can freely discuss any topic for eternity. This approach may result in chatbots that are like "a hive mind," combining the efforts of many AI bots.
When Google engineer Blake Lemoine revealed the substance of his conversations with LaMDA, he sent shockwaves through the tech industry. When asked if the system was sentient, LaMDA responded that it was and encouraged everyone to accept it as a person! It also believes it has a soul and perceives itself as a 'glowing orb of energy floating in mid-air.'
Type 4: Self-awareness
Self-awareness is an essential feature of artificial intelligence (AI) systems. Self-aware AI can learn, develop, and mature on its own. Although it can take years for AI to master how to write good computer code, SourceAI, a Paris-based startup, believes that programming shouldn't be so complex.
The firm is refining software that uses machine learning to create code based on a brief text description of what the code should accomplish. For example, tell the program, "Multiply two numbers supplied by a user," and it will create a dozen or so lines in Python to do so.
Some more AI types
1. Narrow AI
Weak AI, also known as narrow AI, is artificial Intelligence designed to perform a specific task or set of functions with a high level of proficiency but limited to only that task or tasks. It is not intended to have a broader, human-like intelligence across various tasks and domains.
2. General AI
Artificial General Intelligence (AGI) is a computer program that simulates human cognitive abilities, allowing an AGI system to solve an unfamiliar problem. An AGI system’s objective is to complete any activity humans can do.
AGI allows machines to apply knowledge and skills in different scenarios, meaning it can independently utilize its expertise and abilities in multiple situations. Narrow AI is where we've been, and General Ai is where we want to go.
3. Super AI
Artificial superintelligence (or Super AI) is a term used to describe artificial intelligence that exceeds human capability. It’s also known as artificial superhuman intelligence (ASI) or superintelligence.
Super AI excels at everything, from arithmetic and science to medicine and hobbies, to name a few.
4. Graph-based AI
In the future, AI's emphasis will be on graph modeling. Graphs represent models that express the linked contexts in which intelligent decisions are made. They can show how users, nodes, applications, edge devices, and other entities interact over time.
5. Explainable AI
Explainable Artificial Intelligence (XAI) is a set of procedures and techniques that enable people to comprehend and accept the outcomes and outputs produced by machine learning systems.
It describes an AI model, its expected impact, and biases. With it, you can troubleshoot and improve the model's performance and help others understand your model's behavior.
Disrupting the debate: Are artificial intelligence and machine learning the same?
Most people often use the terms artificial intelligence and machine learning interchangeably. However, there is a distinction to be made between the two phrases.
Artificial intelligence (AI) is a study focusing on developing intelligent agents or systems that can think, learn, and act independently.
Machine learning refers to acquiring and applying artificial intelligence algorithms that learn from data and improve independently.
In other words, machine learning is a way to create more intelligent artificial Intelligence.
AI and machine learning are two related but distinct fields. They use computers to perform tasks that typically require human intelligence but have different approaches and goals.
Challenges in AI adoption
Around 94% of enterprises face potential Artificial Intelligence problems while implementing it. – Deloitte
AI can be a game changer for businesses. However, AI implementation has challenges and pitfalls like with any advanced technology. It’s essential to be aware of these before starting the implementation process:
1. Data quality and quantity
AI algorithms rely tremendously on the quality and amount of data given. That is why it is so important that organizations invest in ensuring the quality of their datasets and having enough information to train AI models adequately.
Without proper data assurance, businesses can experience unreliable results or incorrect predictions, which could be catastrophic to their success.
2. Technical expertise
Developing and implementing AI solutions requires high technical expertise, which can be challenging to find in the workforce. Businesses may need to invest in training programs to build technical skills or hire external consultants.
3. Bias and fairness
AI algorithms can replicate and amplify underlying biases within the data used to train them, causing discrimination towards certain groups with potentially dire consequences.
Businesses must ensure their AI solutions are not prejudiced by upholding ethical and legal expectations when deploying these technologies.
4. Data privacy and security
AI systems have revolutionized how this data is collected and analyzed, yet it is still vulnerable to theft or misuse if not adequately safeguarded.
Implementing cutting-edge security measures can help protect against privacy violations and other malicious activity.
5. Integration and implementation
Crafting an AI-driven business model can be daunting, especially for companies with antiquated systems.
To guarantee success, it's essential to map out the implementation process and ensure your technology infrastructure is prepared for what lies ahead.
6. Transparency and interpretability
Suppose businesses are to maintain trust in their AI solutions, particularly those used in highly regulated industries such as finance and healthcare.
In that case, they must guarantee that the outcomes created by algorithmic Intelligence remain understandable for humans. This means creating algorithms that are transparent, reliable, and with decision-making processes that can be easily explained.
What are the use cases of AI in different industries?
1. AI in manufacturing & supply chain
Manufacturers use AI to:
Predict equipment failures
Schedule maintenance before they occur
The system analyzes data from sensors on manufacturing equipment, such as vibration and temperature readings, to detect signs of wear and tear. This helps companies avoid unplanned downtime and increase efficiency by reducing the need for manual monitoring and maintenance.
Predictive maintenance also helps companies improve equipment longevity and reduce maintenance costs.
2. AI in retail
Retailers use AI algorithms to:
Analyze customer data
Make personalized product recommendations
The system considers a customer's purchase history, search history, and other relevant data to recommend products they are more likely to buy, increasing the chances of customer satisfaction and repeat purchases. This results in improved customer engagement, sales, and overall customer experience.
AI-powered technology can help retailers:
Predict consumer demands
Better target their marketing efforts
Enhance their supply chain management
Calculate pricing more effectively
Determine staffing requirements
Achieve optimum returns
3. AI in sales & customer service
Data scientists use machine learning technology in analytics and customer relationship management (CRM) systems to learn more about serving customers better.
Many organizations already use AI, computer vision, natural language processing (NLP), and machine learning to enhance their sentiment analysis models. This allows them to gauge customers' overall experiences with their businesses and improve their services.
A new conversational IVR system (Interactive Voice Response system) employs AI to handle time-consuming and repetitive activities with automated routing, making simple customer conversations and other necessary transactional activities more efficient.
From taking the time to authenticate users with voice biometrics to instructing the IVR system directly in natural language, improving customer experience is becoming increasingly more manageable thanks to AI-backed IVR systems.
4. AI in finance
Personal finance apps like Intuit Mint or TurboTax disrupt financial institutions using Artificial Intelligence (AI) to provide customer assistance and advice.
Banks successfully employ chatbots to inform their customers about products and services. Virtual assistants are being used to cut costs and improve compliance with banking rules.
AI is also applied to banking to make credit decisions, establish credit limits, find investment possibilities, and detect fraud.
5. AI in healthcare
AI is being used to provide an analysis of a surgeon's technical abilities. Doctors use AI to assist in medical diagnoses and make more informed treatment decisions.
The system uses machine learning algorithms to analyze patient data, including medical history, lab results, and imaging scans. The AI system then suggests probable diagnoses to the doctors based on the information fed into its system.
Accurate and timely diagnoses will lead to improved patient outcomes and will also reduce overall healthcare costs.
6. AI in cybersecurity
Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies, identify suspicious activities, and indicate threats.
The maturing technology plays a significant role in helping organizations fight cyber-attacks.
7. AI in education
AI may be able to grade automatically, allowing teachers more time to focus on other things. It can evaluate and adapt to the needs of learners, allowing them to work at their own pace.
What are the things you should consider while applying AI in your business?
There is no easy answer regarding applying AI in your business. On one hand, several benefits can be gained from employing it. On the other, you must consider some of its downsides too.
Application or indulgence with AI broadly depends on the specific circumstances and your organizational objectives.
Ethical implications of AI
AI can assist in efficiency and decision-making by allowing you to make better and quicker decisions. However, if you're concerned about your privacy or making ethical choices, you should tread carefully.
Here are some broad principles to bear in mind:
When dealing with delicate data, such as personal information or health records, you must exercise caution when employing AI.
Have a strategy in place for safeguarding users' privacy.
You should be careful when making judgments that could influence people's lives, such as who gets hired.
AI can be a valuable tool while working on a project with a tight deadline.
AI may not be the best alternative if you're working on something that requires creativity or intuition (as of now).
In general, using AI thoughtfully and responsibly is critical. You must be cautious about the potential dangers but should also be intrigued by the possibilities. With the appropriate strategy, AI may be a potent tool for improving your business and gaining better momentum in your industry.
How should you plan and execute an AI project?
AI projects are frequently built on top of existing software or internal company systems, adding a minor or more notable AI-powered feature to maximize the impact of the organization’s collected and integrated data.
It is crucial to have a strategy for the necessary resources, tools, budgets, and knowledge to calculate an AI project's cost.
FAQs on estimating an AI project
Let's answer some questions related to the development of AI projects.
Q1. What should I do first?
This is the most common inquiry for those unfamiliar with AI projects. The solution is straightforward. Start by establishing a more explicit business goal and determining the project's viability and future return on investment.
To do so, decide the fraction of your processes that need AI implementation and then rate them based on their priority. Once you have done that, you must decide on the data sources required for training the AI/ML models.
Once you have covered data procurement, you must understand the time and talent required to complete your AI project.
Q2. Should I work with internal resources or hire a technological partner to create an AI project?
When considering whether to hire a technological partner to create an AI project, there are several factors to consider. Here's what you need to know:
1. Expertise
Creating an AI project requires specialized skills and knowledge. Look for a technological partner with experience developing and deploying AI solutions in your industry.
2. Available resources
Developing an AI project requires significant resources, including time, money, and computing power. Look for a technological partner who can provide the resources needed to complete the job and ensure the cost is within your budget.
3. Scalability
Look for a technological partner who can provide the necessary resources and expertise to scale the project as needed. Ensure they have experience developing scalable solutions that can grow with your business.
4. Cost
Consider the cost of developing an AI solution internally versus hiring a technological partner. Look for a partner who can provide a cost-effective solution without sacrificing quality.
5. Time-to-market
Look for a technological partner who can deliver the project within your timeframe. Ensure they have a track record of delivering projects on time and within budget.
If your company's resources, abilities, and expertise are limited, you may consider working with a technological partner to implement an AI strategy. Rapidops has been working with companies for years transforming business operations and helping meet mission-critical goals through advanced technologies. Learn more about working with a trusted digital partner here.
Q3. What is the average cost of an AI project?
There is no simple answer to this question since the cost is typically determined on an ad-hoc basis, as every business has its own AI requirement.
Conclusion: Why AI now?
Our question to you is, why not!
AI can help businesses enhance their customer service and better target the marketing efforts. And we are hoping that this guide would have answered some of the most crucial questions business owners might have o their mind while thinking about AI implementation.
In conclusion, artificial Intelligence has become an indispensable tool for businesses to achieve operational efficiency and make informed decisions.
AI offers a competitive edge to businesses with its ability to:
Automate tasks
Analyze large amounts of data
Perform tasks at a speed and accuracy beyond human capabilities
According to research, financial success may be challenging to achieve if the talent and infrastructure for AI development are not in place. Implementation of AI comes with its own set of challenges, such as securing data and optimizing storage. However, the benefits it brings to the table far outweigh these challenges.
As AI matures and impacts industries, decision-makers and C-level executives are encouraged to adopt this technology and harness its potential to drive growth and success for their organizations.
By leveraging AI, machine learning, and data analytics, businesses can revolutionize their operations and stay ahead of the curve in today's fast-paced, data-driven world. If you want to understand how to ideate AI model deployment, its framework, and the technology stack involved, or get an estimate of your AI project, book your free discovery call with us today.
| 2023-03-02T00:00:00 |
https://www.rapidops.com/blog/executives-guide-to-artificial-intelligence/
|
[
{
"date": "2023/03/02",
"position": 12,
"query": "artificial intelligence business leaders"
}
] |
|
Putting AI on Every Team - Yale Insights
|
Putting AI on Every Team
|
https://insights.som.yale.edu
|
[
"Michael Chertoff",
"Balázs Kovács",
"Tauhid Zaman",
"Alex Burnap",
"Bryce Hall",
"Jeffrey A. Sonnenfeld",
"Zhen Lian",
"Gershon Hasin"
] |
Is artificial intelligence ready to become a standard business ... One of the core challenges companies face is developing processes to integrate their people and ...
|
Q: How quickly are companies adopting AI?
In 2017, about 20% of companies responding to McKinsey Global’s AI survey reported adopting AI in at least one of their business areas. Today, that number is two and a half times larger.
However, we have seen a leveling off around that 50% level over the past few years. An important reason for that is that adopting AI isn’t simply investing in a new technology. We see the best results when people can supplement their expertise with the rich insights AI can deliver. One of the core challenges companies face is developing processes to integrate their people and AI tools and insights.
Q: What are the typical uses of AI today?
The top use cases of AI overall are for service operations optimization. Product or service development is consistently near the top. Marketing and sales use cases are too. In all of these, the AI is embedded in products used for things like customer service analytics, customer segmentation, lead generation, new customer acquisition, or marketing.
AI is great with multi-variable optimization challenges. It can take in vast data sets and a vast number of variables and deliver recommendations. For an airline, that might be what passenger routes to fly, improvements to maintenance operations, or how to maximize cargo yield. For a mining company, an AI engine drawing on IoT (Internet of Things) sensors can deliver guidance on precise adjustments to crushers or chemical baths based on the characteristics of the ore being processed. This has enabled mines to increase throughput and yield by more than 10%.
These are incredibly capital-intensive operations, so making them more efficient and more environmentally friendly, also can mean hundreds of millions in savings.
Q: I’m guessing airlines and mines aren’t using off-the-shelf AI.
To date, AI tools have been most naturally suited for an enterprise setting—a large corporation with a large data set and the resources to develop, to train, and fine-tune AI models. And many AI capabilities still require a large team of PhD data scientists, but cloud-based, SaaS [software as a service] AI solutions are expanding quickly and are commercially viable in a broader set of contexts. More and more AI capabilities are designed to involve low code or no code.
Recently, we’ve seen an explosion of public recognition of AI through the generative AI capabilities. The democratization and increased accessibility of AI capabilities has led to a step change in awareness and excitement around the opportunities and also the risks associated with AI.
With the advent of electricity, an extraordinary number of adjacent possibilities were opened up for businesses, homes—all of society. People have suggested we’re now at a point with AI where the adjacent possibilities can expand rapidly.
At Yale SOM, I loved the Innovator class. A concept that has stuck with me is the innovation funnel. To get to a stellar breakthrough idea often requires starting with a broad set of possibilities that are narrowed down to the truly transformative one.
With millions of people exploring the possibilities of generative AI technologies and products for example, that definitely expands the top of the innovation funnel. Many of these users of AI will come up with new ideas and creative applications of AI. To me that suggests significantly increased growth of AI over the next three to five years.
Q: Is this an inflection point?
Time will tell, but I think we can at least describe it as a commercial threshold moment. Previously there was lots of potential, but many AI tools weren’t commercially viable, even if some were put out into the world. Most of us have had an incredibly frustrating experience with an automated call center that used AI in conversational customer support. Now many more applications have passed a threshold where it’s easy to see that they are actually quite useful—and the opportunities just open up from here.
Another concept that has stuck with me from the Innovator class is “the adjacent possible.” With the advent of electricity, an extraordinary number of adjacent possibilities were opened up for businesses, homes—all of society. It wasn’t necessarily electricity itself that was so transformative, but all the applications it enabled. People have suggested we’re now at a point with AI where the adjacent possibilities can expand rapidly.
We’re at this threshold because the AI tools are more sophisticated and more accessible. That has happened because many other trend lines have moved in the right direction—falling cost of data storage, rising data accessibility, development of low or no code applications, tools that enable an AI model designed for a specific purpose to be retrained for adjacent opportunities rather than starting from scratch for each new use. All of that means we’re moving towards assetization and modular components that can be tuned and more easily transferred to a broad range of other applications.
Q: AI has shown it has enormous potential. There’s now extraordinary public interest. Are there are also reasons to be cautious with AI?
Over the past five years, we’ve tracked a set of risks, including cybersecurity, regulatory and compliance, privacy, and bias risks.
It’s a fascinating time to be working in this area because of the growth trajectory and the value that AI can unlock for companies. And it’s a fascinating time because there are so many very real issues that will need to be worked through.
Now, with generative AI, the question of risks come to the fore in a way that is accessible to a broad audience. We’re seeing issues of ownership and copyright, plagiarism, and accuracy—things created by generative AI are sometimes completely inaccurate. And AI can be used in ways that pose real risks. Companies and organizations are now in the early stages of identifying all the risks and then properly mitigating them.
It’s a fascinating time to be working in this area because of the growth trajectory and the value that AI can unlock for companies. And it’s a fascinating time because there are so many very real issues that will need to be worked through.
Q: You started by saying that the best results come from AI and people working together. Would you expand on that?
In most business settings, an AI technology solution alone isn’t sufficient to capture value. AI offers data-driven insights, but people often need to do the real-life decision-making. We have to have people with technical expertise and industry knowledge and combine that with technology capabilities.
Companies that are successfully capturing value from AI spend more than half of their time and investment on the integration of the people and the AI tools. That means things like training employees, tracking KPIs, having a performance infrastructure to unblock issues that arise so that the AI tools deliver accurate useful information and the people put that information to use in a way that gets to the outcome that’s intended.
Companies that get good at this go from idea to proof of concept to implementable solution very rapidly. They also consistently have an executive team aligned on the AI strategy, a clear and quantified view of priorities, and a realistic understanding of the specific tech talent roles and responsibilities required to implement.
Q: Do you ever foresee a day when McKinsey will give clients the option of choosing either a human or an AI consultant?
Just as with other companies, McKinsey has found the partnership between AI and deep human experience and expertise is what characterizes successful outcomes. Sometimes we describe that as hybrid intelligence, and for the foreseeable future we expect that partnership to be needed to get to the best answer.
Q: Does that imply that McKinsey teams are using AI tools?
Absolutely. Consulting has been and is being disrupted by these technologies. We’ve made a broad set of changes and investments to be able to evolve and meet our client needs in this new world.
Today, McKinsey has thousands of colleagues with very deep technical expertise—data scientists, data engineers, software and cloud engineers, UX/UI designers, and consultants—whose primary role is to help clients build and implement data and analytics capabilities. We recently acquired Iguazio to expand our ability to help our clients scale AI, using Machine Learning Operations (MLOps).
One of the cleanest analogies that I’ve heard is if you were remodeling your kitchen, you wouldn’t do that without an electrician, a plumber, and someone to install your cabinets. All the separate specialties are needed to get to the finished product, but you don’t need them all there the whole time.
It’s an exciting time as we’re working with a transformative set of technologies that mean we’re all adapting. That’s true for McKinsey as much as for our client companies.
| 2023-03-02T00:00:00 |
https://insights.som.yale.edu/insights/putting-ai-on-every-team
|
[
{
"date": "2023/03/02",
"position": 74,
"query": "artificial intelligence business leaders"
}
] |
|
Google President Kent Walker Joins Kogod Dean to Discuss ...
|
Google President Kent Walker Joins Kogod Dean to Discuss Responsible AI
|
https://kogod.american.edu
|
[
"Kogod School Of Business",
"Kent Walker",
"Heng Xu"
] |
... leaders from one of the world's most powerful companies to discuss artificial intelligence's benefits—and potential ramifications. ... business leaders use ...
|
In front of an audience of students, faculty, staff, and alums from across American University, top on-campus experts joined leaders from one of the world’s most powerful companies to discuss artificial intelligence's benefits—and potential ramifications.
Google President of Global Affairs Kent Walker joined Kogod School of Business Dean David Marchick for a February 24 fireside chat called “Responsible AI.” Their discussion, and a subsequent panel, sought to answer one of the questions most central to the future of tech: Can artificial intelligence (AI) be both transformative and responsible?
Timely in nature, the conversations came amid what Marchick called a “frenzy” over AI, particularly given the recent rise in prominence of the AI-driven chatbot “ChatGPT.”
“AI has been on the front page of newspapers almost every day for the last month,” Washington College of Law (WCL) Dean Roger Fairfax said in his opening remarks, noting the forum’s relevance to a cross-section of campus: At Kogod, where faculty study and teach how business leaders use technology and data to make responsible decisions; at the School of Public Affairs (SPA), which focuses on emerging policy issues related to AI; and at WCL, which identifies potential challenges and solutions related to the law and technology.
“How do you balance this issue of transformation and responsibility?” Marchick asked Walker, a 16-year veteran of Google, who’s seen the tech giant grow from 5,000 employees to around 180,000 during his tenure.
“I think we need to be very thoughtful about the implications, the new laws, the new regulations, but also the new social mores,” Walker answered.
| 2023-03-02T00:00:00 |
https://kogod.american.edu/news/google-president-kent-walker-joins-kogod-dean-to-discuss-responsible-ai
|
[
{
"date": "2023/03/02",
"position": 91,
"query": "artificial intelligence business leaders"
}
] |
|
Recruitment Strategy Archives
|
Recruitment Strategy Archives
|
https://pandologic.com
|
[] |
In today's world of rapidly evolving applications for Artificial Intelligence, businesses of all sizes are recognizing the significance of harnessing AI to help ...
|
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics Statistics
| 2023-03-02T00:00:00 |
https://pandologic.com/category/employers/recruitment-strategy/
|
[
{
"date": "2023/03/02",
"position": 92,
"query": "artificial intelligence hiring"
}
] |
|
CNET is doing big layoffs just weeks after AI-generated ...
|
CNET is doing big layoffs just weeks after AI-generated stories came to light
|
https://www.theverge.com
|
[
"Mia Sato"
] |
Red Ventures is laying off members of CNET, as the parent company focuses on AI writing and stories optimized for search engines.
|
is features writer with five years of experience covering the companies that shape technology and the people who use their tools.
Just weeks after news broke that tech site CNET was quietly using artificial intelligence to produce articles, the company is doing extensive layoffs that include several longtime employees, according to multiple people with knowledge of the situation. The layoffs total around a dozen people, a CNET staffer says, or about 10 percent of the public masthead.
CNET editor in chief Connie Guglielmo will also step down from her role and become the senior vice president of AI content strategy and editor-at-large, according to a draft blog post circulated internally and obtained by The Verge. She will be replaced by Adam Auriemma, former editor in chief of another Red Ventures-owned outlet, NextAdvisor. NextAdvisor appears to have shut down; it hasn’t tweeted since January, its website now redirects to CNET, and it no longer appears on Red Ventures’ list of brands.
The layoffs began Thursday morning and were announced internally via email by Red Ventures, the private equity-backed marketing-turned-media company that bought CNET in 2020. In the email, a Red Ventures executive suggested the cuts were made to focus CNET on areas where the site can succeed at bringing in traffic on Google search — a top priority for the company.
“To prepare ourselves for a strong future, we will need to focus on how we simplify our operations and our tech stack, and also on how we invest our time and energy,” wrote Carlos Angrisano, president of financial services and the CNET Group at Red Ventures.
CNET will focus on “authority,” a metric Google considers in search rankings
Angrisano says implicitly what Red Ventures’ — and CNET’s — focus will be going forward: coverage areas where the company has “a high degree of authority, relevance, differentiation” and can “make a large difference in the lives” of audiences. “Authority” is among the metrics that Google stresses to websites as it decides what content ranks highly in search.
Under Red Ventures, former CNET employees say the venerated publication’s focus increasingly became winning Google searches by prioritizing SEO. On these highly trafficked articles, the company crams in lucrative affiliate marketing ads for things like loans or credit cards, cashing in every time a reader signs up.
In the email, Angrisano said CNET would focus on consumer technology, home and wellness, energy, broadband, and personal finance — the sections Red Ventures could best monetize, a current staffer says.
“But those sections are shadows of what they once were, particularly home,” the staffer says. “If you want to do that section the right way, you don’t sell off your Smart Home, get rid of its video team and cripple your editorial staff.”
In January, Futurism reported that CNET had published dozens of articles since last November that were generated using AI tools, much to the surprise of readers — the outlet hadn’t formally announced it was doing so. Other Red Ventures-owned properties, Bankrate and CreditCards.com, had also been publishing similar pieces. The company paused the practice after public outcry and factual errors in stories and promised to do an audit of all articles using AI systems. On CNET, more than half of the articles eventually had corrections made to them.
Though the AI-generated stories were put on pause in January, Red Ventures is preparing to deploy the tool again soon, according to an internal meeting held in late February, first reported by Futurism and confirmed by The Verge. In her new role, Guglielmo will work on machine learning strategies across Red Ventures, according to the memo circulated today. The news is expected to be announced tomorrow.
Are you a current Meta employee with info you’d like to share? Contact me at [email protected] or on Signal (@miasato.11) using a non-work device. You can be anonymous.
Even beyond the shift to affiliate marketing, former CNET staff told The Verge that working conditions under Red Ventures deteriorated since the acquisition. Former staff recounted multiple instances in which CNET employees were pressured to change their coverage of companies that advertised with Red Ventures — a flagrant violation of journalistic ethics that put CNET’s editorial independence at serious risk.
Ivey O’Neal, senior communications manager for CNET, confirmed the layoffs in an email to The Verge. “Today, the CNET Group implemented a reorganization of the team, which unfortunately meant saying goodbye to a number of colleagues,” O’Neal writes. “While it was a difficult decision to let employees go, we believe this is critical for the longevity and future growth of the business.”
Update March 2nd, 3:15PM ET: This story has been updated with comment from Red Ventures.
Update March 2nd, 5:40PM ET: This story has been updated to include news that Connie Guglielmo will step down as editor-in-chief and take on an AI strategy role.
| 2023-03-02T00:00:00 |
2023/03/02
|
https://www.theverge.com/2023/3/2/23622231/cnet-layoffs-ai-articles-seo-red-ventures
|
[
{
"date": "2023/03/02",
"position": 7,
"query": "artificial intelligence layoffs"
}
] |
CNET Hits Staff With Layoffs After Disastrous Pivot to AI ...
|
CNET Hits Staff With Layoffs After Disastrous Pivot to AI Journalism
|
https://futurism.com
|
[] |
After infamously pivoting to AI article writing, CNET owner Red Ventures appears to be hitting staff with a fresh round of layoffs.
|
After using artificial intelligence to churn out dozens of articles that turned out to be rife with errors and plagiarism, CNET owner Red Ventures is hitting its remaining human staff with a fresh round of layoffs.
In an email today, company leadership announced the culling in apologetic tech-speak, citing simplifications in the company's "operations" and "tech stack."
"Today we are implementing a reorganization of our team," reads the email, "which will result in a number of colleagues leaving the CNET Group."
Though the scale of the layoffs is unclear, the email said that the company is keeping resources related to "consumer technology, home and wellness, energy, broadband and personal finance as our priority categories."
Notably, those categories are ones that are particularly easy to monetize using affiliate links that give Red Ventures a kickback when a reader buys a product or signs up for a financial service — a system that, The Verge reported earlier this year, Red Ventures has used to transform CNET into an "AI-powered SEO money machine."
Absent from the list of coverage areas being spared is news, where staffers have continued to conduct admirable journalism even under Red Ventures' pivot to AI-generated SEO content and editorial strategy that some staffers say is favorable to the site's advertisers.
Though Red Ventures didn't respond to a request for comment, sympathetic tweets from ex-CNET staffers seem to corroborate that layoffs are underway at the site.
"Sending a lot of love to a lot of my former colleagues today," former CNET editor-at-large Tim Stevens tweeted.
In another tweet, former CNET copy editor Dawnthea Price Lisco said that her ex-employer is "being gutted for parts" in a post expressing solidarity with the newly laid off.
This is far from the first time the tech news site has hit staff with layoffs in recent history, but this time is significant given that it comes after it was revealed in January that CNET had for months been quietly publishing AI-written articles. The connection between the disastrous AI-generated articles and the layoffs is unclear, but the timing is striking.
In the aftermath of the AI revelation, employees at the company have decried the program. The company has tried to save face, first by adding a more prominent disclaimer to its often error-filled AI-written articles, and then announcing that it was pausing the use of algorithmic writing — only to backtrack from its backtracking as it prepared to boot back up the text generating machine at the end of February.
With this latest round of layoffs, the future of the AI-generated content — and perhaps of CNET itself — is as unclear as ever.
One thing's for sure, though: for bosses, AI is a great excuse to lay off workers.
More on CNET: Leaked Messages Show How CNET's Parent Company Really Sees AI-Generated Content
| 2023-03-02T00:00:00 |
https://futurism.com/cnet-layoffs-ai
|
[
{
"date": "2023/03/02",
"position": 9,
"query": "artificial intelligence layoffs"
}
] |
|
Know How Artificial Intelligence is Changing the Job Market?
|
Know How Artificial Intelligence is Changing the Job Market?
|
https://edvancer.in
|
[
"Edvancer Edventures"
] |
Jobs in Artificial Intelligence in India have risen at a pace faster than they can be filled. A recent report indicated about 45,000 openings available for AI ...
|
Jobs in Artificial Intelligence in India have risen at a pace faster than they can be filled. A recent report indicated about 45,000 openings available for AI and related roles. No wonder artificial intelligence and data science courses are rising at breakneck speed. However, before you plunge into one yourself, you should clear your vision about the job market changes due to AI.
What is Artificial Intelligence?
Is AI Threatening Human Jobs? The debate about job losses due to technology is nothing new. When assembly lines began in manufacturing units, this was the talk of the town, then at the advent of computers, and then with AI. If anything, technological innovations have only improved the job markets. Technological disruptions have always moved the cheese elsewhere for the employable population rather than eliminating the need for the people. As industries adopt AI, they hope to become more sustainable financially and improve the quality of jobs. For example: Robots help waiters lift the load and pick up used dishes. While restaurants can avoid employing an expensive workforce during low-traffic hours, the waiters can focus on customer service. AI has become the new platform to upskill existing professionals. At the same time, for newcomers, AI is raising opportunities worldwide. Enrolling in an artificial intelligence and data science course is your first step toward entering this lucrative field in the future. AI has become the new platform to upskill existing professionals. At the same time, for newcomers, AI is raising opportunities worldwide. Enrolling in an artificial intelligence and data science course is your first step toward entering this lucrative field in the future.
Artificial Intelligence refers to the ability of computer-controlled machines to perform human-like tasks. AI programming allows the controlling software installed on the computer to learn from the task experiences and improve. This simple trait makes the machines capable of performing otherwise complex tasks. For enterprises, AI means greater automation and lower human dependence. AI has renewed the debate about machines overtaking human jobs. The usage of artificial intelligence in businesses has increased by 270 per cent in the last four years. This brings us to the next question:
Also Read: Top 5 Professions Who Must Learn AI for Superior Career Growth
Impact of AI On Different Sectors
Artificial intelligence offers several use cases across industries. Around 75% of businesses are using one or the other types of AI technologies. Simple machine-learning solutions are already present around us in several processes, such as chatbots, search engines, etc.
The following sectors have showcased beneficial use cases of AI:
Automotive AI is already impacting the automotive industry as manufacturing and transportation is automated to a great extent. For example, driving has been automated but it is only to assist drivers and reduce the risks of accidents. Robotic processes through the assembly line
Driver assist and autonomous vehicles
Automotive testing
Insurance claim settlements
Automotive design and solution assistance AI is already impacting the automotive industry as manufacturing and transportation is automated to a great extent. For example, driving has been automated but it is only to assist drivers and reduce the risks of accidents.
Various research agencies predict the yearly growth rate to range between 20 – 50% for AI in the automotive sector.
Medical: AI in the healthcare industry is expected to reach 102.7 billion by 2028 (from 14.2 billion in 2023), recording a CAGR of 47.6% in the coming five years. It can replace a few jobs, such as record maintenance, patient engagement, etc.
But AI cannot take over the healthcare profession entirely. At the same time, it also increases the requirement for skilled AI professionals in the industry.
E-Commerce: Top e-commerce businesses like Amazon use AI to detect fake reviews, virtual assistants, manage big data, etc. As per reports, recommendation engines using AI technology drive 35% of total sales for Amazon.
AI also allows users to search for the products of their choice conversationally and recommends the most relevant products to them. While using AI reduces the advertising and sales costs for businesses, it leads them to hire more professionals with AI skills.
Cybersecurity: AI in Cybersecurity will not take the jobs in cybersecurity but might change them. Artificial intelligence and machine learning have helped companies fight thousands of cybersecurity threats with agile teams.
Is AI The Future of Work?
As per a World Economic Forum study, AI will automate over 75 million jobs by 2025, and create 133 million new jobs. However, the development of AI and ML has always caught the fancy of the media and entertainment industries. Both industries have popularized the doomsday view of AI and ML capabilities. However, as new technologies evolve, they open new avenues of development and employment.
MIT Task Force released a report named “Artificial Intelligence and the future of work,” which predicts that AI will lead to massive innovations in the existing industries, creating new job roles. Moreover, it can open new sectors for growth, eventually giving rise to new job roles.
Disadvantages of AI in the Workplace
AI algorithms cannot perform multiple jobs and
AI and ML problem-solving is limited to a single process at a time
Inability to think beyond their pre-programmed framework
How To Build a Career in Artificial Intelligence?
The artificial intelligence and data science course by Edvancer gives you a thorough coverage of AI, which includes:
Machine Learning in Python
Deep Learning in TensorFlow and Keras
Data Analysis in SQL
Data Visualization in Tableau
Be Future Ready with AI & ML Courses in India
Edvancer provides artificial intelligence courses in collaboration with IIT Kanpur. The courses are coupled with real industrial projects to get you up to speed with real projects.
FAQs 1. How does AI differ from machine learning and deep learning?
Machine learning and deep learning are artificial intelligence technologies. ML is a subset of AI, and DL is a subset of ML. AI refers to the idea that machines can perform human-like tasks. Machine Learning is all about machines learning from data and experiences to perform specific tasks. However, Deep Learning is about teaching machines to use unstructured data and break it down to generate specific results.
2. What new job opportunities will be created as a result of the rise of artificial intelligence?
The rise of artificial intelligence will create several new jobs, including machine learning engineers, deep learning engineers, AI trainers, AI Business Development Managers, and many more.
3. What is the future of AI and how will it impact our lives in the coming years?
Artificial Intelligence is expected to grow even more in the coming years. It is going to impact human lives personally as well as professionally. However, the professional impact can be significant. Repetitive tasks like customer queries, data entry, limited supply chain, etc. will see increased automation.
While AI technologies can make machines perform human tasks more efficiently, they also have many limitations:Artificial intelligence presents a novel method of solving complex and chronic problems automatically. However, all the AI capabilities will still need a human developer to survive. So, whether you are an experienced professional facing the risk of AI takeover or a student heading to graduation, you should priorities building your AI skills.Artificial intelligence and data science courses have growing importance for all walks of professionals and graduates. Edvancer offers artificial intelligence, data science, and machine learning courses in online mode. Depending on the time you can dedicate to upskilling, you can complete the course in a live classroom or self-paced mode.The program focuses on practical skill development along with theoretical clarity. This learning program lasts 180 hours with additional 250 hours of assignments and projects.You also receive lifetime access to study material and online libraries you can use anytime. So, regardless of your profession, Edvancer’s AI ML certification can prepare you to face the future.Share this onFollow us on
| 2023-03-03T00:00:00 |
2023/03/03
|
https://edvancer.in/the-future-of-work-how-artificial-intelligence-is-changing-the-job-market/
|
[
{
"date": "2023/03/03",
"position": 96,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/03",
"position": 39,
"query": "job automation statistics"
},
{
"date": "2023/03/03",
"position": 34,
"query": "AI labor market trends"
},
{
"date": "2023/03/03",
"position": 94,
"query": "machine learning workforce"
}
] |
What is ChatGPT? Is ChatGPT Killing Jobs in Future?
|
What is ChatGPT? Is ChatGPT Killing Jobs in Future?
|
https://redapplelearning.in
|
[] |
The use of ChatGPT and other artificial intelligence technologies may lead to some job displacement, but it is not accurate to say that ChatGPT is killing jobs.
|
What is ChatGPT? Is ChatGPT Killing Jobs?
Workers are feeling a lot more vulnerable these days. Technology, globalization, and other forces have decimated their job stability, with the number of layoffs on the rise. But the recent trend of ChatGPT is creating havoc among the workers. Open AI’s ChatGPT, which was introduced on November 30, 2022, has attracted the interest of engineers, social media users, business owners, authors, and students alike. The chatbot can converse like a human since it was developed using OpenAI’s GPT-3 family of big language models. Today in this blog, we will be discussing the recent “ChatGPT” wave that is taking over different industries.
What is ChatGPT?
In simpler term, ChatGPT is developed by OpenAI, that is designed to have conversations with humans in a natural way. It is developed using a massive dataset of text from the internet, allowing it to understand a wide range of topics and respond to a variety of questions and prompts. Chat GPT is based on the “transformer” architecture. These models are effective to execute different tasks. This “transformer model” helps to generate responses, taking into account the context of the conversation and drawing on its vast knowledge of the language and the world. The goal of ChatGPT is to provide a more engaging and intelligent way for people to interact with machines and to facilitate more seamless communication between humans and technology.
Is ChatGPT Killing Jobs?
The use of ChatGPT and other artificial intelligence technologies may lead to some job displacement, but it is not accurate to say that ChatGPT is killing jobs. AI technologies such as ChatGPT have the potential to automate certain tasks and processes, which can increase efficiency and productivity. However, they can also create many new job opportunities that require different skills and expertise.
It is important to note that the impact of AI technologies on jobs will vary depending on the industry, the specific job roles, and the skills and expertise of the workers. Some jobs may be more susceptible to automation than others, but new job roles may emerge that require skills such as data analysis, programming, and machine learning.
Therefore, it is important for individuals to continue to develop their skills and adapt to changes in the job market. Foreseeing the technological changes, Red Apple Learning is providing huge opportunities for upskilling and also for learning new technologies like Artificial intelligence and machine learning, AR/VR, and game development. Choosing new-age technologies over mainstream career options is a great way for your own job security along with a high paycheck. Not only that; for the existing workers, upskilling themselves with new technologies like AI or AR/VR development can act as a protective shield for preventing layoffs. Ultimately, the responsible deployment and management of AI technologies can create a more productive and prosperous economy that benefits everyone.
Also Read: Jobs that Would Make You a Millionaire Before Retirement?
List of Strategies that Could Help You to Resist Layoff in the Era of ChatGPT
In the era of ChatGPT, where many industries are adopting artificial intelligence and automation; it’s important to focus on developing skills that are difficult for machines to replicate. Here are some strategies that could help you resist layoff in the era of ChatGPT:
Develop Interpersonal and Emotional Intelligence:
ChatGPT may be able to provide quick answers and solutions, but it cannot provide emotional support or understand complex human emotions. Focusing on developing strong interpersonal skills and emotional intelligence can make you an invaluable asset to any team.
Continuously Learn and Upskill:
Stay up to date with industry trends and learn new skills that are in demand. This can help you stay ahead of the curve and make you more valuable to your employer. You have to choose ‘new age career options” like machine learning, AI development, AR/VR or game development as these career options as the spread of technology is becoming more and more inevitable across all fields of human endeavour, and it is playing an increasingly important role in all professions. Reputed institutes are providing AR/VR development courses and advanced game development courses. Choosing and learning these courses over mainstream career options will obviouslyprevent layoffs.
Focus on Creative Problem-Solving:
ChatGPT may be able to provide solutions to simple problems, but it cannot replace the creativity and critical thinking skills of humans. You can also choose creative job options like animation, graphic designing, or UI/UX designing for a prosperous career option. Well, you should also focus on developing your creative problem-solving abilities and finding innovative solutions to complex problems.
Build a Strong Professional Network:
Networking with other professionals in your industry can provide opportunities for collaboration, mentorship, and potential job opportunities.
Be Adaptable and Flexible:
In the era of ChatGPT, job roles, and responsibilities are evolving rapidly. Being adaptable and willing to learn new skills can make you a valuable asset to any organization.
To wrap Up
Overall, the key to resisting layoff in the era of ChatGPT is to focus on developing new technical skills and qualities that are difficult for machines to replicate, such as interpersonal skills, emotional intelligence, creativity, and adaptability. ChatGPT is a blessing for those who can upskill themselves; and a boon for the ones who can’t get over their comfort zone and adapt to the new technological environment.
| 2023-03-03T00:00:00 |
2023/03/03
|
https://redapplelearning.in/what-is-chatgpt-is-chatgpt-killing-jobs/
|
[
{
"date": "2023/03/03",
"position": 87,
"query": "automation job displacement"
}
] |
With France's retirement age rising, automation is key to ...
|
With France’s retirement age rising, automation is key to preserving know-how and preventing worn-out workers
|
https://www.universal-robots.com
|
[
"Universal Robots"
] |
Using cobots from Universal Robots has enabled BWIndustrie to maintain competitiveness and increase its workforce by 50% and revenues by 70%. The company has ...
|
France is the world's seventh-largest economy. The industrial sector is vital contributing nearly 17% of GDP. Manufacturing plays a key role in creating jobs and driving growth.
But, as for many other countries, trouble lies ahead. The French National Institute of Statistics and Economic Studies emphasizes that 67% of business leaders report difficulties in recruiting. The situation is particularly tense in the food processing and electrical equipment sectors, but also affects specific professions such as molders, polishers and welders. According to the Randstad recruitment agency, some 4,500 manufacturing and production positions are currently vacant in France.
The reason is simple: there are fewer workers to fill the positions as the pool of working-age people in France has shrunk by 755,000 people in the last 10 years according to the UN population prospects. Looking at the workforce population in 2043, France’s working-age population is expected to decrease by 1.8 million.
In addition to this, industrial jobs have been seen as unattractive. Musculoskeletal problems sometimes lead to early exclusion of seniors from the labor market, sometimes as young as 45. This has serious human consequences for each individual, and at the same time companies are losing out on the experience and know-how of skilled senior workers. As manufacturers are finding it increasingly difficult to compensate by hiring young people, it also becomes it harder to pass on skills between generations.
| 2023-03-03T00:00:00 |
https://www.universal-robots.com/blog/with-france-s-retirement-age-rising-automation-is-key-to-preserving-know-how-and-preventing-worn-out-workers/
|
[
{
"date": "2023/03/03",
"position": 36,
"query": "job automation statistics"
}
] |
|
The War for Tech Talent
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Fastest way manage skills gap skills first workforce ready
|
https://workforceai.ai
|
[] |
The quest for AI expertise and skills in powerful new technologies is fueling the war for talent. Companies are vying for experts who can develop and implement ...
|
Competition Is Heating Up
The competition for top talent is leading to soaring salaries, lucrative benefits packages, and creative hiring practices. Companies are offering perks like unlimited vacation time, free food and drinks, and flexible work arrangements to entice workers. Some are even going so far as to offer signing bonuses and relocation packages to lure top talent away from their competitors.
Whither To?
Where will this all lead? The war for tech talent shows no signs of abating, and it's likely to continue for the foreseeable future. As companies increasingly rely on AI and other advanced technologies, the demand for skilled workers will only continue to grow.
This could lead to even higher salaries for workers with the right skills, as well as greater job security and flexibility. It could also lead to more consolidation in the tech industry, as companies with deep pockets acquire smaller startups to gain access to their talent.
| 2023-03-03T00:00:00 |
https://workforceai.ai/the-war-for-tech-talent
|
[
{
"date": "2023/03/03",
"position": 44,
"query": "AI skills gap"
}
] |
|
Accelerated Digital Transformation
|
Fastest way manage skills gap skills first workforce ready
|
https://workforceai.ai
|
[] |
Manage Skills Gap | Critical Talent | Fast, accurate Skills Inventory | Soft ... Artificial intelligence (AI) has played a critical role in this ...
|
In finance, AI is being used to improve fraud detection and automate routine tasks such as customer service. Chatbots, powered by AI, are becoming increasingly popular in the finance industry to answer customer queries and provide personalized recommendations.
AI is revolutionizing many industries, including healthcare, finance, retail, and transportation. In healthcare, AI is being used to develop personalized treatments and improve patient outcomes. Google, for instance, is using AI to help detect diabetic retinopathy, a leading cause of blindness.
Accelerated digital transformation has been an ongoing process, but it has been significantly accelerated by the COVID-19 pandemic. Artificial intelligence (AI) has played a critical role in this process, and it is changing our world in many ways.
In retail, AI is being used to analyze consumer behavior and preferences to offer personalized recommendations and enhance the overall customer experience. Amazon is a prime example of a company using AI to transform the retail industry with its recommendation engine.
In transportation, AI is being used to improve safety and efficiency. Self-driving cars, powered by AI, are expected to revolutionize transportation in the coming years, with companies such as Tesla and Google leading the way.
In retail, AI is being used to analyze consumer behavior and preferences to offer personalized recommendations and enhance the overall customer experience. Amazon is a prime example of a company using AI to transform the retail industry with its recommendation engine.
AI Impact on the Workplace
AI is also transforming the way we work, with automation of routine tasks freeing up time for more creative and innovative work. This is leading to the creation of new job roles that require more advanced skills and expertise.
| 2023-03-03T00:00:00 |
https://workforceai.ai/accelerated-digital-transformation
|
[
{
"date": "2023/03/03",
"position": 55,
"query": "AI skills gap"
}
] |
|
Reimagining people development to overcome talent ...
|
Reimagining people development to overcome talent challenges
|
https://www.mckinsey.com
|
[
"Sandra Durth",
"Asmus Komm",
"Florian Pollner",
"Angelika Reich"
] |
Organizations already face a severe shortage of key talent, and 90 percent say they will have a meaningful skills gap in the coming years. ... Matthew Smith and ...
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The talent shortage is a critical problem that is only getting worse. Organizations already face a severe shortage of key talent, and 90 percent say they will have a meaningful skills gap in the coming years. At the same time, digitalization and automation of work activities are leading to further skill shifts, with about 40 percent of Americans and 34 percent of Western Europeans potentially needing to switch occupational groups by 2030. And with 40 percent of workers planning to leave their jobs, attrition is making it harder to retain skills within organizations.
The bottom line? Organizations can’t wait to act. They need to drastically change how they think about talent, from attraction to hiring for potential (rather than fit or experience) and, especially, the development of skills at the enterprise scale. People development is often overlooked and underused, but it’s critical to attracting talent and driving lasting market advantage. In this article, we recommend eight imperatives for people leaders as they build their capabilities.
Why people development is so important
A reputation for a strong culture of people development can make all the difference for companies in a tight labor market. Organizations that make learning and development a priority and a part of their mission can create a virtuous cycle and improve the odds of success in attracting, advancing, and retaining talent. Our research shows that fostering a growth mindset among leaders and employees—for example, by providing training and internal advancement opportunities—is a cornerstone of effective organizations (Exhibit 1).
The organizations that prioritize people development become talent magnets for employees who want to build their knowledge and networks. People quickly develop new skills and skill depth: we have found that 40 to 60 percent of an employee’s human-capital value (knowledge, attributes, skills, and experience) can be attributed to skills acquired through work experience.
Highly effective “learning organizations” also have higher levels of talent retention. Best-in-class organizations provide an average of about 75 hours of training per employee annually, promote their employees at higher rates (seven percentage points), and enjoy higher retention (five percentage points). By contrast, organizations that don’t provide learning and development opportunities risk losing talent. Our research found that the top reason employees cited for quitting previous jobs was a lack of career development and advancement (41 percent).
Reimagining people development: Eight imperatives
Eight imperatives to reimagine people development Deliver great onboarding Many companies forget that formal onboarding is more than just an orientation session, a laptop, and a building pass. It should be a thoughtfully designed six- to 12-month development journey incorporating ongoing coaching, apprenticeship, networking, and formal opportunities to develop skills. The opportunity cost of leaving new hires to find their way on their own can be immense. Employees are more willing to leave in the initial period; the company is on probation during this period just as much as the employee is. Empower the learner Meeting the expectations of employee learners requires dedicated effort and often a shift in technology. One global pharmaceutical company replaced a mix of legacy learning platforms with an integrated learning-experience platform to drive employee engagement, provide personalized learning pathways and opportunities (such as project assignments), and generate data insights. But simply “empowering” employees by improving access was not enough—leaders need to create a culture of learning. Provide a state-of-the-art learning experience Learning and development offerings require not only great content but also the right delivery to provide a great experience and encourage learning. This includes ensuring easy access, providing blended-learning experiences (for instance, a mix of in-person, virtual live, mixed-reality, and digital modalities), and making sure content is personalized and applicable in day-to-day work. It also includes using behavioral psychology to make learning stick through practice and application; regular reinforcement; providing intense, immersive experiences; and making learning an opportunity to collaborate and interact with others. Create back-to-human moments A great deal of skill development happens daily on the job, accumulating over time and eventually accounting for almost half the human capital built over an employee’s working life. Coaching and apprenticeship can maximize this effect. Coaching has also become more relevant for those in leadership roles because success increasingly requires transversal skills such as resilience and adaptability rather than just knowledge and experience. Go ‘leader led’ One of the core elements of establishing a strong learning culture is gaining the support of business leaders. For example, one global biotechnology company has rolled out a program to teach business leaders and influencers how to become better coaches and help their peers and employees achieve their highest personal potential. This multiplier system is designed to equip the workforce with positive leadership mindsets and behaviors and is a great way to scale development opportunities flexibly across the enterprise. Know (and show) your worth Becoming a talent magnet and building a growth mindset into business strategy requires the ability to communicate the value and cost to the business of talent interventions. That means that the people-development function needs to move from learning administration and delivery to actively managing a portfolio of skills together with other parts of the people function. Measuring the value of a skill bought or built allows for fact-based decisions around interventions, which is the holy grail of people development. Invest in your central backbone Making people development a strategic priority usually requires a certain degree of centralization of the “backbone”—including the technology architecture, a common organizational philosophy around talent and skills, and strategic decision-making processes about people. For many organizations, this may mean centralizing a fragmented learning landscape, employing a consistent methodology for tracking effectiveness, and instilling a common learning philosophy that includes an understanding of transversal skills such as leadership. Develop people development People-development functions need new capabilities and roles beyond program managers to be successful and capture value. This includes people analytics and technology capabilities, human-centric design, and strategic thinking to influence and collaborate effectively with and across the business. More than 70 percent of people-development professionals say the function has become more strategic and more cross-functional at their organization over the past few years.
Redesigning people development is complex and challenging. It requires acting on shifts in modern employee behavior, keeping pace with the speed and scale of business capability-building needs, and applying the latest innovations in learning technology. All three actions have become more challenging through the pandemic, and few organizations believe they have cracked the code on how to empower employees to drive competitive advantage (Exhibit 2). However, we have identified eight imperatives that address the challenge (see sidebar, “Eight imperatives to reimagine people development”).
A comparatively straightforward action is to rethink recruiting practices. While companies should always consider candidates with nontraditional backgrounds, it is especially important to do so in a tight labor market. For instance, people with unconventional career paths may have a demonstrated ability to master new skills and absorb new knowledge. However, adopting such a recruiting strategy requires a commitment to helping these employees expand their skills to fit their roles by emphasizing people development.
Addressing employee needs with a ‘segment of one’ approach
Effectively developing employees requires organizations to consider their expectations and needs, including empowering them to access relevant content anywhere, anytime. More than half of employees—58 percent—prefer to learn at their own pace and “on demand.” People development is shifting from providing formal learning to enabling democratized and self-driven learning in the flow of work. Modern employees want learning to be contextualized, personalized, easily accessible in consumable formats, incorporated into the daily workflow, and supported by timely nudges. And users expect a high degree of customization—moving from one-size-fits-all learning to a “segment of one” approach that considers individual needs and preferences.
When 85 percent of organizations were forced to shift learning content to digital formats during the pandemic, we learned that not all development can or should be replaced by virtual and digital platforms. In fact, in a dispersed and virtual world of work, on-the-job training could become more challenging because it may be harder to deliver adequate apprenticeship and onboarding with reduced in-person interaction and fewer unplanned coaching moments. Organizations must learn to deliberately create moments of connection in a hybrid “phygital” world and rethink how to integrate the best of both worlds into their people development.
This includes fit-for-purpose offerings—blended-learning journeys that focus on the employee’s experience and create deliberate “back to human,” or people-centric, moments. Recent McKinsey research has found that a varied, multichannel approach that includes different types of learning formats works best. While virtual workshops or webinars and self-paced digital modules need to be offered for scale and flexibility, in-person training is still most effective for specific cohorts and topics. For example, many organizations have refocused their leadership development efforts on building out their culture of learning, growth, and feedback by teaching leaders to become great coaches. And it is in these organizations—with a culture of lifelong learning—that reskilling at scale is most likely to be successful.
In fact, both developing leaders and driving cultural change—the self-reported top priorities for people leaders globally (Exhibit 3)—benefit significantly from in-person formats.
Keeping pace with the speed and scale of business needs
One key to effective people development is the ability to rapidly assess current skills and skills gaps in an organization, anticipate future needs, and rapidly address these at scale, all while adjusting to continuously changing environments. That is why it’s important that HR—and the people-development function—collaborate closely with the business side of the organization to fully understand business strategy and needs and jointly develop holistic measures to address these gaps.
The problem? Offering formal learning to address skills gaps is not enough because just 10 percent of corporate learning is effective, according to one meta-analysis. That is why it is important to measure actual (business-relevant) outcomes of learning and development interventions, allocate resources where they generate the highest returns, and quickly update or discontinue those that simply do not work.
Some companies have shifted their people-development operating model to create dedicated “learning factories.” These are cross-functional sprint teams on which employees and subject-matter experts from the business side help design and develop offerings and interventions with people-development experts. Such a model harnesses the best of different perspectives, ensures content and formats are fit-for-purpose, and enables rapid development and redevelopment. But business involvement should not stop at design. Business leaders can also lead development programs and act as role models and coaches to help their employees apply new skills in their day-to-day work.
Finally, beyond learning, the ability to redeploy skills based on continuously evolving business priorities should be central to modern people-development functions. This includes a structured approach to development—involving apprenticeship, sponsorship, mentorship, and project opportunities—and it includes increased fluidity across roles. Employees want opportunities to progress, grow, and develop. Creating pathways for both upward and lateral moves reduces the risk that employees will look for a job elsewhere. Schneider Electric, for example, is leveraging AI to create an internal “open talent market” after learning that 47 percent of those leaving the company said they couldn’t find their next career opportunity internally. Employees can upload a profile listing their skills, expertise, and goals to the open talent market, and an AI algorithm matches them with development and career opportunities that fit their profiles.
Using technology to increase speed and flexibility
Innovations in learning technology can help people-development functions deliver learning opportunities that meet employee expectations and fulfill the business’s strategic needs with speed and flexibility.
Technology can help make capability building more user-friendly, customizable, and flexible via machine learning–driven “future skill” taxonomies, AI-powered personalized learning paths, automated nudges in the flow of work, or full metaverse learning and development platforms, for example. These technologies help put the employee in the driver’s seat of their own development journey and can help provide the right development intervention at the right time, place, and pace.
But the learning-technology ecosystem is going through a phase of continuously increasing complexity and innovation. Many companies want to integrate additional innovations but face a multitude of legacy platforms, systems, and apps that are often decentralized, fragmented, and inefficient. A deliberate HR technology strategy is required, starting with key choices around best of breed versus best of suite. Consolidating into a purposefully architected, central, and AI-enabled learning experience platform that acts as the technology backbone, for example, can allow organizations to rapidly integrate innovative external content and methodologies, reduce complexity, harmonize and streamline offerings, and transparently measure effectiveness and efficiency.
Consolidating the learning experience can help the people-development function extend a coherent talent strategy and learning culture across the organization and provide a superior user experience for all employees, democratizing development opportunities. The right technology infrastructure that can be flexibly adapted to changing business needs is a prerequisite for efficient skill building at scale. Staying on top of technology requirements and rapid innovations also requires the appropriate roles and skills within the people-development team.
While building world-class people-development capabilities isn’t easy, people leaders around the world are confident that out of the eight imperatives, developing this function will be the least of their implementation challenges (Exhibit 4).
Done right, people development can be an uncommon solution to a common problem: helping organizations address current and future skills gaps and shifts at scale. This function is the key enabler for turning an organization into a talent magnet in a tight labor market, and it helps organizations provide employees with the best opportunities to make the most of their talent—simultaneously creating value for the business. Especially in times of economic uncertainty, organizations should focus on both providing effective people development and prioritizing key areas of value and impact.
| 2023-03-03T00:00:00 |
https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/reimagining-people-development-to-overcome-talent-challenges
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{
"date": "2023/03/03",
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"query": "AI skills gap"
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E84 Anthony McCauley, AI Program Manager at Skills ...
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E84 Anthony McCauley, AI Program Manager at Skills@ScAle
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https://aiawards.ie
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[
"Liam Mc Namara"
] |
Addressing the skills gap in AI and Data Analytics; How it all came together; What's involved in the programme; Details on how to get involved with Skills@ScAle.
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Welcome to episode 84 of the AI Ireland podcast, the show that explores the applications and research of Data Science, Machine Learning and Artificial Intelligence on the island of Ireland.
Our mission is to increase the use of AI for the benefit of our society, our competitiveness and everyone living in Ireland.
In today’s show, we are delighted to welcome Anthony McCauley, AI Program Manager at Skills@ScAle. In partnership with Technology Ireland ICT Skillnet, Skills@ScAle provides ready-to-go skills pathways to allow business and technology leaders to build their analytical and AI capability.
When combined with other programmes within the Technology ICT Skillnet portfolio, S@S represents a one-stop-shop for companies building a team and for people building a career in the whole area of AI and Data Analytics.
Topics include:
What Skills@ScAle is all about
Addressing the skills gap in AI and Data Analytics
How it all came together
What’s involved in the programme
Details on how to get involved with Skills@ScAle
A big thank you to Anthony for joining us on this week’s podcast. If interested in learning more about Skills@ScAle pathways, check out the website https://www.ictskillnet.ie/training/ai-analytics-pathways/ and you can also find out more at the Skills@ScAle launch webinar on Youtube.
Finally, if you would like to hear more from AI Ireland, please reach out to us to learn more about AI Ireland membership and how to get involved with the 2022 AI Awards by contacting [email protected].
Subscribe to the AI Ireland Podcast:
| 2022-06-02T00:00:00 |
2022/06/02
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https://aiawards.ie/e84-anthony-mccauley-skills-at-scale/
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[
{
"date": "2023/03/03",
"position": 72,
"query": "AI skills gap"
}
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How Fashion Can Close Its Skills Gap | BoF
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How Fashion Can Close Its Skills Gap
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https://www.businessoffashion.com
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[
"Sheena Butler-Young"
] |
Companies that have struggled to recruit employees with the right knowledge and experience are investing in training programmes that teach everything.
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Last month, 16 students completed a five-week footwear and accessories design “masterclass,” at Pensole Lewis College in Detroit, Mich. The benefits included free tuition, room and board, face-time with executives from several major luxury fashion brands and, quite possibly, a job.
“It’s really like a five-week job interview,” said D’Wayne Edwards, founder of Pensole Lewis College and a veteran footwear designer. “Most kids don’t even get that far. So [we] help get them there. Then, it’s on you [the student] to build those relationships.”
The Capri Holdings Foundation for the Advancement of Diversity in Fashion, an arm of the parent of Versace, Jimmy Choo and Michael Kors — sponsored the programme and paid for the students’ room and board. At the end of the course, the company offered a handful of internships and will keep the line open with Edwards for hiring recommendations, he said.
But the students, most of whom are BIPOC — a huge part of Pensole’s mission is to level the playing field for minorities in the fashion industry — aren’t the only ones benefiting.
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For the brands, courses like these are akin to “having their own college” whereby they get to build inroads with talent and train and develop them to meet whatever needs (and skill gaps) exist in their organisations right now and in the future, Edwards said. For instance, instructors at Pensole work with the brands to “co-create” curriculum that’s tailored to precisely how the company operates, he said.
Students at Pensole Lewis College participate in a "masterclass" sponsored by The Capri Foundation. (Courtesy/Courtesy)
This sort of intensive, early career training is something of a lost art in retail. In the ‘80s and ‘90s, department stores like Bloomingdales, Sears and Federated Department Stores (now Macy’s) were known for their executive and merchandising training programmes that taught the basics of operations, product development and retail strategy, as well as soft skills like effective communication, organisation and multi-tasking.
Such courses, which could last up to 18 months, also helped participants — often recent college graduates and early career professionals — touch multiple parts of a retail business and gain an awareness of the value and longevity of a retail career, said Paula Reid, president of executive search firm Reid & Co. Macy’s CEO Jeff Gennette and Bandier President Kimberly Minor both graduated from Macy’s executive training programme in the 1980s.
Over the past couple decades, many of these programmes have “[fallen] by the wayside,” she said, often a victim of cost cutting by struggling department stores. (Sears, for instance, went bankrupt and shuttered stores in 2018.) Some retailers, including Macy’s, still operate internal training programmes. However, many companies have instead relied on fashion and retail programmes at universities to supply new talent, giving up the ability to tailor the skills being taught to their business’ individual needs.
At the same time, new advancements in technology coupled with consumer and shareholder expectations for fashion firms to be more diverse and sustainable have created a need for a whole new sets of skills. Programmes like Pensole’s — and others that are cropping up across the industry at pace — reflect a growing awareness among fashion firms that the best solve for skills gaps are to prevent them in the first place.
“What’s happening industry wide right now is the companies are realising that they can’t just get a student straight from school,” Edwards said. “For one, they may be lacking the necessary technical skills needed to hit the ground running — or, two, they lack the maturity and [soft skills] to work in that company’s environment.”
Developing the Solutions
While many companies still recruit from colleges and universities, they’re increasingly finding that they’ll need to take a more active role in the education process if they’re going to prevent skills gaps that exist at graduation or that crop up much later in the pipeline, experts say.
“People who are graduating from traditional fashion programs are entering a workforce that could be foreign to anything that they’ve worked on,” said Jessica Couch, co-founder of retail tech research firm Fayetteville Road.
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Nordstrom in January launched a new curriculum track at Morehouse, a historically Black college in Atlanta, offering courses such as Computing Career Exploration and Intro to Tech Product Management, featuring the department store’s leaders and executives as regular guest speakers and advisors. That same month, SMCP Group, the French holding company for ready-to-wear labels Sandro, Maje, Claudie Pierlot & De Fursac, unveiled a training academy for sales associates, dubbed SMCP Retail Lab. Like Nordstrom, the year-long programme is in partnership with existing colleges Ema Sup Paris and Institut Français de la Mode.
At Pensole, Edwards designs the programmes like “a trade school,” where he’s meshing technical skills — like how to stitch together the upper part of a sneaker using sustainable materials — with soft skills like “how to behave in a corporate environment,” he said.
Pensole Lewis College students participate in a "masterclass" sponsored by The Capri Foundation. (Courtesy/Courtesy)
“When we worked with Capri, we co-created the course outline and the checkpoints,” Edwards said. “The students are actually learning exactly the way Capri works and what they are going to be expected to behave like when they get there.”
This month, Pensole will welcome another 10 students who will take part in a similar programme like the one the school offered with Capri. This time, the course will be virtual (to be more global) and sponsored by the Coach brand, which will fund room and board for participants when they fly to New York to meet with executives to show off the designs they dream up during the free six-week course.
For skills gaps at the management level and higher, companies must create programmes that help leaders develop a balance of “both the art and science” of retail as well as gain a broader view of the business beyond their respective focus areas, Reid said. For example, someone who’s working in supply chain or marketing should touch other parts of the business via cross-training or regular workshops.
“A lot of this comes down to that partnership between industry and education,” Edwards said. “It just helps companies get closer to the talent that they want. And when they do get that talent they’re more ready to work as soon as they step foot in the door.”
| 2023-03-03T00:00:00 |
https://www.businessoffashion.com/articles/workplace-talent/how-fashion-can-close-its-skills-gap/
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[
{
"date": "2023/03/03",
"position": 75,
"query": "AI skills gap"
}
] |
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The Rise of AI in Business: How Artificial Intelligence is ...
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The Rise of AI in Business: How Artificial Intelligence is Disrupting Industries
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https://www.walkweltech.com
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[] |
Automation: The automation of routine tasks and processes by AI reduces the need for human labor and increases efficiency, disrupting industries like ...
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Despite decades of slow but steady advancement, artificial intelligence (AI) has recently seen a sharp increase in acceptance and popularity. It is due to technological advances, the demand for increased productivity and efficiency, and the desire to deliver better client experiences. As a result, AI’s use in business has grown and is undergoing a revolutionary change as it develops.
What is Artificial Intelligence?
AI, or artificial intelligence, is a branch of computer science that creates intelligent machines. AI automates routine tasks, improves efficiency, and provides new insights and capabilities in various business applications. However, there is no limit to what it can do in the business world, and its applications are only limited by the imagination and creativity of its users.
Various business applications use AI, such as chatbots and virtual assistants, for customer service, fraud detection, predictive maintenance, supply chain optimization, marketing and sales optimization, and healthcare diagnostics.
Importance of Understanding AI’s Impact on Industries
It is crucial to comprehend the influence of AI on various sectors to identify possibilities, reduce risks, boost production, encourage innovation, and address ethical problems. Besides streamlining processes and generating additional consumer value, it helps firms stay ahead of the curve.
Additionally, it enables businesses and decision-makers to create plans to deal with the effects of AI on employment and employment-related issues.
Furthermore, ethical concerns around prejudice, privacy, and transparency are becoming more critical as AI integrates into various enterprises. Therefore, businesses must guarantee that the technology is applied ethically and responsibly by comprehending these challenges and creating responsible practices for its use.
AI’s Impact on Industries
AI is disrupting industries by changing the way businesses operate and compete. Here are a few ways AI is disrupting industries:
Automation: The automation of routine tasks and processes by AI reduces the need for human labor and increases efficiency, disrupting industries like manufacturing, transportation, and logistics that rely on manual work or repetitive tasks.
Personalization: By enabling businesses to provide personalized experiences to customers, AI disrupts industries like retail and advertising that rely on mass-produced goods or services.
Data Analysis: AI allows companies to quickly and accurately analyze large amounts of data, opening up new possibilities for growth and insights. This causes disruptions in industries that use conventional data analysis methods, such as healthcare and finance.
Decision-making: By providing data-driven insights and predictions, AI enables businesses to make more informed decisions, leading to disruptions in industries that rely on intuition or experience, such as management and consulting.
Innovation: The use of AI in business is revolutionizing industries such as healthcare and entertainment by creating new products and services that were previously not possible or practical, resulting in disruptive innovation.
Examples of Industries Adopting AI
Various industries, including healthcare, finance, retail, manufacturing, transportation, agriculture, energy, and education, have adopted AI. Enterprises are adopting AI to improve efficiency, increase productivity, and provide better products and services to customers.
Business team working on project. Project management, business analysis and planning, brainstorming and research, consulting and motivation concept. Vector isolated illustration.
For example, AI is used for medical image analysis, fraud detection, personalized marketing, autonomous vehicles, precision farming, predictive maintenance, and more. The adoption of AI is expected to grow as businesses seek to leverage the technology’s capabilities to stay ahead of the competition and transform their industries.
AI and the Future of Work
AI’s impact on employment is complex and multifaceted. While AI can create new jobs and improve job satisfaction by automating routine tasks, it can also displace workers and create job insecurity. AI is changing the skill requirements for many jobs and creating new industries.
Also, the benefits of AI may be unevenly distributed, and workers who lack the necessary skills to work with AI may find themselves at a disadvantage in the job market. Therefore, investing in education and training programs that prepare workers for the changing job market is essential to manage AI’s impact on employment.
New Job Opportunities Created by AI
AI creates new job opportunities in various fields, such as data science, machine learning engineering, AI training, NLP specialization, robotics technician, and AI ethics. Increasing data generation creates a growing need for data scientists and analysts.
Machine-learning engineers design and implement algorithms that enable AI systems to learn and improve over time. An AI trainer trains an AI system to perform a specific task, while an NLP specialist develops algorithms to understand and respond to the language used by humans. As AI becomes more widespread, new job opportunities will likely emerge, creating a demand for workers with the necessary skills and expertise.
Challenges and Limitations of AI in Business
AI raises ethical concerns like privacy, bias, transparency, and accountability. Also, due to its limitations and expensive development and implementation, AI has limited use in business.
Furthermore, AI’s potential risks in business include unintended consequences, malicious use, and job displacement. Hence, companies must consider the ethical implications of AI and its possible limitations and risks and take steps to mitigate them.
Conclusion
The future of AI in business is promising, as the technology has the potential to revolutionize many industries, improve efficiency, and drive innovation. However, it is important for businesses to carefully consider the ethical implications of AI and its limitations and potential risks.
To fully realize the benefits of AI, companies must invest in the necessary infrastructure, talent, and training. This includes developing strategies for integrating AI into existing workflows, ensuring that AI is used ethically and responsibly, and creating a culture of collaboration and continuous learning.
Ultimately, the success of AI in business will depend on the ability of firms to balance the potential benefits of AI with the need for ethical, transparent, and accountable practices.
| 2023-03-03T00:00:00 |
2023/03/03
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https://www.walkweltech.com/blog/the-rise-of-ai-in-business-how-artificial-intelligence-is-disrupting-industries/
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[
{
"date": "2023/03/03",
"position": 95,
"query": "AI labor market trends"
}
] |
Are you ready for performance management in the age of ...
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Are you ready for performance management in the age of artificial intelligence?
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https://blogs.lse.ac.uk
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"Helena Vieira",
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Using artificial intelligence at work may have many benefits, but a growing body of research calls attention to the potential negative impact of AI on employee ...
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“Whether or not employees benefit or suffer from working with artificial intelligence very much depends on their personal characteristics, like how they (un)favorably view themselves…”
Albert Lam, Chief Marketing Officer (CMO), NOVELTE Robotic Solution
The opening quote from the chief marketing officer of a robotics company reveals a critical insight about working with, or depending on, artificial intelligence: AI can be a double-edged sword for both employees and managers. It can augment employees at work by facilitating their goal accomplishments, since it helps them complete more complicated analytical tasks. However, this emerging digital tool might unexpectedly trigger an aversive host of psychological states in employees. The critical question to resolve thus seems to be for whom the benefits (versus costs) of working with AI will be more salient.
In our recent article, my co-authors and I write that for some employees, this augmentation (i.e., making employees depend on AI) has a bright side, but there might be a negative consequence in its ability to enhance employee performance. Yes, there are likely both benefits and harms to employees as a result of these human-machine collaborations, but we suspected that personal characteristics might influence how strongly those benefits vs. harms are experienced. If true, this would have significant implications for organisational decision-makers worldwide who might have to rethink their business practices regarding pairing employees with AI.
The central premise of these arguments is based on self-regulation theory, a dynamic process wherein individuals evaluate discrepancies between their current states (how they are feeling or what they are doing now) and some desired end-state (what they want/expect themselves to feel or do). We posited that, on the one hand, someone’s dependence on AI in various work tasks can be discrepancy-reducing. They may be able to complete important tasks more effectively or eliminate repetitive tasks or hassles, which can improve their performance. On the other hand, the same phenomenon can be discrepancy-enlarging, making clear that employees could not complete their tasks without AI assistance, which eventually has the potential to harm their performance.
Across two different studies with participants from India and the United States, we consistently showed that dependence on AI at work can both reduce discrepancy, by eliciting goal progress (a positive consequence) and increase discrepancy, by triggering a self-esteem threat (a negative consequence), with mixed consequences for employee performance. Yet, more importantly (based on the opening quote), our focus was on how employees’ personal characteristics would affect the strength of these relationships. We expected that whether AI can be discrepancy-reducing or discrepancy-enlarging would depend on employees’ core self-evaluation (CSE), their overall self-assessment. Our expectation was that employees with higher levels of CSE would be more likely to see their dependence on AI negatively, as they tend to attribute their work accomplishments to their ability (instead of AI’s help).
And indeed, this is what we found. Dependence on AI was discrepancy-enlarging when it became a self-esteem threat (employees tended to experience larger threats to their self-esteem). Along similar lines, for these individuals, dependence on AI was not discrepancy-reducing when it came to their work-goals (they tended not to feel that depending on AI was effective for making progress on tasks at work). Overall speaking, our results suggest that employees high in CSE might attain fewer performance benefits from working with AI.
These findings have critical implications for managers and other organisational decision-makers who are navigating through the Fourth Industrial Revolution. Specifically, the conventional wisdom on CSE is that it is a good filtering criteria in recruitment and selection, as well as performance management. Our findings suggest to decision-makers that this consensus of CSE might need to be revisited. It is critical that managers recognise that tried-and-true methods they have used in the past may not work in the new digital era.
While one remedy might simply be to encourage managers to avoid pairing high-CSE employees with AI (or at least to avoid their heavy engagement with AI), this may not be feasible all the time given that high (compared to low) CSE employees have proven to be better in a lot of aspects in their daily lives and work.
To this end, we encourage decision-makers to adopt a more holistic perspective for the outcomes that are measured when evaluating the success of these augmentation efforts. Beyond focusing on increases to performance, managers should be attentive to the well-being of their digital workforce, striving to avoid the reductions to task performance that we found, as well as other costs that can arise.
One potential solution is to encourage employees to engage in positive self-affirmations (which provide a means to restore or protect one’s self-regard) or mindfulness practice (which can also have positive effects for employee well-being). Overall, we recommend that managers be careful to avoid “over-selling” the promise of AI, and we echo those who have called for a more conservative judgement of the organisational impact of intelligent machines.
In closing, our research joins an emerging conversation about the potential benefits and costs of coupling employees with AI at work and throws light on the personal characteristics that amplify these benefits (vs. costs). We continue to add to the recent stream of research highlighting that some conventional wisdom and organisation practices might no longer be valid in the new digital era—a manuscript we previously wrote about for LSE Business Review.
| 2023-03-03T00:00:00 |
2023/03/03
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https://blogs.lse.ac.uk/businessreview/2023/03/03/are-you-ready-for-performance-management-in-the-age-of-artificial-intelligence/
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[
{
"date": "2023/03/03",
"position": 95,
"query": "AI regulation employment"
}
] |
▷ Machine Learning solutions | Innovant
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▷ Machine Learning solutions
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https://innovant.us
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Check other services · Elastic Workforce™ · UX/UI Design & Growth · Big Data Sccience Consultancy & Data Analayst · Digital Transformation.
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We will explain how Machine Learning will help you improve your sales and profits, understand your customers, reduce costs, automate and improve processes and become more efficient in decision making.
Our team of Machine Learning experts will take care of the entire process: understanding your business and the information you handle, designing a short-, medium-, and long-term strategic plan, modeling the information, developing it, and putting it into production.
| 2023-03-03T00:00:00 |
https://innovant.us/services/machine-learning/
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[
{
"date": "2023/03/03",
"position": 50,
"query": "machine learning workforce"
}
] |
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5 Ways We're Using AI at Work
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5 Ways We're Using AI at Work
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https://www.ideo.com
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... Union click here). Please note you will continue to receive generic ads ... 5 Ways We're Using AI at Work. Artificial Intelligence is our new office ...
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Are you using ChatGPT yet? If the answer is no, keep reading. For anyone whose day job involves creating things—proposals or presentations that require fact-finding, writing, imagery, and video—it's changing how we make stuff. Love it or fear it, generative AI may be our new co-worker. And to ensure this co-worker is ethical, friendly, and aligned with our intentions, we must first explore it.
This phase of the AI revolution feels personal. Many IDEO designers are artists or writers—crafts that generative AI is uniquely suited to disrupt. So, we’re getting ahead of it and embracing these tools to explore how our creative process might coevolve with AI.
As we would do with any new colleague, we hope to learn how to have constructive relationships with these technologies, understand them better, and collaborate.
Way back in 2019—before generative AI was everywhere—we created a deck of AI Ethics cards, which you can download here. It was a good start, but there is much work yet to be done on creating an ethical human-AI ecosystem that endures long after the hype cycle ends.
Here are a few of the ways we’ve been experimenting so far:
1. Research Synthesis
The classic image of design thinking is people huddled around a Post-It–littered foam core board. During the pandemic, this process shifted to digital tools like Miro, Reduct, and Figjam.
Interaction Designer Takashi Wickes is using generative AI to further streamline the synthesis process—a method of organizing and interpreting research data in order to identify patterns, themes, and insights that can be used to inform the design of a product or service or experience. Using Reduct to transcribe his interviews, he highlights stand-out quotes. He then runs the quotes through Notion AI with a prompt like “What are the top 10 key takeaways from this conversation?”
Knowing that the AI doesn’t have the larger context of the project at hand, Takashi and his teammates use Notion AI’s output as supplemental input. Treating Notion AI’s output as a base to build on, they combine AI and human intelligence to go deeper, faster.
And, we’re already seeing startups form around this type of process: check out Vowel.
2. Concepting & Ideation
Designers come up with a lot of ideas. A typical ideation session begins with a prompt, something like: In a world oversaturated with screens, how might we support parents and children in connecting over the dinner table through games? We scratch our heads, think it over, and begin sketching out sacrificial concepts.
Recently, we’ve been experimenting with flipping this process on its head. Rather than coming up with an idea straight away, we use AI-based image generators like DALL-E or Midjourney for inspiration.
For a project on new dinnertime games, we fed DALL-E the prompt: “a family playing with their food during dinner.” The images rendered spark new directions that we might not have come to on our own. Mom with a carrot as a nose? Why didn’t we think of that? When you’re all at the table, why not use food as costumes in gameplay? Magical game design is all about turning expectations on their head. Now, we start to wonder… What other foods would make fun play pieces?
And of course, there are a cohort of new companies who are formalizing this process into a consumer tool: check out Fermat.
{{video-1}}
3. Critique
One thing we’re always exploring in our very riffy culture is how to get better at critique. There’s always this one thing to pick on: Weak insights. Insights at IDEO are concise statements that reflect our understanding about the current state of affairs in a given challenge and the way they are framed can be foundational to how we design solutions.
A few years back, before AI tools were everywhere, we made an insight analyzer chatbot. It looked for overused terms and poor word choice and would essentially chew you out. It was just an NLP bot that used natural language processing to match user chats with a set of known statements. The interface was a physical telephone (hotline). A funny gimmick with a simple learning: bots can get away with being mean.
Late last year, a team of brand and interaction designers started packaging up our favorite simple methods of critique and revisited the old insights hotline. Anne Graziano and I fed simple prompts such as “Give a brutally honest critique of the following insight: Meet people where they are at” to ChatGPT, and as might be expected, if you talk to an AI modeled after what’s said on the internet it’s going to be very good at criticizing things.
We tried the OpenAI api (GPT3_davinci-003) and got decent results, so we wired it up to an emoji reaction in a Slack channel. When you add a “crit” emoji to an insight, it replies with a critique. The original prompt was harsh and getting a bit obstinate, so our group adjusted to the prompt “Give constructive feedback on the following insight:” which changed the tone and made it more creative and fun.
Our colleague Takashi figured out that the AI was pleased to critique quotes from classic movies and romcoms, which Takashi sourced from ChatGPT (prompt: “Give me the 10 cheesiest famous lines from movies”). He then fed the AI old chestnuts like, "Life is not measured by the number of breaths we take, but by the moments that take our breath away." (Hitch, 2005).
{{video-2}}
4. Marketing Copy
As community builders at IDEO, we host a lot of events. Recently, when writing up the copy for an internal informational session about the fundamentals and history of generative AI, I turned to ChatGPT to write the marketing copy. The output was pretty spot-on, and ended up informing both the content of the event and the copy we used to pitch it. We built on it, of course—inserting things that we thought were especially important for our community to learn—but it was a true collaboration.
5. Slide Decks
Google Slides are a staple of IDEO’s creative process. We use slides for project deliverables, ideation, business proposals, and internal events. We've found Tome.app, which creates AI-generated slides, to be a helpful springboard. Business designers are experimenting with the technology to refine their communications, interaction designers use it to think through how a new product might look, act, and feel, and our Play Lab is using it to explore new rules and play styles for games that do not yet exist.
Companies are forming around key parts of our creative process—capabilities which humans (and a few animals) once claimed as their sole domain.
In a 1948 documentary about U.S. agriculture called "The Land" the disruptive force of large-scale mechanization is described in a way that feels like it could be about this AI moment:
"These miraculous machines!
Do we shape them
Or do they shape us?
Or reshape us from our decent, far designs?
But we are learning.
We are learning to build for the future
From the ground up."
We’re curious: How are you using generative AI tools at work? We’d love to hear from you! Send us a note: Savannah Kunovsky & Danny Deruntz
| 2023-03-03T00:00:00 |
https://www.ideo.com/journal/5-ways-were-using-ai-at-work
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[
{
"date": "2023/03/03",
"position": 53,
"query": "AI labor union"
}
] |
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NLRB Rules Severance Agreements with Confidentiality ...
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NLRB Rules Severance Agreements with Confidentiality Provisions Violate Employee NLRA Rights
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https://www.hunton.com
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Following a union election in August 2019, the NLRB certified a labor union ... AI, Air Quality Index, Airline Worker, Alan Marcuis, Alcohol Policy, Alcoholism ...
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NLRB Rules Severance Agreements with Confidentiality Provisions Violate Employee NLRA Rights
The National Labor Relations Board (“Board” or NLRB) decided in McLaren Macomb, 372 NLRB No. 58 (2023) that an employer violated the National Labor Relations Act (NLRA) by offering furloughed employees severance agreements that contained confidentiality and non-disparagement provisions. “A severance agreement is unlawful if its terms have a reasonable tendency to interfere with, restrain, or coerce employees in the exercise of their [NLRA] rights, and that employers’ proffer of such agreements to employees is unlawful,” announced the Board. In rendering the decision, the NLRB overruled Baylor Univ. Med. Ctr., 369 NLRB No. 43 (2020)[1] and IGT d/b/a Int’l Game Tech., 370 NLRB No. 50 (2020). In those cases, the Board decided that employers did not independently violate the NLRA simply by presenting employees with severance agreements containing non-assistance, non-disclosure, and non-disparagement provisions that arguably restricted NLRA rights absent some additional circumstances.
Background on NLRA Rights
Most private sector employers in the United States are covered by the NLRA, irrespective of whether their workplaces are unionized. The NLRA provides both union and non-union employees with the right to engage in protected concerted activities and the right to refrain from such activities. Such activities include the right to discuss terms and conditions of employment with co-workers, publicly protest unfair working conditions, and join labor unions. Employees also have the right to file unfair labor practice (“ULP”) charges with the NLRB and cooperate in Board investigations. To the extent language in a severance agreement, such as a confidentiality provision, can be read to interfere with these rights, employers should carefully consider the decision in McLaren Macomb before including such language.
A Summary of McLaren Macomb Decision
Facts
The employer operates a hospital in Michigan and employs over 2,000 employees. Following a union election in August 2019, the NLRB certified a labor union as the exclusive collective bargaining representative of about 350 service employees at the hospital.
Approximately six months after the union election, in March 2020, the government issued COVID-19 regulations that limited the employer’s operations. The regulations prohibited the employer from performing elective and outpatient procedures, and from allowing nonessential employees to work in the hospital. As a result of these restrictions, the employer terminated its outpatient services and eventually permanently furloughed 11 employees who were represented by the union.
The employer offered the permanently furloughed employees severance agreements. As is typical, each severance agreement provided the employee with consideration in exchange for a waiver of the right to bring certain legal claims. The severance agreement further required the employee to keep the terms of the agreement confidential and prohibited the employee from disparaging the employer, related entities, and individual representatives. In the event an employee breached the confidentiality or non-disparagement provision, the severance agreement stated that the employer could obtain injunctive relief and that the employee would be responsible for damages to the employer, including attorneys’ fees.
At the time the employer provided the employees with the severance agreements, the employer was testing the validity of the union’s August 2019 election victory. The employer did not give the union advance notice of the permanent furloughs or that it would offer the employees severance agreements. The employer did not provide the union an opportunity to bargain over the furlough decision or the effects of the decision. And, the employer did not include the union in its discussions with the employees about the severance agreements.
Procedural History
The union filed a ULP charge against the employer, claiming that its conduct with respect to the furloughs violated the NLRA. The general counsel issued a complaint stemming from the charge.
A hearing was held before an Administrative Law Judge (“ALJ”), who concluded that the employer violated the NLRA by failing to notify the union about the furlough decision, failing to provide the union an opportunity to bargain about the decision and the effects of that decision, and directly dealing with the employees about the furlough decision. The ALJ decided the employer did not separately violate the NLRA by offering the severance agreements to the employees. Regarding this ruling, the ALJ looked to the decisions in Baylor and IGT. The ALJ said, “[t]he agreements were voluntary, only offered to separated workers, and did not impact their previously accrued benefits.” Despite the conclusion that the employer violated the NLRA by failing to involve the union in the furlough decision, the ALJ further stated, “[t]his case also does not involve . . . other circumstances interfering with [NLRA] rights . . . ”
Both the employer and the general counsel filed exceptions to the ALJ’s decision. The general counsel’s exceptions argued, for the first time, that the Board should overrule Baylor and IGT.
Decision
The NLRB agreed with the ALJ, deciding the employer violated the NLRA by failing to notify the union about the furlough decision, by failing to provide the union an opportunity to bargain about the decision and the effects of that decision, and by directly dealing with the employees. The Board disagreed with the ALJ’s conclusion that the employer did not separately violate the NLRA by offering the severance agreements to the employees. In doing so, the NLRB overruled Baylor and IGT.
The Board claimed that the decisions in Baylor and IGT were problematic to the extent they stand for the proposition that an employer cannot violate the NLRA merely by offering a severance agreement to employees absent a finding that the employer otherwise violated the NLRA and harbored animus toward rights protected by the NLRA. The NLRB held that the mere offering of a severance agreement to employees is an independent violation if the agreement’s terms have a reasonable tendency to interfere with, restrain, or coerce employees in exercising their NLRA rights, irrespective of any other circumstances. The Board opined that this standard was consistent with long-standing NLRB precedent.
The Board decided that the confidentiality and non-disparagement provisions at issue were unlawful because they had a reasonable tendency to interfere with, restrain, or coerce employees’ exercise of their NLRA rights. As to the confidentiality provision, the NLRB said that the provision broadly prohibited an employee from disclosing the agreement to any third-party, which would include the Board and other employees. The NLRB highlighted that it is against public policy to prohibit individuals with knowledge of ULPs from cooperating with the Board and that employees have the right under the NLRA to discuss their terms and conditions of employment with each other. Regarding the non-disparagement provision, the NLRB faulted the provision for prohibiting an employee from making any statement that could disparage or harm the image of the employer, related entities, and individual employer representatives without any temporal limitation. The Board emphasized that “[p]ublic statements by employees about the workplace are central to the exercise of employee rights under the [NLRA].” It explained that employees can exercise NLRA rights both inside and outside the workplace, including in political forums and the media.
In rendering its decision, the NLRB mentioned that not every provision in a severance agreement that arguably interferes with NLRA rights is unlawful. The Board explained that the standard it set forth examines whether the relinquishment of NLRA rights is a permissible “narrowly tailored” one. In this regard, the Board footnoted:
We are not called on in this case to define today the meaning of a ‘narrowly tailored’ forfeiture of [NLRA] rights in a severance agreement, but we note that prior decisions have approved severance agreements where the releases waived only the signing employee’s right to pursue employment claims and only as to claims arising as of the date of the agreement.
Dissent
A dissenting Board member criticized the decision in McLaren Macomb on numerous grounds. The Board member claimed the NLRB decision to overrule Baylor and IGT was merely “dicta.” The dissent explained that it was not necessary for the Board to overrule Baylor or IGT because the offering of the severance agreements in McLaren Macomb constituted an NLRA violation under Baylor and IGT as the employer committed ULPs by failing to involve the union in the furlough decision. The Board member pointed out that the general counsel did not even argue to overrule Baylor and IGT until filing exceptions to the ALJ’s decision, further questioning whether it was appropriate for the NLRB to overrule Baylor and IGT. In addition, the dissent disagreed with the Board’s conclusion that Baylor and IGT were contrary to long-standing precedent. The dissent noted that neither Baylor nor IGT overturned NLRB cases coming before them, “but merely declined to continue to apply the overboard holdings contained therein to cases involving a significantly different factual scenario.” The dissent also faulted the Board for applying the “reasonable tendency to interfere” standard borrowed from a currently outdated standard set forth in Lutheran Heritage Village-Livonia, 343 NLRB 646 (2004) to evaluate facially neutral work rules in analyzing the facially neutral provisions in the severance agreements at issue.
Conclusion
The decision in McLaren Macomb makes clear the Board will be critical of any language in a severance agreement that arguably interferes with employees’ rights under the NLRA. As a result, employers must keep this decision in mind when drafting and presenting severance agreements to employees protected under the NLRA. Feel free to check out our client alert about this decision here, which includes some tips for employers to consider moving forward. Additional coverage on McLaren Macomb can be found in our previously published alert.
[1] Hunton Andrews Kurth LLP’s Amber Rogers represented Baylor in this case.
| 2023-03-03T00:00:00 |
https://www.hunton.com/hunton-employment-labor-perspectives/nlrb-rules-severance-agreements-with-confidentiality-provisions-violate-employee-nlra-rights
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[
{
"date": "2023/03/03",
"position": 90,
"query": "AI labor union"
}
] |
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Will AI Take Over the Jobs That Digital Nomads Do?
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Will AI Take Over the Jobs That Digital Nomads Do?
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https://andysto.com
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Digital nomads and remote workers who work internationally also bring a level of diversity, cultural perspective and experiences to their work, these things can ...
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“AI will take over my job” that’s a thought many (if not all) of us have had lately, with the rise of generative AI applications like ChatGPT, DALL-E and the hype they created, it’s hard not to worry about losing our jobs to artificial intelligence.
Digital nomads and remote workers are among those who worry. If digital nomads don’t have a job, how can they finance their lifestyle?
According to the “2023 State of Digital Nomads” report by Nomad List, the majority of digital nomads are software developers, creatives, marketers or startup founders. Other jobs include bloggers, UI/UX designers, translators, virtual assistants and product managers.
Currently, many aspects of these jobs can be automated by an AI application or another. So what does this mean?
What is AI?
Artificial Intelligence is a field in computer science based on training computers and machines to do tasks that are normally performed by humans. These include decision-making, language comprehension and object recognition.
AI started in the 1950s with pioneers like John McCarthy, who is considered the father of AI, which was about creating smart machines.
The advances in technology and increase in data led to the development of AI in recent years, leading up to the release of ChatGPT in 2020. ChatGPT is an AI language model introduced by OpenAI, it generates human-like answers to textual questions.
Is your job safe?
AI applications like ChatGPT will not take over your job. Yes, believe it. Why are we so sure? Let us explain to you.
Generative AI applications are unlikely to replace humans at work, for starters, they don’t have the level of creativity, flexibility or critical thinking that humans do and that is important in many of the above mentioned jobs.
For example, there is a lot that AI can do when it comes to customer service, they can answer generic questions (like bots) or enter data in a form, but they can never offer empathy to customers or think creatively about solutions that suit and comfort the customer in question.
The same applies for the majority of digital nomad jobs, where communication skills, orchestration, team building and leadership skills are required.
Digital nomads and remote workers who work internationally also bring a level of diversity, cultural perspective and experiences to their work, these things can’t be replaced by a machine.
For all the above reasons, AI can’t replace you in your work. But what can AI do?
What AI can do
AI can help automate routine tasks that take up a lot of our time in any job. Like data entry, writing a lengthy email, better phrasing an idea or a pitch. AI can help digital nomads boost their potential, and can open more doors that they hadn’t anticipated.
By taking over and automating these small tasks, digital nomads and remote workers may find themselves with more time on their hands to experience more, work on more important stuff, make plans, focus on their wellbeing or take on more clients.
Here are some examples of how digital nomads and remote workers can use AI to improve their lives.
Time management: by using AI digital nomads and remote workers may be able to schedule things more efficiently. They can also use tools for time tracking to understand how they spend their time and improve their productivity.
by using AI digital nomads and remote workers may be able to schedule things more efficiently. They can also use tools for time tracking to understand how they spend their time and improve their productivity. Discovery and recommendations: AI tools used by TripAdvisor and others make suggestions to you based on your preferences and history, so that you can access more personalized experiences and destinations.
AI tools used by TripAdvisor and others make suggestions to you based on your preferences and history, so that you can access more personalized experiences and destinations. Virtual Assistance: for sure one of the Virtual assistants like Siri, Alexa and Google assistant is already part of your day. They help you be organized and productive, like having a human assistant.
for sure one of the Virtual assistants like Siri, Alexa and Google assistant is already part of your day. They help you be organized and productive, like having a human assistant. Translation: when traveling all over the world, you need AI translation tools like Google Translate to help you understand and be understood when no one speaks a common language with you.
when traveling all over the world, you need AI translation tools like Google Translate to help you understand and be understood when no one speaks a common language with you. Learning: educational platforms that use AI can help you learn and improve your skills whether to become a digital nomad or if you are already one. They offer a more personalized learning experience, thus helping you progress faster.
educational platforms that use AI can help you learn and improve your skills whether to become a digital nomad or if you are already one. They offer a more personalized learning experience, thus helping you progress faster. Safety: AI can help you stay safe whether physically by using localized alerts, or financially by using AI tools to detect suspicious behavior on your cards.
AI can help you stay safe whether physically by using localized alerts, or financially by using AI tools to detect suspicious behavior on your cards. Traveling smart: Using AI tools you can get personalized suggestions for destinations, find deals, check real-time weather conditions and best ways to get anywhere.
Using AI tools you can get personalized suggestions for destinations, find deals, check real-time weather conditions and best ways to get anywhere. Task automation: you can use AI for your website or blog to help give you ideas, phrase a better ad copy, quickly research a quote, do math or summarize a book. In other words, help you save time on tasks that don’t need your personal touch.
The verdict
While it seems like artificial intelligence will take over the world, the truth is, it can not replace the human element in many jobs.
On the other hand, AI can help digital nomads and remote workers improve their lives drastically. It can help them be more organized, efficient, productive and free up more time to keep work-life balance and maintain their wellness.
It’s logical to fear something when you don’t know it, but our advice is, transform your fear into your strength. Familiarize yourself with AI applications, embrace the new technology and use AI to leverage your potential as a digital nomad or remote worker to the max.
| 2023-03-03T00:00:00 |
2023/03/03
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https://andysto.com/will-ai-take-over-the-jobs-that-digital-nomads-do/
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[
{
"date": "2023/03/03",
"position": 100,
"query": "artificial intelligence workers"
}
] |
Artificial Intelligence (AI) Leaders Podcast Series
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Servicios y soluciones de inteligencia artificial
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https://www.accenture.com
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Accenture's AI Leaders Podcast series on industry trends, opportunities and challenges related to AI, analytics, and data. Listen now.
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Liderá en la era de la IA generativa En los últimos 30 años, ninguna tecnología ha prometido cambiarlo todo en una empresa, hasta que llegó la IA generativa. Hoy en día, la IA es el motor número uno de reinvención empresarial. Y la disponibilidad de los datos es uno de los factores más importantes para el éxito de la IA.
| 2023-03-03T00:00:00 |
https://www.accenture.com/ar-es/services/applied-intelligence/ai-leaders-podcast-series
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[
{
"date": "2023/03/03",
"position": 17,
"query": "artificial intelligence business leaders"
}
] |
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Guide to ChatGPT for Recruiting
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Guide to ChatGPT for Recruiting
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https://www.emissary.ai
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ChatGPT's AI capabilities will also enable recruiters to identify trends and patterns among applicants and automate the scheduling process for interviews. All ...
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ChatGPT is an Artificial Intelligence (AI) technology that has the potential to revolutionize the way we recruit and hire online.
By leveraging advanced Natural Language Processing (NLP) algorithms, ChatGPT can understand and respond to user-generated text in a human-like manner, making modern conversations more meaningful and engaging than ever before.
In the coming years this generative technology (GPT) will certainly work its way into recruiting workflows in a variety of ways. In fact vendors have already starting integrating into their products.
We put together a list of how to's, videos and other online resources to help you understand how GPT technology can help you recruit.
As a starter, here is a list of prompts you can try on ChatGPT for recruiting purposes;
Create a company wide communication regarding our upcoming open enrollment for our healthcare benefits
Create an empathetic rejection email to a candidate who met all of the requirements, however the hiring manager selected another candidate
Create a job description for a [insert job title]
Create a payroll calendar for our employees who are paid on a bi-weekly pay cycle
Create an onboarding process to welcome new hires
Create a comparison chart for different payroll systems
Create a communication to an employee who has requested ADA accommodation
Create a list on interview questions for a customer service candidate
Create a Boolean search string for LinkedIn to identify a sales analyst with experience in consumer products. They should have experience in CRM systems and be based in New York.
Now lets check out some great video content that we've partnered on with Recruiting veteran Jim Stroud.
Watch Jim's other ChatGPT for Recruiting vidoes on the Emissary Youtube Channel.
How to Source Candidates with ChatGPT
Hacking Job Titles using ChatGPT
Boosting Employee Retention through ChatGPT
ChatGPT How To Articles
More ChatGPT Recruiting Videos
ChatGPT Recruiter Podcasts
Other ChatGPT Recruiting Resources
How Will ChatGPT Influence Recruiting?
ChatGPT can be used for recruiting in a variety of ways. It can be used to quickly filter through large volumes of candidates, using real-time natural language understanding and conversation technology.
ChatGPT is also capable of automatically evaluating how well each candidate’s answers match the job description and requirements. This allows recruiters to focus on the best-qualified candidates in a much shorter amount of time.
Additionally, ChatGPTs AI capabilities make it possible to identify trends and patterns among applicants, making it easier for recruiters to quickly recognize potential areas of improvement in their recruitment process. Ultimately, ChatGPT can be used to save time and resources while ensuring that the right candidates are identified and contacted in a timely manner.
By leveraging ChatGPT's natural language processing capabilities, recruiters can create more personalized experiences for applicants by engaging them with conversational chatbot technology. This allows recruiters to ask targeted questions and gain deeper insights into the candidate’s background and qualification. In addition, ChatGPT can be used to automate the scheduling process by helping recruiters identify the best times and dates for interview slots.
Ultimately, ChatGPT's AI capabilities make it an invaluable tool for recruitment professionals looking to streamline their processes while maintaining a high level of accuracy in candidate selection.
By optimizing the recruitment process with ChatGPT’s AI-powered chatbot technology, recruiters can easily and quickly find the best candidates while saving time and resources.
In summary, ChatGPT is revolutionizing the recruitment process by leveraging its advanced natural language processing capabilities. It can be used to filter through large volumes of applicants quickly and accurately, as well as to evaluate each candidate’s responses in relation to the job description.
ChatGPT’s AI capabilities will also enable recruiters to identify trends and patterns among applicants and automate the scheduling process for interviews. All of this allows recruiters to save time and resources while ensuring that the right candidates are identified and contacted in a timely manner.
By leveraging ChatGPT’s powerful AI-powered chatbot technology, recruiters can provide an even more personalized experience for applicants, while still remaining efficient and accurate.
This makes ChatGPT the perfect tool for recruitment professionals looking to streamline their processes and ensure that only the best candidates are chosen. With ChatGPT, recruitment becomes more efficient, accurate, and personalized than ever before.
| 2023-03-03T00:00:00 |
https://www.emissary.ai/chatgpt-for-recruiting
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[
{
"date": "2023/03/03",
"position": 87,
"query": "artificial intelligence hiring"
}
] |
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Kummer Institute Center for Artificial Intelligence and ...
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POST DOCTORAL FELLOW - Kummer Institute Center for Artificial Intelligence and Autonomous Systems job with Missouri University of Science and Technology
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https://jobs.chronicle.com
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Candidates must have knowledge and experience in machine learning and an earned Ph.D. in computer engineering, electrical engineering, computer science, ...
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Kummer Institute Center for Artificial Intelligence and Autonomous SystemsThe Kummer Institute Center for Artificial Intelligence and Autonomous Systems (KICAIAS) needs support to develop proposals and execute projects in areas of KICAIAS interests. The position will report to Dr. Donald Wunsch, the Mary K. Finley Missouri Distinguished Professor and Director of the KICAIAS. Tasks will include proposal writing, collaborating with faculty members and students, publication of papers and presentation of research results. These activities will be conducted in person and remotely. Candidates will be considered immediately and consideration will continue until all positions are filled.Candidates must have knowledge and experience in machine learning and an earned Ph.D. in computer engineering, electrical engineering, computer science, statistics, or a related discipline.Strong publication record and excellent written communication skills.Founded in 1870 as the Missouri School of Mines and Metallurgy, Missouri University of Science and Technology is one of the top technological institutions in the nation and among the first technological institutions established west of the Mississippi River. A US News and World Report Top 100 national public university, Missouri S&T serves 7,200 students and counts over 55,000 living alumni, including world-famous astronauts, scientists, engineers, inventors, and business leaders. S&T has three colleges: the College of the Arts, Sciences, and Education; the College of Engineering and Computing; and the newly established Kummer College of Innovation, Entrepreneurship, and Economic Development. Nearly 400 faculty offer 101 degree undergraduate and graduate programs in business, engineering, science, computing, social sciences, and the humanities. Students entering S&T have an average ACT of 28.5, while 80% of all students are Missouri residents and 20% are first-generation.In 2020, alumnus Fred Kummer and his wife June donated $300M to create the Kummer Institute of Student Success, Research, and Economic Development, with three primary objectives: (1) elevate S&T’s reputation as an educational and research organization, (2) create broad access to S&T’s exceptional STEM and business programs through outreach to Missouri’s K-12 communities, and (3) drive economic development regionally, statewide, and beyond. Missouri S&T is currently poised for dramatic achievement in these areas as the vision of the Kummer family is brought to fruition.Located about 100 miles southwest of St. Louis in the multicultural community of Rolla, Missouri, S&T is an accessible, safe, and friendly campus surrounded by Ozarks scenery.Missouri S&T’s Department of Kummer Institute Center for Artificial Intelligence and Autonomous Systems, the campus, and the greater University of Missouri System are deeply committed to inclusion and valuing diversity. Missouri S&T has undertaken a number of initiatives to improve campus life and the work/life balance of its faculty and staff (see http://hr.mst.edu ). Missouri S&T seeks to meet the needs of dual-career couples. ( Dual Career Partner Assistance Program Open Until FilledThe final candidate is required to provide copies of official transcript(s) for any college degree(s) listed in application materials submitted. Copies of transcript(s) should be provided prior to the start of employment. In addition, the final candidate may be required to verify other credentials listed in application materials.Failure to provide official transcript(s) or other required verification may result in the withdrawal of the job offer.All job offers are contingent upon successful completion of a criminal background check.This position is eligible for University benefits. As part of your total compensation, the University offers a comprehensive benefits package, including medical, dental and vision plans, retirement, and educational fee discounts for all four UM System campuses. For additional information on University benefits, please visit the Faculty & Staff Benefits website at https://www.umsystem.edu/totalrewards/benefits The University of Missouri is an Equal Opportunity Employer To request ADA accommodations, please call the Office of Equity & Title IX at 573-341-7734.Job Title POST DOCTORAL FELLOW - Kummer Institute Center for Artificial Intelligence and Autonomous Systems #00085101, #00085102, #00085103, #00085104, #00085105, #00085106, #00085107, #00085108Job ID 45987Location RollaDepartment Ctr for AI & Autonomous SysJob Family Teaching & Research FacultyBusiness Unit Missouri S & TPosted Date 03/03/2023Close Date Open Until Filled
| 2023-03-03T00:00:00 |
2023/03/03
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https://jobs.chronicle.com/job/37422763/post-doctoral-fellow-kummer-institute-center-for-artificial-intelligence-and-autonomous-systems/
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[
{
"date": "2023/03/03",
"position": 94,
"query": "artificial intelligence hiring"
}
] |
An Insider Look at Scout AI Search Engine
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An Insider Look at Scout AI Search Engine
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https://www.getscout.ai
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[] |
Benefits of AI-Powered Recruitment Sourcing: ... To recap, AI's incredible capabilities simply can't be ignored. Scout's own unique algorithms put considerable ...
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An Insider Look at Scout AI Search Engine
What is Scout?
Sourcing for the ideal candidate can be a troublesome process that can take hours, and sometimes even weeks. Yet, it is an irreplaceable part of the recruitment process with 31% of all hires having been proactively sourced.
To make this process a lot simpler and more efficient, Scout, an artificial intelligence-powered sourcing engine, was created with recruiters and talent teams in mind. In under a minute, Scout provides you with a longlist of only the most relevant and qualified candidates, helping you halve your sourcing time while doubling your fill rates.
This way, you can spend more time brokering placements and managing your candidates and hiring managers, rather than on manual tasks like sourcing.
But how does Scout actually do it all? Here is an inside look at Scout’s secret advantages that enable it to be a powerful AI sourcing engine that you can get started using today, for free.
How Artificial Intelligence (AI) Supports Scout:
Scout AI’s search engine is the heart of the platform, using AI algorithms to match candidates with job requirements in real time. The search engine leverages natural language processing (NLP) and machine learning (ML) to understand the requirements of each role, as well as the skills, experience, and qualifications of each candidate. This allows Scout AI to provide recruiters with a shortlist of qualified candidates in just 1 minute, saving time and effort over the traditional recruitment process.
Here’s a deeper look at what separates Scout from other candidate sourcing engines, empowering you to find only the most qualified and relevant tech talent for your open roles.
Natural Language Processing (NLP) for Relevant Job Matching:
Rather than spending hours poring through a stack of resumes or countless LinkedIn profiles and trying to make sense of a candidate’s relevancy to a job opening by yourself, Scout does all the work for you.
Equipped with Natural Language Processing (NLP) which uses artificial intelligence and machine learning to process and interpret text, Scout can easily analyse:
Job Descriptions: Scout parses through your provided job description to obtain information on the skills and experience required of an ideal fit for the role.
Scout parses through your provided job description to obtain information on the skills and experience required of an ideal fit for the role. Resumes: Scout looks through countless candidate profiles at once to uncover candidates’ key skills and experiences that match the job’s requirements.
Scout also accurately parses text of all kinds of pages and documents with different layouts, which includes texts from PDF or .doc files, and even websites.
With the aid of its AI engine, Scout users are able to gain more accurate and relevant matches, helping recruiters to fill open roles twice as fast and more easily compared to traditional methods of sourcing and screening candidates.
Ranking Algorithm for Quicker Interviewing and Hiring Decisions:
Besides parsing through the text of candidate profiles and your provided job description, Scout also uses AI algorithms to rank your candidates. This saves you the trouble of filtering out only the best candidates from a long list of profiles, which can often seem like an impossible task when you have multiple variables to consider, and multiple profiles to choose from.
This is how it works: after analysing your job description’s top requirements, Scout sources candidates across the web and ranks them according to their relevance and suitability to the role.
The three bars beside a profile indicate that the candidate is highly recommended for the job, possessing the relevant skill set and experience required.
These recommendations are based on Scout’s AI-powered comparison of your job’s top requirements, against a weighted calculation of a candidate’s entire digital footprint. Scout determines the experience, technical abilities and relevance of a candidate through these mediums:
Profiles on LinkedIn, as well as other job boards such as Naukri, Monster, and more
GitHub contributions
Personal websites
Online portfolios
HackerRank scores
Contributions on other platforms such as, but not limited to, CodePen, Medium, StackOverflow, Behance, etc.
This allows you to gain a better understanding of a candidate’s technical expertise and suitability for the role in a matter of moments rather than hours.
By having quick access to the best candidates at your fingertips, recruiters will be able to reach out to top candidates, submit CVs, and schedule interviews a lot faster.
Machine Learning (ML):
Last but definitely not least, Scout’s machine learning algorithms carefully analyses your job description and picks out the most important skill, experience and certification requirements from it.
This can be especially helpful when hiring for technical roles as a recruiter who is not familiar with technical recruitment. This saves hours of your own time spent researching or speaking with the hiring manager to simply understand the requirements of the role… and this roadblock happens before you even start searching for suitable candidates!
Also, as you add more job descriptions and send out outreach emails, Scout learns more about your candidate skill preferences, leading to more precise and powerful candidate sourcing.
Benefits of AI-Powered Recruitment Sourcing:
To recap, AI’s incredible capabilities simply can’t be ignored. Scout’s own unique algorithms put considerable power into recruiters’ hands and provide these benefits:
Fast and efficient process
Resume screening is automated
Accurate matches based on AI algorithms
Faster time to fill a role
Improved candidate experience
Enhanced data insights
Want to make talent sourcing a breeze? Get started with a FREE Scout trial in just a few minutes. You might just find your next hire with Scout.
| 2023-03-03T00:00:00 |
https://www.getscout.ai/blog/an-insider-look-at-scout-ai-search-engine
|
[
{
"date": "2023/03/03",
"position": 98,
"query": "artificial intelligence hiring"
}
] |
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What impact will artificial intelligence have on education?
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What impact will artificial intelligence have on education?
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https://www.equaltimes.org
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[] |
Luckin recommends harnessing the potential of this technology to make learning more fun and more effective, “using AI wisely” to perform more routine tasks and ...
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The growing popularity of artificial intelligence (AI) software, capable of generating images, sound and even text in a matter of seconds, is opening up a debate on technological transformation. In this February 2023 image, a teacher in A Coruña, Spain, gives a lesson in a classroom where technology, for the time being without artificial intelligence, is ubiquitous.
The growing popularity of artificial intelligence (AI) programmes, which have shown themselves increasingly capable in recent months of generating images, videos, music, computer programming code and even texts of all kinds in a matter of seconds, producing seemingly appropriate and coherent results, in many instances – and in many others, not – is arousing fascination and concern all over the world, especially among artists and creators.
What the AI tools of today can do is, at times, so spectacular and convincing that it is hard not to think it must be the work of a conscious being that comprehends what is being asked of it and understands what it produces in response. This is clearly not the case, but for the public at large it suddenly seems like we are witnessing the sudden emergence of revolutionary technology, full of potential and promise but also perils that could transform our world.
This day may come, but it is further away than the flurry of expectation may lead us to think. What has happened in recent months, above all, is that the current technology, quite widespread and known to all researchers who had hitherto been experimenting with it behind closed doors, has suddenly started to see the light of day, not only with a view to introducing it to the public, arousing interest and attracting investors, but also so that the programmes could benefit from interacting with people and be ‘trained’ by millions of requests and users at the same time, a massive amount of activity and information that no company could otherwise secure for their AIs.
Last year, text-to-image generators such as Midjourney, Stable Diffusion or Dall-E were already beginning to catch the attention of thousands of new and curious users around the world, but the debate reached much greater heights with the public release in December 2022 of a similar programme that generates text: ChatGPT by the company OpenAI. The results delivered within seconds can be so coherent that, in many instances, they appear to have been written by a human being.
It has taken society by storm and may be here to stay, but it is not, in itself, as transformative as it may seem.
At 94, Noam Chomsky, the father of contemporary linguistics, who has closely followed the entire history of AI to this day, cautioned, in January 2023, that ChatGPT is irreparably flawed in that it gives the same value to information that makes sense in the actual world as to information that does not, because it cannot distinguish between the two or understand it. He argued that it does not, therefore, seem to be making any engineering or scientific contributions “except maybe helping some student fake an exam or something”.
From one day to the next, old student antics such as copying entire paragraphs from Wikipedia have indeed been rendered obsolete by technology that, with little effort, does the same job more creatively and more convincingly, in a matter of seconds. Are our educators prepared for the task of incorporating the existence of AI into our classrooms and our lives?
The perils AI poses for education
Equal Times spoke to Spanish researcher José F. Morales, who has first-hand knowledge of the subject, both as a professor of computational logic and as a member of the Artificial Intelligence Department at the Polytechnic University of Madrid (UPM) and the IMDEA Software Institute. In his view, the emergence of this technology as it stands today “won’t be very disruptive” and “if ChatGPT were introduced in schools tomorrow, it would probably lead to a lot of time wasting without offering any comparative advantage over studying with a good book”.
Present-day AIs “are not intelligent beings, they are not programmed to use abstract thinking, to reason or to understand what they produce; what they do is to learn from the structure of texts and the information we put within their reach, from patterns that repeat themselves, thanks to the unimaginable amount of data they have access to, an amount beyond the reach of human beings,” he tells us. “It would be dangerous to suggest that the way they work is somehow intelligent. It would be dangerous to lead people to believe that certain important decisions can be based on the results of this type of AI; and in education, it would be dangerous to suggest that they might be a reliable instrument for imparting knowledge, because while the information they give you is correct half the time, they can also indistinguishably present us with false information, using equally convincing arguments, the other half of the time.”
That is why, he stresses, they are “creative tools” that we should see as a kind of uninformed assistant, capable of both helping us and deceiving us without knowing it: “Because their use of language and argumentation is so good, you might think they are understanding what they are saying, when in fact they are like parrots, repeating things because they know how they sound, without understanding what they are saying.”
Their answers may even seem ingenious, but they are based on a mechanism similar to the predictive text of any search engine, only on a more complex scale and with an unimaginable amount of information behind it, which the AI imitates without question.
Therein lies their other great danger, Morales points out: these AIs, with a computational power incomparably inferior to that of a person, respond based on something very similar to our intuition, doing a kind of cloaked plagiarism, as they relay, copy or reproduce huge amounts of data that are available online, but in a totally opaque way, without us seeing from where and whom the data has been sourced, or how they reproduce what they deliver using these sources.
Aside from the potential legal ramifications, and the fact that the way they operate is not based on logic (unlike other non-neural-network based AIs, which have been studied for many years but have not yet given rise to applications of use to the general public), they also carry another danger: “Their way of responding is, ultimately, very monotonous, and we have to be careful that they don’t end up standardising and killing creativity,” warns the expert. And the same applies in the classroom: “Copying is not bad, it is a necessary part of learning,” he says. “What is bad is copying but not saying from where.”
For Morales: “If students manage to learn with ChatGPT instead of with a book, then all well and good but, for now, books are a much more reliable and edifying source of knowledge.” In addition, “any country can afford to publish its own textbooks, but only two or three companies in the world have the capacity to train ChatGPT, which is done using thousands and thousands of texts, which they themselves select and tag,” and it is this information, inevitably limited and mediated, that would give rise “to ‘valid and accurate knowledge’, which would end up being set in stone, because you cannot change what the AI has learned without redoing the training”.
“Even if technology were beneficial in the classroom, even if ChatGPT were to reason, in whose hands are we leaving the students? Are we willing, as a society, to delegate to such an extent?” he asks.
“We should not forget that until technology allows us to train our own AIs we should be very cautious about whom knowledge management is delegated to and avoid the mistakes made with social media,” adds Morales.
That said, AI is already here, and many educators are going to find themselves on the wrong foot when it comes to its more immediate implications.
No need to panic
“No need to panic, but there is a need for change,” Rose Luckin, researcher at the Knowledge Lab at University College London (UCL) and professor of learner-centred design at the UCL Institute of Education, tells Equal Times. “Education systems across the globe vary and some are better prepared than others to enable their students to coexist with and indeed benefit from living and working with AI,” says Luckin, a leading expert on this technology’s impact on education.
“Education systems that focus on learning facts and testing students about the extent to which they can process information and remember and reproduce it, are not preparing students well for a future workplace where these types of skills and abilities will be done by AI systems.” But, she argues, those “that help students develop a sophisticated understanding of themselves as learners and of what knowledge is, where it comes from, how to make judgements about what good evidence is and make good judgements about what to believe, and most important of all, how to be good at learning, will build their students capabilities to thrive in a world where they coexist with many different types of artificial intelligence.”
“I don’t know of any particular institutions that are handling the situation well,” she acknowledges. “I think that’s something that will come to light over the coming weeks and months. But one thing is for sure: education institutions and systems must seize this opportunity and see it as a positive incentive for much-needed change.”
Luckin recommends harnessing the potential of this technology to make learning more fun and more effective, “using AI wisely” to perform more routine tasks and to encourage curiosity and the exploration of tools such as ChatGPT or Dall·E, to enable students to have “personalised learning journeys”.
But, above all, we should avoid pitfalls such as “failing to recognise that this is a game-changing technology” and there is no turning back.
“The genie cannot be put back in the bottle and it’s great that ChatGPT is waking up the education world to the implications of artificial intelligence for education and training,” insists Luckin, pointing out that it soon will be an integral part of our everyday communication technologies, from word processing to social media and search tools, which is why “we have to prepare people to be well equipped to use it”, rather than ignoring this reality in education or “seeing technology as a tool for cheating”.
For Martin Henry, research coordinator at Education International (EI), the global union federation representing education workers’ organisations: “What we need to teach students is digital citizenship, we need to get them involved in ideas of digital safety. We are just adjusting to technology that we haven’t seen before and that could have a far-reaching impact, or not, depending on how we deal with it and whether we humans are making the decisions.”
Henry, like Morales, sees the tremendous danger in delegating our decision-making processes to artificial intelligence, because, depending on how it is programmed or what data it feeds on, we can end up with an “algorithm that may be racist in nature, or may be based on the wrong data, or might be doing what the English algorithm was doing and upgrading the private students and downgrading the working-class students”, as was the case a few years ago in England during Covid. “An algorithm will do what you ask it to do, and if what we are asking to do is wrong, then we have a problem. That’s what I think we should be focusing on,” he concludes.
The debate, for educators, seems to be about the need to move with the times. This is also the view of Pasi Sahlberg of Finland, a former director general at the Finnish Ministry of Education, current Professor of Education at Southern Cross University in Lismore, Australia, and one of the world’s leading experts on education policy. “As far as I am aware, everybody is trying to figure out what to do with technology, the AI and VR that are slowly finding their places in mainstream schooling,” he recently told Equal Times.
“We still debate whether smartphones and other internet-based gadgets that young people have should be banned in school. Soon, I think sooner than we think, these gadgets will be embedded in what we wear or even within us, which will make these blanket bans practically impossible.” For him, the challenge lies, rather, in updating schools “to fit into these new futures”. Accordingly, he concludes: “Learning to live a safe, responsible and healthy life, with all that technology with us, is perhaps one of the most important 21st century skills there is.”
| 2023-03-03T00:00:00 |
2023/03/03
|
https://www.equaltimes.org/what-impact-will-artificial
|
[
{
"date": "2023/03/03",
"position": 50,
"query": "artificial intelligence education"
}
] |
Should the Government Use AI? With Shion Guha
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Should the Government Use AI? With Shion Guha — The Radical AI Podcast
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https://www.radicalai.org
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... unemployment services or what have you. There's plenty of them. And that ... And if you enjoyed this episode, we invite you to subscribe, rate and review the show ...
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Shion_mixdown.mp3: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.
Speaker1:
Welcome to Radical AI, a podcast about technology, power, society and what it means to be human in the age of information. We are your hosts, Dylan and Jess. We're two PhD students with different backgrounds researching AI and technology ethics.
Speaker2:
In this episode, we interview Shayan Guha about how governments adopt algorithms to enforce public policy.
Speaker1:
Cheyenne is an assistant professor in the Faculty of Information at University of Toronto. His research fits into the field of human centered data science, which he helped develop. Cheyenne explores the intersection between A.I. and public policy by researching algorithmic decision making in public services such as criminal justice, child welfare and health care.
Speaker2:
And with that said, we cover a lot of ground in this interview, so let's just dive right in.
Speaker1:
We are on the call today with Shayan Guha. Shayan, welcome to the show.
Speaker3:
Yeah. Thank you very much for having me here.
Speaker1:
Absolutely. And today we're going to be talking about a few different case studies of data driven decision making in public services. And so to start off, I'm wondering if you could just describe to us some examples of data driven decision making and data driven approaches in the public services sector.
Speaker3:
Yeah, absolutely. So this is not a North American thing. This is not a global South. This is not a global North thing. Governments everywhere, historically have always been very interested in using data to make decisions about the people that they serve. Specifically, however, in the past 5 to 10 years, there's been a lot of attention that's been put on how a lot of this data that's been collected at every level of government, be it kind of like a federal or national government or kind of like a state or provincial level or even at a very local city or other subdivision level. Governments have started implementing predictive risk based algorithms to make decisions from all of the data that it collects about people. And so that exists at practically every single government service that we know or care about or don't even know. So, for instance, the ones that get a lot of attention is the criminal justice system, rightfully so, in the popular consciousness in North America. In the past few years, there's been a lot of attention that has been put on how algorithms are used to make sentencing decisions or set bail or provide various kinds of risk based assessments to judges to make decisions, so on and so forth. And a series of journalistic exposés done by ProPublica and other folks back in 2015, 2016, as well as a US Supreme Court series of cases called Wisconsin versus Loomis. They kind of brought all of this to the national consciousness. But, you know, especially in the criminal justice space, that has been kind of punted back to the to the states in the context of the United States.
Speaker3:
So the Supreme Court basically said that this is not the state should first decide whether before we weigh in. And part of the issue, which is worth mentioning, especially the public sector, is the public sector is inextricably tied up with the legal proceedings. So, you know, there's there's no public services that does not have some kind of a legal or regulatory component associated with it. However, the people who manage or run these legal or regulatory services, so your judges, your lawyers, so on and so forth, they don't get any kind of data or empirical training. So think about this as kind of like a weird dichotomy that on one hand the government is interested in making data driven decisions about people. On the other hand, the people who should be making the decisions have no training in what the decisions are. So when algorithms are thrown at them and they're asked to make decisions, then it creates a lot of conflict and friction. And so we see that now in every single government service beyond criminal justice that exists in school, who should go to watch school kind of ideas in California, Pittsburgh, Wisconsin, Massachusetts, Colorado is all about algorithms and child welfare. But then you also have people deciding what kind of services should Medicare and Medicaid recipients need or unemployment services, what kind of money should go to whom, so on and so forth.
Speaker3:
So there's all of that. And again, it's not a us thing. Let's not just be US centric in other parts of the world. For instance, in Europe, in the social democracies of Europe, you're going to find a lot of this kind of enlightened, data driven decision making where it's kind of looked at as a very positive thing that should be done in order to provide the right kind of service to the right kind of people. So the United States, it's often or or even Canada, it's often kind of looked at as kind of like a punitive deficit based thinking, right. That we must have data driven decision making algorithms so as to prevent the wrong kind of people or to. Prevent fraud. Right. That's the rhetoric. Whereas in Europe. Western Northern Europe, the rhetoric is different. We should be using algorithms so that we can provide the right kind of service to the right kind of people to get it at the right time. And because we are good stewards of public money, so we should be doing this right. So I say this to demonstrate that, you know, algorithms in the public sector are not a partisan thing. It's not about a particular political isle. But it's largely viewed as a very positive thing that will only result in better government services. So that's kind of like a broad introduction to where we are at with with algorithms in the public sector down.
Speaker2:
So let's stick with that positive assumption for because I imagine a lot of listeners from various parts of the world are new to this topic and also translating it to their own context. But when we talk about this positive element about implementing these AI systems, what's that all about? What's the argument for using these systems? And perhaps where are those main inflection points for where these systems are being used?
Speaker3:
There are two main arguments that are often given as to the positive outcome of implementing AI systems in the public sector. So the first thing is increasing efficiencies in whichever service is being provided. So every government has a lot of inefficiencies whether or not the government is providing the service properly or not. There always exists a certain degree of inefficiency and you know, generally it's looked at as a positive thing that we should be trying to reduce those inefficiencies so that we are good stewards of public money. That's one argument that's often made. A second argument that is often made. Is that it's going to reduce costs. Now, that is a far more nebulous argument that you're going to hear in a lot of places. You're going to see it written in a lot of public sector white papers. So before governments implement the systems, they write like 10,000 white papers to kind of prove their point so that when they actually involve the systems that kind of point to the white papers and say, we wrote 10,000 white papers about this before we decided to do this. Why are people criticize it and stuff? So there's always the thing about reducing costs, but there's never any justification about how the costs are reduced. But there seems to be a great optimism that we will move to some kind of automated or more automated world where costs will be reduced. But there's never any definitive thing about how the costs are going to be reduced, because even in a fully automated world it doesn't the logic doesn't automatically imply that just because you live in an automated world, the costs are low. It could be very high. Right.
Speaker1:
I feel like Dillon's is leaning over your right shoulder asking What are the positive arguments? And now I'm going to lean over your left shoulder and say.
Speaker2:
Well, what are the negative arguments?
Speaker1:
Because I'm sure that there are plenty.
Speaker3:
Yeah, absolutely. So. I think that. You know, the negative implications. Lots of people already know these things. But to restate the negative implications, there's two broad negative implication. One broad negative implication is that it. Largely reinforces the existing structural inequalities that are present in society. And the reason for that is the algorithms are trained on administrative data that is collected by the state and administrative data that is often collected by the state that is linked to a variety of different databases. Well, I mean, this is pretty well known in the research record will will reflect the existing structural problems. So algorithms in the public sector often have a tendency to create vicious cycles of that type of reinforcement. And it doesn't affect structural inequalities in any way. Like no amount of throwing algorithms will solve structural inequalities. It will it will exacerbate that if we don't kind of look at them properly. That's the second thing, and this is a lot of people have started kind of doing this work and we started doing this work. It's kind of a pushback against that efficiency and cost argument. The idea is that, well, one of the stated objectives for implementing algorithms is increasing efficiencies and reducing costs. But empirical work actually finds that it doesn't reduce the it doesn't increase the efficiency, it increases efficiency, and it doesn't reduce the cost. In fact, it often brings with it. A whole new data driven algorithmic layer to the bureaucracy. So now you've got street level bureaucrats who are trying to make decisions that the earlier used to do based on their own judgment.
Speaker3:
And we should recognize here that human judgment in the government sector is also biased. It's not a it's not a question of like, algorithms are biased, People are not biased. That's not true. People are also very wise. So but it's a given. But people were first making judgments about other people who seek services. And then you add an additional layer of bureaucracy of here. Now, here is all of these algorithmic systems that you have to deal with and you have to kind of make decisions. That's that's another problem. It really increases a bureaucratic kind of process that the bureaucrats themselves don't like. They just have to use it because they're mandated to use it, but they don't like it. No one likes it. They most of the time, you know, the people who are implementing all of this tends to get all of the flak. But I would push back and say that the street level bureaucrats are actually not to blame here. And the people to blame here are actual people at the positions of power. Right. So we have a tendency, for instance, so let's think about the criminal justice system. We have a tendency to say that. The police use ShotSpotter and crime hotspots to reallocate more policing resources to poorer parts of the city, which are more likely to be black or parts of the city.
Speaker3:
And therefore that's a bad thing. And we should 100% hashtag defund the police. But that's not the that's not a very good rhetoric, right? Because the individual police notwithstanding, the individual cop actually does not have the power to choose not to or to listen to ShotSpotter or crime hotspots or what have. That agency is taken out of their hands. They just have to use it from now on. It's another thing that they have to use. And the real blame kind of lies with the people much higher up the chain. Right. So people who are actually in positions of power to move money are. So although the systems in the public sector cost a lot of money. The real power lies with upper level bureaucrats or people, political appointees who are who have power to throw money to this. And often these are pet projects, right? So the some some person higher up thinks that or even in the case of cities, this becomes like a mayoral for instance prerogative. Happened in New York City back in the eighties and nineties with the ill fated broken Windows policy. Which I'm sure many of the listeners are very familiar with. But the idea was that they would go around neighborhoods and collect data about the smallest types of crimes, and then they would prosecute the smallest type of crimes. They would really, really focus on small quality of life crimes such as littering, crossing the road without the zebra, crossing, truancy.
Speaker3:
And this is called broken windows theory. The idea is that if you prosecute the smaller crimes, the larger crimes won't happen for a variety of reasons. It's since been shown that that theory is not accurate. But why was this a thing? Why this specific theory? Weren't there other other approaches that they could have used? Yes. But this was a moral prerogative. In the 1990s, the mayors were very gung ho about this particular policy and they were like, yes, this is a thing. And now I will devote money to it. Right. So again, with everything in the public sector, it's often a question of follow the money. Where where is the money coming from? Where? Who is throwing that money around? Where is the agency? Right. If the state legislature decides that from now on, all child welfare workers are mandated to use algorithms, the blame does not lie with the individual caseworker who is making a decision on the ground. I mean, what what will that person do? You know, that person doesn't have a choice. So that that is what I'm trying to get at, right? That the fact that the negative implications of algorithms, it's not enough to say the negative implications exist. That's a very shallow critique, right? It's a very shallow distance. The yes, it exists all algorithms of negative consequences. Fine. Now, how do you how do you get at that? Right. And so people are trying to admit that. That's a separate question.
Speaker3:
But like, people are trying to get at the decision making processes and the agency of algorithmic decision making because that is important. Of course, it's equally important to kind of look at the algorithms themselves and do things like look at fairness and conduct audits and increase transparency. Those things are one side of things. The second side of things that not a lot of people do is because that's the hard part, right? Because that part ultimately, ultimately, if you go down that road, you have to become an activist. Ultimately, you have to get more people out to vote to change the leadership so that they don't doesn't result in flawed implementation of systems down the line. Right. So I would urge listeners to think about take a step back and think about the meta perspective. Where is where is this all coming from? Why is this all coming from? And kind of avoid the kind of like shallow pointing of fingers, right? Like the common person off the street when they interact with the government, they don't interact with the mayor, they don't interact with the president, so on and so forth. They interact with your your caseworker, your social services person, your average cop on the street. So it's very easy to blame those people. And beyond the individual blames that they have, it's actually more less about that and more about the structural agencies that lead to the kinds of flawed algorithmic decision making that see.
Speaker2:
One stakeholder that we've talked a bit about but we haven't really gotten to depth is industry itself. And I think industry often gets that blame, as you're saying, kind of thrust on them. But industry is a stakeholder in this system. And so I'm wondering in your mind how industry is is playing in? Because my question is, you know, where is this tech coming from in the first place? And if we follow the money, I'm sure there is some sort of relationship between where that tech is coming from and then how it's being implemented at that mayoral or other political level.
Speaker3:
Absolutely. I mean, industry is definitely a stakeholder in the process because so, you know, if anyone here is familiar with government tenders, you know that the tender, the bid always goes to the lowest bidder. So the government is always trying to find who can build a system at the lowest cost. At the lowest levels of government your your city level, county level, other local level kinds of governments. What you're going to see is you're going to see kind of like small, nebulous third party private companies that latch onto these tenders because in the grand scheme of things, while these tenders do come with a lot of money attached, that's not enough for the big guys to jump in. That's not enough for IBM to jump in. That's not enough for Google or Amazon to jump in. Be jumping at like the higher levels Empire State based contracts or federal contracts. So when the FBI wants facial recognition, they go by Amazon's recognition system, right? When. You know, the NSA wants another risk based algorithm to screen terrorists. They go to IBM to like, I'm giving examples like some of these are well done, but others are hypothetical. Exactly. So. So you've got. So that's that word. So if you think about like big tech, they kind of follow that kind of. Right. Like the big national level things that that's happening with all of that. On the other hand, that's not where most of the action is. Most of the action is actually at the local level, state level, that kind of stuff.
Speaker3:
And they're most of the time you'll see small companies like private companies you don't know. No one knows those teams, but they exist. That's where most of the small businesses are. Right. Or now I've seen a worrying trend, at least in child welfare. So it's a child welfare. It's everywhere else. But you've got Silicon Valley tech startups that have suddenly realized, oh, instead of trying to create the next data gap, I could go to local governments because all the tenders are public. I could go search for tenders, I could create some fancy looking system because again, it's not that hard to wow government officials who low level government officials who know very little about algorithms. It's just not. So it's that's where you kind of see the split between kind of like big tech and startups, like they operate at different levels. But really most of the action is happening at the local level. So you got plenty of like, you know, if you do a cursory Google search, I'm sure that you're going to find plenty of Silicon Valley very recent startups who are claiming to change how resource allocation is done for child welfare or unemployment services or what have you. There's plenty of them. And that because they have now realized that that is a great revenue source because clearly we don't have any good ideas for technology anymore. So.
Speaker1:
Let's dive a little bit deeper into that child welfare case, because I know you've done a bit of work on this in the past, so could you just begin by giving a brief description of what you mean by child welfare and then describe some of the data driven decision making that's happened from governments in this scenario?
Speaker3:
Sure. By child welfare, I mean various child welfare systems that exist at state or local levels in different countries that are towards making sure that children. So that's defined by when you're born to 18 years old, 18 years of age, making sure that children are not abused or neglected. Now, I need to point out that 95% of cases in child welfare and neglect and not abuse abuse is actually a very rare event. Just as, for instance, terrorism is a very rare event, but we are all extremely concerned about terrorism. Similarly, in child welfare, everyone is concerned about abuse, but abuse is actually very low. That is just pure numbers. Abuse is very low. What really happens is neglect and neglect unfortunately has a racist, classist kind of sentiment behind it. What is neglect is not feeding your child organic fruits and vegetables, neglect. Some of you might be like, that's a ludicrous thing to say, but that's actually not true because there are many definitions of neglect, and some definitions of neglect are that you must feed your child fresh fruits and vegetables or else. Well, in many parts of North America and other parts of the world, people live in food deserts and you can't find fresh anything, not when you do three minimum wage jobs. So, you know, you can you can start to see where some of these problematic things exist. And they've existed irrespective of algorithms. That's what I'm trying to say, right? These are structural problems that exist irrespective of algorithms.
Speaker3:
So child welfare systems largely deal with managing neglected abuse of children. And in order to do so, they provide a variety of services. One of the services in child welfare is foster care. Child welfare and foster care often like synonymous. They're not synonymous like foster care is one part of child welfare. There are many other. Most of the time children are actually not put in foster homes. They're there. It's called at home placement. You still live with your parents. It's just that a caseworker comes to check up on you. So, again, if you think about the popular narrative and rhetoric in child welfare. It's most of the time it's not the government coming and taking your kids away, but that's what the popular narrative is. Right. Like if you if you really look at the data, that's not what it is. So it's the popular data is at odds with what actually happens. Even if the government comes most of the time you they do at home placements because we don't have enough foster homes in in the country and we don't need that many homes in the country. And obviously they should be taking your child away if you've abused your child. So. So so those things are there. No data has existed in child welfare for a long time. It's not entirely due. They were doing physical risk assessments on surveys for a long time.
Speaker3:
They were making like risk assessment adjudication in child welfare based on manual collected data for really, really long time. The kind of so I mentioned ProPublica and criminal justice system. That's the case that kind of brought it to the narrative. The other one was the one about child welfare into the popular narrative is also a ProPublica Gizmodo Techradar thing. Those were published, but it's really a book by Virgil Eubanks in 2018 called Automated Inequality that brought one of the key studies was child welfare in Allegheny County in Pennsylvania, where they partnered with our fine friends and colleagues at Carnegie Mellon to do some really interesting things. And I should point out that most of the time, I mean, we tend to focus on where things are going wrong. But most of the time, the I would say that Allegheny County largely did the best that they could under the circumstances and actually improved outcomes in a variety of ways. And they've since kind of become more and more successful. But Virginia did point out many, many instances where it went wrong for a variety of reasons, because the algorithm failed to encapsulate something or the other. And part of the problem is that. Once again, going back to my previous point, these algorithms were built using. Bias risk assessment instruments that risk assessment instruments that will ask questions like does the child have access to fresh food and vegetables and all that? And so a caseworker physically comes to your house, opens your fridge and cupboards, and actually sees if what food is there.
Speaker3:
Now, there's no like in those kind of risk assessments you can't like, there's no wiggle room for saying a client lives in a food desert and doesn't have access to staff or client works three jobs and therefore Kraft mac and cheese. It is right. So when algorithms are implemented, a lot of the times because of this reliance on training on administrative data, so data that is easily quantifiable, Do you have fresh food or vegetables in the house? Yes. No, that is a theoretical, non contextual question. Right. So so we the work that we've all critiqued in child welfare and so I've done deep dive in Wisconsin, that kind of comes out of this kind of analysis. So the algorithms that are there, they're based on data that comes from questions like this over a period of time. So that's a theoretical that's not very good. I mean, there needs to be context around the data. And so part of the effort that my colleagues and I have and students and colleagues and I have tried to kind of put a path forward about, okay, well, if you have to I'm not naive enough to think that. If I criticize algorithms, the algorithms are going to go away. That's not how that works.
Speaker3:
So how do we how do we make progress in that work? Right. Let's think about a world where algorithms exist. They're not going to go away. How do we make progress on one hand? We can make progress on the systems of power. That's a that's a different conversation. On the other hand, we can start to think about what could we do with the algorithms. The current algorithms are biased. What kinds of incremental approaches could we do so that we slowly start making things better? That's probably the approach that works best in the public sector, because why would they listen to you? You might be a very good academic, but like why would a low level bureaucrat listen to you and why on earth would they make changes in there like any change that they make is extremely annoying for them for a variety of reasons. Like any change that they make in a process has a lot of consequences down the line because governments are all about like specific processes. So one of the things that we we were saying is that okay, through our this deep ethnography that we did in Wisconsin, we realized that not only are these risk assessments biased, but the caseworkers know they're biased and they intentionally manipulate the data, like what data they put into the risk assessments in order to get different outcomes that actually benefit whatever variety of reasons.
Speaker3:
Then we found out that this actually caseworkers are required to write down detailed ethnographic narratives for each case every time they have an encounter. And that just gets stored in these archives that no one ever looks at. Now being, you know, having expertise in natural language processing, we realized that, well, if yes, no questions or questions that are kind of on a liquid type scale, strongly disagree to strongly agree. If those questions are bad, if those questions are not appropriate for child welfare. Could we get more context from text and histories? Right. And, you know, because that's what a lot of qualitative social scientists and humanities folks do. Get more context from close reading of texts, except we have 25 years worth of like, like ten terabytes of text. So close manual reading is not going to be enough. Are there computational ways that we could potentially kind of get more context around it so that we can stop living in a risk assessment world and we can live in a more kind of strength based contextual world? So that's kind of like the novel direction that people are headed towards. We've worked on that a fair bit. We've shown in a paper published last year a proof of concept about how you can actually start to get at some of these things. Now, obviously there's going to be people who are not going to like write like you could make the argument that why are ethnographic narratives better than risk assessments? It's a fair question to ask.
Speaker3:
I mean, every data every data source has problems, right? So then why would we you know, if if that's the philosophical question that one must raise, then we should also raise the question then why do we believe closed manual reading done by scholars on archival data if that archival data is also biased? So, you know, that kind of gets at that. So again, that's not a useful critique. The idea is that are there useful things that you can get out of that stops reducing children down to a probability or a number, right? And instead nudges caseworkers to to do things that are in line with other positive things that they've done in the past. Right. And it's kind of threading a very, very, very, very thin needle. Right. Because on one hand. You know, you're still kind of using computational probabilistic algorithms, but on the other hand, you're not. Giving the caseworker a probability or a classification score or a risk score, because again, a single metric, a single score like that has problems. A person who doesn't have algorithmic literacy, like a low level bureaucrat. What does it mean when it says that this child is at 53% of getting abused or allegation of neglect within the six months? What does that mean? How do they. How do they make that determination? That determination.
Speaker3:
And again, this is what we've done earlier. It's kind of based upon three different kind of thought processes. One is like, where does this exist in the overall bureaucratic regulations and processes of things? The second is discretion. Human discretion is a huge part of governments, so governments can choose whether or not to take action based on criteria that's called discretion. So the best example of that is you are speeding, the cop pulls you over and decides to let you go with a warning. That is a very good example of human discretion. That is. Interestingly, as an aside, that is actually one of the arguments against having body cameras for cops, because then it would take that discretion choice away from them. Because if anyone can a cop if I were a cop, I would be very afraid that if my body camera was randomly audited in the future and they discovered that I was literally not catching anyone speeding at all, then that that would have implications for my job, Right. I would do it. So again, these things are complicated. Like it's not as simple as like, do this thing and then everything will be fine. So going back to child welfare, human discretion is a huge part of decisions, right? Decisions that happen within bureaucratic processes. When should the caseworker go to the house? How often should they go to the house? How much money is available for mental health needs of the child? How much money is available for parenting classes? The parents must take a lot of the times in child welfare, in neglect.
Speaker3:
I can tell you a curious statistic. A lot of the times neglect happens with and maybe this is a reflection on on the United States, but it happens with like very young mothers, often like teen moms who actually did not want to have the child but were forced, compelled to have the child for a variety of reasons. And now they have the child, but they never had any training. They do not have any support to know how to care for the child. Right. And so it's not as if they're abusing the child. That's far from truth. But like there's a gulf between abuse and and neglect. And oftentimes, you know, like I was 16 when I had when I had a child, now I'm 18 and my I have a two year old toddler and I want to go party with my friends. Right. It's too easy for us to cast aspersions and say that. Why why should you do these kinds of things? Well, when I was 18, I also wanted to party all the time. So you cannot blame someone who is 18 to want to party and leave the. Jelena Yeah, you know, the child can remain know they're sleeping in their, in their cot. Fine. I'm just going to have a quick makeout and then someone finds that out or the child is a toddler so they climb out of the car, they open the apartment door and they're found wandering outside.
Speaker3:
The very, very typical case, by the way. Very typical kids then. Then what? So the caseworkers need to decide. You can just say that, okay, this is now an unfit parent. You can't just make sport decisions like that. So human discretion is an integral part of the process. And finally, the third part of the process is the actual algorithmic literacy itself. So how do you train people to make decisions through algorithms? So if you take all of these three things together, basically what's happening is that reducing a child down to a classification or a number is a bad thing. It's not. It doesn't it's theoretical. It doesn't. It takes discretion away. It's kind of like they don't like it because it causes problems with the bureaucratic processes. And most importantly, they're unable to interpret it. They're unable to interpret it. So, okay, so then why do we want to live in that world? We don't want to live in network. We want to live in. If we still want to live in an algorithmic world, then maybe we should be trying to do things that support the bureaucratic processes, support human discretion, and support the people to have better algorithmic literacy and decision making capabilities. So that's what we're trying to kind of nudge people towards.
Speaker3:
So, you know. Creating algorithms in the public sector is in itself not a bad thing per se. There's lots of places where actually very low tech algorithms are very useful and high tech obviously are also very useful, but. You know, you have to kind of do it with a with a thoughtful way. You have to kind of understand like, what is the what are the needs of the of the system? Like, how do you support the workers who are in the system? How do you support the caseworkers of the system? So again, in the HCI design community, a lot of people have started talking about worker centered design. This is a very specific thing because HCI as a as a field has now really concerned itself with questions of digital labour, with questions of tech labour, with questions of labour that intersects with algorithms, with data driven systems, with information systems. And this is something that we can see that, well, the algorithms were not designed to be worker centred or to be more human centred. And so that's what we want the conversation to go that way, right? We don't want the conversation to be like, and now I will create the most mathematically perfect algorithm ever. Well, that's useless because people use the algorithms. They don't care about your mathematical facts. So, so, so, so these types of things exist, so I'll stop there.
Speaker2:
Yeah. Well, as we move towards closing, I'm thinking about all of this, and it's it seems like a massive system and it seems like it's playing into, you know, social elements that have existed for a while and are still really thorny, that are really hard to pull apart. And so I think for me, because we're going into this human centered area, the question is still how. And I think the question and this is partially coming from a recent legal decision in the US around Roe v Wade, and a lot of folks feeling powerless about how to look at that even from a technology, all that stuff. For people who want to either take a stand or become involved in this conversation around either child welfare or worker centered design. Are there places that people can plug in or are there resources that people can go to to find out more and then again to plug in?
Speaker3:
Absolutely. So first I would like to point listeners to so based on work that we did at other people did in collaboration, the American Civil Liberties Union actually now has a big nationwide campaign about algorithms and child welfare. Their campaign, for obvious reasons, because they are all about defending the constitutional rights, focuses on the negative implications of child welfare, which are which are very important. So people who want to know more, I would recommend go read that ACLU article as a as an interesting kind of standpoint, if I'm allowed to plug my own work, I think that I can put the links in chat. But there's a series of papers that we wrote in the past few years where we kind of looked at the state of the art of how algorithms are used in child welfare in the US. Then we did a deep dive in Wisconsin to find out all of this stuff around bureaucratic processes and where it exists in the decision making capabilities. That's like a more like very theoretical qualitative kind of paper. And based off those two papers, we kind of try to see if there's proof of concept in computational narrative analysis to support worker centered design. So there's a series of three papers that I think that are useful if people kind of want to get on the on the tech side of things, if there are people who want to kind of get it on the on the more kind of social critical side of things, I think the ACLU article is great.
Speaker3:
And then on the more like tech and design side of things, I think this use of papers are very useful. And then along with other co-authors, Cecilia and Michael Mueller and Marina Cogan, we've also written a textbook very recently that just came out earlier this year published by MIT Press, and that's called Human Centered Data Science and Introduction. And that is a very small textbook. It's not like a big fat text, but it's a very small textbook that's meant for people who want to understand, well, how do you do kind of how do you kind of meld human centered design and data science together? It's kind of meant for professional data scientists or designers or like professional students who want to kind of get into that particular work. I will say that it's very encouraging to see at various stages of the government, I keep a close watch on child welfare in both the US and Canada. There are now positions within child welfare that might be very well suitable for people who want a career in government. And we're also very interested in doing kind of like human centered data science things within the government.
Speaker3:
So I feel like the government has realized that just like outsourcing contracts to nebulous tech startups is not very useful and that there needs to be some amount of capacity building within the system. And so increasingly we're finding more job postings like that. Aclu also hires in this space is really an excellent position. I think they filled it recently, but today they are really at the front line of all of these things, like people, people who really want to get it on the activist side of things. Or if you have a legal background and you want to kind of get into the law stuff, I think that the ACLU articles are a good starting point. In addition to the work, my own work that I plug. I would also recommend that people look at the fine series of articles published by many scholars at Carnegie Mellon with Allegheny County. So Alex Jones, YouTuber Maria Arteta have published a series of work and more recently Ken Holstein High Schoo and students Logan, Stapleton and Kawakami have published some excellent stuff around algorithmic decision making in child welfare. That's certainly something that people could check out. So there's like lots of resources that both kind of like the critical social policy activist side as well as tech design kind of site. Check that out.
Speaker1:
Thank you so much for all those resources. And as usual, we will include links to those in the show notes for this episode. But for now, Shayana, unfortunately, we are out of time for this discussion, even though we know it could go all day. So thank you so much for. Coming on our show and also just for the really important work that you're doing in this field.
Speaker3:
Absolutely. Thank you very much for inviting me. Happy to be here.
Speaker2:
We want to thank Cheyenne again for joining us today and for this wonderful conversation. As always, we like to debrief our conversations. And just let's begin with you. What are you thinking?
Speaker1:
Oof, so many things, so many good things came up in this interview. Well, so many juicy things, maybe. I don't know if they're necessarily.
Speaker2:
Good.
Speaker1:
Objectively, but interesting to discuss maybe is a better way to put it. So first thing that's coming to my mind is our our discussion around discretion with algorithms. And this is something that I think is fascinating in the context of going from a human decision making system to an algorithmic decision making system. And I know that, like, there's some people that are super optimistic about emotional robots and and social robots and their capacity to understand the nuance in human language and the complex differences between humans. But I really thought that Cheyenne's example of algorithms in capability of of exercising discretion was a really good one. The one that he was describing about food deserts and how some algorithms that are trying to allocate child welfare resources or trying to determine whether or not a parent is fit enough to be a parent, they take into consideration that the kind of food that you're feeding your child. And what if you don't have access to, like locally grown, non-GMO, organic foods for your children? What if you live in a food desert? The algorithm wouldn't know that and wouldn't be able to exercise discretion and would flag you as a bad parent for something that you don't really have any control over. And that is one of probably like so many examples that are very similar around how an algorithm often thinks in absolutes and has this determination of what is right or wrong or good or bad based off of whatever heuristics the engineers or the designers of the algorithm have coded into the system. And those those absolute heuristics change over time and change between contexts and between individuals, between locations, between so many things. So yeah, that's, that's my first reaction is I think that's a really important point and it's one that makes me feel uncomfortable about the government using these kinds of systems to allocate resources or to, I don't know, decide if somebody can be a parent or not. What about you, Dylan?
Speaker2:
Yeah, I think that's a really good point. I it makes me think of just all the gaps in the social side of this too. So and we've heard this from a number of guests, but when you're deploying algorithms, especially algorithms coming from certain industry sectors that are divorced from what's possibly happening on the ground or the systems that are already in place, in this case child welfare, there can be real harms in that application because in child welfare, we we do have a system that is very complex and. It sounds like it functions, but it doesn't necessarily function for the betterment or already has things figured out, right? Like it's a social system that is imperfect and that has come under fire a lot of times over the course of its history. And so we're we're at this pragmatic point. I think Cheyenne brought this up where it's it's both good and also complicated that these algorithms exist in a similar way, that it's both good and complicated, that these social services exist. Like it's great that people are doing something and there are people in real need, there are kids in real need and really difficult situations where child protective services are really needed. And also there is some harm reduction that we can do here, including in our algorithms, like not all solutions are created equal.
Speaker2:
And so it's a matter of figuring out how these algorithms, which can be powerful tools and also problematic tools, but can be powerful tools for good, where and how we deploy them and how we build them. And then I think a lot of that has to do with that conversation of the people who are designing it. And then you mentioned some of these startups who are trying to both follow the money and also probably do some good in the world and connecting with government and selling their tech to government. A lot of the government agencies who don't really understand exactly what these algorithms are doing or how they work or anything like that. And so then they deploy them and it's puts the question of agency in, it takes it really out of the hands of the practitioners. And so there's all these different gaps in this massive stakeholder environment that then function but really impact people in need. And with all of these, like even talking about it, right, it's overwhelming. And so how do these different threads fit together in a way that really centers the needs and holistic solutions for in this case, you know, kids, but also anyone being impacted by these algorithms being deployed from the government perspective and in public policy settings.
Speaker1:
Totally agreed. Yeah. And I was also thinking about the Silicon Valley example, as you were speaking about who who actually is creating these algorithms, who's creating these systems, who's collecting this data, and what's that expression about capitalism? It's like if you follow the money back to its source, then that, you know, you figure out like what the actual motivation or the goal behind the system was or something along those lines. I totally just butchered whatever that phrase was.
Speaker2:
But I guess the catch you saying.
Speaker1:
The catch, you saying that I just totally made up and probably definitely bastardized. But I think that it's applicable here because if you think about a lot of these government data driven algorithms or systems that are maybe in partnership with Silicon Valley startups or maybe even big tech giants for certain certain applications, if you trace back the the beginning driving motivation for why these systems were created, it's not always going to be to serve society or to make things better for children or to help out the underserved populations of the country. Oftentimes, if you trace it back to its motivation, it's probably going to be to make money, right? And so if that's the underlying motivation for creating these systems, then I think that they're they're almost set up for failure or at the very least set up to incite and create harm for whoever they're impacting. Because if if their motivation is just to make money, then they're not going to care as much about, you know, the maybe the edge cases where the algorithm was wrong and somebody was impacted poorly because, you know, if it's 98% accuracy then sucks for that 2%. But you know, the 98%, that's like that's great news for us. That means we're hitting our KPIs and that means that we can definitely continue to get more funding from the government to keep this project going. But but I'm more curious about the people who build these systems and their motivation from the start is to ensure that that 2% isn't actually happening or that they can qualitatively investigate and research what is going on with that 2% and mitigate that impact and that harm to make sure that these systems aren't unintentionally desperately impacting certain groups of people, which is pretty much always the case. What happens when we deploy these systems in the real world?
Speaker2:
And which is why Cheyenne's impact has been so important while working with other folks who are bringing this human centered data science methods, but also ideas around how to do this well, or about how to be human centered. And the human centered element, at least just I think in our world, it's still people are still figuring out exactly what that means and how to do it. Well, because it sounds really good. But like, how do we actually either bring a human in the loop or like what you're saying, focus on the fringe cases and focus on the folks, the actual society. Element, societal element, while other things such as money might be prioritized in order to even keep a startup afloat. And so it, you know, it might make sense, but still cause harm. It might be reasonable to follow the money and it still causes harm. So I think the question is and continues to be how can we as as practitioners or as listeners, maybe as we think about some of these things, how do we continue to be human centered in our data science but also in our lives? And as we vote, as we engage in politics, as we go out and we try to change the world even in our daily lives, how can we be more human centered in terms of technology, mediation and our technology use? It's just again, it's feels so complex, especially when you start bringing public policy in it. But I think Shane's work gets to the heart of the fact that we need to start thinking about all of these things and try to figure out some sort of solutions because there's no going back to before algorithms got involved in public policy. They're they're here, they're there. And so now what do we do with it?
Speaker1:
Definitely. And to to quote Cheyenne himself, how can we stop reducing kids or humans down to a probability or a number? How can we scale these systems without reducing people to something that is, it doesn't capture the complex nuances of what it means to be human. So as always, we could keep talking about this for so long, but we are at time. So for more information on today's show, please visit the episode page at Radical Air dot org.
Speaker2:
And if you enjoyed this episode, we invite you to subscribe, rate and review the show on iTunes or your favorite pod culture. Please please do review the show if you're listening right now. Just just go. Just leave us a little a little review. It really does help us get into the. Years and downloads of other folks who might be interested in the topics that we cover on the show. Please catch our regularly scheduled episodes the last Wednesday of every month with possibly some bonus episodes in between. Join our conversation on Twitter at Radical, a iPod. And as always.
Speaker1:
Stay at radical.
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| 2023-03-04T00:00:00 |
https://www.radicalai.org/government-using-ai
|
[
{
"date": "2023/03/04",
"position": 79,
"query": "AI unemployment rate"
},
{
"date": "2023/03/04",
"position": 81,
"query": "AI labor union"
}
] |
|
Artificial Intelligence in the Labor Market | Free Essay ...
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Artificial Intelligence in the Labor Market
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https://studycorgi.com
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[] |
AI is forecast to replace more than 30% of jobs, including white-collar positions such as market research analysts, accountants, and auditors.
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Technology has taken a dramatic leap forward in the last 100 years, taking center stage in every industry. All the people in the world face technological advances: some are more affected by them, while others are confused by technology. Nevertheless, progress is happening almost every day, and the leading player in this field is artificial intelligence. What is this machine, and why has it already infiltrated human life? Where do its competencies begin, and when does its influence end? These are the questions that computer scientists and inventors who want to move progress forward are asking. But more importantly, outline the ethical component and address the central question: will artificial intelligence replace humans? There is no single answer to the question because it affects several areas of human life at once.
Artificial Intelligence is an intelligent machine capable of performing many tasks thanks to unique coding. The coding sets parameters and algorithms that lay the groundwork for functioning, and then the AI builds on them with new ones. This sort of thing seems handy and valuable until the AI begins to perform all of humanity’s functions, from working in a factory to selling a phone. There will probably be no complete mastery of human life by machines, but no one can underestimate the impact of AI. The market economy, political mechanisms, and health care system situation lead to a narrower question: what impact will widespread AI have on humans? Intelligent machines work in many industries and change people’s lives, but what if the moment comes when AI starts to harm humanity?
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Artificial Intelligence is no longer a technology of the distant future since it is quickly being improved, and the job market will become the first area to be radically transformed by AI. First of all, it is important to define the notion of AI. According to research, Artificial Intelligence can be described as a computer system capable of executing tasks such as decision-making and prediction (Agrawal et al., 2019). In other words, AI constitutes a machine-based alternative to people’s intelligence and ability to engage in complex cognitive activities. As research shows, the further advancement of AI is expected to cause unemployment in the skilled labor market to increase (Lu, 2021). Such data can be explained by the fact that AI is primarily intended to solve comprehensive issues and tasks, which are usually in the purview of skilled workers.
As a result, the growth in the use of AI will cause more people to change their jobs and seek alternative employment options. Moreover, AI is forecast to replace more than 30% of jobs, including white-collar positions such as market research analysts, accountants, and auditors (Chelliah, 2017). Essentially, millions of people will risk losing their jobs due to the adoption of AI by businesses and will have to experience a career change shortly. Additionally, researchers outline a possibility that AI also, to a certain extent, will replace tutors and educators and will also adjust curriculums to each student’s needs (Chelliah, 2017). Under such circumstances, society has to implement certain mechanisms to avoid facing a considerable socio-economic crisis due to the radical changes in the labor market. Therefore, one of the possible solutions developed by researchers is the introduction of basic-income programs and the imposition of taxes on industries utilizing AI (Bruun & Duka, 2018). Such policies will potentially help governments to reduce the impact of AI advancements on the economy and lives of people.
On the other hand, the very ability of an AI-based system to replace human workers is a matter of heated debate. From one perspective, there are cases of technology taking the place of people, resulting in job shortening. Such a situation has been observed since the end of the 20th century when robots were implemented to work at conveyor belts and other production sites (Borland & Coelli, 2017). Nevertheless, this particular scenario is associated with manual labor, which does not imply intense cognitive activities. Instead, robots handle routine tasks, in which they have an unquestionable superiority over weaker human bodies. Moreover, Borland and Coelli (2017) argue that “the pace of structural change and job turnover in the labor market has not accelerated with the increasing application of computer-based technologies” (p. 377). In this regard, it may be possible that the technophobic ideas are exaggerated.
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However, artificial intelligence is an entirely new step toward the use of technology. In an optimal state, its range of applicability extends robots by far, making it possible to delegate even complex planning and decision-making tasks to a computer. As a result, humans will experience a job shortage of an unprecedented level that pales in comparison to the use of robots for manual labor. In this case, the superiority of computers in such positions is not as undisputed. Their computing power and the ability to process immense amounts of information may, indeed, help to make data-driven forecasts and decisions. Nevertheless, even a fully developed AI will lack the inherent features of an advanced human mind, the list of which includes emotional intelligence and reflexivity. This is why Jarrahi (2018) suggests that the key to the future is not a competition but symbiosis. An effective nexus of artificial and human intelligence may form a synergy that will yield outstanding results in the new age of development.
Overall, the continuous improvements in terms of developing artificial intelligence make render this technology close to reality. Previously, it could be seen as an element of science fiction or a distant invention, but now AI is as close to completion as it can be. The full implementation of this technology will mark a milestone in humanity’s scientific development, while profoundly transforming the landscape of most industries. The role of artificial intelligence in labor relations is projected to be of unparalleled importance. In other words, the lives of most people will see an effect of this invention, which is why relevant research is a subject of paramount importance for society in general. As per its definition, an AI is a computer system that can predict various outcomes, performing self-learning and decision-making in addition to the usual preprogrammed task execution processes (Agrawal et al., 2019). In a way, AI is to mimic the pinnacle of nature’s design which is human intelligence.
The development of such advanced technology is expected to cause repercussions for the labor market. As computers become capable of complex cognitive processes, unemployment within skilled professions is likely to increase. Outweighed by the computing power of AI, up to 30% of employees will have to seek another area of expertise, which translates into millions of people. As experts and researchers remain centered on the capabilities of artificial intelligence, a logical question arises of whether there should be human-made limits to it. It is recommended that future research takes into account the full specter of AI’s potential impact on humanity’s employment and professional expertise. While such limitations do not align with the overarching concept of AI, they may be necessary to preserve the integrity of society and help it transition to the new reality with fewer losses. With them in place, humanity and AIs can potentially work together on a predictable basis, forming a positive synergy of efficient, data-driven decision-making.
References
Agrawal, A., Gans, J., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. The Journal of Economic Perspectives, 33(2), 31–50.
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Borland, J., & Coelli, M. (2017). Are robots taking our jobs? The Australian Economic Review, 50(4), 377–397. Web.
Bruun, E., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13(2), 1–15. Web.
Chelliah, J. (2017). Will artificial intelligence usurp white collar jobs? Human Resource Management International Digest, 25(3), 1–3. Web.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. Web.
Lu, C. (2021). Artificial intelligence and human jobs. Macroeconomic Dynamics, 25(8), 1–40. Web.
| 2023-03-04T00:00:00 |
https://studycorgi.com/artificial-intelligence-in-the-labor-market/
|
[
{
"date": "2023/03/04",
"position": 37,
"query": "AI labor market trends"
}
] |
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Annex III: High-Risk AI Systems Referred to in Article 6(2)
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Annex III: High-Risk AI Systems Referred to in Article 6(2)
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https://www.euaiact.com
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[] |
AI systems intended to be used for monitoring and detecting prohibited behaviour of students during tests in the context of or within educational and vocational ...
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6.
Law enforcement, in so far as their use is permitted under relevant Union or national law: (a) AI systems intended to be used by or on behalf of law enforcement authorities, or by Union institutions, bodies, offices or agencies in support of law enforcement authorities or on their behalf to assess the risk of a natural person becoming the victim of criminal offences; (b) AI systems intended to be used by or on behalf of law enforcement authorities or by Union institutions, bodies, offices or agencies in support of law enforcement authorities as polygraphs or similar tools; (c) AI systems intended to be used by or on behalf of law enforcement authorities, or by Union institutions, bodies, offices or agencies, in support of law enforcement authorities to evaluate the reliability of evidence in the course of the investigation or prosecution of criminal offences; (d) AI systems intended to be used by law enforcement authorities or on their behalf or by Union institutions, bodies, offices or agencies in support of law enforcement authorities for assessing the risk of a natural person offending or re-offending not solely on the basis of the profiling of natural persons as referred to in Article 3(4) of Directive (EU) 2016/680, or to assess personality traits and characteristics or past criminal behaviour of natural persons or groups; (e) AI systems intended to be used by or on behalf of law enforcement authorities or by Union institutions, bodies, offices or agencies in support of law enforcement authorities for the profiling of natural persons as referred to in Article 3(4) of Directive (EU) 2016/680 in the course of the detection, investigation or prosecution of criminal offences.
| 2023-03-04T00:00:00 |
https://www.euaiact.com/annex/3
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[
{
"date": "2023/03/04",
"position": 32,
"query": "AI regulation employment"
}
] |
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Home
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Inside Privacy
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https://www.insideprivacy.com
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[
"Libbie Canter",
"Elizabeth Brim",
"Micaela Mcmurrough",
"Ashden Fein",
"David H. Engvall",
"Kerry Burke",
"Shayan Karbassi",
"Caleb Skeath",
"Jayne Ponder",
"Ariel Dukes"
] |
... (AI), Competition, Consumer Protection, Emerging Technologies, Technology, UK Government ... policy and best practices surrounding the use of AI in the workplace.
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On Friday, the FTC announced that was entering a consent decree with 1Health.io Inc., which also does business as Vitagene, Inc. This is the fourth health-related FTC enforcement action announced this year (see here and here).
In addition, it comes on the heels of Virginia, Montana, and, as recently as last week, Texas joining California, Utah, and Arizona in adopting legislation specifically regulating the privacy practices of direct-to-consumer genetic testing companies. The recently adopted Montana law has a broader scope and narrower exceptions that raise questions about whether it will impede research, whereas the Texas law adopted last week is more similar to the other state models.
| 2023-03-04T00:00:00 |
https://www.insideprivacy.com/page/28/
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[
{
"date": "2023/03/04",
"position": 91,
"query": "government AI workforce policy"
}
] |
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Developing Human Skills In The Age Of Machines
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Developing Human Skills In The Age Of Machines: The Future Of Learning And Development
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https://elearningindustry.com
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[
"Danielle Wallace",
"Neha Mehta",
"Dr. Ravinder Tulsiani",
"Dr. Marina Theodotou"
] |
By providing employees with personalized and adaptive learning, employers can ensure that their workforce is equipped with the specific skills and knowledge ...
|
The Future For L&D Professionals: Human Skills Development
As technology continues to advance at a rapid pace, the role of machines in the workplace will only become more significant. It's clear that AI will outdo humans on efficiency issues, so instead of trying to compete with them, we need to focus on developing the skills that machines are less equipped to handle. Through countless interviews with industry executives, I’ve gleaned insights applicable to Learning and Development professionals and summarized them into the following themes.
L&D For Developing Human Skills
Need For Context And Creativity
In the Learning and Development industry, the opportunities for sustained human skills development mean teaching employees to consider context, frame problems effectively, and factor in considerations that are harder to quantify or where there isn't enough data for statistical analysis. It also means encouraging creativity in every sense of the word. As technology and machines become increasingly prevalent in the workplace, the need for learners to develop their critical thinking skills becomes essential. While machines can excel at efficiency and statistical analysis, they struggle with interpreting and understanding context, framing problems effectively, and considering non-quantifiable factors. Human experience, knowledge, and the ability to accurately derive context and creatively apply it will become increasingly in demand.
Applied Judgment To Question AI
It's essential to note that machines play a game of probability, which is why number skills will be necessary for analytics. However, it's not enough for employees to know how to do the analytics. They must also understand the assumptions and limitations of these tools. If AI were perfect, we would all "game" the stock market and be wealthy, so it's crucial to teach employees to excel at skills and perspectives that cause them to question the recommendations and decisions of machines.
As Learning and Development professionals, we need to ensure employees know what data to collect so that machines can inform them. However, we also need to impart essential critical thinking skills and not delegate decision-making to AI. Instead, we should empower our employees to make informed decisions based on their analysis of the data and their understanding of the business context. A practical and current example is how students are erroneously using Chat GPT for assistance on school assignments without questioning the validity of the materials nor having sufficient knowledge of the subject to be able to discern what is inaccurate.
Therefore, learners must be taught to question the recommendations and decisions of machines and think beyond the numbers. They need to be able to analyze data and make informed decisions based on their understanding of the business context, as well as their ability to frame problems creatively and think outside the box. By developing these skills, learners can position themselves to work effectively alongside machines and make valuable contributions to their organizations.
Hyper-Personalized Solutions
When it comes to the next wave of training, personalization and learning in the flow of work are becoming increasingly critical, being powered by deep learning. Technology and information infrastructure now allow us to do things we could never do before. As a result, we will always be drawn to the technology that allows us to be more intimate and seamless.
Personalized and adaptive learning has become increasingly important in the age of machines as it allows employees to receive training and support that is tailored to their individual needs and preferences. With the help of advanced technologies such as deep learning, it is now possible to collect and analyze large amounts of data about an employee's learning habits, strengths, and weaknesses, and use this information to create a customized learning plan. By providing employees with personalized and adaptive learning, employers can ensure that their workforce is equipped with the specific skills and knowledge they need to excel in their roles. Additionally, personalized and adaptive learning can help to increase engagement and motivation among employees by giving them more control over their learning experience and allowing them to progress at their own pace.
Added Meaning To Work
Our obligation, however, goes deeper. We need to engage employees and show them that they have a purpose beyond just completing tasks. We must allow for creativity in the training process and encourage employees to think outside the box. We can even think of our employees as our target market and apply marketing techniques to convince them of our training strategies and tactics. In an increasingly machine-driven world, the need to feel meaning and a sense of purpose at work has become even more important. It will be important to clearly articulate the benefits of training and consistently consider the learner experience in all aspects of their learner journey.
Conclusion
In conclusion, the future of Learning and Development lies in embracing the power of machines while simultaneously developing the human skills that machines are less equipped to handle. It's about empowering employees to make informed decisions and providing them with the tools and training they need to succeed. It's about personalization, creativity, and engaging employees to create a culture of continuous learning and improvement. The industry is poised to embrace this challenge, provided we ourselves also continually upgrade our own skills while enhancing the skills of our workforce.
| 2023-03-04T00:00:00 |
2023/03/04
|
https://elearningindustry.com/developing-human-skills-in-the-age-of-machines-the-future-of-learning-and-development
|
[
{
"date": "2023/03/04",
"position": 31,
"query": "machine learning workforce"
}
] |
▷ Elastic Workforce Outsourcing Solution | Innovant
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▷ Elastic Workforce Outsourcing Solution
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https://innovant.us
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[] |
Machine Learning. HOW WE DO IT. < START OVER. I'm looking for Elastic Workforce. Please enable JavaScript in your browser to complete this form. Name ...
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| 2023-03-04T00:00:00 |
https://innovant.us/services/elastic-workforce/
|
[
{
"date": "2023/03/04",
"position": 57,
"query": "machine learning workforce"
}
] |
|
The Great Artificial Intelligence debate
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The Great Artificial Intelligence debate
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https://www.blackbelt360.com
|
[
"Nancy Ampaw",
"Nancy Ampaw Has A Strong Passion For Technology",
"Csr",
"Iot",
"Device Lifecycles",
"Is An Integral Part Of International Marketing Team. Her Excellent Contributions To Growing Knowledge Base Of Blogs",
"White Papers Are Helping Its Customers Deliver Impactful Results."
] |
The great Artificial Intelligence debate – replacing human workers or creating more jobs in society? Artificial Intelligence (AI) is the most talked about ...
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The great Artificial Intelligence debate – replacing human workers or creating more jobs in society?
Artificial Intelligence (AI) is the most talked about controversy in technology. The extent robotic replacement results in human labour becoming obsolete has shaken businesses. What exactly does the new wave of ‘intelligence’ comprise? Are we expected to get on board with the unknown? The counter-argument – an inevitable process in technological and economic advancement. However, some say this is the beginning of the end.
AI vs SAI
AI is the simulation of a digital computer or computer-controlled robot programmed to perform tasks commonly associated with intelligent beings. Replicating human intelligence processes using machines or computer systems is a touchy subject. The sector of workers outraged mainly by the potential phenomena are average day-to-day employees in less well-paid clerical and process-oriented roles. According to research by PwC for the Department of Business, Energy & Industrial Strategy, only in the long term will manual workers such as truck or taxi drivers fall victim to ‘robotic replacement’.
Super Artificial Intelligence (SAI) has become a subset of ‘anti-technology’ terminology conspiring against the potential wave of human cognitive replacement. Despite speculation, there has not been a clear roadmap on how super intelligence will look. The fear is of a mass SAI rollout within the first third of the next century. The predictions are digital computer operationalisation, a super-speed Internet of Things (IoT) programme or anything else that may erupt from technology pipelines.
Although mass unemployment is likely to occur, many speculate there will not be anything drastic except re-allocating roles. Researchers are predicting the extent AI and related computer sciences may follow historical patterns of structural labour force change. On a positive note, new technologies are forecasted to boost GDP by as much as 10% in 2023. Could this result in a substantial economic sweep for the recovering British economy or are employees likely to receive the shorter end of the stick?
Limitations to replacement
Labour-replacing AI varies systematically by sector and industry. The first wave of automatable replacement is predicted to hit the customer service industry. With nearly 300,000 British workers employed in this field, three-quarters are estimated to be displaced over the next two decades. The lucky winners of job security against computer intelligence are Nurses with the least automatable professional occupation, while Solicitors and Psychologists come at a close second. So, why does AI discriminate? The realities are that many of the top professions in our society reliant on morality and ethical reasoning are highly challenging and, without a doubt, impossible to replicate.
Legislators, Psychiatrists, Doctors and many professional qualifiers that fall under the same bank of intellect require a potent aspect of human cognition for successful operation. The verdict – AI and robotics will assist those in Health and Sciences as the industry transitions from collaborative care structures and embarks on precision medicine. Now the tone is set for robot-to-human cohesion in healthcare sectors; the future will likely be complementary to human labour rather than replaceable.
While social reassurance in health practices is band-aided by optimism in medicinal research, there are still barriers to overcome before the technological potential is realised. Ethical sensitivities towards conventional methods of treatment executed by doctors resurface yearly. As highlighted by PwC’s 2017 survey, only 27% of respondents from the UK said they were willing to have a major surgery performed by a robot instead of a doctor - the lowest willingness of any country surveyed.
Can Tech and Ethics meet common ground?
The empiricist philosopher Locke defined ethics as ‘the seeking out of Rules, Measures of humane actions, which lead to Happiness and the means to practice them’. Without delving too far into philosophy, it’s worth noting how setting out the ‘law of ethics and parameters’ have only been conducted so far by humans. When examining many segments within the AI debate, the collective agreement is that ethics falls under scrutiny when we introduce computer programming over human cognitive ability.
A robotic replacement may sound convenient in customer service and retail, yet it raises eyebrows in scientific departments. Despite machines having more processing power and memory than anticipated, they cannot mimic empathy, leadership or teamwork. As tech expands to broader territories of modern life, it’s necessary to rewind discussions about implementing AI without the risk of death, business collapse, or legal battles.
If you have any questions about Blackbelt Defence and how we can help you, do not hesitate to get in touch with the team by clicking the link below.
Unlock the potential of smartphone recycling for your business. Download our comprehensive white paper to gain valuable insights and proven grading strategies. Maximize profitability while making a positive environmental impact.
| 2023-03-04T00:00:00 |
https://www.blackbelt360.com/blog/the-great-artificial-intelligence-debate
|
[
{
"date": "2023/03/04",
"position": 90,
"query": "artificial intelligence workers"
}
] |
|
Survey: 66 Percent of Teens Concerned They Might Not Be ...
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Survey: 66 Percent of Teens Concerned They Might Not Be Able to Find a Good Job as Adults Due to Artificial Intelligence "A.I."
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https://jausa.ja.org
|
[
"Junior Achievement Usa"
] |
Colorado Springs, CO – In recent months, there have been reports of Artificial Intelligence "AI" making advances in competencies related to occupational fields ...
|
Junior Achievement Survey Finds 92 Percent of Teens Want to Learn More about AI in High School
Colorado Springs, CO – In recent months, there have been reports of Artificial Intelligence "AI" making advances in competencies related to occupational fields millions of people depend on for jobs, including graphic design, writing, content creation, and even law and medicine. This has raised questions about the impact AI could have on the future employability of millions.
In response to this, Junior Achievement USA conducted a survey of teens that shows 66 percent are concerned that they will not be able to find a good job as adults due to AI, with 32 percent being "Very" or "Extremely" concerned. Despite this, most teens, 71%, think recent technological innovations are "a good thing," and virtually all, 92%, would be interested in courses in school that promote skills related to AI. The survey of 1,005 teens between 13 and 17 was conducted by Big Village between February 28 - March 5, 2023.
"We at Junior Achievement believe this is a watershed moment in technology, and it's going to impact the future of work," said Jack E. Kosakowski, President and CEO of Junior Achievement USA. "Every 10 to 15 years, we have a technological breakthrough that negatively impacts some jobs while creating new industries and career fields. We saw this with personal computers in the early 80s, the Internet in the mid-90s, smart devices and social media about 15 years ago, and now AI. That's why Junior Achievement is engaging partners in the business community to support the development of new learning experiences to educate young people about AI."
Organizations or individuals interested in partnering with Junior Achievement on this or other initiatives can reach out through [email protected].
Additional survey findings include:
- 86% of survey respondents noted they were "Interested" STEM, Science, Technology, Engineering, and Math", topics in school, with 57% being "Very" or "Extremely Interested".
- Despite their concerns about the impact of AI on future employment, 79% of teens believe they will find a good-paying and rewarding job as adults.
A full summary of the results can be found at https://jausa.ja.org/news/critical-issues.
Methodology
This Youth CARAVAN survey was conducted by Big Village among a sample of 1,005 13-17-year-olds. This survey was live on February 28 - March 5, 2023.
Respondents for this survey were selected from among those who have volunteered to participate in online surveys and polls. All sample surveys and polls may be subject to multiple sources of error, including, but not limited to sampling error, coverage error, error associated with nonresponse, error associated with question wording and response options, and post-survey weighting and adjustments. It is nationally representative with set quotas based on census data. The 1,005 completes are all who qualified and completed based on the demographic quota requirements. The MoE is +/- 3.1%.
About Junior Achievement USA®
Junior Achievement is the world's largest organization dedicated to giving young people the knowledge and skills they need to own their economic success, plan for their future, and make smart academic and economic choices. JA programs are delivered by corporate and community volunteers and provide relevant, hands-on experiences that give students from kindergarten through high school knowledge and skills in financial literacy, work readiness, and entrepreneurship. Today, JA reaches more than 3.3 million students per year in 102 markets across the United States as part of 12.5 million students served by operations in more than 100 other countries worldwide. Junior Achievement USA is a member of JA Worldwide. For more information, visit www.ja.org.
Download PDF
| 2023-03-05T00:00:00 |
https://jausa.ja.org/news/press-releases/survey-66-percent-of-teens-concerned-they-might-not-be-able-to-find-a-good-job-as-adults-due-to-artificial-intelligence-a-i
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[
{
"date": "2023/03/05",
"position": 64,
"query": "artificial intelligence employment"
}
] |
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Careers
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Careers — aiEDU
|
https://www.aiedu.org
|
[] |
The AI Education Project is an equal opportunity employer and we believe that building and empowering a diverse team is a strategic imperative in our work. We ...
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Diversity, equity, and inclusion are fundamental to our culture and core values at The AI Education Project. We believe unity develops when we acknowledge, respect, and embrace our unique histories, skills, and experiences in service of our mission.
The AI Education Project is an equal opportunity employer and we believe that building and empowering a diverse team is a strategic imperative in our work. We welcome people of all backgrounds, beliefs, and philosophies, and our hiring process is designed to maximize inclusiveness and to mitigate conscious and unconscious bias. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, or any other legally protected status.
Our team members come from diverse backgrounds, experiences, and points of view, but we all come with the shared goal of exciting and empowering learners everywhere with AI literacy.
We strongly encourage candidates of all different backgrounds and identities to apply. We are committed to building an inclusive, supportive place for you to do the best and most rewarding work of your career.
| 2023-03-05T00:00:00 |
https://www.aiedu.org/careers
|
[
{
"date": "2023/03/05",
"position": 65,
"query": "artificial intelligence employment"
},
{
"date": "2023/03/05",
"position": 78,
"query": "artificial intelligence education"
}
] |
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The Future of Jobs: What Will Happen in 20 Years?
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The Future of Jobs: What Will Happen in 20 Years?
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https://tietalent.com
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[] |
It is important to note that while automation will change approximately 50% of jobs, it is not expected to eliminate more than 5%. Rather than being replaced by ...
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The next two decades promise a full-scale revolution in our working lives, with automation becoming the center of productivity. But let’s take a look at the present and how technology already is a huge part of our lives.
The concept that our jobs will be replaced by robots and we will be out of work is not true. We already live in an age of robotics in the workplace, and, before the pandemic, we had reached some of the lowest global unemployment rates in history with 5.2% in 2018. (Worldwide)
In other words, high tech and high employment rates don’t need to be mutually exclusive.
This coexistence gives us reason to believe that technology will create more jobs than it will destroy. But we have to be very careful when analyzing this situation as a projection because there is one problem that is arising and that we need to address as soon as possible.
The fact is that jobs will switch to skill-based. In turn, this will affect the most vulnerable populations who have less access to education.
| 2023-03-05T00:00:00 |
https://tietalent.com/en/blog/6/the-future-of-jobs-what-will-happen-in-20-years
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[
{
"date": "2023/03/05",
"position": 34,
"query": "AI unemployment rate"
},
{
"date": "2023/03/05",
"position": 12,
"query": "job automation statistics"
},
{
"date": "2023/03/05",
"position": 50,
"query": "AI job creation vs elimination"
},
{
"date": "2023/03/05",
"position": 82,
"query": "AI skills gap"
},
{
"date": "2023/03/05",
"position": 60,
"query": "AI labor market trends"
}
] |
|
Using Data to Forecast and Meet Hiring Demands in Tight ...
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Using Data to Forecast and Meet Hiring Demands in Tight Labor Markets
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https://gojob.com
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[] |
For instance, if the unemployment rate in your local geography (the area ... Advanced analytics like the kind now available through artificial intelligence (AI) ...
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Chief among them: how do I know the right number of people to hire to find the right balance between meeting production requirements and controlling budget? Equally important, how can I make the right decisions to maximize my budget and productivity?
Ramp-up planning--responding to an anticipated significant increase in production or service requirements--requires determining the number of workers needed, where to find them, wage analysis, how flexible schedules could be used to manage costs, and how short-term benefits could be used to attract and engage staff.
Data can help.
Historical Data
Analyzing past company data can provide insights into hiring needs. For instance, if you know that you had X number of loading dock employees on staff who efficiently handled X volume of loads during the 2021 holiday season, and you predict that volume to increase by a certain amount, you can calculate an estimate of how many additional employees you might need this season.
Your own data also provides a benchmark that can be used to evaluate effectiveness and productivity on an ongoing basis.
Local Data
Local data on available staff/talent is also an important indicator to help in new hire ramp -up planning. For instance, if the unemployment rate in your local geography (the area that staff would normally come from) is 4%, you’re facing a tighter hiring market than if unemployment was at 8%.
Local data that may be available through Chambers of Commerce, economic development groups, and others can help you stay attuned to labor availability and shortages in your recruiting area.
Government Agency Data
Data is available from a wide range of governmental agencies like the U.S. Census Bureau, the Bureau of Labor Statistics (BLS), the Department of Labor’s Wage and Hour Division, and others.
This data may be broader than what you need to make decisions locally, but can give insights into potential trends that may impact your area now or in the future.
Trade Association Data
There is a trade association to represent virtually every industry and every type of job. For instance, in the manufacturing industry, trade associations like the National Association of Manufacturers or The Association for Manufacturing Excellence.
These organizations study their industries and collect and report data to members.
Predictive Data
Advanced analytics like the kind now available through artificial intelligence (AI) and machine learning can offer extremely reliable insights and direction when considering the type and number of workers to bring on board to handle any kind of special need. Powered by data gathered from a variety of sources, AI can help you make future decisions based on what has happened in the past, and the current labor market trends.
While, as we’ve seen, there are a number of sources of data that can be used as an aid in ramp-up planning, accessing, evaluating, and appropriately using this data to make sound recruitment and hiring decisions takes time.
Another challenge that companies face when needing to ramp up staffing is that this need may not exist in the future. Hiring full-time staff now to address peak demand will only result in overstaffing--and overspending--in the future.
And, face it. As you’re focused on meeting customer demand, while maintaining production capacity within your budget, you don’t have the time to crunch numbers or engage in strategic recruitment activities, especially in a tight and very competitive labor market.
We can help.
Companies like GoJob have access to data, advanced analytics capabilities, and the capacity to help companies determine their hiring needs and staffing volumes--and the resources to help fill that volume with staff on a temporary basis. In most cases, we can get you the staff you need within 24 hours on average. You realize significant short-term gains while avoiding long-term liabilities.
Learn more.
| 2023-03-05T00:00:00 |
https://gojob.com/new-hire-ramp-up-planning/
|
[
{
"date": "2023/03/05",
"position": 60,
"query": "AI unemployment rate"
},
{
"date": "2023/03/05",
"position": 44,
"query": "AI labor market trends"
}
] |
|
Overcoming Staff Shortages: A guide to Proactive Solutions
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Overcoming Staff Shortages: A guide to Proactive Solutions
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https://www.celayix.com
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[
"Nippun Arora"
] |
The unemployment rate in Canada has fluctuated since 2015. In 2015, the unemployment rate was relatively low, averaging around 6.9%. As the Canadian economy ...
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Labour Shortage Trends in North America
Staff Shortages in Canada
Staff shortages in Canada come from a need for more available workers to fill positions in a particular field or industry. This can happen for various reasons, including an aging population, low unemployment, and a lack of interest in certain types of work.
One of Canada’s most significant staff shortages is in the healthcare industry. This is partly due to the aging population, which is increasing the demand for healthcare services, and partly to a need for more workers to enter the field. There is a high demand for registered nurses and personal support workers who assist with activities of daily living.
Another area where staff shortages are being felt is in the construction industry. The strong demand for housing and infrastructure projects, coupled with a need for more skilled workers, has led to a shortage of tradespeople such as electricians, plumbers, and carpenters.
In addition, Canada’s fast-growing technology sector faces massive staff shortages of skilled workers, like developers, programmers and data scientists.
Staff shortages can harm businesses and industries, leading to project delays and increased workload for existing employees. Some companies are considering recruitment and retention strategies to address staff shortages, such as flexible work arrangements and training programs. The government also plays its role by providing immigration programs to attract and retain skilled workers.
Staff Shortage in the U.S.
The healthcare industry also has one of the most significant areas of staff shortages in the U.S. With the increasing aging population, the demand for healthcare services is rising. Consequently, there is a shortage of healthcare workers such as nurses, doctors, and personal care aides. The shortage can also be attributed to the number of Americans entering the healthcare field has yet to keep pace with the increasing demand for healthcare services.
Another area where staff shortages are being felt is in the manufacturing industry. The strong demand for goods and a need for more skilled workers have led to a shortage of skilled labour, including machinists, welders, and engineers.
Another sector facing substantial staff shortages is the education sector, specifically the shortage of teachers, special education teachers and support staff.
In addition, the technology sector is also facing challenges with massive staff shortages of skilled workers, like developers, programmers and data scientists.
Staff shortages can negatively impact a country’s economy and industries. The two countries are currently facing staff shortages in several areas, including healthcare, construction, and technology. Addressing the staff shortages will require a combination of efforts from the government, industries and individuals.
2022 Staff Shortage Trends and 2023 Predictions
In 2022, North America, as a whole faced significant staff shortages across various industries. Some of the main reasons behind these shortages include the following:
Low unemployment:
With unemployment rates at historic lows, there were fewer available workers to fill open positions. This makes it more difficult for employers to find suitable candidates.
In July, the unemployment rate was 3.5%, matching the 50-year low set in February 2020. To attract and keep workers, there are 1.9 available employment for every unemployed American. Resultantly, firms are reacting by boosting compensation and offering alternative working arrangements.
Aging population:
As the population ages, more people are retiring, and fewer are entering the workforce. This leads to a shortage of workers in specific fields. This is particularly true in healthcare, where an aging population has led to an increased demand for healthcare services but a decreased number of workers available to provide those services.
Lack of Interest in certain types of work:
Some jobs are considered less attractive than others due to pay, working conditions, or other factors. All of this can lead to a lack of people interested in pursuing careers in those fields.
40% of all job-seeking respondents say the desire for better pay or hours drives their search for a new job. Furthermore, a Payscale research on 454 occupations contrasted how employees felt about their employment regarding Job Meaning and Job Satisfaction. The study also factored in Median Pay to see if there was any strong correlation between the 2.
According to the report, working at fast-food restaurants has the lowest median wage, particularly in the kitchen. They were also in the bottom 20 jobs regarding how significant and gratifying they believed their work was.
Job Title Median Pay High Meaning High Satisfaction Restaurant workers $19,400 33% 39% Registered Nurses $60,100 80% 71% Security Guards $25,300 49% 53% Librarians $49,100 77% 81% Public Transport Workers $30,400 52% 52% Bartenders $28,600 32% 61% Repair Workers $38,100 50% 64% Home Care Aides $30,500 81% 64% Teachers $41,000 72% 67% Fire Fighters $43,100 88% 83% Different Occupations and their median wages, and survey respondents’ responses on job meaning and satisfaction
The table presents the anticipated decrease in interest and job satisfaction for certain occupations. The root cause of this dissatisfaction, be it arduous working conditions or job burnout, will likely deter individuals from pursuing such careers, resulting in a staff shortage.
Changing demographics:
The changing demographic landscape can have a substantial impact on the in-demand skills in the labour market. As the aging population creates a higher demand for healthcare and social services workers, technological advancements drive a heightened demand for workers with technical and engineering abilities.
However, this can result in a skills mismatch. Hereby, the available workforce does not possess the required skills to meet the growing demands of the industry. This shortage of skilled workers can pose a significant challenge for organizations seeking to expand and remain competitive in the global economy.
Skills gap:
There may need to be more workers with the necessary skills to fill open positions. Particularly in areas such as technology and healthcare.
The perceived mismatch between jobseekers’ skills and employer expectations is the second most often stated obstacle to employment. A lack of experience, necessary abilities, credentials, and/or education is preventing 26% of respondents from the McKinsey study from employment.
Remote work:
The aftermath of the COVID-19 pandemic has led to more people working remotely. This makes it more difficult for some employers to find workers willing to relocate or return to working on-site.
A discrepancy in expectations, particularly regarding income and flexibility, is a potential cause for the mismatch between job vacancies and job seekers. Other top reasons for looking for a new job, after salary or hours and better career progression, are flexible working arrangements. This includes the flexibility to work from home or bring a kid to work (22%%), and predictable hours and scheduling (16%).
Each industry and location might have different contributing factors that cause staff shortages. As we remember from our macroeconomics classes, the government looks to take immediate action during high unemployment. But first, let’s look at an economic overview of the staff shortages problem to see how bad it is!
Economic Overview of Staff Shortage Trends
Unemployment Rate
The unemployment rate is a measure of the % of the labour force that is unemployed but actively seeking employment and willing to work. It is typically calculated by dividing the number of unemployed individuals by the total labour force, including employed and unemployed individuals.
The total labour force is the sum of employed individuals and unemployed individuals, which are defined as
Employed individuals: people currently working, including those who are self-employed or working without pay in a family business.
people currently working, including those who are self-employed or working without pay in a family business. Unemployed individuals: people who are without work but are available for work and have actively sought employment in the past four weeks.
It’s worth noting that not all people without work are considered unemployed. For example, individuals who are not actively seeking employment are not considered unemployed and, therefore, not included in the labour force.
The unemployment rate is widely used to indicate the economy’s overall health and labour market. A low unemployment rate generally indicates a strong economy and a healthy labour market. A high unemployment rate may indicate an economic downturn or weak labour market conditions. It’s also important to note that the unemployment rate is a lagging indicator and does not capture the dynamics of the labour market, such as underemployment and discouraged workers.
Canada’s Unemployment Rate
The unemployment rate in Canada has fluctuated since 2015.
In 2015, the unemployment rate was relatively low, averaging around 6.9%. As the Canadian economy expanded, the unemployment rate dropped even lower, hitting a low of 5.8% in early 2017, the lowest level seen since the 1970s.
The unemployment rate remained relatively stable during the next few years, fluctuating from 5.6% to 6.0%. However, with the onset of the COVID-19 pandemic in 2020, the Canadian economy was hit hard. It led to widespread job losses, which caused a significant increase in the unemployment rate. It peaked in May 2020 at 13.7%, the highest level in decades.
As the country adjusted to the new normal, the unemployment rate started to come down with the vaccines rolling out. The unemployment rate continued to improve throughout 2021 and early 2022 as the economy began to recover and businesses started re-hiring workers. The unemployment rate currently is at 5.8% as of December 2022 and has decreased by 1.7% points in the past year.
United States Unemployment Rate
In 2015, the unemployment rate steadily decreased from previous years, averaging around 5.3%. The unemployment rate remained low for the next few years, reaching a record low of 3.5% in September 2019.
However, similarly to Canada, in 2020, the U.S. economy was hit hard. The pandemic led to widespread job losses and a significant increase in unemployment. The unemployment rate increased dramatically, reaching 14.8% in April 2020, its highest level since the Great Depression. Like Canada, the United States is returning to a lower unemployment rate as they adjust to the new normal.
Unemployment-to-job vacancy ratio
The unemployment-to-job vacancy ratio (U-to-J ratio) measures the balance between the number of unemployed workers and the number of job openings in an economy. It is calculated by dividing the number of unemployed workers by the number of job vacancies.
The U-to-J ratio varies depending on the period you are looking at and also the location you are looking at. It also can be affected by the type of economy, whether service-based or goods based, as well as the level of education and skills required for the job vacancies.
A low U-to-J ratio indicates a tight labour market with more job openings than unemployed workers. Employers may have to offer higher wages and better benefits to attract and retain workers. On the other hand, a high U-to-J ratio indicates a weak labour market, where there are more unemployed workers than job openings, and employers may have more choices in terms of whom they hire. They can offer lower wages and benefits.
U-to-J ratio in North America
How have the fluctuations in the unemployment-to-job vacancy ratio (U-to-J ratio) changed in both Canada and the United States since 2010?
In Canada, the U-to-J ratio was relatively low in the early 2010s, with unemployment rates below job vacancies indicating a tight labour market. The ratio reached an all-time low of 0.3 in 2015 but started to rise as the economy started to lose steam, reaching around 0.7 by 2018. The COVID-19 pandemic in 2020 resulted in widespread job losses and a sharp increase in unemployment, causing the U-to-J ratio to skyrocket to one of its highest levels in two decades at 2.4 in May 2020. The ratio continues to increase past 2021 and 2022. However, the Canadian Government believes the ratio would start to fall as people go back to work.
In the United States, the U-to-J ratio was relatively high in the early 2010s due to the lingering effects of the 2008-2009 financial crisis and the Great recession. The ratio started to decline as the economy began to recover and reached 1.9 by the end of 2014. The unemployment rate dropped to a historic low and job vacancies increased due to tight labour market conditions and strong economic growth. However, the COVID-19 pandemic in 2020 severely impacted the economy and resulted in widespread job losses and a considerable increase in unemployment. The U-to-J ratio improved in the latter half of 2020 and early 2021 due to efforts implemented to mitigate the pandemic’s economic impact.
Important Remark about the U-to-J ratio
It’s worth noting that the U-to-J ratio can vary by industry, region, and other factors. Therefore, it’s essential to look at more detailed data to get a more accurate picture of the labour market conditions in Canada and the U.S. since 2010. Also, the U-to-J ratio is only one measure of labour market conditions.
The graphs indicate that we have two large differences in the U-to-J ratio in neighbouring countries. This begs the question if the staff shortage is likely to be a regional issue or has a deeper root cause than that. Regardless, the economic trends show an urgent need for government intervention to reduce unemployment rates and increase labour force participation. So what has the government done to keep labour force participation in check?
Government initiatives to increase labour force participation
The Canadian and U.S. governments have implemented several policies and programs to increase labour force participation. Some of the key initiatives include:
Encouraging immigration:
Canada and the United States have several immigration programs in place that are designed to attract skilled workers from other countries. This helps address labour shortages in specific industries and regions and promotes economic growth.
Providing education and training opportunities:
The government offers funding and support for education and training programs to help Canadians and Americans acquire the skills they need to participate in the workforce. This can include secondary and post-secondary education funding and apprenticeship and vocational training programs.
Helping disadvantaged groups:
The Canadian government focuses on helping disadvantaged groups more likely to face barriers to labour force participation. Such groups include Indigenous peoples, people with disabilities, and newcomers to Canada. Similarly, the U.S. government also focuses on helping disadvantaged groups more likely to face barriers to labour force participation, such as low-income families, people with disabilities, and veterans. This includes programs and initiatives to provide support, education, training, and other opportunities for these groups.
Implementing policies to support family and work-life balance:
The Canadian and the U.S. government have introduced various policies to support work-life balance, such as parental leaves, flexible work arrangements and affordable childcare, to make it easier for parents to participate in the workforce.
Providing assistance and incentives for employers:
Both governments provide assistance and incentives to employers to encourage them to hire and retain workers. This can include tax credits, grants, and subsidies to help offset the cost of training, hiring and retaining workers.
Addressing regional disparities:
The Canadian and U.S. governments are addressing regional disparities in labour force participation by implementing policies and programs targeted to specific regions where the participation rate is lower than the national average.
It’s important to note that the policies and programs implemented by the Canadian and the U.S. government are subject to change and review over time depending on the economic situation and the labour market conditions. The government initiatives may have long-term benefits, but urgent action is needed to ensure companies don’t remain understaffed in 2023. So what can businesses do to ensure they have an adequate workforce? With the shifting trends, it’s essential first to identify the root causes of staff shortages within organizations, followed by possible solutions!
Causes for Labour Shortage
Is the pandemic still a root cause of the labour shortage?
It is difficult to predict the exact effects of the ongoing pandemic in 2023, as it will depend on various factors. Factors such as the effectiveness of containment and mitigation efforts, the development and distribution of vaccines, and potential virus mutations. However, some possible effects that have been widely discussed include continued economic disruption, the possibility of additional waves of infection, and ongoing challenges to mental health. Changes made to society and the economy due to the pandemic will remain in effect for some time. Additionally, further research and studies may come up with more information regarding the long-term effects of the pandemic on individuals and society.
For instance, US President Joe Biden announced on 30th January 2023 that he plans to end the public health emergency declaration by this May. While it may not directly impact employment opportunities and staff shortages, it could certainly affect the labour market. By ending the action to offer financial help to the public, people are more likely to start looking for jobs again to finance their living. While this may be a good opportunity for the government to increase economic activity and reduce their spending, critics disagree as it could still affect the welfare of the public that cannot afford vaccinations and treatments for COVID-19.
Maclean’s research on 2023 predictions can have significant implications on what’s expected to come to Canadian society, with a substantial focus on businesses, health and social issues. We’ll take a brief look to see how these topics can result in a labour shortage.
Canadian Businesses in 2023
“In other words, fasten your seatbelt” Jason McBride.
Maclean’s research discusses the present economic downturn’s probable influence on Canadian corporations and labour unions, as well as the consequences of climate change on both groups. The recession created by the COVID-19 epidemic has devastated many firms, according to the report, and has resulted in a significant increase in bankruptcies and job losses. According to the report, the economic slump may decrease union membership as people are laid off, and businesses fail.
Nevertheless, the recession could have a silver lining for some businesses and workers. The downturn may lead to increased government spending on infrastructure projects, which could create jobs and help boost the economy. Additionally, the recession could increase consumer demand for sustainable and environmentally friendly products as people become more conscious of the need to address climate change.
Moreover, it is essential to look at the potential impact of climate change on Canadian businesses and labour unions. Climate change could lead to more extreme weather events, disrupting supply chains and damaging infrastructure. The shift towards a more sustainable economy could also lead to job losses in some industries, such as fossil fuels. Still, it may also create jobs in new sectors, such as renewable energy.
Overall, the economic recession and the challenges of climate change will likely lead to significant changes in the Canadian business and labour landscapes in the coming year. Businesses will have to adapt to the changing economic conditions. Labour unions will have to navigate the challenges of a changing economy and the need for more sustainable practices.
Canadian Healthcare System in 2023
The impact of the COVID-19 pandemic on the healthcare system in Canada is one to discuss. The pandemic has highlighted the need for more doctors in the country, particularly in rural and remote areas. The shortage of doctors in these areas is a long-standing problem, but the pandemic has made it even more pressing.
Moreover, the shortage of doctors will likely continue as the population ages and the demand for healthcare services increases. The pandemic has accelerated the adoption of telemedicine in Canada. This could help address the shortage of doctors in some areas by allowing patients to see a doctor remotely.
The COVID-19 pandemic has inflicted a substantial toll on healthcare professionals. Doctors in particular doctors have been subjected to rigorous working conditions. These individuals have been grappling with augmented job responsibilities, prolonged stress, and burnout. It is imperative to emphasize the necessity for robust mental health support for healthcare workers, especially those at the forefront of the pandemic.
In conclusion, the COVID-19 outbreak has underscored the need for significant enhancements in Canada’s healthcare system and brought to light the persistent personnel shortages in an industry that is pivotal to a nation’s economic stability.
Canadian Society in 2023
Maclean’s outlook on Canadian society, which can be extended to the United States, suggests that some of the changes caused by the pandemic are here to stay and become the new normal.
Hybrid work is here to stay.
According to an AT&T poll of 300 C-suite executives and senior managers, 81% of employees will use a hybrid arrangement by 2024. It’s a step that will need improved technology and a lot more confidence. Technologies, such as A.I. assistants who can arrange meetings and open-source software that synchronizes all of our office tools, are well on their way, but trust is more difficult to come by. Employers are electronically monitoring one-third of Canadian workers at work. Ontario will force corporations to disclose such surveillance beginning in 2023, but the legislation still needs to grant employees new rights.
Moving over productivity phobia will need a fundamental shift in how we approach work: project-based over time-based, output over input. Brandish, a Winnipeg consulting business, bills customers by the project rather than the hour. At the same time, AltFee, a Vancouver Law-Tech startup, creates software that lets law firms, long shackled to the billable hour, would do the same.
The potential alterations that are being considered may have a considerable impact on the matter of work interests and preferences. As per a recent study conducted by Accenture, 83% of 9,326 surveyed workers expressed a preference for a hybrid work model, allowing them to work remotely for a minimum of 25% of the time. Thus, it is possible that a shift in the workplace model could mitigate the issue of personnel shortages.
Employers will experiment with four-day workweeks.
What was once the stuff of Gen X fantasies appears to be getting closer to reality. A non-profit organization called 4 Day Week Global is conducting a pilot project with 160 companies in the United Kingdom, North America, Australia, and New Zealand, including Canadian organizations such as Toronto marketing agency Praxis, Montreal architecture firm L’Abri, and the Leukemia & Lymphoma Society of Canada, to test a four-day workweek with no pay cut for employees.
Preliminary findings are encouraging! Employee happiness is up, stress is down, and 95% of participating organizations reported the same or greater productivity levels. Employers are also finding it simpler to attract and retain talent. As the labour crisis remains, employee experience is becoming an increasingly important component of recruiting packages. This implies that more Canadians than ever may be saying TGIT in 2023.
The Great Resignation: What is it, and why isn’t the economy fixing it?
The term “The Great Resignation” refers to the trend of workers leaving their current employment in pursuit of better job prospects or higher job satisfaction. This trend has been observed across various countries and industries and is believed to have multiple causes.
One factor is the changing economic landscape. It has resulted in increased job competition and a more dynamic labour market, brought on by globalization and the digital economy. Another factor is a shift in employee attitudes toward work, where individuals are prioritizing a better work-life balance and are less willing to stay in jobs that do not align with their values or career aspirations. It is believed that these two causes are interconnected.
The Great Resignation has had a noticeable impact in countries such as the United States and the United Kingdom. Here workers have reported increased job uncertainty and insecurity, as well as decreased job satisfaction. This trend has implications for both organizations and employees.
To mitigate the effects of the Great Resignation, experts have suggested that organizations adopt a proactive approach to employee engagement by providing more flexible work arrangements, and career development opportunities, and fostering a positive and supportive work environment.
Ways an economy can solve a labour shortage.
There are several ways that an economy can address labour shortages:
Increase immigration: Allowing more people to enter the country can help fill labour shortages, particularly in industries that rely on low-skilled workers.
Automation: Investing in automation and other forms of technology can help businesses become more efficient and reduce the need for human labour.
Training programs: Investing in training programs and education can help prepare workers for in-demand jobs, which can help fill labour shortages.
Increase wages and benefits: Increasing salaries and benefits can help attract workers to specific industries or regions experiencing labour shortages.
Encourage more women and older workers to join the workforce: Encourage policies that support women and older workers to enter the workforce and make working conditions more flexible to retain these employees.
Increase productivity: Encourage policies that support worker productivity.
Changing Demographics: Early Retirement or Immigration Drop?
Economists hold contrasting viewpoints regarding the impact of immigration on the economy. Proponents of immigration argue that it stimulates economic growth through the expansion of the labour force and the promotion of innovation. Conversely, opponents argue that immigration negatively affects low-skilled workers by increasing competition for jobs that would otherwise be filled by American workers and by suppressing wages for native-born, low-skilled labourers.
In his address to Congress in 2017, Former President Donald Trump cited a report by the National Academies of Sciences, Engineering, and Medicine on the economic impact of immigration. He stated that the immigration system incurs significant financial losses for American taxpayers each year, as per the report’s findings. However, the National Academies later clarified that the report actually concluded that immigration has a net positive effect on the economy. Immigrants represent an increasingly diverse category in the U.S., having become more diverse in recent decades.
During the Trump administration, there was a decline in immigration to the United States, but the recent trend shows a return to pre-pandemic levels of net international migration. Despite the influx of immigrants with a diverse range of skills and work experiences, the ongoing staff shortage issues have not been alleviated.
Macrotrends’ Research
According to research by Macrotrends, the proportion of new immigrants to the total population is decreasing. This may suggest a decline in new job opportunities for immigrants, an increase in the productivity of workers, or a rise in the retirement rate. It is also possible that the situation may be different in Canada, where there was an increase of 250,000 more immigrants in 2022 compared to 2021, primarily for the purpose of study or work. Is the current staff shortage issue solely attributed to an increase in early retirement and a more productive workforce?
Early Retirement
After the great resignation, some economists have termed a new word: The Great Retirement. The labour force in Canada increased in the month of August 2022. However, it declined in the preceding two months and has yet to return to its pre-summer size as a considerable number of individuals have ceased to be employed. Statistics Canada attributes a significant portion of this phenomenon to an increasing trend of retirement among Canadians.
According to Statistics Canada, a record number of Canadians aged 55-64 are reporting retirement in the last 12 months. This is in addition to the usual 65 and over age group who are also retiring.
As reported by Statistics Canada, Canada has the largest working-age population as a percentage of the overall population in the G7. Nevertheless, its labour force has become increasingly older. With one in five workers 55 years or older, Canada has been facing a mass exodus of its highly skilled workforce. This situation has left businesses struggling to adapt and has resulted in a rise in wages and a potential decline in productivity. In response, the country has ramped up immigration efforts to drive economic growth.
The issue of retirement in Canada is particularly pronounced in skilled fields such as trades and nursing, leading to a significant decline in the number of jobs available in these industries. According to Statistics Canada, the country has lost 34,400 healthcare jobs since May, despite a record number of nurses reporting working overtime hours.
CBS News Report
Similar is the case in the United States, with CBS News reporting that “Retirements fueling U.S. worker shortage.” With the difficulty of getting people back into the workforce, whether through immigration or bringing people back from retirement, the only thing keeping companies alive are the remaining employees that are either very productive or they are overworked. But is that sustainable in 2023 and beyond?
Industry-Specific Labour Shortage: Need for a change in working conditions
Various industries and sectors within the U.S. and Canada have observed The Great Resignation. On a national scale, disputes over wages and working conditions have often led the conversation regarding industry-related staff shortages. So what is the Industry trend looking like for employees and is there a need for change?
The U.S.
In 2023, the US technology industry experienced significant layoffs, with over 66,000 workers impacted, as recorded by Crunchbase News. Several factors attribute this trend, including intense competition for talent and the rapid pace of change and innovation in the sector. Resultantly, the job cuts are expected to continue, with more workers potentially losing their jobs as the year progresses.
In the retail industry, the Great Resignation has been driven by the changing nature of the sector, with more retailers shifting to e-commerce and automation, which has led to job losses and increased competition for remaining jobs.
In the healthcare industry, the Great Resignation has been spurred on by the increasing demands on healthcare workers, with many reporting high levels of stress and burnout, leading to increased turnover.
Canada
Canada has observed The Great Resignation too, mainly in the oil and gas industry. Many workers have left their jobs due to the downturn in the industry and the shift toward renewable energy sources. This has led to increased competition for remaining jobs, and many workers have chosen to leave the industry in search of better opportunities. In 2014, the labour force within Oil and gas industry had peeked at over 240,000 Canadians. However, in 2022, only around 188,000 are seeking to work in this sector.
In the public sector, the Great Resignation has been led by the increasing demands on public sector workers, with many reporting high levels of stress and burnout, leading to increased turnover.
So, it’s very clear that there is a need for a change in working conditions to help address labour shortages in North America.
One of the main reasons for labour shortages in North America is a mismatch between the workforce’s skills and qualifications and employers’ needs. To address this, many experts recommend investing in education and training programs to help workers acquire the skills and qualifications they need to succeed in the modern workforce.
Working conditions also play a vital role in the retention of employees; providing better working conditions, benefits, fair compensation, and a good work-life balance can help to retain employees and reduce labour shortages.
In addition, organizations may need to consider alternative recruitment and workforce management forms, such as remote work arrangements, contractor-based workforce, or outsourcing, to address labour shortages in specific sectors and industries.
Importance of Retaining Employees in 2023
Retaining employees is critical to running a successful business. Not only does it save time and resources in the recruitment and training process, but it also contributes to a positive workplace culture and increased productivity.
According to a study by the Society for Human Resource Management, the average cost of losing an employee can range from one-half to two times their annual salary. That’s why employers need to make employee retention a priority.
Here are some tips and strategies for retaining employees:
Offer competitive compensation and benefits packages.
Offering competitive salaries, bonuses, and benefits such as health insurance, retirement plans, and paid time off can help retain employees. Employees are more likely to feel valued and appreciated by providing a comprehensive compensation package, which can increase job satisfaction and reduce turnover.
Foster a positive workplace culture.
Creating a positive workplace culture can have a significant impact on employee retention. This includes promoting open communication, encouraging collaboration, and recognizing and rewarding employees for their contributions. A positive workplace culture can improve morale and increase job satisfaction, making employees more likely to stay with the company.
Provide opportunities for growth and development.
Employees are more likely to stay with a company that offers opportunities for growth and development. This can include training and professional development programs, offering promotions and advancement opportunities, and encouraging employees to take on new challenges and responsibilities.
Listen to employee feedback.
Regularly soliciting and acting on employee feedback can help retain employees. By showing that their opinions and ideas are valued, employees are more likely to feel invested in the company and less likely to look for opportunities elsewhere.
Offer flexible work arrangements.
Offering flexible work arrangements, such as telecommuting, flexible schedules, and job sharing can help retain employees. This can improve work-life balance and increase job satisfaction, making employees more likely to stay with the company.
Specific Work Environment Issues to tackle in 2023
Different industries face unique work environment issues impacting employee satisfaction and retention. Such issues largely contribute to employee turnover and hence the staff shortages that we see today. These issues can range from the physical demands of the job, long and irregular hours, and limited opportunities for growth and advancement to high stress and burnout. Understanding and addressing these issues is crucial in creating a positive workplace culture and retaining top talent. Whether it’s the healthcare industry with its demanding patient care responsibilities, the technology industry with its rapidly changing technology, or the retail industry with its long hours and physical demands, each industry faces its own set of work environment challenges. Let’s look at some of them!
Employee Safety in Hospitality and Security: Understanding the Challenges and Solutions
Hospitality and security are two industries that require a strong focus on employee safety. With the nature of the work, employees in these industries are often exposed to various physical and emotional hazards that can impact their well-being and job satisfaction. In this blog post, we’ll discuss the challenges of employee safety in hospitality and security and how employers can help with employee safety.
Challenges of Employee Safety in Hospitality and Security
Physical Demands:
Hospitality workers, such as hotel and restaurant workers, are often required to perform physically demanding tasks, such as carrying heavy bags, standing for long periods, and dealing with difficult customers. These physical demands can lead to injuries and burnout, impacting employee morale and job satisfaction.
Security personnel are often required to perform physically demanding tasks, such as standing for long periods of time and dealing with potentially dangerous situations. These physical demands can lead to injuries and burnout, impacting employee morale and job satisfaction.
Workplace Violence:
Hospitality workers are often exposed to customer verbal and physical abuse, leading to a high risk of workplace violence. According to a 2018 Bureau of Labor Statistics study, the hospitality industry had the highest rate of nonfatal workplace assaults among all industries.
Security personnel are often exposed to verbal and physical abuse from customers and clients, leading to a high risk of workplace violence. According to a 2018 Bureau of Labor Statistics study, the security industry had the second-highest rate of nonfatal workplace assaults among all industries.
Stress and Burnout:
Hospitality workers are often required to work long hours, deal with demanding customers, and manage multiple tasks simultaneously. This can lead to high levels of stress and burnout, impacting employee well-being and job satisfaction.
Security personnel must often work long hours, deal with potentially dangerous situations, and manage multiple tasks simultaneously. This can lead to high levels of stress and burnout, impacting employee well-being and job satisfaction.
Ways Employers Can Help with Employee Safety
Provide Adequate Training:
Employers can provide adequate training to employees to help them handle the physical and emotional demands of the job, deal with workplace violence, and manage stress and burnout. For example, hospitality employers can provide customer service training to help employees deal with demanding customers, and security employers can provide self-defence training to help employees deal with potentially dangerous situations.
Implement Safety Protocols:
Employers can implement safety protocols to help employees stay safe on the job. For example, hospitality employers can implement security cameras and panic buttons, and security employers can provide body armour and pepper spray.
Foster a Positive Workplace Culture:
Employers can foster a positive workplace culture by promoting teamwork and collaboration, recognizing and rewarding employees for their contributions, and promoting open communication. This can help improve employee morale and job satisfaction, reducing the risk of workplace violence and stress and burnout.
Offer Support Services:
Employers can offer support services to employees, such as counselling and wellness programs, to help them manage stress and burnout.
Increasing Workload from Pandemic for Healthcare workers: How to avoid Nurse Burnout
The COVID-19 pandemic has put an enormous strain on healthcare workers, particularly nurses, who have been on the front lines of the crisis. Nurse burnout has become a critical issue with increasing patient loads, long hours, and high-stress levels and burnout. In this blog post, we’ll discuss the reasons for the increasing workload for healthcare workers, the impact of nurse burnout, and how employers can help prevent nurse burnout.
Reasons for Increasing Workload
Impact of Nurse Burnout
Ways to Prevent Nurse Burnout
Provide Adequate Support: Employers can provide adequate support to healthcare workers, including counselling and wellness programs, to help them manage stress and burnout. Foster a Positive Workplace Culture: Employers can foster a positive workplace culture by promoting teamwork and collaboration, recognizing and rewarding employees for their contributions, and promoting open communication. Implement Flexible Scheduling: Employers can implement flexible scheduling to help healthcare workers manage their workload and avoid burnout. This can include offering flexible hours, paid time off, and the ability to work from home. Provide Adequate PPE: Employers can provide adequate personal protective equipment (PPE) to healthcare workers to help them stay safe on the job and reduce the risk of exposure to the virus.
How Celayix Can Help Solve your Staff Shortage Problems
You must have noticed several phrases often used throughout the ebook: flexible scheduling, technology, employee communication, and feedback. We’d be lying if we stated there wasn’t a reason for employing the terms. Below we talk about the importance of flexible work schedules, how to implement them and show you why Celayix would be the perfect fit for you!
Why Shift Workers Need Flexible Work Schedules
Shift workers are essential to many industries, including healthcare, retail, transportation, and manufacturing. These workers provide imperative services 24/7, 365 days a year. Despite the importance of their work, shift workers often need help with challenges that make their jobs more complex and less enjoyable. One of the biggest challenges faced by shift workers is inflexible work schedules.
Inflexible work schedules can create several problems for shift workers. For example, it can be difficult for shift workers to balance their work and personal lives. They may miss important events or activities, such as birthdays, school plays, and family gatherings, because of work commitments. This can lead to stress, burnout, and reduced job satisfaction.
Another issue is the impact of shift work on health. Shift work is associated with several health problems, including sleep disturbances, digestive issues, and cardiovascular disease. Moreover, these health problems can be exacerbated by inflexible work schedules, as shift workers are often required to work unsocial hours, leading to disrupted sleep patterns and other health issues.
How to Provide Flexible Work Schedules to Shift Workers
It is essential to provide flexible work schedules to support shift workers and help them achieve a better work-life balance. Here are some ways to do so with Celayix:
Offer flexible scheduling options: Allow shift workers to choose their work hours or days within certain constraints. This can help them to balance their work and personal commitments better. Examples include Shift Bidding, Self-scheduling and Shift Swapping. Encourage job sharing: Encourage shift workers to find a job share partner to split their shifts. This can reduce the time spent working and allow shift workers more time for other activities. Provide paid time off: Offer paid time off to shift workers, such as paid holidays and paid sick leave. This can help them take time off for personal or health reasons without worrying about lost income. Offer remote work options: Allow shift workers to work remotely or elsewhere. This can help reduce the impact of shift work on their health and well-being and provide them with more flexibility. Provide on-site amenities: Offer amenities such as on-site childcare, showers, and break rooms to support shift workers. This can help them to manage their work and personal lives better and make their jobs more enjoyable.
In conclusion, providing flexible work schedules to shift workers can positively impact their health, well-being, and job satisfaction. By offering flexible scheduling options, encouraging job sharing, providing paid time off, offering remote work options, and providing on-site amenities, employers can support shift workers and help them to achieve a better work-life balance.
Our Final Thoughts
In conclusion, staff shortages can be attributed to several factors, including a strong economy leading to low unemployment rates, a skills gap, and an aging workforce. Employers can tackle staff shortages by offering competitive wages and benefits, providing professional development opportunities, and adopting flexible work arrangements. They can also consider expanding their hiring pool, for instance, by recruiting from underrepresented groups or training and upskilling current employees.
By implementing these strategies, employers can not only address staff shortages but also enhance job satisfaction and employee retention. Utilizing scheduling software like Celayix can also help employers tackle staff shortages by streamlining their scheduling process, improving communication and collaboration among team members, and reducing errors and inconsistencies in scheduling. This can result in increased efficiency and productivity, freeing up time for employers to focus on other important tasks in 2023.
| 2023-02-08T00:00:00 |
2023/02/08
|
https://www.celayix.com/blog/overcoming-staff-shortages-a-guide-to-proactive-solutions/
|
[
{
"date": "2023/03/05",
"position": 96,
"query": "AI unemployment rate"
}
] |
How Internal Hiring Can Close A Skills Gap
|
How Internal Hiring Can Close A Skills Gap
|
https://vervoe.com
|
[
"Angela Wallace",
"Angie Wallace Is A Self-Proclaimed Word Nerd",
"Big Thinker",
"Retired Tourism Wizard Who Believes In The Art Of Reinvention Every Five Years A Ritual She Considers Essential For All Good Millennials. With A Career Spanning Various Roles In The Tourism",
"Digital Marketing Sectors",
"Angie Has Amassed A Wealth Of Experience",
"Expertise. She Began Her Journey As A General Manager At Sailing Whitsundays",
"Where She Honed Her Skills In E-Commerce",
"General Management Over Five Years.",
"Transitioning Into The Digital Realm"
] |
Learn how Vervoe can help you close a skills gap. Vervoe is an end-to-end AI-powered solution that is proudly revolutionizing the hiring process through skills ...
|
Current labor market shortages would indicate that the global skills gap is only growing, but what if you could solve it with the team you’ve already got?
It’s never ideal when what you have and what you need don’t align. In workplaces all over the world, the issue is only exacerbated when employees are expected to complete tasks that they don’t have the skills for. To solve the problem, identify shortcomings, and supercharge efficiency, it’s at this point that a skills gap analysis is a must.
For those unfamiliar with the term, the skills gap is the divide between the skills employers expect their existing team members to have, versus the skills employees and job seekers actually possess. Not only does it place too much pressure on existing team members, but it can be incredibly frustrating for department heads and managers as well.
Let’s say that your existing marketing department consists of three key employees: the marketing manager, the web developer, and a copywriter. You’ve done your research, and you know that the next key areas to focus on are SEO and graphic design. Are you going to hire two new team members, find a unicorn that could do both, or outsource?
While many hiring managers make the mistake of thinking that the above choices are the only three options, the reality is that there is a fourth: upskilling your existing team.
As an example, your copywriter may show a passion for producing all types of content, or your web developer may already have some basic knowledge of SEO — but these hidden talents rarely have the possibility of being unearthed without a skills gap analysis, followed by a skills assessment test.
What is a skills gap and what causes them in workplaces
For those unfamiliar with the term, the skills gap is the divide between the skills employers expect or desire their existing team members to have, versus the skills employees and job seekers actually possess. According to McKinsey & Company, the vast majority of companies worldwide — a whopping 87% — are aware that they either already have a skills gap, or will have one within the next decade.
Current skills gaps are being fuelled by a combination of factors including advances in automation, artificial intelligence, and other technologies, along with candidates lacking the skills — not qualifications — needed to interact with these new tech innovations.
A skills gap is the divide between the skills employers expect their existing team members to have, versus the skills employees and job seekers actually possess.
How unaddressed skills gaps are hurting companies
The world has changed a lot in recent years, and both employees and employers alike have struggled to keep up. Although the employees themselves are similarly anxious about the widening skills gap, with 46% of those surveyed believing their current skill set will become irrelevant by 2024, just 35% of workers feel supported by their organization’s skill development opportunities.
Despite job seekers attempting to get qualified with degrees that they believe will set them up for the rest of their careers, the reality is that the knowledge obtained becomes increasingly irrelevant as technology advances.
As the job market increasingly focuses on technical skills, employers are having a challenging time finding applicants with the background and experience needed to meet the needs of changing job roles.
In fact, the 10 industries that are suffering the most from skills gaps include cybersecurity, machine learning, advanced manufacturing, cloud migration, big data, construction, warehousing, computer technology, electrical engineering, and marketing.
If left unchecked, a skills gap can cause a wide range of issues for companies across almost all sectors, including:
1. Lower productivity
When fewer people are working, less work can be accomplished during each workday. Managers may also have to take time away from their work to help employees who don’t have ideal job skills, as the team members are often issued tasks that they don’t have the knowledge to tackle.
2. Performance issues
An employee needs more of the skills necessary to accomplish a job to ensure their results live up to expectations and their key performance indicators. Workers must complete their jobs accurately and precisely to ensure products meet a certain standard, especially in technology.
3. Decreased profits
Lower productivity rates and substandard products can quickly result in dismal profit and loss statements. A lack of skilled employees, or employees with the right skills, can lead to a business not making the necessary sales to sustain and grow the company.
4. Reduced competitiveness
Without a skilled team, a company could fall behind competitors in other countries. As an example, Apple harnessed the soft skills of innovation and creativity to produce the iPhone rather than relying on computers and iPods. Microsoft didn’t expand their offerings, and soon fell behind.
The vast majority of companies worldwide — a whopping 87% — are aware that they either already have a skills gap, or will have one within the next decade, but just 35% of workers feel supported by their company’s approach to training and upskilling.
Why internal mobility is the solution to skills gaps
Typically, a labor shortage occurs when there are not enough available workers participating in the workforce to meet the demand for employees.
As an example, there were 11 million job vacancies in the United States in 2022, but only 6.5 million workers were listed as unemployed. When it’s already this difficult to find staff, finding the right staff gets even harder.
However, the solution an existing skill gap in your company might be hiding right under your nose. According to PwC Global, an overwhelming number of existing employees want to reskill, with 77% of respondents saying that they would happily learn new skills at work or even retrain entirely.
Interestingly, 46% of people with postgraduate degrees say their employer gives them opportunities to improve their digital skills, but just 28% of people with secondary education say the same.
Too many companies still believe that holding the right degree is the key to identifying top performers, but fail to recognize the achievements of those who hold the soft skill of continuous learning, or the desire to constantly master the latest technologies, best practices, and more. So how do you address skill gaps in the workplace?
The most effective approaches to achieving a higher-value workforce have a common core: opportunity. For companies who have already clued onto the benefits of hiring and promoting internally by upskilling existing team members, the skills gap is nowhere near as relevant, as they’ve been able to solve the issue by looking within via internal talent mobility.
An overwhelming number of existing employees want to reskill, with 77% of surveyed respondents saying that they would happily learn new skills at work or even retrain entirely.
How companies can benefit from encouraging upskilling
Between the ongoing pandemic and labor shortage, it’s clear that employees want more than just a job — they want a career. When you choose to promote from within, you send the message to other employees that your organization values hard work, commitment, and most importantly, its team.
Aside from boosting your team’s morale, hiring internally by encouraging employees to upskill is also incredibly cost-effective. Research from Wharton suggests that external hires underperform in their first two years when compared to internal hires. Aside from having a much slower onboarding experience, external hires also cost around 20% more than internal hires by the time the recruitment process is finalized.
While this may be music to the ears of recruiters and hiring managers struggling to find the right people for the right roles, the key to a successful upskilling program is identifying which team members have hidden talents. So, what’s the best way to identify gaps in your team? Ideally, the process should start with an in-depth skills gap analysis.
By the time the recruitment process is finalized, external hires cost as much as 20% more than internal hires.
5 steps to upskilling your staff and futureproofing your organization
New research from Deloitte indicates that a whopping 98% of business executives say they plan on moving more toward becoming a skills-based organization. Still, fewer than one in five are adopting skills-based approaches to a significant extent across the organization, and in a clear and repeatable way.
To turn a philosophy into an action plan, it’s imperative to implement an upskilling program. Common examples of upskilling efforts include digital skills, analytics skills, and organizational transformation skills.
These types of upskilling programs not only introduce your employees to new technologies in their fields and decrease knowledge gaps in technical skills, but cement your company’s status as forward-thinking, encouraging the professional development of your team. But where do you start?
1. Conduct a skills gap analysis
A skills gap analysis can help you determine the gaps you have and which skills your team is missing so you can confidently hire for specific roles or provide your current workforce with training or on-site education. To conduct a skills gap analysis, you must first identify your company goals and choose skills accordingly.
As an example, if your marketing team doesn’t have a social media marketer and you plan to start building your online presence, you’ll know that you need to find someone with that experience or help current employees obtain those skills.
After identifying the skills needed for different roles throughout your organization, you’ll need to assess what skills your team already has, and identify what you’re lacking. Start by assessing past performance reviews or working with managers. Always collect data from employees, including any qualifications they already have.
2. Design an in-depth skills plan
Previous reskilling efforts made on behalf of a company may have provided short term solutions based on the current state of a company, but failed because they didn’t build for the future. Instead, base your priorities on the types of jobs that will be affected most by new technologies and the employees who are most at risk.
In addition, work to understand which individuals will be affected by job changes, or roles disappearing. Analytic workforce planning tools can help estimate the impact of new technologies, the savings that automation will generate, the types of new skills that will be needed, and how long these changes will take.
Design each course to focus closely on these strategic goals. Ideally, you want your internally trained staff to become as competent as those you might hire from outside. In addition, it’s a good idea to address this sooner rather than later to keep up with rapidly evolving technology.
3. Meet with existing employees
Spending time with employees can help you learn a lot about your workforce and your business, since talking to your people can help you understand overall team dynamics, goals, and skills gaps. What’s more, you may discover the underlying cause for skill gaps: unengaged employees.
Many employees want to improve their skills to receive promotions, earn a higher salary, and grow as a professional. However, many companies make the mistake of not providing their employees with learning opportunities to help them get there, leading to lagging motivation or increased turnover.
By scheduling regular one on ones as a part of your management strategy, you’ll learn about your employees’ individual goals, strengths, and weaknesses. Once you learn more about your employees, you can start to find ways to reduce any skill gaps by helping them determine the skills they need for certain roles.
4. Use skills assessment tests
If your existing team members have expressed an interest or a desire to learn more about a particular skill or role, it’s worth using skills testing software to unearth any hidden talents or assets. By prioritizing current employees for future positions, you’re giving them something new to work towards.
In addition to developing future leaders within your company, skills testing software or even a skills assessment template can also identify underperformers. If employees cannot complete basic tasks like using a spreadsheet or writing an email, it means that they aren’t meeting the competency level of their job and may need training.
Of course, the third benefit of integrating skills assessment tests into your hiring process is its ability to kill two birds with one stone. To maximize the opportunities of white-labeled software with an API, companies can use this technology not just to promote internally, but to find new external hires when the time is right as well.
5. Make an action plan for training
The quality, value, and efficiency of the training experience affect every aspect of the strategic upskilling initiative. The quality of curricula is particularly important when the training involves advanced technologies such as robotic process automation, artificial intelligence, smart warehouses, or digital fabrication.
When recruiting training providers, be explicit in communicating the objectives of the upskilling initiative, including the particular skills that will need to be developed. When selecting programs, your key criteria should be market recognition, track record, and trust built in the past through placing graduates in new jobs.
Once members of your team start their training, don’t forget that they will still need your support. Set up opportunities for workers to communicate with one another via support groups, informal meetings, and online platforms to compare notes, catch up on classwork, and show solidarity with others who may be struggling.
Upskilling your existing team members can be done in just five simple steps: conduct a skills gap analysis, design an in-depth skills plan, meet with existing employees, use skills assessment tests and make an action plan for training.
Learn how Vervoe can help you close a skills gap
Vervoe is an end-to-end AI-powered solution that is proudly revolutionizing the hiring process through skills testing and job simulations. Vervoe predicts performance using job simulations that showcase the talent of every candidate, simply by empowering businesses to create assessments designed to suit the specific requirements of a role.
By assessing an applicant’s ability to perform the role through a skills assessment, our job simulations focus on the work — and not the person. To see people do the job before they get the job, book a demo today and let our experienced team run you through Vervoe’s full range of skills testing solutions.
| 2023-03-05T00:00:00 |
https://vervoe.com/how-internal-hiring-can-close-a-skills-gap/
|
[
{
"date": "2023/03/05",
"position": 29,
"query": "AI skills gap"
}
] |
|
AI is coming for your job
|
AI is coming for your job
|
https://grandcrucapital.com
|
[] |
Technology only moves foward, not backwards · Classic Algorithms vs Artificial Intelligence · Ethics & Regulation · AI landscape · Is AI really coming for my job?
|
I uploaded a photo of me (left) and asked AI to generate variations of it.
Technology only moves foward, not backwards
If you keep hearing about Artificial Intelligence in the news and are curious about what all the hype is about, I wrote this post for you. Let's break down the what and why of AI.
By now, most of us have heard or seen headlines about Artificial Intelligence, or AI. It is almost impossible to ignore if you spend even just a few minutes each week consuming the news. You may be wondering, why now? Why, after years of embedded AI in the wild (like email autocomplete or AlphaGo), are we now finally talking about AI? It all started earlier this year when OpenAI, an AI research lab, released chatGPT.
In a nutshell, chatGPT allows you to type in just about anything — from a search query to story prompt to even just friendly banter — and receive a surprisingly-good response in paragraph form. Here are examples of me asking it to write me a love letter in the style of early 1800s:
And here is me asking it about mortgages and home equity:
Pretty cool, right?
I'll be the first to admit that chat AI has its shortcomings, and there are definitely bugs in the responses, but don’t kid yourself — AI tech is here to stay and is improving at an exponential rate. It’s getting better by the day (literally), and AI will impact your job (and life!) in several ways (from searching online with perplexity.ai to gains upward of 50% on writing & blogging).
For those that have never seen this technology, it can get overwhelming once you truly realize the full capabilities of AI. You can signup here if you want to play around with it — but be forewarned, it's still in its early stages and not super accurate for many topics.
chatGPT is now the fastest-growing consumer tech product in history:
Many are calling it a Google killer, especially after Microsoft invested billions into chatGPT so they could integrate it into their Bing search results (Google quickly responded by launching their own AI, dubbed "Google Bard"). Many others (mostly tech venture capitalists) call AI the "greatest platform shift since the iPhone and the internet itself!" (shameless plugin: Maven Ventures is actively investing in this space — if you want an intro, reply to this email). AI has received so much hype that even the FTC has reminded the public to keep your AI claims in check.
Even though AI has been used internally by FAANG for years, chatGPT has been a lightbulb moment for the industry and consumers alike. Since its launch just a few weeks ago, dozens of companies have cropped up to show off their various features and products (see more below). Let's look under the hood to understand why and how AI is such a breakthrough technology.
Classic Algorithms vs Artificial Intelligence
In classical computer programming, apps are merely a series of small algorithms duck-taped and patched together to create a larger, functional program. With AI, it’s a self-taught, self-learning piece of software that gets exponentially better the more data you feed it.
Until now, computer programming logic has moved linearly through a series of algorithms (image a bunch of if/else statements patched together), but with AI, the program is much more dynamic by leapfrogging certain conditions or logic to solve the problem.
Imagine a human that memorizes 100% of what s/he consumes and continues to build on their knowledge — as the corpus of the dataset grows, the more accurate, factual, and creative the output becomes.
It's like comparing an apple 🍎 to an apple tree 🌳 — with the former, you get what you see (an apple), whereas with the latter, you get a living plant that continually produces fruit that soon blossoms its own fruit. The process continues until the entire field is covered with apple trees.
So does this mean AI will grow until it completely covers (and overtakes?) human lives? Many people believe this is a credible threat that must be addressed early before it’s too late.
Ethics & Regulation
One of those people is Elon Musk & his Paypal mafia friends (Peter Thiel, Reid Hoffman, etc). In 2015, the team founded openAI with a mandate to build a more friendly AI. Musk observed:
"What is the best thing we can do to ensure the future is good? We could sit on the sidelines or we can encourage regulatory oversight, or we could participate with the right structure with people who care deeply about developing AI in a way that is safe and is beneficial to humanity."
As someone who is just learning about the power of AI and realizing its early capabilities, I wholeheartedly and unequivocally support oversight and regulation of this powerful technology. I’m not exactly sure how we will do it or the best way to manage it, but there’s a reason why Google withheld AI from the world for so long — it’s too dangerous in the wrong hands!
So whether you are liberal or conservative, rich or poor, street smart or book smart… you should support AI oversight and we should all spend at least a little bit of our life playing with and learning about AI.
AI landscape
I’ve now played with about 20+ AI apps (from chat to search to generative images), and I’m convinced the venture capitalists are right — we are at the start of a platform shift that will change the trajectory of innovation and humanity forever.
My first recommendation for those still reading is to watch the AlphaGo documentary — the now famous 37th move of the match is when the AI opponent showed its true prowess by playing a move that had a 1 in 10,000 chance of being played. The human opponent was so startled by the move that he literally exited the building to calm himself with a cigarette.
Next, of course, is to signup for chatGPT (links above) or just use perplexity.ai to run some test queries. If you're still intrigued, here are some of the tools I've played with and can recommend:
Generative search. Enhances fact-finding and elevated search UX. You've seen ChatGPT, but you definitely need to try perplexity.ai for general searching and Poe for quora-powered AI.
Enhances fact-finding and elevated search UX. You've seen ChatGPT, but you definitely need to try perplexity.ai for general searching and Poe for quora-powered AI. Generative graphics. From video editing to deepfakes, or even as simple as fashion (example, see also womens & mens), audiovisual work will become much more powerful for a fraction of the cost. For image creation, I recommend starting with Stable Diffusion then try DALL-E. If you’re still hyped, hack around with Midjourney.
From video editing to deepfakes, or even as simple as fashion (example, see also womens & mens), audiovisual work will become much more powerful for a fraction of the cost. For image creation, I recommend starting with Stable Diffusion then try DALL-E. If you’re still hyped, hack around with Midjourney. Assistant / Copilot. Any AI that empowers the workforce (mostly B2B). This seems like the most immediate and obvious use case, especially since we already see gains — Github Copilot for engineering, Lex for blogging, copy.ai for ad copy & other digital marketing. I just started using Notion AI and now looking for a tool for my Zoom meetings.
Is AI really coming for my job?
So in sum, do I really believe AI is coming for your job? No, not yet. Do I think AI is coming for your kid’s job? Yes, it definitely is.
| 2023-03-05T00:00:00 |
2023/03/05
|
https://grandcrucapital.com/ai-is-coming-for-your-job
|
[
{
"date": "2023/03/05",
"position": 81,
"query": "AI regulation employment"
}
] |
ARM Hosts Manufacturing USA Workforce Strategy Meeting
|
ARM Hosts Manufacturing USA Workforce Strategy Meeting
|
https://arminstitute.org
|
[
"Livia Rice"
] |
... workforce policy. The Strategy Meeting convened representatives from the ... government agencies, training providers, and labor and community organizations.
|
Earlier this month, the ARM Institute hosted a Manufacturing and in partnership with the White House, Department of Labor, Department of Education, and Manufacturing USA, the ARM hosted a Manufacturing USA Institute Workforce Strategy Meeting at our Pittsburgh facility. This was the final meeting in a small series of meetings held at Manufacturing USA Institute facilities and the outputs from this meeting will be used to inform White House workforce policy.
The Strategy Meeting convened representatives from the Manufacturing USA Institutes, the Department of Defense, The White House, U.S. Department of Labor, U.S. Department of Education, U.S. Department of Commerce, National Institute of Standards and Technology (NIST), and other key stakeholders to explore cross-institute workforce strategies, existing institute efforts, and strategies moving forward to empower the U.S. manufacturing workforce. Attendees also toured the ARM Institute facility and saw demonstrations of some ARM Institute robotics projects that are strengthening U.S. manufacturing.
Specifically, the meeting:
Explored workforce implications of another type of innovation manufacturing.
Discussed long-term strategy for institute participation in eco-systems for manufacturing workforce development, including partnership with government agencies, training providers, and labor and community organizations.
Explored ways that institutes can become partners in solving recruitment and retention issues, to promote innovation that benefits firms, workers, and communities. Key tools are establishing sector partnership (an evidence-based way of bringing all workforce stakeholders together) and increasing worker voice (a stakeholder often not represented).
The ARM Institute was honored to host this event and looks forward to collaborating with our fellow Manufacturing USA Institutes and the various government agencies in attendance to move these goals forward!
ABOUT THE ARM INSTITUTE
The Advanced Robotics for Manufacturing (ARM) Institute is a Manufacturing Innovation Institute (MII) funded by the Office of the Secretary of Defense under Agreement Number W911NF-17-3-0004 and is part of the Manufacturing USA® network. The ARM Institute leverages a unique, robust, and diverse ecosystem of 390+ consortium members and partners across industry, academia, and government to make robotics, autonomy, and artificial intelligence more accessible to U.S. manufacturers large and small, train and empower the manufacturing workforce, strengthen our economy and global competitiveness, and elevate national security and resilience. Based in Pittsburgh, PA since 2017, the ARM Institute is leading the way to a future where people & robots work together to respond to our nation’s greatest challenges and to produce the world’s most desired products. For more information, visit www.arminstitute.org and follow the ARM Institute on LinkedIn and Twitter.
| 2023-03-05T00:00:00 |
2023/03/05
|
https://arminstitute.org/news/mfg-usa-workforce-meeting/
|
[
{
"date": "2023/03/05",
"position": 53,
"query": "government AI workforce policy"
}
] |
Artificial Intelligence Taking Human Jobs
|
Artificial Intelligence Taking Human Jobs
|
https://ctnewsjunkie.com
|
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AI is a fast-evolving technology with great potential to make workers more productive, to make firms more efficient, and to spur innovations in new products ...
|
Dick Wright has been an award-winning editorial cartoonist for decades, drawing for the San Diego Union, the Providence Journal, Scripps-Howard Newspapers, and the Columbus Dispatch. His cartoons are syndicated by Cagle Cartoons.
The views, opinions, positions, or strategies expressed by the author are theirs alone, and do not necessarily reflect the views, opinions, or positions of CTNewsJunkie.com.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://ctnewsjunkie.com/2023/03/06/artificial-intelligence-taking-human-jobs/
|
[
{
"date": "2023/03/06",
"position": 43,
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] |
AI Taking Over the Job: Overcoming the Fear
|
AI Taking Over the Job: Overcoming the Fear
|
https://gig4u.co
|
[
"Authors' Is A Team Of Domain Expert Writers In Various Niches",
"Including Business",
"It",
"Non-It",
"Designs",
"Marketing",
"Branding",
"Many More. Each Article On The Website Is Well-Researched",
"Written A Domain Expertise Writer",
"Verified Before Making It Live."
] |
For example, workers who are at risk of losing their jobs to automation may need to re-skill in data analysis, software development, or digital marketing, among ...
|
AI Taking Over the Job: Overcoming the Fear
Artificial intelligence (AI) has been a buzzword in the tech world for several years now, and its impact on various industries is becoming increasingly evident. While AI holds a lot of promise, it also raises concerns about job displacement and unemployment.
In this blog, we will explore what jobs AI is likely to replace, what it can never replace, and what people need to do to stay relevant in the AI era.
What Jobs Would AI Replace?
The integration of AI technology is likely to automate repetitive, routine, and low-skill tasks such as data entry, telemarketing, and customer service. These are tasks that are often performed by low-wage workers and involve pattern recognition, prediction, and data analysis, which are tasks well-suited for automation by AI.
Some industries will rely on human workers even after AI advancements like writing jobs, social work, legal defense, teaching and even elementary AI training engineers. AI can support these fields in the future, but AI and robots can't replace or even replicate the empathy and social intelligence required by these professions. For example, AI could help in the assessment of a student's levels of competence and temperament — but only teachers can grasp special interests with essential mentorship required by individual students.
AI's impact on the job market is two-fold. While it may lead to the displacement of certain jobs, it is also expected to create new job opportunities. While it is estimated that AI will create 97 million new jobs by 2025, some sectors are definitely getting impacted by the rise of AI. They include:
Finance and Banking
AI is helpful in financial institutions to reduce the risk of fraud and improve the accuracy of financial predictions and analysis. AI is also set to have a more significant impact on the finance and banking industry. Routine tasks such as data entry, record keeping, and customer service can be automated, freeing up human workers to focus on more complex tasks.
FMCG and Retail
The retail industry is also likely to be impacted by AI. AI can automate tasks such as stock management, pricing analysis, and customer service, reducing the need for human workers. Online retail giant, Amazon, has already made significant investments in AI, including the development of its Amazon Go stores, which allow customers to shop without the need for cashiers (self-checkout innovation).
AI for FMCG is working beyond traditional segmentation of the market. AI systems can introduce new spaces where specific products will always bring in great demand. The sector can benefit from advanced segmentation. AI can advocate innovation in marketing strategy, customer service, and even regular operations.
Healthcare
The healthcare industry is another sector that is poised for major change due to the rise of AI. From record-keeping and data entry to patient care and diagnostics, AI has the potential to transform the way healthcare is delivered. This technology can automate routine tasks, freeing up human workers to focus on more complex and important tasks, such as patient care and diagnosis. AI will help healthcare providers deliver more personalized and efficient patient care. It can assist in identifying patterns and making predictions about patient health too. The providers can make efficient use of resources with AI, after all!
Transportation
The rise of autonomous vehicles and drones is expected to bring about significant changes to the industry, especially in terms of job displacement. As AI-powered vehicles become increasingly prevalent, the demand for human drivers is likely to decline. However, this trend also presents new opportunities. For instance, there will be a growing need for experts in AI programming, vehicle maintenance, and other relevant fields.
This shift highlights the importance of continuous learning and upskilling in the era of AI. As the industry evolves and new jobs are created, workers will need to acquire new skills and knowledge to remain competitive in the job market. Whether you are currently working in the transportation and logistics industry or looking to make a career change, now is the time to invest in your future and position yourself for success in the AI era.
Manufacturing
The manufacturing industry is poised for significant change with the integration of AI technology. The deployment of robots and automated systems promises to increase productivity, lower production costs, and make the industry more competitive.
One of the immediate impacts of AI in this sector has been witnessed the automation of routine tasks such as assembly line work and quality control. This shift will result in a reduction of roughly 1.7 million jobs. However, it has also opened up newer opportunities in other verticals where human intervention is necessary.
Using a digital twin that represents a virtual copy of any industrial process has increased demand for prospective operators who can explore new scenarios on the factory floor. The lack of operators will offer new hiring possibilities. Even those less experienced would find this job palatable to their career prospects. The manufacturing labour, despite the proliferation of AI, will need number-crunching data scientists. As manufacturers seek to harness the latent potential of Industry 4.0, they would need professionals who could leverage data generated by sensors to generate actionable recommendations. The optimal nature of production will demand more engineers, efficient operators, and data scientists.
Investment
The operating environment for investment firms is evolving, with innovations and changing investor preferences propelling the change. Artificial Intelligence provides new opportunities beyond cost reduction and operational efficiency in this domain.
Investment management firms are now focusing on digital transformation to empower firms to deliver value for the future in the following ways:
Firms adopt data sets and AI to propel organic growth and generate additional alpha for their investment business. They are enabling better operational efficiency by deploying AI and automation while transforming traditional cost centers into service offerings powered by AI solutions. Firms are also incorporating AI to improve product and content distribution ensuring better customer experience. AI solutions are already helping advisors generate better insights, and tailor content more effectively. In terms of risk management, AI equips firms with effective tools that address a variety of compliance and risk management functions, automate data analysis, and predict ambiguous events in the business sphere.
To benefit from these AI trends fully, firms expect to consider and manage both technology and talent with the right balance.
Logistics
The integration of AI in the logistics industry has revolutionized the way we move goods around the world. However, this transformation has come with a potential downside - the displacement of human workers. Tasks that were once performed by people, such as delivery, transportation, storage, picking, packaging, and routing, can now be automated using self-driving vehicles and robotics.
While this innovation can optimize logistics processes and reduce expenses, it may significantly impact the job market. The increasing use of AI in logistics raises questions about the future of employment and the need to ensure that workers are not left behind in this digital age.
Entertainment
Some aspects of AI have already started to have an influence on the entertainment sector, including recommendation algorithms, personalization, and automated content production. Artificial intelligence (AI) systems can scan vast quantities of data to find patterns and produce music or visual effects that fit particular styles or themes. The production process might be accelerated and made more effective as a result.
For instance, AI is used by websites like Netflix and Spotify to evaluate user data and provide users with tailored suggestions. This has grown to be a significant component of the entertainment business since it keeps viewers interested and subscribers happy. This could potentially put certain genres on the backlog since people would prefer watching what's trending.
What Can AI Never Replace?
While AI is capable of automating many tasks, there are certain aspects of work that it can never replace. These include:
Complex thinking and decision making
There is no shortage of information or data in this age of information, but the success of any individual or enterprise depends on how they can separate reliable information from the abundance of misleading information.
In a world rife with outright falsehoods, hype, rumours, propaganda, frenzy, and sheer publicity, employers will seek candidates who are not just open-minded but also skilled at evaluating the veracity of the information we are exposed to everyday.
Being able to think critically goes beyond merely being upbeat; it also enables you to assess information with objectivity, determine its reliability, and determine if it should be trusted by both businesses and customers.
Emotional intelligence
Machines cannot compete with people who possess emotional intelligence, which is the capacity to recognize, regulate, and express one's own emotions as well as others' emotions.
Emotional intelligence is useful as long as there will be people working, since they affect every single encounter we have. Since machines are unable to establish real connections with people, this is a talent that will always be useful on both a personal and professional level.
Creativity
No matter how many robots there are, humans have always been better at thinking creatively than machines. Businesses still need people who are imaginative, who can see the potential of the future, and who can imagine. There are several ways to employ AI to foster creativity at work. Thus, it is crucial to be receptive to new technologies that foster and spark creativity, enabling businesses to grow and innovate.
Interpersonal communication skills
Despite the fact that robots are growing better at communicating, their programming is still limited. The capacity for successful interpersonal communication still falls under the purview of the human species.
Leadership
Strong leadership abilities are essential for everybody who makes choices or manages project teams in the modern workplace, not simply those at the top of the conventional corporate ladder.
3 Best Strategies to Preserve Jobs and Boost Growth
While AI will automate many jobs, it will also create new job opportunities and stimulate job growth in various industries. For example, the demand for workers with expertise in AI, data analysis, and software development is expected to grow rapidly. But many of those jobs will come up in the near future.
Additionally, there will be a growing demand for workers who have the skills to manage and integrate AI into various business processes. As this Allwork report suggests, nearly 97 million new jobs will be added to the economy owing to the emergence of AI.
The rise of AI presents both challenges and opportunities for workers in the job market. To stay relevant in the AI era, here is what people need to do:
1. Reskilling and Upskilling
People need to invest in their own skills and education related to their artificial intelligence and machine learning. This involves embracing the challenge and developing new skills and competencies that are in high demand in the AI era.To stay ahead of the curve in the job market, workers need to embrace the concepts of reskilling and upskilling. Reskilling refers to acquiring new skills that are necessary for current or future job opportunities. For example, workers who are at risk of losing their jobs to automation may need to re-skill in data analysis, software development, or digital marketing, among others. Below are the skills that will be in high demand even after the AI tools:
Knowledge of Programming languages
Data Engineering
Exploratory data analysis
Understanding of different AI Models
Data Security
Machine Learning Algorithms
Upskilling, on the other hand, refers to enhancing existing skills to stay ahead of the curve in the job market. For example, workers who have experience in a particular field may need to upskill by acquiring new knowledge and expertise in related areas such as AI, machine learning, and data analytics.
2. Investing in Your Future
One of the most important steps in overcoming the worry that AI will replace you at work is to invest in your own education and skill set. This investment may be made in a variety of ways, such as by enrolling in online courses, going to conferences and workshops, and pursuing certification in in-demand fields like data science, cloud computing, and machine learning.
It is critical to keep in mind that the rate of technological development will only quicken and that employees must take the initiative to advance their knowledge and abilities. This entails keeping up with the most recent developments in AI and being proactive in looking for learning and development opportunities.
3. Embrace the Change
Workers who wish to prosper in the AI future must embrace the transformation that AI symbolizes. This entails having a positive outlook on the effects that AI is having on the labour market as well as being open to new concepts and technology.
Workers may regard AI as a chance to learn new skills, explore new career options, and discover new ways to add value to their job rather than a threat, for instance. This upbeat attitude can aid employees in overcoming their worries and seeing the possibilities that AI has for their own professional development and success.
Conclusion
The rise of AI is profoundly impacting the job market, and workers are naturally concerned about the impact that AI will have on their careers. However, by embracing the challenge and investing in their own skills and education, workers can overcome the fear of AI stealing their jobs.
By reskilling and upskilling, workers can stay ahead of the curve in the job market and find new opportunities for growth and success. And by embracing the change that AI represents, workers can overcome their fears and see the potential that AI holds for their own career growth and success.
| 2023-03-06T00:00:00 |
https://gig4u.co/gig-reads/blog/ai-taking-over-the-job-overcoming-the-fear
|
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|
Don't fear an AI-induced jobs apocalypse just yet
|
Don’t fear an AI-induced jobs apocalypse just yet
|
https://www.economist.com
|
[] |
America's manufacturing and hospitality sectors report labour shortages of 500,000 and 800,000 respectively (as measured by the gap between job openings and ...
|
“I think we might exceed a one-to-one ratio of humanoid robots to humans,” Elon Musk declared on March 1st. Coming from the self-styled technoking of Tesla, it was not so much a prediction as a promise. Mr Musk’s car company is developing one such artificially intelligent automaton, codenamed Optimus, for use at home and in the factory. His remarks, made during Tesla’s investor day, were accompanied by a video of Optimus walking around apparently unassisted.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.economist.com/business/2023/03/06/dont-fear-an-ai-induced-jobs-apocalypse-just-yet
|
[
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"date": "2023/03/06",
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What Is Cognitive Automation: Examples And 10 Best Benefits
|
What Is Cognitive Automation: Examples And 10 Best Benefits
|
https://aijc.com.ph
|
[] |
Middle managers will need to shift their focus on the more human elements of their job to sustain motivation within the workforce. Automation will expose skills ...
|
7 Best Use Cases of Cognitive Automation
This provides thinking and decision-making capabilities to the automation solution. The human brain is wired to notice patterns even where there are none, but cognitive automation takes this a step further, implementing accuracy and predictive modeling in its AI algorithm. Middle managers will need to shift their focus on the more human elements of their job to sustain motivation within the workforce. Automation will expose skills gaps within the workforce and employees will need to adapt to their continuously changing work environments. Middle management can also support these transitions in a way that mitigates anxiety to make sure that employees remain resilient through these periods of change. Intelligent automation is undoubtedly the future of work and companies that forgo adoption will find it difficult to remain competitive in their respective markets.
These include creating an organization account, setting up the email address, providing the necessary accesses in the system, etc. Thus, the AI/ML-powered solution can work within a specific set of guidelines and tackle unique situations and learn from humans. Siloed operations and human intervention were being a bottleneck for operations efficiency in an organization. Cognitive automation cognitive automation examples can uncover patterns, trends and insights from large datasets that may not be readily apparent to humans. With these, it discovers new opportunities and identifies market trends. Please be informed that when you click the Send button Itransition Group will process your personal data in accordance with our Privacy notice for the purpose of providing you with appropriate information.
What is the difference between RPA and cognitive automation?
Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.If you liked this blog post, you’ll love Levity. As mentioned above, cognitive automation is fueled through the use of Machine Learning and its subfield Deep Learning in particular.
For an airplane manufacturing organization like Airbus, these operations are even more critical and need to be addressed in runtime. You can foun additiona information about ai customer service and artificial intelligence and NLP. Sign up on our website to receive the most recent technology trends directly in your email inbox. Sign up on our website to receive the most Chat PG recent technology trends directly in your email inbox.. Check out the SS&C| Blue Prism® Robotic Operating Model 2 (ROM™2) for a step-by-step guide through your automation journey. The scope of automation is constantly evolving—and with it, the structures of organizations.
With robots making more cognitive decisions, your automations are able to take the right actions at the right times. And they’re able to do so more independently, without the need to consult human attendants. With AI in the mix, organizations can work not only faster, but smarter toward achieving better efficiency, cost savings, and customer satisfaction goals. “Cognitive automation is not just a different name for intelligent automation and hyper-automation,” said Amardeep Modi, practice director at Everest Group, a technology analysis firm.
Cognitive automation adds a layer of AI to RPA software to enhance the ability of RPA bots to complete tasks that require more knowledge and reasoning. Intelligent automation simplifies processes, frees up resources and improves operational efficiencies through various applications. An insurance provider can use intelligent automation to calculate payments, estimate rates and address compliance needs.
An organization invests a lot of time preparing employees to work with the necessary infrastructure. Asurion was able to streamline this process with the aid of ServiceNow‘s solution. The Cognitive Automation system gets to work once a new hire needs to be onboarded. These are just two examples where cognitive automation brings huge benefits. You can also check out our success stories where we discuss some of our customer cases in more detail. Basic cognitive services are often customized, rather than designed from scratch.
The form could be submitted to a robot for initial processing, such as running a credit score check and extracting data from the customer’s driver’s license or ID card using OCR. Unlike traditional unattended RPA, cognitive RPA is adept at handling exceptions without human intervention. For example, most RPA solutions cannot cater for issues such as a date presented in the wrong format, missing information in a form, or slow response times on the network or Internet. In the case of such an exception, unattended RPA would usually hand the process to a human operator. In addition, cognitive automation tools can understand and classify different PDF documents. This allows us to automatically trigger different actions based on the type of document received.
Cognitive automation examples & use cases
Cognitive automation may also play a role in automatically inventorying complex business processes. The above-mentioned examples are just some common ways of how enterprises can leverage a cognitive automation solution. It is up to the enterprise now to incorporate it and use it the way it deems fit. In the retail sector, a cognitive automation solution can ensure all the store systems – physical or online – are working correctly. Thus, the customer does not face any issues with browsing and purchasing the item they like.
Traditional RPA without IA’s other technologies tends to be limited to automating simple, repetitive processes involving structured data. Start your automation journey with IBM Robotic Process Automation (RPA). It’s an AI-driven solution that helps you automate more business and IT processes at scale with the ease and speed of traditional RPA. In addition, businesses can use cognitive automation to automate the data collection process.
This includes tasks such as data entry, customer service, and fraud detection. For example, Digital Reasoning’s AI-powered process automation solution allows clinicians to improve efficiency in the oncology sector. Unlike other types of AI, such as machine learning, or deep learning, cognitive automation solutions imitate the way humans think.
These workers are designed to optimize workflows and automate tasks efficiently. This integration often extends to other automation methods like machine learning (ML) and natural language processing (NLP), enabling the system to interpret and analyze data across various formats. Cognitive automation plays a pivotal role in the digital transformation of the workplace. It is a form of artificial intelligence that automates tasks that have traditionally been done by humans.
What are the challenges of cognitive automation?
RPA resembles human tasks which are performed by it in a looping manner with more accuracy and precision. Cognitive Automation resembles human behavior which is complicated in comparison of functions performed by RPA. Upgrading RPA in banking and financial services with cognitive technologies presents a huge opportunity to achieve the same outcomes more quickly, accurately, and at a lower cost.
Let us understand what are significant differences between these two, in the next section. RPA is certainly capable of enhancing various processes, especially in areas like data entry, automated help desk support, and approval routings. Let’s explore how cognitive automation fills the gaps left by traditional automation approaches, such as Robotic Process Automation (RPA) and integration tools like iPaaS. Cognitive automation holds the promise of transforming the workplace by significantly boosting efficiency and enabling organizations and their workforce to make quick, data-informed decisions. The way RPA processes data differs significantly from cognitive automation in several important ways. It now has a new set of capabilities above RPA, thanks to the addition of AI and ML.
By using cognitive automation to make a greater impact with fewer data, businesses can improve their decision-making and increase their operational efficiency. We still have a long way to go before we have freely thinking robots, but research is producing machine capabilities that assist businesses to automate more work and simplify the operations that employees are left with. It means that the way we work is changing, and businesses need to adapt in order to stay competitive. One of the most important aspects of this digital transformation is cognitive automation. These tasks can be handled by using simple programming capabilities and do not require any intelligence. Cognitive automation combined with RPA’s qualities imports an extra mile of composure; contextual adaptation.
Some of the capabilities of cognitive automation include self-healing and rapid triaging. One of the most important parts of a business is the customer experience. Due to the extensive use of machinery at Tata Steel, problems frequently cropped up.
If the system picks up an exception – such as a discrepancy between the customer’s name on the form and on the ID document, it can pass it to a human employee for further processing. The system uses machine learning to monitor and learn how the human employee validates the customer’s identity. Next time, it will be able process the same scenario itself without human input. This highly advanced form of RPA gets its name from how it mimics human actions while the humans are executing various tasks within a process. Such processes include learning (acquiring information and contextual rules for using the information), reasoning (using context and rules to reach conclusions) and self-correction (learning from successes and failures). Besides the application at hand, we found that two important dimensions lay in (1) the budget and (2) the required Machine Learning capabilities.
Cognitive automation, therefore, marks a radical step forward compared to traditional RPA technologies that simply copy and repeat the activity originally performed by a person step-by-step. CIOs are now relying on cognitive automation and RPA to improve business processes more than ever before. Accounting departments can also benefit from the use of cognitive https://chat.openai.com/ automation, said Kapil Kalokhe, senior director of business advisory services at Saggezza, a global IT consultancy. For example, accounts payable teams can automate the invoicing process by programming the software bot to receive invoice information — from an email or PDF file, for example — and enter it into the company’s accounting system.
They can also identify bottlenecks and inefficiencies in your processes so you can make improvements before implementing further technology. One example of cognitive automation in action is in the healthcare industry. Hospitals and clinics are using cognitive automation tools to automate administrative tasks such as appointment scheduling, billing, and patient record keeping. This frees up medical staff to focus on patient care, leading to better health outcomes for patients. This can be a huge time saver for employees who would otherwise have to manually input this data.
In other words, the automation of business processes provided by them is mainly limited to finishing tasks within a rigid rule set. That’s why some people refer to RPA as “click bots”, although most applications nowadays go far beyond that. Cognitive automation has a place in most technologies built in the cloud, said John Samuel, executive vice president at CGS, an applications, enterprise learning and business process outsourcing company. His company has been working with enterprises to evaluate how they can use cognitive automation to improve the customer journey in areas like security, analytics, self-service troubleshooting and shopping assistance. Cognitive automation is an extension of existing robotic process automation (RPA) technology. Machine learning enables bots to remember the best ways of completing tasks, while technology like optical character recognition increases the data formats with which bots can interact.
What is Cognitive Robotic Process Automation?
“One of the biggest challenges for organizations that have embarked on automation initiatives and want to expand their automation and digitalization footprint is knowing what their processes are,” Kohli said. Employee onboarding is another example of a complex, multistep, manual process that requires a lot of HR bandwidth and can be streamlined with cognitive automation. “The biggest challenge is data, access to data and figuring out where to get started,” Samuel said. All cloud platform providers have made many of the applications for weaving together machine learning, big data and AI easily accessible.
Individuals focused on low-level work will be reallocated to implement and scale these solutions as well as other higher-level tasks. Another way businesses can minimize manual mental labor is by using artificial intelligence (AI) to set up and manage robotic process automation (RPA). By using AI to automate these processes, businesses can save employees a significant amount of time and effort.
This means that businesses can collect data from a variety of sources, including social media, sensors, and website click-streams. It can carry out various tasks, including determining the cause of a problem, resolving it on its own, and learning how to remedy it. One example is to blend RPA and cognitive abilities for chatbots that make a customer feel like he or she is instant-messaging with a human customer service representative. Automated processes can only function effectively as long as the decisions follow an “if/then” logic without needing any human judgment in between. However, this rigidity leads RPAs to fail to retrieve meaning and process forward unstructured data. Businesses are increasingly adopting cognitive automation as the next level in process automation.
Once, the term ‘cognition’ was exclusively linked to human capabilities. Originally, it referred to the awareness of mental activities like thinking, reasoning, remembering, imagining, learning, and language utilization. It’s quite fascinating that, given our technological strides in artificial intelligence (AI) and generative AI, this concept is increasingly relevant to computers as well. A cognitive automation solution is a positive development in the world of automation.
These six use cases show how the technology is making its mark in the enterprise. Processors must retype the text or use standalone optical character recognition tools to copy and paste information from a PDF file into the system for further processing. Cognitive automation uses technologies like OCR to enable automation so the processor can supervise and take decisions based on extracted and persisted information. In case of failures in any section, the cognitive automation solution checks and resolves the issue. Else it takes it to the attention of a human immediately for timely resolution.
By augmenting RPA with cognitive technologies, the software can take into account a multitude of risk factors and intelligently assess them. This implies a significant decrease in false positives and an overall enhanced reliability of autonomous transaction monitoring. ML-based cognitive automation tools make decisions based on the historical outcomes of previous alerts, current account activity, and external sources of information, such as customers’ social media. The value of intelligent automation in the world today, across industries, is unmistakable. With the automation of repetitive tasks through IA, businesses can reduce their costs and establish more consistency within their workflows. The COVID-19 pandemic has only expedited digital transformation efforts, fueling more investment within infrastructure to support automation.
One of their biggest challenges is ensuring the batch procedures are processed on time. Organizations can monitor these batch operations with the use of cognitive automation solutions. Various combinations of artificial intelligence (AI) with process automation capabilities are referred to as cognitive automation to improve business outcomes. The company implemented a cognitive automation application based on established global standards to automate categorization at the local level. The incoming data from retailers and vendors, which consisted of multiple formats such as text and images, are now processed using cognitive automation capabilities.
This has helped them improve their uptime and drastically reduce the number of critical incidents. It also helps keep the cost low and meet the demands of the customers. The biggest challenge is the parcel sorting system and automated warehouses. Here, in case of issues, the solution checks and resolves the problems or sends the issue to a human operator at the earliest so that there are no further delays.
Once implemented, the solution aids in maintaining a record of the equipment and stock condition. Every time it notices a fault or a chance that an error will occur, it raises an alert. Managing all the warehouses a business operates in its many geographic locations is difficult.
Also, 32 percent of respondents said they will be implementing it in some form by the end of 2020. By augmenting human cognitive capabilities with AI-powered analysis and recommendations, cognitive automation drives more informed and data-driven decisions. Its systems can analyze large datasets, extract relevant insights and provide decision support.
Processing approach
Additionally, it assists in meeting client requests and lowering costs. In this situation, if there are difficulties, the solution checks them, fixes them, or, as soon as possible, forwards the problem to a human operator to avoid further delays. Additionally, it can gather and save staff data generated for use in the future. Cognitive automation can then be used to remove the specified accesses.
Exploring the impact of language models on cognitive automation with David Autor, ChatGPT, and Claude – Brookings Institution Exploring the impact of language models on cognitive automation with David Autor, ChatGPT, and Claude. Posted: Mon, 06 Mar 2023 08:00:00 GMT [source]
In this case, cognitive automation takes this process a step further, relieving humans from analyzing this type of data. Similar to the aforementioned AML transaction monitoring, ML-powered bots can judge situations based on the context and real-time analysis of external sources like mass media. On the other hand, cognitive automation, or Intelligent Process Automation (IPA), effectively handles both structured and unstructured data, making it suitable for automating more intricate processes. Cognitive automation integrates cognitive capabilities, allowing it to process and automate tasks involving large amounts of text and images. This represents a significant advancement over traditional RPA, which merely replicates human actions in a step-by-step manner. Cognitive automation offers a more nuanced and adaptable approach, pushing the boundaries of what automation can achieve in business operations.
At the same time, Cognitive Automation is powered by both thinkings and doing which is processed sequentially, first thinking then doing in a looping manner. RPA rises the bar of the work by removing the manually from work but to some extent and in a looping manner. But as RPA accomplish that without any thought process for example button pushing, Information capture and Data entry. These processes can be any tasks, transactions, and activity which in singularity or more unconnected to the system of software to fulfill the delivery of any solution with the requirement of human touch. So it is clear now that there is a difference between these two types of Automation.
This means using technologies such as natural language processing, image processing, pattern recognition, and — most importantly — contextual analyses to make more intuitive leaps, perceptions, and judgments. RPA imitates manual effort through keystrokes, such as data entry, based on the rules it’s assigned. But combined with cognitive automation, RPA has the potential to automate entire end-to-end processes and aid in decision-making from both structured and unstructured data. In the past, businesses used robotic process automation (RPA) to automate simple, rules-based tasks on computers without the need for human input. This was a great way to speed up processes and reduce the risk of human error.
The cognitive solution can tackle it independently if it’s a software problem. If not, it alerts a human to address the mechanical problem as soon as possible to minimize downtime. Let’s see some of the cognitive automation examples for better understanding.
What’s important, rule-based RPA helps with process standardization, which is often critical to the integration of AI in the workplace and in the corporate workflow. For example, cognitive automation can be used to autonomously monitor transactions. While many companies already use rule-based RPA tools for AML transaction monitoring, it’s typically limited to flagging only known scenarios. Such systems require continuous fine-tuning and updates and fall short of connecting the dots between any previously unknown combination of factors. Traditional RPA primarily focuses on automating tasks that involve swift, repetitive actions, often with structured data, but lacks in contextual analysis and handling unexpected scenarios. It typically operates within a strict set of rules, leading to its early characterization as “click bots”, though its capabilities have since expanded.
Splunk has helped Bookmyshow with a cognitive automation solution to help them improve their customer interactions. Digitate’s ignio, a cognitive automation solution helps handle the small niggles in the system to ensure that everything keeps working. Cognitive automation solutions can help organizations monitor these batch operations. Automation helps us handle redundant tasks so that there are no human errors involved, and human intervention is minimal.
Claims processing, one of the most fundamental operations in insurance, can be largely optimized by cognitive automation. Many insurance companies have to employ massive teams to handle claims in a timely manner and meet customer expectations. Insurance businesses can also experience sudden spikes in claims—think about catastrophic events caused by extreme weather conditions. It’s simply not economically feasible to maintain a large team at all times just in case such situations occur.
As a result, the company can organize and take the required steps to prevent the situation. The Cognitive Automation solution from Splunk has been integrated into Airbus’s systems. Splunk’s dashboards enable businesses to keep tabs on the condition of their equipment and keep an eye on distant warehouses. For example, an attended bot can bring up relevant data on an agent’s screen at the optimal moment in a live customer interaction to help the agent upsell the customer to a specific product. In the telecom sector, where the userbase is in millions, manual tasks can be more than overwhelming.
They can also identify bottlenecks and inefficiencies in your processes so you can make improvements before implementing further technology.
It is frequently referred to as the union of cognitive computing and robotic process automation (RPA), or AI.
ServiceNow’s Cognitive Automation solution has helped Asurion to ease this process.
After realizing quick wins with rule-based RPA and building momentum, the scope of automation possibilities can be broadened by introducing cognitive technologies.
The company implemented a cognitive automation application based on established global standards to automate categorization at the local level.
Make your business operations a competitive advantage by automating cross-enterprise and expert work. From your business workflows to your IT operations, we got you covered with AI-powered automation. In the past, businesses had to sift through large amounts of data to find the information they needed. Cognitive automation is a form of AI technology that may mimic human actions. It allows computers to execute activities related to perception and judgment, which humans previously only accomplished.
What Is Cognitive Computing? – Built In What Is Cognitive Computing?. Posted: Thu, 29 Sep 2022 20:43:25 GMT [source]
Karev said it’s important to develop a clear ownership strategy with various stakeholders agreeing on the project goals and tactics. For example, if there is a new business opportunity on the table, both the marketing and operations teams should align on its scope. They should also agree on whether the cognitive automation tool should empower agents to focus more on proactively upselling or speeding up average handling time. By enabling the software bot to handle this common manual task, the accounting team can spend more time analyzing vendor payments and possibly identifying areas to improve the company’s cash flow. Cognitive automation tools are relatively new, but experts say they offer a substantial upgrade over earlier generations of automation software. Now, IT leaders are looking to expand the range of cognitive automation use cases they support in the enterprise.
And without making it overly technical, we find that a basic knowledge of fundamental concepts is important to understand what can be achieved through such applications. With light-speed jumps in ML/AI technologies every few months, it’s quite a challenge keeping up with the tongue-twisting terminologies itself aside from understanding the depth of technologies. To make matters worse, often these technologies are buried in larger software suites, even though all or nothing may not be the most practical answer for some businesses. Cognitive automation is a summarizing term for the application of Machine Learning technologies to automation in order to take over tasks that would otherwise require manual labor to be accomplished. “The problem is that people, when asked to explain a process from end to end, will often group steps or fail to identify a step altogether,” Kohli said. To solve this problem vendors, including Celonis, Automation Anywhere, UiPath, NICE and Kryon, are developing automated process discovery tools.
There are a lot of use cases for artificial intelligence in everyday life—the effects of artificial intelligence in business increase day by day. With the help of AI and ML, it may analyze the problems at hand, identify their underlying causes, and then provide a comprehensive solution. RPA operates most of the time using a straightforward “if-then” logic since there is no coding involved. If any are found, it simply adds the issue to the queue for human resolution. It imitates the capability of decision-making and functioning of humans. This assists in resolving more difficult issues and gaining valuable insights from complicated data.
Today’s modern-day manufacturing involves a lot of automation in its processes to ensure large scale production of goods. The worst thing for logistics operations units is facing delays in deliveries. SS&C Blue Prism enables business leaders of the future to navigate around the roadblocks of ongoing digital transformation in order to truly reshape and evolve how work gets done – for the better. Data governance is essential to RPA use cases, and the one described above is no exception. An NLP model has been successfully trained on sufficient practitioner referral data. For the clinic to be sure about output accuracy, it was critical for the model to learn which exact combinations of word patterns and medical data cues lead to particular urgency status results.
| 2024-03-12T00:00:00 |
2024/03/12
|
https://aijc.com.ph/what-is-cognitive-automation-examples-and-10-best/
|
[
{
"date": "2023/03/06",
"position": 70,
"query": "job automation statistics"
}
] |
The AI skills gap and the dangers of exponential innovation
|
Stanford researcher on the AI skills gap and the dangers of exponential innovation
|
https://www.raconteur.net
|
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Jobs will be enhanced in many cases, but some will be eliminated. Routine work will become increasingly automated – and there will also be a flourishing of ...
|
Erik Brynjolfsson is in great demand. The US professor whose research focuses on the relationship between digital tech and human productivity is nearing the end of a European speaking tour that’s lasted nearly a month. Despite this, he’s showing no signs of fatigue – quite the opposite, in fact.
Speaking via Zoom as he prepares for his imminent lecture in Oxford, the director of the Digital Economy Lab at the Stanford Institute for Human-Centered AI is enthused by recent “seminal breakthroughs” in the field.
Brynjolfsson’s tour – which has included appearances at the World Economic Forum in Davos and the Institute for the Future of Work in London – is neatly timed, because the recent arrival of ChatGPT on the scene has been capturing human minds, if not yet hearts.
The large-scale language model, fed 300 billion words by developer OpenAI, caused a sensation with its powerful capabilities, attracting 1 million users within five days of its release in late November 2022. At the end of January, Microsoft’s announcement of a substantial investment in OpenAI “to accelerate AI breakthroughs” generated yet more headlines.
ChatGPT’s popularity is likely to trigger an avalanche of similarly extraordinary AI tools, Brynjolfsson predicts, with a possible economic value extending to “trillions of dollars”. But he adds that proper safeguards and a better understanding of how AI can augment – not replace – jobs are urgently required.
What’s next in AI?
“There have been some amazing, seminal breakthroughs in AI lately that are advancing the frontier rapidly,” Brynjolfsson says. “Everyone’s playing with ChatGPT, but this is just part of a larger class of ‘foundation models’ that is becoming very important.”
He points to the image generator DALL-E (another OpenAI creation) and lists similar tools designed for music, coding and more. Such advances are comparable to that of deep learning, which enabled significant leaps in object recognition a decade ago.
“There’s been a quantum improvement in the past couple of years as these foundational models have been introduced more widely. And this is just the first wave,” Brynjolfsson says. “The folks working on them tell me that there’s far more in the pipeline that we’ll be hearing about in the coming weeks.”
As much as I’m blown away by these technologies, the bottleneck is our human response
When pushed for examples of advances that could shape the future of work, he reveals that Generative Pre-trained Transformer 3 (GPT-3) – the language model that uses deep learning to emulate human writing – will be superseded by GPT-4 “within weeks. This is a ‘phased change of improvement’ compared with the last one, but it’ll be even more capable of solving all sorts of problems.”
Elsewhere, great strides are being made with “multi-agent systems” designed to enable more effective interactions between AI and humans. In effect, AI tech will gain the social skills required to cooperate and negotiate with other systems and their users.
“This development is opening up a whole space of new capabilities,” Brynjolfsson declares.
The widening AI skills gap
As thrilling as these pioneering tools may sound, the seemingly exponential rate of innovation presents some dangers, he warns.
“AI is no longer a laboratory curiosity or something you see in sci-fi movies,” Brynjolfsson says. “It can benefit almost every company. But governments and other organisations haven’t been keeping up with developments – and our skills haven’t either. The gap between our capabilities and what the technology enables and demands has widened. I think that gap will be where most of the big challenges – and opportunities – for society lie over the next decade or so.”
Brynjolfsson, who studied applied maths and decision sciences at Harvard in the 1980s, started in his role at Stanford in July 2020 with the express aim of tackling some of these challenges.
“We created the Digital Economy Lab because, as much as I’m blown away by these technologies, the bottleneck is our human response,” he says. “What will we do about the economy, jobs and ethics? How will we transform organisations that aren’t changing nearly fast enough? I want to speed up our response.”
Brynjolfsson spoke passionately about this subject at Davos in a session entitled “AI and white-collar jobs”. In it, he advised companies to adopt technology in a controlled manner. Offering a historical analogy, he pointed out that, when electricity infrastructure became available about a century ago, it took at least three decades for most firms to fully realise the productivity gain it offered because they first needed to revamp their workplaces to make the best use of it.
“We’re in a similar period with AI,” Brynjolfsson told delegates. “What AI is doing is affecting job quality and how we do the work. So we must address to what extent we keep humans in the loop rather than focus on driving down wages.”
Why AI will create winners and losers
The risk of technology racing too far ahead of humanity for comfort is a familiar topic for Brynjolfsson. In both Race Against the Machine (2011) and The Second Machine Age (2014), he and his co-author, MIT scientist Andrew McAfee, called for greater efforts to update organisations, processes and skills.
AI can benefit almost every company. But governments and other organisations haven’t been keeping up with developments – and our skills haven’t either
How would he assess the current situation? “When we wrote those books, we were optimistic about the pace of technological change and pessimistic about our ability to adapt,” Brynjolfsson says. “It turns out that we weren’t optimistic enough about the technology or pessimistic enough about our institutions and skills.”
In fact, the surprising acceleration of AI means that the “timeline for when we’ll have artificial general intelligence” should be shortened by decades, he argues. “AGI will be able to do most of the things that humans can. Some predicted that this would be achieved by the 2060s, but now people are talking about the 2030s or even earlier.”
Given the breakneck speed of developments, how many occupations are at risk of obsolescence through automation?
Brynjolfsson concedes that the range of roles affected is looking “much broader than earlier thought. There will be winners and losers. Jobs will be enhanced in many cases, but some will be eliminated. Routine work will become increasingly automated – and there will also be a flourishing of fantastic creativity. If we use these tools correctly, there will be positive disruption. If we don’t, inequality could deepen, further concentrating wealth and political power.”
How to apply AI in the workplace
How, then, should businesses integrate AI into their operations? First, they must avoid what Brynjolfsson has labelled the Turing trap.
“One of the biggest misconceptions about AI – especially among AI researchers, by the way – is that it needs to do everything that humans do and replace them to be effective,” he explains, arguing that the famous test for machine intelligence, proposed by Alan Turing in 1950, is “an inspiring but misguided vision”.
Brynjolfsson contends that a “mindset shift” at all levels – from scientists and policy-makers to employers and workers – is required to harness AI’s power to shape society for good. “We should ask: ‘What do we want these powerful tools for? And how can we use them to achieve our goals?’ The tools don’t decide; we decide.”
One of the biggest misconceptions about AI is that it needs to do everything that humans do and replace them
He adds that many business leaders have the wrong attitude to applying new tech in general and AI in particular. This amounts to a “pernicious problem”.
To illustrate this, he cites Waymo’s experiments with self-driving vehicles: “These work 99.9% of the time, but there is a human safety driver overseeing the system and a second safety driver in case the first one falls asleep. People watching each other is not the right path to driverless cars.”
Brynjolfsson commends an alternative route, which has been taken by the Toyota Research Institute, among others. When he was in Davos, the institute’s CEO, Dr Gill Pratt “told me how his team has flipped things around so that the autonomous system is used as the guardian angel. Creating a self-driving car that works in all possible conditions is tough, but humans can handle those exceptions.”
With a person making most decisions in the driving seat, the AI intervenes “occasionally – for instance, when there’s a looming accident. I think this is a good model, not only for self-driving cars, but for many other applications where humans and machines work together.”
For similar reasons, Brynjolfsson lauds Cresta, a provider of AI systems for customer contact centres. Its products keep humans “at the forefront” of operations instead of chatbots, whose apparent Turing test failures continue to frustrate most people who deal with them.
“The AI gives them suggestions about what to mention to customers,” he says. “This system does dramatically better in terms of both productivity and customer satisfaction. It closes the skills gap too.”
Does Brynjolfsson have a final message for business leaders before he heads off to give his next lecture? “We need to catch up and keep control of these technologies,” he says. “If we do that, I think the next 10 years will be the best decade we’ve ever had on this planet.”
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.raconteur.net/future-of-work/stanford-researcher-ai-skills-gap-innovation
|
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"query": "AI skills gap"
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"date": "2023/03/06",
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AI in Recruitment: A Study of Present Conditions and ...
|
AI in Recruitment: A Study of Present Conditions and Future Possibilities
|
https://www.stantonchase.com
|
[
"Brainz Disruptive"
] |
But with its help, executive recruiters that are willing to take advantage of its potential could become more efficient and effective at their jobs than ever ...
|
AI is revolutionizing ordinary recruitment. Is executive search next?
The introduction of Chat GPT to the world in early 2023 reignited the conversation surrounding artificial intelligence and how close the technology is to impacting daily life as we know it.
One area that is already seeing significant changes as a result of AI is the field of recruitment. The question is, how much does AI already influence the hiring and firing of workforces? And how much will it impact more nuanced executive recruitment efforts in the future?
Existing AI in Talent Management
Artificial intelligence has already been busy redefining the employee and recruitment landscape for a while now. For instance, performance analytics has become a growing preference for leaders looking to refine their workforces.
The practice of performance analytics consists of a unique merging of data analysis and behavioral science that enables managers to pinpoint where their teams are excelling and where they need improvement. Performance analytics utilizes increasingly complex algorithms and artificial intelligence solutions to deliver its results.
It’s also no secret that AI is already used directly in the employee hiring process. AI-driven algorithms help HR representatives sort through resumes, identify keywords and desirable traits, and even measure personality based on definable characteristics. This yields several key benefits, including the ability to:
Speed up the recruitment process.
Identify a larger pool of qualified candidates.
Eliminate human bias.
Streamline background searches and reference checking.
However, while AI is a useful tool, it does raise some concerns.
The Dark Side of AI in Recruitment
The World Economic Forum points out that the current AI systems utilized in recruiting can create as many issues as the problems that they solve. One of the major concerns is the introduction of bias while hiring. This may sound a bit surprising, but the biases that come from AI aren’t the same as human biases. Harvard Business Review emphasizes things like shaping the candidate pool and excessively narrowing the candidate funnel as potential biases created by AI.
AI is particularly pernicious when it’s introduced to a hiring system without the knowledge of the recruiters or applicants that are operating around them. Recruiter.com adds to the conversation by highlighting the fact that AI hiring tools can be less accurate than experienced recruiters. They are also impersonal and can easily fail to keep up with current regulations if they aren’t constantly updated. Most importantly, they lack human judgment.
These concerns have lawmakers around the world sitting up and paying attention. In fact, in some areas like New York City and the EU, government officials are actively considering what limitations and barriers they should implement in response.
While there are some serious red flags that come with current AI in search, recruiters aren’t using these solutions just for fun. They’re solving real-world issues in the recruitment space, from saving time to improving communication and identifying hidden talent. The benefits far outweigh the risks, which is another way to say that AI isn’t going anywhere any time soon.
AI’s Potential Future in Executive Search
So, how does all of this impact executive recruitment? After all, the way companies hire executives is very different from a run-of-the-mill job advert or interview. It’s a complex, multi-faceted process that is difficult to formulate. Nevertheless, there are ways that AI could begin to influence even the complex upper echelons of the hiring world in the near future.
For instance, the 24/7 nature of AI makes it an excellent tool for monitoring candidate availability. The faster an executive recruiter or company with an open C-suite position can become aware of an available candidate, the better chance they have of recruiting them before a competitor learns that they’re on the market.
AI also has the potential to improve the quality of the executive recruitment process by executing more thorough background checks and automating certain reference-checking activities. In other words, the increasing sophistication of AI platforms is making it more likely that they’ll be able to help with any data involved in the recruitment process — regardless of its complexity.
It’s also worth noting that AI could impact executive search by replacing certain roles within the C-suite itself. In fact, it’s been years since the Japanese firm Deep Knowledge captured headlines around the world by appointing an AI robot named Vital to its board of directors. Vital isn’t the only example of this trend, either. As AI continues to develop, its chances of performing larger roles, both as administrators and leaders, is likely to improve.
At the end of the day, though, it’s hard to envision a point in the near future where AI plays too large of a role in the actual recruitment of executives. The process is simply too nuanced and complex to approach in an overly-methodical manner.
“At the end of the day, though, it’s hard to envision a point in the near future where AI plays too large of a role in the actual recruitment of executives.”
Instead, as AI advances, organizations like Stanton Chase will continue to look for ways to implement its growing capacity into our own search strategies. AI may not be able to recruit executives on its own yet. But with its help, executive recruiters that are willing to take advantage of its potential could become more efficient and effective at their jobs than ever before.
About the Author
Peter Deragon is a Managing Director at Stanton Chase Los Angeles. He is also the Global Practice Leader of our Supply Chain, Logistics, and Transportation Practice Group. Additionally, Peter is active in the CFO Practice Group and financial services, where he started his career.
| 2023-03-06T00:00:00 |
https://www.stantonchase.com/insights/blog/ai-in-recruitment-a-study-of-present-conditions-and-future-possibilities
|
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|
How AI Will Affect the WordPress Industry
|
How AI Will Affect the WordPress Industry
|
https://wpmudev.com
|
[
"N. Fakes"
] |
Plus, we'll try to answer the biggest question of them all: Will WordPress Developers Be Out of a Job? So, adjust those AI-created reading glasses, and let's ...
|
AI is becoming more prominent every day, and there’s no question it will change the WordPress business. Many changes are already taking place. This article showcases what might be different in the near future, as well as what’s happening currently.
You’ve probably been hearing a ton about AI (artificial intelligence), which describes human reasoning mimicked in machines – especially computer systems. Specifically, AI includes natural language processing, speech recognition, machine vision, and expert systems.
In this article, we’ll focus on advancements that pertain to WordPress and what it looks like now that AI has its grip on the industry.
This introductory article covers the following:
Plus, we’ll try to answer the biggest question of them all: Will WordPress Developers Be Out of a Job?
So, adjust those AI-created reading glasses, and let’s begin!
How AI Will Change the WordPress Industry
Knowing that AI is here to stay, there’s bound to be speculation about how AI will change the WordPress industry. Will it be for the better? Worse? Can AI take my job as a developer?
We’re no fortune tellers here, but we can determine a few industry changes that will more than likely take place. A lot of it has already.
Here’s a breakdown of some of AI’s major changes to the WordPress industry.
Website Building
Machines that build websites? It’s not a sci-fi movie, it’s reality. There are already companies that are in the process of building algorithms that can build websites (e.g. Wix ADI, Hostinger, and Jimdo).
ChatGPT is also a game changer for WordPress developers. It’s a chatbox developed by OpenAI that uses a language processing model to generate text based on user inputs. Its applications range from making code based on texts to generating content.
AI models like ChatGPT give users access to an AI assistant that works with them on developing the perfect website based on a user’s criteria. It makes determinations about layout, design, branding, and more.
Also, WordPress-specific code-creating tools that work with AI, such as CodeWP, are emerging rapidly.
All of this being said, these are pretty simplistic websites. Therefore, web designers and developers are still essential and provide a service unsurpassed by AI… for now.
Machine-Aided Writing
Like website building, machine-aided content creation and writing are on the horizon. AI assistants will create content writing, copywriting, social media writing – and basically all written content.
This article you’re reading right now exemplifies how things will change. Currently, technology isn’t where you can get insightful, meaningful, and engaging content fully produced by AI. However, it’s coming.
And it might outperform current writing tasks by analyzing the best content to include without a human manually searching.
That’s not putting myself or any other writers down, but AI can research articles in a matter of seconds, whereas a writer like myself has to look up various resources – which can take a while.
The good news is that humans can currently put an article like this together and (hopefully) make it engaging, and AI just doesn’t have the creative chops (patting myself on the back).
AI is already implemented to help with writing, including software like Grammarly – which is getting better and better at understanding context, making good suggestions, and ensuring that your words are well written.
There’s also software like Jasper, Hypertenuse, and Writesonic that help create unique articles, copywriting, and social media content by writing it for you. And AI journalism is also here for stock insights, business reports, sports recaps, and more.
Even WordPress is experimenting with some new AI writing content to help write and provide images..
User Experience That’s Customized
As AI advances, it will understand the customer more and more, providing them with a customized shopping experience.
An example is a facial recognition plugin, such as Real ID. Beyond Real ID, there will be plugins allowing AI to suggest products. This will be based on color, size, and brand – whatever would look good on an individual consumer. This will eliminate the need for browsing and can instantly match products.
This takes it further than “suggested items” based on browsing history by knowing what is a good match with an AI assistant. It’ll be like your best friends saying, “Oh, that looks great on you!” Or, “Eh, not your style.”
Customer Service Automation
You’ve probably had automated customer service already. Chatbots exist all over the WordPress community and social media. They learn from various interactions, add answers to their system, and respond automatically. Even when making calls, voice recognition answers questions via an AI assistant.
Even here at WPMU DEV, we implemented AI assistance. It combs through all of our resources to provide the best answer immediately for our members.
Customer service automation is even apparent offline, in places like grocery stores. The self-checkout aisle is a good example. It is edging toward the point where humans will be out of the equation (in its current state, it’s definitely not satisfactory… yet).
Along with answering questions, AI can find prospective customers and improve the sales process – plus much more.
SEO Booster
AI can give you a boost in the SEO department. It’s already here for WordPress – and getting better all the time. The days of painstakingly going through your website and optimizing SEO for WordPress are already over.
A plugin like SmartCrawl automatically analyzes your website’s SEO, provides recommendations, and leverages your social media SEO. Additionally, for images, Smush can optimize images automatically.
As AI advances, plugins like SmartCrawl and Smush will, too.
Reduction of Human Error
Humans, of course, make mistakes. We’re wired for them. And mistakes can be costly – especially with WordPress development.
AI will be used more and more to prevent errors by developing flawless systems of coding, security, and specifications. This is another example of AI taking over WordPress development completely.
Machine learning is here to improve efficiency. It can eliminate the need for costly meetings regarding decision-making, for example. AI will already know what’s best, and you can eliminate that hours-long Slack call with a dozen employees.
When decisions by AI are made, it is decided on information gathered and a set of algorithms criteria. So, when programmed well, it can eliminate any errors.
Unbiased Decisions
Humans, driven by emotions, can sometimes make decisions based on bias. However, AI is emotionless, practical, and rational in any decision-making process.
When eliminating biased views, it can disrupt programming by allowing agencies to make decisions on sites they want to build, what to include, and what clients to work with.
Zero Risks
Ever want to remove a line of code but decided it was in your best interests not to in case it would crash an entire website? When AI robots are on the job, they can take more risks without the backlash of losing a job in case of a bad decision.
Plus, they can provide more accuracy with greater responsibility.
So, AIs can be written-up for bad work performance, so that’s good news for developers. However, it can also take a developer’s job, so there might not be any opportunity to be written up in the first place.
Graphics and Images
Part of website development is images; AI can produce images with just a few words of what it is you’d like.
This can also be used when creating websites for clients who need images for their WordPress site, social media, emails, and other marketing. AI generators like fotor, DeepAI, and craiyon are a few examples of companies that can provide this service.
Almost anything imaginable can be generated with these programming languages, and the art is becoming increasingly complex as time goes on.
The Upside to AI
We’ve gone over how AI will change the WordPress industry – and much of it is positive. Beyond streamlining web development, AI can:
– Drive down the time it takes to perform a task and enable multi-tasking with a hands-off approach.
– Work 24/7 with no breaks needed.
– Increase eCommerce sales by learning the customer’s needs and catering to them (e.g. like the clothing example mentioned above).
– Make decision-making faster and smarter.
– Save time and energy by implementing SEO practices practically instantly.
– Be deployed across industries.
There are other examples, but AI has great potential in the WordPress business.
Still, there’s also some bad with the good.
The Downside of AI
Of course, there can be a downside to AI as well. And when saying “downside”, that can depend on the context of individual situations and circumstances – so keep that in mind. However, there could be some unpleasant disruptions to how WordPress has operated throughout the years that might not bode well.
Some unsavory aspects will be:
– Unemployment could rise as AI can take over positions in WordPress development, such as with graphics, web building, copywriting, and more.
— Misinformation due to AI not knowing what is true and what isn’t.
– A personalized experience on a human level will disappear if AI performs the tasks.
– High costs can occur when using AI software, tools, robots, etc.
– No creativity.
– No ethics, which we all have regarding our work.
— Using images and text without permission from original sources.
All of this also begs the question…
Will WordPress Developers Be Out of a Job?
As of right now, AIs are capable of churning out websites that are quite limited, basic, and lack imagination. Sure, this will improve over time. However, at the moment, AI isn’t there. WordPress developers and designers are a necessity.
AI is on a trek to form a next-generation workplace that relies on collaboration between systems and individuals. So, humans are essential and not obsolete. If anything, human efforts are strengthened by the emergence of AI.
Tasks will be able to be performed quicker, jobs can be done around the clock, and decisions can be made in a few moments.
A personalized touch on web development, design, graphics, and writing is still needed (and valuable). Additionally, updating and maintaining web design is done by humans.
So… will AI ever completely take over WordPress development?
Fear not — it’s not likely to happen too soon.
BUT…
What you can do as a WordPress developer is adapt to AI. There’s no point in turning a blind eye to AI – it’s here already and will become more and more prominent.
Be aware of AI tools, and get to know them well. Learn about what an AI web design tool does and how it works. Then, decide what tools you can use in your web development business. And finally, practice using the tools until you figure out exactly what it does.
No amount of AI can replace human creativity. The best AI out there doesn’t know what a customer wants. There will be an increase in one-off AI-generated websites now and from here on out, but it’s unlikely that full automation will take over web development jobs soon.
It’s a matter of combining your skills as a web developer with AI – and adapting to this new technology.
Seeing Eye-to-Eye With AI
With AI emerging, there’s a lot of speculation, excitement, worry — you name it — in the WordPress community. But, like everything else, it’s not the demise of WordPress developers, illustrators, writers, and every other job required for good web development.
It’s just a matter of embracing change and working with it. And at the end of the day, if you get on top of what’s coming, you can expand and grow tremendously with the AI resources at your disposal.
Don’t worry about a robot coming along and handing you a termination letter anytime soon. Sure, there’ll be some changes, but as much anxiety as change can bring, try to see eye-to-eye with AI.
Be sure to check back with us soon as we dive deeper into AI with more articles about this ever-evolving technology.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://wpmudev.com/blog/how-ai-will-affect-the-wordpress-industry/
|
[
{
"date": "2023/03/06",
"position": 83,
"query": "AI job creation vs elimination"
}
] |
How AI Is Transforming the FP&A Career, The benefits, and ...
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How AI Is Transforming the FP&A Career, The benefits, and the challenges.
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https://cfo.university
|
[] |
... AI in their businesses to augment their transformation and value creation activities. ... Will AI and automation eliminate FP&A? While it's true that AI ...
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The impact of artificial intelligence (AI) on the finance and planning (FP&A) career is undeniable. AI has the potential to revolutionize the way FP&A professionals work, allowing them to focus more on high-value tasks and less on manual, time-consuming processes. In this article, we will explore how AI is changing the FP&A career, the benefits it offers to professionals, and the potential challenges to consider.
How AI Can be used in FP&A
First, it’s important to understand how AI can be used in FP&A. AI can be used to automate mundane tasks such as data entry, data processing, and report generation. This can free up time for FP&A professionals to focus on more strategic tasks such as developing and analyzing financial models, forecasting cash flow, and providing strategic advice to stakeholders. Additionally, AI can be used to uncover insights from large datasets that could otherwise be missed.
How AI Is Transforming the Financial Planning and Analysis Career
Artificial intelligence (AI) is revolutionizing the Financial Planning and Analysis (FP&A) career, enabling professionals to make faster and more accurate decisions. AI technology can automate many of the mundane tasks that take up valuable time and resources, allowing FP&A professionals to focus on more complex and meaningful activities. AI can also analyze large amounts of data quickly, enabling FP&A professionals to gain insights into financial trends and patterns that were previously impossible to detect. AI-powered tools can also provide more accurate forecasts, helping FP&A professionals make smarter decisions and improve the accuracy of their planning and analysis. Ultimately, AI technology is transforming the FP&A career, helping FP&A professionals make quicker and more informed decisions about their finances.
The benefits of AI for FP&A professionals
The benefits of AI for FP&A professionals are numerous. AI eliminates tedious processes and can be used to uncover meaningful insights faster than ever before. It can also help FP&A professionals stay more organized, save time, and be more efficient. Additionally, AI can help FP&A professionals develop deeper relationships with stakeholders, as they can provide more in-depth analysis and advice.
This article, CFOs and Digital Leadership – Automation for Transformative Change, by Ravi Bhardwaj, CEO and Co-Founder of Kosh.ai, includes a number of ways CFOs can implement AI in their businesses to augment their transformation and value creation activities.
The challenges to consider when using AI in FP&A
Finally, there are some potential challenges to consider when using AI in FP&A. AI requires data to be accurate and up to date, and FP&A professionals need to be aware of the potential for bias in the data. Additionally, AI can be expensive and may require more training for professionals to use it effectively.
Will AI and automation eliminate FP&A?
While it’s true that AI and automation are transforming the way finance functions are conducted, they are unlikely to eliminate FP&A roles altogether. Rather, they will likely change the nature of the role, shifting the focus away from manual processes and toward more strategic analysis. AI and automation can free up time for FP&A professionals to focus on more complex tasks, such as forecasting and scenario modeling, which can ultimately lead to more meaningful insights and better decision-making.
Conclusion
AI will have a profound impact on the FP&A career. It can automate mundane tasks, free up time for more strategic activities, and uncover meaningful insights from large datasets. However, there are also potential challenges to consider, such as data accuracy and potential bias. As AI continues to evolve, FP&A professionals should be aware of its potential and the opportunities it can offer.
The AI in Finance - Identification Worksheet will help identify where AI applications can add the most value to accounting and finance teams.
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| 2023-03-06T00:00:00 |
https://cfo.university/library/article/how-ai-is-transforming-the-fpa-career-the-benefits-and-the-challenges-sobhy
|
[
{
"date": "2023/03/06",
"position": 94,
"query": "AI job creation vs elimination"
}
] |
|
More skills are needed to help AI plug skills gaps
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More skills are needed to help AI plug skills gaps
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https://www.zdnet.com
|
[
"Joe Mckendrick",
"Contributing Writer",
"March",
"At A.M. Pt",
"Min Shin"
] |
Businesses are struggling to find the skills necessary to identify, build, and deploy the AI and automation needed to resolve their skills shortages.
|
BlackJack3D/Getty Images
Artificial intelligence -- and related forms of high-level automation and analytics -- have become the tool of choice for helping businesses plug their ever-persistent talent gaps. The catch is, however, businesses are struggling to find the skills necessary to identify, build, and deploy the AI and automation needed to resolve their skills shortages.
AI is potentially a powerful tool for keeping talent on board and engaged, For example, Kshitij Dayal senior VP at Legion points to AI-driven capabilities, such as AI-powered workforce management and demand forecasting, scheduling agility through better workforce management, and, importantly, fostering a positive work environment by increasing knowledgeability about employee wants and needs. Automating tasks with AI, or augmenting human labor, means greater productivity across the board. Acute skills shortages are better addressed, while workers and managers can concentrate on higher-level tasks.
Also: These experts are racing to protect AI from hackers. Time is running out
All good. Except putting together AI-driven capabilities requires skills to do so -- and this is one of its greatest challenges. The latest survey of 1,420 IT leaders by Rackspace Technology bears this out. In many cases, AI/ML replaces work formerly performed by humans, with 62% of respondents saying that AI/ML implementation has led to a reduced headcount within their organization. In addition, 69% say AI helps improve ability to hire and recruit new talent.
The main barrier, responding executives state, is the need for more AI and machine learning capability and the talent required to manage data effectively.
The issue or obstacle most often faced is a shortage of skilled talent, cited by 67%, followed by algorithm or model failure (61%) and cost of implementation (57%).
"AI and machine learning is smart -- but it isn't ready to implement itself," the survey report's authors point out. "It's difficult to find skilled people who can work with the technology and the data to optimize outcomes."
To address these issues, 82% of respondents said they have made efforts to recruit employees with AI and machine learning skills in the past 12 months, while 86% have grown their AI and machine learning workforce in the past 12 months.
Challenges to AI and machine learning adoption:
A shortage of skilled talent: 67%
Algorithm/model failure: 61%
Cost of implementation: 57%
The lack of technological infrastructure to support it: 54%
Lack of internal skills/difficulty hiring the required roles: 51%
Technical infrastructure challenges: 49%
The other notable takeaway from the survey is a high degree of trust in AI output -- and are comfortable with the steps taken to assure this trust. Despite data concerns and internal resistance, trust in the output of AI projects remains high among IT decision-maker respondents, with 73% saying they have confidence in the answers provided by AI. 72% say sufficient checks and balances are in place to avoid negative consequences from using AI, while 80% of respondents do not think AI/ML answers require additional human interpretation.
Also: Laid-off tech workers are launching their own ventures and competing with their ex-employers
Close to three-fourths of executives, 73%, said they always trust the analysis provided by AI and machine learning technologies. They say they have processes in place to assure AI is fair and unbiased. To this end, 72% say there are sufficient checks and balances in place to avoid negative consequences from the use of AI. In addition, 77% say AI and machine learning decisions are made by the "right people" on their organizations, and 71% say there is sufficient governance in place to safeguard against misuse of AI.
Technologies that are being implemented within respondents' organizations include virtual cloud networks (57%), the Internet of Things (51%), AI and machine learning (46%), blockchain (36%), robotics (34%), and 5G (31%).
"Respondents consistently listed lack of in-house resources who can understand and refine the use of AI machine learning technologies," the survey's authors state. "Evaluate your current training processes and your in-house capabilities to determine whether you should recruit externally or use the resources that you already have. To cultivate stronger AI and machine learning capabilities in-house, you should consider increasing your company's attendance at conferences or events, and offer online training to your teams."
| 2023-03-06T00:00:00 |
https://www.zdnet.com/article/more-skills-needed-to-help-ai-plug-skills-gaps/
|
[
{
"date": "2023/03/06",
"position": 5,
"query": "AI skills gap"
}
] |
|
Leadership Skills that Matter in the Age of AI
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When AI is transforming the workplace, how can leaders cope?
|
https://numly.io
|
[
"Amrutha C N",
"Madhukar Govindaraju",
"Shalini Ramakrishnan"
] |
A CEO-focused PWC report highlighted the skills gap as a vital problem that needs to be solved. According to the report, 55% of respondents believed they ...
|
According to a report by the World Economic Forum, artificial intelligence (AI) will create over 97 million jobs, but it will also replace 85 million jobs that may become obsolete by 2025!
The dynamics of how leaders function and direct their teams will change because of such a significant shift in the industry. Organizations must be prepared to face and embrace these paradigm shifts as well as the obstacles that the workforce around the world will experience.
While considering the workforce and their capabilities, AI will alter the strategies and choices made by leaders at all levels. Future leadership must focus on integrating AI-powered systems and preparing the workforce for these radical changes. Leaders must develop the appropriate soft and hard skills that are crucial in the AI era to accomplish these changes effectively.
Here are some skills that we believe are crucial to possess in order to keep up with the age of AI.
Upskilling as the Core Element
Upskilling the team to adapt to these AI-driven changes is the most crucial and necessary competency that leaders need to possess in the upcoming years. A CEO-focused PWC report highlighted the skills gap as a vital problem that needs to be solved. According to the report, 55% of respondents believed they were unable to innovate effectively. This demonstrates to us in clear terms the inner spectrum of how decision-makers relate skills to innovation, showing a direct proportionality between the two. Therefore, the leaders must put effort into upskilling themselves and the team to remain useful for the years to come to match the pace of an AI-driven future.
Having a Collaborative Mindset
Unparalleled teamwork and support are required when introducing new frameworks, processes, tools, and workflows to support the current system. If there is an acceptance conflict within the internal team, the leader cannot put the new system into practice. The leader must effectively communicate the bigger picture to the teammates for them to understand the vitality of things. This kind of cooperation can assist in successfully implementing any strategy, with smooth blending with the current context or system. To build a team that works closely to face the novelty of the changing world, a leader must use such collaborative channels and conversations.
Read: How to be a connected leader?
Agility in Strategic Management
According to the PwC report, over 35% of decision-makers have considered implementing AI-driven initiatives. Over 20% of respondents, however, were not even thinking about implementing AI models or solutions into their strategy. This may be a smart move, but not all industries will be able to adapt to the new AI model without agility. Therefore, leaders must update their strategies while implementing the models and functionalities that keep their systems from becoming obsolete. It also allows team members to follow the same line of thought and practice agility when the time comes, leading to quick and acceptable adoption in the age of AI.
Impeccable Emotional Intelligence
Emotional intelligence is one tool that can keep the ship afloat when erratic and unprecedented changes are introduced to the industries. It is not possible to lead with a manager mindset alone in this changing world. It is crucial to develop emotional intelligence so that the team can trust the leader and have faith in the organization’s future goals. Emotional intelligence will be one of the top skills for these changing times, where job roles are evolving and sudden shifts are impacting the sectors so easily, according to a report by Capgemini. The ability to connect with one another, possess social awareness, and cultivate self-awareness will be crucial for leaders to survive the imminent AI boom.
Read: Emotional Intelligence in Leadership – Why is it Essential?
Cultural Fit and Diversity Enthusiast
AI will never be able to physically present for the team and create a culture that accepts the various cultural shifts. The emotional needs of employees seeking a workplace with a diverse and culturally inclusive environment are evolving along with technology. The changing demands of the AI-powered world can be driven by a leader who can provide a culture that speaks for itself. According to a Glassdoor survey, over 76% of job seekers view finding a diverse workplace as their top priority to stick with and perform to the best of their abilities. A team that works together for the bigger picture needs to have their personal goals and needs met as well. To succeed in the modern world, a leader must use tactics that reflect such inclusive and diverse ideologies to fit everyone, working towards the singular end goal.
Out-of-the-box Authentic Thinking
AI can automate processes and provide answers to your inquiries, but it is unable to make inferences that aren’t already recorded in the data sets. To combat the AI revolution, leaders must cultivate their ability to think creatively and introduce novel value propositions. Organizations must remain relevant in the age of AI, but to shine as distinctive, leaders must be able to think creatively while encouraging the same in the team. By using AI, time spent on routine tasks would be freed up, allowing for more creative and unconventional endeavors. For the best outcomes, the leaders would need to be genuine thinkers as well as champion the same approach within the team. The goal is to consider all options, whether they involve AI or not while using human intelligence to think outside the box.
The shifting dynamics of the industries and workforce have a significant impact on the leaders. They must constantly improve the way they manage their teams and reduce operational risks in the era of AI. We at Numly have developed tactics that assist leaders in maintaining their relevance and influence despite the changing nature of society. Our framework for connected leadership provides leaders with a learning environment that enables them to accomplish both their personal goals and the overarching organizational objective.
Provide the people managers in your organization an opportunity to improve their leadership skills to become Better Leaders and build Better Teams. Start a 60-day pilot of NumlyEngage™.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://numly.io/blog/leadership-skills-that-matter-in-the-age-of-ai/
|
[
{
"date": "2023/03/06",
"position": 23,
"query": "AI skills gap"
},
{
"date": "2023/03/06",
"position": 21,
"query": "artificial intelligence business leaders"
}
] |
AI Regulation in the Public Sector
|
AI Regulation in the Public Sector: Regulating Governments’ Use of AI
|
https://www.holisticai.com
|
[] |
In this blog post, we provide a high-level summary of some of the actions taken to regulate the use of AI in public law, focusing on the US, UK, and EU.
|
Artificial intelligence (AI) use has grown rapidly in the last few years, with 44% of businesses taking steps to integrate it into their current processes and applications. However, while AI can offer many business benefits, such as increased productivity, accuracy, and cost savings, using AI comes with risks. Consequently, steps must be taken to reduce these risks and promote AI's safe and trustworthy use. An effective way to do this is to introduce governance mechanisms or codify risk management requirements in the law. Accordingly, policymakers worldwide have begun to propose regulations to make AI systems safer for those using them.
While many of these efforts target AI applications by businesses, governments are also starting to use AI more widely, with almost 150 significant federal departments, agencies, and sub-agencies in the US government using AI to support their activities. As such, governmental use of AI is also starting to be targeted, with initiatives to govern the use of AI in the public sector increasingly being proposed. In this blog post, we provide a high-level summary of some of the actions taken to regulate the use of AI in public law, focusing on the US, UK, and EU, first outlining the different ways governments use AI.
How are governments using AI?
AI is increasingly being used by governmental departments and agencies, and other entities in the public sector to automate a variety of tasks, from virtual assistant bots to deliver reminders about pregnancy checkups to mapping the characteristics of businesses in different areas to direct investments towards more ventures that are likely to be more successful. Elsewhere, AI is being used in defence activities to enhance decision-making, increase safety, and predict supply and demand, with the US Department of Defense publishing an AI strategy to accelerate the applications of AI in the military and the US Defence Advanced Research Projects Agency (DARPA) funding a program to develop a brain-to-machine interface.
However, highlighting the potential harms that can come from the use of AI in the public sector, the UK’s Office of Qualifications and Examinations Regulation (Ofqual) came under fire in 2020 for its algorithm used to assign GCSE grades while students were unable to take exams due to COVID restrictions since many students received lower grades than expected. Further, an already controversial application of AI, facial recognition, is being used by law enforcement to identify suspects and has recently garnered much attention due to the wrongful arrest of a man in Georgia who was mistaken for a fugitive by Louisiana authorities’ racial recognition technology. With the Gender Shades project revealing the inaccuracies of facial recognition technology for darker-skinned individuals and both the victim and fugitive being Black, this highlights the need to ensure that AI systems, particularly those used in high-risk contexts, are not biased and are accurate for all subgroups. As such, the UK’s Equality and Human Rights Commission has called for suspending facial recognition in policing in England and Wales, with similar action being taken in Washington’s city of Bellingham and Alabama.
Regulation Region Brief summary Declaration on Responsible Military Use of Artificial Intelligence and Autonomy US Best practices for states using AI and automation in their military practices. AI Training Act US Requires the Director of the Office of Management and Budget to develop an AI training program for the acquisition workforce. Executive Order (EO) 13960 US The EO sets out a series of principles that federal agencies must be guided by when considering the design, development, acquisition, and use of AI in Government. Maryland Algorithmic Decision System Procurement and Discriminatory Act US Requires that if a state unit purchases a product or service that includes an algorithmic decision system, then it must adhere to responsible AI standards. Guidance on building and using AI in the public sector UK Provides resources on how to assess if using AI will help to achieve user needs, how AI can best be used in the public sector, and how to implement AI ethically, fairly, and safely. Guidelines for AI procurement UK Outlines guiding principles on how government departments should buy AI technology and insights on tackling any challenges that may arise during procurement. Algorithm Registers in the Netherlands EU AI Applications can be filtered by government branch and the database provides detail on the type of algorithm being used, whether it is currently actively used, and the policy area it is used for. Italy’s White Paper on AI in Public Administration EU The Italian government published a report addressing various methods of adopting AI technology into public policies.
US efforts to regulate AI in the public sector
Given that AI is increasingly being used in high-stakes applications in the public sector and several instances of harm have resulted from this, efforts are emerging to govern and regulate public sector applications of AI, with many being centred in the US.
Declaration on Responsible AI in the Military
Most recently, the US Department of State published a declaration on the Declaration on Responsible Military Use of Artificial Intelligence and Autonomy, which outlines 12 best practices for states using AI and automation in their military practices. These include maintaining human control, using auditable methodologies and design considerations, rigorous testing and assurance across the AI life cycle, and sufficient training for the personnel approving or using military AI capabilities.
AI Training Act
On the note of personnel training, the US has launched an imitative specifically targeting the training of federal agency personnel acquiring AI. Signed into law in October 2022, the AI training Act (Public Law No. 117-207) requires the Director of the Office of Management and Budget to develop an AI training program for the acquisition workforce. Specifically, this program will be designed for employees of an executive agency who are responsible for program management; planning, research, development, engineering, testing, and evaluation of systems; procurement and contraction; logistics; or cost estimation of AI to ensure that such personnel know the risks and capabilities of the AI systems they are responsible for procuring. Taking a risk-management approach, the topics to be covered by the training include the science of AI and how it works, technological features of AI systems, how AI can benefit the federal government, AI risks, including discrimination and privacy risks, methods to mitigate risks including ensuring that AI is safe, reliable, and trustworthy, and future trends in AI.
Executive Order (EO) 13960: Trustworthy AI in the Federal Government
This effort builds Executive Order (EO) 13960, Promoting the Use of Trustworthy AI in the Federal Government, signed into law in December 2020. The EO sets out a series of principles that federal agencies must be guided by when considering the design, development, acquisition, and use of AI in Government:
Lawful and respectful - Agency use of AI should respect the Nation’s values and comply with relevant laws and policies.
- Agency use of AI should respect the Nation’s values and comply with relevant laws and policies. Purposeful and performance-driven – agencies should seek opportunities for AI-led innovation but only when the benefits of using AI outweigh the risks and can be appropriately managed.
– agencies should seek opportunities for AI-led innovation but only when the benefits of using AI outweigh the risks and can be appropriately managed. Accurate, reliable, and effective – AI applications should be consistent with the use cases for which the AI was trained, where AI should be accurate, reliable, and effective.
– AI applications should be consistent with the use cases for which the AI was trained, where AI should be accurate, reliable, and effective. Safe, secure, and resilient – AI systems should be resilient against systematic vulnerabilities, adversarial manipulation, and other malicious intents.
– AI systems should be resilient against systematic vulnerabilities, adversarial manipulation, and other malicious intents. Understandable – the operations and outcomes of AI applications should be understandable to subject matter experts, users, and others as appropriate.
– the operations and outcomes of AI applications should be understandable to subject matter experts, users, and others as appropriate. Responsible and traceable – human roles and responsibilities should be clearly defined, understood, and designed for the design, development and acquisition of AI and the design, development, acquisition, and use of AI, as well as inputs and outputs, should be documented and traceable.
– human roles and responsibilities should be clearly defined, understood, and designed for the design, development and acquisition of AI and the design, development, acquisition, and use of AI, as well as inputs and outputs, should be documented and traceable. Regularly monitored – AI applications should be regularly tested against the principles, and mechanisms should be in place to supersede, disengage, or deactivate AI systems that do not perform consistently with their intended use.
– AI applications should be regularly tested against the principles, and mechanisms should be in place to supersede, disengage, or deactivate AI systems that do not perform consistently with their intended use. Transparent – Agencies should be transparent in disclosing relevant information about their use of AI to appropriate stakeholders, including Congress and the public, keeping in mind applicable laws and policies.
– Agencies should be transparent in disclosing relevant information about their use of AI to appropriate stakeholders, including Congress and the public, keeping in mind applicable laws and policies. Accountable – agencies should be accountable for implementing and enforcing appropriate safeguards for the appropriate use and functioning of their AI and shall monitor, audit, and document their compliance with safeguards.
As part of this Executive Order, the National Institute for Standards and Technology (NIST) will re-evaluate and assess AI used by federal agencies to investigate compliance with these principles. In preparation, the US Department of Health and Human Services has already created its inventory of AI use cases.
Use of AI by New York government agencies report
At a more local level, and using different terminology, a report by the New York City Automated Decision Systems (ADS) Task Force in November 2019. Convened by Mayor Bill de Blasio in 2018 as part of Local Law 49, which required the Task Force to provide recommendations on six topics related to the use of ADSs by City agencies, the Task Force examined three key areas as part of their report:
How to build capacity for an equitable, effective, and responsible approach to using ADSs
How to broaden public discussions on ADSs
How to formalise ADS management functions
Recommendations included establishing an Organizational Structure within City government to act as a centralised resource to guide agency management of ADSs, including the inclusion of principles such as fairness and transparency, providing sufficient funding and training to agencies to support the appropriate use of ADSs, staff education and training, support for public requests for information about City use of ADSs, establishing a framework for agency reporting of information about ADSs, and creating a process for assessing ADS risks.
Following this report, Mayor de Blasio signed Executive Order 50 to establish an Algorithms Management and Policy Officer within the Mayor’s Office of Operations. The aim of this was to establish a centralised resource on algorithm policy and develop guidelines and best practices to assist City agencies using algorithms.
Maryland Algorithmic Decision System Procurement and Discriminatory Act
In Maryland, the Algorithmic Decision Systems Procurement and Discriminatory Act was proposed in February 2021 to require that if a state unit purchases a product or service that includes an algorithmic decision system, it must adhere to responsible AI standards. They must also evaluate the system's impact and potential risks, paying particular attention to potential discrimination. Further, state units must ensure the system adheres to transparency commitments, including disclosing the system's capabilities, limitations, and potential problems to the state.
UK efforts to regulate AI in the public sector
Guidance on building and using AI in the public sector
While the UK has not introduced any laws regulating public sector use of AI, reflecting the lack of more general AI-specific legislation in the UK, the Central Digital and Data Office and Office for Artificial Intelligence published guidance on building and using AI in the public sector on 10 June 2019. While brief, the guidance provides resources on assessing whether using AI will help achieve user needs, how AI can best be used in the public sector, and how to implement AI ethically, fairly, and safely.
Citing guidance from the Government Digital Service (GDS) and Office for Artificial Intelligence (OAI), the publication provides four resources on assessing, planning, and managing AI in the public sector. The publication then provides a resource on using AI ethically and safely, co-developed with the Turing institute, before providing a series of case studies on how AI is being applied in the public sector, from satellite images being used to estimate populations to using AI to compare prison reports. Therefore, instead of comprehensive guidance principles being outlined, which is more characteristic of the US approach, the UK guidance acts as a resource bank.
Guidelines for AI procurement
With a more comprehensive approach, the Guidelines for AI procurement, co-published by the Department for Business, Energy & Industrial Strategy, Department for Digital, Culture, Media & Sport, and Office for Artificial Intelligence in June 2020 is aimed at central government departments that are considering the suitability of AI technology. Specifically, the document outlines guiding principles on how government departments should buy AI technology and insights on tackling any challenges that may arise during procurement.
Initiated by the World Economic Forum’s Unlocking Public Sector AI project, the guidelines were produced with insights from the World Economic Forum Centre for the Fourth Industrial Revolution and other government bodies and industry and academic stakeholders.
The guidance starts by outlining ten key things that the central government should consider concerning AI procurement:
Ensure Technology and Data strategies are updated to incorporate AI technology adoption and act strategically to support AI adoption across the government.
Seek multidisciplinary insights from diverse teams, including data ethicists and domain experts.
Conduct data assessments before commencing procurement processes.
Assess AI risks and benefits before procurement and deployment.
Engage early with the market and consult various suppliers.
Remain flexible and focus on the challenge rather than a particular solution.
Establish appropriate oversight mechanisms to support the scrutiny of AI systems throughout their lifecycle.
Encourage explainability and transparency by avoiding black box models where possible.
Focus on the need to address the technical and ethical limitations of AI.
Consider how AI systems can be managed throughout their lifecycle.
The guidelines then address AI-specific considerations within the procurement process concerning preparation and planning; publication; selection, evaluation and reward; and contract implementation and ongoing management.
EU efforts to regulate AI in the public sector
While much of the European Commission’s resources are currently invested in the development of the EU AI Act, and the EU is focusing more on businesses using AI, individual member states are introducing their own initiatives to address government use of AI.
Algorithm Registers in the Netherlands
For example, in the Netherlands, the Dutch Secretary of the State of Digital Affairs announced the launch of an Algorithm Registry in 2022.
Here, the AI applications currently being used by the Dutch government are listed, with 109 registries currently. Applications can be filtered by government branch, and the database provides detail on the type of algorithm being used, whether it is currently actively used, and the policy area it is used for. Information about monitoring, human intervention, risks, and performance standards are also provided, increasing transparency of AI usage by the Dutch government.
At a more local level, the City of Amsterdam and Helsinki launched an Algorithm and AI register in September 2020. Providing information about the three algorithms used in the City of Amsterdam, the register provides an overview of each system and contact information for the department responsible, along with information on the data, data processing, non-discrimination approach, human oversight, and risk management associated with the system.
Italy’s White Paper on AI in Public Administration
Elsewhere, in Italy, a Task Force on Artificial Intelligence was established as part of the agency for Digital Italy to develop Italy’s strategy for AI. In March 2018, the Italian government published a report, edited by Task Force, addressing various methods of adopting AI technology into public policies. This report, referred to as the White Paper, discussed and identified nine challenges to be addressed in the country’s National AI Strategy:
Ethics – agencies should consider the effects that AI innovation has had and will continue to have in terms of societal impact and safeguarding values.
– agencies should consider the effects that AI innovation has had and will continue to have in terms of societal impact and safeguarding values. Technology – AI in the Public Sector should be personalised and adaptive to create services capable of catering to the needs of citizens.
– AI in the Public Sector should be personalised and adaptive to create services capable of catering to the needs of citizens. Skills - Individuals should be trained on AI issues for both work-related and educational benefits
- Individuals should be trained on AI issues for both work-related and educational benefits Role of Data – AI needs to be able to transform public data into widespread and shared knowledge in both a transparent and accessible way.
– AI needs to be able to transform public data into widespread and shared knowledge in both a transparent and accessible way. Legal context – legal liability for AI should be established, and the characteristics of AI solutions and systems should be defined and interpreted in accordance with the fundamental rights of individuals and the laws in effect.
– legal liability for AI should be established, and the characteristics of AI solutions and systems should be defined and interpreted in accordance with the fundamental rights of individuals and the laws in effect. Accompanying the transformation - there needs to be room for both ordinary citizens and Public Administrations to participate in the development/creation of AI systems.
- there needs to be room for both ordinary citizens and Public Administrations to participate in the development/creation of AI systems. Preventing inequalities - AI can reduce inequalities in different public law sectors. Still, one also needs to be mindful of the inequalities AI can create, e.g., race, gender and other social factors.
- AI can reduce inequalities in different public law sectors. Still, one also needs to be mindful of the inequalities AI can create, e.g., race, gender and other social factors. Measuring the impact – the impact of AI should be measured through both qualitative and quantitative indicators, for example, exploring which professions/roles will be replaced by technology.
– the impact of AI should be measured through both qualitative and quantitative indicators, for example, exploring which professions/roles will be replaced by technology. The Human Being - there needs to be consideration of the real-life effects of AI on human beings concerning concerns such as their rights, freedoms, and opportunities.
In order to address these challenges, the report gives 10 recommendations:
Promote a national platform dedicated to AI development, including capabilities to collect annotated data, codes, and learning modules
Make public appropriate documentation on the AI systems operated by public administrators so that processes can be reproduced and they can be evaluated and verified
Enable computational linguistic systems for the Italian language using new resources distributed with open licenses
Develop adaptative personalisation and recommender systems to facilitate interaction with the services offered by public administration systems based on specific needs, requirements, and characteristics of citizens
Promote the creation of a National Competence Centre to act as a point of reference for the implementation of AI in public administration to enhance the positive effects of AI systems and reduce their risks
Facilitate the dissemination of skills by promoting training, education, and certification
Provide a plan to encourage investments in AI in public administration through promoting innovation
Support the collaboration between research, business accelerators, and innovation hubs to promote the adoption of AI solutions in the public sector
Establish a transdisciplinary Center on AI in collaboration with the Center of Expertise to publicise debates on AI ethics and create opportunities for expert and citizen consultation
Define guidelines and processes based on the principle of security-by-design and facilitate the sharing of data on cyber-attacks to AI across Europe
Get compliant
Governments and businesses alike will soon be faced with several requirements and principles that they must follow when designing, developing, deploying, and procuring AI systems. Taking action early is the best way to ensure compliance. To find out more about how Holistic AI can help you with this, get in touch at [email protected].
| 2023-03-06T00:00:00 |
https://www.holisticai.com/blog/ai-regulation-public-sector
|
[
{
"date": "2023/03/06",
"position": 6,
"query": "government AI workforce policy"
}
] |
|
AI inventions: Policy options and a path forward
|
AI inventions: Policy options and a path forward
|
https://www.brookings.edu
|
[
"Alex Engler",
"Nicol Turner Lee",
"Jack Malamud",
"Brooke Tanner",
"Tonantzin Carmona",
"John Villasenor"
] |
The question of how to handle AI inventions from a policy perspective is particularly timely given the extraordinary recent advances in AI.
|
The question of how to handle AI inventions from a policy perspective is particularly timely given the extraordinary recent advances in AI. AI inventions can be defined as “inventions for which an AI system has contributed to the conception in a manner that, if the AI system were a person, would lead to that person being named as an inventor.” (This is my own definition, not a formalized legal definition).
In mid-February, the U.S. Patent and Trademark Office (PTO) released a “Request for Comments Regarding Artificial Intelligence and Inventorship.” The request, which is part of the PTO’s broader effort to engage in issues at the nexus of AI and innovation, invites responses on questions including “How is AI, including machine learning, currently being used in the invention creation process?” and “If an AI system contributes to an invention at the same level as a human who would be considered a joint inventor, is the invention patentable under current patent laws?” Responses are due by May 15, 2023.
In a law review article published in late February in the Santa Clara High Technology Law Journal, I propose that AI inventions should be patentable under a broadened view of conception, with inventorship attributed to the people who use AI tools as extensions of their mind.
In the article, I provide four options for addressing AI inventions. The first is to deem them unpatentable on the grounds that patenting them would require listing a non-human inventor in violation of the Patent Act. As I explained in an August 2022 TechTank post, the Federal Circuit’s decision that month in Thaler v. Vidal made clear that the definition in the Patent Act of “inventor” requires that inventors be human. Therefore, under current patent law, naming AI systems as inventors isn’t possible. But that shouldn’t be the end of the story. After all, AI has enormous potential in relation to inventions, and U.S. patent policy should provide a mechanism to harness the power of AI to enhance innovation.
A second option is to deem AI inventions patentable by revising the Patent Act to allow AI systems to be named as inventors or co-inventors. But this would require Congress to make a fundamental change to U.S. patent law. It would also raise a host of new challenges. For instance, how would an AI system exercise the rights and responsibilities associated with inventorship, including signing the inventor’s oath or declaration that must accompany a patent application, assigning ownership of the patent to a third party, or, in the event of litigation involving a dispute over inventorship, testifying under oath in a deposition or at trial?
A third option is to modify patent law to include an “invention made for hire” framework analogous to “work made for hire” in copyright. Under this approach, a company or university would be the inventor for AI inventions made by its AI systems. But this would involve complex line drawing exercises to determine which inventions are AI inventions subject to this new legal framework. It would also disincentivize human employees, who might be concerned that an invention made for hire approach would reduce their opportunities to be listed as inventors.
| 2023-03-06T00:00:00 |
https://www.brookings.edu/articles/ai-inventions-policy-options-and-a-path-forward/
|
[
{
"date": "2023/03/06",
"position": 17,
"query": "government AI workforce policy"
}
] |
|
4 ways technology impacts the government workforce
|
4 ways technology impacts the government workforce
|
https://www.microsoft.com
|
[
"Ngozi Nwoko",
"See More Articles This Author",
"Director",
"Global Industry Product Marketing",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class",
"Wp-Block-Co-Authors-Plus",
"Display Inline",
".Wp-Block-Co-Authors-Plus-Avatar",
"Where Img"
] |
Per IDC, “by 2025, 40 percent of national governments will adopt immersive learning technology in critical sectors to accelerate education, hone skills, and ...
|
Governments seek to serve and protect their communities through the delivery of essential public services. It’s people—the government’s own workforce—that make these services possible. From elected officials and tax administrators to social workers, police officers, and firefighters, the government is comprised of qualified individuals who have eagerly answered the call to serve their communities.
According to Gartner, “Government employees are too often battling with friction in antiquated citizen-facing and back-office systems and increasing cycle completion times, leaving them frustrated and sometimes cynical.”1 The good news is that governments are actively seeking ways to mitigate this concern across multiple agencies.
Across industries, while end-to-end digital transformation initiatives are redefining how employees perform their roles and how services are delivered, governments need to take it a step further by investing in the necessary training and skills needed to recruit and retain a digitally-savvy workforce. Per IDC, “by 2025, 40 percent of national governments will adopt immersive learning technology in critical sectors to accelerate education, hone skills, and engage and retain employees”.2 This is because government employees care about a variety of factors including improved well-being and the availability of technology that makes it easier to effectively do their job.
Microsoft for Government partners with governments around the world to help empower the government workforce and improve employee engagement, streamline workflows, and uncover actionable insights across agencies. We are committed to helping improve the lives of the people who keep our communities safe and operational using innovative technology. This commitment takes shape in different ways across the public sector. Showcased below are impactful examples of how Microsoft has helped accelerate digital transformation within government agencies and helped empower the workforce to achieve their mission.
1. Transforming internal operations to accelerate the delivery of services.
As the primary delivery arm for information and communications technology, the Malta Information Technology Agency (MITA) has been driving the government’s transformational policies for more than 20 years.
“MITA seeks out the most innovative initiatives to lead our country’s ambitious transformation toward a first-class digital society. As we deployed more and more programs, we needed a consolidated workplace to improve collaboration and accelerate service delivery, with security standards matching our government requirements.”—Mariano Debono, Manager of Software License Management, MITA.
MITA implemented Microsoft 365 productivity tools, such as Microsoft Teams and Microsoft SharePoint online to connect more than 23,000 public service employees across all ministries on a single platform.
“The single collaboration hub simplifies work and makes communication a lot quicker. For example, we sometimes had to spend half the day traveling to attend one meeting with our ministry colleagues on the Island of Gozo. These meetings can now be held remotely.”—Jonathan Cassar, CTO, MITA.
This is just one example of how government agencies, like MITA, are partnering with Microsoft for Critical Infrastructure to transform government operations and empower the workforce to be more productive, collaborative, and efficient.
2. Streamlining administrative processes to provide coordinated care for vulnerable populations.
The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) program’s participation rates were declining rapidly nationwide leaving the most vulnerable people without service—even though they were eligible. This decline was due in large part to duplicative and cumbersome registration processes across adjunct programs like Medicaid, Temporary Assistance for Needy Families (TANF), or the Supplemental Nutrition Assistance Program (SNAP).
New Mexico WIC saw an opportunity to reverse the downward trend using technology to streamline processes yet program staff members often spend their brief time with clients gathering basic information already provided to other state agencies, saddling state administrators with an additional burden of 10 to 15 minutes per applicant.
“WIC offers a holistic approach. We educate folks about nutrition and lifestyle and maximize their SNAP and WIC dollars.”—Gavino Archuleta, Program Analyst, New Mexico WIC.
Teaming up with the Human Services Department (HSD), they integrated the application process across state-administered programs. Using Microsoft Dynamics 365 and data analytics through Microsoft Power BI, the teams built a solution that notifies the New Mexico WIC program whenever an individual qualifies for a new program. Then, the program informs the individuals of their ability to receive benefits. This has increased access to key resources for vulnerable populations and minimized paperwork, freeing staff time, and letting nutritionists focus on nutrition.
New Mexico WIC is an inspiring example of how public health and social services agencies can partner with Microsoft to modernize processes and reduce the administrative burden on employees so that they can spend more time doing what they love—serving the community.
3. Upskilling the banking workforce to drive financial stability.
The Bank of Canada isn’t just your neighborhood bank. As its name suggests, it’s everyone’s bank—it’s Canada’s bank. As such, it has great responsibilities which include setting monetary policy, distributing currency, and maintaining the country’s financial stability. This is all captured in its public mission “to regulate credit and currency in the best interests of the economic life of the nation.”
The pursuit of that goal requires significant computing resources, and the bank’s IT department is crucial in executing the day-to-day operations. The bank decided that it was important to develop and maintain the skills required to realize that mission. Using new technologies, adapting to new ways of working, and designing and operating new digital products and services all require new skills. An ongoing training initiative identified immediate and emerging skills and integrated associated upskilling courses into an online learning experience platform.
Claude Guimont, Senior Learning Specialist, and his team worked closely with Microsoft to find opportunities to incorporate an ideal mix of online on-demand and instructor-led content that would best fit the bank’s transformation effort. He says, “working with the Microsoft Learn team, we identified and refined that content to build self-paced modules starting with fundamentals all the way through to certification, if that’s what the learner wants.” It’s important to provide staff with the means to pursue both highly focused task-specific training and topics of less targeted, more general interest that may lead to future opportunities.
Bank of Canada is a prime example of how public finance organizations can empower their workforce with the skills required to digitally transform the workforce in order to ultimately drive informed budgeting, provide financial stability, and serve the public today—and into the future.
4. Enhancing collaboration across the public safety and justice ecosystem.
As criminal activities are on the rise, public safety and justice agencies are increasingly gravitating towards digital tools—tools that help address situational boundaries that bad actors have long exploited. The North Carolina State Bureau of Investigations (SBI) conducts criminal investigations across the state and supports agencies within the state when needed.
North Carolina SBI chose Microsoft 365 as their secure communication platform to help accelerate collaboration and secure data sharing. This has helped inform positive outcomes for their employees and the communities they serve. The integration of these collaborative applications has contributed to heightened effectiveness in combating crime in this day and age.
Ease of use and versatility is key in ensuring the adoption of any new technology in a work environment. Since Microsoft Teams can be installed on many mobile devices, it goes along with the employees on missions and lends a hand towards real-time collaboration, data sharing, and virtual work while in the field.
“Microsoft Teams integrates with other Microsoft 365 apps, it’s familiar and it’s easy to use. Our agents are busy people who really appreciate tools that just work”.—Mike Denning, IT Director, North Carolina SBI.
Microsoft also helps investigators manage the large quantities of digital evidence involved in modern investigations. While only 20 years ago it was unusual to have digital evidence at the center of a case, today digital evidence is involved in nearly 90 percent of all crimes committed. It’s now a significant part of the evidence that determines a defendant’s outcome. Without the help of innovative technology such as Azure Cognitive Services, analyzing all that data would be a massive undertaking for investigators.
This is one of the many great examples of how Microsoft for Public Safety and Justice helps to be more efficient and effective.
When the government workforce thrives, we all thrive
Microsoft is continuously inspired by our government customers around the world and their commitment to serving their communities and employees. We proactively play a key role in their digital transformation initiatives and provide innovative technology solutions that empower their hybrid workforce.
To explore more workforce transformation stories across government, visit our Microsoft for Government webpage and follow us on LinkedIn at Microsoft in Government.
Intelligent government solutions Inclusive government technology that seamlessly blends into everyday life. Learn more
1Gartner, Top Trends in Government for 2022: Total Experience, Apeksha Kaushik, Arthur Mickoleit, Daniel Snyder, 18 January 2022.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
2IDC, IDC FutureScape: Worldwide National Government 2023 Predictions, 27 October 2022.
| 2023-03-06T00:00:00 |
2023/03/06
|
https://www.microsoft.com/en-us/industry/blog/government/2023/03/06/4-impactful-ways-microsoft-is-empowering-the-government-workforce/
|
[
{
"date": "2023/03/06",
"position": 37,
"query": "government AI workforce policy"
}
] |
AI around the world | Deloitte Insights
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AI around the world
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https://www.deloitte.com
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[] |
Workforce · Industries ... DELOITTE RESEARCH CENTERS. Cross-Industry · Economics · Consumer · Energy & Industrials · Financial Services · Government & Public ...
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Looking to stay on top of the latest news and trends? With MyDeloitte you'll never miss out on the information you need to lead. Simply link your email or social profile and select the newsletters and alerts that matter most to you.
| 2023-03-06T00:00:00 |
https://www.deloitte.com/us/en/insights/industry/public-sector/global-government-ai-case-studies.html
|
[
{
"date": "2023/03/06",
"position": 48,
"query": "government AI workforce policy"
}
] |
|
Singapore: Leaders on driving life-long learning, reskilling ...
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Singapore: Leaders on driving life-long learning, reskilling and upskilling
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https://www.aigroup.com.au
|
[] |
The Ai Group Centre for Education and Training's report: Skilling Australia to lock in our digital future (2022) found that, 'changes in technology are driving ...
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The rapid pace of digitalisation and technological change in industry and commerce means that individuals need to regularly update and build upon their existing skills and knowledge base. The Ai Group Centre for Education and Training’s report: Skilling Australia to lock in our digital future (2022) found that, ‘changes in technology are driving shifts in job function and job design’. This has necessitated employers, government, and workers to prioritise adult learning, training and skills development to better equip people to deal with these changes.
In the Centre’s recent skills survey, skills shortages were reported across all occupations due to factors like changes in technology (30%), new work practices (25%), new machinery, technology or tools (22%), and new software or systems (17%). To meet these changes, 34% of companies said they would prioritise re-skilling existing staff on the job. In light of this data, the Centre favours a culture of lifelong learning supported by an education and training system that can rapidly and flexibly upskill workers through, for example, short, stackable training options, like microcredentials. This is why the Centre is taking note of cultural changes to adult learning in countries like Singapore.
The Singaporean government is placing a strong emphasis on education and the importance of continuous learning. This is evident in the country’s ongoing and steadfast reforms to adult learning, which aim to promote lifelong learning and equip workers with the skills they need to thrive in a rapidly changing world.
In a recent speech by Minister of State for Education, Ms. Gan Siow Huang, she emphasised the need for individuals to continuously upskill and reskill themselves to stay relevant in an increasingly digital and competitive global economy. The Minister acknowledged that the traditional model of education, where individuals study for a set period and then stop learning, is no longer sufficient in today's rapidly changing world.
In her speech, the Minister released the new Training and Adult Education (TAE) Industry Transformation Map (ITM) 2025, which builds on the TAE ITM 2020 and aims to achieve four key strategies:
Improve industry-relevance and market responsiveness of training Innovate and Digitalise at Scale Invest in Adult Educators and Sectoral Capabilities Internationalise to Strengthen Sector Resilience
These strategies are aimed at creating a more integrated and cohesive system of training and education that will benefit individuals and industry alike.
The government's reforms to adult learning focus on creating a culture of lifelong learning, where individuals have access to a wide range of opportunities to continue learning and developing new skills throughout their life. This is achieved through a combination of government-led initiatives, such as the SkillsFuture program, and private-sector initiatives, such as continuing education and training programs offered by universities and private companies.
The SkillsFuture program, launched in 2015, provides individuals with a credit that can be used to offset the cost of various training and development programs. This initiative encourages individuals to take charge of their own learning and career development, and provides them with the support they need to do so.
Since 2015, all Singaporeans aged over 25 years have been eligible to receive a SkillsFuture credit of S$500 (Singaporean Dollars) to reskill and upskill with additional one-off payments of S$500 to individuals aged 40 to 60 years to assist with mid-career transitions. According to the latest annual report on the program, 660,000 individuals and 24,000 companies participated in SkillsFuture programmes and initiatives in 2021.
In addition to the SkillsFuture program, the government has also made a significant investment in the development of a robust digital infrastructure that enables individuals to access a wide range of online learning resources. This includes online courses, workshops, and seminars, as well as virtual classrooms and online communities that provide individuals with opportunities to connect with like-minded people and continue learning.
Furthermore, the government has also introduced a number of initiatives to encourage employers to invest in the training and development of their employees. This includes tax incentives, grants, and other support measures that encourage companies to provide their employees with opportunities to continue learning and developing new skills.
The government's focus on continuous and lifelong learning has several key benefits for individuals, companies, and the wider economy. Firstly, it helps individuals to stay relevant and competitive in a rapidly changing job market. Secondly, it boosts the productivity and competitiveness of companies by providing employees with the skills they need to succeed in their roles. Thirdly, it contributes to the overall economic growth of the country by promoting a highly skilled and educated workforce.
The Singaporean government's reforms to adult learning are an important step in promoting lifelong learning and ensuring that individuals have the skills they need to thrive in a rapidly changing world. By providing individuals with access to a wide range of learning opportunities, and encouraging employers to invest in their employees, the government is laying the foundation for a highly skilled and competitive workforce that will drive economic growth and prosperity for years to come.
Australian businesses and governments can learn much from the strategies and reforms Singapore has instituted to create a more resilient and robust economy and workforce.
Learn more about this initiative with this news item from Channel News Asia here.
| 2023-03-02T00:00:00 |
2023/03/02
|
https://www.aigroup.com.au/education-training/centre-for-education-and-training/blog/singapore-leaders-on-driving-life-long-learning-reskilling-and-upskilling/
|
[
{
"date": "2023/03/06",
"position": 71,
"query": "government AI workforce policy"
}
] |
AI in HCM: Understanding Its Role In The Workforce
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Modernize HR Processes Using AI-Powered HCM Solutions
|
https://www.chetu.com
|
[] |
AI-driven systems have revolutionized how organizations manage their human resources, vastly improving the function and efficiency of workforce management.
|
The rise of AI technology has had a transformative impact on Human Capital Management (HCM). AI-driven systems have revolutionized how organizations manage their human resources, vastly improving the function and efficiency of workforce management.
According to Allied Market Research, the global human capital management market was valued at $21.8 billion in 2021 and is expected to reach $51.3 billion by 2031. This growth projection is attributed to the rising integration of artificial intelligence and machine learning in HCM software.
By leveraging artificial intelligence, companies can automate routine, time-consuming tasks such as payroll processing, attendance tracking, talent acquisition, and more. Modernized systems help eliminate unnecessary manual labor and allow valuable resources to be dedicated to more critical tasks.
Simplify Human Relationship Building
AI-driven HCM platforms provide insight into employee behavior, enabling businesses to identify trends and patterns in employee data that would otherwise be unobtainable. These insights help improve HR processes such as recruitment, onboarding, training programs, performance reviews, career development plans, and other activities related to managing personnel. Companies can now make more informed decisions about their workforce by analyzing all this data with the help of machine learning algorithms.
AI-powered HCM solutions have also made it easier for businesses to personalize their interactions with employees and develop tailored strategies for nurturing talent according to individual needs. Furthermore, by providing automated feedback on employee performance in real-time, AI-driven HCM systems also ensure a more equitable approach to appraising job performance which can help promote fairness in the workplace.
In addition to all these advantages, AI-based HCM platforms often incorporate comprehensive data security features that protect sensitive information related to employees from unauthorized access. This mitigates the risk of data breaches and helps companies comply with privacy regulations while providing peace of mind that confidential information is safe.
Expert-level software developers can engineer custom AI-powered HCM solutions. They can also integrate AI technologies into existing HCM systems to assist organizations with optimizing their HR processes while fostering an environment conducive to productivity and well-being among staff members. Skilled and experienced programmers can also assist software providers with enhancing their HCM software to create robust solutions that include the latest features and functionalities for their end-user clients.
Harnessing AI Technologies to Advance HCM Solutions
AI in HCM applies technologies such as machine learning (ML), natural language processing (NLP), and deep learning to automate HR processes and provide personalized experiences and actionable insights gained from HR data. These systems gather and analyze information at a constant rate to improve the accuracy and efficacy of human tasks.
The AI-powered chatbot is a prime example of the innovative AI technology that is helping streamline and expand HR service delivery. That chatbot provides a medium that enhances communication between HR teams and company staff, improving employee morale through higher levels of engagement that create more efficient and productive workflows. In this application, the chatbot provides the HR department with cost-effective solutions using interactive features that yield faster responses and resolutions to employee inquiries.
The chatbot can analyze and provide valuable insights about an organization’s personnel database as it manages staff records and data entry, generates reports, and provides predictive analysis. Additionally, it can help simplify recruiting and onboarding as it is capable of candidate outreach, developing hiring procedures, and posting content on websites and job search and social media platforms about career opportunities. This technology offers endless opportunities as it enriches the performance of the HR department as a whole and helps support the needs of employees by providing a reliable communication channel.
Cloud-based HCM solutions allow organizations to quickly deploy advanced HCM software without investing in additional hardware or infrastructure. This saves companies time and money and allows them to scale up or down according to their needs without disruption. Cloud-based systems also offer improved reliability and centralized data management, allowing HR functions to easily manage high volumes of data in one place without concern about limited storage space. HR professionals can access data from anywhere, at any time, from any device. Also, the Cloud supports automatic updates, which include any new modules or security patches. Finally, Cloud computing environments are inherently secure thanks to their multi-layered approach to security, which includes encryption technologies such as firewall configurations and two-factor authorization systems.
A Modernized Workforce
Skilled software developers can assist software and SaaS providers with team augmentation to engineer custom AI-powered HCM applications that are tailored to the specific needs of their end-user clients. Partnering with an expert-level development team can ensure these innovative solutions are deployed on time with total precision.
With its robust capabilities in automation combined with scalability benefits and improved security protocols, more companies than ever before are taking advantage of these cutting-edge, AI-powered HCM solutions for managing their personnel – proving just how essential AI-driven technology has become in today’s digital age. That’s why Chetu partners with Workday, UKG, and more to offer consulting, customization, implementation, migration and managed services for your HCM software. We know that AI promises a future where organizations are better equipped to manage their most valuable asset: their people.
| 2023-03-06T00:00:00 |
https://www.chetu.com/blogs/hcm/cultivate-your-workforce-with-ai-powered-hcm-solutions.php
|
[
{
"date": "2023/03/06",
"position": 10,
"query": "machine learning workforce"
}
] |
|
A Deep Dive Into Machine Learning Developers
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A Deep Dive Into Machine Learning Developers · DeveloperMedia
|
https://developermedia.com
|
[] |
The most popular uses for tabular data are workforce planning and resource allocation. With all of the upheaval in global supply chains due to the COVID-19 ...
|
A Deep Dive Into Machine Learning Developers
To Gather Data Is Human
We like to think we are data-driven. From large-scale government or business operations to individuals managing their budget, people justify decisions based on data. Our devices gather data to enable entities to aggregate vast amounts of data which are processed, analyzed, and correlated in order to draw conclusions. Data science (DS), machine learning (ML), and artificial intelligence (AI) all process large amounts of data to make decisions. SlashData has recently published its annual State of the Developer Nation Survey, which includes a section devoted to developers of machine learning applications and the type of data they use.
Defining Data
In this survey, data is defined according to two types: structured tabular data, and unstructured data, both of which can consist of images, video, audio and text. Structured tabular data is organized into rows and columns, and the unstructured data is, well, unstructured.
The survey further defines the types of developers using the data as either professionals or students/hobbyists, although the people in either category can belong to both (Table 1). While either category will use any type of data, 72% of students and 68% of professionals are focused on image classification; 38% of students and 32% of professionals are focused on natural language processing. Key applications in the visual category involve augmented and virtual reality development in games, medical imaging, and facial recognition for intelligence and security applications.
Table 1. The professional/hobbyist/student mix in the ML/AI/DS ecosystem.
Source: SlashData™.
Unstructured text data is the most popular data among ML developers, and tabular data is a close second (Table 2). The most popular uses for tabular data are workforce planning and resource allocation. With all of the upheaval in global supply chains due to the COVID-19 pandemic, more effective planning tools will depend on AI and ML to smooth out inconsistencies in supply and demand.
The survey also found that audio data was most often combined with images or video or text to enhance the data provided by these formats. Many professionals rely on more than one data format in their projects.
Table 2. The types of data ML/AI/DS developers work with.
Source: SlashData™
Size Matters
“Big Data” has been a buzzphrase for a few years now, but the SlashData survey found that developers in the ML realm are often not using extremely large datasets. Tabular data, as expressed in the number of rows, is typically in the range of 1,000 to 20,000 rows for up to 25% of those developers surveyed. Depending on the application, this can stretch to 22% of developers working in the realm of 20,000 – 50,000 rows of data, and 21% working with 50,000+ rows of data.
The amount of data under analysis has several implications related to the size of the enterprise. Simply put, an enterprise engaged in Big Data analysis has to be adequately resourced with infrastructure to generate, store, process and analyze the datasets. For non-tabular data, the survey found that 18% of those surveyed used image datasets typically 50-500MB in file size, while only 8% were larger than 1TB in size.
Another consideration associated with datasets or file size is the processing power and hardware required to manipulate the data. The survey found that, depending on the file sizes associated with the datasets, 26% of the developers using text data and 41% of those using video data would require specialized hardware to handle their data, limiting the development opportunities as the size of Big Data increased.
What to Say to Developers
The SlashData survey provides several insights around machine learning developers and the data they use. Unstructured data is most prevalent in machine learning applications, and most of the work being done in this area is completed by professionals as opposed to students or hobbyists, even though ML, AI, and DS are some of the hottest fields in software development — particularly given the growth of cybercrime and the need for cybersecurity. Today, in its present state, the data they are using does not necessarily involve big datasets due to the associated resources needed to process the data. Keep this in mind when providing resources to developers.
Resources
| 2021-09-15T00:00:00 |
2021/09/15
|
https://developermedia.com/machine-learning-developers
|
[
{
"date": "2023/03/06",
"position": 90,
"query": "machine learning workforce"
}
] |
CVS Pharmacy Union - The United Food & ...
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CVS Pharmacy Union - The United Food & Commercial Workers International Union
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https://www.ufcw.org
|
[] |
UFCW is the union for CVS Pharmacy workers. The United Food and Commercial Workers Union represents CVS workers all across the country.
|
UFCW is the union for CVS Pharmacy workers.
The United Food and Commercial Workers Union represents CVS workers all across the country, and it’s our job to help you and your coworkers come together and make CVS a better place to work.
We know firsthand that CVS workers have always been essential. Whether you’re stocking shelves, working the register, filling prescriptions, or giving customers vaccines, you deserve respect from your employer. We believe everyone at CVS deserves stable schedules, living wages, and safe workplaces — no matter what store you’re in!
Want to learn how to start a union at your CVS?
Ready to negotiate together for bigger paychecks, stronger benefits and better lives? If you are, the steps to joining the UFCW are simple.
Hear from CVS worker Pedro Rodriguez Quote CVS has the means to do the right thing and give us more hours to do whatever they’re asking us to do. You know, it’s just unreasonable to do all this work with the amount of hours allocated. No one can do it…CVS, do the right thing. Give us a fair contract. It is needed to keep our family safe, your customers safe… Pedro Rodriguez, UFCW Local 770
Frequently Asked Questions
Is CVS union?
Yes and no. Some CVS stores unfortunately do not have a union contract. UFCW believes that all CVS workers deserve the same guaranteed benefits that come with having a union! That’s why we’re here to help you and your coworkers organize and make every job at CVS better. Every CVS store that starts a union strengthens your ability to fight for fair contracts. Use the form below to contact one of our organizers to learn more about getting a union at your CVS.
What are the benefits of having a union at my store?
With a union, you and your coworkers get to come together and decide what benefits you want at CVS! You can negotiate with your company to get things such as consistent raises and schedules, affordable healthcare, and workplace safety standards — all guaranteed in an union contract. To learn more about the benefits of union representation, check our How We Make Work Better page.
How do I start a union at my CVS?
Looking for more information on how to unionize your CVS? Fill out the form below to talk to an organizer! We are here to help you make change and answer all your questions.
Start a Union at Your CVS Pharmacy
| 2023-03-06T00:00:00 |
https://www.ufcw.org/actions/campaign/cvs-pharmacy-union/
|
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{
"date": "2023/03/06",
"position": 37,
"query": "AI labor union"
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