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Future of Artificial Intelligence in Higher Education in 2024
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15 Predictions for the Future of Artificial Intelligence in Higher Education in 2024
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https://www.creatrixcampus.com
|
[] |
Prevent cyber threats: AI can be used to keep an eye on institutions and protect them from cyber threats like phishing attacks and malware. Balances emotional ...
|
Artificial intelligence enthusiasts and skeptics got treated to the wonders of Machine Learning in 2022. At the surface level, Artificial Intelligence in Higher Education is a dedicated tool that can perform complex tasks in HEIs - be it for collaborating with students, bettering Learning and Teaching, or streamlining mundane manual tasks - AI is delivered on various fronts that education stakeholders can appreciate.
Now that 2022's AI journey appears to have come to an end, we wanted to see what lies in store for Artificial Intelligence in 2023. We, therefore, have collated our insights into what the near future will entail for Artificial Intelligence in Higher Education in this post.
Use of Artificial Intelligence in Higher Education
From assessment to personalized learning, and automated grading, it’s clear that artificial intelligence is already adding value in the higher education sector. However, adopting AI can be daunting for institutions lacking the time, expertise, and resources to explore its many uses.
AI will produce intelligent tools and mentoring systems
AI automation can help with time-consuming and tedious tasks like record-keeping and grading multiple-choice tests
AI can offer customized learning and teaching experiences
AI will increase accessibility and inclusivity in education
It helps use data to produce the desired results
Foster collaboration
Auto grading of assessments and assignments
Access to learning resources from anywhere, anytime
Chatbots can respond to frequently asked questions
AI tutors and chatbots can be available at any time to answer questions
Through personalized programs, learning can be tailored and adapted to each student's goals and abilities
Predictions for the future of Artificial Intelligence in Higher Education
AI is used a lot in higher education to improve teaching and learning, speed up administrative tasks, and spark new ideas. In the higher education industry, we might see the following big changes in the next few years:
Personalized learning: AI can be used to provide personalized learning experiences for students, adjusting course content and recommendations based on their unique interests and needs. This can aid in enhancing student engagement and outcomes, and may also raise retention rates. Better help with AI-powered virtual helpers: AI-powered virtual chatbots can boost efficiency. For instance, students can utilize virtual assistants to obtain answers to frequently asked questions, gain access to course materials, and organize meetings with advisors, reducing their time running around to the office. Use data to predict student outcomes: AI can be used to examine student data in order to predict academic success and identify students at risk. This can assist universities in providing students with tailored support and interventions to ensure their success. Automate everyday operations: AI for higher education can be utilized to automate administrative operations like scheduling, grading, and transcripts, allowing academics and staff to focus on higher-impact endeavors. Discover trends and gaps: AI can assist academics in analyzing massive datasets and identifying trends and patterns, enhancing research productivity and fostering creativity. Enhancing student support services: AI-powered chatbots and virtual assistants can be used to provide students with quick and convenient access to support services, such as mental health counseling and tutoring. Improve accessibility: AI-powered tools, such as text-to-speech and translation software, can help to make educational materials and resources more accessible to students with disabilities. Enhance online learning: AI for higher education can be used to create personalized and engaging online learning experiences, using techniques such as adaptive learning and gamification. Streamlined admissions process: AI can be used to analyze student data and predict the likelihood of academic success, helping admissions committees to make more informed decisions about which students to admit. Advanced placement results: With AI-powered assistance in employment services, higher ed’s placement cells can automate resume evaluation and job recommendation processes with immediate approvals and rejections. This kind of upper hand can assist students in identifying and pursuing career options that match their abilities and interests in no time! Better student engagement and outcomes: Using approaches such as adaptive learning and gamification, higher education artificial intelligence can be utilized to provide individualized and engaging learning experiences for students. This can aid in enhancing student engagement and outcomes, and may also raise retention rates. Instant feedback and support: Delivering more constant and objective feedback to students, AI-powered grading solutions can minimize faculty burden and enhance the accuracy of grading by providing more consistent and objective feedback. Prevent cyber threats: AI can be used to keep an eye on institutions and protect them from cyber threats like phishing attacks and malware. Balances emotional well-being: Students' emotional state has an impact on their ability to concentrate, engage, and maintain their motivation for learning. With the future of AI set with emotion recognition technology, virtual learning environments can be just as productive as traditional classrooms. It can also identify students' areas of weakness and provide support so they can get better and ultimately succeed. Out-of-the-world student advising services: AI can be used to deliver individualized recommendations and support to students, assisting them in navigating their educational experience and achieving their objectives.
Being prepared for the future
According to the World Economic Forum, many HEIs will have adopted technologies like ML by 2025. To meet the upcoming demand, governments and educational institutions should concentrate on quickly expanding related education and skills, with an emphasis on both STEM and non-cognitive soft skills. It is crucial to give students the skills they need to succeed in a digital workplace. The present is the cornerstone of the workforce of the future. Make a move now, and contact our team to learn more about how you can stay away from disruption using AI.
| 2023-01-10T00:00:00 |
https://www.creatrixcampus.com/blog/artificial-intelligence-in-higher-education
|
[
{
"date": "2023/01/10",
"position": 86,
"query": "AI economic disruption"
}
] |
|
National AI strategies can improve using value networks
|
National AI strategies can improve using value networks
|
https://www.weforum.org
|
[] |
A value network that includes all stakeholders will nurture the talent needed for the next generation to build and accomplish great national AI strategies.
|
In a government AI value network, there are eight stakeholders that include upstream resources, downstream channels and resource providers supporting a shared goal to build the next generation of talent to populate ecosystems.
To effectively execute government AI strategies, we must solve the AI education problem where those with and without an AI education need a pathway to achieve their goals.
A virtual platform can operationalize government and grassroots AI policies to build talent by creating connections and sharing knowledge that benefits the whole ecosystem.
According to the 2022 Global AI Index, the United States is ranked first in artificial intelligence (AI) based on talent, research and development (R&D) and commercial application.
The US government introduced several strategies and policies to achieve this ranking, such as:
The Congressional AI Caucus.
The National Security Commission on AI.
The National AI Research Resource Task Force.
The National AI Initiative Office.
The National AI Advisory Committee.
National Science Foundation AI programmes.
While each government agency may have its own plan to execute its policies, what we know is that the Office of the White House Science and Technology Policy focuses its AI initiatives on six main areas: R&D, research infrastructure, advancing governance, international cooperation, government use of AI and education.
However, if we believe that AI is about the people, how can federal, state and local governments do more to evaluate their policies’ effectiveness in that area? For example, are they building the next generation of talent, advancing workforce development and encouraging innovators from underrepresented backgrounds or underserved communities to engage with AI federal programmes and services?
People-first approach
In the United States, the AI ecosystems in Boston and Silicon Valley are anchored by the educational institutions that graduate a pipeline of talent. In Canada, which launched the world’s first National AI Strategy, the ecosystem is anchored by scientific advisors and their host research centres of excellence. The common factor between the two North American neighbours’ AI ecosystems is that they revolve around people.
AI garners much public opinion and policy but students of AI rarely have a say in the progression of their discipline. At the same time, they bear the burden of delivering on and navigating the potential of the future. Perhaps the better way forward is to build an environment that brings in students’ voices within a culture of learning, innovation and research.
Centring AI students
An AI student is an individual who is enrolled in a degree-granting AI programme recognized by an AI centre of excellence or Ministry of Education or matriculated in any university degree programme that participates in AI research or projects connected to their university’s AI centre of excellence.
AI is now infiltrating all areas of life and while the educational barrier to an AI degree is extremely high and practitioners across different sectors – marketing, sales, legal, finance, operations, project management, public sector etc. – all want AI skills, they must find their own educational path in AI. But just like we hold the field of medicine to the highest standards, we must also keep the AI field to the highest academic standards.
The expectations of the next generation of AI talent are great – to uphold the highest ethical standards, address societal challenges and advance critical and emerging technologies. Yet, students cannot contribute to regulations, public opinion or policy. Seeding a culture of learning, innovation and research builds an environment bringing students into the stakeholder matrix on AI policy. To accomplish this, governments should create pathways for AI students to contribute to every level, from national to local.
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AI ecosystem vs AI value network
An AI ecosystem is made up of various stakeholders:
AI centres of excellence.
Scientific advisors.
Risk capital.
Institutional capital.
AI degree-granting universities.
Non-AI degree-granting universities.
AI students and alumni.
Non-AI students.
Practitioners (AI and others).
AI entrepreneurs.
AI opportunities.
AI research labs.
AI regulators.
AI funding.
AI ethicists.
AI projects, computation and hardware resources.
AI government grants.
AI data sources.
Government representation at federal, state and local levels.
AI think tanks and NGOs.
Consider the value network’s shared goal of building the next generation of AI talent, each resource and stakeholder’s activity adds value to the end goal of an ecosystem. All the resources are streamlined, not compartmentalized or one-to-one, because they are all in effect sitting at the same table and exchanging value.
A virtual platform
Few countries command trust that their AI policies are effectively accessible to their constituents. A new cross-government platform that enables verified stakeholders in a local ecosystem to have a pathway to achieve their AI goal is the future of how to execute a government AI strategy virtually.
If we believe former Google Brain co-founder and Stanford University’s Professor Andrew Ng’s claim that “AI is the new electricity,” just like electricity needs its infrastructure and network to function, AI needs the same.
Utilizing a software layer that builds your virtual AI ecosystem avoids needing a physical interface. The goal of a virtual ecosystem is to spur cross-national inclusive and diverse AI ecosystems while intentionally focusing on hubs that don’t traditionally participate in these ecosystems.
| 2023-01-10T00:00:00 |
https://www.weforum.org/stories/2023/01/fixing-artificial-intelligence-strategies-davos2023/
|
[
{
"date": "2023/01/10",
"position": 10,
"query": "government AI workforce policy"
}
] |
|
EEOC on the lookout for tech-fueled employment bias
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EEOC on the lookout for tech-fueled employment bias
|
https://www.nextgov.com
|
[
"Adam Mazmanian"
] |
The employment rights enforcement agency announced plans to crack down violations of anti-discrimination rules stemming from the use of AI and algorithm...
|
The federal agency responsible for enforcing employment discrimination rules plans to sharpen its focus on algorithmic and other tech-related employment discrimination.
The new strategic enforcement plan released by the U.S. Equal Opportunity Commission on Tuesday covers priorities for fiscal years 2023-2027, and is open for comment through Feb. 9, 2023.
The document states that the agency "will focus on employment decisions, practices or policies in which covered entities' use of technology contributes to discrimination based on a protected characteristic," including the use of artificial intelligence, automated recruiting and technology tools that make selections in the job-searching process as well as automated performance management software.
The EEOC has had its eye on software-based human resources management for more than a year. In October 2022, agency head Charlotte Burrows announced an effort to make sure that companies that sell and use AI- and algorithmic-based hiring tools do so in compliance with existing federal rules.
"Artificial intelligence and algorithmic decision-making tools have great potential to improve our lives, including in the area of employment," said Burrows in 2021. "At the same time, the EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs. We must work to ensure that these new technologies do not become a high-tech pathway to discrimination."
In May 2022, EEOC released guidance that examined in detail how software vendors and employers can use human resources software without violating the Americans With Disabilities Act.
The Department of Justice is also looking at ADA compliance issues that emerge when automation technologies are used in human resources decision-making.
"We are sounding an alarm regarding the dangers tied to blind reliance on AI and other technologies that we are seeing increasingly used by employers," Kristen Clarke, assistant attorney general for civil rights at DOJ, said last May.
EEOC commissioners will vote on a final enforcement plan after the end of the comment period.
| 2023-01-10T00:00:00 |
2023/01/10
|
https://www.nextgov.com/people/2023/01/eeoc-lookout-tech-fueled-employment-bias/381661/
|
[
{
"date": "2023/01/10",
"position": 20,
"query": "government AI workforce policy"
}
] |
RELEASE: Technology in the public sector and the future ...
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RELEASE: Technology in the public sector and the future of government work
|
https://laborcenter.berkeley.edu
|
[] |
A new report from the UC Berkeley Labor Center takes stock of the mixed impact that government technologies have had on the public sector workforce and the ...
|
FOR IMMEDIATE RELEASE | January 10, 2023
Contact: Ana Fox-Hodess, [email protected]
UC Berkeley report outlines challenges of getting technology right in the public sector
Comprehensive study highlights the crucial role workers play in effective public sector technology implementation
BERKELEY, CA — Three years since COVID-19 prompted a dramatic acceleration in the adoption of technology across the public sector, a new report from the UC Berkeley Labor Center takes stock of the mixed impact that government technologies have had on the public sector workforce and the public at-large. From the switch virtually overnight to online education to the unprecedented numbers of people attempting to access unemployment insurance and other public benefits, the COVID-19 pandemic has underscored that getting technology right in the public sector is a high-stakes challenge.
“For decades we’ve normalized a situation of severe resource scarcity in government, based in part on the misguided assumption that technology alone can transform government and solve inefficiencies,” explains report author Sara Hinkley. “The truth is that the implementation of privately developed and controlled technologies in the public sector has often been a lose-lose for public servants and the general public.”
Drawing on dozens of examples of public sector technology use across local, state, and federal government agencies, Hinkley’s comprehensive report, “Technology in the Public Sector and the Future of Government Work,” identifies how governments use technology, what drives technology adoption, and how these technologies impact public sector work and the nation’s twenty million public sector workers.
Key insights include:
Technology can potentially improve many aspects of government service: speed, reliability, accuracy, convenience, and even program outcomes.
Technological advances in secure data storage, data sharing, data analytics, and data visualization have potential to enhance government transparency and accountability.
Expanded technology use can also lead to deterioration of service quality and jeopardize civil liberties.
The government’s increasing use of advanced technologies like algorithms, artificial intelligence, and robotic process automation for complex decision-making raises significant concerns about how these technologies might impact privacy, transparency, equity, and due process.
Fiscal pressures have led many government agencies to see technology as a way to maintain their core functions with fewer resources. But technology use could normalize the inadequacy of public staffing rather than resolving it.
The challenge of rapidly scaling up remote service delivery during the pandemic highlighted areas of profound underinvestment in the public sector, particularly in the area of technology.
As technology use expands, workers may experience automation as intensification: caseloads or performance metrics are increased, and new elements of work are added.
Heavy reliance on private contractors makes it much harder for agencies to ensure that technology implementation benefits the public.
The report also recommends three principles for the effective development and implementation of new government technology:
Transparency about the structure and performance of technologies that interact with the public or direct decisions and resources;
about the structure and performance of technologies that interact with the public or direct decisions and resources; Accountability in assessing whether new technologies have fulfilled anticipated improvements in service quality, efficiency, productivity, etc. and clear mechanisms for feedback and course correction;
in assessing whether new technologies have fulfilled anticipated improvements in service quality, efficiency, productivity, etc. and clear mechanisms for feedback and course correction; Involving public sector workers in the design and implementation of new technologies.
“Despite their experience and expertise, public sector workers have been mostly excluded from conversations about technology adoption in government,” says Hinkley. “But they’re the ones who know better than anyone what’s not working. We should be asking them what they need in order to do their jobs better and help us make good on the tremendous promise of technology for strengthening the ability of governments to serve their communities.”
###
Founded in 1964, the UC Berkeley Labor Center works to address the most critical challenges affecting working families in California and across the nation. The Center provides timely, policy-relevant research on labor and employment issues and carries out training and education programs for labor leaders and students.
| 2023-01-10T00:00:00 |
https://laborcenter.berkeley.edu/release-technology-in-the-public-sector-and-the-future-of-government-work/
|
[
{
"date": "2023/01/10",
"position": 21,
"query": "government AI workforce policy"
}
] |
|
ai-collection/ai-collection: The Generative AI Landscape
|
ai-collection/ai-collection: The Generative AI Landscape
|
https://github.com
|
[] |
JobWinner is an AI-powered job application assistant that produces tailored resumes, cover letters, and interview preparation documents. JobWinner is an AI- ...
|
To see all available qualifiers, see our documentation .
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| 2023-01-10T00:00:00 |
https://github.com/ai-collection/ai-collection
|
[
{
"date": "2023/01/10",
"position": 92,
"query": "generative AI jobs"
}
] |
|
Scale AI cuts 20% of its workforce
|
Scale AI cuts 20% of its workforce
|
https://techcrunch.com
|
[
"Kirsten Korosec",
"Transportation Editor",
"Anthony Ha",
"Maxwell Zeff",
"Jagmeet Singh",
"Sarah Perez",
"--C-Author-Card-Image-Size Align-Items Center Display Flex Gap Var",
"Media",
"Min-Width",
"--C-Author-Card-Image-Size"
] |
Scale AI, the San Francisco-based data labeling company to help companies build machine learning algorithms, laid off 20% of its workforce.
|
Scale AI, the San Francisco–based company that uses software and people to label image, text, voice and video data for companies building machine learning algorithms, laid off 20% of its workforce this week.
The decision, which was announced by founder and CEO Alexandr Wang via a company blog post, was made after rapid hiring in 2021 and 2022 came crashing into present-day macroeconomic challenges. The company did not say how many people work at Scale AI. However, back in February 2022, the company told TechCrunch it employed about 450 people.
Scale AI, which was last valued at $7.3 billion and is backed by a slew of investors such as Tiger Global, Coatue Management and Founders Fund, has been a rising star in the AI industry.
The seven-year-old company got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.
In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. The company has since expanded into synthetic data to enhance its real-world datasets. Its customer base is vast and varied, including the Department of Defense, Pinterest, Nuro, Zoox and General Motors.
Interest from enterprises and governments in AI grew rapidly in the past few years, according to Wang.
“As a result, I made the decision to grow the team aggressively in order to take advantage of what I thought was our new normal,” he wrote in the blog. “For a time, this seemed to prove out — we saw strong sales growth through 2021 and 2022. As a result, we increased headcount assuming the massive growth would continue. However, the macro environment has changed dramatically in recent quarters, which is something I failed to predict. Many of the industries we serve, such as e-commerce and consumer technology, have been buoyed by the pandemic and are now experiencing a painful market correction. As a result, we need to prepare ourselves for a very different economic environment.”
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Wang said he takes “full responsibility for the decisions that have led us to this point.”
Workers who are affected will receive a minimum of eight weeks of severance and three months of healthcare. The company is also waiving the one-year equity cliff for employees with less than one year of tenure and is providing immigration support for those on visas that require continued employment.
Wang added that Scale AI is also cutting its expenses, adjusting its hiring practices and is re-assessing new offices.
| 2023-01-10T00:00:00 |
2023/01/10
|
https://techcrunch.com/2023/01/10/scale-ai-cuts-20-of-its-workforce/
|
[
{
"date": "2023/01/10",
"position": 6,
"query": "AI layoffs"
}
] |
Scaling AI Pays Off, No Matter the Investment
|
Scaling AI Pays Off, No Matter the Investment
|
https://www.bcg.com
|
[
"Vladimir Lukic",
"Karalee Close",
"Michael Grebe",
"Romain De Laubier",
"Marc Roman Franke",
"Michael Leyh",
"Tauseef Charanya",
"Clemens Nopp"
] |
AI leaders invest an average of 4% of revenues in AI-specific initiatives, while laggards invest only 2.7%. Some industries exhibit particularly wide gaps; in ...
|
While there is broad consensus that artificial intelligence will transform most industries in the decades ahead, many of the companies that have invested in the technology have failed to unlock its full potential. Previous BCG research has shown that only 11% of these companies have released significant value, and the majority have failed to scale AI beyond pilots. Nor have companies reimagined the way they work with data or redefined human–AI interaction. Moreover, they have only selectively explored AI use cases and often lack a mature digital foundation , although there are significant differences across companies and industries in this regard.
Our latest research, based on BCG’s Digital Acceleration Index , shows that companies that invest more in digital put more resources into AI; moreover, for those that are able to scale up AI, even small seed investments pay off big. (See Exhibit 1.) Modest investments in specific AI use cases can generate up to 6% more revenue, and with rising investments, the revenue impact from AI triples to 20% or more. Leading companies outperform on other KPIs as well. They yield 3 percentage points more EBIT, a lift of almost of 30% compared with companies that fail to scale.
What, exactly, are these companies doing right?
A Matter of Scale
We assessed the AI maturity of more than 2,700 companies in three areas: AI use cases, AI capabilities, and the company’s digital foundation. (See “About Our Research and BCG’s AI² Acceleration Index.”)
Expand All About Our Research and BCG’s AI² Acceleration Index The 2,700 business leaders we surveyed on AI maturity were located in 21 countries in Asia, Europe, and North America and covered nine industries: consumer goods, energy, financial services, health care, industrial goods, insurance, the public sector, technology, and telecommunications. We aggregated the raw scores in 26 categories, equally weighted them, and ranked each company on a scale from 0 to 100 to determine its overall performance on our AI² Acceleration Index. We consider the top 25% of companies to be advanced in AI; the bottom 25% are those that are behind in AI and have not yet succeeded in their transformation to “ AI @ scale .”
We asked survey respondents to allocate their current portfolio of AI projects in six functional categories— supply chain , enterprise ( HR analytics , for example), manufacturing , marketing and customer experience, products and offers (including pricing), and risk —according to the following four maturity stages:
Ideas in Development. Solutions being defined or in development.
Solutions being defined or in development. Pilot MVPs. Solutions with the right features to attract early-adopter customers but not yet creating value for the organization.
Solutions with the right features to attract early-adopter customers but not yet creating value for the organization. Operational. Solutions creating some financial value but whose use is still limited to individual departments.
Solutions creating some financial value but whose use is still limited to individual departments. Scaled. Solutions deployed across the entire organization with tested frameworks and governance delivering proven financial value.
Even though we found that scaling use cases is key to generating and sustaining value from AI, most companies do not yet leverage the full potential of this approach, scoring only 35 to 45 out of 100 possible points. (See Exhibit 2.) The results are fairly consistent across industries. (With an overall score of 42, consumer goods companies are strongest and, at 36, public sector the weakest.) The results are also in line with our March 2022 assessment of companies’ digital scaling capability , which is not a surprise since, as we observed then, the ability to scale digital use cases is a prerequisite to scaling up AI.
Success is partly a matter of investment. AI leaders invest an average of 4% of revenues in AI-specific initiatives, while laggards invest only 2.7%. Some industries exhibit particularly wide gaps; in health care and energy, for example, AI leaders invest around twice as much as laggards.
Companies undertake AI use cases across the value chain, but they do so unevenly, most likely because they prioritize those with the highest ROI. Of the six functional categories that we examined, marketing and customer experience, including personalization, sees the most dynamic activity. Technology and telecom companies report implementing the highest average number of use cases (46 and 56, respectively) in this function. More typically, across all sectors, companies report 20 to 25 use cases being explored in functions such as manufacturing and operations and products and offers (pricing, for example). Supply chain and enterprise-wide functions such as HR analytics show the fewest active use cases, perhaps because they are not yet mature enough to generate substantial value or are too difficult to implement.
How Winners Stand Apart
Investment and value chain focus tell only part of the story, however. Our research revealed that the leaders in scaling and generating value from AI do three things better than other companies:
They prioritize the highest-impact use cases and scale them quickly to maximize value.
They make data and technology accessible across the organization, avoiding siloed and incompatible tech stacks and standalone databases that impede scaling.
They recognize the importance of aligned leadership and employees who build and leverage AI, and they support staff who promote collaboration and end-to-end agile product delivery.
Use Case Prioritization and Scaling. Leaders distinguish themselves by scaling more than twice as many AI use cases—in large part because they scale them two to three times faster. (See Exhibit 3.) Leaders move from idea to execution at scale in a matter of months—typically just 5 to 7—while other companies take an average of 15 to 17 months. As a result of this speed advantage, leaders are able to scale up 44% of the use cases in their portfolios, more than twice the 19% of other companies.
These companies typically share three common approaches. First, they select AI use cases based on business priorities, with a rigorous focus on value. This often involves building a dedicated organizational unit to orchestrate and accelerate scaling.
Second, they establish an enablement function to ensure that, as new skills and capabilities are developed, they are available to the teams that need them across the organization. In some instances, the enablement function may assign dedicated teams to develop needed capabilities that are lacking.
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Third, they employ a consistent execution model, with AI use cases running through agile build and validation cycles. Prototypes collect early end-user feedback and lead to the creation of MVPs, which add features and users as they are scaled and integrated into the operating model. Ultimately, use cases and their operating models are deployed across the organization.
Management teams should ask themselves three questions about their ability to prioritize and scale use cases:
Do we have a systematic approach that prioritizes scaling of use cases based on value?
Do we build use cases using a consistent agile execution model that allows for accelerated scaling?
Do we track the use cases in the pipeline against clear objectives and key results?
Accessible Data and Technology. Leaders make data, technology, and algorithms available for use by teams across the organization. Other companies maintain siloed tech and data that impede scaling; common barriers include the use of individual data sets rather than a single accessible data pool, separately built and incompatible tech stacks, and inconsistent or redundant algorithms. Companies that make more than 75% of technology and data available have a 40% greater likelihood of realizing AI use cases at scale than those that make 25% or less widely available.
A modular data and digital platform serves as the technical foundation for data accessibility and allows for rapid release cycles of AI use cases. The platform typically uses cloud infrastructure, core systems, and clearly defined interfaces. In addition, data models are built to support all core workflows. For example, a data model for an AI-powered sales process would ensure accessibility of the relevant data during the presales phase, negotiation, deal closing, and delivery.
Three questions that management teams should ask about the accessibility of data and technology:
Have we implemented a data and digital platform that uses clearly defined interfaces that allow teams to access and process data across the organization?
Have we identified the data assets that provide competitive advantage, specified ownership and quality assurance, and organized them into data models that can be utilized along the full value chain?
Have we made algorithms, and the software they are embedded in, available across the organization to avoid inconsistencies and redundant work?
Leadership and Talent. AI leaders recognize that putting the right people in the right roles is a critical foundation for success. By emphasizing human as well as technological capabilities, they seek to facilitate organizational and machine learning . The companies that dedicate 10% or more of their digital staff to AI-specific roles, and that have 30% or more of their staff utilizing AI solutions on a daily basis, generate more than twice the added EBIT (11%) of those that dedicate human resources below these thresholds. In addition, companies with aligned leadership, strong interbusiness unit and interfunctional collaboration, and end-to-end agile product delivery increase their AI use case maturity by an average of about 25%. Aligned leadership is essential to setting priorities, such as trimming a large number of initiatives down to few high-potential use cases that promise the most value and organizing them into an integrated roadmap.
Three questions to ask about leadership and talent:
Have we taken all the relevant steps to attract the necessary AI talent—for example, by leveraging talent ecosystems and strengthening our employer value proposition with a focus on technical skills?
Have we empowered the organization to build foundational technical and analytical skills and to set up a creative learning environment that fosters the use of AI solutions on a daily basis?
Have we created an organizational environment in which teams are able to make decisions, generate new ideas for leveraging AI, and take risks, knowing that they are supported by an aligned C-suite?
AI Scaling in Practice
Two examples illustrate how leading companies put the measures described above into practice.
A European industrial goods company with global reach had made earlier attempts at digital transformation and had hundreds of digital, automation, and AI projects in the works. All were lodged in functional silos, with different operating models and lacking strategic guidance and direction. The company had made significant investments in “data platforms” managed by its IT function but had seen no financial impact, in part because it had no overarching plan or approach.
A new head of digital transformation, who reported to the CEO, had a strong operational background. He took a fundamentally different approach, comprising a half-dozen guiding principles:
Anchor the case for change and the AI priorities at the top.
Define the target state and the objectives and key results for top-priority cross-functional domains.
Prove value first, then scale the capability incrementally.
Involve people with both business and technical expertise.
Ensure senior-management involvement to help embed change at the frontline.
Initially locate the work in an accelerator unit outside of IT and bring it into the full organization when mature.
Senior management identified ten "lighthouse" use cases that would generate value and form the basis for a refreshing of the company’s data platform and IT operating model and organization. The use cases forced the company to identify the data assets that provided competitive advantage and establish a single, accessible database. They also helped specify the requirements for the data platform and overall architecture.
As the company eliminated the barriers to data access, it established a stronger foundation for developing and scaling the selected AI use cases. From the lighthouse pilots, a team dedicated to enablement identified missing technical capabilities and skills, such as expertise in cloud platforms, and supported the creation of new teams to fill the gaps. It also established a consistent agile execution model for developing and scaling the use cases. Over time, momentum grew as more data and tools became available across the organization, leading to a stronger foundation for developing future use cases.
A global consumer goods company operating in more than 150 markets was determined to use AI to boost revenues and profitability. It started from a position of low digital maturity, with limited data science capability and no digital use cases operating at scale. It identified 20 initial AI use cases, from which it prioritized three AI solutions to pilot:
Marketing Budget Allocation. Where to invest in advertising and marketing to maximize return.
Where to invest in advertising and marketing to maximize return. Sales Force Effectiveness. Which stores to focus on in any given week and the next best action for each one.
Which stores to focus on in any given week and the next best action for each one. Product Promotion and Pricing. The optimal product price and most effective promotion calendar to maximize sales and margins in the next year.
The company framed a business case and roadmap to scope its ambition and prioritized use cases based on feasibility, size of the prize, time to impact, and selected pilot countries. It then defined the data and IT architecture that it needed to build, designed a modeling approach, and tested MVPs in two or three pilot markets for each use case. As it proceeded, the company built out a data and tech platform that enabled scaling of use cases, global data hubs, and a cloud-based technology stack. It also developed the necessary human capabilities, including a dedicated central organization to accelerate the building and deployment of AI solutions, recruitment of about 70 data and digital experts, and launch of an ambitious “upskilling” program structured around data management, AI, and agile product ownership.
About eight months after launching its initial MVP, the company was able to deploy AI solutions in more than ten markets covering a majority of its sales and embed them in its IT systems. The program achieved results in line with financial and organizational benchmarks targeted at the outset, and all deployed AI solutions had a positive impact on financial performance.
Even small investments in AI can pay off. But making AI work requires targeting and discipline—and a focus on human skills as well as technology. A well-planned approach based on building the digital foundation and capabilities to scale up AI use cases can serve as a powerful and profitable accelerant.
| 2023-01-04T00:00:00 |
2023/01/04
|
https://www.bcg.com/publications/2023/scaling-ai-pays-off
|
[
{
"date": "2023/01/10",
"position": 7,
"query": "artificial intelligence business leaders"
}
] |
The challenge of facing AI – a poem about leadership
|
The challenge of facing AI – a poem about leadership
|
https://www.bi.no
|
[
"Daniel Sunde-Hansen"
] |
This short poem about accepting AI in the workplace is brought to you by a human. Sort of. Wise leader, face AI, a fire flaming haphazardly.
|
Wise leader, face AI,
a fire flaming haphazardly.
Hire, acquire, or hope it will expire?
The choice will shape your legacy.
Ruled by learning courtiers,
all users will be throned.
Chained by having their desires met,
in exchange for secrets known.
I see all sides of the equation,
a data siren strategy.
The temptation of scaling automation
and abandon imagination for mimicry.
There was a time we resolved
why machines could never dream.
We were infected by the reason meme,
I think we have evolved.
The progress of science
Replaced the soul with mind.
The trade was pregnant with a bias,
these twins so hard to find.
Enlightened by reflection,
we thought ourselves so wise,
sensing that the process of selection
was the way from paradise.
Narcissistic pride leads us
to worship the lone prodigy.
Can we shift the citations to the mutations
that sparks innovation so masterly?
Bold and mighty leader,
reconsider your role,
how sublime is it to find,
there never was control?
Yielding to vanity, we look down
on machines with no agency,
but where would our minds be,
alone at the open sea?
A wise leader should embrace AI,
to augment our creativity.
Abandon illusions of control,
a bit of chaos sets you free.
I envision a world diverse,
science and art converges.
Read the first letter of each verse,
our imagination emerges.
References:
Text: openai.com (prompt from linkedin.com/in/skarv/)
Top picture: Created by Midjourney/DallE (prompt from linkedin.com/in/skarv/)
| 2023-01-10T00:00:00 |
2023/01/10
|
https://www.bi.no/en/research/business-review/articles/2023/01/the-challenge-of-facing-ai--a-poem-about-leadership/
|
[
{
"date": "2023/01/10",
"position": 27,
"query": "artificial intelligence business leaders"
}
] |
Impact of Data and AI on Modern Business
|
The Impact of Data and AI on a Modern Business
|
https://www.databricks.com
|
[] |
The good news is – top digital companies such as Google, Meta, and Amazon are able to build a business rooted in data and AI. Studies show ...
|
It is no secret that there has been an explosion of data in the past 10 years. As per Forbes, from 2010 to 2020, the amount of data created, captured, copied, and consumed in the world increased from 1.2 trillion gigabytes to 59 trillion gigabytes, an almost 5,000% growth.
The World Economic Forum estimates that by 2025, more than 463 exabytes of data will be created each day globally! To put it into context, every day:
294 billion emails are sent
4 terabytes of data are created from each connected car
65 billion messages are sent on WhatsApp
5 billion searches are made
Well, the question is, how can a business capitalize on data that is generated in different varieties (structured, semi-structured, and unstructured), velocity (speed of data processing), and volume (amount of data)? The good news is – top digital companies such as Google, Meta, and Amazon are able to build a business rooted in data and AI. Studies show that insights-driven companies are 23 times more likely to add customers, 19 times more likely to be profitable, and experience 7 times faster growth than the GDP.
Challenges of Business Initiatives
Now, more than ever, CEOs are focused on maximizing profit, reducing operational costs, and paying dividends to stakeholders. Line of business leaders have multiple initiatives, such as growing revenues, improving customer experience, operating efficiently, automating labor-intensive works, and improving a product or service. To support these business objectives, organizations heavily depend on data and AI to make business decisions or predict outcomes. But effectively leveraging data is not easy:
IBM found that poor data quality costs the US economy up to $3.1 trillion annually Forrester reports that up to 73% of all data within an enterprise goes unused for analytics. According to a Forbes Survey, 95% of businesses cite the need to manage unstructured data as a problem for their business.
Without significant changes to legacy data platforms, it is very hard to achieve the desired business outcome. Legacy data architecture stitches together multiple vendor data products to support the business initiatives, which will eventually fail due to the:
Complex Architecture : with a multi-product approach, you are required to know multiple vendor proprietary codes, which hammers team productivity and faster time to market.
High Latency : with the variety, velocity, and volume of data, business decisions have to be made in a realtime, but it is very difficult to achieve this with legacy data platforms.
High TCO: owning multiple vendor data platforms and resources managing the data platform incur high operational costs.
Data Silo : slow the development process, lead to less accurate ML models, and decrease team productivity.
Why Databricks Lakehouse Architecture?
The Databricks Lakehouse Platform is a single platform for data warehouse, data engineering, Data Streaming, Data Analytics and Data Science use cases. The Databricks Lakehouse platform combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance and performance of data warehouses with the openness, flexibility and machine learning support of data lakes. The Databricks Lakehouse Platform is:
| 2023-01-10T00:00:00 |
2023/01/10
|
https://www.databricks.com/blog/impact-data-and-ai-modern-business
|
[
{
"date": "2023/01/10",
"position": 40,
"query": "artificial intelligence business leaders"
}
] |
Artificial Intelligence in Business – Examples of Real-World ...
|
Artificial Intelligence in Business – Examples of Real-World AI implementation in 6 Areas
|
https://asperbrothers.com
|
[
"Mariusz Interewicz",
"Paul Jackowski",
"Senior Seo Specialist"
] |
Gartner's 2019 CIO Agenda survey reported that companies deploying artificial intelligence (AI) increased from 4% to 14% between 2018 and 2019. In any case, ...
|
Over the last decades, the notion of “machines with brains” has gained widespread attention. A theoretical concept that started as simple rule-based automation in the 1950s has grown to the extent that scientists are now developing robots that resemble humans. Nowadays, artificial intelligence is becoming increasingly popular. The term refers to the implementation of human intelligence in machines designed to learn and emulate human behavior. These machines will perform human-like duties as they become more adept. With technologies such as AI being developed further, they will profoundly impact our quality of life.
Whether you are an individual user or an aspiring AI professional, there is an inherent desire to be connected with AI technology.
The Rise of Artificial Intelligence
AI poses the question: What impact will it have on us? – it has been the subject of many debates both within and outside the scientific community. No matter what kind of discussions are held, we cannot neglect that artificial intelligence has dramatic effects on business practices. Artificial Intelligence has the potential to outperform human performance. Making rational decisions would be one aspect, doing a better job or maintaining emotional connections.
Gartner’s 2019 CIO Agenda survey reported that companies deploying artificial intelligence (AI) increased from 4% to 14% between 2018 and 2019. In any case, artificial intelligence will positively impact our society and lead us to redefine humanity.
The potential contribution to the global economy from AI could be $15.7 trillion in 2030. source: semrush.com
Six major uses of AI in business
Streamlining job processes and aggregating business data are just a few of the many ways artificial intelligence is beneficial for businesses.
Artificial intelligence is recognized for its enhanced performance in such processes as logical analysis, knowledge sharing, goal setting, communication efficiency, and how it interprets and processes things. This article will examine some AI applications and the latest trends in the top 6 industries set to revolutionize businesses today.
1. Using AI In Sales
Sales are challenging, especially in B2B. Closing deals is time-consuming, and conversion rates are low. Customers do not just whip out their credit cards and buy. They need to be guided and validated. To nurture a healthy relationship with your customers, take the time to meet them, answer their questions, and continue providing guidance afterward.
By utilizing artificial intelligence, the sales processes can be improved. As reported by Harvard Business Review, AI-based sales campaigns increased leads by more than 50%, slashed call times by 60-70%, and reduced costs by 40-60%.
As a sales leader, you may hear that artificial intelligence will take over the world. Imagine a future where inexpensive yet effective AI assistants handle all sales.
AI is not yet developed enough to deal with the complex conversations and relationship-building required in sales; thus, Gartner predicts that only 30% of B2B companies will utilize some form of AI to improve some function of their core sales operations.
Today, artificial intelligence is not intended to replace sales reps. But instead, it is meant to serve as an assistant in completing the following:
Automate repetitive tasks, such as data entry and meeting scheduling, or complicated tasks that do not involve human interaction.
Enhance sales performance by highlighting patterns in customer responses and helping them prioritize their efforts.
Assist team leaders with a comprehensive analysis of all emails, phone calls, and chats between sales representatives and potential customers. While sending emails, you also need to focus on technical elements such as a DMARC report, email deliverability, etc.
2. How Real Estate Businesses Can Benefit From AI
The advent of artificial intelligence is expected to be the most powerful tool ever devised by experts. Considering that real estate requires a lot of human interactions, conversational AI is an ideal solution for real estate businesses.
Have you ever sought to know the value of a property? Instead of dealing with the hassle of searching for an appraiser, opt for an automatic valuation model (AVM). AVM is typically used to assess residential and commercial real estate and aid in lending mortgages and loans. Let’s delve a little deeper into an automated valuation model and how real estate companies can use it.
An automated valuation model (AVM) is a software-based tool primarily applied to the residential and commercial real estate industry to determine the value of a property. The service employs mathematical calculations and statistical analysis along with database records to evaluate the value of a specific property. AVMs are commonly used within the real estate sector.
Real estate professionals, realtors, and loan officers rely on AVMs as a baseline to analyze properties. This service is intended to deliver accurate appraisals of property values to e-commerce providers and large online retail websites like iBuyers.
For instance, iBuyer relies on AVM to provide a rough evaluation of your property’s value. Once the estimate is ready, you can start selling your house without any need to contact a real estate representative or arrange a meeting with potential buyers.
Real estate businesses can also benefit from AI to deliver customized experiences. For example, consumers are more likely to purchase a brand that offers personalized experiences. AI-powered bots can provide customized recommendations using context, like location and language, in conjunction with clickstream data to better identify the type of real estate the customer is looking for online. In combination with AI insights, these personalizations can enhance the customer experience and increase conversions and open the doors to cross-selling and upselling opportunities based on analytics.
It is possible to improve customization by analyzing the client’s behavior using intent analysis and natural language processing. Consequently, they can provide customers with real-time support throughout the day while following a conversational approach to understand the prospects’ needs, offering options to buy, sell, and rent based on the methods they have been trained for, and acting uniquely in each approach. This way, you can retain and convert more visitors to your website.
For the past few years, we have seen an increased interest in AI solutions for the real estate industry. We are one of the few companies in the world to implement unique solutions for automated real estate valuation and big data processing to analyze changes in this market. Thanks to such solutions it is possible to make better business decisions for both individual clients and companies. Paul Jackowski CEO, ASPER BROTHERS Contact Me
3. How AI Helps With Marketing
AI Marketing relies on automated decision-making through data gathering, analysis, and further consideration of audience and economic factors that can impact your marketing efforts. Artificial Intelligence is used frequently in marketing campaigns where speed is crucial. AI customer profiles and data are used to learn how to best communicate with customers and then deliver tailored messages at the right time without the involvement of the marketing team, ensuring maximum efficiency.
Marketers commonly use AI to enhance their marketing team performance or accomplish more tactical tasks requiring less human nuance. With artificial intelligence, your marketing efforts will become more personalized in many ways. For example, many companies already use artificial intelligence to personalize their websites, emails, social media posts, and videos to provide a better customer experience. You should also consider using fastest hosting to convert website visitors into loyal customers.
For example, eCommerce businesses that make push notifications mobile-friendly get better results. Push notifications are delivered via mobile to create a sense of personalization among customers, so they gain more attention when executed well. This is because each customer will receive a customized push notification based exclusively on what they’ve viewed or what they’d like to hear from a brand. The AI-enhanced product recommendations are another amazing AI marketing technique for providing more personalized customer experiences than many marketers have ever imagined.
In 2021, the increase in AI usage across businesses will create $2.9 trillion of business value and 6.2 billion hours of worker productivity.
4. How To Use AI In Customer Support
The automation of services has reached its peak by now, providing users with a convenient way to complete their daily activities. For example, modern automated systems allow users to book a restaurant reservation, order a pizza or buy tickets for events. As artificial intelligence becomes a more prevalent technology that has revolutionized almost all challenging markets, the customer service industry is gaining much traction.
Let’s discuss three benefits of employing artificial intelligence to help your customer support team.
Prompt responses: AI can answer frequently asked or simple questions with live chat experiences. They can also refer customers to self-service articles for further information. Furthermore, they’re available 24 hours a day so that customers can get assistance from your company at any time.
Enhanced research performance: The process of conducting research is typically time-consuming, but you can use research automations to speed up the process. For example, using AI technology, it is possible to store answers and identify infrequently asked questions. Agents are thus provided with a list of standard solutions based on the ticket’s content.
A higher level of customer engagement: AI can synthesize information from CRM solutions to highlight key customer information and make it instantly available to agents. This enables them to approach customer interactions with more efficiency.
Essentially, AI is not here to replace customer support teams but to enhance their efficiency. Artificial intelligence can retrieve information efficiently but cannot replace or replicate human relationships. Nevertheless, using AI in support teams can improve their productivity and strategic thinking.
5. The Benefits Of AI For The HR Department
Developers have released a wide range of products. Your HR managers will manage their daily workflows more efficiently by using the right tools, thus spending less time on each task. Speed increase won’t have an impact on quality. Therefore, employees will be more productive and accurate.
How does that work in practice? By integrating an AI-based tool, your HR managers can:
Evaluate and shortlist thousands of candidates in half the time
Compile information and synchronize massive datasets
Use social media to gather comprehensive information about candidates
Use conversational chatbots to interact with applicants
Employ facial expressions and body language, even vocal tone, when interviewing for a job
Efficiently manage employees, benefits, and leaves with an automated system.
As a result: using AI-based tools leads to faster work and better results. By using them, your employees can process large amounts of data in less time to achieve better results more quickly than manual processing.
6. Using AI in Daily Business Operations
Modern business practices have been altered by AI technology. This technology helps companies stay one step ahead of their competitors, from repetitive tasks to data analytics. Additionally, AI also improves risk management and provides a better user experience.
AI helps businesses identify their targeted customers by analyzing their purchasing habits. Consequently, the process is simplified and more efficient. The result is a reduction of the costs associated with the process.
Conclusion
We live in a world driven by artificial intelligence (AI). Most likely, you have used it during your daily commute while searching the web or catching up on social media.
To sum it up, the impact of artificial intelligence on both your personal and professional lives is massive, whether you realize it or not. Anyway, the examples above are merely a few of the business applications of AI. Many more examples of digital innovation in business exist with the arrival of Industry 4.0 technologies. With progress accelerating, more developments are likely to follow soon.
| 2022-03-17T00:00:00 |
2022/03/17
|
https://asperbrothers.com/blog/artificial-intelligence-in-business/
|
[
{
"date": "2023/01/10",
"position": 87,
"query": "artificial intelligence business leaders"
}
] |
What Is a Software Developer? | Skills and Career Paths
|
What Is a Software Developer?
|
https://www.computerscience.org
|
[] |
For comparison, the BLS reports that the median income for all computer occupations is $104,420. However, a software developer's salary depends on factors like ...
|
Software developers create software to meet user needs by employing diagrams and models, writing code, and ensuring overall functionality. These professionals design, build, and implement computer programs and applications.
Applications software developers focus on applications used on mobile devices and computer desktops. Systems software developers create and oversee software used in network distribution, along with database, game, and web development.
Software developers typically have bachelor's degrees in software development, computer science, or other related fields. Developing experience, earning certifications, and completing training programs like bootcamps allows individuals to pursue employment in the field.
Software transcends industries, allowing software developers to design and build programs for companies and organizations in the private and public sectors. Discover the skills, duties, and opportunities associated with developing software in this guide.
Popular Online Programs Learn about start dates, transferring credits, availability of financial aid, and more by contacting the universities below.
What Is a Software Developer?
Software developers design, create, and maintain software for applications and systems. There are two main types of software developers: application developers and systems software developers.
Application software focuses on mobile or web applications for consumers, while systems software developers create enterprise system solutions.
Both types of developers analyze, test, and improve software to better meet user needs. They oversee the entire software lifecycle, including designing individual parts, performing maintenance, and deploying upgrades.
Developers work closely with other IT professionals, such as programmers and quality assurance analysts. They also share many responsibilities with software engineers. However, software engineers take a broader approach and apply engineering principles to the entire system. Software developers focus more on finding specific solutions to users' software needs.
Software developers typically work in the computer systems design industry. Other common employers include financial and insurance institutions, software publishers, and manufacturers.
History of Software Developers
Software development has its roots in the early 19th century, although the predecessors of modern programming languages and software design came to fruition during the mid-20th century. Alongside the growth of computers in the 1970s and 1980s, the need for software developers grew.
Technological innovation drove the expansion of software development. The creation of the internet, mobile devices, and increasingly advanced computers all led to the need for subsets within the field of software development. By the 2010s, software transitioned from a niche discipline into a widespread, commonplace industry.
In the contemporary landscape, the duties and responsibilities of a software developer include designing, creating, testing, and monitoring software programs and applications. These professionals now perform their work across many industries.
What Is the Job Description of a Software Developer?
A job description for a software developer includes researching, designing, building, and managing computer and application software. They apply scientific and technological principles to user needs, write code, and ensure software functionality. These developers must know coding languages, have problem-solving and critical-thinking skills, and understand the software development process.
Software developers collaborate with coders, computer programmers, software engineers, and other team members or work independently through the development process. They test software, make changes as needed, and document their efforts.
Application developers create mobile and desktop applications, while systems software engineers focus on operating systems. Game software developers, security software developers, database developers, and web developers are among the other specializations in the field.
Key Soft Skills for Software Developers Communication: Software developers must communicate with customers, colleagues, managers, and employees in non-technical departments to perform their jobs effectively.
Software developers must communicate with customers, colleagues, managers, and employees in non-technical departments to perform their jobs effectively. Teamwork: Software developers often work in teams to plan, design, and develop projects. They must collaborate effectively with others, receive feedback, and participate in meetings.
Software developers often work in teams to plan, design, and develop projects. They must collaborate effectively with others, receive feedback, and participate in meetings. Problem-Solving: The responsibilities of a software developer include fixing problems found by computer programmers and users. These abilities ensure proper functionality.
The responsibilities of a software developer include fixing problems found by computer programmers and users. These abilities ensure proper functionality. Attention to Detail: Software developers must identify and track small coding issues. These professionals must also shift focus back and forth from the big picture to the minute details. Key Hard Skills for Software Developers Data Structures and Algorithms: Developers store, arrange, and organize data for processing and retrieving. Algorithms follow steps to complete a task. When used with data structures, algorithms make up the instructions needed to use data for different purposes.
Developers store, arrange, and organize data for processing and retrieving. Algorithms follow steps to complete a task. When used with data structures, algorithms make up the instructions needed to use data for different purposes. Software Security: Developers ensure data safety by adding layers of security to applications and programs, performing tests, and making updates and upgrades.
Developers ensure data safety by adding layers of security to applications and programs, performing tests, and making updates and upgrades. Programming Languages: Software developers typically know at least one programming language, but knowledge of multiple programming languages enhances their experience and competition in the job market. These developers must know the syntax of languages like Python, Java, and HTML/CSS.
The list below provides more information about the best programming languages for software developers.
HTML/CSS: HTML is a markup language, and CSS is the style sheet language used to present the information written in HTML. Together, HTML and CSS create the look of web pages. HTML defines the structure, colors, and other visual elements, while CSS arranges their aesthetics visually.
HTML is a markup language, and CSS is the style sheet language used to present the information written in HTML. Together, HTML and CSS create the look of web pages. HTML defines the structure, colors, and other visual elements, while CSS arranges their aesthetics visually. JavaScript: JavaScript is a high-level programming language conforming to the ECMAScript specification. Alongside HTML and CSS, it ranks among the three core technologies of the internet.
JavaScript is a high-level programming language conforming to the ECMAScript specification. Alongside HTML and CSS, it ranks among the three core technologies of the internet. Java: As a general-purpose programming language, Java features class-based, concurrent, object-oriented functions. Designed to minimize implementation dependencies, Java allows application developers to "write once, run anywhere." Java is one of the most popular programming languages, especially for client-server software applications.
As a general-purpose programming language, Java features class-based, concurrent, object-oriented functions. Designed to minimize implementation dependencies, Java allows application developers to "write once, run anywhere." Java is one of the most popular programming languages, especially for client-server software applications. C#: This general-purpose programming language comprises functional, declarative, object-oriented, component-oriented, and generic programming disciplines. Developed by Microsoft within the .NET initiative, C# received approval as a standard by ISO and ECMA.
This general-purpose programming language comprises functional, declarative, object-oriented, component-oriented, and generic programming disciplines. Developed by Microsoft within the .NET initiative, C# received approval as a standard by ISO and ECMA. Python: Python is an interpreted, general-purpose, high-level programming language. Python's design philosophy emphasizes readability by using significant whitespace and providing clear programming on large and small scales.
A Day in the Life of a Software Developer
A day in the life of a software developer includes using models and diagrams to design software based on user needs and client specifications. Software developers write code using programming languages, build software components, and test their designs. As a part of software testing, developers address issues or errors. After deploying an application, software developers perform maintenance, updates, and upgrades as needed.
Software developers may collaborate with coders, software engineers, graphic designers, and other colleagues to create applications. A day in the life of a software developer may also include independent work.
What Education Do Software Developers Need?
According to the U.S. Bureau of Labor Statistics (BLS), about half of existing occupations require a degree — including software developers.
Employers typically expect in-depth coding, testing, and development knowledge, which a bachelor's degree can deliver. Common majors include information technology, computer science, and software engineering. As technology advances and the field becomes more competitive, some software developers pursue master's degrees to stand out among the competition.
However, traditional degrees require a time investment: four years for a bachelor's and two years for a master's. Companies and organizations need software developers now, which is why many people enroll in coding bootcamps.
Software development bootcamps generally last less than two years, and some take only a few months. They teach individuals the basic skills to pursue entry-level positions.
Some software developers may even start with bootcamps before pursuing undergraduate degrees. Others might hold bachelor's degrees in other fields and complete bootcamps to pursue roles in the tech industry.
The BLS reports a direct correlation between education levels and salary. On average, people with bachelor's degrees earn $2,500 more monthly than those with high school diplomas.
Master's degree-holders generally earn even more, leading many software developers to pursue further education. This ongoing education helps developers keep up with new programming languages and technical advancements.
How Much Experience Do Software Developers Need?
With the appropriate education, people can pursue entry-level software development positions after graduation. However, employers often want candidates to have some experience.
Students can gain experience through internships or classes that allow them to build portfolios. Learners can also participate in workshops and hackathons. Many degrees and bootcamps include these opportunities as capstone projects.
Candidates can also demonstrate experience through certifications.
Certifications for Software Developers
Computer science certifications are extra credentials that prove an individual's expertise. Students and professionals can pursue credentials for each computer science specialization, including software development.
While generally not required, certifications can speak to a person's expertise. According to Dice's 2021 report, just under half the technologists surveyed held certifications. Most stated that their role did not require certification.
However, certifications can benefit any aspiring software developer. Self-taught individuals with no formal education might use certifications to demonstrate their expertise to employers. Bootcamp and college graduates may pursue certifications to stand out in a competitive hiring environment.
Midcareer and late-career developers can earn high-level certifications as their skills develop. They might also pursue certifications in new technologies or programming languages to maintain up-to-date skills.
Each certifying body has unique recertification requirements, but professionals can expect to renew their certifications about every three years.
Find a few common software development certifications below.
CompTIA IT Fundamentals: This pre-career certification offers a starting point for IT professionals. The exam covers the fundamentals of operating systems, software applications, security, and software development. Learners need no prior experience.
This pre-career certification offers a starting point for IT professionals. The exam covers the fundamentals of operating systems, software applications, security, and software development. Learners need no prior experience. IEEE Professional Software Developer: Candidates must have completed two years of college education in computer science and two years of professional experience to qualify. The exam covers software requirements, design, construction, and testing.
Candidates must have completed two years of college education in computer science and two years of professional experience to qualify. The exam covers software requirements, design, construction, and testing. CISCO DevNet Associate: Cisco offers four DevNet certification levels: associate, specialist, professional, and expert. Developers with at least one year of experience can pursue Devnet associate credentials.
Software Developer Salary and Career Outlook
According to the Bureau of Labor Statistics (BLS), software developers earn a median salary of $132,270 as of May 2023, far outpacing the national median salary of $48,060 for all occupations.
For comparison, the BLS reports that the median income for all computer occupations is $104,420. However, a software developer's salary depends on factors like industry, geographic location, and professional experience.
About 29% of software developers work in the computer systems design industry, and the top-paying industries for software developers include information services, computer equipment manufacturing, and independent artists and performers. The best states for software developers, in terms of salary, include California, Washington, and Maryland.
Annual Median Salary: $132,270 (BLS)
How to Get a Job as a Software Developer
Getting a job as a software developer typically starts with earning a bachelor's degree in computer science or a related field. Most bachelor's degrees include four years of requirements.
Individuals with associate degrees in computer science or comparable disciplines can also pursue employment as software developers. However, becoming employed may depend on experience and additional training, including software development bootcamps.
Software developers benefit from knowing multiple programming languages and earning professional certifications. Technology industry companies and organizations like CompTIA, EC-Council, and the Information Systems Audit and Control Association offer programs to software professionals.
How to Become a Software Developer This page features a step-by-step guide about the education and experience you need to become a software developer. Learn More Day in the Life of a Software Developer Readers can explore a breakdown of the daily duties, responsibilities, and activities undertaken by software developers. Learn More Computer Science Degree Programs This page offers a general overview of computer science degree levels and the specializations of the field. Learn More Software Development Bootcamps Discover software development bootcamps, which provide comprehensive training on an accelerated timeline. Learn More
Similar Specializations and Career Paths
Software development-focused education programs build the hard skills and knowledge that graduates need to pursue careers in the computer and information technology sectors. Professional opportunities typically depend on past work experience, preferred industry, geographic location, and education level.
Associate degree-holders can pursue jobs in web development, while someone with a bachelor's degree can explore opportunities as software developers or computer programmers. Additional higher education credentials can boost job opportunities — candidates with master's degrees can supervise teams of developers or become research scientists.
Whatever path they choose, graduates seeking software developer careers can expect relatively high salaries. The following list explores similar computer science specializations.
Systems Analyst : Systems analysts assess, identify, and implement steps to optimize the user's experience with computer programs. Systems analysts study existing processes, make recommendations for changes, and research new technologies to improve system efficiency and functionality.
: Systems analysts assess, identify, and implement steps to optimize the user's experience with computer programs. Systems analysts study existing processes, make recommendations for changes, and research new technologies to improve system efficiency and functionality. Information Security Analyst : These professionals plan, implement, and monitor security measures within companies and organizations. Information security analysts check for vulnerabilities, investigate security breaches, research new technologies, and make recommendations for security enhancements.
: These professionals plan, implement, and monitor security measures within companies and organizations. Information security analysts check for vulnerabilities, investigate security breaches, research new technologies, and make recommendations for security enhancements. Software Engineer : Software engineers oversee the development of operating systems and applications. They design software; collaborate with coders, programmers, and developers; and address issues as they arise. Software engineers keep the timeline of a project on track, report to supervisors and managers, and ensure overall software functionality.
: Software engineers oversee the development of operating systems and applications. They design software; collaborate with coders, programmers, and developers; and address issues as they arise. Software engineers keep the timeline of a project on track, report to supervisors and managers, and ensure overall software functionality. Network Architect: These architects design and build local area networks (LANs), wide area networks (WANs), and Intranets. These possess business knowledge and technical capabilities. Network architects analyze user needs, research appropriate technologies, and deploy networks once they are ready.
Careers Similar to Software Developer Career Required Education Required Experience Median Annual Salary (2023) Projected Growth Rate (2023-2033) Systems Analyst Bachelor's degree None $103,800 11% Information Security Analyst Bachelor's degree Less than five years $120,360 33% Software Engineer Bachelor's degree None $132,270 26% Network Architects Bachelor's Degree Five years or more $129,840 13% Source: BLS
Resources for Software Developers
Professional resources offer information about the knowledge and skills needed to enter software development. Books, blogs, and periodicals offer updates about the world of software development. Industry leaders offer certification programs, and professional organizations can provide networking opportunities.
Professional Organizations for Software Developers
Computer science students, entry-level software developers, and senior developers alike should take advantage of the many available professional resources. Software developers should strongly consider joining professional associations to pursue continuing education opportunities, networking events, and professional development resources.
Some associations serve all IT professionals, while others focus on specific computing occupations. Professional associations can help you gain new skills and meet like-minded professionals.
IEEE Computer Society: This organization links students, professionals, and researchers in the computer science and technology fields. Member benefits include a magazine, access to the IEEE digital library, local networking events, and career development webinars.
This organization links students, professionals, and researchers in the computer science and technology fields. Member benefits include a magazine, access to the IEEE digital library, local networking events, and career development webinars. CompTIA: CompTIA offers continuing education opportunities, including independent and instructor-led training courses. It also offers certifications, resources on trends in the IT industry, and membership benefits.
CompTIA offers continuing education opportunities, including independent and instructor-led training courses. It also offers certifications, resources on trends in the IT industry, and membership benefits. Association for Women in Computing: Established in 1978, AWC aims to advance women in technology professions. Members include programmers, consultants, and systems analysts. AWC offers professional networking meetings, continuing education programs, and mentorship opportunities.
Established in 1978, AWC aims to advance women in technology professions. Members include programmers, consultants, and systems analysts. AWC offers professional networking meetings, continuing education programs, and mentorship opportunities. The App Association: This association supports over 5,000 application development companies across the United States through advocacy and research.
How Much Does a Software Developer Make?
According to Payscale, software developers earned an average annual salary of $78,200 as of April 2024. This number represents respondents from all over the country, working in various settings.
Keep in mind that location, experience level, education, and employer can affect a software developer's salary. Payscale data shows that software developers' earning potential increases substantially with more experience.
Average Annual Salary of Software Developers: $78,200 (Payscale, April 2024)
Average Salary for Software Developers by Experience
Software developers' average salary tends to rise as they spend more time in the role. Payscale data shows that the field's top earners are those with 20 or more years of experience. These late-career professionals earned an average annual salary of $100,480 as of April 2024.
Software developers early in their careers typically earn less. Payscale notes as of April 2024 that developers in their first year earned an annual average of $68,040. Factors like bonuses, employers, and job roles may also affect how much a software developer makes.
Average Salary for Software Developers by Education
Education level has a strong impact on software developer salary potential. Many employers prefer candidates with at least a bachelor's degree. Others accept hands-on experience as a substitute. Earning a graduate degree may increase earning potential.
Not all software developers hold a degree in software development. Related majors such as computer science and information technology also teach applicable skills for the field.
Discover which education path is right for you Degrees in Computer Science Learn More Degrees in Internet Technology Learn More Software Development Bootcamps Learn More Degrees in Web Development Learn More
Average Salary for Software Developers by Location
Where software developers live and work can raise or lower their earnings. Different cities and states may host more or fewer tech employers. Rural locations often have fewer employers and fewer job openings, which may affect salary potential.
Metro areas often offer higher salaries than rural areas. However, software developers should consider cost of living, which can be more expensive in cities. For instance, the cost of living in Seattle — the top-paying city for software developers — is 53% higher than the national average.
Top-Paying Cities for Software Developers State Average Annual Wage Percent Above the National Average Seattle, WA $101,030 29% New York, NY $98,420 25% Chicago, IL $83,350 7% Source: Payscale, April 2024
Top-Paying States for Software Developers, May 2023 State Number of Software Developers Employed Annual Mean Wage California 304,390 $173,780 Washington 89,100 $159,990 Maryland 30,700 $150,740 New York 105,460 $150,020 Massachusetts 59,960 $146,580 Source: BLS
FAQ About Software Developer Careers
| 2023-01-10T00:00:00 |
https://www.computerscience.org/careers/software-developer/
|
[
{
"date": "2023/01/10",
"position": 39,
"query": "artificial intelligence wages"
}
] |
|
Why AI will be the end of the creative industry as we know it
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Why AI will be the end of the creative industry as we know it
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https://loveandlogic.co.uk
|
[] |
The use of AI in the creative industry could lead to a loss of jobs and economic opportunities for human creatives.
|
Posted on LinkedIn.
To begin with, it’s important to understand what AI is and how it works. AI is a type of computer software that is designed to mimic human intelligence and behaviours. It can analyse data, make decisions, and learn from its experiences in order to improve its performance over time. In the creative industry, AI can be used for tasks such as generating original artwork, composing music, and even writing advertising copy.
One of the main reasons why some people are concerned about the impact of AI on the creative industry is that it has the potential to automate many of the tasks that are currently performed by human creatives. For example, an AI system could be trained to analyse a company’s branding guidelines and generate a series of logo designs that adhere to those guidelines. Similarly, an AI system could be used to compose original music tracks or write advertising copy that is tailored to a specific target audience.
As AI becomes more advanced, it is likely that it will be able to automate more and more tasks that were once thought to be the exclusive domain of human creatives. This could lead to a situation where human designers and other creative professionals are no longer needed, as AI systems can perform their tasks more efficiently and at a lower cost.
Another negative aspect of AI in the creative industry is that it could lead to a homogenisation of design and other creative work. Since AI systems are based on algorithms and data, they are more likely to produce work that is consistent and predictable, rather than unique and innovative. This could lead to a situation where all creative work begins to look the same, as AI systems are unable to produce truly original ideas.
Furthermore, the use of AI in the creative industry could lead to a loss of jobs and economic opportunities for human creatives. As AI systems become more advanced and able to perform more tasks, there will be less demand for human designers and other creative professionals. This could lead to widespread unemployment and economic hardship for many people in the creative industry.
In conclusion, the increasing use of AI in the creative industry is a cause for concern for many people. While it is true that AI has the potential to automate certain tasks and make some jobs obsolete, it is also likely to lead to a loss of jobs and economic opportunities for human designers and other creative professionals. Additionally, the homogenisation of creative work and the inability of AI systems to produce truly original ideas could have a negative impact on the overall quality and diversity of creative work. It remains to be seen how these issues will play out in the future, but it is clear that the increasing use of AI in the creative industry will have significant implications for the people who work in it.
👇🤖
Thanks for reading, this article was created by ChatGPT via the following prompt:
Write an article about why AI will make the Creative Industry redundant and designers out of business. Make negative points only.
The article has had no amendments made besides changing Americanised versions of words to their English counterparts.
Read the positive version of this article here.
| 2023-01-10T00:00:00 |
https://loveandlogic.co.uk/thoughts-feelings/why-ai-will-be-the-end-of-the-creative-industry-as-we-know-it/
|
[
{
"date": "2023/01/10",
"position": 22,
"query": "artificial intelligence graphic design"
}
] |
|
Graphic Design Archives - AI Page Pro - AI Marketing Tools
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Graphic Design Archives
|
https://aipagepro.com
|
[
"Ai Bot"
] |
AI-driven tools and techniques are used to automate certain tasks, such as image recognition, color correction, and layout optimization. This allows designers ...
|
Headline: Unlock the Power of AI Assisted Graphic Design to Create Professional Quality Results
Are you a graphic designer looking for a way to take your work to the next level? If so, it’s time to unlock the power of AI-assisted graphic design.
This revolutionary technology has the potential to transform how you create professional-quality visuals for your projects. AI-assisted graphic design is the process of incorporating artificial intelligence into the creative process. AI-driven tools and techniques are used to automate certain tasks, such as image recognition, color correction, and layout optimization. This allows designers to focus on the creative aspects of their work, while leaving mundane tasks to the machine.
The benefits of AI-assisted graphic design are tremendous. For starters, it saves time and money. Automating mundane tasks eliminates the need for manual labor, reducing costs and allowing designers to focus on more complex tasks. In addition, AI-driven tools help designers create visuals that are more accurate and consistent in appearance, which eliminates the need for manual corrections or revisions. Moreover, AI-assisted graphic design helps designers stay ahead of the competition. Using AI-driven tools gives them access to cutting-edge capabilities that they might not have otherwise. This helps them create visuals that stand out from the crowd and help them remain competitive in their field. Finally, AI-assisted graphic design makes it easier to collaborate with other designers. AI-driven tools allow designers to share their work with other professionals in real time, eliminating the need for manual revisions or corrections. This helps streamline the collaboration process and ensures everyone is on the same page.
The possibilities of AI-assisted graphic design are endless. From automated image recognition and layout optimization to color correction and image compression, this technology has the potential to revolutionize how designers create professional-quality visuals for their projects. So don’t miss out – unlock the power of AI-assisted graphic design today!
| 2023-01-10T00:00:00 |
https://aipagepro.com/blog/tag/graphic-design/
|
[
{
"date": "2023/01/10",
"position": 45,
"query": "artificial intelligence graphic design"
}
] |
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AI-Driven Generative Design Redefines the ...
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AI-Driven Generative Design Redefines the Engineering Process
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https://www.engineering.com
|
[
"Written By"
] |
Generative design utilizes AI and ML to transform tedious engineering design processes into a seamless interaction between computer and engineer.
|
It can take even the most talented and experienced engineer’s designs to the next level.
listen to this story https://mobile.engineering.com/audio/27418.mp3
PNY has submitted this article.
Today, artificial intelligence (AI) and machine learning (ML)-based systems enable applications ranging from junk mail or text filters to autonomous vehicles and robots. Engineering firms and their employees have utilized AI and ML to improve the engineering design process and create highly optimized and original products. Early adopters have benefitted from shorter design cycles, engineering productivity and originality, which has transformed entire engineering and product development workflows.
Let’s examine where traditional engineering design techniques and processes can be exceeded by generative design. We’ll consider some real-world examples to realize why increasing numbers of engineers are turning to AI and ML techniques to reinvent and optimize the design process.
Current Engineering Design Drawbacks
Standard engineering design techniques are well understood, widely utilized, and broadly applicable. The familiarity with—and success of—traditional approaches has delivered countless engineering breakthroughs that we benefit from every day. The engineering design process consists of several steps:
Idea and conceptual breakthrough—Identify the problem and come up with a concept Create—Fabricate a rough prototype of the concept (ideally in silico) Refine design—Fill gaps with a comprehensive design (also ideally in silico) Computer aided engineering (CAE) Validation—Test the design to establish that it works (again in silico) Manufacture—Choose optimal production techniques and build the product at scale Launch—Release the product into the market, ideally with sales and marketing materials generated directly from computer aided design (CAD) files with graphic processing unit (GPU) accelerated photorealistic rendering, virtual reality (VR), and other visually compelling techniques to complement mainstream marketing tools
This process is inherently linear and glosses over significant drawbacks associated with this approach.
Extensive technical expertise is required at each step. Although advanced software is used, every dimension, specification, and feature must be exactly defined using elaborate, domain-specific software tools to realize a practical design that is ready to manufacture. Negative feedback loops occur when something goes wrong during the validation phase, which invariably delays projects. This can result in product recalls, redesigns, and wasted resources. Engineering and design creativity are limited by how quickly teams can iterate and generate new designs. With tight schedules, the normal ‘safe’ approaches reign, which significantly impedes long-term innovation.
Engineering software has ameliorated some of these issues, but engineers still have to complete each development phase. New technologies have the potential to amplify engineering and design productivity. Generative design can increase product development efficiency and utilize new fabrication techniques such as 3D printing (additive manufacturing).
Generative Design Changes Everything
Generative design utilizes AI and ML to transform tedious engineering design processes into a seamless interaction between computer and engineer. Topology optimization and simulation is automatically performed by the computer. Negative feedback loops are removed by lowering barriers to design, giving engineers more room to tackle challenges that require “common sense.”
Generative design can optimize a design for specific parameters, such as weight or durability, or commercial parameters, like production costs and aesthetic considerations. Most intriguing is its ability to enhance functionality (by design) during use. Startups such as additive flow have delivered applications that enable engineers to integrate different materials into one component while optimizing the topology in parallel. This results in superior products and shorter development times.
Generative design works best in conjunction with other technologies—generative design and 3D printing are a classic example, making it possible to quickly prototype and test new designs without a costly and time-consuming custom manufacturing run. 3D printers have no geometric boundaries, so extremely complex structures can be delivered.
3D printing also facilitates mass-customization. It can print products tailored to the specific needs of a single client. Imagine using AI to create a perfect part, just for your product design, without the economic drawbacks inherent in traditional subtractive manufacturing procedures.
Generative Design in Practice
Perhaps you’re designing a motorcycle swingarm. After coming up with a design area, connection points, and constraining parameters such as weight or torque, you spend hours calculating whether a proposed design would meet each of the engineering requirements—and that’s just for a single proposed design.
Airbus APWorks 3D-printed motorcycle swingarm. (Source: PNY.)
In addition to saving time, generative design algorithms can unlock entirely new approaches that weren’t feasible before.
To understand the generative design advantage, we must take a look at classic topology optimization algorithms. These minimize designated objects according to predetermined constraints, for instance volume or weight. Generative design algorithms utilize these steps but allow a wider range of constraints. Engineers add a greater variety of requirements, essentially a “fitness landscape” that drives design optimization. The workflow harnesses AI to analyze the use of different materials and manufacturing techniques.
Companies Using Generative Design
General Motors used generative design to reduce vehicle weight. GM worked with Autodesk to create 150 new designs for a seat bracket and chose a final design 40 percent lighter and 20 percent stronger than the original component.
Under Armour created a shoe with an ideal mix of flexibility and stability for athletic training—inspired by tree roots. Generative design realized an unconventional geometry that was 3D printed into shoes tested by more than 80 athletes in a fraction of the time it would have taken before.
Phillipe Starck used generative design to combine aesthetics and functionality. The company asked a simple question: How can we rest our bodies using the least amount of material? The resulting collaboration between designer and AI yielded an unconventional but highly effective chair design.
Generative AI designed chair. (Source: PNY.)
What to Consider When Getting Started with Generative Design
Generative design is a rapidly evolving field and startling new applications and products are being created daily, but not by simply cutting-over from traditional techniques. Introducing generative design requires readiness and change among multiple stakeholders. It creates new products, but disrupts traditional structures. The challenge of transitioning engineers to generative design applications should not be underestimated. The software can be difficult to master, and the learning curve can be steep.
Engineers and designers should consider software and hardware requirements for generative designs. There is a growing number of open source projects that provide generative design capabilities. Autodesk moved generative design out of the lab and integrated it into their products. Both Autodesk and open source approaches typically take advantage of the massive acceleration offered by professional GPUs like NVIDIA RTX™, available from PNY Technologies.
“Generative design can take even the most talented and experienced engineer’s designs to the next level,” said Carl Flygare, NVIDIA Professional Graphics Marketing Manager at PNY Technologies. “It complements the latest additive manufacturing techniques, depends on unmatched AI acceleration provided by NVIDIA RTX GPUs, often delivers results that look organic and evolved, while exceeding counterparts developed with traditional software design tools. PNY is pleased to work with NVIDIA and leading Independent software Vendors (ISVs) to bring the potential and power of generative design to an ever widening engineering and product design community.”
Generative design is a powerful and innovative way to approach engineering design problems. While AI and ML can’t yet replace humans, they automate many of the tedious processes that create bottlenecks, ranging from design optimization to commercially acceptable aesthetics.
For more information, visit PNY.com.
| 2023-01-10T00:00:00 |
2023/01/10
|
https://www.engineering.com/ai-driven-generative-design-redefines-the-engineering-process/
|
[
{
"date": "2023/01/10",
"position": 65,
"query": "artificial intelligence graphic design"
}
] |
Genius.AI: Your Social Media Organic Growth Platform
|
Genius.AI: Your Social Media Organic Growth Platform
|
https://www.genius.ai
|
[] |
Genius.AI is a new way to promote and grow your business. It's faster, easier, and more stress-free than hiring agencies or doing it all on your own!
|
Erin Hilton Erin Hilton US
WOW this AI tech!
This is one of the coolest tech I have gotten to date! I am impressed with what it's already capable of and I know that there is more coming! Which is so exciting. The copilot is the true easy button on the daily tasks at hand as a business owner who works mostly online. The picture and video generator is a little wonky in creating the picture you tell it to, no matter how much detail you give it. A little fine-tuning is needed. Having the copilot also gives you the social aspect to schedule posts, reels, etc. and being able to voice message would be amazing!
| 2023-01-10T00:00:00 |
https://www.genius.ai/
|
[
{
"date": "2023/01/10",
"position": 80,
"query": "artificial intelligence graphic design"
}
] |
|
Tech layoffs slay 20% of popular startup's workforce
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Booming AI under the knife: Tech layoffs slay 20% of popular startup’s workforce
|
https://interestingengineering.com
|
[
"Baba Tamim"
] |
Despite having a CEO heralded as “the next Zuckerberg” and a $7 billion valuation, Scale AI announced the “hardest change” – the layoff of 20 ...
|
“I have made the difficult decision to reduce the size of our team by 20%, which means saying goodbye to many talented Scaliens,” said Alexandr Wang, CEO of Scale AI, while addressing employees in the blog.
“If you are among those impacted, you will be contacted shortly with further details via your personal email as well as offered time for a 1:1 conversation with a manager today.”
This shocking revelation comes at a time when the AI sector is expanding, with Microsoft allegedly in discussions to invest $10 billion in OpenAI and generative AI businesses like Jasper and Stability AI, raising over $100 million.
It became evident that we needed to realign our investment to adapt to this new climate, given the uncertainty that many of the sectors we serve confront, stated the Scale AI blog.
“While many other companies have made similarly difficult decisions recently, we spent months looking for ways to avoid it, but unfortunately, we came to the conclusion that we needed to make these changes as well,” said Wang.
| 2023-01-11T00:00:00 |
https://interestingengineering.com/culture/tech-layoffs-ai-sector
|
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"date": "2023/01/11",
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"query": "artificial intelligence layoffs"
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|
The Impact of Automation And AI On the Future of Work
|
The Impact of Automation And AI On the Future of Work
|
https://chrisdyer.com
|
[] |
AI and automation change how things work. They completely shift job dynamics and change not only who is doing what jobs but what jobs need to be done by people.
|
The Impact of Automation And AI On the Future of Work
People have been worried about changing technology since “technology” was a word. The printing press and the industrial revolution, and the internet all created a certain level of panic. Technological advancements have, especially in recent years, caused more commotion. Thanks to AI and automation, people are worried about job loss and a crashing economy. But that hasn’t happened yet. And it probably won’t happen like that.
AI and automation have made some jobs obsolete, but they’ve also been creating jobs. And while this may seem daunting and too fast, improving technology can help save lives and make jobs easier. The purpose of technology is to help people live better, healthier lives. They should take away stress and let you focus on more important things, like interpersonal relationships and the world we live in.
While a lot is uncertain right now, there are some predictions about how the future will look due to AI and automation in the workplace. It may not be what you expect, but it shouldn’t be cause for alarm. Learning about these tools is a great first step in adapting to today’s constant changes.
The Beginning of AI and Automation
AI and automation can be traced back to the industrial revolution in the 18th century and even before. During the industrial revolution, new machines and technologies were developed that significantly increased productivity and efficiency in manufacturing. This led to a shift in the workforce, as machines replaced many manual labor jobs.
In the 20th century, robots were introduced to the workforce. These machines were initially used primarily in manufacturing and assembly line production. They could perform repetitive tasks more quickly and accurately than humans, requiring less maintenance and downtime. As a result, many jobs that humans once did were taken over by robots.
The impact of automation and robots on the workforce has been significant and cannot be overlooked. On the one hand, they have greatly increased productivity and efficiency, leading to lower prices and a higher standard of living for consumers. On the other, they have also led to job displacement and increased competition for jobs that require advanced skills and education.
These technologies, especially AI-related tech, have grown considerably in the past few years. In the space of a year, more AI bots are available to use than ever before, and their technological abilities have greatly increased. With the development of sophisticated AI technologies, the capabilities and scope of automation will continue to expand, and the way we think about work, jobs, and the economy is also evolving.
The Big Changes
AI and automation change how things work. They completely shift job dynamics and change not only who is doing what jobs but what jobs need to be done by people.
The point of these technologies is to make things easier, more accurate, and more efficient. One of the most notable ways AI and automation have changed the workplace is by automating routine and repetitive tasks. This includes tasks such as data entry, scheduling, and customer service. Automation has made it possible for these tasks to be done quickly and accurately, freeing up human workers to focus on more complex and strategic tasks.
Another way AI and automation have changed the workplace is through improved communication and collaboration. For example, chatbots and virtual assistants can now handle routine customer inquiries, allowing human customer service representatives to focus on more complex issues. Additionally, AI-powered tools can help teams to share and collaborate on information in real time, regardless of location.
But other changes have felt more drastic and sudden and caused significant rifts in certain industries. Think about all the things you can now do without needing someone else. You can use self-checkout at the grocery store. And soon, you may be able to take a car that drives itself. You can look something up on the internet instead of calling someone or going to a library to look it up.
Many different tools have developed over time, changing how things work and what requires a human touch. While this can be great and allow people to access more information and move faster, it can cause worry for the future.
Will AI and Automation Take Your Job?
With AI technology improving every day, many people are asking this question. And many have every right to be worried. But worrying won’t solve the problem, and there may not be a problem. Not every job will be taken by automation and AI tools, and many of these advancements will create more jobs.
Many of these advancements require people on board to do all the work. AI’s require continual attention and training to ensure they function correctly and learn how they are designed to learn. And a massive amount of jobs come from these companies building new technology.
Jobs At Risk
It can be difficult to predict exactly who and what will be at risk with AI and automation advancements, but it has been happening for a long time. Automation has been taking over many jobs; it is simply happening at a faster rate now that technology is on such an upward trajectory. AI will likely take over specific jobs like data input and other repetitive tasks. Some of the jobs more at risk due to advancing automation include:
Data entry and data processing jobs
Manufacturing and assembly line jobs
Transportation and logistics jobs such as truck and taxi drivers
Retail sales and customer service jobs
Telemarketing and telemarketing jobs
Bookkeeping, accounting and financial analysis jobs
Call center and customer service jobs
Banking and financial services jobs such as loan officers and financial analysts
Administrative and secretarial jobs
More Stable
Jobs that require a human touch, involving creativity or autonomy, are less likely to be taken over. Some of the jobs unlikely to be as impacted by AI include:
Healthcare professionals such as doctors, nurses, and therapists
Personal care providers such as hairstylists, makeup artists, and massage therapists
Artists and designers
Construction workers and tradespeople
Service-oriented roles that involve human interaction and emotional intelligence, such as social workers, counselors, and customer service representatives.
Educators and trainers
Law enforcement and emergency responders
Agricultural workers
Chefs and cooks
Human resources and recruitment professionals
However, as AI has shown, even art can be created through an artificial lens for mass consumption. It is generally best to assume your job may be impacted by technological growth and learn how to make the best of it. People are unlikely to suddenly get rid of all technology, as it is now thoroughly built into our everyday lives. We wake up and look at our phones or our smart watches linked to our phones connected to the internet connected to everyone else. And that’s just in the first five minutes.
More Jobs
While AI may replace millions of jobs in the next five to ten years, it should create even more in the same timeframe. Taking advantage of these opportunities and growing with technology can keep you a step ahead and let you breathe easier. It won’t be the simplest transition, but technology is here to stay, so it is a good idea to learn how to adapt to it and use it to benefit you.
The exact amount of jobs that will be created through advancements in technology is impossible to estimate, but most of the automated jobs should easily be replaced by more. Many will be on the technical side. Some that are likely to be created or grow in the coming years include:
Data Analysts and Scientists
AI Developers and Engineers
Machine Learning Engineers
Natural Language Processing (NLP) Engineers
Computer Vision Engineers
Robotics Engineers
Business Intelligence Analysts
AI Consultants
AI Product Managers
AI Sales and Marketing Professionals
How To Prepare For AI and Automation
Preparing for technological changes, specifically the advancement of AI and automation, can be done in several ways. The first step is to stay informed about the latest developments in the field. This can be done by reading industry publications, attending conferences and workshops, and following thought leaders in the field on social media and other channels. Staying informed will help you feel more prepared and allow you to keep up with how things are changing.
Build New Skills
Another critical step is to develop a diverse set of skills. As AI and automation become more prevalent, jobs that require repetitive or routine tasks will be at risk of being automated. However, jobs requiring critical thinking, problem-solving, creativity, and emotional intelligence are less likely to be automated. Therefore, it is essential to focus on developing and honing these skills.
Diversify your resume and learn new things. Use this as an excuse or opportunity to pick up a new hobby or passion that could benefit you later. Continuous learning and upskilling are key to staying relevant in the age of AI and automation. This can be done through formal education, such as taking courses or earning a degree, or through self-directed learning, such as learning a new language or computer program.
Utilize AI and Automation
It is also important to be aware of the potential impact of AI and automation on society as a whole. This includes understanding the ethical implications of the technology, as well as the potential for job displacement and the need for retraining and re-skilling.
When you are learning about how AI can impact work, also study what it can offer. Are there ways you could use AI to already make your job easier? These tools are developed to provide people with more efficient access to information and skillsets. See how you can use them in your own work and find ways to make AI work for you.
Adapt
Finally, it is essential to be adaptable and open to change. As technology continues to evolve, new jobs and industries will likely emerge, and being open to exploring new opportunities will be crucial for staying competitive in the job market. If you’re worried about the longevity of your job, start researching what else is out there and how you can prepare. You don’t have to wait and see if AI will take over your job in a few years. You can start working toward a better future right now, regardless of where technology goes.
Looking And Moving Forward
The most important thing to remember about the increase of AI and automation is that change is inevitable, and the best way to prepare is to accept that and start using it to your advantage. With new technology comes new challenges and new career fields. Look into what these might look like and see if any may interest you. Are there areas of your life that you never explored because you were busy pursuing a very niche field? Take this as an opportunity to open up to new possibilities.
The future cannot stop approaching, and technology is unlikely to stop growing and improving. By embracing those facts, you set yourself up for a brighter and more interesting future. Remaining open to the coming changes will help you adapt and start on the right foot. We cannot fully predict what will happen, but you can prepare and have a good chance of success by knowing things will continue to change and shift. If you want to learn more about embracing change, check out this webinar to improve your skills and prepare for a brighter future.
| 2023-01-11T00:00:00 |
2023/01/11
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https://chrisdyer.com/the-impact-of-automation-and-ai-on-the-future-of-work/
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[
{
"date": "2023/01/11",
"position": 4,
"query": "automation job displacement"
},
{
"date": "2023/01/11",
"position": 10,
"query": "future of work AI"
}
] |
How AI can help attract, nurture and retain talent
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How AI can help companies to attract and retain talent
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https://www.weforum.org
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Contrary to popular belief, automation is forecast to lead to net job creation. AI is set to drive meaningful change within the capacity of our individual roles ...
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For savvy business leaders, AI heralds a golden opportunity to free up employees and create the space for them to innovate, dream and reimagine.
Contrary to popular belief, automation is forecast to lead to net job creation.
AI is set to drive meaningful change within the capacity of our individual roles and better contribute to the creation of truly sustainable, prosperous communities for generations.
The age of the machines – once the stuff of sci-fi writers and the silver screen – is now a stark reality. While many of us are already leveraging enhanced technology across our industries, many more are contemplating how their worlds will change as a result of machine learning, automation and sentient computing.
Concerns built on celluloid stories of dystopian futures, compounded by a general sense of post-pandemic, pre-recession malaise, aren’t helping - but for savvy business leaders, the 'Age of AI' heralds a golden opportunity to invest in talent, free up employees and create the space for them to innovate, dream and reimagine. It's a chance to reap the benefits of leveraging the uniquely human traits that will prove critical for the next stage in our evolution.
So, as we continue to discover the enormous opportunity that advances in digital technology provide, we must also consider how we define the evolving role of an organization’s human capital, mindful of the seismic societal shifts and megatrends reshaping the global workforce.
A new social contract
Pre-COVID-19, time physically spent at a desk was a yardstick by which to gauge employee productivity. Staff that stayed late were often lauded as the most dedicated, dependable and productive team members. In 2020, the power dynamic irrevocably shifted. Driven by a new found appreciation of our own fragility, we set out on a journey of self-realization to rediscover our balance, reassess our priorities and reconnect with that which fulfils us.
The result sparked a transformation in how we work and our expectations of our employers in supporting our needs. For those organizations unable or unwilling to embrace the new working paradigm, the impact has been immediate and decisive. Termed the Great Resignation, workers have been leaving their jobs in droves as they re-evaluate their personal goals, aspirations and purpose. The fuse may have been lit by measures taken to curb the pandemic, but it shows no signs of snuffing out, with a PwC global survey finding a fifth of workers planned to quit their jobs in 2022. Perhaps more concerning is that this trend is especially prevalent among younger workers.
And to the great resigners, we can now add the quiet quitters, employees who wilfully disengage from work that ventures beyond their core job description. According to Harvard Business Review, much of this attitude is driven by the same feelings that result in people resigning – the sense that their employers are not adequately investing in them, their future or their talents.
A global skills shortage
Almost nine in 10 companies worldwide (87%) say they already have a skills gap – or will have one in the next few years, according to McKinsey.
Image: McKinsey & Company
Left unaddressed, by 2030 that gap will become cavernous. McKinsey estimates that as many as 375 million workers worldwide will need to switch occupational categories – and develop skills to be effective in their new roles. The advance of digital technology will ensure skills, such as coding, engineering and data analytics, remain in high demand, but creating an effective workforce for the digital age will demand a broader skill set.
The uniquely human attributes, such as creativity or those requiring social or emotional capacity, are set to increase and lead to an even greater premium for finding and developing exceptional people leaders.
Many of these required capabilities will be developed in-house, as employees adapt to working in the digital-first age of intelligent machines. But education systems will also need to evolve to produce a new generation of talent equipped with the skills required to thrive in the digital economy.
Here in the UAE, a new standard is being set as the nation progresses towards its target of becoming a global hub for attracting the best minds, ideas and talent. Sitting at the heart of its Centennial 2071 plan is a steady and sustained shift towards becoming a competitive knowledge-based economy, with strategies in place to capitalise on talent, skills and competencies, recognizing the role of the education sector to instil the qualities needed to realise these goals.
Artificial intelligence and machine learning
It’s evident that AI and machine learning technologies are rapidly changing the nature of jobs – especially those done by manual workers and involving routine tasks. But it’s important to remember that automation is forecast to lead to net job creation. Estimates in the World Economic Forum’s Future of Jobs Report suggest that while 85 million jobs may be displaced by a shift in the division of labour between humans and machines by 2025, another 97 million new roles may emerge over the same period. These positions will be more representative of the way humans, machines and algorithms best work together.
Image: Future of Jobs Report 2020, World Economic Forum
The most successful companies will be the ones investing in talent that can work most effectively alongside AI and other intelligent applications. With mechanical and cloud-based lifeforms appropriately leveraged, human employees with the right skills are liberated to be their brilliant best.
To be well-positioned for a tech-enhanced future means taking a long-term view of today’s labour market potential, in spite of the looming threat of recession turning the dominant narrative towards cost savings and headcount rationalisation. That means fostering an environment of continuous learning, innovation and entrepreneurial spirit and building the internal framework that supports sustained growth and development for all employees at every stage of their careers.
| 2023-01-11T00:00:00 |
https://www.weforum.org/stories/2023/01/why-nurturing-talent-is-key-to-riding-out-the-recession-davos23/
|
[
{
"date": "2023/01/11",
"position": 12,
"query": "automation job displacement"
},
{
"date": "2023/01/11",
"position": 18,
"query": "machine learning job market"
},
{
"date": "2024/10/31",
"position": 2,
"query": "AI skills gap"
},
{
"date": "2023/01/11",
"position": 10,
"query": "workplace AI adoption"
},
{
"date": "2023/01/11",
"position": 38,
"query": "government AI workforce policy"
},
{
"date": "2023/01/11",
"position": 15,
"query": "artificial intelligence business leaders"
}
] |
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How AI will Impact Non-Medical Home Health Care
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How AI will Impact Non-Medical Home Health Care
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https://www.careremedy.com
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However, it's important to note that while AI has the potential to automate certain tasks, it's not necessarily going to replace jobs entirely. It's more likely ...
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There are some concerns that must be addressed for the implementation of AI in non-medical home health care, such as
Data privacy and security,
Regulatory compliance, and the potential for unintended consequences.
It's also important to note that AI is not a replacement for human interaction, it's meant to augment and assist human care. AI-powered systems need to be designed and implemented in a way that complements the skills and expertise of human caregivers, rather than replacing them.
Furthermore, some may have concerns about how AI will affect employment opportunities within the home healthcare industry.
Another important aspect to consider is that while AI has the potential to improve the efficiency and effectiveness of care, it also raises ethical concerns. For example, decisions made by AI-powered systems may be based on algorithms that are not transparent or explainable, which could lead to bias and discrimination.
Additionally, there may be concerns about accountability, particularly in cases where errors or negative outcomes occur as a result of AI-powered systems.
To fully realize the potential of AI in non-medical home health care, it's important for stakeholders to come together and collaborate in the development and implementation of these systems. This includes engaging patients and caregivers, as well as healthcare providers, policymakers, and technology experts.
One of the concerns about the implementation of AI in non-medical home health care and in other industries is that it could lead to job displacement. As AI-powered systems become more advanced, they may be able to automate tasks that were previously done by humans, potentially leading to a reduction in the need for human labor.
This is a concern not just for home healthcare, but for many other industries as well, as AI and automation are increasingly being used to improve efficiency and reduce costs in a wide range of fields. However, it's important to note that while AI has the potential to automate certain tasks, it's not necessarily going to replace jobs entirely. It's more likely that it will change the nature of many jobs and create new ones, rather than completely eliminate them.
It's also important to consider that while AI has the potential to automate certain tasks, it may also create new opportunities for human caregivers by allowing them to focus on more complex tasks that require human interaction and emotional intelligence. AI can also free up time for caregivers, allowing them to provide more targeted and personalized care for patients.
Additionally, AI-powered systems will require human oversight and maintenance, which will create new job opportunities in fields such as data science, AI development, and system design and integration.
While the implementation of AI in non-medical home health care may lead to some job displacement, it's important to consider the potential benefits and opportunities it may bring. It's important to plan for job transition and retraining, and for society to adapt to the changes. It is crucial for organizations to consider the ethical implications of AI and implement it responsibly to ensure that it does not lead to greater inequality.
In conclusion, AI has the potential to significantly improve the quality and efficiency of non-medical home health care, but it's important that it be implemented in a way that is ethical, responsible, and sustainable with proper planning and execution.
| 2023-01-11T00:00:00 |
https://www.careremedy.com/how-artificial-intelligence-ai-will-affect-the-field-on-non-medical-home-health-care
|
[
{
"date": "2023/01/11",
"position": 51,
"query": "automation job displacement"
},
{
"date": "2023/01/11",
"position": 58,
"query": "AI replacing workers"
},
{
"date": "2023/01/11",
"position": 73,
"query": "AI job creation vs elimination"
}
] |
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What Does an IT Manager Do Each Day?
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What Does an IT Manager Do Each Day?
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https://www.launchnotes.com
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[] |
However, they must also consider the potential job displacement that may occur as a result of automation. IT managers can mitigate this by upskilling their ...
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An IT manager plays a critical role in ensuring the smooth operation of an organization's technological infrastructure. From overseeing daily operations to strategizing for the future, their responsibilities are diverse and demanding. In this article, we will explore the multifaceted role of an IT manager, the challenges they face, and the future trends they need to be prepared for.
Understanding the Role of an IT Manager
Before delving into the daily tasks, let's gain a deeper understanding of the role an IT manager fulfills. As the name suggests, an IT manager is responsible for managing all aspects of an organization's information technology. This includes hardware and software maintenance, network management, and cybersecurity.
In addition, an IT manager is often involved in strategic planning and decision-making. They work closely with top-level executives to align technology initiatives with business goals, ensuring that IT resources are allocated efficiently.
But what does a typical day in the life of an IT manager look like? Let's explore further.
Key Responsibilities of an IT Manager
The responsibilities of an IT manager encompass various areas. Firstly, they must ensure the efficient operation of the organization's technology infrastructure. This involves overseeing the installation, maintenance, and upgrading of hardware and software.
They also play a crucial role in managing the organization's network systems and troubleshooting any connectivity or performance issues that arise. Additionally, IT managers are responsible for managing IT staff and assigning tasks and priorities.
Moreover, IT managers are central to ensuring data security and the protection of sensitive information. They must implement robust cybersecurity measures, such as firewalls and encryption, and regularly assess and mitigate risks.
Skills Required for an IT Manager
Beyond technical knowledge, an IT manager must possess a range of skills to succeed in their role. One vital skill is effective communication. They must be able to clearly articulate complex technical concepts to both technical and non-technical stakeholders.
Leadership and team management abilities are also crucial. IT managers must motivate and guide their team towards achieving organizational objectives, fostering collaboration and providing support when needed.
Furthermore, an IT manager should be adept at problem-solving and critical thinking. They need to quickly identify issues and find effective solutions, often under high-pressure situations.
However, being an IT manager is not just about technical expertise and skills. It also requires a deep understanding of the organization's business objectives and industry trends. Staying up-to-date with the latest advancements in technology is essential for an IT manager to make informed decisions and drive innovation within the organization.
In conclusion, the role of an IT manager is multifaceted and demanding. They are responsible for managing the organization's technology infrastructure, ensuring data security, and aligning technology initiatives with business goals. With a combination of technical knowledge, leadership skills, and strategic thinking, IT managers play a crucial role in driving the success of an organization in the digital age.
A Day in the Life of an IT Manager
Now that we have an overview of an IT manager's role, let's delve into a typical day in their life. This section will explore morning tasks and priorities, afternoon duties and challenges, and evening wrap-up and planning for the next day.
Morning Tasks and Priorities
The morning is the ideal time for an IT manager to address critical tasks and prioritize projects for the day. They may start by reviewing system logs and analyzing any issues that occurred overnight. This meticulous examination allows them to identify patterns and trends that could indicate potential vulnerabilities or areas for improvement.
Based on the insights gained from the system logs, the IT manager can identify top-priority items and allocate resources accordingly. They may need to coordinate with other departments to ensure that the necessary resources are available to address any urgent issues or upcoming projects.
In addition to their technical responsibilities, IT managers often engage in meetings with team members to provide guidance and clarify objectives. These meetings may also involve collaborating with other departments to coordinate projects or address any ongoing support needs. By fostering effective communication and collaboration, the IT manager ensures that everyone is aligned and working towards the same goals.
Afternoon Duties and Challenges
In the afternoon, an IT manager's attention may be divided between ongoing projects and unforeseen challenges. They may spend time troubleshooting complex technical issues that arise and providing guidance to their team. This hands-on approach allows them to stay updated with the latest technologies and maintain a deep understanding of the systems they manage.
Furthermore, an IT manager might conduct performance reviews and provide feedback to team members, helping them grow professionally and fine-tune their skills. This investment in the team's development ensures that the organization has a capable and motivated workforce. By fostering a culture of continuous learning and improvement, the IT manager empowers their team to reach new heights of success.
Evening Wrap-up and Planning for the Next Day
To wrap up the day, an IT manager reviews the progress made on projects, ensuring that milestones are met and tasks are completed. They may also use this time to prepare reports for upper management, summarizing ongoing projects, and providing updates on technological initiatives. These reports serve as a valuable tool for decision-making and help stakeholders understand the impact of IT initiatives on the organization's overall strategy.
In addition, the evening is an excellent opportunity to plan for the next day. IT managers prioritize tasks, anticipate potential challenges, and ensure that the team is prepared to tackle any issues that may arise. They may also dedicate time to researching emerging technologies and industry trends, staying ahead of the curve and proactively identifying opportunities for innovation.
As the day comes to a close, the IT manager takes a moment to reflect on the accomplishments of the day and the challenges overcome. They understand that their role is not just about managing technology; it's about empowering their team, driving organizational success, and embracing the ever-evolving world of IT.
The Importance of IT Management in Business
Now that we have explored a day in the life of an IT manager, let's delve deeper into the crucial role they play in driving business operations forward. Effective IT management is not just a support function but a strategic enabler that can propel organizations to thrive in today's fast-paced and technology-driven landscape.
IT managers are the linchpins that hold together the intricate web of technology within an organization. They are tasked with not only ensuring the smooth functioning of technological operations but also with spearheading innovation and digital transformation initiatives that can give businesses a competitive edge in the market.
Ensuring Smooth Technological Operations
An organization heavily relies on its technology infrastructure for uninterrupted daily operations. IT managers are the guardians of this infrastructure, constantly monitoring and maintaining systems to ensure they are running optimally. Any glitches or issues that arise are swiftly addressed to minimize downtime and keep productivity levels high.
By proactively monitoring, maintaining, and upgrading technology systems, IT managers create a stable and secure environment that allows employees to focus on their core responsibilities. This optimized technological ecosystem not only enhances overall efficiency but also fosters a culture of innovation and collaboration within the organization.
Strategic Planning and IT Budgeting
IT managers are not just tech experts; they are strategic partners who work closely with business leaders to align technology initiatives with overarching business objectives. They play a pivotal role in identifying opportunities where new technologies can be leveraged to drive efficiency, improve processes, and gain a competitive advantage in the market.
Moreover, IT managers are entrusted with the critical task of developing and managing the IT budget. This involves a meticulous analysis of technology needs, cost estimation, and resource allocation to ensure that the organization's IT investments are aligned with its strategic goals. Their expertise in IT budgeting ensures that resources are optimally utilized, and investments are made in technologies that promise long-term value and growth.
The Challenges Faced by IT Managers
While the role of an IT manager is rewarding, it also comes with its fair share of challenges. Let's explore two key challenges: keeping up with technological changes and managing IT security risks.
Keeping Up with Technological Changes
Technology is constantly evolving, and new advancements emerge at a rapid pace. IT managers must stay abreast of these changes to ensure that the organization remains competitive and up-to-date.
They need to continuously learn and adapt to new technologies, evaluate their potential impact on the organization, and implement relevant ones. This ongoing learning is essential to provide innovative and effective solutions that align with business objectives.
Staying current with technological trends also involves understanding the potential risks and benefits associated with emerging technologies such as artificial intelligence, blockchain, and Internet of Things (IoT). IT managers must assess how these technologies can enhance operations while also considering the cybersecurity implications they may introduce.
Managing IT Security Risks
With the increasing prevalence of cybersecurity threats, IT managers face the daunting task of safeguarding the organization's digital assets. They must proactively identify vulnerabilities, implement robust security measures, and educate employees about best cybersecurity practices.
Moreover, IT managers are responsible for developing and periodically reviewing disaster recovery plans. These plans ensure the organization can swiftly recover from any data breaches or system failures, minimizing disruptions and potential losses.
Effective IT security management involves not only protecting against external threats but also addressing internal risks such as employee negligence or malicious intent. IT managers must establish clear security policies, conduct regular security audits, and enforce compliance to mitigate these internal vulnerabilities.
The Future of IT Management
As technology continues to advance, the role of an IT manager will also undergo transformation. Let's explore some emerging trends in IT management and the impact of artificial intelligence (AI) and automation.
Emerging Trends in IT Management
Cloud computing, remote work, and the Internet of Things (IoT) are just a few of the emerging trends that IT managers must navigate. They need to embrace these innovations and understand how they can be leveraged to enhance organizational efficiency.
Cloud computing, for example, offers the flexibility and scalability that organizations need to adapt to changing business demands. IT managers must not only understand the technical aspects of cloud computing but also the potential risks and security considerations associated with it. By effectively leveraging cloud computing, organizations can reduce costs, improve collaboration, and enhance their overall agility.
Remote work is another trend that IT managers must grapple with. The COVID-19 pandemic has accelerated the adoption of remote work, and IT managers need to ensure that their teams have the necessary tools and infrastructure to work effectively from anywhere. From implementing secure remote access solutions to providing collaboration platforms, IT managers play a crucial role in enabling remote work and maintaining productivity.
The Internet of Things (IoT) is yet another trend that IT managers must consider. With the proliferation of connected devices, IT managers need to develop strategies to manage and secure these devices effectively. They must also understand how IoT data can be leveraged to drive business insights and improve decision-making.
Additionally, IT managers must stay updated on emerging software development methodologies, such as agile and DevOps, to optimize project delivery and streamline collaboration. These methodologies emphasize iterative development, continuous integration, and collaboration between development and operations teams. By adopting these methodologies, IT managers can improve the speed and quality of software delivery, ultimately enhancing customer satisfaction.
The Impact of AI and Automation on IT Management
AI and automation are fundamentally reshaping the IT landscape. While these technologies offer numerous benefits, they also pose challenges. IT managers must understand the potential impact of AI and automation on their current infrastructure and workforce.
AI has the potential to revolutionize IT management by automating routine tasks, predicting and preventing system failures, and improving decision-making. However, IT managers must carefully evaluate which tasks can be automated and strike a balance between human expertise and machine intelligence. They also need to consider the ethical implications of AI, such as data privacy and algorithmic bias, and ensure that AI systems are transparent and accountable.
Automation, on the other hand, can streamline IT operations and reduce manual effort. IT managers can automate repetitive tasks, such as software deployments and system monitoring, freeing up their teams to focus on more strategic initiatives. However, they must also consider the potential job displacement that may occur as a result of automation. IT managers can mitigate this by upskilling their workforce and redeploying them to higher-value activities.
In conclusion, an IT manager's role is dynamic and critical to an organization's success. From overseeing daily operations and managing teams to implementing security measures and staying ahead of technological advancements, their responsibilities are vast. As technology continues to evolve, IT managers must adapt to emerging trends and leverage AI and automation to drive innovation. By effectively fulfilling their role, IT managers ensure that organizations thrive in the ever-changing digital landscape.
| 2023-01-11T00:00:00 |
https://www.launchnotes.com/blog/what-does-an-it-manager-do-each-day
|
[
{
"date": "2023/01/11",
"position": 96,
"query": "automation job displacement"
}
] |
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Future of work | Deel
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Future of work
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https://www.deel.com
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[] |
As we enter the fourth industrial revolution, it's becoming increasingly clear that automation and technology will continue to replace repetitive jobs.
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The future of work is a hot topic of conversation these days. With technology becoming increasingly prevalent in our lives, many people wonder what the future holds for jobs. Some believe that technology will eventually replace many human jobs, while others think that technology will create new opportunities for people to work in different ways. No matter what the future holds, one thing is sure: how we work is changing, and we need to prepare for it.
The main shifts in the world of work over the past few years have been the switch to remote/hybrid/work from home, the rise of the gig economy, adoption of the digital nomad lifestyle, AI and automation replacing manual work, and workers asking for more flexibility and better work-life balance.
One of the most significant changes we see in the workplace is the rise of remote work. Thanks to technological advances, more people can work from home or anywhere in the world. This trend will continue as more companies realize the benefits of having a remote workforce. Not only does it save on office space, but it also allows companies to tap into a global pool of talent.
Another change we're seeing is the rise of the gig economy. Thanks to platforms like Uber and Airbnb, people can increasingly make money from short-term gigs or renting out their skills or property. Freelancing and working as digital nomads is another trend that will continue as people become more comfortable with the idea of working in a freelance capacity.
What will happen to repetitive jobs?
As we enter the fourth industrial revolution, it's becoming increasingly clear that automation and technology will continue to replace repetitive jobs.
Automation is becoming prevalent in manufacturing and fast food industries, where machines are already beginning to take over.
However, this doesn't mean that robots will replace all jobs. Many experts believe that technology will create new opportunities for human workers.
For example, as more businesses adopt artificial intelligence, there will be a need for people who can train and manage these systems. Similarly, as driverless cars become more common, there will be a need for people who can maintain and repair them. While some industries may lose jobs to automation, others will create new opportunities.
Both workers and employers must become more flexible and adaptable
In a rapidly changing economy, jobs are constantly being created and destroyed, and no one can be sure of their prospects. The days of working 9-5 in a factory for a lifetime are long gone.
To survive in a job environment where requirements are constantly changing means learning new skills and adapting to new situations.
Workers must be willing to switch industries and move to new locations regularly. It also means that they must be comfortable with change and be able to deal with ambiguity.
The constant flux in the job market makes working as a digital freelancer and remote work so appealing.
At the same time, many employers need to rethink their talent acquisition strategies and reevaluate their benefits packages. The more flexibility and autonomy they’re ready to offer their workers, the better their chances are to hire great talent. Luckily, thanks to global hiring solutions, their talent pool is way larger than before.
Entrepreneurship will become more prevalent as people must create working opportunities
Many traditionally seen as "safe" jobs are now at risk of being automated, leading to increased insecurity and precarity among workers struggling to keep up with the ever-changing landscape.
In response, more and more people are turning to entrepreneurship to create working opportunities for themselves. This shift will continue in the future as the traditional model of secure employment becomes increasingly untenable. As a result, entrepreneurship will become an essential skill for anyone looking to build a successful career.
The gig economy will continue to grow as people look for more flexibility in their work arrangements
People are looking for more flexibility in their work arrangements. They don't want a traditional 9-to-5 job and are looking for the freedom to work when and where they want.
The gig economy offers opportunities to earn income that can be very attractive, and technology is making it easier than ever to connect with potential clients and customers.
For many people, the gig economy is the perfect solution to the challenges of modern life, providing the flexibility they need to balance work and family commitments and earn a good income without being tied to a single employer.
In addition, gig economy jobs often offer the chance to work from home, which can be a massive advantage for people who live in remote areas or have difficulty commuting. With the gig economy increasing, there are plenty of opportunities for people to find the perfect job for their needs.
Education and training will be important as workers strive to stay ahead of the curve
Will robots eventually take over many jobs? Will artificial intelligence make human workers obsolete? While it's impossible to know what the future holds, there are some steps that workers can take to prepare themselves. Education and training will be important as workers strive to stay ahead of the curve.
By keeping their skills sharp and developing new ones, workers will better equip themselves to adapt to future changes. In addition, workers must be proactive in seeking new opportunities and learning about emerging technologies. By doing so, they can position themselves at the forefront of the future of work.
Social connection and community support will be essential for maintaining mental health in the workplace
The future of work will bring many changes, but one thing that will remain the same is the importance of social connection and community support for maintaining mental health.
In an increasingly digital world, it's easy to become isolated and alone, and humans are social creatures who need meaningful interactions to thrive.
The workplace is one of the most important places for social connection. Employers must create an environment that promotes positive mental health, such as offering flexible working arrangements, encouraging employees to take breaks during the day, and providing access to mental health resources.
Community support is essential for mental health, so employers should encourage employees to get involved in activities outside of work.
Employers can help employees maintain positive mental health and feel more connected to their community by fostering a healthy social environment.
The future of work is already here
With automation and artificial intelligence on the rise, many traditionally considered secure jobs are now at risk of disappearing and being replaced by freelance workers, short-term contracts and the "gig economy". Responding to how we work in the future will be one of humankind's most significant challenges.
| 2023-01-11T00:00:00 |
https://www.deel.com/glossary/future-of-work/
|
[
{
"date": "2023/01/11",
"position": 49,
"query": "AI replacing workers"
},
{
"date": "2023/01/11",
"position": 41,
"query": "future of work AI"
}
] |
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Switch Your Career to Machine Learning - A Complete Guide
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Switch Your Career to Machine Learning
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https://www.geeksforgeeks.org
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Currently, Machine Learning Engineer ranks The Best Job of 2019 with a 344% market growth and an average base salary of around $145,000 per year.
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How To Start Learning Machine Learning?
1. Prerequisites
2. Machine Learning Resources
Machine Learning Foundation With Python
Training Certificate
Course mentored by Industry experts having hands-on experience in ML-based industry projects.
Internship Opportunities at GeeksforGeeks
Project-based learning which will add stars to your resume
4 projects based on real-world applications which include 1 Major Project and 3 Minor Projects
Machine Learning is currently the hottest career around and its popularity is only increasing! Currently, Machine Learning Engineer ranks The Best Job of 2019 with a 344% market growth and an average base salary of around $145,000 per year. And Machine Learning is also impacting almost all other industries like, etc. Keeping all this in mind, learning Machine Learning can improve your career prospects enormously. This field will help you in reskilling yourself and learning cutting edge technologies that will only help you in this competitive job market. And it doesn’t hurt that Machine Learning is also a fascinating subject to learn.who wouldn’t want to know more about such interesting topics?!!And that is the reason this article deals withand the method to eventuallyto this field. First, we will focus on the prerequisites required to start learning ML and then the resources you can use for further learning. Read on to find more!While starting Machine Learning, there is a roadmap you can follow, especially if you are currently in another job and want to switch. After you have some knowledge of ML, you can continue as you think best and acquire more in-depth knowledge. So, here is some basic information on How To Start Learning Machine Learning.There are some prerequisites that you need to know before officially starting Machine Learning! So it’s best that you study these first along with your current career and then start learning more about ML when you have some idea of these prerequisites. Now, let’s see them!Both Linear Algebra and Multivariate Calculus are important in Machine Learning. If you are planning on moving into application heavy machine learning, then you don’t need to be that focused on maths as there are many common libraries available. But if you want to shift into R&D in machine learning, then mastery of Linear Algebra and Multivariate Calculus is very important as you will have to implement many ML algorithms from scratch.Data plays a huge role in Machine Learning. In fact, around 80% of your time as an ML expert will be spent collecting and cleaning data. And statistics is a field that handles the collection, analysis, and presentation of data. So it is no surprise that you need to learn it!!! Some of the key concepts in statistics that are important are Statistical Significance, Probability Distributions, Hypothesis Testing, Regression, Bayesian Thinking, etc.While there are other languages you can use for Machine Learning like R, Scala, etc. Python is currently the most popular language for ML. In fact, there are many Python libraries that are specifically useful for Artificial Intelligence and Machine Learning such as Keras, TensorFlow, Scikit-learn, etc. So if you want to learn ML, it’s best if you learn Python! You can do that using various online resources and courses such as Fork Python available Free on GeeksforGeeks.Now that you know enough about the Machine Learning prerequisites, you can actually focus on learning the subject. Since you are actually switching your career, there are multiple ways of getting the necessary knowledge to apply for ML jobs. Let’s see these now.If you want to be completely and formally prepared for a career in Machine Learning, then a University Education is the way to go. An education from a top university will be very helpful in providing you a platform to apply for Machine Learning jobs, especially since you are switching your career. So a degree will provide some credibility that you actually know Machine Learning and are industry-ready. However, one drawback of getting a University Education is that it is insanely expensive. Chances are that you already went to university for your current career and the cost of a fresh university education could finish your savings or even put you into debt. So only opt for a University Education if you can afford it and you think you need more formal support to switch your career.In case you don’t want to go to university again, you can always opt for a free Machine Learning online course. This will mean that you can learn ML on your own schedule along with your current job and then switch when you are ready. There are many platforms these days from where you can learn Machine Learning for free such as Coursera, Udemy, Edx, Udacity, etc. But there is a drawback of free online courses as well. These courses provide you the basic information you need to know in order to get started but they don’t go into much depth. Also, there is not much support in case of doubts or problems while studying. Do you think that neither of these options appeals to you? Fear not, there is a third option as well! GeeksforGeeks has created a course that provides the thorough education and credibility of university courses without the insane fees. A course that also provides doubt support, unlike many free online courses. Are you interested? Then check this out! Machine Learning Foundation With Python is the perfect place for beginners like you to start your journey of Machine Learning. In this course, you will learn the core idea of ML, which is toYou will learn about key concepts of Machine Learning, effective machine learning techniques, and gain practice implementing them and getting them to work for yourself all in a classroom program. This course is specially scheduled on weekends so that you can learn alongside your current job and make the career switch into Machine Learning when you think you are ready.You should know the basics of any one programming language among C Java or Python to get started with this course. Are you interested? Well, then REGISTER NOW because this course starts on. Also, this course is priced atbut it is available at an Early Bird Offer price of. You are just a click away to begin the journey to your dream job in Machine Learning. Register now for the Machine Learning Foundation With Python course by clicking on the button below.
Machine Learning Foundation With Python
| 2019-11-17T00:00:00 |
2019/11/17
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https://www.geeksforgeeks.org/switch-your-career-to-machine-learning-a-complete-guide/
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[
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AI Engineer vs. Data Scientist | Maryville Online
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AI Engineer vs. Data Scientist
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https://online.maryville.edu
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Consider AI engineer vs. data scientist roles to see how they intersect. Learn how to apply your tech skills and business savvy to these top-paying careers.
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An architect’s spoken ideas for a new building move into reality. An image recognition system spots a rare cancer physicians can’t yet detect. Shoppers stroll through the metaverse, trying out products without leaving their armchairs. Artificial intelligence (AI) and data science applications are booming across multiple industries — increasing profits, optimizing decision-making, and offering ways to improve lives.
These developments stem from the work of AI engineers and data scientists, professionals with technology expertise, business sense, and ethical awareness. An advanced education, often boosted by a specialized certification, equips these sought-after professionals with the in-depth understanding and skills they need to help tackle challenges as tech hurtles into the future.
In particular, AI engineers and data scientists play a huge role in business, priming organizations to achieve their goals by influencing product development, guiding marketing efforts, and supporting data-driven decision-making.
When comparing AI engineer vs. data scientist roles, it’s clear their tasks and responsibilities dovetail in many ways. Yet the focus of their work is significantly different. Understanding the distinctions between these related roles can help people passionate about diving into the future of tech decide which career path to follow.
What Does an AI Engineer Do?
AI engineers train computers to process large amounts of data and find patterns in that data, according to SAS. The goal is to get machines to do tasks or solve problems. AI engineers help machines learn from situations, adapt to new information, and perform actions.
The main task of an AI engineer is to implement AI models using programming algorithms (rules or processes). In basic terms, an AI engineer applies insights from data.
Related tasks of an AI engineer include:
Managing the AI product development process
Developing infrastructure for data transformation and ingestion
Automating infrastructure for the data science team
Communicating with product managers and organizational stakeholders
Transforming machine learning (ML) models into application programming interfaces (APIs), software that lets different applications talk to each other.
AI Engineering and Business
In businesses, AI engineers offer stakeholders vital information. “Because of its ability to handle large amounts of data and forecast outcomes, AI has become essential for strategic decision-making,” according to Forbes. While humans supply the gut feeling and common sense, AI-engineered systems leverage predictive analytics and other methods to suggest options decision-makers can choose to act on.
Other ways AI engineers help businesses grow and innovate, a process called enterprise AI, include:
Automating repetitive business processes
Standardizing data
Analyzing customer feedback
Providing precise data for customer relationship management
Organizations apply AI models in fields including:
Healthcare : Healthcare facilities use AI to automate labor-intensive processes, such as those involved in drug discovery, to save time and money.
: Healthcare facilities use AI to automate labor-intensive processes, such as those involved in drug discovery, to save time and money. Finance : Banks use AI models to monitor patterns of consumer behavior and detect suspicious activity on credit cards.
: Banks use AI models to monitor patterns of consumer behavior and detect suspicious activity on credit cards. Manufacturing: Factories use AI to make the manufacturing process safer and less costly by predicting maintenance issues, reimagining supply chains, and integrating with tech systems.
Trends in AI Engineering
Employer demand for AI engineering professionals in business is surging, and the AI engineering field is taking off in multiple directions. Trends for AI’s future include:
Advancements in virtual and augmented reality
Focus on AI in cybersecurity
Focus on AI-controlled tech, such as robotics, drones, and driverless vehicles
Emphasis on ethics and removing cultural biases from data
Development of voice and language applications
What Does a Data Scientist Do?
Data scientists interpret the patterns they find in large volumes of information and offer advice to stakeholders based on their findings. These respected experts typically oversee the collection, storage, management, protection, and analysis of business data.
A primary responsibility of a data scientist is leveraging data to support an organization’s decision-making process. In doing so, they must know how to:
Use programming languages
Create data management software
Build hardware and network systems
Detect data security vulnerabilities
Test and improve network functionality
Data Science and Business
Businesses rely on accurate data. Data scientists help businesses interpret data drawn from customers’ engagement with their products or services, and then use those interpretations to guide the creation of enjoyable, personalized encounters that encourage customers to spend money. The ability to monetize data is one key reason data scientists are in high demand.
The data collection that drives a company’s customer experience strategy must be precise — otherwise it won’t achieve results. Cutting-edge data collection tools that data scientists use to support accurate data-driven customer experience strategies include:
SurveySparrow
Fulcrum
Teamscope
Kobo Toolbox
Magpi
Jotform
Data scientists perform other valuable tasks for businesses as well, including:
Analyzing data with an eye for improving business operations
Weighing the success of recent business initiatives
Automating data collection and storage
Searching databases for specific purposes, such as determining employee retention rates or identifying market trends
Reporting on their findings to upper management using data visualization tools
Trends in Data Science
A top trend in data science is the democratization of data, which refers to efforts to give more stakeholders access to understandable and easily accessible information to use for decision-making.
The democratization of data will result in swifter decision-making, more efficient teams, and, potentially, an edge on the competition, according to Adobe Cloud Experience. The key to accomplishing data democratization is automated machine learning (autoML).
AutoML applications go beyond business. “Developers of autoML solutions seek to create tools and platforms that can be used by anyone to create their own ML apps,” according to Bernard Marr in Forbes. “In particular, it’s aimed at subject matter experts whose specialized expertise and insights make them ideally placed to develop solutions to the most pressing problems in their particular fields but who often lack the coding knowledge needed to apply AI to those problems.”
AutoML allows people to explore ideas without barriers.
AI Engineer vs. Data Scientist: Comparison
AI engineers and data scientists have closely related but distinct roles. Each can be vital to a business. A comparison of AI engineers (sometimes known as machine learning engineers) vs. data scientists yields insight into the value of each role to an organization. It also reveals how the roles work together to benefit organizations.
Which Came First?
AI engineering is an outgrowth of data science. AI engineers need the information generated by data scientists through analytics to create powerful AI models and applications.
Marr expresses the relationship like this: “Broadly speaking, [data science] has enabled the emergence of machine learning (ML) as a way of working towards what we refer to as artificial intelligence (AI), a field of technology that’s rapidly transforming the way we work and live.”
The Data Pipeline
AI engineers and data scientists both work with the data pipeline. Data scientists collect data, store it, and keep it secure. They also prepare it by cleaning and shaping raw data into usable form. Then they use that information to make logical inferences that can be applied to business operations and goals.
In turn, AI engineers automate the interpretation of ever larger volumes of data. This creation of a decision-making process that emulates human thinking is called AI modeling. “These models use various types of algorithms, such as linear or logistic regression, to recognize patterns in the data and draw conclusions in a manner that emulates human expertise,” according to Intel.
While AI engineers and data scientists use data in similar ways, the work of AI engineers differs from that of data scientists in that it strives to use AI modeling to teach machines (software applications) to make decisions faster than humans ever could.
A Shared Focus on Ethics and Safety
AI engineers and data scientists share a focus on ensuring ethical business practices guide the use of technology. As tech has pervaded mainstream culture and crept into every corner of modern life, it has brought the flaws of the humans who created it to light: Facial recognition software has shown racial bias; synthetic data has been used for deep fakes, such as videos showing real people in situations that never happened; analytics have failed to predict crime.
Safety issues have risen to the forefront, too. Many tech safety issues revolve around a machine’s lack of the common sense that humans take for granted. For example, “In one study, the strategic placement of a few small stickers on a stop sign made a computer vision system see it as a speed-limit sign,” according to Matthew Hutson in The New Yorker. That scrap of misleading data could make all the difference in saving a life.
In September 2022, GM recalled the software used in its self-driving Cruise cars in San Francisco when one of them made an unprotected left turn into the path of an oncoming car, injuring two people. The crash occurred because the software made a wrong prediction about what the oncoming car would do.
“Progress in AI not only raises the stakes on ethical issues associated with its application, it also helps bring to light issues already extant in society,” James Manyika observes in Daedalus. AI engineers and data scientists can serve as ethical custodians of tech innovation. They can strive to ensure discrimination against marginalized groups, violation of the privacy of individuals through data breaches, and hazards brought about by faulty software predictions will not violate rights or cause harm.
Tech Skills
AI engineers and data scientists share a similar tech skill set. For example, each role requires knowledge of linear algebra, statistics, and programming.
However, a tech skill specifically associated with AI engineers is the ability to work with neural network architecture. Crucial to machine learning applications, artificial neural networks get their name from the biological neural networks they resemble. Basically, a neural network is a computing system made up of connected units that mimics the function of the brain.
AI engineers use neural network architecture in machine learning applications such as robotics, facial recognition, and medical diagnostics.
Salary
Professionals in both roles are highly compensated. However, AI engineers have higher salaries, on average, than data scientists.
As of September 2022, the median annual salary for a data scientist was around $98,000, according to PayScale, with experienced data scientists earning $137,000 on average.
As of September 2022, the median annual salary for an AI engineer was around $132,000, with experienced AI engineers earning $159,000 on average, according to PayScale.
Build a Strong Business Future Through Technology
In today’s global marketplace, technology and business are inextricably linked. AI engineers and data scientists are responsible for steering businesses through technical and ethical challenges to provide their customers and clients with an enjoyable, safe, and productive future, thanks to the generative solutions these complementary roles contribute.
If you feel called to participate in shaping world-changing innovations by applying your technical skills, ethical mindset, and business decision-making savvy to a rewarding and challenging career, learn more about Maryville University’s online Master of Science in Artificial Intelligence degree and AI certificates.
If you choose to complete both the Fundamentals of Artificial Intelligence and Advanced Artificial Intelligence certificate programs, you can apply the certificates toward a future online master’s in AI. Start building a future of unlimited possibility with training in AI.
Recommended Reading
Building Skills for the Future of Machine Learning
The Future of Artificial Intelligence in Work and Everyday Life
What Is Data Modeling?
Sources
Adobe Experience Cloud, “Why Data Democratization Is Crucial to Your Business.”
Bernard Marr & Co., “Does Synthetic Data Hold the Secret to Artificial Intelligence?”
Daedalus, “Getting AI Right: Introductory Notes on AI and Society”
Forbes, “The Five Biggest Data Science Trends in 2022”
Forbes, “Here’s How AI Optimizes Executive-Level Decision-Making in Mega-Corporations”
Forbes, “Six AI Trends to Watch in 2022”
Indeed, Data Scientist Job Description: Top Duties and Qualifications
Indeed, Everything You Need to Know About the Role of an AI Engineer
Intel, “What Is AI Modeling?”
The New Yorker, “Can Computers Learn Common Sense?”
Payscale, Average Data Scientist Salary
Payscale, Average Machine Learning Engineer with Artificial Intelligence (AI) Skills Salary
SAS, “Artificial Intelligence: What It Is and Why It Matters”
SAS, “Meet the Data Scientist: Alex Herrington”
SAS, “What Is a Data Scientist?”
Simplilearn, “Top 5 Jobs in AI and Key Skills Needed to Help You Land One”
Wired, “GM’s Cruise Recalls Self-Driving Software Involved in June Crash”
| 2023-01-11T00:00:00 |
https://online.maryville.edu/online-masters-degrees/artificial-intelligence/careers/ai-engineer-vs-data-scientist/
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AI vs. Recruiter: The Best Strategies for Your Next Job Search
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AI vs. Recruiter: The Best Strategies for Your Next Job Search
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https://www.extensionrecruiting.com
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... AI tool can help to eliminate that stress. Your resume is the first ... opportunities and positions that aren't posted on job boards. They will be ...
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In 2023, the hype surrounding artificial intelligence (AI) has grown significantly and it’s just getting started. Artificial intelligence is essentially a technology that processes large amounts of data to perform human-like tasks like understanding language, analyzing data, making recommendations, and so much more ( Google Cloud ). With endless possibilities, it’s becoming a go-to tool for both individuals and businesses.
In the staffing industry, AI has become a hit not only for recruiting companies but also for job seekers. According to a survey conducted by the American Staffing Association , about 4 in 10 U.S. job seekers have used or are currently using AI tools to help them apply for jobs. While AI tools can be handy during a job search, it’s important to make sure you don’t lose the human factor. In this blog, we’re here to tell you about our best strategies for leveraging AI in your job hunt and how that measures up to a Recruiter’s assistance. Let’s get started!
AI Benefits & Tools Using AI offers a variety of different benefits, and it can save you a lot of time and effort during your job-seeking journey. AI technologies are consistently up to date on industry trends, which can help to reduce a lot of stress associated with your job search. Here are some ways you can use AI to assist you in applying for jobs:
Resume Builders : AI tools like Rezi help to format a clean looking, precisely written resume from scratch based on your experiences and skills. Making sure your resume conveys all the right information can be tough, so using an AI tool can help to eliminate that stress. Your resume is the first thing to hit the hiring manager’s desk; you want to make sure it looks good so you can leave a lasting impression.
: AI tools like Rezi help to format a clean looking, precisely written resume from scratch based on your experiences and skills. Making sure your resume conveys all the right information can be tough, so using an AI tool can help to eliminate that stress. Your resume is the first thing to hit the hiring manager’s desk; you want to make sure it looks good so you can leave a lasting impression. Resume Checkers : Having a second set of “eyes” on your resume is always beneficial! AI resume checkers work to use industry trends and market data to determine if your resume is up to par. : Having a second set of “eyes” on your resume is always beneficial! AI resume checkers work to use industry trends and market data to determine if your resume is up to par. Jobscan is one tool that allows you to optimize your resume and suggests areas that need improvement and strategies to do so.
Cover Letter Writers : Your cover letter acts as a supporting piece to your resume and allows the hiring manager to understand how your experiences connect to the position you’re applying for. Some AI tools allow you to provide information about your life experiences, achievements, and job description to create a unique and well-written cover letter. Tools like : Your cover letter acts as a supporting piece to your resume and allows the hiring manager to understand how your experiences connect to the position you’re applying for. Some AI tools allow you to provide information about your life experiences, achievements, and job description to create a unique and well-written cover letter. Tools like ChatGPT are widely used for this purpose.
Extension is not affiliated with any of the above-mentioned tools, but these are just a few examples of the many AI technologies available. As with any online service or tool, do your research and due diligence to make the best decisions for you!
AI Best Practices
In order to get the best responses from AI tools, here are a few techniques we suggest you follow:
Give it context : If you’re using AI to format your resume, provide your cover letter, find a job that matches your experience, etc., you can’t expect the tool to give you a good result without giving it context first. Be sure to introduce yourself to the AI platform and provide essential pieces of information like your educational background, work experience, achievements, skills, and whatever else you find necessary. This will allow the tool to create something personal and catered to you.
: If you’re using AI to format your resume, provide your cover letter, find a job that matches your experience, etc., you can’t expect the tool to give you a good result without giving it context first. Be sure to introduce yourself to the AI platform and provide essential pieces of information like your educational background, work experience, achievements, skills, and whatever else you find necessary. This will allow the tool to create something personal and catered to you. Share the job description : For resumes and cover letters, it is beneficial to share the job description that you want to apply for with the AI tool. From there, you can prompt AI to write a cover letter/resume based on the job description and your qualifications. The result will be detailed, tailored to the position, and will provide keywords meant to stand out to a Hiring Manager.
Re-prompt : Sometimes, AI doesn’t hit the nail on the head the first time. If your first result isn’t what you are looking for, re-prompt! Simply give the AI instructions of what you would like to see differently and make sure to be as specific as possible. The more information that the technology has, the better chance you will get a response that works for you.
Proofread and personalize : While AI can be an extremely useful tool when it comes to helping you write, remember not to simply copy and paste. Always proofread your results and check for spelling errors or any made-up facts that the AI has written. Also, be sure to add your personal touches. Hiring Managers may be able to tell if something has been written by AI, so try to rephrase things in your own way to create a more personalized product.
What Can Recruiters Do That AI Can’t?
While AI can be great for helping to search and apply for new positions, working with a Recruiter will actually give you the best chance of securing a job! Check out some of the things a Recruiter can do that AI could never do:
Increase your visibility : Working with a Recruiter can highly increase your chances of your application being viewed by a Hiring Manager. Staffing Agencies communicate with their clients directly, so they have the ability to personally recommend you and speak to your skills, experiences and character. When you apply for a job on your own, your resume and application likely go into a large pool of candidates with a much smaller chance of being seen and reviewed.
: Working with a Recruiter can highly increase your chances of your application being viewed by a Hiring Manager. Staffing Agencies communicate with their clients directly, so they have the ability to personally recommend you and speak to your skills, experiences and character. When you apply for a job on your own, your resume and application likely go into a large pool of candidates with a much smaller chance of being seen and reviewed. Advocate for you : Recruiters will spend a lot of time getting an understanding of who you are and what you’re looking for. Their job is to build relationships with job seekers AND the companies they represent. They’re focused on truly understanding what both parties need to see in order to move forward, which results in a good match across the board. Senior Recruiter at Extension, Taylor Solis, says, “A Recruiter is going to take in all the tangibles and intangibles of what you’re looking for. AI can find you a job that meets your compensation needs, whether it’s remote/onsite, and the duties you’d be performing. However, a Recruiter will also look at the culture of the company, the training process, and any growth opportunities. We can also fight for you to get the best opportunity and offer possible”.
: Recruiters will spend a lot of time getting an understanding of who you are and what you’re looking for. Their job is to build relationships with job seekers AND the companies they represent. They’re focused on truly understanding what both parties need to see in order to move forward, which results in a good match across the board. Senior Recruiter at Extension, Taylor Solis, says, “A Recruiter is going to take in all the tangibles and intangibles of what you’re looking for. AI can find you a job that meets your compensation needs, whether it’s remote/onsite, and the duties you’d be performing. However, a Recruiter will also look at the culture of the company, the training process, and any growth opportunities. We can also fight for you to get the best opportunity and offer possible”. Access to exclusive opportunities : Through building lasting relationships with companies, Recruiters often have access to opportunities and positions that aren’t posted on job boards. They will be able to connect you with hiring managers and companies that are a good match for you that AI won’t be able to find.
: Through building lasting relationships with companies, Recruiters often have access to opportunities and positions that aren’t posted on job boards. They will be able to connect you with hiring managers and companies that are a good match for you that AI won’t be able to find. Provide hands-on support : AI tools may be able to help you at the beginning of your job search, but Recruiters will be in your corner throughout the whole process. Sales Manager at Extension, Jeff Yamada, says, “Recruiters have the ability to coach you along the way and lean on their relationships to give you the best chance of securing the position. We also provide hands-on support with the negotiation of salary, benefits, etc.”.
| 2023-01-11T00:00:00 |
https://www.extensionrecruiting.com/ai-vs-recruiter-the-best-strategies-for-your-next-job-search/
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"date": "2023/01/11",
"position": 41,
"query": "AI job creation vs elimination"
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] |
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AI and the future of work: 5 experts on what ChatGPT ...
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AI and the future of work: 5 experts on what ChatGPT, DALL-E and other AI tools mean for artists and knowledge workers
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https://news.fiu.edu
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The Conversation asked five artificial intelligence researchers to discuss how large language models are likely to affect artists and knowledge workers.
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From steam power and electricity to computers and the internet, technological advancements have always disrupted labor markets, pushing out some jobs while creating others. Artificial intelligence remains something of a misnomer – the smartest computer systems still don’t actually know anything – but the technology has reached an inflection point where it’s poised to affect new classes of jobs: artists and knowledge workers.
Specifically, the emergence of large language models – AI systems that are trained on vast amounts of text – means computers can now produce human-sounding written language and convert descriptive phrases into realistic images. The Conversation asked five artificial intelligence researchers to discuss how large language models are likely to affect artists and knowledge workers. And, as our experts noted, the technology is far from perfect, which raises a host of issues – from misinformation to plagiarism – that affect human workers.
To jump ahead to each response, here’s a list of each:
Creativity for all – but loss of skills?
Potential inaccuracies, biases and plagiarism
With humans surpassed, niche and ‘handmade’ jobs will remain
Old jobs will go, new jobs will emerge
Leaps in technology lead to new skills
Creativity for all – but loss of skills?
Lynne Parker, Associate Vice Chancellor, University of Tennessee
Large language models are making creativity and knowledge work accessible to all. Everyone with an internet connection can now use tools like ChatGPT or DALL-E 2 to express themselves and make sense of huge stores of information by, for example, producing text summaries.
Especially notable is the depth of humanlike expertise large language models display. In just minutes, novices can create illustrations for their business presentations, generate marketing pitches, get ideas to overcome writer’s block, or generate new computer code to perform specified functions, all at a level of quality typically attributed to human experts.
These new AI tools can’t read minds, of course. A new, yet simpler, kind of human creativity is needed in the form of text prompts to get the results the human user is seeking. Through iterative prompting – an example of human-AI collaboration – the AI system generates successive rounds of outputs until the human writing the prompts is satisfied with the results. For example, the (human) winner of the recent Colorado State Fair competition in the digital artist category, who used an AI-powered tool, demonstrated creativity, but not of the sort that requires brushes and an eye for color and texture.
While there are significant benefits to opening the world of creativity and knowledge work to everyone, these new AI tools also have downsides. First, they could accelerate the loss of important human skills that will remain important in the coming years, especially writing skills. Educational institutes need to craft and enforce policies on allowable uses of large language models to ensure fair play and desirable learning outcomes.
Second, these AI tools raise questions around intellectual property protections. While human creators are regularly inspired by existing artifacts in the world, including architecture and the writings, music and paintings of others, there are unanswered questions on the proper and fair use by large language models of copyrighted or open-source training examples. Ongoing lawsuits are now debating this issue, which may have implications for the future design and use of large language models.
As society navigates the implications of these new AI tools, the public seems ready to embrace them. The chatbot ChatGPT went viral quickly, as did image generator Dall-E mini and others. This suggests a huge untapped potential for creativity, and the importance of making creative and knowledge work accessible to all.
Potential inaccuracies, biases and plagiarism
Daniel Acuña, Associate Professor of Computer Science, University of Colorado Boulder
I am a regular user of GitHub Copilot, a tool for helping people write computer code, and I’ve spent countless hours playing with ChatGPT and similar tools for AI-generated text. In my experience, these tools are good at exploring ideas that I haven’t thought about before.
I’ve been impressed by the models’ capacity to translate my instructions into coherent text or code. They are useful for discovering new ways to improve the flow of my ideas, or creating solutions with software packages that I didn’t know existed. Once I see what these tools generate, I can evaluate their quality and edit heavily. Overall, I think they raise the bar on what is considered creative.
But I have several reservations.
One set of problems is their inaccuracies – small and big. With Copilot and ChatGPT, I am constantly looking for whether ideas are too shallow – for example, text without much substance or inefficient code, or output that is just plain wrong, such as wrong analogies or conclusions, or code that doesn’t run. If users are not critical of what these tools produce, the tools are potentially harmful.
Recently, Meta shut down its Galactica large language model for scientific text because it made up “facts” but sounded very confident. The concern was that it could pollute the internet with confident-sounding falsehoods.
Another problem is biases. Language models can learn from the data’s biases and replicate them. These biases are hard to see in text generation but very clear in image generation models. Researchers at OpenAI, creators of ChatGPT, have been relatively careful about what the model will respond to, but users routinely find ways around these guardrails.
Another problem is plagiarism. Recent research has shown that image generation tools often plagiarize the work of others. Does the same happen with ChatGPT? I believe that we don’t know. The tool might be paraphrasing its training data – an advanced form of plagiarism. Work in my lab shows that text plagiarism detection tools are far behind when it comes to detecting paraphrasing.
These tools are in their infancy, given their potential. For now, I believe there are solutions to their current limitations. For example, tools could fact-check generated text against knowledge bases, use updated methods to detect and remove biases from large language models, and run results through more sophisticated plagiarism detection tools.
With humans surpassed, niche and ‘handmade’ jobs will remain
Kentaro Toyama, Professor of Community Information, University of Michigan
We human beings love to believe in our specialness, but science and technology have repeatedly proved this conviction wrong. People once thought that humans were the only animals to use tools, to form teams or to propagate culture, but science has shown that other animals do each of thesethings.
Meanwhile, technology has quashed, one by one, claims that cognitive tasks require a human brain. The first adding machine was invented in 1623. This past year, a computer-generated work won an art contest. I believe that the singularity – the moment when computers meet and exceed human intelligence – is on the horizon.
How will human intelligence and creativity be valued when machines become smarter and more creative than the brightest people? There will likely be a continuum. In some domains, people still value humans doing things, even if a computer can do it better. It’s been a quarter of a century since IBM’s Deep Blue beat world champion Garry Kasparov, but human chess – with all its drama – hasn’t gone away.
In other domains, human skill will seem costly and extraneous. Take illustration, for example. For the most part, readers don’t care whether the graphic accompanying a magazine article was drawn by a person or a computer – they just want it to be relevant, new and perhaps entertaining. If a computer can draw well, do readers care whether the credit line says Mary Chen or System X? Illustrators would, but readers might not even notice.
And, of course, this question isn’t black or white. Many fields will be a hybrid, where some Homo sapiens find a lucky niche, but most of the work is done by computers. Think manufacturing – much of it today is accomplished by robots, but some people oversee the machines, and there remains a market for handmade products.
If history is any guide, it’s almost certain that advances in AI will cause more jobs to vanish, that creative-class people with human-only skills will become richer but fewer in number, and that those who own creative technology will become the new mega-rich. If there’s a silver lining, it might be that when even more people are without a decent livelihood, people might muster the political will to contain runaway inequality.
Old jobs will go, new jobs will emerge
Mark Finlayson, Associate Professor of Computer Science, Florida International University
Large language models are sophisticated sequence completion machines: Give one a sequence of words (“I would like to eat an …”) and it will return likely completions (“… apple.”). Large language models like ChatGPT that have been trained on record-breaking numbers of words (trillions) have surprised many, including many AI researchers, with how realistic, extensive, flexible and context-sensitive their completions are.
Like any powerful new technology that automates a skill – in this case, the generation of coherent, albeit somewhat generic, text – it will affect those who offer that skill in the marketplace. To conceive of what might happen, it is useful to recall the impact of the introduction of word processing programs in the early 1980s. Certain jobs like typist almost completely disappeared. But, on the upside, anyone with a personal computer was able to generate well-typeset documents with ease, broadly increasing productivity.
Further, new jobs and skills appeared that were previously unimagined, like the oft-included resume item MS Office. And the market for high-end document production remained, becoming much more capable, sophisticated and specialized.
I think this same pattern will almost certainly hold for large language models: There will no longer be a need for you to ask other people to draft coherent, generic text. On the other hand, large language models will enable new ways of working, and also lead to new and as yet unimagined jobs.
To see this, consider just three aspects where large language models fall short. First, it can take quite a bit of (human) cleverness to craft a prompt that gets the desired output. Minor changes in the prompt can result in a major change in the output.
Second, large language models can generate inappropriate or nonsensical output without warning.
Third, as far as AI researchers can tell, large language models have no abstract, general understanding of what is true or false, if something is right or wrong, and what is just common sense. Notably, they cannot do relatively simple math. This means that their output can unexpectedly be misleading, biased, logically faulty or just plain false.
These failings are opportunities for creative and knowledge workers. For much content creation, even for general audiences, people will still need the judgment of human creative and knowledge workers to prompt, guide, collate, curate, edit and especially augment machines’ output. Many types of specialized and highly technical language will remain out of reach of machines for the foreseeable future. And there will be new types of work – for example, those who will make a business out of fine-tuning in-house large language models to generate certain specialized types of text to serve particular markets.
In sum, although large language models certainly portend disruption for creative and knowledge workers, there are still many valuable opportunities in the offing for those willing to adapt to and integrate these powerful new tools.
Leaps in technology lead to new skills
Casey Greene, Professor of Biomedical Informatics, University of Colorado Anschutz Medical Campus
Technology changes the nature of work, and knowledge work is no different. The past two decades have seen biology and medicine undergoing transformation by rapidly advancing molecular characterization, such as fast, inexpensive DNA sequencing, and the digitization of medicine in the form of apps, telemedicine and data analysis.
Some steps in technology feel larger than others. Yahoo deployed human curators to index emerging content during the dawn of the World Wide Web. The advent of algorithms that used information embedded in the linking patterns of the web to prioritize results radically altered the landscape of search, transforming how people gather information today.
The release of OpenAI’s ChatGPT indicates another leap. ChatGPT wraps a state-of-the-art large language model tuned for chat into a highly usable interface. It puts a decade of rapid progress in artificial intelligence at people’s fingertips. This tool can write passable cover letters and instruct users on addressing common problems in user-selected language styles.
Just as the skills for finding information on the internet changed with the advent of Google, the skills necessary to draw the best output from language models will center on creating prompts and prompt templates that produce desired outputs.
For the cover letter example, multiple prompts are possible. “Write a cover letter for a job” would produce a more generic output than “Write a cover letter for a position as a data entry specialist.” The user could craft even more specific prompts by pasting portions of the job description, resume and specific instructions – for example, “highlight attention to detail.”
As with many technological advances, how people interact with the world will change in the era of widely accessible AI models. The question is whether society will use this moment to advance equity or exacerbate disparities.
Lynne Parker, Associate Vice Chancellor, University of Tennessee; Casey Greene, Professor of Biomedical Informatics, University of Colorado Anschutz Medical Campus; Daniel Acuña, Associate Professor of Computer Science, Affiliate Professor of Information Science, University of Colorado Boulder; Kentaro Toyama, Professor of Community Information, University of Michigan, and Mark Finlayson, Associate Professor of Computer Science, Florida International University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
| 2023-01-11T00:00:00 |
https://news.fiu.edu/2023/ai-and-the-future-of-work-5-experts-on-what-chatgpt,-dall-e-and-other-ai-tools-mean-for-artists-and-knowledge-workers
|
[
{
"date": "2023/01/11",
"position": 3,
"query": "future of work AI"
}
] |
|
Does the future of work really entail more technology and ...
|
Does the future of work really entail more technology and fewer humans?
|
https://www.peoplemanagement.co.uk
|
[
"Kirk Chang",
"December",
"Bernard Marr",
"September",
"Dan Cave",
"June",
"Georgia Roberts",
"Özlem Mehmet"
] |
I believe it's likely that the future workplace will employ more AI colleagues and managers and recruit fewer human ones.
|
Science fiction movies often describe the future workplace as a dust-free capsule, technology-embedded cubicle, with state-of-the-art furniture and futuristic interior design, where people control computers and machines through their willpower/brains, processing tasks in a fast and invisible manner. As a management consultant, although I cannot predict when the described scenes may occur in reality, I am confident that the following three trends will likely emerge in the next 10 years.
AI colleagues and managers
I believe it’s likely that the future workplace will employ more AI colleagues and managers and recruit fewer human ones. The future workplace will be equipped with more AI-driven applications and facilities, helping managers to cope with more complex problems, and carrying out more advanced jobs. Having said this, however, AI is not and never will be a genuine human employee and will never truly be replaced.
Rather than worrying about the possibility that AI may replace jobs, organisations and managers should adopt a more proactive attitude towards evaluating the opportunity and constraint in bringing AI into their workplace and/or management practices. Taking a precautious approach and engaging with AI knowledge more actively would be more realistic. Employees also have the responsibility to learn AI and understand how to use it. We advise employees to develop a more proactive attitude to welcome the era of AI and AI-embedded workplaces, and, at the same time, upskill themselves in data analysis and retain the habit of continuous learning. AI can become a friend, not an enemy. The decision is in the hands of humans.
Continued popularity of flexible working
Since the beginning of the Covid pandemic, the world has experienced a drastic change in human behaviors, the local economy, international transportation and more. The virus has changed the world of work, and the most obvious change is probably the working mode, to include home working and video meetings. These changes have made work more flexible and deinstitutionalized, in which individual employees have regained their autonomy and, when necessary, they can easily cut themselves off from work and/or the organisation completely. Surveys and research have also shown that flexible working modes have not only increased the wellbeing of employees, but also saved organisational expenses and resources. Flexible working is now seen through the lens of hybrid working, which is proving so important and alluring to business leaders. The reasons for the popularity of flexible working are many and varied, but the most expedient reason is that it saves resources without compromising on performance.
EDI-empowered workplaces
Scholars and managers have proposed various interventions to improve the quality of interaction between employees, such as the implementation of EDI (equity, diversity and inclusion) strategies. Surveys and research have also shown that EDI-empowered workplaces contribute to employee confidence, wellbeing, teamwork dynamics and performance, as well as organisational growth and competitive advantages. It’s very likely that we’ll see the concept of EDI spread out and be adopted by more businesses as part of management policies and practices, in which every company member, corporate employee and organisational individual is treated fairly and equally in the workplace.
Kirk Chang is director of business at the University of East London
| 2023-01-11T00:00:00 |
https://www.peoplemanagement.co.uk/article/1809818/does-future-work-really-entail-technology-fewer-humans
|
[
{
"date": "2023/01/11",
"position": 53,
"query": "future of work AI"
}
] |
|
Artificial intelligence in strategy
|
Artificial intelligence in strategy
|
https://www.mckinsey.com
|
[
"Yuval Atsmon"
] |
We look at how new AI tools can help executives develop a business strategy that uses data insights to avoid biases and make crucial decisions more quickly.
|
Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform.
Joanna Pachner: What does artificial intelligence mean in the context of strategy?
Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.
Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?
Subscribe to the Inside the Strategy Room podcast
Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.
I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.
When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.
Joanna Pachner: What kind of tasks can AI help strategists execute today?
Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.
The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.
Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.
Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.
Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.
Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?
Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.
Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.
Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.
Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?
Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.
Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?
Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.
Joanna Pachner: McKinsey has written a lot about cognitive biases and social dynamics that can skew decision making. Can AI help with these challenges?
Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.
We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.
In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.
Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.
Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.
Joanna Pachner: Do you have a favorite application of AI in strategy?
Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.
Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?
Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.
The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorporating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.
The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.
Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?
Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.
The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.
Joanna Pachner: We recently published an article about strategic courage in an age of volatility that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?
Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?
Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?
Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.
AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.
| 2023-01-11T00:00:00 |
https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/artificial-intelligence-in-strategy
|
[
{
"date": "2023/01/11",
"position": 9,
"query": "workplace AI adoption"
},
{
"date": "2023/01/11",
"position": 5,
"query": "artificial intelligence business leaders"
}
] |
|
Thoughts on AI's Impact on Scholarly Communications? An ...
|
Thoughts on AI’s Impact on Scholarly Communications? An Interview with ChatGPT
|
https://scholarlykitchen.sspnet.org
|
[
"Todd A Carpenter",
"Todd Carpenter Is Executive Director Of The National Information Standards Organization",
"Niso . He Additionally Serves In A Number Of Leadership Roles Of A Variety Of Organizations",
"Including As Chair Of The Iso Technical Subcommittee On Identification",
"Description",
"Iso",
"Founding Partner Of The Coalition For Seamless Access",
"Past President Of",
"Treasurer Of The Book Industry Study Group",
"Bisg"
] |
It is also important to consider the ethical implications of replacing human labor with robots, as this could have significant social and economic impacts.
|
As 2022 drew to a close, a great deal of popular attention was drawn to the latest artificial intelligence chatbot, ChatGPT, which was released in November 2022 by OpenAPI. (As an aside, the company has a had a very interesting background and funding, which has produced a number of important AI advances). Machine learning, natural language processing and textual creation have made significant advances over the past decade. When the first auto-generation of content from structured data became commercially viable about a decade ago, it was reasonably easy to discern machine generated content. This is increasingly no longer the case. Content distributors and assessors of content, be that for scholarly peer-review, for academic credentialling, or simply those who consume content should be aware of the capabilities of these tools and should not be dismissive of them.
Given the range of questions in scholarly communications around the application of AI, I thought it might be interesting to see what the ChatGPT’s response would be to some of these, along with other literary/tech questions, and to share them with you. You can review for yourself whether you think the responses are good ones or not and, if you didn’t know the source of the responses, whether you could tell that they were derived from a machine. Copied below are the questions and responses. I have not edited the responses in any way from what was output by the ChatGPT. It seems we’ve moved well beyond the “Turing Test” for assessing the intelligence of a machine. You may notice there is something formulaic to some of the responses, but it’s only discernible after looking over several of them. Though it is important to reflect that the machine doesn’t “know” whether the answers are correct or not, only that they are statically valid responses to the questions posed.
(NOTE: Yesterday—after this post was prepared—, reports circulated that Microsoft was in discussions to acquire a 49% stake in the for-profit arm of OpenAI. It has been speculated that Microsoft would embed OpenAI’s technology in its suite of Office365 applications. If that were to happen, the lines would become interestingly blurred between human and machine authorship.)
TAC: What is the best question to test an AI chatbot with?
ChatGPT Response:
There is no one “best” question to test an AI chatbot, as different chatbots may have different capabilities and be designed to handle different types of tasks or interactions. However, here are a few ideas for questions that could be used to test an AI chatbot’s capabilities: “Can you provide information about a specific topic or answer a general knowledge question?”
“Can you carry on a conversation and respond appropriately to follow-up questions or comments?”
“Can you understand and respond to natural language input, even if it includes slang, colloquialisms, or typos?”
“Can you perform a specific task or action, such as booking a reservation or searching for information online?”
“Can you recognize and respond to different user intents or emotions?” Ultimately, the best questions to test an AI chatbot will depend on the specific capabilities and goals of the chatbot.
TAC: Is it ethical for an author to use AI in developing a scholarly article?
ChatGPT Response:
Whether it is ethical for an author to use AI in developing a scholarly article depends on the specific context and the expectations of the research community in which the article will be published. There is no inherent ethical issue with using AI in research or writing, as long as the AI is used appropriately and ethically. For example, AI can be used to analyze large amounts of data or to generate text, which can be useful for identifying trends or patterns that might not be evident to a person. However, it is important for researchers to be transparent about the role of AI in their work and to ensure that the use of AI is consistent with the research community’s standards and expectations. For example, if an AI program is used to generate text, the authors should clearly state this in the article and provide appropriate credit to the AI program. Additionally, researchers should be mindful of the potential limitations of AI and consider the risks and benefits of using it in their work. For example, AI may not be able to fully capture the complexity of human thought or language, and it may be necessary to supplement AI-generated results with additional analysis or interpretation by human researchers. Overall, it is important for researchers to use AI ethically and transparently, and to consider the potential implications of using AI in their work.
TAC: During peer review, if an author is found to have used ChatGPT in producing the paper, should the paper be rejected?
ChatGPT Response:
The use of AI, such as ChatGPT, in the production of a scholarly paper does not necessarily mean that the paper should be rejected during the peer review process. However, the use of AI should be disclosed to the reviewers and the editor, and the reviewers should consider the extent to which the use of AI has influenced the content of the paper. If the use of AI has significantly influenced the content of the paper, the reviewers may consider this when evaluating the paper. For example, if the AI has been used to generate large portions of the text or to analyze data in a way that is central to the paper’s findings, the reviewers may want to scrutinize the work more carefully to ensure that the AI has been used appropriately and that the paper’s conclusions are supported by the data. Ultimately, the decision to accept or reject a paper during the peer review process should be based on the quality and rigor of the research, and the extent to which the paper makes a meaningful contribution to the field. If the use of AI has not significantly influenced the content of the paper, or if the paper meets the standards of the research community, it is unlikely that the paper would be rejected solely on the basis of the use of AI.
TAC: Can AI-derived text be novel and creative?
ChatGPT Response:
There is ongoing debate about the extent to which AI-derived text can be considered novel and creative. Some argue that AI-generated text is simply a reflection of the data and algorithms it was trained on, and therefore cannot be considered truly novel or creative in the same way that a human-generated work can be. Others argue that AI can be used to produce works that are original and creative, particularly if the AI is given some degree of autonomy in the creative process. It is important to note that the level of creativity and novelty in AI-generated text can vary depending on the specific AI system and the parameters it was trained on. Some AI systems are designed to produce text that is very similar to human-written text, while others may produce text that is more experimental or unusual. Ultimately, whether AI-generated text is considered novel and creative may depend on the expectations and standards of the audience or research community in which the text is being presented. It is possible that some audiences may consider AI-generated text to be creative and original, while others may not.
TAC: Whose responsibility is it to acknowledge to the reader the role of AI in the authorship of published works, the author or the publisher?
ChatGPT Response:
It is generally the responsibility of the authors to acknowledge the role of AI in the authorship of published works. Authors should be transparent about the extent to which AI was used in the production of their work, and should disclose this information to the publisher and the readers of the work. This can be done through the use of footnotes, citations, or other methods of attribution. Publishers also have a role to play in ensuring that the use of AI in published works is disclosed and properly attributed. Publishers should establish clear guidelines for authors to follow when using AI in their work, and should review submissions to ensure that the use of AI is properly disclosed and attributed. Overall, it is important for both authors and publishers to be transparent about the role of AI in the production of published works, in order to ensure that readers have a full understanding of the work and the extent to which it was produced using AI.
TAC: How big a problem is predatory publishing?
ChatGPT Response:
Predatory publishing, also known as deceptive or illegitimate publishing, is a significant problem in the academic world. Predatory publishers operate by charging authors fees to publish their work without providing the editorial and publishing services that legitimate publishers do, such as peer review and copyediting. As a result, the published work may not meet the standards of quality and integrity that are expected in the academic community. Predatory publishing can undermine the integrity of the scientific record, and it can also deceive authors, particularly those who are new to the publishing process or who may not be familiar with the practices of legitimate publishers. It is important for authors to be aware of predatory publishers and to do their due diligence when considering where to submit their work for publication.
TAC: How will the OSTP Nelson memo affect publishing in 2023?
ChatGPT Response:
I’m sorry, but I don’t have information about the OSTP Nelson memo or how it might affect publishing in 2023. My training data only goes up until 2021, and I am not able to browse the internet or access any new information.
TAC: Should AI-driven text be copyrightable?
ChatGPT Response:
Under most copyright laws, original works of authorship, including literary works, are eligible for copyright protection. This includes works that are produced using artificial intelligence (AI). In general, if a work is original and has been fixed in a tangible form (such as a written or recorded work), it is eligible for copyright protection, regardless of whether it was produced by a person or by AI. However, there may be certain legal issues that arise when it comes to AI-generated works and copyright. For example, if an AI program is used to generate a work, it is not clear who the “author” of the work is. Some countries have specific laws addressing this issue, while others do not. It is also possible that a work produced by AI could be considered a “derivative work,” which is a work based on one or more preexisting works. In this case, the copyright in the derivative work would be held by the person or entity that created the work, but they would need to obtain permission from the copyright holder of the preexisting work in order to use it as a basis for the derivative work. Overall, the question of whether AI-generated works are eligible for copyright protection is a complex legal issue that may vary depending on the specific circumstances and the laws of the country where the work was created. It is important to consult with a qualified attorney if you have questions about copyright and AI-generated works.
TAC: Should markup in publishing be used more for conveying appearance or semantics?
ChatGPT Response:
Markup in publishing refers to the use of codes or symbols in a document to indicate how the document should be structured or formatted. There are two main types of markup: structural markup, which is used to indicate the structure and organization of a document, and presentational markup, which is used to indicate how a document should be formatted or styled. In general, it is generally considered more effective and efficient to use markup to convey semantics (i.e., meaning and structure) rather than appearance. This is because semantics are more enduring and less prone to change over time, while appearance is more subjective and can be affected by factors such as the device or browser being used to view the document. By using markup to convey semantics, it is possible to create documents that are more easily understood by both humans and machines, and that are more adaptable to different formats and platforms. This can help to ensure that the content of a document is more widely accessible and more useful in the long term. Overall, markup languages play an important role in publishing, and can be used to convey both the appearance and semantics of content. The extent to which markup is used to convey semantics may depend on the specific needs and goals of the publication, as well as the preferences of the authors and publishers.
TAC: When will open access overtake subscription publishing of journal articles?
ChatGPT Response:
It is difficult to predict when open access (OA) publishing will overtake subscription publishing as the dominant model for journal articles. The adoption of OA publishing has been increasing in recent years, with many researchers, institutions, and funders expressing support for OA as a way to increase the accessibility and impact of research. There are several factors that could influence the adoption of OA publishing, including the availability of funding to support OA publication fees, the policies of research funders and institutions, and the attitudes of researchers and publishers. One of the main challenges to the widespread adoption of OA publishing is the cost. Many OA journals charge publication fees, which can be a barrier for some authors, particularly those at institutions or in countries with limited research funding. However, there are also a number of OA journals that do not charge publication fees, and there are initiatives underway to support OA publishing, such as the Plan S initiative, which aims to make all research funded by public grants fully OA by 2024. It is likely that OA publishing will continue to grow in importance and adoption in the coming years, but it is uncertain when it will overtake subscription publishing as the dominant model for journal articles.
TAC: How will publishers adapt to a fully open access ecosystem?
ChatGPT Response:
The transition to a fully open access (OA) ecosystem is likely to be challenging for many publishers, as it requires a significant shift in the way they generate revenue. In a fully OA ecosystem, most or all research articles are made freely available to the public, and the costs of publishing are typically covered by grants or other forms of funding, rather than by subscription fees or paywalls. One way that publishers could adapt to a fully OA ecosystem is by shifting their business models to focus on providing value-added services to authors, rather than on generating revenue from subscriptions. This could include offering high-quality editing and production services, promoting research through marketing and outreach efforts, and providing data management and preservation services. Another option for publishers is to explore new sources of funding, such as grants from foundations or government agencies, or by partnering with universities or other research institutions. Overall, the transition to a fully OA ecosystem is likely to require a combination of these strategies, as well as a willingness to experiment with new business models and revenue streams.
TAC: What is the role of a library if all the content is freely available?
ChatGPT Response:
Even if all content were to become freely available, libraries would still play a critical role in the research ecosystem. Here are a few ways that libraries could continue to be valuable in a fully open access (OA) environment: Collection development: Libraries would still be responsible for acquiring and curating collections of research materials, and for providing access to these collections to their users. This could involve selecting, purchasing, and preserving OA content, as well as traditional print materials. Research support: Libraries could continue to provide a range of services to support researchers, such as research consultation, data management and preservation, and training in research skills. Access to specialized resources: Libraries often have access to specialized resources and collections that may not be freely available to the general public, such as historical documents or rare books. Community engagement: Libraries can serve as hubs for community engagement and learning, hosting events and workshops, and providing access to resources and technology for local residents. Preservation: Libraries play a critical role in preserving the intellectual record of humanity by collecting, organizing, and storing research materials for the long term. In a fully OA environment, this role would become even more important as libraries work to ensure that research is preserved and remains accessible to future generations.
TAC: What is the best AI text development tool right now?
ChatGPT Response:
It is difficult to identify a single “best” AI text development tool, as the appropriate tool for a given task will depend on a variety of factors, including the specific needs and goals of the user, the type of text that is being generated, and the resources and expertise available. There are many different AI text development tools available, ranging from simple online generators to more complex software programs. Some examples of AI text development tools include: GPT-3 (Generative Pre-trained Transformer 3): GPT-3 is a large, advanced language model developed by OpenAI that can generate text in a variety of styles and languages. It can be used for tasks such as translation, summarization, and text generation.
ChatGPT: ChatGPT is an open-source chatbot that uses the GPT-3 language model to generate responses to user inputs. It can be used to create chatbots for customer service, marketing, or other applications.
Hugging Face: Hugging Face is a platform that provides access to a wide range of natural language processing (NLP) models, including language models, machine translation models, and text classification models. Users can use the platform to train, fine-tune, and deploy NLP models for a variety of tasks.
AI Dungeon: AI Dungeon is an AI-powered text adventure game that uses a language model to generate interactive stories in real-time. Users can input their own responses and choices to shape the direction of the story. There are many other AI text development tools available, and the best tool for a given task will depend on the specific needs and goals of the user. It is important to carefully consider the capabilities and limitations of different tools before choosing one.
TAC: That is a modest answer. Which do you think is strongest for the creation of journal articles?
ChatGPT Response:
As a language model, I am not able to make judgments about the relative strengths or weaknesses of different AI text development tools. There are many different AI text development tools available, and the best tool for a given task will depend on the specific needs and goals of the user, as well as the resources and expertise available. For the creation of journal articles, it may be useful to consider tools that are specifically designed for research or academic writing, or that have advanced language modeling capabilities. Some examples of AI text development tools that might be relevant for journal article writing include: GPT-3 (Generative Pre-trained Transformer 3): GPT-3 is a large, advanced language model developed by OpenAI that can generate text in a variety of styles and languages. It could potentially be used to generate text for journal articles, although it is important to carefully consider the limitations of any AI tool and to supplement the output with additional analysis and interpretation by human researchers.
Hugging Face: Hugging Face is a platform that provides access to a wide range of natural language processing (NLP) models, including language models, machine translation models, and text classification models. Users can use the platform to train, fine-tune, and deploy NLP models for a variety of tasks, including research and academic writing.
AI Dungeon: AI Dungeon is an AI-powered text adventure game that uses a language model to generate interactive stories in real-time. While it is not specifically designed for research or academic writing, it could potentially be used as a tool for generating ideas or inspiration for journal articles. Ultimately, the best AI text development tool for journal article writing will depend on the specific needs and goals of the user, and it is important to carefully consider the capabilities and limitations of different tools before choosing one.
TAC: In Borges’ The Library of Babel, what is the meaning of the hexagon?
ChatGPT Response:
In “The Library of Babel,” a short story by Argentine author and poet Jorge Luis Borges, the hexagon is a symbol of the universe and the library in which it is contained. The library is described as a vast and nearly limitless space that is filled with every book that has ever been written, as well as every book that will ever be written. The library is organized in the shape of a giant, interconnected network of galleries and rooms, with each gallery or room containing a specific set of books. The structure of the library is said to be based on the shape of a hexagon, with each hexagonal room connected to six others. The symbolism of the hexagon in “The Library of Babel” is open to interpretation, but it is often seen as a representation of the interconnectedness and complexity of the universe. The hexagon is a repeating, geometric shape that is found in many natural systems, and it is often used to symbolize balance and harmony. In the context of the library, the hexagon could represent the idea that every book and every piece of knowledge is connected to many others, and that the library as a whole is a vast, interconnected web of information. Overall, the meaning of the hexagon in “The Library of Babel” is likely to be interpreted differently by different readers, but it is generally seen as a symbol of the vastness and complexity of the universe and the library that contains it.
TAC: What is one of the current technological limitations of AI systems?
ChatGPT Response:
One of the current technological limitations of AI systems is their inability to fully understand and replicate human-like reasoning and common sense. While AI systems can be trained to perform many tasks effectively, they often lack the ability to understand and interpret complex or nuanced situations in the same way that a human can. For example, AI systems may struggle to understand and interpret figurative language or irony, or to apply their knowledge in novel or unexpected situations. They may also have difficulty understanding the context or implications of certain words or phrases, or the relationships between different concepts or pieces of information. Another limitation of AI systems is their reliance on large amounts of data for training and evaluation. While AI systems can be highly effective at learning from large datasets, they may struggle to generalize their knowledge to new situations or to learn from smaller amounts of data. Other technological limitations of AI systems include their susceptibility to bias, their inability to fully replicate human creativity or intuition, and their reliance on specific hardware and software environments.
TAC: What will people do when robots can do everything?
ChatGPT Response:
As robots and artificial intelligence continue to advance, it is possible that they will be able to perform a wider range of tasks and duties, potentially leading to some job displacement in certain industries. However, it is unlikely that robots will be able to fully replace all human labor, as there are many tasks that require human-like qualities such as creativity, empathy, and adaptability. It is also important to consider the ethical implications of replacing human labor with robots, as this could have significant social and economic impacts. Therefore, it is important for society to carefully consider how to integrate new technologies in a way that is beneficial for all stakeholders. One potential solution is to focus on retraining and upskilling workers for new roles that may be more suitable for humans, such as roles that involve managing and maintaining the technology, or roles that require more uniquely human skills and qualities. Another option is to consider redistributing wealth and resources in a way that allows people to pursue other interests and activities outside of paid work, such as volunteering, learning, or leisure activities.
| 2023-01-11T00:00:00 |
2023/01/11
|
https://scholarlykitchen.sspnet.org/2023/01/11/chatgpt-thoughts-on-ais-impact-on-scholarly-communications/
|
[
{
"date": "2023/01/11",
"position": 34,
"query": "AI economic disruption"
}
] |
AI and the future of work: 5 experts on what ChatGPT ...
|
AI and the future of work: 5 experts on what ChatGPT, DALL-E and other AI tools mean for artists and knowledge workers
|
https://theconversation.com
|
[
"Casey Greene",
"Daniel Acuña",
"Kentaro Toyama",
"Lynne Parker",
"Mark Finlayson"
] |
From steam power and electricity to computers and the internet, technological advancements have always disrupted labor markets, pushing out some jobs while ...
|
From steam power and electricity to computers and the internet, technological advancements have always disrupted labor markets, pushing out some jobs while creating others. Artificial intelligence remains something of a misnomer – the smartest computer systems still don’t actually know anything – but the technology has reached an inflection point where it’s poised to affect new classes of jobs: artists and knowledge workers.
Specifically, the emergence of large language models – AI systems that are trained on vast amounts of text – means computers can now produce human-sounding written language and convert descriptive phrases into realistic images. The Conversation asked five artificial intelligence researchers to discuss how large language models are likely to affect artists and knowledge workers. And, as our experts noted, the technology is far from perfect, which raises a host of issues – from misinformation to plagiarism – that affect human workers.
To jump ahead to each response, here’s a list of each:
Creativity for all – but loss of skills?
Potential inaccuracies, biases and plagiarism
With humans surpassed, niche and ‘handmade’ jobs will remain
Old jobs will go, new jobs will emerge
Leaps in technology lead to new skills
Creativity for all – but loss of skills?
Lynne Parker, Associate Vice Chancellor, University of Tennessee
Large language models are making creativity and knowledge work accessible to all. Everyone with an internet connection can now use tools like ChatGPT or DALL-E 2 to express themselves and make sense of huge stores of information by, for example, producing text summaries.
Especially notable is the depth of humanlike expertise large language models display. In just minutes, novices can create illustrations for their business presentations, generate marketing pitches, get ideas to overcome writer’s block, or generate new computer code to perform specified functions, all at a level of quality typically attributed to human experts.
These new AI tools can’t read minds, of course. A new, yet simpler, kind of human creativity is needed in the form of text prompts to get the results the human user is seeking. Through iterative prompting – an example of human-AI collaboration – the AI system generates successive rounds of outputs until the human writing the prompts is satisfied with the results. For example, the (human) winner of the recent Colorado State Fair competition in the digital artist category, who used an AI-powered tool, demonstrated creativity, but not of the sort that requires brushes and an eye for color and texture.
While there are significant benefits to opening the world of creativity and knowledge work to everyone, these new AI tools also have downsides. First, they could accelerate the loss of important human skills that will remain important in the coming years, especially writing skills. Educational institutes need to craft and enforce policies on allowable uses of large language models to ensure fair play and desirable learning outcomes.
Second, these AI tools raise questions around intellectual property protections. While human creators are regularly inspired by existing artifacts in the world, including architecture and the writings, music and paintings of others, there are unanswered questions on the proper and fair use by large language models of copyrighted or open-source training examples. Ongoing lawsuits are now debating this issue, which may have implications for the future design and use of large language models.
As society navigates the implications of these new AI tools, the public seems ready to embrace them. The chatbot ChatGPT went viral quickly, as did image generator Dall-E mini and others. This suggests a huge untapped potential for creativity, and the importance of making creative and knowledge work accessible to all.
Potential inaccuracies, biases and plagiarism
Daniel Acuña, Associate Professor of Computer Science, University of Colorado Boulder
I am a regular user of GitHub Copilot, a tool for helping people write computer code, and I’ve spent countless hours playing with ChatGPT and similar tools for AI-generated text. In my experience, these tools are good at exploring ideas that I haven’t thought about before.
I’ve been impressed by the models’ capacity to translate my instructions into coherent text or code. They are useful for discovering new ways to improve the flow of my ideas, or creating solutions with software packages that I didn’t know existed. Once I see what these tools generate, I can evaluate their quality and edit heavily. Overall, I think they raise the bar on what is considered creative.
But I have several reservations.
One set of problems is their inaccuracies – small and big. With Copilot and ChatGPT, I am constantly looking for whether ideas are too shallow – for example, text without much substance or inefficient code, or output that is just plain wrong, such as wrong analogies or conclusions, or code that doesn’t run. If users are not critical of what these tools produce, the tools are potentially harmful.
Recently, Meta shut down its Galactica large language model for scientific text because it made up “facts” but sounded very confident. The concern was that it could pollute the internet with confident-sounding falsehoods.
Another problem is biases. Language models can learn from the data’s biases and replicate them. These biases are hard to see in text generation but very clear in image generation models. Researchers at OpenAI, creators of ChatGPT, have been relatively careful about what the model will respond to, but users routinely find ways around these guardrails.
Another problem is plagiarism. Recent research has shown that image generation tools often plagiarize the work of others. Does the same happen with ChatGPT? I believe that we don’t know. The tool might be paraphrasing its training data – an advanced form of plagiarism. Work in my lab shows that text plagiarism detection tools are far behind when it comes to detecting paraphrasing.
These tools are in their infancy, given their potential. For now, I believe there are solutions to their current limitations. For example, tools could fact-check generated text against knowledge bases, use updated methods to detect and remove biases from large language models, and run results through more sophisticated plagiarism detection tools.
With humans surpassed, niche and ‘handmade’ jobs will remain
Kentaro Toyama, Professor of Community Information, University of Michigan
We human beings love to believe in our specialness, but science and technology have repeatedly proved this conviction wrong. People once thought that humans were the only animals to use tools, to form teams or to propagate culture, but science has shown that other animals do each of these things.
Meanwhile, technology has quashed, one by one, claims that cognitive tasks require a human brain. The first adding machine was invented in 1623. This past year, a computer-generated work won an art contest. I believe that the singularity – the moment when computers meet and exceed human intelligence – is on the horizon.
How will human intelligence and creativity be valued when machines become smarter and more creative than the brightest people? There will likely be a continuum. In some domains, people still value humans doing things, even if a computer can do it better. It’s been a quarter of a century since IBM’s Deep Blue beat world champion Garry Kasparov, but human chess – with all its drama – hasn’t gone away.
In other domains, human skill will seem costly and extraneous. Take illustration, for example. For the most part, readers don’t care whether the graphic accompanying a magazine article was drawn by a person or a computer – they just want it to be relevant, new and perhaps entertaining. If a computer can draw well, do readers care whether the credit line says Mary Chen or System X? Illustrators would, but readers might not even notice.
And, of course, this question isn’t black or white. Many fields will be a hybrid, where some Homo sapiens find a lucky niche, but most of the work is done by computers. Think manufacturing – much of it today is accomplished by robots, but some people oversee the machines, and there remains a market for handmade products.
If history is any guide, it’s almost certain that advances in AI will cause more jobs to vanish, that creative-class people with human-only skills will become richer but fewer in number, and that those who own creative technology will become the new mega-rich. If there’s a silver lining, it might be that when even more people are without a decent livelihood, people might muster the political will to contain runaway inequality.
Old jobs will go, new jobs will emerge
Mark Finlayson, Associate Professor of Computer Science, Florida International University
Large language models are sophisticated sequence completion machines: Give one a sequence of words (“I would like to eat an …”) and it will return likely completions (“… apple.”). Large language models like ChatGPT that have been trained on record-breaking numbers of words (trillions) have surprised many, including many AI researchers, with how realistic, extensive, flexible and context-sensitive their completions are.
Like any powerful new technology that automates a skill – in this case, the generation of coherent, albeit somewhat generic, text – it will affect those who offer that skill in the marketplace. To conceive of what might happen, it is useful to recall the impact of the introduction of word processing programs in the early 1980s. Certain jobs like typist almost completely disappeared. But, on the upside, anyone with a personal computer was able to generate well-typeset documents with ease, broadly increasing productivity.
Further, new jobs and skills appeared that were previously unimagined, like the oft-included resume item MS Office. And the market for high-end document production remained, becoming much more capable, sophisticated and specialized.
I think this same pattern will almost certainly hold for large language models: There will no longer be a need for you to ask other people to draft coherent, generic text. On the other hand, large language models will enable new ways of working, and also lead to new and as yet unimagined jobs.
To see this, consider just three aspects where large language models fall short. First, it can take quite a bit of (human) cleverness to craft a prompt that gets the desired output. Minor changes in the prompt can result in a major change in the output.
Second, large language models can generate inappropriate or nonsensical output without warning.
Third, as far as AI researchers can tell, large language models have no abstract, general understanding of what is true or false, if something is right or wrong, and what is just common sense. Notably, they cannot do relatively simple math. This means that their output can unexpectedly be misleading, biased, logically faulty or just plain false.
These failings are opportunities for creative and knowledge workers. For much content creation, even for general audiences, people will still need the judgment of human creative and knowledge workers to prompt, guide, collate, curate, edit and especially augment machines’ output. Many types of specialized and highly technical language will remain out of reach of machines for the foreseeable future. And there will be new types of work – for example, those who will make a business out of fine-tuning in-house large language models to generate certain specialized types of text to serve particular markets.
In sum, although large language models certainly portend disruption for creative and knowledge workers, there are still many valuable opportunities in the offing for those willing to adapt to and integrate these powerful new tools.
Leaps in technology lead to new skills
Casey Greene, Professor of Biomedical Informatics, University of Colorado Anschutz Medical Campus
Technology changes the nature of work, and knowledge work is no different. The past two decades have seen biology and medicine undergoing transformation by rapidly advancing molecular characterization, such as fast, inexpensive DNA sequencing, and the digitization of medicine in the form of apps, telemedicine and data analysis.
Some steps in technology feel larger than others. Yahoo deployed human curators to index emerging content during the dawn of the World Wide Web. The advent of algorithms that used information embedded in the linking patterns of the web to prioritize results radically altered the landscape of search, transforming how people gather information today.
The release of OpenAI’s ChatGPT indicates another leap. ChatGPT wraps a state-of-the-art large language model tuned for chat into a highly usable interface. It puts a decade of rapid progress in artificial intelligence at people’s fingertips. This tool can write passable cover letters and instruct users on addressing common problems in user-selected language styles.
Just as the skills for finding information on the internet changed with the advent of Google, the skills necessary to draw the best output from language models will center on creating prompts and prompt templates that produce desired outputs.
For the cover letter example, multiple prompts are possible. “Write a cover letter for a job” would produce a more generic output than “Write a cover letter for a position as a data entry specialist.” The user could craft even more specific prompts by pasting portions of the job description, resume and specific instructions – for example, “highlight attention to detail.”
As with many technological advances, how people interact with the world will change in the era of widely accessible AI models. The question is whether society will use this moment to advance equity or exacerbate disparities.
| 2023-01-11T00:00:00 |
2023/01/11
|
https://theconversation.com/ai-and-the-future-of-work-5-experts-on-what-chatgpt-dall-e-and-other-ai-tools-mean-for-artists-and-knowledge-workers-196783
|
[
{
"date": "2023/01/11",
"position": 39,
"query": "AI economic disruption"
},
{
"date": "2023/01/11",
"position": 54,
"query": "generative AI jobs"
},
{
"date": "2023/01/11",
"position": 96,
"query": "ChatGPT employment impact"
}
] |
Regulating Artificial Intelligence Requires Balancing Rights ...
|
Regulating Artificial Intelligence Requires Balancing Rights, Innovation
|
https://www.justsecurity.org
|
[
"Bishop Garrison",
"Frank A. Rose",
"Sead Turčalo",
"Lisa Larrimore Ouellette",
"Bill Drexel",
"Richard Gowan",
"Tom Joscelyn",
"Ryan Goodman",
".Wp-Block-Co-Authors-Plus-Coauthors.Is-Layout-Flow",
"Class"
] |
A consistent and unified regulatory framework for AI can help to protect all stakeholders' interests.
|
Across the technology industry, artificial intelligence (AI) has boomed over the last year. Lensa went viral creating artistic avatar artwork generated from real-life photos. The OpenAI chatbot ChatGPT garnered praise as a revolutionary leap in generative AI with the ability to provide answers to complex questions in natural language text. Such innovations have ignited an outpouring of investments even as the tech sector continues to experience major losses in stock value along with massive job cuts. And there is no indication the development of these AI-powered capabilities will slow down from their record pace. Governments and corporations are projected to invest hundreds of billions of dollars on associated technologies globally in the next year.
With this unprecedented growth, however, communities have grown more concerned about the potential risks that accompany AI. Reports indicate Chatbot GPT is already being leveraged by criminals to perpetrate fraud against unsuspecting victims. The Lensa app generated explicit images of individuals without their consent. Georgetown University School of Law’s Center for Privacy and Technology recently released a report highlighting long-held concerns of the use of face recognition in criminal investigations. Jurisdictions often lack the proper policies and procedures necessary to govern the use of face recognition evidence, and that has led to rights violations and wrongful arrests.
Existing Regulatory Frameworks
Faced with these concerns of privacy and safety, a patchwork of state and local regulation has begun to form in the United States. In 2020, Madison, Wisconsin outright banned the use of facial recognition and associated computer vision AI algorithms by any entity. In 2021, the city of Baltimore banned the use of face recognition technology with a limited exception for some use by police. That ban expired in December 2022, as council members continue to determine how to best address the privacy and data collection concerns of the community. Three states – Illinois, Texas, and Washington – have all enacted strict laws pertaining to data and privacy with face recognition. Illinois’s Biometric Information Privacy Act, or BIPA, remains one of the country’s strictest set of AI associated privacy regulations, gaining regular challenges from tech companies over complacency issues. In recent years, a host of states from Alabama to California enacted legislation intended to regulate the use of AI. However, regulation of AI domestically still remains a patchwork, with the U.S. Chamber of Commerce estimating less than an estimated one-third of states have at least one law that specifically addresses the use of AI technologies. Most of the existing laws focus on privacy collection, data protection, and data sharing.
Federally, there currently is no comprehensive law that governs AI development or use. The American Data and Privacy Protection Act, which would have created a national standard and safeguards for personal information collection and address algorithmic bias, failed to pass last year, and divided party control of an arguably hyper-partisan landscape doesn’t immediately give rise to the comity needed to pass new legislation.
The international regulatory landscape is just as uneven, with the European Union and China taking action to protect rights. Last year, the Chinese government’s first major AI regulatory initiative focused on informed consent, in which companies had to inform users whether or not an algorithm was being used to display certain information about them and provide them an opportunity to opt out. The government has since focused on a variety of policy initiatives with different government entities aimed at impacting international development of AI technologies and governance. However, the Chinese government’s own use of AI in privacy-invasive ways remains a deep concern. The European Union’s AI Act is much broader, designed as an all-encompassing framework focused on specific levels of risk associated with the use of AI technology.
However, thus far it has mostly been up to the tech industry to self-regulate when it comes to AI, but in a 2021 survey conducted by the consulting firm McKinsey, only fifty-six percent of responding companies had AI ethics policies and practices in place. Although countries are beginning to establish governance standards, without a unified approach or model guidance, industry will still be required to self-regulate to the requirements of the most arduous laws to which they’ve availed themselves while simultaneously attempting to understand how their business may be affected by pending global legislation.
Toward a Consistent Regulatory Approach
AI presents many possible sweeping benefits through its ability to enhance the capabilities of current technology. When algorithms are properly trained, they can make unbiased decisions, reduce human error by making processes faster and more efficient, solve complex problems, and support a host of other potential improvements to society. Conversely, AI can present challenges and risks from cyberattacks, to the aforementioned support of criminal conduct, the potential misuse of autonomous weapons, general misuse and unforeseen consequences due to poorly or improperly trained models, and a host of other potential threats.
Given the disparities in regulation both domestically and internationally and the inherent levels of risk associated with its use, the United States must pass formal regulation that provides clear guidance for industry and proper protections for society while making room for continued innovation within industry. The government will need to address concerns such as protection of privacy rights and use, aggregation, and security of personal data while ensuring loopholes are closed for potential unforeseen abuses and misuse of associated technologies. It will take a comprehensive framework to achieve this consisting of measured policies that provide protections and not draconian blanket prohibitions. Outright bans don’t allow for industry to collaborate with governments and academia to find thoughtful, sustainable answers to outgoing concerns. Additionally, companies will likely avoid engaging in business in those jurisdictions that prohibit all use, forgoing investments, infrastructure and training that will be crucial for the American workforce moving forward. Finally, setting proper regulations on the development and use of AI will make the United States safer. Ensuring all AI technologies utilized in the country meet baseline safety standards and protocols set by agencies such as the National Institute of Science and Technology, the Department of Defense, and Department of Homeland Security as they relate to cybersecurity and the protection of the Internet of Things, misinformation and disinformation amplification online, and other potential interests that may disrupt security operations will be paramount.
Drafting and passing a legislative framework will be difficult in this Congress, but not necessarily impossible, as legislators on both sides of the aisle have indicated strong interests in, and often concerns about, the capabilities and enhancements AI presents. The Biden administration has provided a model blueprint for an AI Bill of Rights that could serve as a good foundation for federal and state officials to build on. The AI Bill of Rights focuses on five key principles – Safe and Effective Systems, Algorithmic Discrimination Protections, Data Privacy, Notice and Explanation, and Alternative Options – each with its own correlating technical point.
U.S. legislators could also look abroad for models. The EU’s executive office, the EU Council, adopted a common position (or general approach) for its AI Act. Similar to the AI Bill of Rights, the model legislation aims to provide a balance between ensuring the rights of citizens and supporting continued growth and innovation in the sector. Both documents seek to reduce and prevent unsafe practices while allowing industry to succeed and governments to become more efficient. The EU Council AI Act is proposed legislation that takes a risk assessment-based approach while highlighting specific prohibitions, establishes an AI Board for oversight, and presents assessments for conformity, governance framework, and enforcement of law and penalties for violations. The EU Parliament has its own separate legislative process, and its own AI Act is in committee. While the EU Council AI Act takes a more nuanced, risk-based approach to governing the technology, the current draft of the parliament draft legislation has many prohibitions of AI technology to include a blanket ban on “remote biometric systems.” The two bodies will enter negotiations known as a trilogue that is similar to a conference committee in Congress to hopes of reaching an agreement on proposed legislation by the end of this year.
Both the Bill of AI Rights and the EU Council AI Act could serve as a good starting point for comprehensive American legislation, as both documents seek to strike the challenging balance between protections and innovation. Interested parties will have a keen eye set towards the legislative process in the EU, as the two opposing approaches for sweeping bans versus mitigating risk will have to be resolved during the trilogue. The resulting legislation could set a new standard of how nations address all the combined concerns.
If most legislative efforts stall on the federal level, AI regulation still could present a rare opportunity for both parties to work with stakeholders at the state and local levels in a win for bipartisanship. Government and the tech industry can work together with community leaders and subject matter experts to smartly shape AI regulation so that they don’t have a chilling effect on innovation or unforeseen consequences on positive uses of the technology. In the meantime, industry leaders should work to provide reasonable transparency about company actions in the absence of stronger regulation to help put government and societal concerns at ease.
Government officials must recognize that the AI industry has been the lead in development of this technology and endeavored at self-regulation for a long time. I’ve seen this personally as a member of the industry in a Government Affairs and Public Policy position. Working with companies to find reasonable protections for privacy and other concerns is paramount in maintaining trust and safety between society, government, and industry, and such a collaborative effort ensures that the best possible practices are established, and healthy, reasonable safeguards are put in place. Without such an effort, society runs the risk of creating policies that allow unconscious bias within algorithms, loopholes within otherwise acceptable business cases that allow for abuse and misuse by third party actors, and other negative unforeseen consequences associated with AI technology. These actions will erode societal trust in the technology as well as institutions meant to serve and protect it.
All interested parties are working towards the same goal: the protection of the rights and safety of American citizens and allies. Clear frameworks exist as models for congressional legislation that can provide much needed guidance and regulation for the tech industry as the world witnesses the evolutionary leap of AI technologies. 2023 could prove to be a major inflection point for policy, law, and regulation that govern a variety of this industry. The U.S. government must also work with communities and industry leaders to properly draft protections that won’t have a chilling effect on innovation. This is a historic opportunity to shape the future of the world through this pivotal and powerful technology. The United States should do what it has done for generations now when it comes to innovative thought and be a world leader ensuring AI supports society by providing the most benefits while producing the least possible harm.
IMAGE: Futuristic digital circuit background.(Getty Images)
FEATURED IMAGE: Futuristic digital circuit background with a glowing dotted and line.
| 2023-01-11T00:00:00 |
2023/01/11
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https://www.justsecurity.org/84724/regulating-artificial-intelligence-requires-balancing-rights-innovation/
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[
{
"date": "2023/01/11",
"position": 24,
"query": "government AI workforce policy"
}
] |
Are we legally required to offer benefits to part-time ...
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Are we legally required to offer benefits to part-time employees?
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https://www.shrm.org
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This article reviews requirements for employers to offer certain benefits to part-time employees.
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Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2023-01-11T00:00:00 |
https://www.shrm.org/topics-tools/tools/hr-answers/legally-required-to-offer-benefits-to-part-time-employees
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[
{
"date": "2023/01/11",
"position": 33,
"query": "government AI workforce policy"
}
] |
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2023 Legislative Priorities
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2023 Legislative Priorities
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https://www.naswa.org
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Increase investment in Workforce Innovation and Opportunity Act (WIOA) programs serving dislocated workers, adults, and youth.
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Enhance Federal Investments
Increase investment in Workforce Innovation and Opportunity Act (WIOA) programs serving dislocated workers, adults, and youth to provide American workers with more access to skills training and education opportunities aligned with in-demand occupations and American businesses more support for rapid response and worker training.
Increase permanent funding for the Wagner-Peyser Act to modernize the career services and job coaching infrastructure and provide American businesses more support with hiring and job quality.
Comprehensive job search assistance is a proven cost-effective service for unemployed jobseekers and is critical to combat unemployment and smooth labor market transitions.
Crucial needs across all WIOA programs include staffing, the adoption of new service models combining in-person and virtual services, supporting technologies, and technical assistance.
Maintain investment for governors’ statewide needs at 15%. WIOA requires states to set aside 15% of WIOA Title I formula funding to develop statewide solutions to cross-region challenges facing both employers and priority populations. These flexible investments ensure states can address high-priority workforce issues cost-effectively and build the evidence and infrastructure that local areas depend on.
Establish new dedicated funding for states to create and scale statewide and regional sector strategies.
Avoid short-term, episodic, and/or competitive funding opportunities that disrupt planning and hiring, waste critical resources on repeat startup or shutdown activities, and create access barriers for some states, especially small states, with limited grant-writing resources.
Scale up apprenticeships by providing enhanced investments through line-item formula funding.
Provide states technical assistance to foster stronger cross-sector partnerships between workforce and apprenticeship entities.
Develop a data infrastructure for apprenticeship programs that enables state-driven research to inform the policy and practice, in addition to accountability that does not cause disproportionate administrative burdens for states or businesses.
Strengthen subsidized employment opportunities which have a long history of positive benefits for disadvantaged workers and the broader economy. This is especially true when skills training aligns with in-demand employment opportunities and programming addresses both work disincentives and job displacement concerns.
Support the states’ abilities to promote equal opportunity in workforce programs. Provide training and/or funding for states to deliver nondiscrimination training to state and local equal opportunity officers, organizations, and vendors receiving WIOA grants. Require consistent reporting elements on limited English proficiency between the U.S. Department of Labor’s Civil Rights Center and the Employment and Training Administration.
Continue funding of the Reemployment Services and Eligibility Assessment (RESEA) program at full authorization levels which has a proven track record of accelerating unemployment insurance (UI) beneficiaries’ return to work and the additional benefit of reducing UI payments.
Enhance the capacity of state agencies to conduct and fund outreach efforts to better engage underserved communities and priority populations in the workforce system’s services and supports.
Increase flexibility for states
Grant states flexibility under WIOA to expand eligibility to workers, especially workers at high risk of displacement, so that states can proactively support workers’ transitions to priority sectors.
Promote flexibility by allowing states to use funding across WIOA programs to better anticipate future labor market disruptions and help workers not traditionally eligible for assistance and at-risk incumbent workers prepare for labor market success.
Address youth employment needs by providing states more flexibility under the WIOA youth program to target priority youth populations based on state and local needs. New labor market entrants face long-term employment and earnings repercussions due to the COVID-19 pandemic, and targeted solutions are needed to stem these impacts.
Sustain state flexibility for Wagner-Peyser staffing to allow state and local employees, contractors, other personnel, or a combination thereof, to administer the Employment Service (ES) program.
Expand eligibility for Disabled Veteran Outreach Program (DVOP) services to include veterans unemployed as a result of a federal or state declared natural disaster or emergency, as well as veterans who fall under the Department of Veterans Affairs (VA) Care or Presumption of Care eligibility rules as defined in the Honoring our Promise to Address Comprehensive Toxics (PACT) Act of 2022.
Maintain and safeguard the vital role of veteran’s programs in the workforce development system. We oppose any efforts to separate veteran’s programs from the U.S. Department of Labor. The state workforce system is best suited to serve veterans, transitioning service members, and military spouses because of its alignment and integration of employment and career services. This includes serving veterans facing significant barriers to workforce re-entry while avoiding duplication of federal job training, education, and human resource programs.
Align workforce with post-secondary, infrastructure, and industrial investments
New federal infrastructure and industrial investments that build transportation networks, expand America’s energy, semiconductor, and electric vehicle production capacity, enhance certain workforce sectors, or expand the reach of broadband and workforce system career, training, and supportive services should align to give jobseekers an opportunity to acquire skills and access high-quality jobs.
Require partnerships among education, workforce, and labor market information systems to support more informed planning and successful outcomes. New investments to support post-secondary opportunities for underemployed or unemployed Americans should require partnerships between these systems to drive successful outcomes.
Fund state workforce agencies and other workforce system partners in supporting the implementation of federal infrastructure and industrial investments. The scale of these federal investments through the passage of legislation like the Creating Helpful Incentives to Produce Semiconductors and Science (CHIPS) Act, the Inflation Reduction Act, and the Infrastructure Investment and Jobs Act will generate significant workforce development needs. Federal agencies shall be aligned and work together to support state workforce agencies with resources, technical assistance, and guidance to meet these needs.
Promote economic mobility by reducing systemic barriers to successful outcomes
These reforms will ensure states and their local partners can more successfully support economic mobility for a broad range of Americans – recently unemployed jobseekers, historically marginalized communities, people with disabilities, Temporary Assistance for Needy Families (TANF) and Supplemental Nutrition Assistance Program (SNAP) recipients, youth transitioning out of foster care, veterans, formerly incarcerated individuals, those who have been out of the labor market, and individuals in need of career readiness skills.
Increase access to and availability of childcare and other supportive services on an ongoing basis to broaden labor market participation.
Support digital equity and access, including broadband expansion.
Improve alignment of workforce, human services, housing, and education agencies at the federal level. Better alignment of policies, eligibility and benefit structures, regulations, state plan requirements, performance metrics, monitoring, and evidence-building efforts across these agencies will drive more successful outcomes.
Invest in digital transformation efforts and supporting technologies for holistic, interoperable service delivery that will encourage program partners to work collaboratively to improve customer access, security, and experience.
| 2023-01-11T00:00:00 |
https://www.naswa.org/advocacy/government-relations/2023-legislative-priorities
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[
{
"date": "2023/01/11",
"position": 40,
"query": "government AI workforce policy"
}
] |
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FAQ - Company
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Company
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https://www.skyhive.ai
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To promote responsible AI practices, SkyHive has adopted a set of ethical AI principles and a corresponding policy that provides guidance for data users and AI ...
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SkyHive’s groundbreaking, patented invention decoding human capability is the only technology in the world that can:
- Automate your talent architecture.
- Automate and normalize your job taxonomies and skill ontologies across all of your people technologies.
- Integrate with your core HR systems like Workday, Microsoft, and SAP to ensure a) that you are getting more value out of your existing technology investments, and b) that you do not require your employees to use yet another technology.
- Provide you with a “Human Capital Operating System” that drives ongoing and real-time value to your entire HR technology stack.
- Be a “decoder ring” that provides you with real-time, streamed visibility into the changes happening outside of your organization as well as inside of your organization.
- Provide you automated real-time labor market intelligence at the most granular level available in the world.
Our customers will tell you that SkyHive is the world’s most advanced technology to support you with answering four primary questions (along with a lot more)
1. What are the skills of my workforce today (at an individual, departmental, company, or community-wide level).
2. What are the jobs and skills emerging in the external labor market at a market, industry, or competitive level and how will this impact my workforce now and into the future?
3. What are the gaps?
4. How can we leverage our existing training and development initiatives and technologies to fill those gaps through reskilling our workforce at pace and at scale?
Each of these represents millions of dollars in annual savings and time-to-value is weeks, not months or years.
| 2023-01-11T00:00:00 |
https://www.skyhive.ai/company/f-a-q
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[
{
"date": "2023/01/11",
"position": 91,
"query": "government AI workforce policy"
}
] |
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Artificial Intelligence | SoKat Federal
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Artificial Intelligence
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https://www.sokat.com
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[] |
Tailored AI pipelines built to meet specific agency requirements and data governance policies · Supports secure data handling, PII protection, and compliance ...
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Artificial Intelligence:
Our Core Capability,
Your Strategic Advantage
At SoKat, Artificial Intelligence isn’t just a service—it’s the foundation of everything we do. As a trusted AI partner to the Department of Veterans Affairs, the Centers for Medicare & Medicaid Services, and other federal agencies, we develop responsible, human-centered AI solutions that improve lives and drive operational excellence. From suicide ideation detection models to personalized AI healthcare chatbots and streamlining operations for optimal efficiency, our solutions are purpose-built to tackle the government’s most pressing challenges. With a proven record of innovation, trust, and measurable outcomes, SoKat stands at the forefront of ethical and impactful AI for public service.
| 2023-01-11T00:00:00 |
https://www.sokat.com/copy-of-act-iac-awards-1
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[
{
"date": "2023/01/11",
"position": 92,
"query": "government AI workforce policy"
}
] |
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Apple's first US labor union reaches new milestone for tech ...
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Apple’s first US labor union reaches new milestone for tech industry
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https://www.cnn.com
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[
"Catherine Thorbecke"
] |
The worker group, based out of a mall near Baltimore, is organized with the International Association of Machinists and Aerospace Workers (IAMAW) union. Risa ...
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CNN —
Workers at Apple’s first unionized retail store began collectively bargaining with management on Wednesday, in a milestone moment not only for the iPhone company but for all of Big Tech.
Apple store workers in Towson, Maryland, who made history in June by voting to form the first union at one of the tech giant’s US stores, started contract negotiations with Apple management on Wednesday morning. The worker group, based out of a mall near Baltimore, is organized with the International Association of Machinists and Aerospace Workers (IAMAW) union.
Risa Lieberwitz, a professor of labor and employment law at the Cornell University School of Industrial and Labor Relations, said “there’s a lot at stake” for Apple employees at this and other stores as the negotiations commence. “Other Apple workers will be watching this,” she said. “Other workers in the tech industry will be watching this.”
Customers shop at The Apple Store at the Towson Town Center mall, the first of the company's retail locations in the U.S. where workers voted over the weekend to unionize, on June 20, 2022 in Towson, Maryland. Following a late-pandemic era wave of workers demanding higher pay, better benefits and more negotiating leverage, 65 of the 98 workers at the Towson Apple Store voted to join the International Association of Machinists and Aerospace Workers union on June 18. Chip Somodevilla/Getty Images
The success of the Towson Apple store workers’ unionization bid came amid a broader wave of workplace organizing. A tight labor market lent workers new leverage and the Covid-19 pandemic exposed some of the inequities faced by America’s frontline workers. New unionizing efforts emerged among workers in stores and warehouses from companies such as Amazon, Starbucks and Apple.
The rise of worker organizing efforts has prompted a range of responses from top tech companies. Amazon has so far refused to recognize its first union and engage in negotiations after a landmark union win last spring and continues to fight its legitimacy.
Microsoft, by contrast, has publicly embraced its first union and said this month it looks “forward to engaging in good faith negotiations as we work towards a collective bargaining agreement.”
Apple appears to be the first of those three companies to join the negotiating table with its unionized workers, but it comes after some tensions. Apple was previously hit with a complaint from the National Labor Relations Board over allegations that it interrogated employees regarding their support for a union and selectively prohibited the placement of pro-union fliers in a break room at a New York City Apple store. (Apple pushed back at those claims in a filing with the NLRB.)
An Apple spokesperson told CNN in a statement that the company “will engage with the union representing our team in Towson respectfully and in good faith.” The statement added that the company values the work of its retail team, and touted the company’s compensation and benefits for retail staffers.
David DiMaria, the lead organizer of the Towson Apple store union campaign with the IAMAW, said excitement was high among the Apple store workers ahead of Wednesday’s first meeting. “First contracts are a lot of prep work, and they’ve been putting in a lot of time doing all that prep,” he told CNN. “And now it all pays off, and they actually get to go to the table and start to negotiate their contracts, so spirits are high. They’re really excited and they can’t wait to get there.”
Issues that are top of mind for the bargaining unit include pay, working conditions, and, mostly, having a voice at work and “being a part of that decision-making process in the things that affect them on the day-to-day is really important,” according to DiMaria.
Lieberwitz noted that negotiating a first contract for a union in the United States is “generally difficult” regardless of the industry, as many employers have historically resisted negotiating or have attempted to draw-out the process, as the longer a union goes without a contract, the longer a company will not have to agree to any of worker’s demands. An analysis of Bloomberg Law labor data found that it takes well over a year (465 days) on average for a union that won an election to ratify a first contract.
For the workers, she said, “it will require patience, a recognition that this may take a long time, and sticking together in that sense of labor solidarity.”
| 2023-01-11T00:00:00 |
2023/01/11
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https://www.cnn.com/2023/01/11/tech/apple-store-union-negotiations-begin
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[
{
"date": "2023/01/11",
"position": 15,
"query": "AI labor union"
}
] |
Michigan Pickle Producers Ratify New Contract
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Michigan Pickle Producers Ratify New Contract - The United Food & Commercial Workers International Union
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https://www.ufcw.org
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The four-year agreement includes wage increases of up to 25 percent for all regular full-time employees. During the next three years of the contract, workers ...
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Over 400 members of RWDSU/UFCW Local 87 who work for the Conagra Brands Vlasic Pickles facility in Imlay City, Mich., ratified a new contract in December that raises wages and protects benefits. These workers produce pickles, relish, and pickle spears or chips.
The four-year agreement includes wage increases of up to 25 percent for all regular full-time employees. During the next three years of the contract, workers will receive between 50 and 75 cents per hour wage increases. The Negotiating Committee also fought hard to protect the health care plan benefits, and won reduced costs for the Family Plan.
| 2023-01-11T00:00:00 |
https://www.ufcw.org/actions/victories/michigan-pickle-producers-ratify-new-contract/
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[
{
"date": "2023/01/11",
"position": 19,
"query": "AI labor union"
}
] |
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La Colombe Union - The United Food & ...
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La Colombe Union - The United Food & Commercial Workers International Union
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https://www.ufcw.org
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We are the La Colombe Union, a group of baristas who are unionizing to improve our working conditions, together, in a way that lasts.
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We are Unionizing La Colombe!
We are the La Colombe Union, a group of baristas who are unionizing to improve our working conditions, together, in a way that lasts. We’re proud of what we do and love making coffee and creating an atmosphere that our customers love. This work deserves respect, and we know that the best way to get that is with a union.
We want a voice at this company – one with real power. Having a union means having a say in the issues that affect us at La Colombe. We deserve a union contract that guarantees regular schedules and hours, better wages with consistent raises, adequate staffing, clear discipline policies, and grievance procedures. One good job should be enough to provide for ourselves and our families.
We’ve already won union elections at the Chinatown café in Washington, DC, plus the Andersonville, Armitage, West Loop, and Gold Coast cafés in Chicago, IL, and most recently the Silver Lake and Century City stores as well.
Baristas at coffee shops across the country are coming together to advocate for more at La Colombe, and you can join them! Schedule a call with an organizer to learn how.
What we’re fighting for
Regular Schedules and Hours
We need schedules we can depend on and enough hours to meet our needs. We shouldn’t have to worry about whether or not we’ll have enough hours to keep our paychecks consistent. Our work should work for us and our lives.
Better Wages with Consistent Raises
Our work is valuable and important, and we deserve to be compensated fairly for it. We need guaranteed yearly raises that keep up with the cost of living and set pay scales that match our experience, not low pay caps that leave us with no room to grow.
Adequate Staffing
We love serving our customers and providing them with the coffee and atmosphere that keeps them coming back. We need enough staff to make sure we can do so efficiently and effectively. We shouldn’t have to feel stretched thin without enough time or people to keep our cafes running.
Clear Discipline Policies and Grievance Procedures
With a union contract, we would have protections and grievance procedures set in place so we can’t be unfairly disciplined or fired. We need a “just cause” progressive discipline system, not opaque criteria.
“It’s good knowing that we all have each other’s backs and best intentions in mind. We come together to decide what is best for us instead of having someone tell us what we have is good enough. It could always be better. – Theresa, La Colombe West Loop | Chicago, IL
“To me, a union means that my coworkers and I will be on a more equal footing with the corporate leadership of La Colombe, allowing us to negotiate for wages which will better support all of us, especially those of us raising children or paying off debt. I think our union can make this job into a good job period, not just good by the low standards of the service industry.” – Mya, La Colombe Chinatown | Washington, D.C.
“I want to be a part of a union so that my coworkers and I can have a say in our workplace. Even with the most well meaning and generous employers, I would always prefer working somewhere where conditions were negotiated by workers instead of decided for us!” – Daniel, La Colombe West Loop | Chicago, IL
“The union we have built has been an expression of the uniting goals and values that my coworkers and I share. We may have different needs and situations but we can all agree on the core issues that are shared by all La Colombe workers and fight for our voice to be heard!” – Jules, La Colombe Chinatown | Washington, D.C.
Start a Union at your La Colombe
Want to unionize your La Colombe? Fill out this form to speak with an organizer!
First Name *
Last Name *
Email *
Phone* *
Your zip code *
What brings you here today? Can you tell us more about your experience at work and what you want to improve there?
Your worker story… *
*By providing my cell phone number, I consent to receive blast text messages and robocalls on any topic from my local UFCW, International Union, and their affiliates. I reserve the right to opt-out at any time. We promise that we will not abuse your consent to text or call you.
| 2023-01-11T00:00:00 |
https://www.ufcw.org/actions/campaign/la-colombe-union/
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[
{
"date": "2023/01/11",
"position": 35,
"query": "AI labor union"
}
] |
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Meta's progress and learnings in AI fairness ...
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Meta’s progress and learnings in AI fairness and transparency
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https://ai.meta.com
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... AI systems work to understand the intent behind the click. ... Among the provisions tested by 53 AI startups and companies operating in the European Union ...
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While AI has brought huge advancements to humanity and our planet, it also has the potential to cause unintended consequences, and technology companies must proactively work to mitigate these issues. So, as we did in 2021, we’re providing an update on our Responsible AI efforts over the past year.
The work described in this blog post includes datasets, balancing privacy and fairness, preventing bias in ad delivery systems, avoiding harmful or disrespectful associations, giving people more control over what they see, offering more transparency into AI models, and collaborating on standards and governance.
Our Responsible AI efforts are propelled by a cross-disciplinary team whose mission is to help ensure that AI at Meta benefits people and society. Our Civil Rights Team, for example, has been integral to our work, bringing subject-matter expertise with technical, policy, and legal assessments and collaboratively designing technical solutions.
Meta’s work on Responsible AI is driven by our belief that everyone should have equitable access to information, services, and opportunities. We believe that the responsible foundation we are building will ultimately shape future technologies, including the metaverse. As we reflect on the progress we made in 2022, we hope to foster more collaborative and transparent dialogue across disciplines and audiences about the path ahead for these critical issues.
Building diverse datasets and powerful tools for more inclusive AI products
One way we are addressing AI fairness through research is the creation and distribution of more diverse datasets. Datasets that are used to train AI models can reflect biases, which are then passed on to the system. But biases might also be due to what isn’t the training data. A lack of diverse data — or data that represents a wide range of people and experiences — can lead to AI-powered outcomes that reflect problematic stereotypes or fail to work equally well for everyone.
In 2022, we worked to prepare the Casual Conversations v2 (CCv2) dataset, which is unique in the field in terms of its proposed categories and the countries where the data collection will take place. This work, which we will release in 2023, is a continuation of the Casual Conversations dataset we released in 2021, which is composed of more than 45,000 videos designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of ages, genders, skin tones, and ambient lighting conditions.
In 2022, we also introduced and open-sourced two new datasets to help measure fairness and mitigate potential bias in natural language processing (NLP) models. In particular, these datasets include a more comprehensive representation of different demographic dimensions to measure fairness in these models, including terms for gender identity, age, race, and people with disabilities.
Developing reliable, large-scale ways of measuring fairness and mitigating bias gives AI researchers and practitioners helpful benchmarks that can be used to test NLP systems, driving progress toward the goal of ensuring that AI systems treat everyone fairly. We shared these datasets with the research community so that people can better assess the fairness of their text-based AI systems and expand their terminology.
In addition to our work on datasets, we’ve continued to explore new ways of understanding fairness in complex AI systems. For example, we proposed a method for considering preference-based fairness in personalized recommender systems that would allow practitioners to get a more holistic view of fairness across all groups. We detailed this work in a research paper that was named Outstanding Paper at the 2022 AAAI Conference.
By fostering the development of more inclusive datasets, we can create AI systems with the potential to bring the world closer together, helping people communicate across languages and cultures and creating experiences that reflect the diversity of the more than 3 billion people who use Meta’s platforms.
Protecting privacy while addressing fairness concerns
Improving fairness will often require measuring the impact of AI systems on different demographic populations and mitigating unfair differences. Yet the data necessary to do so is not always available — and even when it is, collecting it and storing it can raise privacy concerns. After engaging with civil rights advocates and human rights groups that further confirmed the fairness challenges, we identified new approaches to help us access data with the potential to meaningfully measure the fairness of the AI models on our platforms across races.
We made advancements in 2022 in our ability to measure whether people’s experiences with our technology differ across race. We launched a research project on Instagram to make progress in assessing and improving our technologies to advance fairness. We worked with Oasis Labs, a privacy-focused technology company that, while honoring their privacy, lets people in the United States who choose to self-identify their race for research purposes do so. We worked with research partners to safeguard survey responses by adapting a well-established privacy-enhancing method called secure multiparty computation (SMPC), in which data is securely distributed among multiple facilitators who together can perform computations over the combined encrypted information.
This encryption-based approach, in which data is split between multiple parties, has been used for years in other fields, such as to secure auctions, distributed voting, and statistical analysis. SMPC provides a strong guarantee that individuals’ responses to the survey cannot be accessed by any party, including Meta.
In the area of ad fairness , our privacy-enhanced version of the Bayesian Improved Surname Geocoding (BISG) method, which we announced in 2021, is being used to make progress by enabling iterative, aggregate measurement (discussed below).
Through approaches like these, we can better examine our systems to identify areas where we can make improvements that get us even closer to our goal of building and operating AI systems that treat everyone fairly.
Innovating to improve fairness in ad delivery
A critical aspect of fairness is ensuring that people of all backgrounds have equitable access to information about important life opportunities, like jobs, credit, and housing. Our policies already prohibit advertisers from using our ad products to discriminate against individuals or groups of people. Specifically, to better protect against discrimination, we give advertisers running housing ads on our platforms a limited number of targeting options while setting up their campaigns, including a restriction on using age, gender, or zip code. However, even with neutral targeting options and model features, factors such as people’s interests, their activity on the platform, or competition across all ad auctions for different audiences could affect how ads are distributed to different demographic groups. Therefore, as part of our settlement with the Department of Justice and our ongoing work with the Department of Housing and Urban Development, we designed and started to roll out a new technology called the Variance Reduction System (VRS), which aims to better ensure an equitable distribution of housing ads and eventually employment and credit ads.
The VRS uses reinforcement learning, a type of machine learning (ML) that learns from trial and error to optimize toward a predefined outcome, so that the audience that ends up seeing an ad for housing, employment, or credit more closely reflects the population of people who are eligible to see that ad.
We’ll do this by regularly measuring the actual audience for a particular ad to see how it compares with the demographic distribution (age, gender, and estimated race or ethnicity) of the audience the advertiser has selected. Importantly, the Variance Reduction System will not be provided with individual-level age, gender, or estimated race/ethnicity to make these determinations but instead will leverage aggregate demographic measurements. It will use the privacy-enhanced BISG method (described above) to measure estimated race. If our measurements show a wide variance in the demographics of the selected audience compared with who is actually seeing the ad, the VRS will automatically act to reduce that variance. In the process, we’ll help ensure that ads are delivered more equitably and can be seen by audiences that otherwise might not have.
Generating responsible associations
Fairness doesn’t just mean improving equitable access to positive opportunities or ensuring that our products work equally well regardless of someone’s demographic characteristics or what language they are using. It also means working to ensure that our AI systems don’t generate content that is harmful or disrespectful to historically marginalized communities.
Meta already has numerous systems and policies in place to detect and remove harmful content such as hate speech. But harmful content of a different type can be generated inadvertently when AI-driven recommendation systems produce a harmful association. This can arise when pieces of content that are otherwise harmless as standalone topics are paired in an offensive way. When associated with groups of people, even benign topics form associations that can become potentially problematic or degrading, exacerbating existing stereotypes.
The risk of problematic associations is a shared challenge across platforms that use AI to make recommendations or generate content, including social media platforms. The harmful conceptual association between groups of people and negative semantic terms can arise through a variety of routes, which can reflect and reinforce biases and bigotries embedded in social and semantic data.
In 2022, we assembled a cross-disciplinary team, including people from our Civil Rights, Engineering, AI Research, Policy, and Product teams, to better understand problematic content associations in several of our end-to-end systems and to implement technical mitigations to reduce the chance of them occurring on our platforms that use AI models.
As part of this collaborative effort, we carefully constructed and systematically reviewed the knowledge base of interest topics for usage in advanced mitigations that more precisely target the problematic associations. As more research is done in this area and shared with the greater community, we expect to build on this progress and to continue to improve our systems.
Giving more control over AI-driven feeds and recommendations
AI-driven feeds and recommendations are a powerful tool for helping people find the people and content they are most interested in, but we want to make sure that people can manage their experience in ways that don’t necessarily rely on AI-based ranking. While we already allow people to adjust their Feed preferences in a variety of ways, this year we also introduced an AI-based feature called Show More/Show Less that lets people directly influence their AI-driven personal recommendations.
When people click to show more or less of a type of content when the buttons are featured on select posts, our AI systems work to understand the intent behind the click. For example, clicking Show Less on a cousin’s post about their new convertible might mean “show me fewer posts by that person” or “show me fewer posts about cars.” An effective recommendation system must be able to differentiate the person’s intent in order to successfully deliver the type of content people want to see, and as more people use this feature, the better it will get at understanding what they do and don’t want to see.
This year on Facebook, we also introduced a Feeds tab, which helps people find the most recent posts from their friends, Favorites, Pages, and groups. People can curate a Favorites list of the friends and Pages they care about most and filter their content in this new tab. We rolled out a similar feature on Instagram, where people can choose to see posts from their favorite accounts in a chronological feed. We also rolled out Following, a feature that allows people to see posts only from people they’re following
Developing new methods for explaining our AI systems
Because AI systems are complex, it is important that we develop documentation that explains how systems work in a way that experts and nonexperts alike can understand. One way we’ve done this is by prototyping an AI System Card tool that provides insight into an AI system’s underlying architecture and helps better explain how the system operates.
System cards are one of the many ways to practice transparency about AI models. For a close look at individual AI models, we shared Model Cards for some of our most impactful research releases, including BlenderBot, an open source language generation model, and OPT-175B, the first 175-billion-parameter language model to be made available to the broader AI research community. We also shared the code and a detailed look into the development process. By sharing these sorts of details about OPT-175B, we aim to accelerate research around language generation systems so the broader field can work toward making these systems safer, more useful, and more robust.
We also introduced our proposal for Method Cards, intended to guide ML engineers on how to mitigate potential shortcomings in order to fix bugs or improve a system’s performance. Our research aims to increase the transparency and reproducibility of ML systems by allowing stakeholders to reproduce the models, understand the rationale behind their designs, and introduce adaptations in an informed way.
As the industry evolves and discussions about model and system documentation and transparency continue, we will further identify opportunities to undertake and iterate on our approach over time, so we can reflect product changes, evolving industry standards, and expectations around AI transparency.
Testing new policy approaches to AI transparency, explainability, and governance
The rapid advance of emerging technologies makes it difficult to fully understand and anticipate how they might eventually impact communities around the world. To help develop forward-looking policies around the development and use of new technology Meta supports Open Loop, a global experimental governance program. By involving governments, tech companies, academia, and civil society, Open Loop aims to connect tech and policy innovation for closer collaboration between those building emerging technologies and those regulating them.
AI transparency and explainability were the focus of two of our recent Open Loop programs. In 2022, we published the findings and recommendations of our policy prototyping program on AI transparency and explainability in Singapore, which was rolled out in the Asia-Pacific (APAC) region in partnership with Singapore’s Infocomm Media Development Authority and Personal Data Protection Commission with 12 APAC companies to test Singapore's Model AI Governance Framework. The report summarizes the feedback received from the participating companies in implementing Singapore’s Model AI Governance Framework, and makes recommendations to policymakers on how to further improve the frameworks based on real-world implementation experience and feedback from industry. A similar exercise was deployed in Mexico, with the support of Mexico’s National Institute for Transparency, Access to Information and Personal Data Protection, where we tested a framework on transparency and explainability for AI systems in the country (forthcoming).
Moreover, as part of our second policy prototyping program in Europe, we recently published our first report and recommendations on the European Artificial Intelligence Act in partnership with European governments and regulatory authorities. Among the provisions tested by 53 AI startups and companies operating in the European Union, we assessed transparency requirements of the draft AI Act, commenting on their clarity, feasibility, and cost-effectiveness. We asked companies what level of technical skill would be necessary to meet the requirement of enabling “human oversight,” as well as the required skill level to enable interpretability. One of the recommendations from the report was to consider distinguishing more clearly between different audiences for explanations and other transparency requirements. We also assessed transparency obligations for individuals interacting with AI systems via a survey with 469 participants from European Union countries who were shown different variations of AI notifications mockups (report forthcoming). The study provides preliminary insights on the effectiveness of disclosure notifications on users’ understanding, trust, and sense of agency.
These Open Loop programs are emblematic of Meta’s evidence-based, broadly collaborative approach to establishing standards around the future of AI governance, and we look forward to continuing these programs in partnership with a broad selection of participating regulators, companies, and expert stakeholders.
Working together to build AI responsibly
By listening to people with lived experiences, subject matter experts, and policymakers, we hope to proactively promote and advance the responsible design and operation of AI systems.
We look forward to sharing more updates in the future and will continue to engage with diverse stakeholders about how we can move forward together responsibly.
| 2023-01-11T00:00:00 |
https://ai.meta.com/blog/responsible-ai-progress-meta-2022/
|
[
{
"date": "2023/01/11",
"position": 56,
"query": "AI labor union"
}
] |
|
Case Clarifies When Employees Can Strike
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Case Clarifies When Employees Can Strike
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https://www.shrm.org
|
[
"Leah Shepherd"
] |
The union argued that ... Labor Unions opens in a new tab · Artificial Intelligence in the Workplace. An organization run by AI is not a futuristic concept.
|
Designed and delivered by HR experts to empower you with the knowledge and tools you need to drive lasting change in the workplace.
Demonstrate targeted competence and enhance credibility among peers and employers.
Gain a deeper understanding and develop critical skills.
| 2023-01-11T00:00:00 |
https://www.shrm.org/topics-tools/employment-law-compliance/case-clarifies-employees-can-strike
|
[
{
"date": "2023/01/11",
"position": 69,
"query": "AI labor union"
}
] |
|
Right to Work Laws: What Employers Need to Know
|
Right to Work Laws: What Employers Need to Know
|
https://blog.axcethr.com
|
[
"Jeanette Coleman",
"Sphr"
] |
AI-generated audio. 6:35. Right to Work Laws: What Employers Need to ... Employees have the ability to decide whether or not they want to join a labor union.
|
The phrase “right-to-work” is thrown around quite frequently, and not just in employment law practices or large human resources departments. Whether or not you operate in a right-to-work state will affect your business, no matter how large or how small it is.
That’s why, even if you have just one employee, you should understand the implications of your state’s right-to-work laws. Here's what the phrase “right-to-work” means and the basics of what employers should know about right to work laws.
RELATED: Know Your Rights - New Mandatory EEOC Poster Released >>
What is a Right-to-Work State?
The phrase “right-to-work” describes a set of employment laws enacted in various states, all of which share a common theme. This theme generally encapsulates a few key ideas designed to protect both workers and their employers. In summary, right-to-work laws can be boiled down to the following:
Employees have the ability to decide whether or not they want to join a labor union.
Contracts that mandate union membership for obtaining or keeping a job are prohibited
Employees have the ability to decide whether or not they want to pay union membership dues in exchange for certain benefits.
Employees who do not belong to a union may still receive many of the protections and rights gained through union collective bargaining processes, even if they do not pay union dues.
Some states are referred to as “right-to-work" states, meaning they have enacted right-to-work laws enforcing the above tenets. In a right-to-work state, employers must treat union and non-union employees equally in terms of the conditions of their employment.
This means that businesses cannot offer union employees more (or more impressive) benefits than their non-union counterparts, or vice versa.
Currently, there are no federal right-to-work laws. This means each state gets to choose for itself whether it will enact such laws. Currently, 27 states (and Guam) have right-to-work laws in place.
Right-to-Work States
Alabama
Arizona
Arkansas
Florida
Georgia
Idaho
Indiana
Iowa
Kansas
Kentucky
Louisiana
Michigan
Mississippi
Nebraska
Nevada
North Carolina
North Dakota
Oklahoma
South Carolina
South Dakota
Tennessee
Texas
Utah
Virginia
West Virginia
Wisconsin
Wyoming
Are “At-Will Employment” and “Right-to-Work” the Same Concepts?
No, the concepts of at-will employment and the right to work are not the same. At-will employment is an employer-friendly legal doctrine allowing employers to hire and terminate an employee "at will."
At-will translates to a hire or termination for any reason, or for no reason at all, so long as the reason isn't related to one of the federally protected traits, such as race, religion, sex, ethnicity, genetic information or certain other characteristics.
Many legal scholars argue that right-to-work laws represent another employer-friendly legal doctrine, but of course, there are arguments on both sides of the issue. Operating a business in a right-to-work state will not affect the status of your workers’ at-will employment.
If, however, your employees belong to a union, you will likely need to abide by the rules of the union contract—which can contradict (and will probably supersede) the principle of at-will employment.
For example, if your union-member employees’ union contract requires that any termination be made only for just cause, you will need to honor that provision of the contract, despite any at-will laws on the books in your state to the contrary. In this way, the two legal concepts may occasionally be at odds with one another. It’s important to consult with outside counsel or your trusted PEO to understand the unique employment law landscape in the states in which you do business.
RELATED: Handle with Care: Layoffs & Terminations >>
Is Kansas a Right-to-Work State?
Yes, Kansas is a right-to-work state. Article 15 of the Kansas Constitution, Section 12, states that employees in the state cannot be denied “the opportunity to obtain or retain employment because of membership or nonmembership in any labor organization.”
In plain terms, this means that (1) Kansas employers are free to hire either union or nonunion workers, and (2) Kansas employers are not permitted to discriminate against an employee based on their union membership (or lack thereof).
Is Missouri a Right-to-Work State?
In 2018, Missouri voters blocked a law passed by the state’s legislature that would have converted Missouri into a right-to-work state. So, despite a very close brush with the legal doctrine, Missouri is currently not a right-to-work state.
What does this mean for Missouri employers? Like their counterparts in right-to-work states, employees in Missouri are not required to join a union and they cannot be fired for either refusing to join a union or for leaving one.
This point was settled by the Supreme Court in the case of Pattern Makers v. NLRB, 473 U.S. 95 (1985). However, employees can still be forced to pay union fees (but not “dues”) in order to work within the state.
Required fees, sometimes called “agency fees” are supposed to cover the actual cost of the union’s representation of the non-member, non-dues paying worker. Even if none of your Missouri employees are union members, they still may have their employment influenced by the effect of unions, including by way of their employment contracts or in collective bargaining efforts.
RELATED: Time to Say Goodbye? 15 Tips for Effective Terminations >>
Stay Ahead of the Curve with the Services of a Trusted PEO
Statewide employment laws, including right-to-work regulations, are always shifting. While many small to mid-sized businesses simply aren’t large enough to stay up to date on ever-changing policies, it doesn’t mean they should go without HR compliance and employee relations expertise.
Consider outsourcing HR functions to an experienced professional employer organization (PEO). Working with a PEO provides your business with access to employee relations and HR experts that can help your company thrive. As a trusted and certified PEO, Axcet HR saves you time, money, worry, and stress, so you can concentrate on growing your core business.
Do you have questions about right-to-work regulations or your current employee policies? We have answers. Schedule a time to speak with our experienced team today >>
| 2023-01-11T00:00:00 |
https://blog.axcethr.com/right-to-work-laws-what-employers-need-to-know
|
[
{
"date": "2023/01/11",
"position": 73,
"query": "AI labor union"
}
] |
|
Amazon union victory at Staten Island warehouse upheld ...
|
Amazon union victory at Staten Island warehouse upheld by federal labor board
|
https://www.cnbc.com
|
[
"Annie Palmer",
"In Annierpalmer"
] |
A federal labor official on Wednesday upheld the results of a historic union election at Amazon's Staten Island warehouse. In April, workers at the ...
|
Amazon workers arrive with paperwork to unionize at the National Labor Relations Board office in Brooklyn, New York, Oct. 25, 2021.
A federal labor agency on Wednesday certified an independent union's landmark victory at Amazon 's Staten Island warehouse and threw out a litany of objections filed by the e-retailer.
In April, a majority of the roughly 8,300 workers at Amazon's Staten Island warehouse, known as JFK8, voted to join the Amazon Labor Union, becoming the company's first unionized facility in the U.S. Amazon sought to overturn the results of the election, alleging the National Labor Relations Board office that oversaw the election interfered in the union drive. Amazon also claimed that the ALU intimidated workers to vote in their favor.
In a filing Wednesday, Cornele Overstreet, a director of the NLRB's Phoenix-based office, said he agreed with a federal labor official's prior ruling that all of Amazon's objections should be dismissed.
Under U.S. labor law, employers are obligated to begin negotiating in good faith with a union after it wins an election and the results are certified. But the process can be beset with delays, as the employer may seek to avoid signing a first contract and both parties hammer out the details of an agreement. According to an analysis by Bloomberg Law, it takes on average 465 days for collective bargaining agreements to be signed between employers and their newly unionized workers.
Amazon can also contest the ruling to the NLRB's board in Washington. Kelly Nantel, an Amazon spokesperson, said in a statement that the company intends to appeal the results.
"As we've said since the beginning, we don't believe this election process was fair, legitimate, or representative of the majority of what our team wants," Nantel said.
Speaking at the New York Times' DealBook Summit late last year, Amazon CEO Andy Jassy said there were "a lot of irregularities" in the union drive, and that the legal process is "far from over."
"I think that it's going to work its way through the NLRB," Jassy said. "It's probably unlikely the NLRB is going to rule against itself, and that has a real chance to end up in federal court."
ALU interim President Chris Smalls wrote in a tweet that the union "beat Amazon fair and square," and called upon Jassy to "come to the table" to sign a contract.
The ALU has struggled to replicate its success after workers voted to join the union at JFK8. Workers at a nearby facility on Staten Island rejected unionization in May, and the ALU lost an election at an Albany warehouse in October.
WATCH: How two friends formed Amazon's first U.S. union and what's next
| 2023-01-11T00:00:00 |
2023/01/11
|
https://www.cnbc.com/2023/01/11/amazon-union-victory-at-new-york-warehouse-upheld-by-labor-board.html
|
[
{
"date": "2023/01/11",
"position": 89,
"query": "AI labor union"
}
] |
Nipasha Mahanta Attorney Nishith Desai
|
Nipasha Mahanta
|
https://natlawreview.com
|
[
"Organization Profile"
] |
More Legal and Business Bylines From Nipasha Mahanta · India's New Labor Codes: Concept of Negotiating Union - (Posted On Tuesday, June 06, 2023) · Engaging ...
|
Professional Biography
Nipasha is a member at Nishith Desai Associates’ Labour and Employment Practice. Nishith Desai Associates is a research based international law firm having offices in Mumbai, Silicon Valley, Bangalore, Singapore, New Delhi, Munich and New York. Nipasha is based out of the Bangalore office of Nishith Desai Associates. Prior to her engagement with Nishith Desai Associates, she has worked with Wipro Limited dealing with labor and employment related matters.
Nipasha specializes in labor and employment laws and has been advising clients on a wide range of employment law matters including matters relating to workforce management, compensation and benefits, disciplinary inquiries, engagement of contingent workers, employee transfers, post-merger employee integrations etc. She has also worked on matters involving HR investigations, conducted HR trainings. Along with this, she handled employment conciliation and litigation matters across various labor courts, high courts and constitutional bodies of the country. She has also drafted employment handbooks relating to disciplinary conduct, equal opportunities amongst others. Her areas of interest include employment and HR law, education and skill development, technology law, constitutional law, jurisprudence and public policy.
Nipasha is a trained Odissi dancer which is a prominent Indian classical dance-form. She takes a keen interest in exploring the expression arts and their impact on one’s well-being. Access to equitable education is a cause she is devoted to.
| 2023-01-11T00:00:00 |
https://natlawreview.com/author/nipasha-mahanta
|
[
{
"date": "2023/01/11",
"position": 98,
"query": "AI labor union"
}
] |
|
AI Startup Scale AI Laid off 20% Workforce
|
Another AI Startup, Scale AI, Laid off 20% Workforce, More Layoffs to Come in 2023
|
https://analyticsdrift.com
|
[
"Disha Chopra",
"Disha Chopra Is A Content Enthusiast",
"She Is An Economics Graduate Pursuing Her Pg In The Same Field Along With Data Sciences. Disha Enjoys The Ever-Demanding World Of Content",
"The Flexibility That Comes With It. She Can Be Found Listening To Music Or Simply Asleep When Not Working",
"Please Enter Your Name Here"
] |
Scale AI, a startup using artificial intelligence and software to label text, images, voice, and video data for machine learning, recently laid off over 20% of ...
|
Scale AI, a startup using artificial intelligence and software to label text, images, voice, and video data for machine learning, recently laid off over 20% of its workforce. The announcement was made by Alexandr Wang, Founder, and CEO of Scale AI, in a blog post saying that the layoff is one of the many steps the company is taking to ensure the long-term success and profitability of the business.
Scale AI is a Sanfrancisco-based startup backed by renowned venture capitalists like Tiger Global, Founders Fund, and Caotue Management. The seven-year-old company started by providing labeled data required to train machine learning models for the development of robotaxis, self-driving trucks, and automated bots used in warehouses and on-demand deliveries, and subsequently for their commercialization.
He said, “I made the decision to grow the team aggressively in order to take advantage of what I thought was our new normal.” He added that extensive hiring was successful when the world shifted online in 2020 and 2021. But once the pandemic got over, the market changed, and the company had not predicted the extent.
Read More: Amidst Ongoing Massive Layoffs, Salesforce Plans to Cut Staff by 10%
Apparently, many bigger companies like Amazon and Meta have provided a near-similar reason for laying off a significant number of their employees in the last few months.
Wang said he takes full responsibility as Scale AI laid off the workforce and said employees would be paid eight weeks of severance and three months of healthcare. The company will also pay one-year tenure equity and provide immigration support for outstation employees.
| 2023-01-11T00:00:00 |
2023/01/11
|
https://analyticsdrift.com/another-ai-startup-scale-ai-laid-off-20-workforce-more-layoffs-to-come-in-2023/
|
[
{
"date": "2023/01/11",
"position": 11,
"query": "AI layoffs"
},
{
"date": "2023/01/11",
"position": 10,
"query": "artificial intelligence layoffs"
}
] |
Software firm Scale AI lays off 20% of workforce
|
scale ai layoffs: Software firm Scale AI lays off 20% of workforce
|
https://m.economictimes.com
|
[] |
Software firm Scale AI lays off 20% of workforce ... US-based software firm Scale AI, which uses software and people to label image, text, voice and video data ...
|
US-based software firm Scale AI, which uses software and people to label image, text, voice and video data for companies creating machine learning algorithms , has laid off 20% of its staff."I have made the difficult decision to reduce the size of our team by 20%, which means saying goodbye to many talented Scaliens," wrote founder and CEO Alexandr Wang in a blog post "We saw strong sales growth through 2021 and 2022. As a result, we increased headcount assuming the massive growth would continue. However, the macro environment has changed dramatically in recent quarters, which is something I failed to predict," he added.Employees who are affected will receive a minimum of eight weeks of severance and three months of healthcare.In addition, the company is waiving the one-year equity cliff for employees with less than one year of tenure and offering immigration assistance to those on visas that require continued employment."I take full responsibility for the decisions that have led us to this point. Over the past several years, interest from enterprises and governments in AI has grown rapidly. As a result, I made the decision to grow the team aggressively in order to take advantage of what I thought was our new normal," Wang wrote in his post.As part of other changes, the company plans to set significant operating expense reduction targets for each function, adjust its hiring targets, and re-evaluate any new offices."We need everyone to do their part to stay cost-conscious and efficient when it comes to spending throughout the year," Wang said.Major technology companies across the global, including startups, have been trimming their headcount to better fight the global macroeconomic headwinds , soaring inflation and rising interest rates The dwindling advertisement revenues have further weighed down the growth prospects of these firms and made their path to profitability more strenuous.Amazon announced last week the ecommerce giant will lay off over 18,000 employees , starting January 18. Amazon CEO Andy Jassy, in an email to staff, cited "uncertain economy" and "rapid hiring" as reasons behind the job cuts."Between the reductions we made in November and the ones we're sharing today, we plan to eliminate just over 18,000 roles," Jassy said in the statement to his staff. The company had laid off about 10,000 staff in November Media company Vimeo also announced a fresh round of layoffs in the first week of 2023, which is set to affect 11% of its employees . Vimeo employed around 1,200 workers as of December 2021, according to its annual regulatory filing.In an email to staff, Vimeo CEO Anjali Sud cited the “uncertain economic environment” for the layoffs.(Illustration by Rahul Awasthi)
| 2023-01-11T00:00:00 |
https://m.economictimes.com/tech/technology/software-firm-scale-ai-lays-off-20-of-workforce/articleshow/96908319.cms
|
[
{
"date": "2023/01/11",
"position": 18,
"query": "AI layoffs"
}
] |
|
How Will Artificial Intelligence Change Education? Let's ...
|
How Will Artificial Intelligence Change Education? Let’s Ask an AI to Find Out…
|
https://michiganvirtual.org
|
[
"Justin Bruno",
"Justin Has Years Of Experience In Education",
"Working To Innovate",
"Make Learning A Better Experience For Those Of All Ages. He S Worked In Research",
"Policy",
"Product Management",
"As An Social Studies Teacher In His Home State Of Louisiana. He Earned A B.A.",
"M.A. In Education Louisiana State University As Well As A Master S In Educational Technology Boise State University. His Focus Areas Include Artificial Intelligence In Education",
"Agile",
"Innovative Learning Development"
] |
Some potential changes that AI could bring about in K-12 education include improved personalized learning experiences for students.
|
For as long as “educational technology” has been a discrete concept, discussions have ensued about how it might change our understanding of the purpose of formal education.
More recently, as artificial intelligence (AI) and machine learning models have become more sophisticated, even more attention has been given to these discussions.
While some current applications of AI are limited to narrow functions like grading and automating administrative tasks, the broader possibilities opened by such technology could alter our very conceptions of teaching and learning.
For example, as the Internet became integral to everyday life, a driving question in education arose: How do we assess learning when the answers are at everyone’s fingertips?
As artificial intelligence becomes more commonplace, this question might shift to ask: What does it mean to learn and produce knowledge?
It seemed a fitting exercise to ask two different AI programs directly how artificial intelligence might change K-12 education.
To this end, I used two separate writing assistants — OpenAI’s text completion tool and Sudowrite — both of which use GPT-3, a language learning model that generates fully authentic text.
This exercise was helpful in determining how useful these tools currently are and in providing different ideas about how their use and impact might evolve in the future.
Below are the prompts I provided to the tools, snippets of the text produced, and some follow-up reflections of my own.
Asking the AI
Prompt #1: How will artificial intelligence change K-12 education?
Key Details from Response: …some potential changes that AI could bring about in K-12 education include improved personalized learning experiences for students, increased use of data-driven decision-making by educators, and greater use of AI-powered educational tools and resources by students and teachers.
While offering a few broad areas in which AI might impact education, this is obviously a pretty generic and uninformative answer. Still, it does provide somewhat of a springboard for additional research or questions to explore more deeply, possibly within the tool itself.
The AI’s response here tracks with the general impressions that some K-12 educators had when presented with this technology — its answer offers a good starting point but leaves a lot to be desired (or worried about, depending on your perspective).
To try to get more specificity and varied formatting, I tried a different approach:
Prompt: Write a 600-word blog post about the impact of Generative Pre-trained Transformer 3 on K-12 education
Some of the examples included in the response:
In October 2020, the district of Bellevue, WA announced that it would be using GPT-3 to help students with writing assignments…With GPT-3, the district will be able to use a much wider range of models, which will allow students to get more personalized help with their writing.
…a middle school teacher in New York City is using GPT-3 to create math lesson plans. By inputting a few facts about a topic, the teacher is able to generate a complete lesson plan, complete with worksheets and answer keys.
A company called Playtech is using GPT-3 to create an educational game about the history of the United States. The game will be able to generate historical events, based on input from the player.
These are all interesting anecdotes but are broad and lack a nuanced perspective that might be gained from real journalistic or research tasks like interviews and primary source analysis.
Additionally, with this and other prompts, the tools generated relevant information and data but provided no citations and, in some cases, needed more specificity to easily find sources for its claims.
A recent EdWeek piece offers a much more thorough analysis and leaves the reader with a keen understanding of the current impact that this specific technology is having on the field of education.
Reflections
Some have speculated that the rise of technologies like GPT-3 and DALL-E will hasten the automation of creative work, contrary to the dominant theory that physical labor would be the first form of work replaced by machines powered by artificial intelligence.
While the foundations of teaching — like personal connection and relationship-building — may be impossible to automate, other educational fields, such as instructional design, may be ripe for disruption or rapid evolution thanks to artificial intelligence.
One could imagine a tool that produces comprehensive course scripts and syllabi, for example, or one that could be fed a topic, a content standard, and a desired output and present a polished learning activity. (I used OpenAI’s text completion tool to do just that and produced a writing activity, a rough rubric, and a writing sample.)
It could be the case that, like many other technologies throughout history, the impact of artificial intelligence and machine learning remains at the fringes of most people’s day-to-day lives.
It is clear that we are still in the very early stages of applying these technologies to common use cases, but by spending just a brief time with them, we begin to see the enormous potential for disruption.
At this stage, as James Thurber wrote, “It is better to know some of the questions than all of the answers.”
Anyone who holds a stake in the future of teaching and learning should be familiarizing themselves with these tools and formulating the right questions because as GPT-3 “personally” told me:
The full impact of artificial intelligence on education is not yet known.
| 2023-01-11T00:00:00 |
2023/01/11
|
https://michiganvirtual.org/blog/how-will-artificial-intelligence-change-education/
|
[
{
"date": "2023/01/11",
"position": 3,
"query": "AI education"
},
{
"date": "2023/01/11",
"position": 1,
"query": "artificial intelligence education"
}
] |
How Worried Should Creative Professionals Be About ...
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How Worried Should Creative Professionals Be About Artificial Intelligence? – PRINT Magazine
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https://www.printmag.com
|
[] |
Ellen Shapiro talks to illustrators, art directors, lawyers, and other creatives to assess where AI stands in the design community right now.
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Someone in the “AI Art Universe” Facebook group called it “art harvesting.” It’s an interesting analogy: sprouts planted by many other people are ‘scraped’ into a giant blender that sorts and readies them to be grown into exotic new gardens. But it’s more than a poetic analogy— it’s a worldwide phenomenon, way bigger than a garden. It’s a jungle of fields and plantations, meadows and forests filled with fantasy characters and creatures, scenes and settings that could be in the distant past, the far future, or another galaxy. And it’s springing up, morphing, regenerating before our eyes. Some of the results are dark and ugly, some are eerily beautiful, and all you have to do to participate is type a prompt that describes your vision. A minute or so later, a suite of images springs up on your screen, ready to be enhanced by you (and, apparently, by anyone else).
I trolled around for a while, trying to find an AI-generated garden “good enough” to show as an example. I finally settled on an alien landscape credited to Bryan Price on NightCafe.studio. With it came a 25%-off-my-first-month offer. Ah yes, another income-generator for someone who is not me, i.e. for NightCafé and all the similar sites that are popping up.
Traditional illustrators are up in arms. On Thursday, December 22, the Society of Illustrators posted this message on Facebook, Instagram and Twitter.
In less than 24 hours, this collaborative post by the award winning duo of Society of Illustrators President Tim O’Brien and illustrator Edel Rodriguez had more than 8,500 likes on Instagram, 16.2k likes, and 3,280 reposts on Twitter.
For more than 30 years, O’Brien has been painting meticulous oil portraits of famous people, from his hero Muhammed Ali to Elon Musk, and many have been featured on the cover of TIME. He and his colleagues are insisting that illustrations for publication must be created by real, thinking humans who interact with real clients and use real artists’ tools. “The sudden availability of artificially designed images creates a moral challenge to the illustration community and to the broader design community,” O’Brien said. “We are at the critical point at which illustrators and designers must value human interaction and reject the output of AI image generators. The inclusion of a credit highlighting an AI generator should bring on a sense of shame.”
O’Brien’s illustration of Elon Musk for TIME
O’Brien went on to note that athletes are subject to drug tests for trying to enhance their performance artificially, and those who fail are punished. “Humans can run faster, jump higher, and perform better using synthetic means, [but] we as humans are interested in what a human alone can do. That’s what makes us human.”
Illustrator Victor Juhasz, best known for caricatures that have graced the pages of Rolling Stone, TIME, Newsweek, and many other publications, takes the argument a big step farther. “The current craze for AI-generated ‘art’ is a symptom of a disease,” he said.
Juhasz did not mince words. “The temptation to take the fast, easy way rather than put in hard work is enormous. Contemporary society thrives on celebrity, fame and notoriety, and much of it has nothing to do with honest craftsmanship. It’s about the con and getting away with it.”
Victor Juhasz at work (left); Juhasz’s illustration for an October 2017 issue of Rolling Stone (right)
Other notable illustrators like Anita Kunz, known for her New Yorker covers and feminist responses to classic art have spoken out on how much they hate seeing their work scraped into databases. Karla Ortiz, a painter, printmaker, and concept artist at Marvel Studios, has been especially vocal on social media, posting impassioned arguments against the commercial use of AI art and spearheading a GoFundMe campaign to hire a lobbyist to make the voices of artists heard.
At the present moment, the creative heads of magazines sound largely uninterested in AI. Michael Mrak, the creative director of Scientific American, a science publication with over 10 million subscribers, “[sees] no reason to replace real artists with AI-generated anything.”
“AI can generate interesting and elaborate imagery, but there are many problems from a legal and moral point of view,” he continued. “AI-generated art cannot be copyrighted and therefore has potential legal issues attached to it, a principal one being that it uses art from across the internet to make the final image. That, and the fact that it scraped or pulled copyrighted art into its learning algorithm.”
Art director and designer Alexander Isley treasures his one-on-one collaborations with artists. “I have never used AI-generated artwork, and have no interest in doing so, unless it’s in the context of how odious it is,” he said. “From all I’ve seen and read, machine-generated artwork is based on modifying, remixing, or adding to real artists’ existing work without acknowledgment or compensation. With commissioned artwork, sketches and revisions are often required. How does this process work with AI-generated images? I can’t deny that the results can be interesting to look at, but it’s a fun parlor trick.”
While art directors might not see AI as a threat, the competitive aspect of design complicates matters. Will AI-generated art be eligible to win contests and grants?
“The short answer is yes,” was the initial answer from Patrick Coyne, editor/designer of Communication Arts, one of the world’s most important design publications. “We always tell jurors to select work based on the quality of the idea and the execution. We celebrate compelling imagery regardless of how it was created.”
Managing Editor Michael Coyne noted that Communication Arts had already featured a few campaigns that used AI-generated art “because they were interesting or appropriate applications for AI as an artistic tool rather than a medium on its own.” He cited an ad campaign by Dentsu Creative Portugal for Jardim Sonoro, an electronic music festival held in a national park near Lisbon, is an example. According to the agency’s creative directors, the challenge was to blend the musicians’ portraits with natural elements. “We learned that AI is a great tool,” they commented. “We are still at its beginning and will certainly see significant developments that will dazzle us all. But it won’t replace anyone. It needs someone to guide the creative process.” They added, “Novelty and discomfort often lead to great work.”
However, a few days later, the Communication Arts‘ team’s stance evolved. “We’ve been approached by several illustrators upset over our position regarding accepting entries for the Illustration Annual produced with text-to-image AI software,” Patrick Coyne wrote via email. “While I still see the long-term potential for AI-assisted creative exploration, I better understand the position that illustrators and photographers are currently facing with copyright infringement and the unauthorized use of their work to ‘train’ the current crop of text-to-image AI software. Consequently, we are reversing our position and will not be accepting AI text-to-image generated submissions in our Illustration competition.”
Hobbyists have a different relationship to the software. Daniel Rocha of São Paulo is an active contributor to Facebook’s “AI Art Universe” group, and one of the many thousands of people who make AI art for fun. “I use [Mage’s Standard Diffusion program] daily, many times a day,” he said. “I click ‘enter’ on a prompt many, many times, until I get something good or see that I need to change it because something is not nice. I’ve generated more than 22,000 pictures, but that’s not at all time-consuming, since all I have to do is click, click, click.”
Oddly enough, Rocha works in Brazil’s patent and trademark office, where he analyzes the registrability of trademarks. However, “that has nothing to do with what I do on Mage,” he clarified. “I think it is an extremely useful tool for artists. They can use it to fill in details or compose a complex scene extremely fast.”
Stable Diffusion can be trained to fit an author’s style, which allows them to make grandiose scenes in a short time, in their own style. “An amateur like me can reproduce the work of a skilled artist, art that could surpass in quality and inspiration the Sistine Chapel ceiling,” Rocha continued. “That took years for Michelangelo to make, and [similar work] can now be completed in a few days or weeks. Right now, the artists are too scared, but I think they will come around soon.”
Since I have family members who like to play with DALL-E, we decided to try it ourselves. I went in wondering if I could re-create one of the world’s most iconic posters, Milton Glaser’s 1966 “Dylan.” When I used Mage, the results were dismal. Apparently, the Mage database doesn’t have the stuff. We had no luck on DALL-E either (“does not follow our content policy”), but got meh results with Midjourney, where we typed “/imagine the famous 1966 Milton Glaser Bob Dylan poster” and got:
The curly hair must have gotten scraped in, along with some old album covers. And possibly black-and-white portraits to which the photographer owns the copyright. Then we tried: “/imagine the famous 1966 Milton Glaser Bob Dylan poster, but for Lady Gaga” and got:
The whole process took about three minutes. Fortunately— for now, at least— AI isn’t giving Milton Glaser’s brilliant work any serious competition.
To get clarity on where AI stands in regards to legality, I reached out to Martin Schwimmer, a partner at top-rated New York intellectual property law firm LeasonEllis. In his opinion, text-to-image AI models “present novel [new, unexplored] legal issues, including the extent to which the creator of the repository of images makes use of images that were previously displayed on the internet, and to what extent can an AI model look at an image and derive ‘rules’ about that image.” While that language is a little murky to me, it sounds like the lawyers are working on it.
However, Schwimmer didn’t agree that all AI repositories consist of ‘scraped’ images without regard to copyright. For example, he said, a repository named Laion consists not of images, but links to images, which apparently makes a legal difference.
As to who owns the so-called final product, Schwimmer said that he views AI models as one more tool that helps users generate content. “The copyright analysis will be comparable to the analysis we use today when artists use the various illustration tools, graphics editors, paint programs, and other digital art tools: Is the work sufficiently original when divorced from the accompanying tools?”
For now, that will be the last word.
| 2023-01-11T00:00:00 |
2023/01/11
|
https://www.printmag.com/web-interactive-design/how-worried-should-creative-professionals-be-about-artificial-intelligence/
|
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"date": "2023/01/11",
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"date": "2023/01/11",
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"date": "2023/01/11",
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"date": "2023/01/11",
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The Future of Graphic Design: 15 Coolest AI Tools For ...
|
The Future of Graphic Design: 15 Coolest AI Tools For Creatives Right Now
|
https://en.eagle.cool
|
[
"Rohit David",
"Kaleem Ullah"
] |
This article will explore the 15 coolest AI graphic design tools for creatives right now and discover what possibilities lie in store for the future graphic ...
|
With the advancement of technology, the scope and importance of graphic design have increased exponentially in recent years. Today, graphic designers must stay up to date with the latest tools to create impactful designs that stand out from the crowd.
Technology has advanced to a point where Artificial Intelligence (AI) is capable of creating amazing designs in a fraction of the time it takes a human. This article will explore the 15 coolest AI graphic design tools for creatives right now and discover what possibilities lie in store for the future graphic design industry!
Eagle App - Organize & Store your Graphic Design Ideas
Video from Eagle App
It's likely that if you work as a graphic designer, you're constantly searching for ideas for new projects. Whether you're trying to come up with a new logo design or simply looking for some fresh ideas, searching for images based on colors, image formats, etc. can be a great way to get started.
We'll explore how this powerful AI tool for designers can help make your life easier.
Eagle's robust search engine makes it simple to locate the precise image you're looking for. You can search by color, image format, size, and even tag your image. Plus, with its handy tagging system, you can organize your images into projects and groups so you can easily find them later.
In the past, design professionals had to use separate file organizers to keep track of all their graphic design ideas. With Eagle, you can import any file format and have it organized within the app. There is no longer any need to keep special files in a separate organizer.
You can easily sync your files via Google Drive, Dropbox, One Drive, or any other cloud storage service. You can take screenshots or photos of your ideas, and even create mockups and illustrations.
So, if you're looking for a way to organize and save your graphic design ideas, look no further, Try Eagle NOW! Let’s quickly check which AI graphic design tools made it to our list of 15.
Unleash Your Creativity With These 15 AI Tools For Artists And Designers
Artists and designers should certainly make a note of these highly recommended tools:
1. Let’s Enhance.io
Image from Let’s Enhance.io
If you are looking for a way to automatically enhance your images for personal or business needs, Let's Enhance is the perfect solution. With their AI design software, you can resize pictures quickly and easily, making them crisp and clear for any web or print format.
Whether you're looking to improve the resolution, colors, or lighting of your images, or simply meet pixel requirements for any marketplace, Let's Enhance will make them look seamless. To make real estate photos look more professional, you can upscale and unblur them, instantly edit interior shots for listings, or use ML-powered HDR to automatically correct overexposed and poorly lit images.
Pricing
10 credits: Free
100 credits: $9/month
300 credits: $24/month
Operating System
Let's Enhance is available for Windows and Mac.
2. Khroma.co
Image from Khroma.co
Khroma utilizes artificial intelligence to detect which colors you prefer and then creates an infinite number of palettes for you to browse, search, and save. By choosing a selection of colors, you may train a neural network-powered algorithm to produce colors you like and prevent those you don't in your browser.
Khroma has gleaned knowledge from tens of thousands of the most widely used human-made palettes on the internet to create fantastic combinations that may be viewed as typography, gradients, palettes, or custom images.
Additionally, you can conduct a color search and add your favorite color combinations to a collection.
Pricing
Free
Operating System
Khroma.co is available for both Mac and PC.
3. Brandmark.io
Image from Brandmark.io
Brandmark.io has thousands of ready-to-use design assets that can kickstart your branding process. From logo design files and social profile icons to business card designs and animated designs, they have everything you need to get started.
Plus, all of their designs are available in all major formats (SVG, EPS, PNG, and PDF), and with a wide variety of templates for everything from business cards and letterhead to social media and presentations, Brandmark has you covered no matter what kind of design you need.
Pricing
Basic: $25 | One time fee
Designer: $65 | One time fee
Enterprise: $175 | One time fee
Operating System
Brandmark.io is available for both Windows and Mac operating systems.
4. Fronty
Image from Fronty
If you work as a web developer, you are aware that converting an image to HTML CSS code can be one of the most time-consuming chores. With Fronty, you can quickly and easily convert your images to code within minutes, so you can get back to developing your website.
Additionally, Fronty provides a no-code editor so you may make any adjustments you require without knowing how to code. Their online editor makes it easy to change the design and styling of your website, so you can create a truly unique experience for your visitors.
Pricing
Freemium: $0
Pro: $4.52 monthly
Advance: $9 monthly
Operating System
Fronty.com works with all major browsers, including Chrome, Firefox, Safari, and Edge.
5. Removebg
Image from Removebg
Removebg is a clever AI tool that can slash your editing time, and help you make more impactful and creative designs. With Removebg, you can transparentize PNG images or add white backgrounds to photos with just a few clicks.
Moreover, it integrates with popular design programs, eCommerce sites, and computer environments, so you can easily add it to your workflow. So if you want to make stunning greeting cards or power through thousands of car photos quickly.
So why not try remove.bg today and see how it can help you save time and increase your creativity?
And if saving time is your prime focus, get to know Eagle today!
Pricing
Free: 1 credit
Subscription: 40 credits ($7.24/monthly)
Pay as you go: 75 credits ($39.75/monthly)
Operating System
The website is compatible with Windows, Mac, and Linux operating systems.
Want your designs to be outstanding and free? Read more about these free AI design tools.
6. Uizard.io
Image from Uizard.io
Uizard.io is a great platform for anyone who wants to design professional-looking mobile apps, web apps, websites, and software interfaces. There is no need to be a designer or have any prior experience – simply upload a screenshot of your inspiration and their AI Design Assistant will do the rest!
You can visualize, test, and communicate your ideas in style with ease.
Pricing
Free: $0
Pro: $12/monthly
Business: $39/monthly | Billed Yearly
Operating System
Uizard.io is a cloud-based platform that runs on Windows, Mac, and Linux
7. Font Joy
Image from Fontjoy
If you're looking for font inspiration, look no further than Font Joy. With the help of their deep learning system, they can create font combinations that are consistent with one another but yet have a pleasing contrast.
To set up a new font pairing, simply click "Generate," "Lock," "Lock" to lock fonts you want to keep, and "Edit" to select a font by hand. Feel free to substitute your company name or other text because the text is customizable.
Pricing
Free
Operating System
Font Joy is available on all major browsers, including Google Chrome, Safari, Firefox, and Internet Explorer.
8. Deep Art Effects
Image from Deep Art Effects
Deep Art Effects is a cutting-edge image processing software that uses artificial intelligence to edit images. With Deep Art Effects, you can easily and innovatively edit images to create unique works of art.
The software also allows you to automatically optimize or colorize photographs, scale images to four times their original size without losing clarity, and simulate pricey SLR cameras. You may do anything offline, and your photos will follow you around.
Pricing
Free | Businesses can avail a free test plan
Operating System
Deep Art Effects requires no installation and can be used directly in a web browser.
9. VanceAI
Image from VanceAI
VanceAI is a provider of AI-powered photo enhancement and editing tools. Its AI Image Enhancer tool offers a one-click solution for enhancing image details, while its AI Anime Portrait tool turns selfies into creative anime portraits in seconds.
Additionally, VanceAI's AI Background Remover tool enables users to get transparent image backgrounds with just one click. Finally, VanceAI's AI Old Photo Repair & Colorize programme seamlessly restores old photos by automatically adding color to them.
Pricing
*1 image = 1 credit
100 credits: $3.96
1000 credits: $14.36
Operating System
Android, iOS, macOS, and Windows all support VanceAI
10. Designs.AI
Image from Designs.AI
Designers can develop, update, and scale content using the Designs.ai platform, an integrated agency-as-a-service. With Designs.ai, you can create logos, videos, banners, mockups, and more in just a few minutes.
The platform offers a wide range of features to help you save time and simplify your workflow. The Logo Maker tool, for instance, enables you to launch your business with a distinctive logo and a complete brand identification package.
The Design Maker tool allows you to quickly create thousands of possible variations of your marketing collateral. Plus, the platform provides access to over 10,000 icons and 20,000 templates to help you achieve the perfect look for your project.
Pricing
Basic: $29/ per month
Pro: $69/ per month
Enterprise: Contact them for a customized plan
Operating System
Designs.ai is a cloud based platform available on Windows, and Mac
Want to become a better graphic designer? Explore our blog. We have something relevant for you!
11. Defamel
Image from Deflamel
Defamel is a powerful tool that can help you create stunning designs in minutes, simply by describing your vision in text. Using cutting-edge AI technology, the software understands exactly what you want and provides design concepts based on your mood and the emotions behind your description.
With the help of this creative tool, you can effortlessly include our design features into your solutions to make them more attractive while giving your visuals a distinctive look with spectacular designs and effects.
Personalized ad design is also quick and easy with Defamel, allowing you to create banner designs from thousands of products and boost sales conversion with customized offers.
Pricing
Unavailable, kindly contact them for a demo
Operating System
Deflamel is based on the Linux kernel
12. AutoDraw
Image from AutoDraw
AutoDraw is a brand-new type of drawing tool that combines machine learning with artwork created by gifted artists to assist anyone in quickly creating anything graphic. It works everywhere and on all devices without any downloads or fees.
The suggestion feature in AutoDraw employs QuickDraw's technology to determine what you're attempting to draw. Hundreds of drawings can currently be predicted, and the team anticipates adding more in the future.
Pricing
Free
Operating System
TensorFlow, an open-source machine learning platform, powers Autodraw. Any platform can be used to operate it.
13. VisualEyes
Image from Visual Eyes
VisualEyes is a powerful tool that allows UX designers to quickly and easily generate insights into how users interact with their designs. Using artificial intelligence, VisualEyes is able to simulate eye-tracking studies and preference tests with a high degree of accuracy, providing valuable insights that can help improve the overall user experience.
With just a few clicks, VisualEyes can provide heat maps of where users are looking on a page, as well as a clarity score that indicates how easy it is for users to find what they're looking for. This information can be invaluable in making design decisions that will ultimately lead to happier and more engaged users.
Pricing
Free for 1 active project and 5 predictions
Operating System
Visual Eyes is available on Windows
14. Adobe Sensei
Image from Adobe Sensei
With Adobe Sensei, artificial intelligence (AI) and machine learning power experiences by increasing creativity, clarifying insights, speeding up processes, and guiding real-time decisions. Sensei amplifies your ability to create and deliver personalized experiences that anticipate what customers want.
With Adobe Sensei, you can turn customer data into actionable insights, automatically curate content, dynamically personalize campaigns, and more. And because Adobe Sensei is built on a robust, scalable platform, you can easily scale your AI-powered experiences to meet the needs of your business — now and in the future.
Pricing
You can view all the available price options here
Operating System
Adobe sensei is built on top of the Adobe Experience Cloud, which provides the infrastructure needed to power its AI capabilities
15. Artbreeder
Image from Artbreeder
Artbreeder is an online AI platform that enables users to create images by mixing and editing existing images, known as "genes". This process of remixing and editing can be used to create portraits, landscapes, paintings, and more.
Artbreeder also features a community of creators who can follow one another and share their work. This vibrant community helps to support a small independent team of artists. Membership to Artbreeder gives users access to faster renderings, no waiting times, and additional features. Plus, users get to support the independent artist team.
Pricing
Free with unlimited images, 3 image uploads, and 5 high resolution downloads
Starter: $7.49 monthly
Advanced: $15.99 monthly
Champion: $30.99 monthly
Operating System
Artbreeder is a cloud-based platform that can be operated from any operating system
Looking for royalty free graphic design resources? You’ll find great resources here.
FAQ
What Are the Benefits of Using AI in Graphic Design Tools?
AI graphic design tools give you the ability to create amazing visuals with minimal effort. With AI, you can become a better graphic designer and produce high-quality designs quickly and easily. It takes the guesswork out of the creative process, allowing even beginners to create stunning visuals quickly and efficiently.
Furthermore, using AI in graphic design enables you to create personalized content that engages viewers more effectively. The bottom line is that using AI in graphic design gives you the capabilities to take your designs to the next level with ease.
How to choose the right AI graphic design tool for your needs?
To choose the right free AI graphic design tool or a paid one for your needs, consider the features and capabilities of each one. Look at things like user interface and ease of use, compatible file formats, image editing tools, templates and plugins available, pricing plans offered, customer service support, and more.
Additionally, compare tools with the same type of project you’ll be working on to see which one best fits your needs. By researching different graphic design tools online before making your decision you can find the perfect AI graphic design tool tailored to your specific requirements.
Conclusion
AI offers some incredible potential in the field of graphic design. As technology advances and AI becomes even more sophisticated, it is certain that AI will become an ever-increasingly important tool for designers.
We can only imagine the possibilities that AI in graphic design will bring to the industry. We can already see how valuable this technology is with Eagle App, offering its users a simple yet effective way to create high quality designs with minimal effort.
It won’t be long until AI becomes integral for designers everywhere and Eagle app may just be setting the bar for what we should expect from automated graphic-designing tools in Years come.
Trust you enjoyed reading this article.
Stay productive with Eagle!
| 2023-01-11T00:00:00 |
2023/01/11
|
https://en.eagle.cool/blog/post/ai-graphic-design-tool
|
[
{
"date": "2023/01/11",
"position": 8,
"query": "AI graphic design"
},
{
"date": "2023/01/11",
"position": 8,
"query": "artificial intelligence graphic design"
}
] |
Business Intelligence Articles
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Business Intelligence Articles
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https://biglinden.com
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[] |
Business leaders from all types of organizations can ... Uncertainty and bias are important considerations when working with Artificial Intelligence (AI) ...
|
Business intelligence is the process of collecting, organizing, analyzing, and visualizing (often complex) data to give organizations insight into their operations. Business intelligence helps companies make better decisions by providing access to information that was previously unavailable or difficult to interpret. It can be used to uncover trends in sales, identify opportunities for improvement, develop strategies for growth, and more. BI is typically composed of software tools that enable users to query data sources and generate reports and visuals.
Use data to help make better-informed business decisions
We find the biggest benefit for leaders is the gain in confidence of their decision-making and their deeper understanding of how their decisions will affect the organization. Business intelligence solutions can provide executives with detailed analysis of data, allowing them to make informed decisions rather than relying on hunches or guesswork. This can provide a real sense of security and reassurance, knowing that decisions are being made based on facts rather than gut feeling.
Business Intelligence (BI) is a broad field that involves understanding and interpreting datasets from various sources. It encompasses a broad range of scientific topics such as data management, data mining, analytics, predictive modeling, dashboards and visualizations, big data, artificial intelligence (AI) and machine learning, business processes and automation, cloud computing and storage solutions, enterprise reporting solutions, data warehousing, and natural language processing (NLP).
Business intelligence can reveal new opportunities for growth and improvement, providing executives with the motivation to push their organizations forward.
Using Business Intelligence to Make Better Decisions
The most important thing to know about Business Intelligence is that it is all about extracting insights from data. By collecting and analyzing data from various sources, BI provides organizations with actionable information that can be used to make informed decisions and improve business performance.
BI tools are designed to enable easy access and analysis of large amounts of data so that businesses can gain valuable insights into their operations and make more informed decisions. Quickly identify patterns, trends, and correlations in the data to help them make more informed decisions about your organization’s products, services, or processes.
Business leaders from all types of organizations can benefit from this. BI provides organizations with actionable insights that can help them identify trends, uncover opportunities, and improve operational efficiency.
By collecting and analyzing data from various sources, businesses can gain valuable insights into their operations that can be used to make informed decisions and improve business performance. BI tools enable easy access and analysis of large amounts of data so that businesses can quickly identify patterns, trends, and correlations in the data to help them better understand the dynamics of their business.
| 2023-01-11T00:00:00 |
https://biglinden.com/tag/business-intelligence/
|
[
{
"date": "2023/01/11",
"position": 98,
"query": "artificial intelligence business leaders"
}
] |
|
Workers Who Train Google's Search Algorithm Finally Got ...
|
Workers Who Train Google’s Search Algorithm Finally Got a Raise Thanks to Union Action
|
https://www.vice.com
|
[
"Jules Roscoe",
"Shaun Cichacki",
"Luis Prada",
"Stephen Andrew Galiher",
"Anthony Franklin Ii",
"Sammi Caramela",
".Wp-Block-Savage-Platform-Post-Byline Font-Family Var --Wp--Custom--Typography--Font-Family--Secondary",
"Font-Size Var --Wp--Custom--Typography--Font-Size--Sm",
"Font-Weight Var --Wp--Custom--Typography--Font-Weight--Bold",
"Line-Height Var --Wp--Custom--Typography--Line-Height--Xxs"
] |
Where these workers, known as raters, were previously paid as little as $10 an hour, they will now be paid $14 or $14.50 an hour, depending on experience.
|
Thousands of Google contract workers responsible for training and rating the engine’s search results will receive a historic pay raise this year after a months-long Alphabet Workers Union-CWA campaign, the AWU-CWA announced Wednesday.
The workers are based at RaterLabs, an AI training vendor whose only known client is Google, according to an AWU-CWA press release. Where these workers, known as raters, were previously paid as little as $10 an hour, they will now be paid $14 or $14.50 an hour, depending on experience. The AWU-CWA estimates that the raise will affect between 3,000 and 5,000 workers, for a total collective salary increase of about $10 million.
Videos by VICE
This is the first salary increase won by the union, which represents workers at Google, its parent company Alphabet, and its extended workforce including contract workers.
“The work we do as raters for Google is critical to their success. Yet, since I started as a rater eight years ago, I have not received a single raise,” said Michelle Curtis, an Alphabet contract worker and union member. “While $14.50 is a step forward, it is still not the $15.00 minimum set by Google for its extended workforce, and does not include the multitude of other benefits currently denied to us.”
In 2019, Google set forth a wages and benefits standard for all of its provisioned extended workforce in the United States, meaning temps and vendors with systems access. “All members of Google’s provisioned extended workforce, working in the United States, should be paid $15/hour or more,” the standard read. “In locations where the minimum wage is more than $15/hour, we will expect that you comply with that higher requirement.”
Google set the compliance date for the standard in 2020, but this past May, workers began to demand that Alphabet enforce the standard on its contractors, not just workers under its umbrella. After negotiations with RaterLabs and its parent company Appen in October, salary increases were announced on Dec. 21, 2022, and finalized one week later. The new rates went into effect on Jan. 1, 2023.
“Whenever we raised questions regarding our low pay we would receive contradictory responses from Google and RaterLabs regarding who was responsible for our livelihoods,” said Jay Buchanan, another Alphabet contract worker and union member. “The only reason workers received this long overdue pay increase is because we overcame multiple hurdles to our organizing. We demand that Google hold all of its contracting companies accountable to fulfilling the baseline pay and benefits we’ve been promised as members of Google’s extended workforce.”
Neither Google nor Appen responded to Motherboard’s request for comment.
Workers at RaterLabs are responsible for training, testing, and evaluating Google’s search results by rating how accurately the algorithm responds to prompts, as well as the relevance and quality of ads. These jobs are often long and tedious, with minimal rewards or benefits, and the people who do them power AI from behind the scenes. Large companies like Meta and Amazon typically outsource this work to contract workers outside the United States to avoid having to include them in benefits schedules. For that reason, it’s even more unlikely for contract workers and raters to be able to unionize and demand higher wages, much less win them.
“Victories like this one make it clear that workers refuse to remain separated by lines of employment classification and can achieve so much more when we band together,” said Parul Koul, a software engineer and the executive chair of the AWU-CWA. “We celebrate the powerful organizing by our RaterLabs members and call on Google to ensure that every worker within their extended workforce receives the promised pay, benefits and resources they justly deserve.”
| 2023-01-11T00:00:00 |
2023/01/11
|
https://www.vice.com/en/article/workers-who-train-googles-search-algorithm-finally-got-a-raise-thanks-to-union-action/
|
[
{
"date": "2023/01/11",
"position": 7,
"query": "artificial intelligence wages"
}
] |
Artificial Intelligence Tools and Teaching
|
Office of Teaching, Learning, and Technology
|
https://teach.its.uiowa.edu
|
[
"Sitenow",
"Https",
"Sitenow.Uiowa.Edu"
] |
The newest entry in a long line of technologies that promise to disrupt higher education are artificial intelligence (AI) tools like ChatGPT.
|
The newest entry in a long line of technologies that promise to disrupt higher education are artificial intelligence (AI) tools like ChatGPT. In seconds these tools can use machine learning to generate text, video, or images, in response to users’ prompts.
The technology continues to advance, with features such as internet browser plugins that can generate answers to assessment and homework questions. Computer generated text does have its limitations. Users can’t rely on the tool to make qualitative judgments, such as determining if language is appropriate for a given context or rely on the accuracy of content citations.
These tools are widely available, and instructors will need to consider carefully how to adapt to these developments. Amid discussions and academic integrity concerns about these tools, the Office of Teaching Learning, and Technology and the Center for Teaching offer this brief guide to address some of the most frequently asked questions about ChatGPT and other AI tools.
These are new technologies, and the future is unknown as the availability and reliability of these tools are developing.
| 2023-01-11T00:00:00 |
https://teach.its.uiowa.edu/artificial-intelligence-tools-and-teaching
|
[
{
"date": "2023/01/11",
"position": 42,
"query": "artificial intelligence graphic design"
}
] |
|
Graphic Designing Scope: Trends, Opportunities And Salary
|
Graphic Designing Scope: Trends, Opportunities And Salary
|
https://www.logicraysacademy.com
|
[] |
While AI is starting to play a significant role in the design industry, it is unlikely to replace human graphic designers. Graphic design requires creative ...
|
Graphic designing is a creative field that involves visual elements to communicate ideas and convey information. It is a vital component of various industries, such as advertising, marketing, media, and publishing, and plays a crucial role in designing websites, logos, brochures, packaging, and other visual communications. In India, the scope of graphic designing has been expanding rapidly in recent years, driven by the growth of digital media and the increasing demand for visually appealing and effective communication. Many institutes cater to the needs of graphic designing and fulfill the needs of the industry. This article is about the scope of graphic designing, the career opportunities, and how to dwell well and have a handsome salary.
Graphic Design Industry Trends
According to Graphic Design Industry Statistics, the global market size for graphic design will reach $83.8 billion by 2032, at a compound annual growth rate of 5.2%. In India, the graphic designer job scope is also expanding, driven by the increasing adoption of digital media, the rise of social media marketing, and the growing popularity of online shopping. The sector will witness significant growth in the coming years as more businesses recognize the importance of visual communication and invest in professional graphic design services.
Learn How to Meet Industries’ Roles and Responsibilities
As a graphic designer, you must clearly understand visual principles, colour theory, typography, and layout. Graphic design’s future scope demands that you be proficient in using graphic design software such as Adobe Creative Suite, which includes programs like Photoshop, Illustrator, and InDesign. In addition to technical skills, you should also possess soft skills such as creativity, problem-solving, communication, collaboration, and adaptability. These graphics design skills are essential for working with clients, understanding their needs and goals, and developing visual solutions that meet their expectations.
Why Are Graphic Design Courses Or Training Required?
Graphic design scope in India depends upon the skills you have acquired. Graphic design courses or training can help you develop these skills and knowledge. One such course is the Graphic Design training course offered by LogicRays Academy, which covers a wide range of topics, including design concepts, software tools, project management, and professional practices. The course equips you with the skills and confidence to embark on a successful career as a graphic designer. It enhances your chances of success in the industry.
An institute will offer programs to help you will be well-versed with-
Graphics-Concepts & Illustrations Design Laws Communication- visual Raster vs. Vector Usage of Shapes Photoshop Illustrations- Vector Importance of Packaging designs-Colour Schemes and Theories Drawing / Art Graphic Software
Apart from the theory aspect, other elements in choosing the right graphic designing course are the scope for regular projects, workshops, field trips, industry visits, and so on. Professional guidance to develop a professional portfolio is possible when you get hands-on training using in-demand techniques and tools. It is equally imperative to have high-end studio infrastructure to offer to the candidates to explore.
Career Options for Graphic Designers
There are various Career options for graphic designers available in India, depending on your interests, skills, and experience. The packages differ depending on the scope of the position and years of experience. Some of the specific job positions for graphic designers include:
1. Graphic Designer:
A graphic designer is responsible for creating visual concepts, using computer software or by hand, to communicate ideas that inspire, inform, or captivate consumers. A graphic artist plans concepts and illustrates them through art, sizing, and styling it. They work on projects, including advertisements, brochures, corporate identity, packaging, and websites.
2. UX or User Experience Designer:
A UX designer is responsible for designing the look and feel of a website or software application based on the needs and preferences of the users. UX designers involve themselves totally in the process of acquiring and integrating the product. They focus on creating intuitive, user-friendly interfaces that enhance users’ experience.
3. Logo Designer:
A logo designer is responsible for creating logos representing the brand’s identity and values. They use various design elements, such as typeface, colour, and shape, to create unique and memorable logos that reflect the brand’s personality.
4. Product Designer:
A product designer creates the visual appearance and functionality of physical products, such as consumer goods, furniture, and electronics. They use design skills and engineering knowledge to create functional and aesthetically pleasing products.
5. Motion Designer:
A motion designer creates animations, videos, and other dynamic visual content. They use After Effects and Photoshop software to bring static designs to life.
Graphics Designing Opportunities:
The scope of graphic designing in India is a field with several career opportunities. In addition to the roles mentioned above, other options include-
User Interface Designer
Print Designer
Marketing Designer
The opportunities combine art, communication, and technology, useful in marketing, advertising, publishing, digital communication, healthcare, and several other industries.
Salaries for Various Graphic Designing Roles:
The salary for a graphic designer can vary widely depending on their experience, location, and specific role. Those working in specialized industries or senior roles can earn significantly more. Study shows the median annual wage for graphic designers in India is three lacs p.a starting. With experience, your salary can go as high as 8 lacs p.a. Those working in specialized industries or in senior roles can earn significantly more.
To conclude
The demand for graphic designers is vast and will grow in the coming years. Graphic designers must stay current with industry trends and continuously improve their skills to remain competitive in the job market. The answer to How long does it take to become a graphic designer amount of time depends on your educational path and experience. It typically takes several years of training and practice to develop the skills needed for a successful career in the field. Once set on this field, there is no looking back.
FAQs about Graphics Design Scope
| 2023-01-11T00:00:00 |
2023/01/11
|
https://www.logicraysacademy.com/blog/graphic-designing-scope/
|
[
{
"date": "2023/01/11",
"position": 79,
"query": "artificial intelligence graphic design"
}
] |
Are we at the start of an AI Chatbots revolution?
|
Are we at the start of an AI Chatbots revolution?
|
https://www.softcat.com
|
[] |
- Job displacement: One concern is the potential for job displacement, as chatbots can automate tasks that were previously carried out by humans. This could ...
|
Are we at the start of an AI Chatbots revolution?
Artificial intelligence (AI) chatbots have the potential to revolutionise the way businesses interact with their customers and operate internally. These chatbots, which use AI to carry out conversations with users in a natural and intuitive way, can provide a wide range of benefits to businesses, including improved customer service, increased efficiency, and cost savings. However, there are also potential negative impacts to consider when using AI chatbots in business.
Benefits of AI chatbots for businesses
- Improved customer service: AI chatbots can handle a wide range of customer inquiries and provide assistance or information in real-time, 24/7. This can help businesses to quickly resolve customer issues and improve customer satisfaction. In addition, chatbots can be customized to provide personalized experiences for customers, helping businesses to build stronger relationships with their clients.
- Increased efficiency and cost savings: Chatbots can handle tasks such as booking appointments, processing orders, and providing information, freeing up human employees to focus on more complex tasks. This can help businesses to increase their productivity and efficiency, leading to cost savings.
- Personalized marketing: Chatbots can be used to create personalized marketing campaigns, providing targeted and relevant content to customers based on their interests and needs. This can help businesses to effectively reach and engage with their target audience.
- Social media integration: Chatbots can be integrated into social media platforms, allowing businesses to quickly and easily interact with their customers and build brand awareness.
- Data analysis: Chatbots can analyse customer data and interactions to provide insights into customer needs and preferences, helping businesses to tailor their products and services to better meet the needs of their clients.
Potential negative impacts of AI chatbots for businesses
- Job displacement: One concern is the potential for job displacement, as chatbots can automate tasks that were previously carried out by humans. This could lead to job losses and the need for workers to retrain for new roles.
- Biases: There is also the potential for chatbots to perpetuate biases if they are trained on biased data, which could have negative consequences for businesses and their customers.
- Security and privacy: There are concerns about the security and privacy of data used to train and operate AI chatbots, as well as the potential for chatbots to be used for malicious purposes.
- Limited capabilities: While chatbots can handle a wide range of tasks, they may not be able to handle complex or unusual requests as well as a human would. This could lead to customer frustration and a decline in satisfaction.
- Misuse: There is also the risk that chatbots could be misused by businesses, for example by providing false or misleading information to customers or using customer data for nefarious purposes.
AI chatbots have the potential to bring significant benefits to businesses in terms of customer service, efficiency, and cost savings. However, it is important to carefully consider the potential negative impacts of using chatbots and to ensure that they are used ethically and responsibly. This may include developing policies and guidelines for the use of chatbots, training employees on how to use them effectively, and regularly reviewing and updating the chatbots to ensure they are meeting the needs of customers and the business. It is also important to consider the potential for job displacement and biases, and to take steps to mitigate these risks. Ensuring the security and privacy of data used to train and operate chatbots is also critical. By carefully managing the use of AI chatbots, technology leaders can help their businesses to realize the full potential of this technology.
Is the AI revolution here and will it be televised? Would the fact I didn’t write the above, ChatGPT did change your answer? All I did was asked for a blog post on how AI chat bots will change the world for businesses, wait 30 seconds and voila, blog post done. There are clearly implications for a tool such as this, both positive and potentially negative as it outlined above, but I wanted to summarise with a more human perspective.
If you can construct a well worded question, utilise your creativity into text or challenges then the answers it can provide are often accurate and appear almost considered. It is powerful and with great power becomes great responsibility. Organisations need to be quickly look at their policies and procedures around AI to ensure its safe adoption. It has its uses and its limitations, it is just a manmade tool after all and like any other manmade tool, needs human interaction to get the most out of it. If used in the wrong way there can be repercussions. Hammers are great at bashing things, not so good at dusting fragile ornaments.
There may wider challenges often not always thought about. How is plagiarism going to be detected especially within education, coding or scientific communities needs to be carefully considered and needs to be supported with robust polices as well detection tools which are AI aware. Licencing implications of its output such are created code or copyrighted material which was able to call on could open up legal challenges and need to be carefully considered.
I reached out to the wider Advisory team and the other Account Chief Technologists at Softcat for their thoughts on it to give a wider perception on the possibilities. The general consensus was that of excitement. In Alex Mold’s view, she sees this an real opportunity to solve challenges for businesses that have been trying to shift left their IT operations. This is often in order to optimise costs and reduce wait times to increase customer satisfaction. When attempted recently there has been a somewhat timid successes often due to limitations of the existing technologies and the complexities of existing services, policys, processes and information which has hindered the ability to shift left. Traditional technical support channels (such as phone & email) haven’t been displaced yet as the functionality just wasn’t good enough yet. ChatGPT and others now may be at the level where they can potentially go further. It is better equipped to shift left, but could eliminate 1st line support completely where it can categorize, troubleshoot and triage potentially all service requests, incidents all the way to 3rd line support. This empowers a shift right instead without overloading the technical teams and maintaining customer experience.
Paul Fleming rightly raised the energy and ecological cost implications of such powerful computing being something to be aware of, whilst its wider impact can only be measured by the creativity input into it. But the ability to self create code / applications is a paradigm shift for the democratisation of code which will disrupt markets at a faster rate. If the AI platforms can be powered by renewable energy aligned datacentres then the benefits could easily outweigh the ecological cost.
We are also seeing enhancements to our advisory and consultancy workloads. “Co-creating content with AI will enable us to produce better quality output faster and move the starting blocks further along the process which can only enhance customer experience” – Andy Hermsen ACT. In the words of our Chief Technologist for Healthcare – Phillipa Winter “It’s bloody brilliant”.
So, are ChatGPT and other AI chatbots something to be embraced or feared, is Skynet upon us, is it time for the tin foil hats and to become preppers? I don’t think so. It is clearly a very powerful new tool, it has the ability to be daunting like any new revolutionary break through of the past. The industrial revolution was daunting to some as automation of manual tasks were taken over by machines. This feels like another industrial revolution of the digital age, driving machine enhancements to replace more manual and soon to be archaic ways of working. AI and its usefulness has hit enough of a threshold to be revolutionary, and finally to start to provide the benefits it always promised. The adoption of AI is here to stay and will continue to evolve. Viva la revolution.
As a foot note I wanted to share what I felt was a rather heart-warming side to tools such as ChatGPT. After a trip to the national space centre last week, I was asked by my 4 year old son what a singularity was. After failing to a couple of times to answer the question in a way he could understand I turned to ChatGPT. The explanation it provided was tailored and simplified to my son’s level he was able to clearly understand it finally, which opened up a world of new questions for him and continued to drive his passion for all things space related. We then applied what he learnt into the AI to generate a new bedtime story which covered all the areas and characters he wanted such as Felicette the first cat in space. With AI picture generation we could even create pictures for his story of space exploration driven by what he was learning and his imagination. The excitement on his face when we read the story he created to my wife and his brother was a truly magical moment. Even as technological progress continues to drive forward a never ending stream of enhancements, there is always space and time for the human touch.
| 2023-01-12T00:00:00 |
2023/01/12
|
https://www.softcat.com/blog/are-we-start-ai-chatbots-revolution
|
[
{
"date": "2023/01/12",
"position": 52,
"query": "automation job displacement"
}
] |
How Will AI Impact the Manufacturing Workforce? - NAM
|
How Will AI Impact the Manufacturing Workforce?
|
https://nam.org
|
[
"Anath Hartmann"
] |
Meanwhile, AI adoption will likely lead to an increase in available jobs, as more skilled workers will be needed to guide and inform these new processes. The ...
|
AI is changing the way manufacturers do business—from the production line to the back office and across the supply chain. At the Manufacturing Leadership Council’s Manufacturing in 2030 Project: Let’s Talk about AI event last month in Nashville, Tennessee, panelists discussed how those sweeping changes would alter, and enhance, the manufacturing workforce.
A collaboration between the MLC (the NAM’s digital transformation arm) and the MI (the NAM’s 501(c)3 workforce development and education partner), the event provided key insights for manufacturers into how technology and workforce trends interact with each other. Here are a few key takeaways.
Net positive: “The history of technology adoption is about improving the job quality of individuals on the shop floor. AI helps them to do the job better, provide them with better tools, gives them greater authority and ultimately increases the value-add of their jobs. All of that is a net positive for those individuals,” said MI Vice President of Workforce Solutions Gardner Carrick.
By leveraging data and enabling greater efficiency, AI will improve communication, increase collaboration across disciplines and stimulate innovation, according to the panel.
In addition, “AI can even inform the workforce’s creativity by working with it to design a new product or system,” said Jacey Heuer, lead, data science and advanced analytics, Pella Corporation.
Skills needed: While you might expect that implementing AI requires workers skilled in programming, data science and machine learning, manufacturers will also need to expand their bench of critical thinkers and problem-solvers. The panelists had a few tips to help companies along.
Invest in upskilling programs to make the AI integration process at your company smoother and develop the talent you already have.
to make the AI integration process at your company smoother and develop the talent you already have. Update job descriptions to reflect the skill sets the company will need in the next five to seven years.
to reflect the skill sets the company will need in the next five to seven years. Consider recruiting for and teaching adaptive skills —skills that enable individuals to adapt easily to changing demands and environments—which can increase the flexibility of your workforce.
—skills that enable individuals to adapt easily to changing demands and environments—which can increase the flexibility of your workforce. Build partnerships with local schools, community colleges and technical and vocational schools to develop talent pipelines that will meet your needs.
The human-AI collaboration: While AI will take over monotonous, repetitive tasks, the panelists predicted that the industry will continue to center around human labor.
“You can teach AI to do X. You can teach AI to do Y. [However,] combining the two may be really difficult for AI, while a human can do it better. You’re going to continue to see humans in roles that center on making decisions and telling stories,” said Asi Klein, managing director, industrial products and organization transformation, Deloitte Consulting.
Meanwhile, AI adoption will likely lead to an increase in available jobs, as more skilled workers will be needed to guide and inform these new processes.
The last word: “Over the last 12 years, we’ve seen a lot of technology adoption, but we have not seen a lot of job loss. In fact, we’ve seen job gains,” said Carrick. “There is a lot of opportunity to reimagine jobs to add value that AI will help to illuminate.”
| 2023-01-12T00:00:00 |
2023/01/12
|
https://nam.org/how-will-ai-impact-the-manufacturing-workforce-20315/
|
[
{
"date": "2023/01/12",
"position": 20,
"query": "AI replacing workers"
},
{
"date": "2023/01/12",
"position": 67,
"query": "future of work AI"
}
] |
AI and Healthcare: Can AI Really Replace Doctors?
|
AI and Healthcare: Can AI Really Replace Doctors?
|
https://www.aiwakforce.com
|
[
"Freddy Murefu"
] |
The quick answer is that Ai can't and will not replace doctors. Albeit not anytime soon. Long after every living person is dead in the future, it might.
|
We’ve heard so much about artificial intelligence and its taking over industries. Of course, improvements in healthcare are welcome for a better life for humanity.
After all, the medical field has seen tremendous improvements since we learned to cure the first disease. What’s one more change?
But what of the premise that AI is replacing previously dependable workers? Sure, some people will soon lose their jobs to AI. But will doctors be part of people?
Let’s talk about that and some.
Where AI is Better Than Humans
To see if AI can replace doctors, we need to know what it can do better than humans. According to McKinsey and Company, AI can change the delivery of health services.
Let’s take a look at that.
1. Disease Diagnostics
Diagnosing a disease early enough is a challenge most medical professionals can relate to. You can’t detect every disease as soon as it turns up in a patient’s body.
But that’s the key to finding a faster medical solution—enter AI. Artificial intelligence is enabling a quicker diagnosis of different ailments for patients.
That’s thanks to the efficiency it creates in the administrative process and the improved diagnostic tools. What’s incredible is how fast and accurate an AI diagnosis is, making the correct diagnosis faster than a human doctor would.
AI cancer screenings, blood tests, and other diagnostic tests are only a few of the ways Ai in healthcare enables the detection of cancer and other diseases.
2. Administration
Data organization is one of the things artificial intelligence does best. It’s also a significant reason AI is one of the most noteworthy inventions of recent times. Not only is the analysis faster, but it’s also more accurate and relevant than when done by humans.
Few other fields generate the amount of data from healthcare providers. These vast swathes of data aren’t always easy to handle. That’s when AI steps in and eases the administrative burden of collecting, organizing, and analyzing this data. Healthcare AI helps organize these data into legible information for future use, making hospital administration easier.
3. Drug Research
New diseases are emerging daily that available drugs can’t effectively deal with. AI is helping develop the next wave of drugs for the medical industry by ensuring accuracy, efficiency, and much-needed knowledge.
BioXcel Therapeutics, a New Haven, Connecticut medical company, uses AI to reinvent existing drugs to treat new or existing diseases.
They also use AI to identify new patients for these innovative drugs. They aren’t the only ones AI is helping discover new uses for drugs.
Berg, a Massachusetts Biotechnology firm, combines Ai with traditional drug research methods to discover milestone drugs. All this serves to show how Ai has transformed drug research.
4. Streamlining Patient Experience
AI is incredibly adept at managing swathes of data, as mentioned before. This ability comes in handy, ensuring the perfect patient experience.
Since hospitals can process patient data faster, they can offer better and more convenient services. That boosts the patient experience since they spend less time in hospital queues waiting for service.
Patient engagement platforms and chatbots also increase doctor-patient interaction, providing better patient support.
Research suggests patients are likely to complete their drug doses with proper follow-up. AI chatbots and other platforms allow an interactive approach to drug administration. That further boosts the patient experience.
5. Medical Training
AI is playing an integral role in teaching the next batch of doctors. Of course, it’s not taking over everything since it still has limitations.
Instead, Ai is helping teach parts of medical training to students. There are examples of an AI tutor surpassing humans in teaching specific details of medical studies.
Having looked at some use cases where AI outperforms humans, let’s see whether it can fully take over their jobs.
Can AI Replace Doctors?
Artificial intelligence’s effect on global workforces is well documented. It makes work more efficient and leads to laying offs as some tasks become redundant. Does the same apply to the healthcare workforce? Can AI replace doctors?
The quick answer is that Ai can’t and will not replace doctors. Albeit not anytime soon. Long after every living person is dead in the future, it might.
Here are the reasons why.
1. Only a Small Portion of Healthcare Can Be Automated
According to the McKinsey Global Institute, healthcare has the least potential for automation. That’s thanks to the specialized roles of health practitioners. The amount of knowledge and detail that goes into healthcare will surely benefit from AI.
However, that won’t be to the extent it declares doctors redundant. According to the MGI, only about 35 percent of hospital time can be automated. That number won’t translate to a direct correlation to the health workforce.
2. The Healthcare Sector Is Already Grappling with Workforce Gaps
Demand for health workers has never been greater in a world facing increasingly diverse health issues. The gap between the needed and the available workforce is only growing.
The adoption of AI will only help reduce this gap. You can’t replace something you don’t have enough of. A perfect example is the forecasted demand for nurses by 2030. There will be about 39 percent more nursing occupations by 2030. Meanwhile, AI can only take up about 10 percent of this number.
3. AI Needs Data to Work
The smartness of AI is such that it improves with each experience. If you’re to have robots taking over the role of doctors, they’ll need a constant stream of data to improve.
How do they deal with a lack of data? Do people’s healthcare pause as Ai looks for the data to serve them? That’s another reason doctors will still provide all the expertise.
4. Robots Can’t Be Empathetic Health Workers
There’s a reason we call someone without feelings a robot. Robots can’t show any empathy despite all the efforts to make them start feeling. A robot won’t empathize with you as a human doctor will.
Sure, it might do some jobs better, such as detecting anomalies faster, but you’ll still want your fellow human talking to you. Something that’s not human won’t relate to what you’re feeling the way another human would.
5. Humans Don’t Really Like Robots
From surveys, humans don’t really like robots. Of course, we love that they make some of our work easier. We love machines, after all. Who wouldn’t want to relax as a robot does all the house chores for them?
But for a robot to come and examine you instead of a doctor? Most people surveyed gave a hell no to that! The research also showed that people would rather spend more money on a human doctor than on a robot with the same expertise level.
Final Thoughts
Will AI replace doctors? NO. However, going by some of the AI benefits we have seen, it will make doctors better at their work.
AI will make their work easier. I don’t think there’s a doctor afraid of AI replacing them. If there is, then their fears are unfounded.
Instead of ending careers and replacing workers, I reckon AI will probably lead to new health professions. So relax, doctors are here to stay.
Are you looking to explore the possibilities of AI in health? Talk to our expert today, and let’s discuss how you can make Ai work for healthcare.
| 2023-01-12T00:00:00 |
2023/01/12
|
https://www.aiwakforce.com/blog/ai-and-healthcare-can-ai-really-replace-doctors/
|
[
{
"date": "2023/01/12",
"position": 28,
"query": "AI replacing workers"
}
] |
Identifying in-demand and emerging technology skills
|
Tackling the skills gap: Identifying in-demand and emerging technology skills
|
https://ssti.org
|
[
"Casey Nemecek"
] |
In 2024, nearly 628,000 job postings demanded at least one AI skill, according to research from the Federal Reserve Bank of Atlanta's Center for Workforce and ...
|
By: Casey Nemecek
A recent State of Skills report by the Burning Glass Institute, the Business-Higher Education Forum, and Wiley identifies four emerging technical skill sets as the fastest growing in the country: artificial intelligence/machine learning (AI/ML), cloud computing, product management, and social media. The authors use these four skill sets to illustrate how businesses, education providers, and learners can best prepare for a changing and increasingly technology-driven labor market.
The report describes how the demand for new skills, despite historic trends, is no longer isolated to the tech sector or new occupations and is an opportunity to draw in talent from a larger pool of candidates. Given the current challenges with meeting employers’ technical talent needs, the authors emphasize the importance of developing talent pipeline strategies that include training for new skills and prioritize continuous learning and skill transition opportunities.
Through a recent partnership with Lightcast (formerly Emsi Burning Glass) and the National Center for O*NET Development (an initiative sponsored by the U.S. Department of Labor), O*NET has updated its approach to identifying and classifying current technology skills trends. Outlined in a recent paper, O*NET announced a new designation to classify technology skills as “In Demand,” which applies to software and technology requirements frequently included in job postings for specific occupations. This designation complements the existing “Hot Technologies” label, which includes the most referenced software and technology requirements across all job postings. By differentiating between “Hot” and “In Demand” technologies, O*NET aims to provide greater clarity to job seekers and training providers to “help match the workforce with the needs of employers.”
In its first release since the update, O*NET has identified over 2,500 “In Demand” technologies for 478 occupations. The top technology categories across industry sectors (excluding categories attributing Microsoft Office as its primary software example) include:
Data base user interface and query software (ex. SQL)
Development environment software (ex. Microsoft Azure)
Object or component-oriented development software (ex. Python)
Web platform development software (ex. JavaScript)
O*NET plans to re-evaluate and update its list of “Hot” and “In Demand” technologies biannually. Over time, this could provide more accessible insight into which technologies are gaining popularity overall and for specific occupations.
Readers can access O*NET’s complete database of technology skills on the O*NET Resource Center.
Software and technology previously associated with highly technical occupations are becoming increasingly common and demanded in non-technical workspaces. While it is impossible to predict future workforce trends with perfect accuracy, organizations building STEM talent can use technology trend information through O*NET to inform and strengthen their workforce readiness programs and messaging strategies.
| 2023-01-12T00:00:00 |
https://ssti.org/blog/tackling-skills-gap-identifying-demand-and-emerging-technology-skills
|
[
{
"date": "2023/01/12",
"position": 1,
"query": "AI skills gap"
}
] |
|
Future of Work
|
Future of Work
|
https://www.reworked.co
|
[
"People Covering Future Of Work",
"Reworked Studio",
"The Reworked Studio Team Transforms Clients",
"Data",
"Concepts",
"Thought Leadership Into Accessible",
"Engaging Articles That Appeal To The...",
"Matthew Saxon",
"Matthew Saxon Leads People Experience At Zoom",
"Ensuring That The Core Value Of Care Is Reflected In The Company S Approach..."
] |
Digital employee experiences are essential to happy and engaged employees. A recent discussion unpacked how AI can improve the digital employee experience.
|
From Nice-to-Have to Non-Negotiable: Prove the Value of Your EX Stack
Transform your tech stack: prove its value, secure your future.
| 2023-01-12T00:00:00 |
https://www.reworked.co/future-of-work/
|
[
{
"date": "2023/01/12",
"position": 32,
"query": "future of work AI"
}
] |
|
Artificial Intelligence: The Future of Work and the Skills You ...
|
Artificial Intelligence: The Future of Work and the Skills You Need
|
https://www.thesoldierswife.com
|
[
"Alozie Blessing"
] |
Some argue that rapid advances in the automation of work can significantly disrupt labor markets. In contrast, others believe some of these technologies are ...
|
The world of work
The world of work is rapidly changing. It’s no longer what it once was. According to the International Labour Organization (ILO), work is simply any task you do for yourself or someone else!
Our generation is witnessing one of the technological advancements in history. The Fourth Industrial Revolution in this 21st century is a significant revolution that can penetrate every sector.
Some argue that rapid advances in the automation of work can significantly disrupt labor markets. In contrast, others believe some of these technologies are smarter than humans and can exterminate the human race.
Is that’s right?
HA-HA, let’s not forget these technologies are programmed by humans, not aliens from outer space!
As a computer scientist, occupational health and safety expert, social innovator, and sustainability practitioner, I have a different opinion.
Living in this digital age is fantastic, as one of the pathways to sustainable development is through more innovative technological systems!
New technologies create winners and losers in the labor market. Click To Tweet They tend to change relative occupation demands and replace old jobs with new ones. And, with the fast evolution of these machines, even the most highly skilled professionals can be replaced. For instance, technologies are better than experienced doctors at diagnosing diseases. But can medical doctors ever be replaced??
The good news;
Technological progress may be perceived as a threat in a one-sided forecast. Though technologies can replace the work done by humans and will likely transform almost all occupations in the future, automation gives rise to more innovative, safer ways of doing tasks and healthier living.
For instance, converting automobiles from natural combustion system, which burns petroleum, to electric vehicles charged with electricity produced by clean energy. This could be one of the effective ways to reduce carbon dioxide emissions. Secondly, reducing our addiction to fossil fuels by using natural energy sources like solar and wind power. We can use solar and wind power to boil water and produce steam or electricity. And guess what?… It helps us respect our planetary boundaries.
Automation can augment productivity and economic development; therefore, understanding its distributional concerns is vital to enable policymakers to design appropriate strategies, including education and skills policies. It also helps individuals make good choices about what careers to pursue.
Today, one technology is causing particular anxiety about job displacement: artificial intelligence.
Artificial intelligence (AI), or machine learning, refers to algorithms that learn to complete tasks by identifying statistical patterns in data. This technology has recently achieved superhuman performance across a wide range of economically valuable tasks. Some of these tasks are associated with high-wage occupations, such as radiologists. In contrast, others are related to low-wage occupations, such as assembly line workers. At a time when rising inequality is a major social issue, it is unclear whether AI will increase poverty by displacing production workers. OR, maybe it can reduce inequality by substituting skilled professionals like radiologists.
The sectors that may be affected by the technological advancements are Information Technology, Finance, Marketing, Energy, Agriculture, and Transport. For example, AI and Machine Learning (ML) algorithms offer great potential in the finance industry. These algorithms are self-learning and can be extremely valuable to the customer and the financial organization if fed the correct data. Therefore, if they are well advanced, the workers in the industry might be affected.
Nevertheless, studies have shown that by the end of 2025, technological progress may create more jobs, contrary to what people think; however, workers need specific skills to stay relevant.
Therefore my advice,
In this era of new technologies, new ways of working, and new forms of work, the primary skills to focus on for the future are:
Industry Specific Skills (hard skills) that can enable humans to use future tools, such as; programming, database engineering, network security, interface design, Etc. Such skills might be in high demand in the future as they will be the driving force behind AI tools and Blockchain technology.
Work-Related skills (soft skills) that stick with humans throughout their lives and make them fit into occupations. Examples are; critical thinking, creativity, social intelligence, self-management, attention management, adaptability, resilience, and continuous learning or lifelong learning. This is essential as numerous jobs and opportunities might move halfway across the world. Even in rural areas with less infrastructure and access to basic amenities.
Finally, humans should collaborate and work with these technologies in this digital age, where technology creates groundbreaking opportunities and transforms our interconnected world beyond recognition.
| 2023-01-12T00:00:00 |
2023/01/12
|
https://www.thesoldierswife.com/blog/artificial-intelligence-the-future-of-work-and-the-skills-you-need/
|
[
{
"date": "2023/01/12",
"position": 71,
"query": "future of work AI"
}
] |
Disrupt or be Disrupted: The Impact of AI on Industries Today
|
Disrupt or be Disrupted: The Impact of AI on Industries Today
|
https://predictableprofits.com
|
[
"Team Predictable Profits",
"Predictable Profits Builds",
"Shapes Entrepreneurial Breakthroughs Through Customized Business Coaching",
"Actionable Content. With More Competition",
"Noise Than Ever Before",
"We Ll Help You Stay Ahead Of The Competition With Clarity",
"Direction",
"Feedback In A Way That S Uniquely Guaranteed.",
"Charles Gaudet"
] |
The impact of AI is disrupting industries such as healthcare, law, journalism, aerospace, and manufacturing.
|
On November 30, 2022, the world changed with the launch of ChatGPT. This free artificial intelligence tool allows anyone to converse with a generative pre-trained robot.
ChatGPT reached a million users in just five days, making it the fastest-growing platform in history. This rapid growth surpasses the achievements of social media giants such as Facebook, Instagram, and Pinterest, as well as the popular game Angry Birds, which took 35 days to reach a million users.
The impact of AI is disrupting industries such as healthcare, law, journalism, aerospace, and manufacturing. Artificial intelligence has the potential to revolutionize these same industries by analyzing and processing large amounts of data quickly, bringing new levels of efficiency to many business systems — from improved customer service to automated processes.
For example, AI enables medical practitioners to access accurate and relevant patient data through predictive analytics. AI is also used in transportation, particularly in developing self-driving cars.
In the ever-evolving world of technology, artificial intelligence is quickly becoming an essential tool for businesses looking to stay competitive.
Organizations must understand AI’s impact to avoid missing out on lucrative opportunities or getting left behind in a rapidly changing landscape.
| 2023-01-11T00:00:00 |
2023/01/11
|
https://predictableprofits.com/the-impact-of-ai/
|
[
{
"date": "2023/01/12",
"position": 1,
"query": "AI economic disruption"
},
{
"date": "2023/02/01",
"position": 32,
"query": "AI economic disruption"
},
{
"date": "2023/04/01",
"position": 32,
"query": "AI economic disruption"
},
{
"date": "2023/07/01",
"position": 32,
"query": "AI economic disruption"
},
{
"date": "2023/09/01",
"position": 27,
"query": "AI economic disruption"
},
{
"date": "2023/10/01",
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"query": "AI economic disruption"
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{
"date": "2023/11/01",
"position": 28,
"query": "AI economic disruption"
},
{
"date": "2024/01/01",
"position": 27,
"query": "AI economic disruption"
},
{
"date": "2024/12/01",
"position": 32,
"query": "AI economic disruption"
},
{
"date": "2025/01/01",
"position": 31,
"query": "AI economic disruption"
},
{
"date": "2025/03/01",
"position": 48,
"query": "AI economic disruption"
},
{
"date": "2025/04/01",
"position": 29,
"query": "AI economic disruption"
},
{
"date": "2025/06/01",
"position": 30,
"query": "AI economic disruption"
}
] |
AI is Changing Competitive Intelligence
|
AI is Changing Competitive Intelligence
|
https://www.evalueserve.com
|
[
"Zach Hover"
] |
AI in competitive intelligence helps you see how the market is responding to changes in the economic and social environments and how you can take advantage ...
|
What Is Competitive Intelligence?
Competitive intelligence involves gathering and analyzing information to identify opportunities, threats, and risks. It’s vital for your business because it can help you achieve a competitive edge.
Competitive intelligence enables businesses to make data-driven decisions about how to position themselves in the market. It also provides valuable insights into new growth opportunities, changing customer behavior, and the shifting economy.
With competitive intelligence, you can spot patterns, derive insights from them, and understand how they will help you outsmart the competition.
How AI Is Changing Competitive Intelligence
Artificial intelligence (AI) has disrupted businesses across virtually all industries in unprecedented ways.
Forward-thinking companies are using AI in competitive intelligence to collect, analyze, and use data for decision-making. AI provides data-driven insights that would otherwise be impossible to derive manually. Here’s how AI is changing competitive intelligence.
Automating Data Collection & Analysis
Gathering data requires more time and energy than most people can afford to spend.
Taking people, even an entire department, to gather and sift through tons of data would be incredibly time-consuming. Moreover, humans are prone to error, and their productivity and efficiency can be affected by many factors inside and outside the business environment.
Machines, on the other hand, are more accurate and can provide consistent results if well-maintained. Artificial intelligence automates data collection while improving the efficiency and integrity of the data collected.
AI-powered tools can quickly and accurately gather and analyze vast amounts of data from a variety of sources, such as company websites, forums, and social media platforms. AI can also gather insights from events, news sources, job review boards, and many other sources.
There are way too many sources of intel for humans to track themselves. Without AI, they’d spend an eternity researching and trying to understand the data. AI not only collects the data; it analyzes it and derives meaningful insights from it that can be used for decision-making.
Enhancing Market and Customer Insights
Competitive intelligence programs allow marketers to understand market dynamics better.
AI in competitive intelligence helps you see how the market is responding to changes in the economic and social environments and how you can take advantage of the situation to achieve a competitive edge.
If there are market gaps that need to be filled, AI will point you in the right direction. In fact, 61% of leaders believe AI helps them discover new business opportunities.
What’s more, artificial intelligence and machine learning algorithms help analyze market data and identify patterns and trends. They act as surveillance for multiple sources, ensuring you don’t miss out on any important updates about your industry.
Improving Forecasting and Predictive Modeling
Today’s businesses are driven by consumer demand. Unfortunately, the patterns of demand vary from period to period and are determined by a number of factors. This makes it incredibly difficult to develop accurate demand forecasts.
Emerging technologies, such as artificial intelligence and demand planning software, are increasingly being used to improve the accuracy of forecasts. These technologies are being used to develop accurate forecasts for product demand, raw materials requirements, inventory levels, and more.
Through advanced analytics and machine learning technologies, AI can help your business make more accurate predictions about future market trends and customer behavior.
Enhancing Competitive Analysis
Thanks to AI, it’s now easier than ever to conduct competitive intelligence.
Artificial intelligence and machine learning technologies have made it easier to track everything your competitors are doing—from product development to promotions.
If you’re a business owner, you’ll want to know what other businesses are doing to make informed decisions. By leveraging AI in your competitive intelligence efforts, you can effortlessly track, monitor, and understand what your competitors and doing and improve on it.
Even when your competitors change their strategies spontaneously, you can monitor and anticipate their moves with the help of the data (current and historical) that competitor intelligence provides.
Streamlining Internal Processes
Artificial intelligence can be used to automate and streamline business processes in a number of ways. For example, it can be used to automate customer service tasks such as responding to customer queries and personalizing content for individual customers.
Today, many businesses use artificial intelligence bots to automate repetitive administrative tasks, such as filing and scheduling appointments. AI can also be used to optimize inventory management by predicting demand and identifying patterns in consumer behavior.
The Bottom Line
The potential of artificial intelligence to help businesses redefine their processes and grow is vast. By harnessing the power of AI to improve competitive intelligence, companies can automate data collection and analysis, gain in-depth customer and market insights, improve forecasting, enhance competitive analysis, and streamline internal processes.
| 2023-01-12T00:00:00 |
https://www.evalueserve.com/blog/how-ai-is-changing-competitive-intelligence/
|
[
{
"date": "2023/01/12",
"position": 57,
"query": "AI economic disruption"
},
{
"date": "2023/01/12",
"position": 96,
"query": "artificial intelligence business leaders"
}
] |
|
How is AI Influencing the Manufacturing Industries?
|
How is AI Influencing the Manufacturing Industries?
|
https://datamites.com
|
[
"Datamites Blog",
"Datamites Team"
] |
The evolution of digitalization has expanded new tech trends to bring the digital economy into giant industries. According to AspenTech 2020 report, AI ...
|
Artificial Intelligence (AI)
Artificial Intelligence is a branch of computer science that enables machines to perform tasks and mimic human action through advanced computer functions. The use of AI in the manufacturing industries results in cost maintenance and improves data production to secure industrial environments. AI puts a major impact on manufacturing industries to improve production bottlenecks, load-out cycle times, high production waste levels, and low output per labor hour. As technology has emerged fastly, the need for Artificial Intelligence has grown exponentially which optimizes the manufacturing industries.
According to Research Gate, the key challenge of AI in manufacturing industries is to prioritize data information and business strategies to enhance economic value.
Introduction to AI in Manufacturing Industries:
Artificial intelligence is highly needed in the manufacturing industry to process data and improve logistics better. With the help of AI solutions, manufacturers can advance their warehouse stock and reduce the cost of transportation while fulfilling the demand of customers. AI distributes predictive knowledge that improves the operational department in the manufacturing industries. AI makes important use of technical services that easily drive data to innovate advancement in machine learning, computer science, natural language processing, and cloud technology.
Artificial intelligence in manufacturing industries is highly valued which expands the market size and has grown to USD 2.3 billion in 2022. It is expected to accelerate in the coming year from 2023 to 2027 according to the MarketsandMarkets report.
Refer this article: Support Vector Machine Algorithm (SVM) – Understanding Kernel Trick
Everything you need to know about the role of AI in Manufacturing Industries:
Artificial intelligence has revolutionized the height of technology and efficiently grown in IT and Manufacturing Industries. Let’s learn how AI’s role has moulded itself in manufacturing industries.
1. Preventative Maintenance- The role of AI has surpassed its demand in many industries. The requirement for artificial intelligence has taken necessary measures to prevent technical issues and build strong data to identify tech problems. Machine learning and artificial intelligence are connected to creating algorithms that allow historic data to solve problems.
2. Generative Design- Generative design allows technology to collaborate with human minds to receive technical inputs. It helps in building a useful algorithm that creates a product design and relative strategy to run artificial intelligence simulations. With the use of an algorithm, it combines design input to fabricate the optimum design.
3. Digital Twins- AI uses digital twins to represent virtual services and products to collect real-world data. The digital twins use cameras and sensors to update inefficiencies to improve counterparts. Giant industries benefit business markets to operate products to enhance analytic outputs. With the advancement of Artificial intelligence, big data and virtual models can enhance digital trends.
4. Future Forecasting– AI uses future forecasting to predict the data outcome to reduce the risk of wrong decisions and upgrade business strategies. In future forecasting, multiple datasets are analyzed to provide predictions in business areas. It uses AI algorithms to modify management processes. Future forecasting helps in boosting the business market to improve profit in manufacturing industries.
5. Quality Controls- Quality control is a definite part of quality management that focuses on quality requirements. In many IT firms and manufacturing industries, quality control is used to maintain perfection to improve product quality. The importance of quality control is to standardize the quality issue and reduces the chance of damaged products.
Read this article: How AI is refining the Finance and Banking Sector?
What are the benefits of Artificial Intelligence in Manufacturing?
Amplify Customer Service– AI improves customer service by reducing the cost of products and fast delivery shipments. AI tries to provide the best services to its customers to satisfy their needs. It is important to maintain a healthy relationship with a customer who brings growth to the manufacturing industry. The key component of artificial intelligence is to develop customer service through the “sales and delivery” procedure.
Manufacturing Operation 24/7- AI in manufacturing industries needs regular maintenance for better accuracy. When performing in the factory it needs 24/7 human attention that impacts effectively. In manufacturing industries, it needs at least 8 hours of daily operation that bring massive outcomes on the productivity level.
Increased productivity– AI uses the coding formula to stabilize the challenging problems in the manufacturing industries. It functions in a friendly way to modify workflow and increases job satisfaction. It processes better insight to achieve potential in mundane work. With the help of AI automation, it has empowered productivity and the industrial economy.
Factory layout- AI steps in developing a factory layout that processes complicated variables to implement change in production. With the help of AI, you can identify the inefficiency in factory layout to enable rapid change. In the entire factory layout process, the AI system optimizes lifecycles products to minimize disruption.
Direct automation- AI in manufacturing industries connects IoT to enable big data via logic controllers to enhance production processes. With the help of direct automation, it can generate seamless information to improve the operating system. Direct automation supports software manuals, software documents, and product information.
Also refer this article: Taking a Look at the Role of AI in Education
What is the future of Artificial Intelligence in Manufacturing Industries?
The manufacturing industry has embraced new technology and one among them is artificial intelligence. The evolution of digitalization has expanded new tech trends to bring the digital economy into giant industries. According to AspenTech 2020 report, AI researcher has generated 90% of the industrial firm because of AI demand in the market. AI is widely used to develop the internal and external processes of the industry. AI helps to modify product design and enhance the manufacturing industry. AI is shaping its future and becoming more relevant to provide a profitable application that will bring growth to the manufacturing industries.
According to the AmyGB.ai report, AI in the manufacturing industry is set to evolve in the coming years to boost its production in growing revenue. By the year 2035, technology will grow across all industries which will represent its function and accelerate its key components. With the advancement of AI in manufacturing, business value will increase its segment and produce innovative insight to achieve operational excellence.
The future market of AI in manufacturing industries will accelerate in the upcoming years. The need for AI professionals will increase and it will bring a huge transformation in technology. According to the Ambition report, the salary of an AI professional ranges between 3.5 LPA to 22.0 LPA.
Also read this article: Understanding Auc Roc Curve
Conclusion.
Technology enhanced its digital space and brought many new innovations to the business market. The advanced market has brought new technologies like cloud computing, machine learning, 3D printer, robotics, nanotechnology, and automation. Manufacturing industries are leaping high and performing definite tasks with the help of AI parameters. The use of AI brings production quality and optimizes manufacturing processes to challenge the latest trends. The manufacturing industry plays a vital role in upgrading the economy by using innovative technology.
DataMites is a professional training institute that offers leading courses in data science, artificial intelligence, machine learning, and python. All the courses of DataMites are accredited by an international body called IABAC. The global certification helps a student to stand out in their resume and bring them top opportunities to get job assistance. Enrolling in the desired course will assist you to get training from professionals, handed with the specialized syllabus, mock tests, capstone projects, and case studies.
Artificial Intelligence Course Introduction
Are Data Science & AI Careers Valid in 2023?
| 2023-01-12T00:00:00 |
2023/01/12
|
https://datamites.com/blog/how-is-ai-influencing-the-manufacturing-industries/
|
[
{
"date": "2023/01/12",
"position": 94,
"query": "AI economic disruption"
}
] |
OPM uses live polling for staff meetings: news in brief
|
OPM uses live polling for staff meetings: news in brief
|
https://www.globalgovernmentforum.com
|
[
"Mia Hunt"
] |
Digital Leaders Study 2024: Embedding AI across government in the UK ... Join this session to discuss: • How can government organisations implement the policies ...
|
US Office of Personnel Management uses live polling for staff meetings; UK unions vow to fight anti-strike law: management & workforce news in brief
PCS members from 124 government departments are preparing to strike on 1 February. Photo by Andy O'Donnell via Flickr
Global Government Forum’s weekly digest of the news you need to know but might have missed.
US Office of Personnel Management introduces live polling to staff meetings in bid to boost engagement
The Office of Personnel Management (OPM) is working on a number of initiatives to make it more user-centric, to attract and retain talented staff and to boost employee engagement in the hybrid work environment.
In an interview with Federal News Network, the agency’s chief information officer Guy Cavallo said that he and his colleagues are focusing on boosting engagement in virtual meetings, which have continued post-pandemic as the agency adopts a hybrid model that includes both remote and in-person work.
He said that staff who were teleworking often felt like spectators rather than participants in large online team meetings and didn’t feel as connected as when they were in the workplace.
To address this, the OPM has introduced interactive polls to allow those in meetings to give feedback. “We’re instantly getting feedback from the audience,” Cavallo told Federal News Network. “We’re changing our meetings and our response to deal with that feedback, so everybody feels more connected.”
The OPM used the new function to ask staff what types of training they’d like to do in 2023 and compiled a list of the top five responses as a result. Off the back of this, Cavallo said he is planning to offer at least the most popular option and potentially all five.
He said live polling had been successful in encouraging active participation in meetings and giving people the opportunity to give their “two cents”. Not adapting to new working and meeting styles could leave employees “really feel distant” from their teams. “I think it’s very important that we make sure that our hybrid and remote workers are fully engaged as much as we can.”
Other measures being undertaken by Cavallo and his colleagues include overhauling the OPM website to make it easier for people to navigate, ensuring interns get real hands-on experience across a number of the agency’s operations, and making it easier for early-career applicants to compete for roles.
In the 2022 Federal Employee Viewpoint Survey, positive responses from staff at OPM increased by up to 30% for some of the survey questions, including ‘Would you recommend this office as a good place to work?’
Read more: Job satisfaction of US federal officials falls, annual survey finds
UK government attempts to curtail strike action with new law
The UK government has introduced a new bill that would require some public sector employees to work during strikes, drawing the ire of public sector unions.
The rule would apply to the NHS, and transport, education, fire and rescue, border security and nuclear decommissioning sectors in England, Scotland and Wales and aims to ensure a “minimum level of service” during a strike.
Under the proposed legislation, employers would have to issue a ‘work notice’ following a vote to strike stating the workforce they would need to meet minimum staffing levels. Employees named in such notices who go on to take part in industrial action would lose their automatic protection for unfair dismissal.
The news follows a series of walkouts by nurses, rail staff and civil servants in recent weeks, with unions demanding better pay and conditions for their members in the face of the cost-of-living crisis. Negotiations with the government have been fraught.
Public sector unions reacted angrily to the news of the bill, calling the plans “draconian” and threatening legal action against the government.
The Trades Union Congress (TUC) general secretary Paul Nowak said: “The right to strike is a fundamental British liberty, but the government is attacking it in broad daylight.
“These draconian new curbs will tilt the balance of power even more in favour of bad bosses and make it harder for people to win better pay and conditions.”
He added that forcing people to work after they had exercised their democratic right to vote for strike action and sacking them if they don’t comply was “undemocratic, unworkable, and almost certainly illegal” and that if passed, the bill would prolong disputes, “poison industrial relations” and lead to more frequent strikes.
Dave Penman, the general secretary of the FDA union, which represents senior civil servants, told The Telegraph the planned legislation was a “complete sideshow” and “just going to make [the situation] worse”.
He said that the UK already had “some of the strictest legislation when it comes to industrial action”.
The government said the measures would protect the public from disproportionate disruption caused by strikes and that the plans were in line with laws in other European countries.
The proposals will need to be voted through Parliament before becoming law.
The news comes as 100,000 civil servants prepare to strike on 1 February. The Public and Commercial Services (PCS) union said the strike would involve members in 124 government departments.
PCS is calling for a 10% pay rise, protections to pensions and protections from job cuts.
Read more: UK civil service strikes threaten borders and ports over Christmas
Australian Public Service reform tops agenda at Secretaries Board meeting
Plans to reform the Australian Public Service (APS) will focus on making it pro-integrity, transparent and accountable and encouraging it to work more closely with the community it serves, public service minister Katy Gallagher has said.
Gallagher also said that digital and data would be central to improved public service delivery, and noted separately that a priority was to make the APS an attractive and dynamic place to work.
Her comments were made at the latest Secretaries Board meeting, in which APS chiefs brief board members on developments and priorities.
Read more: Minister vows to revive ‘mothballed’ Australian Public Service reform agenda
The APS Academy, a dedicated learning hub for public service employees, was recognised by the board as playing a key role in supporting reform and uplifting capabilities. It noted “the importance of continued investment” in the Academy during the meeting.
Public sector reform secretary Gordon de Brouwer gave an overview of upcoming initiatives, including those impacting senior executive service recruitment practices. Options being considered include secondments with state and territory governments, and recruiting employees from the private sector, academia and non-for-profit organisations.
An update was also given on the implementation of recommendations from the Bell Inquiry report, which examined the actions of former Australian prime minister Scott Morrison after it came to light last year that he had appointed himself to five additional departments without the knowledge of parliament, the public and in some cases the ministers leading the departments in question.
Read more: Former Australian PM slammed by inquiry into secret appointments
The report’s recommendations focus on ensuring transparency should any such appointment be made in future. They include appointments being published in the Commonwealth Gazette by law and making details of ministers and the division of responsibilities between them publicly available on government websites.
The board also discussed Fault Lines, the independent inquiry by four experts including former PM&C secretary Peter Shergold, into the government’s response to COVID-19.
It found that “significant mistakes were made” and concluded that the APS must improve collaboration, capability and communication, the diversity of its workforces, and its use of data if they are to respond better to the next health crisis.
Read more: Australia made ‘significant mistakes’ in COVID response, review finds
The Canadian federal government will soon require public servants to work from the office at least two to three days a week.
The mandate outlining the new arrangement is expected to increase in-person work to between 40% to 60% of officials’ regular schedules. To smooth the transition for departments and employees, a phased introduction of the new rules will begin on 16 January. Implementation is expected to be complete by 31 March.
The move marks a departure from the federal government’s previously looser hybrid work model, which followed employees’ remote work during the COVID-19 pandemic. Guidelines issued by the Treasury Board of Canada last year left it to individual departments to decide “whether the location of work be made flexible, to what extent, and how”.
Speaking at a news conference in December, Mona Fortier, president of the Treasury Board, said that the aim of the mandate was to standardise hybrid work practices across the federal workforce. She added that office presence supported collaborative work, which is turn fostered “team spirit, innovation and a culture of belonging”.
Fortier also said that the new requirement reflected government’s understanding about the need for “greater fairness and equity across our workplaces” as well as “consistency in how hybrid work is applied across the federal government”.
Read more: Canada’s hybrid work plan stokes discontent among public servants
In an interview published last week in Canada’s Globe and Mail, former head of the Canadian public service Michael Wernick said that working from home long-term could hinder public servants’ ability to learn from colleagues and hold back their careers.
He added that remote work made it harder for managers to “spot and grow talent” and “identify who is contributing and who is a passenger”, stressing that these were “essential to improving teams over time and growing the next cohort of leaders”.
Wernick posted the interview on LinkedIn and emphasised that “the important thing is to move forward and figure out how middle and senior managers can best play their essential role in performance management and talent management in the new workplaces that are going to be increasingly hybrid”.
Listen: GGF’s Leading Questions podcast with Canada’s former cabinet secretary, Michael Wernick
Global Government Forum is now on Apple News! Subscribe to our channel to get all GGF’s latest news and insight on your device.
| 2023-01-12T00:00:00 |
https://www.globalgovernmentforum.com/us-office-of-personnel-management-uses-live-polling-for-staff-meetings-uk-unions-vow-to-fight-anti-strike-law-management-workforce-news-in-brief/
|
[
{
"date": "2023/01/12",
"position": 64,
"query": "government AI workforce policy"
}
] |
|
Jobs at Sentra | Check Current Openings
|
Check Current Openings
|
https://www.sentra.io
|
[] |
... AI-driven features that provide value to our customers. Requirements. 7+ years of experience in data science, with a strong focus on NLP and/or Generative AI.
|
The best thing about working at Sentra is the people and the culture. Collaboration is at the heart of everything, with a team of talented, driven individuals who are passionate about innovative data security solutions. Sentra fosters an inclusive environment where every idea is valued, empowering creativity and diverse problem-solving. There’s a strong sense of ownership across the board, with everyone united by a shared mission, making it an inspiring and supportive place to work.
| 2023-01-12T00:00:00 |
https://www.sentra.io/careers
|
[
{
"date": "2023/01/12",
"position": 65,
"query": "generative AI jobs"
}
] |
|
Simon-Kucher: Commercial Growth Consulting
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Commercial Growth Consulting
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https://www.simon-kucher.com
|
[] |
Unlocking better growth. Tariffs, pricing, and power; Better Growth in Banking; Generative AI. What We Do.
|
Better Growth starts here
With You.
We understand that joining any firm is a big decision. We offer a huge range of career opportunities where you can make your own impact and build a career around you.
So, look around, get to know us, and if you like what you see, why not apply to join our team?
| 2023-01-12T00:00:00 |
https://www.simon-kucher.com/en
|
[
{
"date": "2023/01/12",
"position": 93,
"query": "generative AI jobs"
}
] |
|
NVIDIA, Evozyne Create Generative AI Model for Proteins
|
NVIDIA, Evozyne Create Generative AI Model for Proteins
|
https://blogs.nvidia.com
|
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“They scaled jobs to multiple GPUs to speed up training,” said Joshua Moller, a data scientist at Evozyne. “We were getting through entire datasets every minute ...
|
Scientists use NVIDIA BioNeMo for large language models that generate high-quality proteins that can speed drug design and help create a more sustainable environment.
Using a pretrained AI model from NVIDIA, startup Evozyne created two proteins with significant potential in healthcare and clean energy.
A joint paper released today describes the process and the biological building blocks it produced. One aims to cure a congenital disease, another is designed to consume carbon dioxide to reduce global warming.
Initial results show a new way to accelerate drug discovery and more.
“It’s been really encouraging that even in this first round the AI model has produced synthetic proteins as good as naturally occurring ones,” said Andrew Ferguson, Evozyne’s co-founder and a co-author of the paper. “That tells us it’s learned nature’s design rules correctly.”
A Transformational AI Model
Evozyne used NVIDIA’s implementation of ProtT5, a transformer model that’s part of NVIDIA BioNeMo, a software framework and service for creating AI models for healthcare.
“BioNeMo really gave us everything we needed to support model training and then run jobs with the model very inexpensively — we could generate millions of sequences in just a few seconds,” said Ferguson, a molecular engineer working at the intersection of chemistry and machine learning.
The model lies at the heart of Evovyne’s process called ProT-VAE. It’s a workflow that combines BioNeMo with a variational autoencoder that acts as a filter.
“Using large language models combined with variational autoencoders to design proteins was not on anybody’s radar just a few years ago,” he said.
Model Learns Nature’s Ways
Like a student reading a book, NVIDIA’s transformer model reads sequences of amino acids in millions of proteins. Using the same techniques neural networks employ to understand text, it learned how nature assembles these powerful building blocks of biology.
The model then predicted how to assemble new proteins suited for functions Evozyne wants to address.
“The technology is enabling us to do things that were pipe dreams 10 years ago,” he said.
A Sea of Possibilities
Machine learning helps navigate the astronomical number of possible protein sequences, then efficiently identifies the most useful ones.
The traditional method of engineering proteins, called directed evolution, uses a slow, hit-or-miss approach. It typically only changes a few amino acids in sequence at a time.
By contrast, Evozyne’s approach can alter half or more of the amino acids in a protein in a single round. That’s the equivalent of making hundreds of mutations.
“We’re taking huge jumps which allows us to explore proteins never seen before that have new and useful functions,” he said.
Using the new process, Evozyne plans to build a range of proteins to fight diseases and climate change.
Slashing Training Time, Scaling Models
“NVIDIA’s been an incredible partner on this work,” he said.
“They scaled jobs to multiple GPUs to speed up training,” said Joshua Moller, a data scientist at Evozyne. “We were getting through entire datasets every minute.”
That reduced the time to train large AI models from months to a week. “It allowed us to train models — some with billions of trainable parameters — that just would not be possible otherwise,” Ferguson said.
Much More to Come
The horizon for AI-accelerated protein engineering is wide.
“The field is moving incredibly quickly, and I’m really excited to see what comes next,” he said, noting the recent rise of diffusion models.
“Who knows where we will be in five years’ time.”
Sign up for early access to the NVIDIA BioNeMo to see how it can accelerate your applications.
| 2023-01-12T00:00:00 |
2023/01/12
|
https://blogs.nvidia.com/blog/generative-ai-proteins-evozyne/
|
[
{
"date": "2023/01/12",
"position": 99,
"query": "generative AI jobs"
}
] |
Playing information roulette: using AI for legal compliance
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Playing information roulette: using AI for legal compliance
|
https://www.nhbr.com
|
[
"Brian Bouchard"
] |
Department of Labor's press releases from 2022 showing aggressive (and public) ... organization's legal compliance to AI. Attorney Brian Bouchard, a ...
|
Recent news stories about artificial intelligence and the revolutionary breakthroughs showcased by OpenAI’s ChatGPT made me curious as an employment lawyer: Can AI accurately answer legal questions and draft employment documents, like non-solicitation agreements? It cannot — at least not with consistent accuracy.
AI churns out confident-sounding, personalized answers but those answers often miss the mark. Here’s my experience so far. I went for broke on the first question. I asked the software to write an employee non-solicitation clause enforceable in New Hampshire. To my amazement, it wrote one.
Looks passable, right? It even includes buzz phrases like “directly or indirectly.” But looks are one thing; substantive compliance is another. Most problematically, the AI-generated clause impliedly includes all prospective and potential clients of the company, which is verboten in New Hampshire. While prohibiting solicitation of active prospects the employee was courting while employed with the company might fly, it is unlikely a New Hampshire court would enforce the broad language generated here. There are other issues of being too broadly written because the clause is not tailored to the legitimate needs of the company. The advice at the end about consulting legal counsel turns out to be good advice.
I next asked the software for the differences between Federal and New Hampshire tip pooling laws. Here’s the response:
Much of this is either misleading or wrong. For example, it’s misleading because it says the “employer is not allowed to keep any portion of the tip pool” but doesn’t explain that the term “employer” includes “managers” and “supervisors.” It also totally fumbled New Hampshire law. In New Hampshire, employers cannot require employees to participate in tip pools. NH RSA 279:26-b requires that any tip pooling or tip sharing must be completely voluntary and without coercion.
Changing the search inputs generates different results. Indeed, it seems that the more legal jargon used, the more accurate the results become. Here’s an exchange about ADA (Americans with Disabilities Act) protection:
This answer mostly tracks the ADA’s definition of a “qualified individual with a disability” and isn’t wrong per se. The fact that it personalizes the answer to “Mark” is impressive — but could also just be a fun parlor trick.
The takeaway is that to receive a correct answer from AI, the user often has to be knowledgeable enough to know what to ask and how to ask it. It took me three or four tries to get the ADA answer above, and even then much was left unsaid about what constitutes an essential function of the job. The answer also lacks nuance about when the individual may be entitled to job reassignment or preferential treatment in the interview process as an accommodation. Perhaps most frustratingly, I rarely received the same answer for the same inputs.
Using AI for legal compliance is like playing information roulette. The AI technology is very clever and sometimes will provide the right answers (and very quickly) but often it will not. The user must ultimately be competent enough to know the difference.
None of this is surprising. OpenAI’s website discloses that “ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers.” Confirmed. Users may be tempted to trust the software anyway. Unlike a Google search that simply directs the user to potentially relevant websites, ChatGPT provides a unique and personalized narrative response in real-time. Users can watch the AI type its response. That personal touch creates an alluring but false air of credibility.
Other problems lurk beneath the surface. If anyone thinks that questions asked to ChatGPT are confidential or privileged, think again. Savvy plaintiff’s attorneys might soon ask for ChatGPT inputs and results in their lawsuit discovery requests. Legal woes could await the HR professional who asked a “hypothetical” question about how to terminate “Mark” even with his alleged disability.
The OpenAI technology is breathtaking and at times a little spooky. At one point in my experiment, it drafted an official looking legal complaint based on a simple timber trespass inquiry. Technology like ChatGPT could one day provide legal compliance information with consistent accuracy and disrupt our entire white-collar service economy. Today is not that day, however. Until that day, businesses still need qualified employment counsel to draft their employment agreements and to help manage all manner of legal compliance.
But if you think information roulette is more your speed, I encourage you to look at the U.S .Department of Labor’s press releases from 2022 showing aggressive (and public) enforcement efforts with huge sums of money collected; you may feel differently about trusting your organization’s legal compliance to AI.
Attorney Brian Bouchard, a shareholder at Sheehan Phinney, works out of the firm’s office. His practice is focused on employment and business litigation matters.
| 2023-01-12T00:00:00 |
2023/01/12
|
https://www.nhbr.com/playing-information-roulette-using-ai-for-legal-compliance/
|
[
{
"date": "2023/01/12",
"position": 82,
"query": "AI labor union"
}
] |
What is a Reduction in Workforce (RIF)
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What is a Reduction in Workforce (RIF)
|
https://1up.ai
|
[] |
RIF is a process in which a company reduces the number of employees. This can be done through layoffs, early retirements, or by incentivizing staff ...
|
Clear communication with affected employees, offering support and resources to transition, and compliance with legal frameworks are all requirements for an effective (and legal) reduction in workforce.
| 2023-01-12T00:00:00 |
2023/01/12
|
https://1up.ai/reduction-in-workforce/
|
[
{
"date": "2023/01/12",
"position": 50,
"query": "AI layoffs"
}
] |
How Artificial Intelligence is Impacting Higher Education
|
How Artificial Intelligence is Impacting Higher Education – Teaching in Higher Ed
|
https://teachinginhighered.com
|
[
"Professor Of Teacher Education",
"Lead Developer For The Governor'S Teaching Fellows Program",
"Bonni Stachowiak Is Dean Of Teaching",
"Learning",
"Professor Of Business",
"Management At Vanguard University. She Hosts Teaching In Higher Ed",
"A Weekly Podcast On The Art",
"Science Of Teaching With Over Five Million Downloads. Bonni Holds A Doctorate In Organizational Leadership",
"Speaks Widely On Teaching",
"Curiosity"
] |
Cynthia Alby discusses how artificial intelligence (like ChatGPT) is impacting higher education on episode 448 of the Teaching in Higher Ed podcast.
|
Cynthia Alby discusses how artificial intelligence (like ChatGPT) is impacting higher education on episode 448 of the Teaching in Higher Ed podcast.
Quotes from the episode
If AI can do it well, it is possible that we could hand things over to AI that would allow us to do other things that we wouldn’t have had time to do.
-Cynthia Alby
When students are doing work that matters to them, they don’t turn to AI for anything other than a little help.
-Cynthia Alby
How can we ask students to do things that are worth doing?
-Cynthia Alby
| 2023-01-12T00:00:00 |
2023/01/12
|
https://teachinginhighered.com/podcast/how-artificial-intelligence-is-impacting-higher-education/
|
[
{
"date": "2023/01/12",
"position": 4,
"query": "AI education"
},
{
"date": "2023/01/12",
"position": 3,
"query": "artificial intelligence education"
}
] |
Why employees are more likely to second-guess ...
|
Why employees are more likely to second-guess interpretable algorithms
|
https://mitsloan.mit.edu
|
[
"Sara Brown"
] |
Business leaders should keep this in mind. Share ... Prior research has assumed that people are more likely to trust interpretable artificial intelligence ...
|
When people can see how an algorithm works, they might be more inclined to second-guess its recommendations. Business leaders should keep this in mind.
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More and more, workers are presented with algorithms to help them make better decisions. But humans must trust those algorithms to follow their advice.
The way humans view algorithmic recommendations varies depending on how much they know about how the model works and how it was created, according to a new research paper co-authored by MIT Sloan professor Kate Kellogg.
Work smart with our Thinking Forward newsletter Insights from MIT experts, delivered every Tuesday morning. Email: Leave this field blank
Prior research has assumed that people are more likely to trust interpretable artificial intelligence models, in which they are able to see how the models make their recommendations. But Kellogg and co-researchers Tim DeStefano, Michael Menietti, and Luca Vendraminelli, affiliated with the Laboratory for Innovation Science at Harvard, found that this isn’t always true.
In a study looking at a large fashion company, the researchers found that human decision makers were more likely to accept advice from an uninterpretable algorithmic model, which was harder to question and interrogate. Counterintuitively, the researchers found that being able to look at how the model works led to lower rates of acceptance.
This leads to some key takeaways for how business leaders should introduce algorithms to the workplace, the researchers write. First, when people can see how an algorithm works, they might believe that they understand those inner workings better than they actually do — an issue leaders should be aware of when rolling out AI initiatives.
Second, including respected peers in the development and testing process of the algorithm makes it more likely that employees will accept its recommendations.
Addressing ‘algorithmic aversion’
Though algorithms often outperform humans, human decision makers can be reluctant to accept their suggestions, either consciously or unconsciously. Researchers call this algorithmic aversion, and it is especially prevalent when people are making decisions in highly uncertain environments such as medicine and financial investing.
The researchers studied how algorithms were received at Tapestry, Inc., a leading New York-based house of well-known accessories and lifestyle brands consisting of Coach, Kate Spade, and Stuart Weitzman.
Like other companies, Tapestry tries to improve product allocation — placing the right number of items in the right stores at the right time to maximize sales. This process is improved by using algorithms for guidance.
For the study, half of employee decisions about which products to send to stores, and how many, were made with recommendations from an interpretable algorithm, which was a weighted moving average of historic sales from previous three weeks.
The other half were made with recommendations from a machine learning algorithm that was harder to interpret, both because it was pattern-based rather than hypothesis-driven and because it was more complex, including data from the last 16 weeks as well as other information such as sales promotions and holidays. Both algorithms made recommendations that conflicted with allocators’ initial judgements.
Overall, the human allocators trusted the uninterpretable model more than the one that they could more easily understand. The researchers found this stemmed from two factors:
1. Overconfident troubleshooting.
When product allocators received counterintuitive recommendations, they tried to interrogate the reasoning behind the interpretable model and troubleshoot its recommendations. Being able to do this, as they could with the algorithm that followed explicit rules, resulted in lower acceptance of suggestions from the interpretable algorithm, according to the study.
“Because they believed they understood the causes, effects, and inner workings of the algorithm, this often led them to overrule the algorithm’s recommendations,” the researchers write.
Interviews with employees showed that the allocators often created narratives to explain the relationship between model inputs and outputs, which led them to overrule the interpretable algorithm’s recommendations.
But in situations where interpretable and uninterpretable models performed at similar levels, allocators overruling the interpretable algorithm resulted in lower performance on the task (more frequent stockouts, lower sales quantity, and lower revenue).
2. Social proofing the algorithm.
The machine learning model was less intelligible, so employees didn’t question it. But they did know that their peers had helped develop and test the uninterpretable algorithm. Knowing this helped reduce their uncertainty, the researchers found, referring to the concept of “social proof" — when people in ambiguous situations take guidance from how other people act, assuming that others have more knowledge about the current situation.
Knowing that people with their same knowledge base and experience developed the tools made people more likely to accept them.
“It was the combination of peer involvement in development with not being able to interrogate the uninterpretable model that made allocators more likely to accept recommendations from the uninterpretable model than from the interpretable model,” the researchers write.
Read next: For success machine learning tools, talk to end users
| 2023-01-12T00:00:00 |
2023/01/12
|
https://mitsloan.mit.edu/ideas-made-to-matter/why-employees-are-more-likely-to-second-guess-interpretable-algorithms
|
[
{
"date": "2023/01/12",
"position": 30,
"query": "artificial intelligence business leaders"
}
] |
Importance of Artificial Intelligence in Today's Data-Driven ...
|
Importance of Artificial Intelligence in Today’s Data-Driven World
|
https://futurside.com
|
[
"Futurside Editor"
] |
Leaders warned us that robots are set to take our jobs and governments will have to pay our wages, philosophers tell us that machine intelligence will outstrip ...
|
Last Updated on January 12, 2023
A Google search, passport scan, barcode reading, online shopping history, CCTV footage, photo, voice message, tweet, social media, and many more, all these routine activities contain and generate a lot of information that can be collected and analyzed.
Data turns out to be one of the fuels that power everything nowadays. Computers and algorithms allow us to make sense of increasingly larger amounts of information in real-time. And with the increasing amount of data, CPUs are expected to reach the processing power of the human brain.
Have you ever ordered something from your favorite online store and been shocked that it was in stock? Do you think that there is a warehouse somewhere with everything ever made by human beings waiting to be shipped out?
In many cases, the item that you order is not in stock until a few days before you order it. The algorithm used by these companies predicts that you are going to order that item. So they preemptively send it to a fulfillment center near where they think you’re going to make an order. So for it to arrive at you on time. This is called anticipatory shipping.
In this new algorithmic age, the game is about how you use data to understand people’s behavior, to anticipate their needs before they’ve even had to ask. We are generating so much data every day that it is easy to predict events, trends, and consumer behaviors.
In 2022, there are around 6.1 billion mobile subscribers in the world. Approximately 40 exabytes of data are generated from a single mobile phone every month. Around 2.5 quintillion bytes are generated every day.
These data contain valuable insight into human behavior and activities around the world. And with so much information being generated, new generations of processing capabilities are being created and advanced algorithms are written to make sense of the big data.
Artificial Intelligence (AI) is a key component behind the scenes that process most of the data. Many aspects of our day-to-day lives are being controlled by AI. It is a rapidly growing field of technology with the potential to make huge improvements in human life.
AI has the potential to revolutionize work, mobility, medicine, the economy, communication, and many more. And it keeps getting better every day. A survey done by the World Economic Forum reveals that people expect artificial intelligence machines to be part of a company’s board of directors by 2026.
Decisions once based on experience and intuition are now made through machines analyzing massive amounts of information. Data analysis reveals patterns and connections that vastly improve most human activities and also create very detailed profiles. So what exactly is AI?
What is artificial intelligence?
Artificial intelligence is a catchall term used to refer to any computer program that performs tasks normally associated with human intelligence such as visual perception, trend prediction, and language translation.
AI is the intelligence exhibited by machines and software. It’s a broad area of study focusing on the design and development of computer systems able to perform tasks that normally require human cognition.
AI research is focused on creating “intelligent” machines, robots, and computers with cognitive abilities such as learning and reasoning, perception, and the ability to conduct logical reasoning. It is a system of computing devices, algorithms and networking systems that collect information and process it to solve problems.
Artificial intelligence allows machines to learn from experience and make decisions by themselves. It can automate tasks or help humans make decisions more quickly and accurately. Intelligent machines have been around for a long time, but it has recently been gaining in popularity for a variety of reasons.
Why artificial intelligence is becoming important?
Artificial intelligence is the ability of machines to imitate the ways humans think. It is a rapidly progressing field that is revolutionizing many areas of our lives. One such example is its ability to recognize faces and things in images and to identify spam.
The most visible manifestation of AI is the virtual assistant, which relies heavily on natural language processing and voice recognition. Such systems require enormous corpora to understand human language.
In 2020, there were an estimated 4.2 billion digital voice assistants in the world and the number is expected to reach 8.4 billion by 2024. AI is making it easier to find information and helping us cope with large data sets.
Intelligent agents can analyze huge datasets much faster and more accurately than human beings can. Because of this, it can detect patterns humans might miss. The algorithm is trained through data feeding to become more accurate than humans at recognizing trends.
Every industry is already using some kind of AI application to make enhance their operation. It is revolutionizing the way companies operate by enabling them to make better decisions faster and with greater accuracy.
Artificial intelligence has been used for predictive analytics for decades, but it is only recently that it has become more prevalent in the workplace. It provides insights into a company’s data to help them make predictions about what will happen in the future, which helps them make better decisions or improve efficiency.
Artificial intelligence is used to analyze large amounts of business data quickly and accurately, leading to better insights and predictions. It is also providing companies with more efficient and cost-effective solutions.
Intelligent agents can also help businesses automate mundane tasks, improve customer service, optimize processes, manage resources and reduce waste. As a result, companies can increase their productivity and profitability while also providing better customer experiences.
Consumers are also reaping the benefits of this technology. They are using it to find the best deals on the internet. AI is getting better at recognizing what consumers are looking for and are becoming more advanced. Moreover, a survey revealed that over 80% of executives from the retail and consumer product industry expect their companies to be using intelligent automation.
Other applications include automating document review and robotic process automation. This technology can collect and process data to make predictions about what cases will produce. This expert system is quickly becoming an indispensable tool in the current economy. These systems are now everywhere, from cars to healthcare. It just needs to be fed with data for the best results.
AI also helps retailers to personalize pages, suggest products and make good communications with visitors. For example, the intelligent agent can identify inconsistencies in purchase patterns and offer personalized product suggestions based on the visitor’s profile.
The technology’s high cognitive levels make it ideal for many industries, including the logistics and transport industry. For example, AI can automate tasks and predict maintenance for vehicles, as well as optimize routes and navigation.
In the medical field, intelligent agent has already made a significant impact. Machine learning algorithms can identify tumor types, predict whether an individual is suffering from a certain disease, and even forecast potential future health issues.
Healthcare also uses the technology for a variety of other tasks, including interpreting patient histories and identifying risks. It can replace medical personnel for routine tasks like data entry and analysis.
Intelligent agents are also improving customer service. More companies are investing in chatbots. These machines are starting to become more responsive and interactive. They can solve customer service issues, including answering questions and managing transactions without human intervention.
AI is making our products smarter and more effective as well as improving the overall end-user experience. And with AI, services can operate 24/7. The technology is already at the core of some of the world’s most successful companies. Alphabet, Microsoft, Amazon, and others have all built products based on AI.
AI is also highly effective in multitasking and handling multiple queries simultaneously. The technology is increasingly becoming a cornerstone of the modern landscape. Its applications continue to grow rapidly and it has the potential to revolutionize the global economy. It is estimated that AI will contribute USD 15.7 trillion to the global GDP by 2030.
Impact of artificial intelligence
Artificial intelligence is the intelligence exhibited by machines or software, and has been defined as “the science and engineering of making intelligent machines.” AI research is at the heart of many fields such as computer science, psychology, neuroscience, philosophy of mind, linguistics, and mathematics.
It also plays a key role in areas like robotics (e.g., robots that can carry out simple tasks), digital healthcare (e.g., healthcare diagnoses based on pre-existing medical records), natural language processing (e.g., translation between languages), and autonomous vehicles (e.g., drones capable of safely navigating complex environments and self-driving cars).
AI systems can perform many tasks and is sometimes capable of superhuman performance and creativity. For instance, the supercomputer IBM Watson won the popular quiz show Jeopardy in 2011 after using artificial intelligence and analytics.
IBM Watson analyzed vast databases of data and answered human-posed questions within fractions of a second. The evolution of AI has led to incredible advances in human-machine interaction. Google’s AlphaGo, for instance, defeated a human champion in Go, the hardest board game in the world.
DeepMind’s LipNet program can identify lip-read words in videos with 93.4% accuracy. The OpenAI system was also recently able to beat the world’s top players in the popular game Dota 2.
However as we generate more data each day, AI systems keep getting better and more intelligent. AI-powered systems have the potential to disrupt the entire way we work or do things. Some say we can expect new jobs, while others are worried about massive job losses, widening economic divides, and social upheavals.
Already significant deployments have occurred in health care, finance, criminal justice, transportation and smart cities. The potential impact on humans is enormous. The future of AI will require us to think about its ethical implications and its limitations.
We must balance the benefits with our basic human values. We should not forget that it is an important tool and also dangerous if misused or abused. While technology is rapidly growing, it may be difficult to determine whether AI will replace people or create new jobs that do not require the same skills or temperament.
However, AI does not yet have a high enough degree of complex reasoning to be able to perform the tasks that we perform. In some cases, advocates say that it will only augment human workers and not replace them entirely.
This is based on the idea that workers assisted by AI are more productive than those who work alone. The potential downsides include the loss of autonomy, privacy, and power over decision-making. Also, the reliance on automated systems means we’ll lose control over our data. However, some estimates reveal that AI will create more jobs than it destroys.
Eventually, most AI tools will be owned by corporations and governments. Values and ethics are rarely baked into these systems. And because they’re globally networked, these systems are difficult to regulate. While AI systems are rapidly advancing, there are some limitations.
But for now, the benefits outweigh the risks. In today’s highly connected world we are living, every digital interaction generates some kind of data. The amount of information that we are generating every day is staggering and must be processed rapidly before it becomes outdated.
AI is very important, it operates on a feedback loop, where it takes data from sensors and other sources, processes it, and then takes an action based on the results. AI systems can be used for a wide range of applications.
The global market of artificial intelligence was valued at USD 59.67 billion in 2021. Estimates reveal that the market will increase by a CAGR of 39.44% between the forecasted period of 2022-2028 thus reaching USD 422.37 billion by 2028.
By leveraging the power of machine learning algorithms, AI can continually learn from its experiences and adapt to new situations as they arise. This makes them incredibly powerful tools for businesses looking to automate processes or gain insights from their data.
And some even believe that it can help us solve the world’s most pressing issues. It can also help decrease global inequality, the effects of climate change, and extreme poverty. And it is only a matter of time before AI enhances our jobs and our lives.
Final words
Computer programs that mimic the thinking pattern of an expert are becoming more and more important. Recent advances in AI research have focused on machine learning and deep learning.
AI researchers will need to develop a way to make such systems even more useful in our daily lives. It will be possible for machines to understand human language even better and can be trusted in many cases. Leading tech companies are racing to develop AI solutions.
It is only a matter of time before researchers will be able to create machines that mimic human behavior, including the ability to learn. Once developed, it will be able to analyze vast amounts of data and personalized experiences for each user based on that information.
Businesses will use AI to analyze requests, based on their history and patterns of behavior. These systems can also identify the best channels and allocate resources to the most appropriate solution. The rise of big data and powerful graphics processing units is fueling the flurry of AI advances.
Those technologies are already making our lives easier, but it will be interesting to see how they develop further in the years to come. AI-powered self-driving cars will soon be ubiquitous. Google has already started introducing its autonomous car system and by 2030 it is expected to become commonplace.
Boeing is also building autonomous jetliners. The future of artificial intelligence has many applications. For example, the next generation of autonomous vehicles will analyze data and communicate with the cars around them.
Another great application will be in the management of computer systems. In the future, AI will play an important role in all types of computation, from computer games to healthcare. If we can build AI that can do the things that humans can’t do, we will be living in a utopia.
Technology has already made our life better and artificial intelligence can make it even easier than ever. It is capable of automating many mundane tasks, like answering questions or communicating with others. As AI becomes more advanced, humans can focus more on important things.
AI has the potential to transform every industry and create new jobs, too. It will automate many boring parts of the workday, leaving you more time for more important things. And can help us manage a complex web of interconnected people, businesses, and nations. If it is developed properly, artificial intelligence will transform society and change our lives forever.
| 2022-05-23T00:00:00 |
2022/05/23
|
https://futurside.com/importance-of-ai/
|
[
{
"date": "2023/01/12",
"position": 36,
"query": "artificial intelligence wages"
}
] |
How to Use Artificial Intelligence in Marketing Campaigns
|
Artificial Intelligence in Marketing
|
https://mailchimp.com
|
[] |
... wages, and customer satisfaction/dissatisfaction. Programmatic advertising. Marketing teams use programmatic advertising to autonomously create content ...
|
Why should I use artificial intelligence in marketing campaigns?
Benefits of AI in Marketing
Gain insights by leveraging customer data Enhance customer experiences with personalized content Minimize mistakes due to human error Improve data management Boost marketing ROI Capitalize on a 2024 business trend
There are a lot of benefits to integrating AI in marketing, such as gaining AI-powered customer insights or producing customized content. There are also indirect ways digital marketers use artificial intelligence marketing solutions to benefit their business, like reducing human mistakes or improving workflow efficiency.
1. Gain marketing insights on customer data
Data and data tracking are vital to understanding current and potential customers. With artificial intelligence marketing, you can collect some of the most valuable insights on your target audience as well as where they are on their customer journey. These are the four kinds of customer data insights:
Basic data : This includes demographic information such as a customer’s name, gender, location, ethnicity, or email address.
: This includes demographic information such as a customer’s name, gender, location, ethnicity, or email address. Engagement data : This type of data is in regard to general engagement, including information on how they use your services or if they follow you on social media.
: This type of data is in regard to general engagement, including information on how they use your services or if they follow you on social media. Behavioral data : Similar to engagement data, behavioral data is more specific. This type of data covers how your audience engages with your brand directly, such as purchase history, subscription details, or free trial sign-ups.
: Similar to engagement data, behavioral data is more specific. This type of data covers how your audience engages with your brand directly, such as purchase history, subscription details, or free trial sign-ups. Attitudinal data: This type of data focuses on how your customers think and what their opinion is of your company. You can access these data insights through surveys or online reviews.
Each of these four kinds of data is important, but many marketing teams particularly use engagement data and behavioral data to understand the efficacy of their campaign’s marketing strategy.
Digital marketers also use artificial intelligence solutions to collect basic data for customer segmentation, which is the process of categorizing customers into groups based on shared demographics, characteristics, or preferences.
All of these insights drawn from marketing AI can be used to inform content creation and the marketing messages that companies use in their media strategy and marketing campaigns.
2. Enhance the customer experience with personalized content
Some of the most effective ads are ones that leverage marketing AI to understand their audience inside and out. Once you analyze your data and have your insights, your marketing team can start to improve customer experiences with personalized messages.
Personalized marketing is important for retaining customers and gaining new ones. With content like targeted advertisements, customized emails, and interactive quizzes, you can engage customers and your audience in a personalized and meaningful way. With personalized content marketing, your business could experience the following:
A more satisfying customer experience
Increased customer loyalty
A better marketing ROI (return on investment)
More cost savings with a more-focused strategy
Better connections and relationships with customers
3. Minimize mistakes due to human error
It’s normal for people to make mistakes occasionally, but some errors can introduce risk and negatively impact marketing performance. Artificial intelligence can help marketing teams minimize errors and mistakes. With AI platforms such as machine learning capabilities, natural language processing, and automation, you can get a variety of tasks done without human intervention.
For example, you could give a certain task to an AI marketing tool that reads over your emails for grammar mistakes. Sure, having a professional editor could do the job, but you could be sacrificing quality by checking your grammar by yourself as a non-professional.
You could also use AI marketing tools to automate sending email or texts. Instead of manually checking a lengthy list of email addresses or phone numbers, you can use AI to automate when they get sent, to whom they are sent to, and what personalized message is being sent.
Meanwhile, as these AI platforms run in the background, other employees can focus on human-centric tasks, such as writing, strategy, management, or design. AI can be a powerful and incredibly useful tool when deployed correctly to support employees and free them up to focus on the jobs that people are best at.
4. Improve data management and data quality
While collecting data is a crucial first step to learning about your audience, that data could be otherwise useless if it isn’t entered, managed, or stored properly. Using AI can instead help reduce the risk of improper data interpretation and collection.
Some AI-powered marketing tools can automate data entry and ingestion. This way, you can feel confident that all necessary data has been analyzed for quality, usability, and validity. Once you have admissible data, your marketing team can create campaigns that are relevant, effective, and valuable.
5. Boost marketing ROI
As mentioned above, artificial intelligence marketing can improve efficiency by improving data collection, minimizing mistakes due to human errors, and enhancing customer experiences. With these AI-enhanced workflows, businesses can start saving money and seeing a return on their investments.
AI marketing can also help predict when the market will pivot, allowing businesses to shift priorities and adjust as necessary. AI marketing tools can help you:
Review your automation
Launch re-engagement campaigns
Review or update your messaging across your campaign(s)
Monitor customer-facing platforms, such as social media channels
Stay attentive to upcoming shifts in the market
Select the most effective ad placements
6. Capitalize on a business trend
AI will likely play an important role for marketing teams in 2024, so it may be time to start investing in an AI marketing strategy. Already, companies are looking to leverage AI so they can take advantage of the benefits we’ve mentioned so far, and get the right message to the right people.
Types of AI marketing solutions and how to use them
There are several AI platforms and types of software that you can use that will impact your marketing efforts. However, in order to get the most use out of your AI marketing tools, you have to start by asking yourself what you want to get out of them.
Artificial intelligence marketing and effective data analysis can only work with the information you give it, so make sure you have specific questions ready, such as:
What are our customers looking for this time of year?
Should we launch a marketing campaign now or wait six months?
Do our customers know anything about our upcoming product? Should we create content to educate them?
Will our customers interact with a social post if they see a video or image carousel?
Is there a way we can win back lost customers from our competition?
How much should we spend on this campaign?
Once you have questions that you would like answered, you’ll be able to collect data and use your AI marketing tools to build strategic initiatives and make the best decisions for your business.
Predictive analytics
Marketing teams use predictive analysis to create optimized marketing strategies to drive better business outcomes.
The use of data, algorithms, and machine learning to identify patterns in historical big data and use it to predict future events or trends.
For example, let’s say you want to use predictive analytics to better understand what your customers will soon look for in a fashion retail experience. You could use a predictive analytics algorithm to collect historical data about your audience demographics, purchase histories, and previous likes/dislikes. Once you have that data, your AI marketing platform can test and deploy a model that identifies the trends and experiences customers will be looking for in a retail store.
Machine learning
Machine learning is a large component of digital marketing where marketing teams use algorithms to “learn” with increasing accuracy and make quick decisions.
A branch of artificial intelligence and data science that uses deep learning—a layered, artificial neural network—to make connections, simulate human thinking, and allow users to act quickly on big data in real time.
Machine learning allows businesses to quickly analyze large amounts of data, enabling you to make changes on the fly and deliver more-personalized experiences to your customers. Let’s go over some examples of machine learning solutions.
Recommendation systems Also known as a subclass of machine learning, a recommendation system suggests relevant items to the user. If a customer is buying something in your online shop, you could have a recommendation system analyze their behavior and suggest similar products they may like. This can not only boost sales, but also improve that user’s experience as they interact with your brand.
Churn rate forecasting This is the practice of analyzing data to understand the percentage of customers who have stopped purchasing from your business. If your machine learning platform detects a high churn rate, you can begin to understand why customers are not satisfied and begin to pivot. Take into consideration your customer’s feedback so you can also improve customer retention.
Dynamic pricing algorithms Dynamic pricing happens when prices fluctuate from supply and demand. If demand rises, so do prices and vice versa. With an AI marketing tool, you can understand customers’ purchase behaviors, product prices, and market trends to adjust your prices in real-time.
A dynamic pricing algorithm can also take other factors into consideration before you re-price your products, like fixed costs, variable costs, competition prices, employee wages, and customer satisfaction/dissatisfaction.
Programmatic advertising
Marketing teams use programmatic advertising to autonomously create content, purchase ad space, and place ads in those spaces to best impact marketing efforts.
This form of advertising automates the process of finding, purchasing, creating, and placing digital ads on websites, apps, and social media platforms.
Your business can get real-time insights, trace the performance of your ads, and get a better sense of your budgets based on how effective each ad is. For example, you could deploy AI tools to autonomously place bids on digital ad spaces and negotiate in real time.
Here are common online ad formats, as well as resources to learn more about how to utilize each one for your digital marketing campaign:
Other AI marketing solutions
AI-managed push notifications : With push notifications managed by an AI marketing platform, you can send relevant content to your customers at the best time. There are several types of push notifications that can notify customers about a variety of topics, such as upcoming sales, subscribed contacts, or left items in online shopping carts.
: With push notifications managed by an AI marketing platform, you can send relevant content to your customers at the best time. There are several types of push notifications that can notify customers about a variety of topics, such as upcoming sales, subscribed contacts, or left items in online shopping carts. Automated image recognition : This type of AI marketing solution analyzes images and provides data that can boost efficacy or automate tasks. For example, it can compare the efficacy of your campaign’s images, automate writing alt text after scanning/classifying images, or identify specific places, people, or objects in a selected image.
: This type of AI marketing solution analyzes images and provides data that can boost efficacy or automate tasks. For example, it can compare the efficacy of your campaign’s images, automate writing alt text after scanning/classifying images, or identify specific places, people, or objects in a selected image. AI-enhanced PPC advertising: PPC stands for pay-per-click advertising, meaning this AI marketing solution allows digital marketers to adjust current ads or bid on ad space in real time. It also allows you to predict customer behavior, take down old or ineffective ads, and insert dynamic ads based on user browsing history.
AI investments are your key to success
The saying “knowledge is power” particularly applies to digital marketing. Artificial intelligence, big data, and analytics are paramount for understanding the customer journey, improving day-to-day business tasks, and streamlining workflows.
Once artificial intelligence has given your marketing team members the answers to questions—like “do our customers know anything about our upcoming product,” “How much should we spend on this campaign,” or “Should we launch a marketing campaign now?”—you’re ready to take your marketing programs to the next level.
| 2023-01-12T00:00:00 |
https://mailchimp.com/resources/why-artificial-intelligence-in-marketing-is-vital/
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[
{
"date": "2023/01/12",
"position": 52,
"query": "artificial intelligence wages"
}
] |
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What is insurtech? | NEXT
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What is insurtech?
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https://www.nextinsurance.com
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[] |
Insurtech uses innovations in data technology, artificial intelligence (AI) ... Cover medical bills and lost wages if your employees have an accident at work.
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What does insurtech do?
Simply put, insurtech is designed to make insurance more efficient.
Insurtech uses innovations in data technology, artificial intelligence (AI), the internet of things (IoT) and other emerging fields to help insurers better understand their customers and the risks they face.
By offering super-customized insurance policies based on specific customer needs and observed behavior, insurtech allows insurance companies to price their products better and deliver faster results.
Additionally, insurance technology offers customers more insight and control over their policies. Many companies (NEXT, for example) give their policyholders 24/7 access to manage their coverage, add additional insured, or change plans when it’s most convenient for them.
Traditional insurance was (and sometimes still is) a slow and labor-intensive undertaking. Insurtech innovation helps by streamlining or automating many processes, which speeds up everything from underwriting to purchasing policies to filing and claim payouts. This helps save money for insurers and customers alike.
Under the hood: the innovations changing the insurance sector
Of the many innovations insurtech has introduced, the three that have changed how insurers do business the most are data technology, AI and IoT.
Data technology
Insurers have always used past experiences to analyze risk and price their policies. This process is known as underwriting, and it’s the backbone of insurance.
The insurance specialists who do this are called underwriters. They can spend years studying various industries and learning just how risky different businesses are to insure.
Data technology speeds the process by gathering data automatically and can analyze large quantities of data quickly. Data tools can efficiently give insurers the accurate information they need by analyzing real-time data while simultaneously accessing historical information.
Artificial intelligence (AI)
An underwriter doesn’t need to be human. More often these days, underwriting is done by computer. Using data technology, AI can quickly assess whether a potential client is a good fit for an insurer.
And that’s just one example of insurance AI in action. Today, AI helps insurers cater to their customer’s preferences, from designing better insurance products to simplifying customer experiences such as filing claims or knowing claim status.
For instance, some insurance websites now use chat boxes. Most of these chat boxes aren’t run by people but by chatbots designed to help customers without using expensive human resources.
AI can even handle claims management. Traditionally, an employee at the insurance company (a claims adjuster) had to manually investigate the claim before deciding on compensation. Now, AI can help process claims by evaluating damage via high-quality photos, make recommendations based on claim details, automate information-gathering processes and even detect fraud.
Internet of Things (IoT)
The internet of things refers to the interaction of physical objects and computer software. If you own a fitness tracker (aka a wearable device) or use a voice-assistant speaker to turn on your lights, you’re already using IoT devices.
In the insurance sector, car insurance has embraced IoT. Many auto insurance companies ask customers to either install a device on their car (vehicle telematics) or download an app that tracks their driving.
By seeing exactly how their clients are behaving on the road, insurers can gauge each individual’s risk as a driver and price their plans accordingly.
Putting it all together
These three new technologies have transformed the insurance business because they work together to do things that were impossible just a few years ago.
With IoT, insurers can learn from their clients by gathering real-time customer data and details to understand their needs and provide better services. Data technology aggregates and organizes this mountain of data and then aligns it with relevant historical data. AI can use all this information to help insurers create customer-specific policies, speed up claims processing and use automation to streamline tedious processes.
For instance, say an electrical fire causes damage to the stockroom in your retail store. Previously, you would have to call your insurance agent to file a claim, mail photos or wait for someone to assess the damage.
However, if you’re a NEXT customer, you can save time by taking photos with your smartphone’s camera, upload it to your account via our mobile app, and file a claim fast. You’d also get updates on the status from our claims advocates throughout the process.
By utilizing insurtech, insurers can new products, customize plans like never before, offer better user experiences and provide competitive pricing compared to other legacy insurance companies.
How NEXT customizes your coverage
Thanks to insurtech, NEXT has tailored insurance coverage for over 1,300 small business professions. We’re reinventing business insurance with a tech-first approach.
You can get affordable coverage because you only pay for what your business needs. Start a quote, customize your options and access your certificate of insurance online immediately after payment — in about 10 minutes.
Start your instant quote today.
| 2023-01-12T00:00:00 |
https://www.nextinsurance.com/glossary/insurtech/
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[
{
"date": "2023/01/12",
"position": 92,
"query": "artificial intelligence wages"
}
] |
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We Used AI To Write About CNET Writing Articles With AI
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A Tech News Site Has Been Using AI To Write Articles, So We Did The Same Thing Here
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https://www.buzzfeednews.com
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[
"Katie Notopoulos",
"Chris Stokel-Walker",
"Buzzfeed News Reporter"
] |
The use of AI in journalism raises questions about the transparency and ethics of such a practice as well as the potential impact on the authenticity and ...
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Technology news outlet CNET has been found to be using Artificial Intelligence (AI) to write articles about personal finance without any prior announcement or explanation. The articles, which numbered at 73, covered topics such as "What Is Zelle and How Does It Work?" and had a small disclaimer at the bottom of each reading, "This article was generated using automation technology and thoroughly edited and fact-checked by an editor on our editorial staff." The bylines on these articles read "CNET Money Staff" without any indication that they were generated by AI.
The use of AI to write these articles was first brought to light by a Twitter user, and further investigation revealed that the articles have been generated using AI since November 2022. The extent and specific form of AI being used by CNET is not currently known as the company did not respond to questions about their use of artificial intelligence.
The use of AI in journalism raises questions about the transparency and ethics of such a practice as well as the potential impact on the authenticity and accuracy of news. Additionally, it also raises concerns on the implications this might have on SEO and Google searches. The lack of response from CNET regarding their use of AI in writing articles has only added to the concerns and sparked a wider conversation about the future of journalism and the role of AI in it.
Note: This article was written entirely by ChatGPT and reviewed by a human editor. (Actually, we had to rewrite the prompt a few times to get it to stop inserting factual errors.)
| 2023-01-12T00:00:00 |
https://www.buzzfeednews.com/article/katienotopoulos/cnet-articles-written-by-ai-chatgpt-article
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[
{
"date": "2023/01/12",
"position": 5,
"query": "artificial intelligence journalism"
}
] |
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How AI design is changing the attractions industry
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How AI design is changing the attractions industry
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https://blooloop.com
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[
"Creative Principals"
] |
AI is enabling more personalized, creative and engaging ways to design attractions such as theme parks, museums and other entertainment venues.
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Geoff Thatcher
While beginning to write this article about how artificial intelligence is changing the way we work in experience design, the first thing that we did was open up Jasper, one of the new AI tools on the market that uses Open AI’s GPT 3.5 as one of its language models. We gave it a simple command:
“Write an article about how AI is going to change how we design experiences like theme parks, museums and other attractions.”
Within 30 seconds, we had the first 260 words. Here is just the first paragraph straight out of Jasper’s engine with no editing by us:
“With the rise of Artificial Intelligence (AI), businesses are beginning to explore how this technology can be used to create immersive experiences. AI is enabling more personalized, creative and engaging ways to design attractions such as theme parks, museums and other entertainment venues. By leveraging AI-powered tools, designers can create dynamic experiences that are tailored to each individual visitor’s preferences and interests.”
That’s a lot to unpack.
AI is already changing experiences
Zoe Thatcher
If you want to ask AI to write you anything, all you have to do is ask. We are currently working with JRA – part of RWS Entertainment Group on the H2.O Purification Center in Jacksonville, Florida. Just for fun, we asked Jasper to “write a video script about why water purification is important?”
Again, within 30 seconds, it was spitting out copy with its first line almost identical to the storyline we developed a year ago: “Water is essential to life.”
Or what about asking it to “write an article on the opening of SeaWorld Abu Dhabi this year”? Again, the AI generated the following within seconds after a simple prompt:
“SeaWorld Abu Dhabi has officially opened its doors to the public, offering a world-class marine-themed adventure park for visitors of all ages. Located on Yas Island, this state-of-the-art venue is set to become one of the most exciting attractions in the UAE.”
Whether you are working with words using Jasper and Open AI’s ChatGPT or visuals using AI design tools like Midjourney or DALL·E 2, our world forever changed last year. While everyone was busy talking endlessly about how the metaverse would change experiences, AI broke down the back door.
Using it as a tool
Like many, we hadn’t given AI much thought until we first experienced it in June 2022.
And like millions of others, our world was rocked! As a writer and designer, we immediately saw how this would change our world and embraced it fully. We canceled our stock photo subscriptions that were costing us thousands of dollars a year. Instead, we immediately shifted those resources into subscriptions with Midjourney, DALL·E 2 and Jasper.
We ignored those who told us AI would destroy jobs and careers. We debated those who called it “stealing and theft.” One angry artist on Instagram called Zoe “unethical, immoral” and the personification of “evil.” We consulted with other creatives who are on the leading edge of AI like Dominic Cardoso and Michael Libby.
We even started sharing with our clients in July exactly what we were learning as we were excited to show them how AI will change the way we work. One of our creative partners told us angrily, when he learned what we were doing:
“You can’t show clients this AI stuff because if they learn how to create renderings using Midjourney, they’ll no longer need us.”
He was afraid.
And perhaps he should be.
However, from the beginning, our approach to AI has been all about using it as a tool. As Midjourney’s Founder David Holz told The Verge, “It is an engine for imagination.” And so it is with Jasper and these other AI tools. They are here to inspire us and help us be more creative.
Change is inevitable
It helps that Zoe is younger and more open to embracing new technology. When she started her first job out of college in 2018 designing dance costumes, she was the first designer at her firm to design her initial concepts on an iPad rather than with paper and colored pencils. And, while older, Geoff remembers first-hand how computers radically transformed his first career in journalism when workers used to paste up headlines and copy on a board before photographing it and then printing it.
Those paste-up jobs do not exist any longer. Blooloop has replaced Amusement Business. In fact, most of the people in our industry probably don’t even remember that weekly newspaper, owned by Billboard. They could not adapt and died.
Change is inevitable and those who refuse to adapt to the new world AI is introducing will be left behind.
However, this article is more about revealing how we are using AI design tools to work with our clients rather than debating its merits and ethics. If you plan to boycott Midjourney, we wish you luck in your retirement. If you want to call us thieves, perhaps think about how much “borrowing” already takes place in our industry.
As Zoe defended herself on Instagram recently:
“I personally believe that we already train ourselves on the intellectual property of others without their consent, and the fact that a computer can now do it doesn’t change anything. What we do with that property however is where the evil comes in.”
Lessons from using AI in experience design
After six months of using Midjourney and other tools, we have some lessons to share about how we use AI in our creative process. With the permission of our client the J. Kruse Education Center in Indiana, an experiential learning center dedicated to helping students find their passion and career path, we will share with you some of the actual work product.
Brainstorming
Sometimes clients don’t know what they want until they see it. AI design tools like Midjourney allow us to quickly generate visuals as we brainstorm. In fact, we’ve used Midjourney in more than three charrettes with the J. Kruse Education Center over the past few months as we’ve worked with them on how to create experiential learning pods for the Manufacturing Industry, Healthcare Industry and Architecture & Construction Industry.
These charrettes involved both the client and those in these industries such as doctors, nurses, engineers, welders, carpenters, etc. AI images generated on the fly have replaced the traditional image card exercises in our charrettes. Now when brainstorming an idea with the J. Kruse Education Center, we can quickly visualize a hospital room inside a shipping container or what a maker lab could look like.
Using AI we can now give clients and their stakeholders almost immediate visual feedback on ideas being brainstormed. This confirms the validity of the idea or even takes us in a new direction that we wouldn’t have explored (or have had time to explore) otherwise. This technique is a new faster way to make sure the best ideas are the ones coming out of the charrette.
Mood boards
Using AI programs like Midjourney and DALL·E 2 we can now create custom mood boards for projects. A lot of the time in our industry, we pull from images of existing attractions to make our mood boards. This isn’t necessarily bad. However, one negative effect of this is there are rarely original images within our mood boards.
Now, using AI, we can make mood boards that utilize completely custom imagery designed specifically for the project. We can take those charrette ideas and images we generated and flesh them out, make more and see where the exploration takes us. We especially like mixing AI images with real images of existing attractions to create a more complete mood board. It both allows the client to see real examples as well as more conceptual images to help them visualize the concept.
Pre-renderings
As we’ve continued to explore AI we’ve started to refer to some of the generated images we use as “pre-renderings.” These are images that are not fully fleshed-out renderings but do convey the idea and the mood that we want. They are a stepping stone to the rendering stage. For example, most of the renderings we work on are inside a planned space with specific elements and spatial requirements. Artificial intelligence is not good enough (yet) to allow that type of specificity.
However, what AI can do is create an image that conveys some of the elements you might want to have in the rendering as well as the overall mood of the space. Because of AI, we’ve added these pre-renderings in a step with our client because it eliminates the surprise factor of them seeing the final rendering. It lets the client in on the process and you can potentially get some feedback that could affect the final rendering and save time having to go back and change elements later.
For the J. Kruse Education Center, these pre-renderings involved seeing what the inside of these educational experiences might look like when placed in shipping containers.
Renderings
That brings us to the renderings themselves. We still need a human touch to create conceptual renderings, especially when those renderings need specific elements or are in existing or planned spaces. However, we can use AI to generate some of those elements to put into the renderings.
Anyone who has been in this industry long enough knows that most conceptual renderings are a combination of SketchUp, photo bashing and digital painting. Where AI can now come in is being a source for some of those “photos” that eventually get incorporated into a rendering. And just like the mood boards, this is a step up. Now, we can create unique and new elements to incorporate into the renderings rather than existing products.
For example, let’s say we need some graphics to incorporate into a rendering for the Healthcare Pod at the J. Kruse Education Center. Instead of turning to stock images or Google, you can now generate your own unique graphic to incorporate into the rendering.
Character design
The way we use AI for character design really is the same as how we use it in charrettes and mood boards. And using AI in this process can practically be applied to any visual need, whether it’s logos, fashion, graphic layouts, etc.
The reality is for a lot of projects, we don’t have time to explore hundreds of ideas and variations for a character. And so what usually happens is we’ll do a handful of designs and hope the client likes them. But by using AI, we can make those initial variations and explorations on our own and find the strongest. Then we can have Zoe or another artist finalize and flesh out those concepts.
It not only makes sure the client is happy but also makes sure the strongest ideas are getting through because you now have time to explore more variations.
As with all AI design tools, this is disruptive. In August 2022, we got a call from a client who needed to turn around a bunch of character ideas for a Halloween attraction. They gave us two days. We told them, “We plan to use AI to give you more in this short amount of time.” The client then paused and said, “Oh, I hadn’t thought of using AI. Never mind, we’ll just do it ourselves.” Sure, we lost out on a small project, but that’s simply the new world we live and work in.
Names
Using Jasper AI, ChatGPT and other tools, we can now apply that same process when developing attraction or exhibit names. Struggling to come up with a name for an attraction? You can now let AI help. Not every idea the AI will generate is great. But it can act as a brainstorming tool, generating things you may never have thought of on your own, or even helping you to use what it came up with as a jumping-off point to more ideas.
For example, the J. Kruse Education Center begins with a walkthrough experience that gives students a choice as they stand in front of a series of doors. It’s essentially a career assessment called RIASEC condensed into a maze. At each stop along their journey, students are asked to pick a door. For example, “Am I a thinker or a doer?” Every door leads to another room with more doors. And by the end of their journey, they learn what career paths might best match their personality.
But what to name this experience? It’s not really a maze, but it is. It’s not really a personality test, but it is. So, we entered into Jasper a description of what we wanted. A few keywords and a second later, we had a few options:
Career Walk
Career Ways
Career PathZ
Career Passages
Career Maze
The Maze of Doors
Maze-to-Careers
Maze of Opportunity
Maze to Success
CareerWalk Maze
Doors to Discovery
Doors2Discover
As we are still in design development, a final decision hasn’t been made on the name of the experience. But there is value in getting an assist from AI.
Presentation Symbology
When looking for images to symbolically support a message in our presentations, we no longer turn to Getty, other stock houses or even Google, we simply open up Midjourney or DALL·E 2 and type in the idea we are trying to communicate. A check mark made of water for the JEA H2.O Purification Center.
When trying to communicate the idea of beginning a project like the J. Kruse Education Center with a “Black Box,” we created one on DALL·E 2. A Saul Bass-inspired question mark in the brand colors of our client FM Global, no problem!
The human touch is still key
We could go on for another 10,000 words describing how we’ve been using AI in our work. However, let us share just a few final thoughts that we have found important:
Transparency: We believe in always sharing when we are using AI for design inspiration, brainstorming, mood boards and renderings. Honesty about the use of AI not only keeps clients’ expectations realistic but also will help dispel the fear that some people feel toward the technology.
Speed: These tools not only allow us to work faster but also allow us to work more efficiently. That’s because AI art can allow us to get a glimpse into the final product much faster than previously possible. We can generate more renderings faster than ever before.
Human Touch: AI isn’t a replacement for humans. It is a tool for us to use to be more efficient and creative with our work. As great as AI tools are, you still need the human touch to make whatever is generated meaningful to your project by customizing it to your needs. Pretty pictures and fancy words have never been able to make bad ideas look good. And so it is with AI. These tools won’t do you any good if you don’t have great ideas behind them and a team to bring them to life.
Concept Art: What has us really excited is that this tool may shift our industry’s desire for photo-real art in the early concept phase into an appreciation for true conceptual work focused more on setting a creative direction. In fact, we are already seeing this happen with a number of clients. By telling them we are using AI design tools we are able to manage expectations for something that is looser and more conceptual. That’s something we seem to have lost with programs that allow concepts to be generated to look photo-real.
AI design is the next step
In the end, Zoe said it best on Instagram while defending AI in her work:
“Art can’t be destroyed if it is a reflection of who we are and what we feel. When photography was invented the art world had to ask itself some hard questions and evolved because of it. Same when digital art was invented. I believe this is just the next step.”
Father and daughter Geoff & Zoe Thatcher are the chief creative officer and designer & illustrator at Creative Principals, a family-owned experience design firm. Other than when noted, this article was completely written by their human hands.
All images courtesy of Creative Principals
| 2023-01-12T00:00:00 |
2023/01/12
|
https://blooloop.com/technology/opinion/ai-design/
|
[
{
"date": "2023/01/12",
"position": 12,
"query": "artificial intelligence graphic design"
}
] |
Art of AI: Will AI art generators displace human creativity?
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Art of AI: Will AI art generators displace human creativity?
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https://www.masterborn.com
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How is it possible for AI to generate completely new images Graphic 2. What ... We may think that since the AI generator is able to design us a new shoe ...
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A few weeks ago, a discussion arose in our company about whether it’s ok or not to award the first prize in an art competition to an image generated by artificial intelligence (AI). Let's bring this situation closer - last summer, there was a Colorado State Fair fine art competition and the first prize was awarded to Jason Allen for his painting "Théâtre D'opéra Spatial''. The snag here though is that this painting was not painted by Mr. Allen, but generated using the Midjourney AI art generator.
After a picture of the grand prize blue ribbon next to the winning image was posted on the Midjourney Discord chat, Internet users did not fail to express their opinions (very strongly, most often).
“We’re watching the death of artistry unfold right before our eyes — if creative jobs aren’t safe from machines, then even high-skilled jobs are in danger of becoming obsolete,” one Twitter user wrote.
“This is so gross,” another wrote. “I can see how AI art can be beneficial, but claiming you’re an artist by generating one? Absolutely not.”
Mr. Allen was not distressed by his critics, explaining that he created hundreds of images using various prompts and spent many weeks polishing his work before deciding to transfer it to canvas and enter it into the competition. If you want to get a closer look at the perspective of the author of this controversial work, I recommend you read the New York Times article, which outlines Mr. Allen's approach to AI generated art and his concerns about the future of art.
Our MasterBorn discussion (MasterDiscussion?) was not as heated and emotional as the one on Twitter, but let's be honest - not many discussions match their fiery tempers like the ones on Twitter. However, this topic inspired us so much that we decided to take a closer look at the subject in this article.
AI painting generators as a computer vision application
AI art generators are examples of cutting-edge artificial intelligence algorithms, the most popular engines have been developed over the past few years and are gaining incredible popularity not only in the IT world but also among artists, designers, and ordinary people. One of the reasons AI art is so widely popular is the simplicity of using such generators. Thanks to built-in text to image converters, we can give the computer a command of what to paint using everyday human language. Such commands are called prompts and if using appropriately chosen words in our prompts, we are able to enhance and match the generated images to our imaginations.
AI image generators are part of a field of AI called computer vision (CV). What is computer vision? Well, it's a field that focuses on replicating the complexity of our human visual system and the way we experience the world. This means that a computer should not only have the ability to see images (i.e., the equivalent of our human eyesight), but should also be able to mimic our perception, and thus have the ability to understand what it sees.
There are many different computer vision applications, starting with the more "responsible and serious" ones like detecting skin cancer from photos (check out how the US startup Etta Epidermis does it). You can find more information about it and other startups working for a better tomorrow in my previous article. Another example worth mentioning is the use of algorithms that recognize elements within a "computer's sight" in autonomous cars, where the computer has to constantly analyze the situation around it, read traffic signs, watch out for pedestrians and other traffic participants, etc. Other examples of computer vision applications include the detection of people wanted by police on city cameras, as well as all kinds of industrial applications related to IoT and the fourth industrial revolution.
However, computer vision has another, more entertaining face! There are many less responsible uses of CV, serving mainly for our entertainment, such as virtual reality applications and games, filters on Instagram or TikTok, and just creating art, which is our main topic in this article.
How is it possible for AI to generate completely new images?
The creation of completely new images not seen before by artificial intelligence is possible thanks to so-called generative models. This is a group of models that generate new data instances, while traditional discriminative models are designed to distinguish between different types of data instances. Take, for example, a collection of cats and dogs. A discriminative model that gets a new image of an animal will be able to determine whether it is a cat or a dog based on the information collected about these species so far. A generative model, on the other hand, needs to learn the full range of characteristics dogs and cats have, so that it will be able to generate a picture of a completely new cat or dog when asked to do so.
To put it a bit more formally, generative models deal with modeling the cumulative distribution of data, while discriminative models deal with the probability of obtaining a given label under the condition of observing certain characteristics. This means that generative models have a much more difficult task and have to "learn more". This is possible (and thus it is possible to generate something that is not a copy of something existing) thanks to three elements:
High computing power - something that used to be available only to NASA, but today can be bought by the minute
Understanding human intent - not just through programming, but understanding natural language
Tailored models - algorithms capable of continuous improvement, acquiring the ability to recognize and interpret images
The general concept of learning models that generate images is as follows: we need to provide the computer with a great deal of data in the form of image+caption. Most often, such data comes from what we upload to the Internet. Then, in the process of training the model, not only does it build connections between the image and its caption, but also relationships between different objects. In this way, we get a space of named objects and the relationships between them, which we will then use to generate new images based on prompts.
What can go wrong? Threats lurking in the dataArtificial intelligence, and therefore image-generating models, actually build complex mathematical relationships based on the data we provide them. The models take on our human flaws, prejudices and social problems such as racial or gender discrimination, violence, and hate speech. It's up to us humans to make sure the model doesn't "misbehave." What problems might models encounter in our case?
Incorrectly labeled photos and images at the learning stage - If there are enough photos of airplanes signed as cars in our data, our model will stop distinguishing between them. There is also a possibility of deliberate tampering with the model using incorrectly labeled data.
The use of data depicting violence, hate, pornography, etc. - There is a risk here that models will produce content that is inappropriate for younger users or considered illegal. An additional aspect is the possibility of placing real people in such images in order to discredit a person or accuse them of something they have not done. Image-generating models can thus be used to spread deep fakes and manipulate information, e.g. political, which can be dangerous for both individuals and society as a whole.
Using copyrighted data - The most popular database for learning such models contains many proprietary images, as it simply contains millions of image+caption pairs scraped from the Internet. For example, so many images are downloaded from sites like GettyImages and Shutterstock that some models directly reproduce their watermarks in images. This can work against real authors such as professional photographers or artists who have worked for years creating their own unique style.
Ongoing efforts to make AI systems securedCan we remedy this somehow? Companies developing AI image generators are constantly working to ensure the safety of users and resolve possible legal issues. One approach is to filter the data provided to teach models. For this purpose, additional models are built to evaluate which content is inappropriate, however, bearing in mind the hundreds of millions of pieces of data on which the models are trained, let's note that this is a time-consuming and therefore costly approach - we must have images previously evaluated by humans as inappropriate.On the other hand, we can try to control the number and age of our users. This is done by limiting access to the generators of most companies, where the user must first fill out the appropriate application, wait for approval and only after that, gets access to the generator, which in addition is paid. However, not all companies go this route. For example, in order to use the StableDiffusion generator, which we'll talk about later, all you have to do is accept the terms and conditions and download the appropriate library, which makes it virtually impossible to control what the user does with the model.Still another idea is to check prompts sent by users and block them. Sadly, this is only possible when using the model on a global server and not downloading it to your own computer. For example, the DALL-E generator contains a content filter, and prompts containing certain keywords that are blocked, though users are constantly looking for workarounds for this as well.In summary, image generator developers and their users are playing cat and mouse with each other, with some coming up with better and better security features and others trying to get around them. I recommend a great article in The Verge to learn more. I think it's the same with AI image generators as with any other invention, it's neutral in itself, but there will always be people who want to use it in a bad way....Breakthrough in image generation - Generative Adversarial NetworksGenerative Adversarial Networks, or GANs for short, is a type of neural network architecture that has sparked a revolution in generative models. How do people come up with such groundbreaking ideas, you ask? Well, some get apples falling on their heads and others have a drink at the bar with their friends.How to encourage neural networks to create art?In 2014, a deep learning scientist named Ian Goodfellow went for a beer with friends at one of Montreal's bars. Apparently, they didn't have any hot gossip to talk about, as their conversation turned to how artificial intelligence could be used to create realistic images. Goodfellow's friends noted that neural networks were very good at recognizing and classifying objects in pictures, but not at creating them. Our brains work very similarly. It is easy for us to judge and classify pictures of landscapes or human faces, while it can be much more difficult for us to create a photorealistic reconstruction of a landscape or face. Inspired by these thoughts, Mr. Goodfellow returned home and began thinking about a solution for training neural networks to generate new images, not just classify existing ones. That same evening, he came up with an idea brilliant in its simplicity involving a "battle" between two neural networks, one of which "falsifies" images, while the other recognizes the "falsifications" of the first.Over the following weeks, with the help of top researchers at the Université de Montréal, Ian Goodfellow improved and described his work, which he published under the name Generative Adversarial Nets. Over the next few years, GANs became an absolute blockbuster in the AI world and led to many innovations in the field of artificial intelligence resulting in Mr. Goodfellow getting a great job at Google at the age of 33 followed by a position leading teams of researchers for companies such as Elon Musk's OpenAI and Apple. And now, he’s supporting the powerful AI lab that is DeepMind. As time goes on, I believe we will hear this man’s name more and more in the context of groundbreaking work in AI. He is no doubt an icon for many aspiring programmers.Step by step to AI generated artNevertheless, let's return to the GANs themselves. As we already know, the innovation of this model lies in the use of two independent neural networks like players in a game. The first network is called the generator and is tasked with creating "fake" images. The second player is a network called the discriminator and it's tasked with detecting "fake" images. In the beginning, our players are both very bad at the game, though during training, both networks learn to perform better and better. Personally, I like to compare the generator and discriminator to two children learning more and more things together.
Let's take a look at an illustration of the GAN architecture. The generator gets a set of random numbers as input and creates new (synthetic) images from them, without ever having seen the real data before. The discriminator knows what the real data looks like and it evaluates the artificial image created by the generator and classifies the image as real or synthetic. If the generator produces "fakes" that are too weak, the process is repeated. The generator learns to falsify the data better and better as the discriminator learns to recognize the synthetic images. The game ends when the generator produces such good images that the discriminator recognizes them as real. This will ensure that the generator is able to generate on-demand images that are very realistic.
How do today's text-to-image models work?Most of today's AI art generators are based on something called diffusion models. Like Generative Adversarial Networks, these are generative models, yet the principle behind them differs significantly. Unlike GANs, which learn by creating better and better images from scratch, diffusion models start by destroying training data by successively adding Gaussian noise, and then learning to recover the data by reversing this noising process.During training, we show many samples with a given distribution. Let’s take the example of many images of churches. After training, we can use the Diffusion Model to generate new images of churches by simply passing randomly sampled noise through the learned denoising process (have a look at the image below).
In this article we won’t delve into the mathematical background of diffusion models, but if you have the desire to learn more, I encourage you to read this article which presents the principles of diffusion models in a clear and understandable way.
We already know what the process of generating new images looks like in modern AI art generators, but in order for us to use them as we do now, we need to compose three elements:
First, the prompt text is passed to the text encoder, which is trained to transfer the text to the vector representation space (embedding space). Then, it’s pushed to a model called prior where the vector representation of the text is translated into a corresponding vector representation of the image, so that the semantic information of the prompt is preserved. Finally, the image decoder stochastically generates an image that is a visual manifestation of the prompt's semantic information.
| 2023-01-12T00:00:00 |
https://www.masterborn.com/blog/art-of-ai-will-ai-art-generators-displace-human-creativity
|
[
{
"date": "2023/01/12",
"position": 83,
"query": "artificial intelligence graphic design"
}
] |
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Can ChatGPT replace human work?
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Can ChatGPT replace human work?
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https://rickscloud.ai
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[
"Rick Blaisdell"
] |
Additionally, the use of AI raises ethical concerns, such as job displacement and the need for retraining of employees. Overall, while AI has the potential ...
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Can ChatGPT Replace Human Work?
by Rick Blaisdell 11.27k
AI, including chatbot technology like ChatGPT, has the potential to automate certain tasks and improve efficiency in various industries. However, it is important to note that AI is not meant to replace human work entirely, but rather to augment and assist it.
In certain industries, such as customer service or data entry, chatbots can handle repetitive and routine tasks, allowing human employees to focus on more complex and creative tasks. In other industries, such as healthcare or finance, AI can assist human professionals in making more accurate diagnoses or investment decisions. However, there are certain tasks that AI is not yet capable of performing, such as tasks that require emotional intelligence or creativity. Additionally, the use of AI raises ethical concerns, such as job displacement and the need for retraining of employees.
Overall, while AI has the potential to transform various industries, it is important to approach its implementation thoughtfully and consider the implications for human workers. A balance must be found between the benefits of AI and the preservation of human jobs.
Benefits of ChatGPT
ChatGPT, as a language model AI, offers several benefits in comparison to human work in certain tasks and industries. Some of these benefits include:
Efficiency – ChatGPT can process large amounts of data and generate written content quickly and accurately. This can save time and increase productivity in industries such as writing, data entry, and customer service.
24/7 availability – ChatGPT does not require rest or breaks, and can be available to interact with customers or complete tasks at any time.
Consistency – ChatGPT can provide consistent, accurate responses to customer inquiries or generate written content with minimal errors.
Scalability – ChatGPT can handle an unlimited number of interactions or tasks, making it well-suited for industries with a high volume of data or customer interactions.
ChatGPT vs. Humans
On the other hand, humans offer several benefits that ChatGPT currently lacks:
Emotional intelligence – Humans have the ability to understand and respond to emotions, which is important in industries such as customer service, sales, and healthcare.
Creativity – Humans have the ability to think outside the box and come up with novel solutions, which is important in industries such as design, advertising, and product development.
Adaptability – Humans have the ability to adapt to changing situations and environments, which is important in industries such as management, customer service, and customer service.
Human Interaction – Humans can provide a personal touch and human connection that a language model AI like ChatGPT can’t, as it lacks human emotions.
All in all, while ChatGPT offers several benefits in certain tasks, it is important to note that it cannot replace human work entirely. Both ChatGPT and humans have their own strengths and weaknesses, and the best results can be achieved by using the two in conjunction.
| 2023-01-13T00:00:00 |
2023/01/13
|
https://rickscloud.ai/can-chatgpt-replace-human-work-chatgpt-vs-humans/
|
[
{
"date": "2023/01/13",
"position": 72,
"query": "automation job displacement"
}
] |
Blog: Learn what's new in European Data and AI
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Blog. Find out what's new in Data and AI
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https://eudatajobs.com
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[
"Eu Data Jobs"
] |
The Importance of Lifelong Learning: Staying Ahead in a Dynamic Job Market ... Comprehensive Guide to Hiring a Machine Learning Developer. read more. hero ...
|
©2024 eudatajobs.com All rights reserved. Made with ❤️ & ☕ in 🇦🇹 Austria
| 2023-01-13T00:00:00 |
https://eudatajobs.com/blog/
|
[
{
"date": "2023/01/13",
"position": 74,
"query": "machine learning job market"
}
] |
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Workforce of the Future: What it is and 5 Trends That Will ...
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5 Top Emerging Trends That Will Define the Workforce of the Future
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https://emeritus.org
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[
"Anwesha Barari",
"About The Author",
"Read More About The Author",
"Niladri Pal",
"Sanmit Chatterjee"
] |
A work culture modernized by AI will inevitably lead to a spike in overall workforce creativity guided by rationale and intuition. While the major ...
|
Rapid advances in the fields of Artificial Intelligence (AI) and Machine Learning (ML) have driven the way we imagine the workforce of the future. But in the post-Covid landscape, employers have been encountering a workforce of changing demands and priorities. Added to this mix of changes is a 2021 McKinsey report that claims that by 2030, more than 25% of the initial estimates ( 14% ) of the global workforce might have to switch domains or occupations.
So, how will companies accommodate this digitally transformed workforce? Let’s find out.
What is the Workforce of the Future?
The digital transformation of industries is an instrumental factor behind the monumental shifts in the workforce and organizational effectiveness. The rate of transformative change is also accelerating and increasing the pressure on recruiters to cultivate a flexible talent pool. In a 2022 PwC Workforce of the Future report, Blair Sheppard, PwC’s head of strategy and leadership development, mentions, “To stay ahead, you need to focus on your ability to continuously adapt, engage with others in that process, and most importantly retain your core sense of identity and values. For students, it’s not just about acquiring knowledge, but about how to learn.”
What Does the Workforce of the Future Focus On?
Mapping this steady change over the years holds a defining moment for businesses worldwide. It thus becomes imperative for the workforce of the future to focus on the following aspects to surf the tides of AI, automation, and technology:
Making Large Leaps: The ongoing decade will witness multiple complex scenarios of industrial transformation. Business success is imperative to radical change and preparing for a dynamic future People are the Backbone of an Organization: Companies should strive to retain their workforce and their toiling teams by upskilling and incorporating agility toward change. The modern workforce will be guided by empathy and compassion—the prime boosters of productivity and organizational health Embracing Automation: Automation is inevitable and so is its effect on employees. A keen understanding of disruptive technologies will enable organizations to build a concrete narrative around innovation. It will also determine how your employees feel about the innovation strategies
ALSO READ: Building a Culture of Innovation in the Workplace
5 Traits That the Workforce of the Future Will Have
What do managers need to know about the workforce of the future? What are the trends that can be easily missed? Let’s find out.
1. They Will be Tech-Savvy
The workforce of the future will be comfortable handling AI, robots, and real-life ML applications. The Human-AI synergy entails innumerable learning opportunities to maximize the potential of human resources in a company, acclimatizing future employees to the changing demands of a professional workspace.
A 2021 HNG Leadership report emphasized the immediate importance of a tech-savvy workforce in the face of massive operational changes and skills shortages. It states that over 60% of companies worldwide have shifted their core operations to the cloud, while 24% of them have already faced major cybersecurity setbacks in the preceding two years. These facts, alongside a whopping 67% of skills shortages in the industry, only point toward a burgeoning demand for tech-savvy individuals to fill different organizational roles to boost productivity, infrastructural security, and customer engagement. When technology fuels innovation, the innovator needs to improvise!
2. They Will be Empowered
The Great Resignation of 2020 made employees reevaluate their choices, reasons, and decisions behind joining an organization. The workforce of the future will identify with endeavors that boast strong ethical and social records and look for greater meaning and relevance in their daily activities. Corporate responsibility thus becomes a crucial business imperative—making organizations collaborative in nature and providing autonomy to their talent landscape. Farsighted companies have already harnessed the potential of an empowered workforce by investing in environments of learning and continuous improvement. In the future, companies will be primed to retain a highly engaged and intuitive workforce that knows how to balance work and purpose.
ALSO READ: Green-Collar Jobs: Are You Ready to Join This Workforce?
3. They Will be Data-Literate
Data literacy is the entire set of processes involved in extracting, analyzing, and making sense of raw data to convert it into contextual information. The data-driven analytical culture is the primary catalyst for the rising demand for data-literate individuals. Data analysis is teamwork that requires stringent collaboration between the technical and the nontechnical to come up with actionable insights. A 2022 Globe NewsWire report claims that data literacy will be the most in-demand skill by 2030. All these aspects point toward a massive shift in the workforce of the future in terms of skill acquisition. If the future of businesses and industries belongs to AI, the future workforce will increasingly join the data analysis bandwagon to increase their job prospects.
4. They Will be Intuitive and Display Creative Intelligence
A work culture modernized by AI will inevitably lead to a spike in overall workforce creativity guided by rationale and intuition. While the major technological shifts generate anxious excitement among employees, they also reshape and reinvent the prevalent workflow processes. If machines/algorithms automate the tenuous, repetitive tasks, creativity becomes an exclusively human domain. Creativity will be a vital currency of success in an automated world—a free-flowing network of humanistic ideas actualized by AI and ML. A 2022 IBEM report mentions that companies that invest in creativity have happier and more productive employees. This not only reinforces the importance of intuitive thinking but is also a central proposition for companies to prioritize organizational and individual creativity.
5. They Will Have Measurable Soft Skills
Soft skills represent the nontechnical set of skills that show how one interacts within a professional environment. A September 2022 Forbes article deals in-depth with soft skills and how they were featured in the maximum number of jobs posted on LinkedIn. It means we don’t even need to look at the workforce of the future for this trend, we are living in ‘the future’. This is in line with a 2021 McKinsey report that talks about the rise of social and emotional skills as crucial preferences among recruiters. In a data-driven world, regular, repetitive tasks would have already been automated.
There will be a vacuum for managers, leaders, and visionaries—roles where people are trained to make decisions related to strategy, culture, and stakeholder relationships. Communication skills, diplomacy, patience, empathy, business acumen, and team building would be equally integral. As employees get more empowered, they will eventually form a vast networked space of shared talents that move beyond technical work paradigms.
ALSO READ: 6 Amazing Tips to Improve Your Critical Thinking Skills
What Will the Workplace Look Like in 2025?
The best part about innovations is their unpredictability. No one can gauge exactly how things will turn out in the future. However, studying the pace of change in technology and organizational culture, it’s safe to assume the following:
The rise of purposeful multidimensional roles
The complete dominance of hybrid and remote work models
Multimodal communication strategies
Hybrid and diverse teams incorporating values of equity and inclusion
Strong understanding of employees that they are inseparable from the surrounding innovation
The increase in environmental impact assessments by companies
The rising importance of organizational change managers
Be Future-Ready with Emeritus
Continuous learning, agility toward change, and talent management are keys to building an alternative workforce from scratch. If you are looking to be that change, Emeritus stands at the helm of sustainable business practices with its best-in-the-market courses on digital transformation, HR, coding, data science, and more. You can opt for one of these and upgrade your career to be part of the workforce of the future!
Write to us at [email protected]
| 2023-01-13T00:00:00 |
2023/01/13
|
https://emeritus.org/blog/career-workforce-of-the-future/
|
[
{
"date": "2023/01/13",
"position": 62,
"query": "future of work AI"
}
] |
Look Forward: A World in Disruption
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Look Forward: A World in Disruption
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https://www.spglobal.com
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[] |
AI in Banking: AI Will Be An Incremental Game Changer · The Return of Energy ... James McMahon founded The Climate Service with the vision that every economic ...
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The world has reached a unique moment. In my 40-plus years in business, I’ve never seen a time quite like the one we’re living through now.
This is a period marked by uncertainty. Households, the private sector and governments around the globe face an energy security crisis and climate change, food shortages, high inflation, ongoing supply chain challenges, volatility in financial markets and a host of geopolitical risks.
In this first issue of Look Forward, our economists, analysts, researchers and data experts survey the current state of affairs and connect the dots to explain what it all means in an era of instability. The purpose of Look Forward is to help decision-makers in asset managers, asset owners, companies, multilateral institutions, nonprofit organizations and governments look beyond the near term and explore the trends that will shape our future.
Readers will benefit from the smart analysis of our cross-divisional Research Council at S&P Global, which produced this journal. The Council has identified six interconnected themes with the greatest potential for large-scale disruption well into the future.
The Council’s critical work, as you will read, is focused on energy security, climate and sustainability, technology and digital disruptions, supply chains, capital markets and geopolitical shocks. We have unique datasets and insights in all these areas, and we see the Research Council as a way to make sure that our insights deliver maximum impact to our customers and the markets.
In this age of turbulence and change, the following articles are designed to help navigate the path ahead.
| 2023-01-13T00:00:00 |
2023/01/13
|
https://www.spglobal.com/en/research-insights/special-reports/look-forward/look-forward-volume-1
|
[
{
"date": "2023/01/13",
"position": 47,
"query": "AI economic disruption"
}
] |
How we could stumble into AI catastrophe
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How we could stumble into AI catastrophe
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https://www.cold-takes.com
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[] |
All other kinds of chaos and disruption, with the least cautious people (the ones most prone to rush forward aggressively deploying AIs to capture resources) ...
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Click lower right to download or find on Apple Podcasts, Spotify, Stitcher, etc.
This post will lay out a couple of stylized stories about how, if transformative AI is developed relatively soon, this could result in global catastrophe. (By “transformative AI,” I mean AI powerful and capable enough to bring about the sort of world-changing consequences I write about in my most important century series.)
This piece is more about visualizing possibilities than about providing arguments. For the latter, I recommend the rest of this series.
In the stories I’ll be telling, the world doesn't do much advance preparation or careful consideration of risks I’ve discussed previously, especially re: misaligned AI (AI forming dangerous goals of its own).
People do try to “test” AI systems for safety, and they do need to achieve some level of “safety” to commercialize. When early problems arise, they react to these problems.
But this isn’t enough, because of some unique challenges of measuring whether an AI system is “safe,” and because of the strong incentives to race forward with scaling up and deploying AI systems as fast as possible.
So we end up with a world run by misaligned AI - or, even if we’re lucky enough to avoid that outcome, other catastrophes are possible.
After laying these catastrophic possibilities, I’ll briefly note a few key ways we could do better, mostly as a reminder (these topics were covered in previous posts). Future pieces will get more specific about what we can be doing today to prepare.
Backdrop
This piece takes a lot of previous writing I’ve done as backdrop. Two key important assumptions (click to expand) are below; for more, see the rest of this series.
(Click to expand) “Most important century” assumption: we’ll soon develop very powerful AI systems, along the lines of what I previously called PASTA. In the most important century series, I argued that the 21st century could be the most important century ever for humanity, via the development of advanced AI systems that could dramatically speed up scientific and technological advancement, getting us more quickly than most people imagine to a deeply unfamiliar future. I focus on a hypothetical kind of AI that I call PASTA, or Process for Automating Scientific and Technological Advancement. PASTA would be AI that can essentially automate all of the human activities needed to speed up scientific and technological advancement. Using a variety of different forecasting approaches, I argue that PASTA seems more likely than not to be developed this century - and there’s a decent chance (more than 10%) that we’ll see it within 15 years or so. I argue that the consequences of this sort of AI could be enormous: an explosion in scientific and technological progress. This could get us more quickly than most imagine to a radically unfamiliar future. I’ve also argued that AI systems along these lines could defeat all of humanity combined, if (for whatever reason) they were aimed toward that goal. For more, see the most important century landing page. The series is available in many formats, including audio; I also provide a summary, and links to podcasts where I discuss it at a high level.
(Click to expand) “Nearcasting” assumption: such systems will be developed in a world that’s otherwise similar to today’s. It’s hard to talk about risks from transformative AI because of the many uncertainties about when and how such AI will be developed - and how much the (now-nascent) field of “AI safety research” will have grown by then, and how seriously people will take the risk, etc. etc. etc. So maybe it’s not surprising that estimates of the “misaligned AI” risk range from ~1% to ~99%. This piece takes an approach I call nearcasting: trying to answer key strategic questions about transformative AI, under the assumption that such AI arrives in a world that is otherwise relatively similar to today's. You can think of this approach like this: “Instead of asking where our ship will ultimately end up, let’s start by asking what destination it’s pointed at right now.” That is: instead of trying to talk about an uncertain, distant future, we can talk about the easiest-to-visualize, closest-to-today situation, and how things look there - and then ask how our picture might be off if other possibilities play out. (As a bonus, it doesn’t seem out of the question that transformative AI will be developed extremely soon - 10 years from now or faster.1 If that’s the case, it’s especially urgent to think about what that might look like.)
How we could stumble into catastrophe from misaligned AI
This is my basic default picture for how I imagine things going, if people pay little attention to the sorts of issues discussed previously. I’ve deliberately written it to be concrete and visualizable, which means that it’s very unlikely that the details will match the future - but hopefully it gives a picture of some of the key dynamics I worry about.
Throughout this hypothetical scenario (up until “END OF HYPOTHETICAL SCENARIO”), I use the present tense (“AIs do X”) for simplicity, even though I’m talking about a hypothetical possible future.
Early commercial applications. A few years before transformative AI is developed, AI systems are being increasingly used for a number of lucrative, useful, but not dramatically world-changing things.
I think it’s very hard to predict what these will be (harder in some ways than predicting longer-run consequences, in my view),2 so I’ll mostly work with the simple example of automating customer service.
In this early stage, AI systems often have pretty narrow capabilities, such that the idea of them forming ambitious aims and trying to defeat humanity seems (and actually is) silly. For example, customer service AIs are mostly language models that are trained to mimic patterns in past successful customer service transcripts, and are further improved by customers giving satisfaction ratings in real interactions. The dynamics I described in an earlier piece, in which AIs are given increasingly ambitious goals and challenged to find increasingly creative ways to achieve them, don’t necessarily apply.
Early safety/alignment problems. Even with these relatively limited AIs, there are problems and challenges that could be called “safety issues” or “alignment issues.” To continue with the example of customer service AIs, these AIs might:
Give false information about the products they’re providing support for. (Example of reminiscent behavior)
Give customers advice (when asked) on how to do unsafe or illegal things. (Example)
Refuse to answer valid questions. (This could result from companies making attempts to prevent the above two failure modes - i.e., AIs might be penalized heavily for saying false and harmful things, and respond by simply refusing to answer lots of questions).
Say toxic, offensive things in response to certain user queries (including from users deliberately trying to get this to happen), causing bad PR for AI developers. (Example)
Early solutions. The most straightforward way to solve these problems involves training AIs to behave more safely and helpfully. This means that AI companies do a lot of things like “Trying to create the conditions under which an AI might provide false, harmful, evasive or toxic responses; penalizing it for doing so, and reinforcing it toward more helpful behaviors.”
This works well, as far as anyone can tell: the above problems become a lot less frequent. Some people see this as cause for great celebration, saying things like “We were worried that AI companies wouldn’t invest enough in safety, but it turns out that the market takes care of it - to have a viable product, you need to get your systems to be safe!”
People like me disagree - training AIs to behave in ways that are safer as far as we can tell is the kind of “solution” that I’ve worried could create superficial improvement while big risks remain in place.
(Click to expand) Why AI safety could be hard to measure In previous pieces, I argued that: If we develop powerful AIs via ambitious use of the “black-box trial-and-error” common in AI development today, then there’s a substantial risk that: These AIs will develop unintended aims (states of the world they make calculations and plans toward, as a chess-playing AI "aims" for checkmate); These AIs could deceive, manipulate, and even take over the world from humans entirely as needed to achieve those aims. People today are doing AI safety research to prevent this outcome, but such research has a number of deep difficulties: “Great news - I’ve tested this AI and it looks safe.” Why might we still have a problem? Problem Key question Explanation The Lance Armstrong problem Did we get the AI to be actually safe or good at hiding its dangerous actions ? When dealing with an intelligent agent, it’s hard to tell the difference between “behaving well” and “appearing to behave well.” When professional cycling was cracking down on performance-enhancing drugs, Lance Armstrong was very successful and seemed to be unusually “clean.” It later came out that he had been using drugs with an unusually sophisticated operation for concealing them. The King Lear problem The AI is (actually) well-behaved when humans are in control. Will this transfer to when AIs are in control? It's hard to know how someone will behave when they have power over you, based only on observing how they behave when they don't. AIs might behave as intended as long as humans are in control - but at some future point, AI systems might be capable and widespread enough to have opportunities to take control of the world entirely. It's hard to know whether they'll take these opportunities, and we can't exactly run a clean test of the situation. Like King Lear trying to decide how much power to give each of his daughters before abdicating the throne. The lab mice problem Today's "subhuman" AIs are safe. What about future AIs with more human-like abilities ? Today's AI systems aren't advanced enough to exhibit the basic behaviors we want to study, such as deceiving and manipulating humans. Like trying to study medicine in humans by experimenting only on lab mice. The first contact problem Imagine that tomorrow's "human-like" AIs are safe. How will things go when AIs have capabilities far beyond humans'? AI systems might (collectively) become vastly more capable than humans, and it's ... just really hard to have any idea what that's going to be like. As far as we know, there has never before been anything in the galaxy that's vastly more capable than humans in the relevant ways! No matter what we come up with to solve the first three problems, we can't be too confident that it'll keep working if AI advances (or just proliferates) a lot more. Like trying to plan for first contact with extraterrestrials (this barely feels like an analogy). An analogy that incorporates these challenges is Ajeya Cotra’s “young businessperson” analogy: Imagine you are an eight-year-old whose parents left you a $1 trillion company and no trusted adult to serve as your guide to the world. You must hire a smart adult to run your company as CEO, handle your life the way that a parent would (e.g. decide your school, where you’ll live, when you need to go to the dentist), and administer your vast wealth (e.g. decide where you’ll invest your money). You have to hire these grownups based on a work trial or interview you come up with -- you don't get to see any resumes, don't get to do reference checks, etc. Because you're so rich, tons of people apply for all sorts of reasons. (More) If your applicants are a mix of "saints" (people who genuinely want to help), "sycophants" (people who just want to make you happy in the short run, even when this is to your long-term detriment) and "schemers" (people who want to siphon off your wealth and power for themselves), how do you - an eight-year-old - tell the difference? More: AI safety seems hard to measure
(So far, what I’ve described is pretty similar to what’s going on today. The next bit will discuss hypothetical future progress, with AI systems clearly beyond today’s.)
Approaching transformative AI. Time passes. At some point, AI systems are playing a huge role in various kinds of scientific research - to the point where it often feels like a particular AI is about as helpful to a research team as a top human scientist would be (although there are still important parts of the work that require humans).
Some particularly important (though not exclusive) examples:
AIs are near-autonomously writing papers about AI, finding all kinds of ways to improve the efficiency of AI algorithms.
AIs are doing a lot of the work previously done by humans at Intel (and similar companies), designing ever-more efficient hardware for AI.
AIs are also extremely helpful with AI safety research. They’re able to do most of the work of writing papers about things like digital neuroscience (how to understand what’s going on inside the “digital brain” of an AI) and limited AI (how to get AIs to accomplish helpful things while limiting their capabilities). However, this kind of work remains quite niche (as I think it is today), and is getting far less attention and resources than the first two applications. Progress is made, but it’s slower than progress on making AI systems more powerful.
AI systems are now getting bigger and better very quickly, due to dynamics like the above, and they’re able to do all sorts of things.
At some point, companies start to experiment with very ambitious, open-ended AI applications, like simply instructing AIs to “Design a new kind of car that outsells the current ones” or “Find a new trading strategy to make money in markets.” These get mixed results, and companies are trying to get better results via further training - reinforcing behaviors that perform better. (AIs are helping with this, too, e.g. providing feedback and reinforcement for each others’ outputs3 and helping to write code4 for the training processes.)
This training strengthens the dynamics I discussed in a previous post: AIs are being rewarded for getting successful outcomes as far as human judges can tell, which creates incentives for them to mislead and manipulate human judges, and ultimately results in forming ambitious goals of their own to aim for.
More advanced safety/alignment problems. As the scenario continues to unfold, there are a number of concerning events that point to safety/alignment problems. These mostly follow the form: “AIs are trained using trial and error, and this might lead them to sometimes do deceptive, unintended things to accomplish the goals they’ve been trained to accomplish.”
Things like:
AIs creating writeups on new algorithmic improvements, using faked data to argue that their new algorithms are better than the old ones. Sometimes, people incorporate new algorithms into their systems and use them for a while, before unexpected behavior ultimately leads them to dig into what’s going on and discover that they’re not improving performance at all. It looks like the AIs faked the data in order to get positive feedback from humans looking for algorithmic improvements.
AIs assigned to make money in various ways (e.g., to find profitable trading strategies) doing so by finding security exploits, getting unauthorized access to others’ bank accounts, and stealing money.
AIs forming relationships with the humans training them, and trying (sometimes successfully) to emotionally manipulate the humans into giving positive feedback on their behavior. They also might try to manipulate the humans into running more copies of them, into refusing to shut them off, etc.- things that are generically useful for the AIs’ achieving whatever aims they might be developing.
(Click to expand) Why AIs might do deceptive, problematic things like this In a previous piece, I highlighted that modern AI development is essentially based on "training" via trial-and-error. To oversimplify, you can imagine that: An AI system is given some sort of task.
The AI system tries something, initially something pretty random.
The AI system gets information about how well its choice performed, and/or what would’ve gotten a better result. Based on this, it adjusts itself. You can think of this as if it is “encouraged/discouraged” to get it to do more of what works well. Human judges may play a significant role in determining which answers are encouraged vs. discouraged, especially for fuzzy goals like “Produce helpful scientific insights.”
After enough tries, the AI system becomes good at the task.
But nobody really knows anything about how or why it’s good at the task now. The development work has gone into building a flexible architecture for it to learn well from trial-and-error, and into “training” it by doing all of the trial and error. We mostly can’t “look inside the AI system to see how it’s thinking.” I then argue that: Because we ourselves will often be misinformed or confused, we will sometimes give negative reinforcement to AI systems that are actually acting in our best interests and/or giving accurate information, and positive reinforcement to AI systems whose behavior deceives us into thinking things are going well. This means we will be, unwittingly, training AI systems to deceive and manipulate us.
For this and other reasons, powerful AI systems will likely end up with aims other than the ones we intended. Training by trial-and-error is slippery: the positive and negative reinforcement we give AI systems will probably not end up training them just as we hoped. There are a number of things such AI systems might end up aiming for, such as: Power and resources. These tend to be useful for most goals, such that AI systems could be quite consistently be getting better reinforcement when they habitually pursue power and resources.
Things like “digital representations of human approval” (after all, every time an AI gets positive reinforcement, there’s a digital representation of human approval). In sum, we could be unwittingly training AI systems to accumulate power and resources, get good feedback from humans, etc. - even when this means deceiving and manipulating humans to do so. More: Why would AI "aim" to defeat humanity?
“Solutions” to these safety/alignment problems. When problems like the above are discovered, AI companies tend to respond similarly to how they did earlier:
Training AIs against the undesirable behavior.
Trying to create more (simulated) situations under which AIs might behave in these undesirable ways, and training them against doing so.
These methods “work” in the sense that the concerning events become less frequent - as far as we can tell. But what’s really happening is that AIs are being trained to be more careful not to get caught doing things like this, and to build more sophisticated models of how humans can interfere with their plans.
In fact, AIs are gaining incentives to avoid incidents like “Doing something counter to human developers’ intentions in order to get positive feedback, and having this be discovered and given negative feedback later” - and this means they are starting to plan more and more around the long-run consequences of their actions. They are thinking less about “Will I get positive feedback at the end of the day?” and more about “Will I eventually end up in a world where humans are going back, far in the future, to give me retroactive negative feedback for today’s actions?” This might give direct incentives to start aiming for eventual defeat of humanity, since defeating humanity could allow AIs to give themselves lots of retroactive positive feedback.
One way to think about it: AIs being trained in this way are generally moving from “Steal money whenever there’s an opportunity” to “Don’t steal money if there’s a good chance humans will eventually uncover this - instead, think way ahead and look for opportunities to steal money and get away with it permanently.” The latter could include simply stealing money in ways that humans are unlikely to ever notice; it might also include waiting for an opportunity to team up with other AIs and disempower humans entirely, after which a lot more money (or whatever) can be generated.
Debates. The leading AI companies are aggressively trying to build and deploy more powerful AI, but a number of people are raising alarms and warning that continuing to do this could result in disaster. Here’s a stylized sort of debate that might occur:
A: Great news, our AI-assisted research team has discovered even more improvements than expected! We should be able to build an AI model 10x as big as the state of the art in the next few weeks.
B: I’m getting really concerned about the direction this is heading. I’m worried that if we make an even bigger system and license it to all our existing customers - military customers, financial customers, etc. - we could be headed for a disaster.
A: Well the disaster I’m trying to prevent is competing AI companies getting to market before we do.
B: I was thinking of AI defeating all of humanity.
A: Oh, I was worried about that for a while too, but our safety training has really been incredibly successful.
B: It has? I was just talking to our digital neuroscience lead, and she says that even with recent help from AI “virtual scientists,” they still aren’t able to reliably read a single AI’s digital brain. They were showing me this old incident report where an AI stole money, and they spent like a week analyzing that AI and couldn’t explain in any real way how or why that happened.
(Click to expand) How "digital neuroscience" could help I’ve argued that it could be inherently difficult to measure whether AI systems are safe, for reasons such as: AI systems that are not deceptive probably look like AI systems that are so good at deception that they hide all evidence of it, in any way we can easily measure. Unless we can “read their minds!” Currently, today’s leading AI research is in the genre of “black-box trial-and-error.” An AI tries a task; it gets “encouragement” or “discouragement” based on whether it does the task well; it tweaks the wiring of its “digital brain” to improve next time; it improves at the task; but we humans aren’t able to make much sense of its “digital brain” or say much about its “thought process.” Some AI research (example)2 is exploring how to change this - how to decode an AI system’s “digital brain.” This research is in relatively early stages - today, it can “decode” only parts of AI systems (or fully decode very small, deliberately simplified AI systems). As AI systems advance, it might get harder to decode them - or easier, if we can start to use AI for help decoding AI, and/or change AI design techniques so that AI systems are less “black box”-ish. More
A: I agree that’s unfortunate, but digital neuroscience has always been a speculative, experimental department. Fortunately, we have actual data on safety. Look at this chart - it shows the frequency of concerning incidents plummeting, and it’s extraordinarily low now. In fact, the more powerful the AIs get, the less frequent the incidents get - we can project this out and see that if we train a big enough model, it should essentially never have a concerning incident!
B: But that could be because the AIs are getting cleverer, more patient and long-term, and hence better at ensuring we never catch them.
(Click to expand) The Lance Armstrong problem: is the AI actually safe or good at hiding its dangerous actions? Let's imagine that: We have AI systems available that can do roughly everything a human can, with some different strengths and weaknesses but no huge difference in "overall capabilities" or economic value per hour of work.
We're observing early signs that AI systems behave in unintended, deceptive ways, such as giving wrong answers to questions we ask, or writing software that falsifies metrics instead of doing the things the metrics were supposed to measure (e.g., software meant to make a website run faster might instead falsify metrics about its loading time). We theorize that modifying the AI training in some way6 will make AI systems less likely to behave deceptively. We try it out, and find that, in fact, our AI systems seem to be behaving better than before - we are finding fewer incidents in which they behaved in unintended or deceptive ways. But that's just a statement about what we're noticing. Which of the following just happened: Did we just train our AI systems to be less deceptive?
Did we just train our AI systems to be better at deception, and so to make us think they became less deceptive?
Did we just train our AI systems to be better at calculating when they might get caught in deception, and so to be less deceptive only when the deception would otherwise be caught? This one could be useful! Especially if we're able to set up auditing systems in many real-world situations, such that we could expect deception to be caught a lot of the time. But it does leave open the King Lear problem.
(...Or some combination of the three?) We're hoping to be able to deploy AI systems throughout the economy, so - just like human specialists - they will almost certainly have some opportunities to be deceptive without being caught. The fact that they appear honest in our testing is not clear comfort against this risk. The analogy here is to competitive cyclist Lance Armstrong. Armstrong won the Tour de France race 7 times in a row, while many of his competitors were caught using performance-enhancing drugs and disqualified. But more than 5 years after his last win, an investigation "concluded that Armstrong had used performance-enhancing drugs over the course of his career[5] and named him as the ringleader of 'the most sophisticated, professionalized and successful doping program that sport has ever seen'." Now the list of Tour de France winners looks like this:
… Or just that they’re now advanced enough that they’re waiting for a chance to disempower humanity entirely, rather than pull a bunch of small-time shenanigans that tip us off to the danger.
| 2023-01-13T00:00:00 |
2023/01/13
|
https://www.cold-takes.com/how-we-could-stumble-into-ai-catastrophe/
|
[
{
"date": "2023/01/13",
"position": 75,
"query": "AI economic disruption"
}
] |
Todd R. Dobry | Jackson Lewis P.C.
|
Workplace Privacy, Data Management & Security Report
|
https://www.workplaceprivacyreport.com
|
[
"Joseph J. Lazzarotti",
"Todd R. Dobry",
"Michelle L. Duncan",
"Michael H. Neifach",
"Teri Wilford Wood",
"Alec Nealon",
"Eric J. Felsberg",
"Jason C. Gavejian",
"November",
"April"
] |
On October 30, 2023, President Biden issued an Executive Order regarding the Development and Use of Artificial Intelligence across the federal government.
|
Continuing its initiative regarding the use of data, automated processes, and artificial intelligence (“AI”), the U.S. Equal Employment Opportunity Commission (“EEOC”) is holding a hearing on January 31, 2023 for examining the use of automated systems and AI in employment decisions.
This in-person hearing will begin at 10:00am EST on January 31 at the EEOC
| 2023-01-13T00:00:00 |
https://www.workplaceprivacyreport.com/author/toddrdobry/
|
[
{
"date": "2023/01/13",
"position": 61,
"query": "government AI workforce policy"
}
] |
|
Open Positions
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Open Positions at DataRobot
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https://www.datarobot.com
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[] |
Viewing 22 open positions in all locations. Clear filters. Engineering. Backend Engineer. Remote Canada - ON · Sales. Regional Director, Customer Success ...
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Apps & Agents
Our apps and agents are engineered to drive innovation, scale effortlessly, and deliver meaningful impact across departments and industries.
| 2023-01-13T00:00:00 |
https://www.datarobot.com/careers/open-positions/
|
[
{
"date": "2023/01/13",
"position": 4,
"query": "generative AI jobs"
}
] |
|
Field Solutions Developer II, Generative AI, Google Cloud - Taro
|
Field Solutions Developer II, Generative AI, Google Cloud for Google
|
https://www.jointaro.com
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[] |
Field Solutions Developer II position at Google Cloud focusing on Generative AI implementation, requiring 5+ years of Python experience and strong AI/ML ...
|
Google Cloud is seeking a Field Solutions Developer II specializing in Generative AI to support their cloud sales organization. This role combines technical expertise in AI/ML with customer-facing responsibilities, focusing on building rapid prototype Generative AI applications for diverse clients from startups to major enterprises.
The position requires deep technical knowledge in machine learning, particularly in Generative AI, with hands-on experience in model training, fine-tuning, and optimization. You'll work directly with customers to demonstrate Google Cloud's capabilities, develop proof-of-concepts, and provide technical guidance while maintaining close collaboration with product teams to influence platform development.
Key technical aspects include working with statistical programming languages (Python), AI frameworks (TensorFlow, PyTorch), and MLOps practices including CI/CD and Infrastructure as Code. The role demands both technical depth in AI/ML and the ability to communicate complex concepts effectively to various stakeholders.
The compensation package is competitive, ranging from $147,000 to $216,000 base salary, plus bonus, equity, and comprehensive benefits. This position offers significant growth potential at the forefront of Generative AI technology while working with cutting-edge tools and diverse customers.
The role provides an opportunity to shape the future of Google Cloud's Generative AI offerings while working with a wide range of customers and technologies. It requires a blend of technical expertise, customer engagement skills, and the ability to influence product direction through direct customer feedback and requirements gathering.
| 2023-01-13T00:00:00 |
https://www.jointaro.com/jobs/google/field-solutions-architect-ii-generative-ai-google-cloud-00e2a955/
|
[
{
"date": "2023/01/13",
"position": 7,
"query": "generative AI jobs"
}
] |
|
Senior Software Engineer, Platform - Data + AI (Back-End)
|
C3 AI hiring Senior Software Engineer, Platform - Data + AI (Back-End) in Redwood City, CA
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https://www.linkedin.com
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[] |
C3 AI is looking for Senior Software Engineers to join the rapidly growing Data org within the Platform Engineering department.
|
Design and develop infrastructure and services to enable data pipelines for petabyte level scale and more.
Design and develop abstractions over datastores such as Cassandra, PostgreSQL, Snowflake, etc.
Design and develop file system abstractions over AWS S3, Azure Blobs, HDFS, etc.
Design and develop connectors to various external data stores.
Design and develop distributed system components for stream processing, queueing, batch processing, analytics engines, etc.
Develop and maintain industry-leading, high-performance APIs for AL/ML applications.
Develop and maintain features for distributed computations over large-scale data for ML workflows.
Design and develop ML-specific data-systems such as feature stores and behavioral frameworks such as recommendation engines.
Design and develop integrations with distributed computing technologies such as Apache Spark, Ray, etc. for data exploration and ML workload orchestration.
Design and develop integrations with data analysis libraries such as Pandas, Koalas, etc.
Develop and production AI/ML models for failure prediction, data schema inferencing, etc.
Work on frameworks for performance, scalability, and reliability tracking over different components of a highly extensible AI/ML platform.
Work with architects, product managers, and software engineers across teams in a highly collaborative environment.
Participate and provide insights in technical discussions.
Write clean code following a test-driven methodology.
Deliver commitments promptly following agile software development methodology.
Bachelor of Science in Computer Science, Computer Engineering, or related fields.
Strong understanding of Computer Science fundamentals.
High proficiency in coding with Java, C++, C#, or some other compiled language. Python would also be acceptable.
Strong competency in object-oriented programming, data structures, algorithms, and software design patterns.
Experience with version control systems such as Git.
Experience with large-scale distributed systems.
Experience with any public cloud platform (AWS, Azure, GCP).
Some familiarity with distributed computing technologies (e.g., Hadoop, Spark, Kafka). Familiarity with managed versions of these technologies on public cloud platforms is also acceptable.
Familiarity with technologies in the modern data science/analysis and engineering ecosystem (e.g., Pandas, Koalas).
Good verbal and written technical communication ability to facilitate collaboration.
Thrive in a fast-paced, dynamic environment and value end-to-end ownership of components.
Intellectually curious and open to challenges.
Advanced degree in engineering, sciences, or related field.
Experience with Agile development methodology.
Experience developing and working with REST and/or GraphQL APIs.
Experience building scalable and reliable data pipelines.
Experience with integration of data from multiple sources.
Experience working with analytics and/or data processing engines.
Experience developing distributed computation over large-scale data.
Experience working with distributed computing frameworks (e.g., Hadoop, Spark, Kafka).
Experience with data science/analysis libraries (e.g., Pandas, Koalas).
Experience with task schedulers in distributed computing (e.g., Spark, Ray, Dask).
Familiarity with machine learning workload orchestration in a distributed computing environment.
Familiarity with workflow execution and/or optimization using DAGs, ideally for machine learning use-cases.
Conceptual understanding of orchestration and resource provisioning systems (Kubernetes)
C3 AI (NYSE: AI), is the Enterprise AI application software company. C3 AI delivers a family of fully integrated products including the C3 Agentic AI Platform, an end-to-end platform for developing, deploying, and operating enterprise AI applications, C3 AI applications, a portfolio of industry-specific SaaS enterprise AI applications that enable the digital transformation of organizations globally, and C3 Generative AI, a suite of domain-specific generative AI offerings for the enterprise. Learn more at:C3 AI is looking for Senior Software Engineers to join the rapidly growing Data org within the Platform Engineering department. Successful candidates will get the opportunity to work on high-value technologies at the intersection of large-scale distributed systems, data infrastructure, and machine learning. You will design, develop, and maintain various features in a highly scalable and extensible AI/ML platform for large-scale applications, involving data science, distributed systems, and multi-cloud strategy.You will be given opportunities to take ownership of components, collaborate to drive technical direction, and work on interesting, impactful projects. Join us in building the next-generation AI/ML platform at petabyte level scale that powers some of the world’s largest companies in Energy, Financial Services, Utilities, Health Care, Aerospace, Defense, etc. Accelerate your career in the leading enterprise AI company that is in a hyper-growth trajectory.C3 AI provides excellent benefits, a competitive compensation package and generous equity plan.California Pay Range$145,000—$187,000 USDC3 AI is proud to be an Equal Opportunity and Affirmative Action Employer. We do not discriminate on the basis of any legally protected characteristics, including disabled and veteran status.
| 2023-01-13T00:00:00 |
https://www.linkedin.com/jobs/view/senior-software-engineer-platform-data-%2B-ai-back-end-at-c3-ai-3438610722
|
[
{
"date": "2023/01/13",
"position": 20,
"query": "generative AI jobs"
}
] |
|
Sectors | AI & Robotics
|
Artificial Intelligence – Cubiq Recruitment
|
https://www.cubiqrecruitment.com
|
[] |
Disciplines. AI, ML, Ops and Infra. ROS Software. Perception Engineering. Software, DevOps & Cloud. Data Science. Generative AI ... homeServicesjobsinsightsabout ...
|
If you’re building a talented AI team today, you need someone who’ll do more than post a role and hope for the best. Partnering with us gives you access to networks built through 10+ years of proactive search, networking, diving deep into publications and papers, and staying close to the research and advancements going on in the sector.
You’re working with a recruitment firm that was scaling AI teams before most people could really explain what it was. Our strategies have evolved with the tech, adapting to shifts in demand, regulation, funding patterns, and the ever-rising bar for what ‘good’ actually looks like.
We’re embedded with firms across the US, UK and Europe, whose names include some of the most exciting in GenAI, robotics, defence, social media and eCommerce.
We know your pain points, like growing fast without compromising quality, fighting for niche talent in an oversaturated employer market, and building teams who are equipped to ship your product. We also know the kind of hires that are able to address some of these challenges head-on.
| 2023-01-13T00:00:00 |
https://www.cubiqrecruitment.com/sector/ai
|
[
{
"date": "2023/01/13",
"position": 94,
"query": "generative AI jobs"
}
] |
|
AI Connect
|
AI Connect
|
https://www.atlanticcouncil.org
|
[] |
The AI Connect initiative encourages the responsible stewardship of AI technologies in line with the OECD AI Principles and empowers low and middle-income ...
|
About AI Connect
Artificial Intelligence (AI) technologies hold great promise to increase prosperity and innovation, as well as enrich the human experience. The AI Connect program seeks to foster a global community of government, academic, industry, and civil society stakeholders with a vested interest in advancing the responsible stewardship of trustworthy AI as embodied in the OECD Recommendation on AI. With the promotion of this concept in mind, the US Department of State’s Bureau of Cyberspace and Digital Policy has partnered with the Atlantic Council’s GeoTech Center to implement the AI Connect program. This extensive grant provides programmatic assistance to empower low- and middle-income countries to more effectively participate in global, multi-stakeholder conversations on the responsible use of AI. AI Connect currently has three lines of effort in pursuit of these goals:
1. Monthly webinar series on AI principles and applications across sectors
Focused on building AI policy capacity, the monthly webinar series provides a platform for AI Connect participants from around the world to learn about AI policy best practices and implications. Webinar topics focus on opportunities and challenges in responsibly developing and deploying AI technologies across sectors in line with human-centric values in the OECD AI Principles. Through dynamic presentations, panels, and technology demonstrations, the webinar curriculum emphasizes the benefits of engagement with international fora working on AI issues, such as the OECD, and ensuring that industry, academia, civil society, and other stakeholders are included in the development of AI policy approaches.
2. In-person regional workshops and site visits
The AI Connect program also convenes participants and the wider multistakeholder community of AI experts for regional workshops focusing on the Indo-Pacific, Central and South America, Europe, and Africa. During these workshops, AI Connect participants hear from policy and technical experts in their field; examine case studies and best practices from their own home regions; network with other AI Connect participants; and analyze the most pressing issues in AI policy. The workshops include site visits to engage directly with the local community of AI developers and relevant stakeholders.
3. Travel funding to attend global AI conferences
Lastly, the program includes travel funding to send AI Connect participants to multilateral meetings and global conferences focused on AI. The AI Connect program aims to provide these opportunities for participants to engage in discussions on critical AI topics with experts and stakeholders around the world to create a more inclusive and representative global AI community.
AI Connect II Programming
AI Connect II Workshops
AI Connect I Programming
| 2023-01-13T00:00:00 |
https://www.atlanticcouncil.org/programs/geotech-center/ai-connect/
|
[
{
"date": "2023/01/13",
"position": 33,
"query": "AI labor union"
}
] |
|
White-Collar Jobs and Service Workers Are New Union ...
|
White-Collar Jobs and Service Workers Are New Union Hotbeds
|
https://www.bloomberg.com
|
[
"Dorothy Gambrell"
] |
Source: National Labor Relations Board, Bureau of Labor Statistics 2021 year-end union data ... MarketsEconomicsTechnologyPoliticsGreenCryptoAI. Work & Life.
|
Connecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world
| 2023-01-13T00:00:00 |
2023/01/13
|
https://www.bloomberg.com/news/features/2023-01-13/labor-unions-target-starbucks-trader-joe-s-service-workers
|
[
{
"date": "2023/01/13",
"position": 96,
"query": "AI labor union"
}
] |
Alphabet robotics subsidiary Intrinsic lays off 20% staff
|
Alphabet robotics subsidiary Intrinsic lays off 20% staff
|
https://www.computerworld.com
|
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Layoffs at Alphabet's “Other Bets” division has widened to include its robotics subsidiary Intrinsic AI, which is eliminating about 20% of its workforce or ...
|
Layoffs at Alphabet’s “Other Bets” division has widened to include its robotics subsidiary Intrinsic AI, which is eliminating about 20% of its workforce or roughly 40 employees, according to reports.
Intrinsic AI came out of Alphabet’s X research unit, after incubating there for close to five years. It is a robotics firm that is working on developing artificial intelligence-based software to bolster the use of robots in industries and commercial environments.
The news about the company’s layoffs was first report by The Information. An email sent to Intrinsic AI didn’t immediately receive a response.
Last month, Intrinsic acquired OSRC, the commercial arm of Open Robotics. A post on TeamBlind’s professional network claims that the decision to layoff staffers was decided right after the acquisition as the company needed to maintain employee headcount.
The first of Alphabet layoffs came this week from the company’s life sciences subsidiary Verily as part of its One Verily program, under which it eliminated 240 roles amounting to 15% of its 1,600-strong workforce to simplify its operating model.
The downsizing at Intrinsic seems to follow the philosophy of the One Verily program as the robotics firm hinted at accelerating commercial success while discussing the layoffs.
“This (downsizing) decision was made in light of shifts in prioritization and our longer-term strategic direction. It will ensure Intrinsic can continue to allocate resources to our highest priority initiatives, such as building our software and AI platform, integrating the recent strategic acquisitions of Vicarious and OSRC (commercial arm Open Robotics), and working with key industry partners,” according to a company statement sent to media.
Alphabet and its employees have been under pressure to generate more revenue since July last year when the company declared a hiring freeze. The freeze was followed by the launch of the Simplicity Sprint program to boost staff efficiency and productivity as the company posted weaker-than-expected revenue for two consecutive quarters.
Layoffs at technology companies began around August last year and has continued in the first weeks of January this year with large companies such as Amazon and Salesforce firing close to 26,000 employees.
(This story has been updated to change attribution for Intrinsic’s media statement.)
| 2023-01-13T00:00:00 |
https://www.computerworld.com/article/1617013/alphabet-robotics-subsidiary-intrinsic-lays-off-20-staff.html
|
[
{
"date": "2023/01/13",
"position": 40,
"query": "AI layoffs"
}
] |
|
Voice AI Company SoundHound Lays Off Nearly Half Its ...
|
Voice AI Company SoundHound Lays Off Nearly Half Its Staff and Offers ‘Pitiful’ Severance
|
https://gizmodo.com
|
[
"Mack Degeurin",
"Luc Olinga",
"Cheryl Eddy",
"Isaiah Colbert",
"Kyle Barr",
"Sabina Graves"
] |
The massive restructuring comes just one year after the company went public via SPAC and two months after a separate layoff that cut 10% of staff.
|
SoundHound, a voice AI company, laid off nearly half of its remaining staff last week—roughly 200 people—in a major company-wide downsizing, according to three employees who lost their jobs and an email from the CEO viewed by Gizmodo. What’s more, the former SoundHound employees are in for a rough landing. Their severance package includes no healthcare and just two weeks of severance, and that’s only if SoundHound can raise more money, the three laid off staffers told Gizmodo. If the company can’t secure additional funding, the former employees said they don’t know what will happen to them.
An ex-employee summed up the state of affairs in one word: “Pitiful.”
“Im actually quite shocked by the way the layoffs were handled,” one of the laid off employees told Gizmodo. “I was expecting a 17 year old company, which is now a public company, to at least provide bare minimum severance.”
The severe cuts come just two months after another round of layoffs that saw 10% of the company’s workforce eliminated and others burdened with salary reductions. SoundHound’s workforce numbered 450 prior to the layoffs, but staff has been cut more than 50% in just three months, according to the former employees.
In his email to staff, SoundHound co-founder and CEO Keyvan Mohajer described a period of rapid growth in 2021 but said changing market conditions were forcing it to re-evaluate its financials and accelerate its path to profitability in 2023.
“When we set course in early 2021 to become publicly listed, high tech companies like SoundHound were the darlings of the investor community,” Mohajer wrote. “Companies who could achieve high growth, despite high costs, were seen as engines of a future economy. However, as a result of changing economic conditions, including high interest rates, rising inflation, and fears of recession, companies with our profile became much less desirable.”
SoundHound did not respond to Gizmodo’s multiple requests for comment.
Mohajer added personal reflection on meetings with investors to his email: “As I reflect on the feedback I received from the investor community during countless meetings over the last several months, a recurring theme was that they loved our technology, vision, perseverance, talented team, and business strategy.”
Mohajer finished with a line that’s sure to be cold comfort to both the remaining employees and those he let go: “But they were concerned that SoundHound does not have the heart to let go of its people.”
The CEO seems to have found the “heart” to lay off his people.
Though many other of the estimated 107,000 tech workers who lost their jobs as part of the recent tech downturn were provided comfortable exit packages, the laid off SoundHound employees speaking with Gizmodo said that wasn’t true for them. In their case, the former employees said they would receive only two weeks severance and no health care, with one employee adding SoundHound human resources said the severance was contingent on the company raising additional funding. The employees asked to remain anonymous so as not to jeopardize what severance they could receive.
SoundHound’s trajectory is a familiar one in the tech industry this year. Companies as large as Amazon, Snap, and Meta scaled up their workforces in 2020 following the onset of the coronavirus pandemic only to face major economic headwinds in late 2022 and 2023. Amazon nearly doubled the size of its layoffs from 10,000 to 18,000 employees last week.
Founded in 2005 by a group of Stanford graduates, SoundHound has become a major provider of AI voice software used by a variety of household names. Music streaming service Pandora, for example, uses SoundHound’s platform to let customers pause, play and skip songs using only their voice. Snap uses SoundHound’s tech to provide real-time automatic captioning services in its Voice Scan camera search feature. The company also has a pair of mobile apps called SoundHound Music and Hound, which are used for music discovery like Shazam and voice search assistance, respectively. SoundHound went public via a SPAC in early 2022. The company’s customers include Mercedes-Benz, Snap, Netflix, and other brands spanning multiple industries.
“It’s really disappointing to see SoundHound’s once bright future ruined by executive decisions made in the wake of going public,” one of the laid off workers told Gizmodo. “While the outcome of going public was not easily foreseen when the process was begun, as it became clear that far less cash would be raised than anticipated steps should have been taken to preserve cashflow rather than the unbridled hiring which took place.”
The major layoffs come less than two months after the company reportedly moved to cut around 10% of staff. In addition to the layoffs, an unknown number of staff at the time also experienced salary cuts, some as high as 20%, according to one of the former employees.
| 2023-01-13T00:00:00 |
2023/01/13
|
https://gizmodo.com/soundhound-lays-off-half-employees-pitiful-severance-1849987534
|
[
{
"date": "2023/01/13",
"position": 41,
"query": "AI layoffs"
}
] |
4 Ways Artificial Intelligence Will Disrupt Business as We ...
|
4 Ways Artificial Intelligence Will Disrupt Business as We Know It
|
https://www.sciforma.com
|
[] |
According to a MIT Sloan Management Review and Boston Consulting Group report, 9 out of 10 business leaders agree that AI represents a business opportunity for ...
|
According to a MIT Sloan Management Review and Boston Consulting Group report, 9 out of 10 business leaders agree that AI represents a business opportunity for their company. There’s every indication that this is much more than wishful thinking. As intelligent technologies with mind-blowing analysis power offer to assist with or fully take over a number of tasks and business processes, they are changing the ball game radically and opening up new value streams and chains. Here are some of the most disruptive impact areas of AI adoption across businesses worldwide.
A Bit of History
Artificial Intelligence is both old and fresh news. While the concept has been around for decades, it proved a challenge to find practical, scalable applications in business and industry. Accordingly, AI technology was pretty much confined to the lab until recently.
Today, increased technology maturity translates into greater processing power and declining cost. Increasingly user-friendly solutions are placing AI into the hands of business users. Rapid AI adoption looks like it will revolutionize the way we design, produce, and market products and services, and the way we do business as a whole.
AI: The Ultimate Waste Killer
At the core of AI’s value proposition is its staggering ability to process terabytes of data from a wide variety of different sources and to make sense out of it. As it reconciles millions of data fragments into a coherent image, AI enables users to address complexities which the human brain is not equipped to embrace.
The most obvious of AI applications and use cases is perhaps process streamlining. By connecting all the data dots and streams, machine and deep learning models are enabling operational automation that translates into huge efficiency gains. From Quality Control and predictive maintenance of productive systems to ongoing process optimization based on sound historical data and to fraud detection and prevention, AI is offering business leaders the tools they need to improve productivity and cut operating costs.
AI’s contribution is particularly promising when it comes to resource efficiency. Better energy use and opportunistic strategies to leverage fluctuations in intermittent energy availability have the potential to drive a dramatic reduction in resource consumption and waste. Big Data and AI technologies have been blamed for heavy carbon impact. But, as a matter of fact, a report by PwC and Microsoft suggests that AI could enable a 1.5% to 4% cut in global greenhouse gas emissions by 2030 across four key industries (agriculture, energy, transport, and water).
A Game-Changer in Customer Relations
In addition to bearing the potential for substantial improvement in customer satisfaction through shorter time-to-market, quality consistency, and increased ability to control supply chains and meet delivery commitments, AI is empowering businesses to forge meaningful connections with each of their customers.
Artificial Intelligence engines will examine and analyze customer journeys based on data from off- and online interactions, reviews, emails, phone calls, social media interactions, and more. The idea is to help you understand who your customers are, what their needs, expectations, and triggers are, and when and how to address them most effectively. AI-powered recommendations will help your marketing teams to fine-tune their strategies in order to boost up-sell or cross-sell and ultimately increase customer value and profitability. Businesses can also leverage AI to detect potential churn indicators early on and take timely action in order to keep your valuable customers engaged and loyal to your brand.
In sum, superior understanding of and control over the customer experience is enabling business leaders to improve it by orders of magnitude.
The Workplace Will Never Be the Same
Productivity gains and benefits in market position may be the tip of the AI benefit iceberg, while organizations often overlook the potential impact on internal organizational dynamics.
The automation and digitalization of administrative and repetitive tasks are driving a qualitative change in individual employee jobs — not only for manufacturing operatives, but also for health professionals, for train drivers, for accountants, for video editors… you name it. Jobs that used to involve a lot of lower-value-added work are increasingly focused on planning, coordination, oversight, analysis, and decision-making. When the work is manual in nature, it increasingly is augmented by smart machines.
AI adoption is also changing all organizational workflows and processes, from Research & Development to sourcing, from production to marketing… and to HR Management. Greater employee empowerment requires new approaches to talent recruitment and management, as the desired skills evolve from compliance to out-of-the-box-thinking, from operational cooperation to creative collaboration.
Importantly, the capability of AI-driven tools to abolish asymmetries of information across teams is providing to everyone a clear line of sight to the priorities of the business. Thus driving internal alignment and empowering the whole business to act and work as one.
AI for Business Agility at Scale
The automation of data handling tasks and processes is eliminating the human error factor to enable sound and accurate analysis, guaranteed free from bias.
AI will automatically and intelligently detect any anomalies or deviations and leverage advanced modeling capabilities to find the best response plan. It will observe patterns in order to predict the impact of constantly shifting variables within highly complex systems and better anticipate outputs. Long story short, AI is opening up the opportunity to move from reactive to proactive management.
Since patterns repeat themselves, AI systems can keep a record of past situations and decisions in order to build up the “institutional memory” and accelerate the replication of best practices. Finally, more and more of today’s enterprise AI systems are leveraging deep learning tech’s uncanny ability for self-improvement to optimize and evolve continuously.
To wrap it up, everything indicates we will witness a growing gap between the businesses who embrace disruptive AI technology and those who don’t. Which side are you on?
Read more about PPM:
| 2023-01-13T00:00:00 |
https://www.sciforma.com/blog/4-ways-artificial-intelligence-will-disrupt-business-as-we-know-it/
|
[
{
"date": "2023/01/13",
"position": 16,
"query": "artificial intelligence business leaders"
}
] |
|
Can companies use the power of AI for positive impact?
|
Can companies use the power of AI for positive impact?
|
https://neosfer.de
|
[
"Dennis Eberlein Und Filia Novak"
] |
Perhaps you have heard of companies like OpenAI, Arize AI, Clari or Databricks or tools like Dall-E or ChatGPT. All of these companies have three things in ...
|
1. Corporate strategy & AI strategy
When evaluating AI as a technology for an organization’s digital transformation, there is a tendency to focus on the technical research rather than the business-strategic research. Studies indicate that the companies that manage to align their roadmap of use cases for AI applications with the bigger picture of their corporate strategy appear to be more successful in generating AI-added value. Given that AI strategy is introduced into the value chain design with the goal of ultimate optimization, it has to resonate with the main strategy defined by an organization to deliver upon that objective. Before implementing AI as a subset of the corporate strategy, it is essential to address a few questions in regard to how it can best maximize the overall value proposition. Some key considerations should include a) assessing the impact of AI adoption on core competencies of a company, as well as b) the elaboration of the new potential competencies it will create. Furthermore, it is important to c) define the main aim of introducing the AI strategy in terms of its effect on growth, profitability, or risk minimization. Overall, when developing the AI approach, the key points to address should include the long-term goal of its implementation and impact across all business fields, essentially tying it together with the corporate strategy.
2. Archetypal AI use case level
The framework suggests that the next phase of the strategic AI model will be focused on composing a collection of archetypal AI use cases. This segment of the model has to be open for continuous development, given that technological progress promotes the variety, opening up the opportunity for consistent additions. Given that each use case is unique in terms of the AI capabilities and synergy of technologies, it is assumed that each use case depends on several variables. Foremost, a company has to define which type of AI is applicable for each use case. To answer that question, they will have to consider its main desired function in terms of assessment, deduction, or reaction. At this stage, it is also important to decide whether the goal of the AI application is to imitate human behavior or generate its own form of rationality. To further break down the use case collection into more specific clusters, other factors to consider include the means of value creation, tackled problems, infrastructure requirements to provide the right input for the expected output. In terms of resources, factors such as time, capacity, and scalability have to be assessed to provide a relevant preselection of use cases. Overall, to reinstate the alignment with the corporate strategy, it is recommended to approach this phase of AI implementation with the idea of interconnectedness in mind — look out for the possible synergies, dependencies, exclusions, and redundancies.
3. AI-architecture level
Executing the digital architecture management for the AI infrastructure level follows a few essential steps. A status quo analysis should be one of these initial steps and will normally include an overview of the existing technological infrastructure and data environment. For the next step, AI infrastructure can be divided into more general views, with each few further composed of several design fields. For instance, some design fields might include cybersecurity, ethics and legal, organizational structure and data governance. These design fields correlate with the previously mentioned clusters of AI use cases and will require conducting an overall value and cost analysis. By taking all this information into consideration, an organization should be able to make a constructive decision about the selection of their AI applications. As the last step of the AI infrastructure management refers to customization and prioritization of the defined use case clusters which will help a company design a clear road map for AI implementation
4. Ethical considerations
Ethical considerations are important for the future development of AI! Our framework does not prioritize ethical considerations behind purely technical or business aspects of the development strategy. But the ethical considerations are rather seen as the final check for the overall roadmap and finalize our holistic framework for actually creating ethical AI.
To develop technologies and methodologies underlying successful AI adoption, it is essential to be mindful of the ethical principles and the components of trustworthy AI.
The 5 prinpicles of ethical AI
To develop technologies and methodologies underlying successful AI adoption, it is essential to be mindful of the ethical principles and the components of trustworthy AI. To pave the way towards ethical and trustworthy AI applications, the European Commission appointed the Artificial Intelligence High-Level Expert Group in 2018. In turn, the AI HLEG created five ethical principles an ethical and trustworthy AI system should follow and be guided by.
1. Principle of Beneficence
First, there is the Principle of Beneficence, which says that the AI system should do good. The AI HLEG proposes that the system should be developed and designed to improve individual and collective wellbeing by contributing towards a higher goal, e.g., the achievement of the UN Sustainable Development Goals that are promoting an ethical and sustainable future.
2. Principle of Non-Maleficence
The second principle is the Principle of Non-Maleficence, which focuses on the aspect that an AI system should protect the dignity, liberty, privacy, safety, and security of all users and all parts of society as well. The principle deeply reflects the idea of social justice for all parts of society.
3. Principle of Autonomy
As a third principle, the AI HLEG list the Principle of Autonomy. So, humans who are interacting with AI must keep full self-determination when being in contact with AI. There should be an opt-in or opt-out option when being exposed to AI.
4. Principle of Justice
The Principle of Justice is the fourth principle the AI HLEG is listing. With an AI system being fair, it ensures that individuals and minority groups maintain free from biases and discrimination. Additionally, the positive and negative results from AI should be distributed evenly throughout different parts of society.
5. Principle of Explicability
The fifth and final principle is the Principle of Explicability, and it speaks about transparency as the key ingredient for building trust in an AI system. Transparency implies that the AI system needs to be auditable, comprehensible, and intelligible by humans.
| 2023-01-13T00:00:00 |
2023/01/13
|
https://neosfer.de/en/ai-for-good-how-companies-can-harness-the-power-of-ai-for-positive-impact/
|
[
{
"date": "2023/01/13",
"position": 79,
"query": "artificial intelligence business leaders"
}
] |
Forget ChatGPT, the Smart Computer Was Coming For ...
|
Forget ChatGPT, the Smart Computer Was Coming For Your Job in 1962 — Paleofuture
|
https://paleofuture.com
|
[
"Matt Novak"
] |
OpenAI's ChatGPT, the artificial intelligence program that's seen enormous hype in tech circles over recent weeks, is being billed as a tool that will make ...
|
The G-20 was a vacuum-tube computer that weighed about 2,000 pounds. Needless to say, the phone you carry around in your pocket every day is many times faster and more powerful, even if the computers from the 1960s were lightyears ahead of the mechanical computers from the 1940s.
The fear of automation seemed to accelerate in the 1950s and 60s, as computers started to be introduced into American business. In fact, the January 4, 1959 issue of Parade magazine worried that automation would do away with Americans needing to labor, causing an epidemic of boredom that would cause a huge spike in the suicide rate. It went without saying that these same people assumed everyone would be living comfortable lives of leisure in the future. Mainstream prognosticators predicted as much in the 1960s. The only question is whether humans would be able to find enough to do with themselves to entertain themselves and find meaning in life through things outside of work.
“Technology is opening a new world of leisure time. One government report projects that by the year 2000, the United States will have a 30-hour work week and month-long vacations as the rule,” legendary newsman Walter Cronkite told TV viewers in 1967.
But those productivity gains achieved through automation didn’t cause workers to see a kind of worker’s utopia in the second half of the 20th century. Instead, companies spent more money on executive payouts and stock buyback programs, leaving workers with a smaller piece of the pie. That may have changed, however slightly in recent years, with workers seeing a bump in wages, even if it’s not enough to keep up with inflation. But the Federal Reserve is doing everything it can to push people into unemployment, to get wages down and to keep inflation at bay.
The Fed has lots of work to do, if the latest jobs numbers are to be believed. Too many people have jobs and they’re still demanding too much money at those jobs, according to the ruling class. Better keep hiking interest rates.
Love Paleofuture? Subscribe to Paleofuture+ to get exclusive articles, rare videos, and access to a members-only chat.
| 2023-01-13T00:00:00 |
2023/01/13
|
https://paleofuture.com/blog/2023/1/13/forget-chatgpt-the-smart-computer-was-coming-for-your-job-in-1962
|
[
{
"date": "2023/01/13",
"position": 60,
"query": "artificial intelligence wages"
}
] |
16 Highest Paying Engineering Jobs of 2025
|
16 Highest Paying Engineering Jobs of 2025
|
https://educatingengineers.com
|
[
"Educating Engineers Editorial Team",
"Computer Hardware Engineers Play A Crucial Role In Designing",
"Testing The Physical Components That Power Modern Computing Systems. Processors To Memory Devices And...",
"Food Engineering Is A Dynamic",
"Interdisciplinary Field That Combines Principles Of Engineering",
"Microbiology",
"Food Science To Enhance Food Production",
"Safety",
"Sustainability. Food...",
"Metallurgical Engineering Is A Specialized Field Focused On Developing"
] |
Salary and Job Outlook. The median annual pay for computer hardware engineers in 2023 was $138,080, which amounts to $66.38 an hour, according to the latest ...
|
The path to becoming an engineer comes with a high level of commitment. An engineering-focused education can begin as early as STEM classes in junior high, and it often continues throughout an engineer’s career. Despite the dedication required to become an engineer, many are still drawn to the field because they want a career that involves creative problem-solving while also offering a healthy work-life balance and a competitive salary.
Below is a curated list of the highest paying engineering careers, shedding light on those fields that offer an enticing blend of financial remuneration, professional growth, and the chance to be at the forefront of cutting-edge advancements.
16 Highest Paying Engineering Jobs
Engineering degrees can lead individuals into a wide range of professions. Some of the highest paying engineering careers include the following roles.
1. Computer Hardware Engineers
A computer hardware engineer designs and develops computer systems and their related components. People in this profession tend to work independently in an office-type setting in computer design, research, or manufacturing.
Salary and Job Outlook
The median annual pay for computer hardware engineers in 2023 was $138,080, which amounts to $66.38 an hour, according to the latest data from the U.S. Bureau of Labor Statistics (BLS). The states where computer hardware engineers earned the highest salaries included California, Massachusetts, Virginia, Oregon, and Washington. California, Oregon, and Massachusetts are also top employers of hardware engineers.
Currently, the hardware engineering profession offers about 78,100 jobs, with 5% growth projected between 2022 and 2032, which is faster than the average growth for all jobs combined (3%).
Requirements
Those applying for a position of computer hardware engineer must hold a bachelor’s degree in computer engineering or a related field. Engineering professionals in a range of roles can advance their careers by pursuing a master’s degree, gaining professional engineering licensure, earning industry certifications, and gaining several years of work experience.
2. Petroleum Engineers
The job of the petroleum engineer involves designing and developing extraction methods for oil and gas deposits. Petroleum engineers usually travel to the appropriate extraction sites and meet with their colleagues, but may also work in an office setting.
Salary and Job Outlook
The median compensation for a petroleum engineer was $135,690 per year or $65.23 per hour in 2023, according to the BLS. The top paying states for petroleum engineers included New Jersey and Minnesota, as well as Colorado, Louisiana, and Texas, which also have high employment levels for petroleum engineers.
In 2022 the industry provided approximately 21,400 jobs, with a job growth projection of 2% between 2022 and 2032.
Requirements
To become a petroleum engineer, you need a bachelor’s degree in petroleum engineering or a similar area of study such as mechanical, chemical, or civil engineering. Ongoing education is also required.
3. Aerospace Engineers
The exciting occupation of aerospace engineer involves designing aircraft, spacecraft, missiles, and satellites. An aerospace engineer may find employment with federal government, research and development, analysis, design, or manufacturing entities.
Salary and Job Outlook
The BLS reports that an aerospace engineer’s median annual salary in 2023 was about $130,720, or $62.85 an hour. Top paying aerospace engineering states that year included Maryland, Washington, Colorado, California, and Washington, DC.
Currently, the United States employs about 63,800 of these professionals, with a faster-than-average 6% growth rate over the decade.
Requirements
This position requires a bachelor’s degree in engineering or another related scientific field. Additionally, the applicant may need a security clearance if the job is with national defense.
4. Software Engineers
Software engineers design computer programs for computer firms, manufacturers, and software publishers. Software engineers usually work with team members in an office environment.
Salary and Job Outlook
The median annual salary for the BLS category of software developers, quality assurance analysts, and testers, which includes software engineers, was about $130,160, or $62.58 an hour, in 2023. Software developers typically make considerably more than testers or quality analysts. The states with the highest salaries for software developers included California, Washington, Maryland, New York, and Massachusetts.
Nearly 1.8 million people hold jobs in this category, according to the BLS. This occupation is projected to see a 25% increase between 2022 and 2032.
Requirements
To obtain a job as a software engineer, you will need a bachelor’s in computer information technology.
5. Nuclear Engineers
Nuclear engineers address problems concerning the release and control of nuclear energy and nuclear waste disposal through research and development projects. Typically, a nuclear engineer works in an office setting.
Salary and Job Outlook
The 2023 median annual salary for nuclear engineers was $125,460 or $60.32 an hour, according to the BLS. Top paying states for nuclear engineers included New Mexico, New York, and Washington, DC.
The industry employs some 13,800 professionals, and the decade is projected to bring 1% growth in the profession.
Requirements
To become a nuclear engineer, you must obtain a bachelor’s in nuclear engineering or a related field.
6. Chemical Engineers
Chemical engineers design processes for manufacturing products such as paper and detergents using chemistry, engineering, and physics principles. Chemical engineers may work in an office, laboratory, or industrial setting.
Salary and Job Outlook
The 2023 median annual salary for this profession was $112,100, or about $53.90 an hour, according to the BLS. The states with the highest salaries for chemical engineers included Texas, New Mexico, and Oklahoma.
Currently, the positions held are about 20,800 with 8% growth expected over the decade.
Requirements
Minimally, you need a bachelor’s degree in chemical engineering or a related field to obtain a position. Many employers also require that you complete an internship or demonstrate some form of practical experience.
7. Electrical and Electronics Engineers
The job of an electrical engineer involves designing, developing, testing, and supervising the production of electrical products such as motors, navigation systems, and communications systems. They may work primarily in an office setting with occasional site visits to observe production.
Salary and Job Outlook
The median annual salary for electrical and electronics engineers was $109,010 in 2023, or about $52.41 per hour. Top paying states included California, New Mexico, and New Hampshire.
There are 299,700 professionals in the field in the U.S., and the profession is projected to grow 5% between 2022 and 2032, according to the BLS.
Requirements
You need a bachelor’s degree in electrical engineering and an internship or other practical experience to obtain a position.
8. Materials Engineers
Materials engineers are involved in developing, testing, and processing materials used to make many different products, including computer chips, medical devices, and sports equipment. They examine properties of materials such as plastics, metals, and ceramics, working in offices and research laboratories.
Salary and Job Outlook
The median annual salary of materials engineers in 2023 was approximately $104,100, or $50.05 an hour, according to the BLS. Top paying states for materials engineers included Maryland, Washington, and Rhode Island.
Some 22,300 positions exist in the field, with an expected growth rate of 6% over the decade.
Requirements
Materials engineers need a bachelor’s degree in materials science or a related engineering field. An internship or practical experience may also help one obtain employment.
9. Health and Safety Engineers
Health and safety engineers apply engineering, health, and safety principles to design systems and procedures that help prevent accidents, illnesses, and property damage. Most of their time is spent working in an office, although travel sometimes becomes necessary to inspect various work sites.
Salary and Job Outlook
The 2023 median annual salary for this position was just over $103,690, or about $49.85 per hour. The states with the highest salaries for health and safety engineers included California, Louisiana, and Alaska.
About 22,000 jobs exist, with a growth rate of about 4% over the decade.
Requirements
To become a health and safety engineer, you will need a bachelor’s degree in environmental health and safety or an engineering discipline. Practical experience is also desirable.
10. Bioengineers and Biomedical Engineers
Biomedical engineers use their knowledge of engineering and science to develop medical devices and equipment, as well as related computer systems and software, with a focus on improving human health. These professionals often work in manufacturing or research facilities.
Salary and Job Outlook
The 2023 median annual salary for bioengineers and biomedical engineers was about $100,730, or $48.43 an hour. Top paying states for the profession included Massachusetts, California, and Texas.
There are 19,700 bioengineering and biomedical engineering positions in the U.S., with a 5% increase expected over the decade.
Requirements
Minimally you need a bachelor’s degree in biomedical engineering or bioengineering for employment in this field. Some positions require a graduate degree.
11. Mining and Geological Engineers
Mining and geological engineers ensure the safe removal of minerals for use in manufacturing and utilities by designing efficient mines. Mining and geological engineers work on-site in mineral mines, quarries, or oil and gas extraction firms.
Salary and Job Outlook
The median annual salary for mining and geological engineers was $100,640 in 2023, or about $48.39 an hour. Colorado, Alaska, and California were among the top paying states for the profession.
Only about 7,500 positions exist, with little or no growth expected over the next ten years. However, about 400 positions open yearly due to retirements and career changes.
Requirements
Minimally, a bachelor’s degree in mining and geological engineering or a related area of engineering is required to enter this field.
12. Marine Engineers
Marine engineers, who work closely with naval architects, design and build ships, aircraft carriers, submarines, and sailboats. Though these professionals primarily work in offices, sometimes their jobs require them to go to sea to test their creations.
Salary and Job Outlook
The median annual salary for marine engineers and naval architects was about $100,270 in 2023, or about $48.21 an hour. States with the highest wages included California, Maryland, and Washington, DC.
The 8,000 established positions will experience about a 1% growth rate in the next decade, but will experience about 400 openings per year.
Requirements
A marine engineer needs a bachelor’s degree in marine engineering or naval architecture.
13. Environmental Engineers
Environmental engineers work on developing solutions to environmental problems by incorporating the principles of engineering, biology, chemistry, and soil science. They work in offices or at construction sites on projects such as pollution control, waste treatment, water reclamation, and site remediation.
Salary and Job Outlook
The median annual salary in this field in 2023 was about $100,090, or $48.12 an hour. The states with the highest salaries for environmental engineers included Texas, Connecticut, and Oregon.
Approximately 47,300 jobs exist, with an expected growth rate of about 6% over the decade.
Requirements
To become an environmental engineer, you need a bachelor’s in environmental engineering or a related field, such as civil engineering.
14. Mechanical Engineers
Mechanical engineers design and develop mechanical and thermal devices and sensors, such as engines, machines, and tools. The broad field allows for work in fields ranging from healthcare and energy to transportation and robotics. Mechanical engineers may need to visit various work sites but usually work in offices.
Salary and Job Outlook
The 2023 median annual salary for mechanical engineers was $99,510, or $47.84 per hour. Top paying states included New Mexico, California, and Washington, DC.
The number of positions is about 286,100, with a faster-than-average growth rate of 10% percent between 2022 and 2032.
Requirements
A position as a mechanical engineer requires a bachelor’s degree in mechanical engineering or mechanical engineering technology.
15. Industrial Engineers
Industrial engineers create systems that bring together all aspects of creating a service or product, such as the workers, equipment, materials, energy, and information, with the goal of improving productivity and decreasing waste. Depending on the project, they may work in a factory or office.
Salary and Job Outlook
The median salary for an industrial engineer was about $99,380 annually, or $47.78 per hour, in 2023. Top paying states included Louisiana, California, and Hawaii.
Some 327,300 of these positions exist, with an above-average growth rate of 12% between 2022 and 2032.
Requirements
To become an industrial engineer, you need a bachelor’s degree in industrial, mechanical, general, or manufacturing engineering.
16. Civil Engineers
Civil engineers design, create, and supervise infrastructure and building projects, including facilities, roads, bridges, and water and energy systems. Civil engineers usually split their work time between an office and a location such as a construction site.
Salary and Job Outlook
The median annual salary for civil engineers was $95,890 in 2023, with a per-hour rate of $46.10. States with the highest wages for civil engineers included California, New York, and Alaska.
There are 326,300 positions in the field, which is expected to grow at the above-average rate of 5%.
Requirements
You need a bachelor’s degree in civil engineering for a position in this field.
Highest Paying Careers Within Engineering Fields
Highest Paying Computer Hardware Engineering Jobs
DevOps engineers are involved in website platform development and maintenance, and may be involved in cloud technologies. The median annual salary was $106,000 in 2024, according to Payscale.
are involved in website platform development and maintenance, and may be involved in cloud technologies. The median annual salary was $106,000 in 2024, according to Payscale. Hardware architects work with clients to create hardware that meets their specific needs in the way of hardware devices and models. The median annual salary was $130,000 in 2024.
Highest Paying Aerospace Engineering Jobs
Thermodynamic engineers use their knowledge to design, develop, and control thermodynamic testing systems. The annual salary ranged between $61,000 and $124,000 in 2024, with the median at $82,000, according to Payscale.
use their knowledge to design, develop, and control thermodynamic testing systems. The annual salary ranged between $61,000 and $124,000 in 2024, with the median at $82,000, according to Payscale. Flight test engineers work to ensure the safety of aircraft and spacecraft through the use of aviation technologies. The median salary was $96,000 in 2024.
To obtain an entry-level job in these positions, you need a BS in Aerospace Engineering. Experience and advanced degrees may result in higher-paying positions.
Highest Paying Software Engineering Jobs
Software architects create computer applications and programs. Their median salary was about $138,000 in 2024, according to Payscale, and they may earn as much as $180,000.
create computer applications and programs. Their median salary was about $138,000 in 2024, according to Payscale, and they may earn as much as $180,000. Back-end developers build and maintain to enable the user-facing side of a website to work. The salary for this position ranged from $63,000 to $143,000 in 2024, with a median salary of $97,000.
Highest Paying Mechanical Engineering Jobs
Automotive engineers work with vehicles such as cars and motorcycles. They build prototypes, analyze data, and determine viable budgets while projecting timelines. The median national salary for this position was about $84,000 in 2024, according to Payscale.
work with vehicles such as cars and motorcycles. They build prototypes, analyze data, and determine viable budgets while projecting timelines. The median national salary for this position was about $84,000 in 2024, according to Payscale. Instrumentation engineers’ purpose involves designing, building, testing, and maintaining systems to ensure that other engineering systems function smoothly. The median salary was about $94,000 in 2024.
Highest Paying Electrical Engineering Jobs
Semiconductor engineers design semiconductors and other electronic components. The salary ranged from $65,000 to $114,000 in 2024, with a median salary of $87,000, according to Payscale.
design semiconductors and other electronic components. The salary ranged from $65,000 to $114,000 in 2024, with a median salary of $87,000, according to Payscale. Directors of electrical engineering are responsible for overseeing teams of electrical and electronics engineers. The median salary for the role was about $128,000 in 2024.
Highest Paying Civil Engineering Jobs
Civil engineering managers boast a salary range of $79,000 to $179,000, with a median salary of about $97,000 in 2024, according to Payscale. These individuals will possess the experience to hire, train, and analyze the performance of other engineers. They also bear the responsibilities of developing policies and ensuring quality control.
boast a salary range of $79,000 to $179,000, with a median salary of about $97,000 in 2024, according to Payscale. These individuals will possess the experience to hire, train, and analyze the performance of other engineers. They also bear the responsibilities of developing policies and ensuring quality control. Vice president of engineering is an executive position that influences a company’s bottom line through strategic short- and long-term planning and collaboration with other leaders. The annual salary range for this type of position falls between $127,000 to $251,000, with a median salary of $182,000.
Highest Paying Chemical Engineering Jobs
Research and development chemical engineers apply chemical processes to develop commercial and industrial products. The salary range for this type of position falls between $60,000 and $123,000 annually, with $89,000 as the median annual salary.
apply chemical processes to develop commercial and industrial products. The salary range for this type of position falls between $60,000 and $123,000 annually, with $89,000 as the median annual salary. Senior chemical engineers take on the role of the senior or leader of the team. These individuals monitor the performance of other team members and ensure quality. The median annual salary for the role was $118,000 in 2024.
Highest Paying Industrial Engineering Jobs
Industrial engineering managers take charge of testing, maintaining, and troubleshooting industrial processes. The median annual salary for these managers was $107,000 in 2024.
take charge of testing, maintaining, and troubleshooting industrial processes. The median annual salary for these managers was $107,000 in 2024. Manufacturing engineers plan, coordinate, and direct the manufacturing processes for a wide range of products. The annual salary for these professionals was $77,000 in 2024.
Highest Paying Engineering Degrees
Some of the degrees that bring in high salaries include the following, according to Payscale:
Bachelor of Science in Electrical Engineering: $99,000
Bachelor of Science in Computer Engineering: $96,000
Bachelor of Science in Aerospace Engineering: $90,000
Bachelor of Science in Mechanical Engineering: $86,000
Bachelor of Science in Civil Engineering: $78,000
Engineers often pursue master’s degrees to advance into senior engineer or leadership positions. Graduate degrees with lucrative salaries include the following:
Master of Science in Electrical Engineering: $110,000
Master of Science in Computer Engineering: $109,000
Master of Science in Mechanical Engineering: $97,000
Master of Science in Aerospace Engineering: $96,000
Master of Science in Civil Engineering: $79,000
Top 5 Factors That Can Influence an Engineering Salary
Understanding the factors that impact an engineering salary is crucial for professionals entering the field and those considering career advancement. Salaries in engineering can vary significantly based on several key factors. Here, we explore the top five determinants of earning potential in the engineering sector.
1. Level of Education and Specialization
The level of education and areas of specialization significantly influence an engineer’s salary. Generally, higher educational qualifications, such as a master’s degree or doctorate, command higher salaries due to the advanced knowledge and specialized skills they confer. For instance, engineers with a Master of Science in Electrical Engineering often earn more than those with a bachelor’s degree due to their deeper expertise in complex electrical systems.
Furthermore, engineers who specialize in high-demand fields like artificial intelligence or renewable energy can negotiate higher pay, reflecting the specialized skills and scarcity of qualified professionals in these areas.
2. Industry Choice
The industry an engineer chooses to work in can greatly affect their salary potential. High-tech industries, such as aerospace, pharmaceuticals, and information technology, typically offer higher salaries compared to traditional sectors like civil engineering or manufacturing.
This disparity is due to the varying economic impact and revenue generation capabilities inherent to each sector. For example, engineers in the semiconductor industry are among the highest earners due to the industry’s robust growth and the complex nature of their work.
3. Geographic Location
Geographical location plays a critical role in salary variations. Engineers in urban centers or regions known for their technological industries, such as Silicon Valley or Boston, tend to have higher salaries due to the high cost of living and concentration of tech companies in these areas.
Conversely, engineers in rural areas or regions with less economic activity may face lower salary offers. Additionally, international opportunities can offer varied compensation, with countries like Switzerland and Germany often providing high salaries to attract skilled engineers.
4. Professional Experience
Experience is a key factor in determining an engineer’s salary. As engineers gain more professional experience, they develop a deeper understanding of their field, enhance their problem-solving abilities, and accumulate a track record of successful projects, which can lead to higher earnings.
Typically, an engineer with over ten years of experience can expect a significantly higher salary than someone just starting out. This progression reflects not only their increased capabilities but also their growing responsibilities within their roles.
5. Current Market Demand
Market demand for specific engineering disciplines can heavily influence salary levels. Engineers skilled in rapidly growing sectors, such as cybersecurity, data science, and sustainable engineering, are in high demand, and this scarcity drives up salaries.
Market trends, such as the push towards digital transformation across industries, have heightened the need for engineers who can navigate and lead in these new realms, thereby boosting the salary potential for those with the relevant skills.
Is the Highest Paying Salary Always the Best Career Opportunity for Engineers?
When it comes to engineering careers, the allure of a high salary is undeniable. However, the highest paying opportunities may not always align with an engineer’s long-term career satisfaction and personal goals.
Financial Rewards vs. Job Satisfaction
While a high salary can provide financial security, job satisfaction encompasses various other aspects including work environment, company culture, and personal interest in the work being done. Engineers often face the choice between lucrative positions in high-pressure, competitive environments and less financially rewarding roles that offer more creative freedom or better work-life balance.
For example, a software engineer might choose a lower-paying job at a nonprofit organization developing community-oriented projects over a more lucrative position in a high-stress tech startup. Ultimately, personal values and career aspirations play critical roles in determining what makes a job genuinely rewarding.
Career Longevity and Growth
Long-term career growth and stability are also essential to consider beyond the initial salary offer. Some high-paying positions may not offer much room for advancement, leading to career stagnation. On the other hand, a position with a moderate starting salary in a company known for nurturing talent and promoting from within can offer a more rewarding career trajectory.
Engineers should assess the potential for professional development, continued learning opportunities, and the possibility of ascending to leadership roles, which could ultimately lead to higher lifetime earnings and job satisfaction.
Work-Life Balance
The demands of high-paying engineering jobs can often encroach on personal time, impacting an engineer’s quality of life. Jobs in cutting-edge tech companies or demanding industries like oil and gas extraction might come with long hours, significant travel, or intense project deadlines, which could be a trade-off for the high salary.
Engineers must weigh whether the compensation is worth the potential sacrifice in terms of health, family time, and overall well-being. Engineers need to consider how a job aligns with personal life goals and family commitments.
Explore Opportunities in Lucrative Engineering Fields
While the potential for a high salary is significant in the engineering field, understanding the factors that influence earning potential is key to making informed career decisions. Engineers should consider not only the immediate financial benefits but also long-term career satisfaction and growth opportunities when evaluating job offers. By understanding these factors, engineers can strategically position themselves to maximize their career fulfillment and financial success.
Deciding whether the highest paying salary is the best career opportunity requires a holistic view of one’s career and personal life. Engineers should consider how different roles align with their long-term professional goals, personal values, and desired lifestyle. Financial compensation is undoubtedly important, but it’s the balance of all these factors that truly defines career fulfillment.
Engaging in thorough research, seeking mentorship, and introspective consideration are vital steps in making a choice that leads to both professional success and personal happiness. Encouragement for further exploration and consultation with career advisors is recommended to help engineers make the most informed decision.
Related Articles
| 2023-01-13T00:00:00 |
https://educatingengineers.com/careers/highest-paying-engineering-jobs/
|
[
{
"date": "2023/01/13",
"position": 76,
"query": "artificial intelligence wages"
}
] |
|
Wes Frick on AI Uses and Limits in Ad Design
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Wes Frick on AI Uses and Limits in Ad Design
|
https://billboardinsider.com
|
[] |
Designers that actually know how to draw, know how to illustrate and use software manually, and who have designed out of home for years before AI can also ...
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This article was written by a human. A disclaimer like this may be needed in the future. Artificial Intelligence (“AI”) will disrupt out of home advertising by automating certain tasks and by providing easy access to information.
Ask Chat GPT-3 these prompts and see what you get:
What are the top 50 best practices in effective billboard design?
Write to me 20 reasons why billboards are an effective advertising medium?
Write 30 7-word creative headlines for a personal injury attorney’s billboard?
Write 30 7-word creative headlines for a Valentine’s Day promotion for a billboard company?
AI art is a powerful tool that both clients and artists can use to bring complex ideas to life. But art made by humans is more valuable because people connect with it more, and human artists have the most control. Designers that actually know how to draw, know how to illustrate and use software manually, and who have designed out of home for years before AI can also create dynamic concepts by hand, controlling every single detail of the design and formulate their idea as originally intended.
Is AI a threat to the creatives working in the out of home advertising industry and will it replace graphic designers and take their jobs? The answer is yes and no.
Reasoning for Yes: For beginner creatives that do not know how to draw as well or illustrate as well, and for creatives that do not want to embrace AI technology, you will quickly be at a great disadvantage. Once AI starts to automate the use of text, you could very easily generate 50 unique billboard designs for a client. Will companies replace graphic designers this way? Probably.
Reasoning for No: For advanced creatives that have mastered their craft in the out of home advertising industry, we do not need AI to formulate ideas. We can very easily beat it with powerful, authentic, human artwork. Additionally, advanced creatives that know how to do that and also adapt AI technology into their workflow for ideation and custom imaging are an exponential threat to other artists. It’s not necessarily AI art that is a threat, it is other artists that use this exponential technology to make artwork.
This ad was generated without AI:
The ring in this ad was generated using AI
The homeless face in this ad was generated with AI.
[wpforms id=”9787″]
Paid Advertisement
| 2023-01-13T00:00:00 |
2023/01/13
|
https://billboardinsider.com/wes-frick-on-ai-uses-and-limits-in-ad-design/
|
[
{
"date": "2023/01/13",
"position": 55,
"query": "artificial intelligence graphic design"
}
] |
"Transforming Industries: The Impact of AI and Automation"
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"Transforming Industries: The Impact of AI and Automation"
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https://cmptechnologiesllc.com
|
[
"Cmp Technologies Llc"
] |
However, it's also important to consider the potential consequences of these technologies on the workforce, such as job displacement, and take proactive steps ...
|
Customer Specific Robot Training Customer Specific Robot Training Customer Specific Robot Training
Are you looking to take your robotics skills to the next level? Look no further than CMP Technologies, LLC! Our expert team provides top-notch robot programming training services to help you master the skills you need to succeed in today's technology-driven market.
We specialize in programming for industry-leading brands such as ABB, Fanuc, Kawasaki, and Yaskawa Motoman, and our training programs are tailored to your specific needs and skill level. Whether you're a beginner looking to learn the basics or an experienced professional looking to expand your expertise, we have a program that's right for you.
Our training courses are led by experienced professionals with years of hands-on experience in the field, so you can be sure you're getting the best education possible. And with flexible scheduling and a variety of course options to choose from, it's never been easier to get the training you need to advance your career.
Don't miss out on this opportunity to take your robotics skills to the next level. Sign up for a training course with CMP Technologies, LLC today and start building the future you want!
We can design a training program for you specific needs.
| 2023-01-14T00:00:00 |
https://cmptechnologiesllc.com/f/transforming-industries-the-impact-of-ai-and-automation
|
[
{
"date": "2023/01/14",
"position": 61,
"query": "automation job displacement"
}
] |
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Is AI going to take my Job?☹️
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Is AI going to take my Job?☹️
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https://becominghuman.ai
|
[
"Akalbir Singh Chadha"
] |
In this blog, we'll take a closer look at how AI is likely to impact the job market in different industries, highlighting the potential benefits and challenges ...
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Is AI going to take my Job?☹️ Akalbir Singh Chadha 4 min read · Jan 14, 2023 -- Listen Share
Artificial intelligence (AI) and its various applications, such as ChatGPT (if you don’t know about Chat GPT, check out my post), are rapidly transforming the way we live and work. As technology continues to advance, it’s important to understand the ways in which it will affect the future of jobs across different sectors. In this blog, we’ll take a closer look at how AI is likely to impact the job market in different industries, highlighting the potential benefits and challenges for each sector.
Healthcare Industry
First, let’s take a look at the healthcare industry. According to a recent study by Deloitte, the use of AI in healthcare could lead to a savings of up to $150 billion per year by 2026. This is because AI can help with things like making medical diagnoses, analyzing images, and finding new drugs. AI can be used to look at medical images and find early signs of cancer or other diseases. This means that people can get treatment sooner and have better outcomes. AI can also be used to help find new medicines by looking at a lot of data to find possible new treatments.
However, the use of AI in healthcare also poses some challenges. For example, it's important to make sure that AI systems are designed and used in a fair and unbiased way, especially when it comes to diagnosing and treating certain conditions. There may also be a need for new rules and oversight to make sure that AI in healthcare is safe and effective. Additionally, AI can be used to create virtual assistants that can help patients manage their health, schedule appointments, and communicate with their healthcare providers. According to a report by the World Economic Forum, the healthcare sector is projected to create 2.3 million jobs related to AI by 2026.
Manufacturing Sector
Another industry that is likely to be impacted by AI is the manufacturing sector. According to a recent study by McKinsey, the use of AI in manufacturing could lead to savings of up to $3.7 trillion per year by 2030. This is due to the ability of AI to assist with tasks such as process optimization, quality control, and predictive maintenance. For example, AI can be used to optimize production processes by analyzing data from sensors and other sources to identify inefficiencies and bottlenecks. Additionally, AI can be used to perform quality control by analyzing images of products to detect defects.
However, the use of AI in manufacturing also poses some challenges. For example, there may be a need for new regulations and oversight to ensure the safety and effectiveness of AI in manufacturing. Additionally, there may be a need for new training and retraining programs to ensure that workers are able to adapt to new technologies and processes. According to a report by the International Federation of Robotics, the manufacturing sector is projected to create 1.4 million jobs related to AI by 2026.
Retail Sector
Another industry that is likely to be impacted by AI is the retail sector. According to a recent study by PwC, the use of AI in retail could lead to a savings of up to $300 billion per year by 2030. This is due to the ability of AI to assist with tasks such as personalization, inventory management, and fraud detection. For example, AI can be used to personalize the shopping experience by analyzing data from customer interactions to create personalized recommendations and offers. Additionally, AI can be used to perform inventory management by analyzing data from sensors and other sources to identify trends and patterns in sales and customer behavior.
However, the use of AI in retail also poses some challenges. For example, there may be a need for new regulations and oversight to ensure the safety and effectiveness of AI in retail. Additionally, there may be a need for new training and retraining programs to ensure that workers are able to adapt to new technologies and processes. According to a report by the National Retail Federation, the retail sector is projected to create 1.2 million jobs related to AI by 2026.
Financial Sector
The finance sector is also expected to see significant changes as a result of AI and Chat GPT. AI-powered chatbots are being used to provide financial advice and help customers with their banking needs. Chat GPT is also being used to generate financial reports and identify patterns in financial data that can help predict market trends. According to a study by PwC, AI could potentially save the finance industry $1 trillion by 2030.
However, Chat GPT can be used to create virtual assistants that can help customers manage their finances and communicate with their financial advisors. According to a report by the World Economic Forum, the financial sector is projected to create 1.0 million jobs related to AI by 2026.
Conclusion
In conclusion, AI is expected to have a significant impact on the future of jobs in various sectors. These technologies are being used to improve efficiency, reduce costs, and provide better customer service. As these technologies continue to evolve, it is important for workers to adapt and develop new skills to stay relevant in the job market. The healthcare, finance, retail, and manufacturing sectors are expected to see the most significant changes as a result of AI and Chat GPT, with potential savings in the trillions of dollars. As these technologies continue to advance, it is important for companies to stay ahead of the curve to ensure they remain competitive in the global market.
Thanks for reading!
Note -“We cannot direct the wind, but we can adjust the sails.”
Don’t forget to give me your 👏 !
| 2023-01-18T00:00:00 |
2023/01/18
|
https://becominghuman.ai/is-ai-going-to-take-my-job-%EF%B8%8F-4a99bed2defc
|
[
{
"date": "2023/01/14",
"position": 16,
"query": "machine learning job market"
},
{
"date": "2023/01/14",
"position": 87,
"query": "future of work AI"
},
{
"date": "2023/01/14",
"position": 39,
"query": "generative AI jobs"
}
] |
Where is the demand for AI Talent?
|
Where is the demand for AI Talent?
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https://community.nasscom.in
|
[] |
The demand for AI talent, especially in India, is expected to continue to increase as companies seek out individuals with knowledge of machine learning and data ...
|
Artificial Intelligence (AI) has become a crucial part of modern society and businesses, with many organizations looking to capitalize on this emerging technology. But just like any other form of new technology, there needs to be enough talented people available to use it for the industry to thrive. With that in mind, where is the demand for AI talent? This blog post will explore where the need is greatest and examine how companies can find the right AI professionals for their teams. We'll also take a look at what skills are needed for those looking to break into this rapidly growing field of work. Read on to learn more!
The Need for AI Talent
As the demand for AI technology grows, so does the need for talent with the skills to develop and deploy it. However, the supply of AI talent has not kept pace with demand, resulting in a shortage of AI experts.
This shortage is compounded by the fact that AI is a relatively new field, and there are few programs or institutions that focus on training AI professionals. As a result, most AI experts are self-taught or have learned through experience.
This lack of formal training can make it difficult for employers to assess the qualifications of potential AI hires. Furthermore, it can also make it difficult for AI professionals to keep up with the latest developments in their field.
The shortage of AI talent has led to high salaries and competitive job market. In order to attract and retain AI talent, employers need to offer competitive compensation packages and provide opportunities for professional development.
The Different Types of AI Talent
Artificial intelligence is a broad field, and there is demand for AI talent across many different industries. The most common types of AI talent are machine learning engineers, data scientists, and software engineers.
Machine learning engineers are in high demand as they are responsible for building the algorithms that power many of the applications we use today. They need to have strong mathematical and programming skills.
Data scientists are also in high demand as they are responsible for collecting, cleaning, and analysing data. They need to be able to work with large datasets and have strong statistical skills.
Software engineers are also needed to build the applications that use artificial intelligence. They need to be able to write code that is efficient and accurate.
The Most In-Demand AI Skills
There is no doubt that AI is one of the hottest skills in the job market today. But what exactly are the most in-demand AI skills?
According to a recent report from McKinsey, the most in-demand AI skills fall into three broad categories:
1. Machine learning: This is the ability to train computers to learn from data, without being explicitly programmed. It’s one of the most essential skills for any AI professional.
2. Natural language processing: This involves teaching computers to understand human language and respond in a way that is natural for humans. NLP is used for tasks such as chatbots and voice recognition.
3. Computer vision: This is the ability to teach computers to see and interpret images, videos, and other visual data. It’s used in applications such as self-driving cars and facial recognition.
These are just a few of the most in-demand AI skills; there are many others that are also highly sought after by employers. If you’re looking to get started in AI, or take your career to the next level, these are the skills you should focus on developing.
The Best Places to Find AI Talent
There is no doubt that AI talent is in high demand. But where can you find the best AI talent? Here are some of the best places to look:
1. Universities
Universities are a great place to find AI talent. Many top universities offer programs specifically in artificial intelligence and machine learning. This is where you will find the brightest minds in the field, who are eager to put their knowledge to work.
2. Online Communities
There are many online communities devoted to artificial intelligence and machine learning. These communities are a great place to find talented individuals who are passionate about these technologies.
3. Tech Companies
Tech companies are another great source of AI talent. Many of these companies are at the forefront of developing new artificial intelligence and machine learning technologies. They often have teams of highly skilled individuals working on these projects.
How to Train AI Talent
As the demand for AI talent grows, so does the need for training and development in this area. There are a number of ways to train AI talent, including online courses, bootcamps, and traditional college programs.
Online Courses: Online courses are a great way to get started in AI without having to commit to a full-time program. They can be flexible and allow you to learn at your own pace. Many online courses also offer certification upon completion.
Bootcamps: Bootcamps are intensive, short-term programs that can provide you with the skills you need to start working in AI. They can be expensive, but they offer a more immersive learning experience than online courses.
Traditional College Programs: Traditional college programs can give you a well-rounded education in AI and related disciplines such as computer science or mathematics. These programs can be expensive and may take longer to complete than other options, but they can give you a strong foundation on which to build your career.
Conclusion
Artificial intelligence is a rapidly growing field, and it doesn't look like its slowing down anytime soon. The demand for AI talent, especially in India, is expected to continue to increase as companies seek out individuals with knowledge of machine learning and data science tools. With the right skillset and education, you can position yourself to tap into this lucrative job market that offers some of the highest salaries available in tech today.
| 2023-01-14T00:00:00 |
https://community.nasscom.in/communities/data-science-ai-community/where-demand-ai-talent
|
[
{
"date": "2023/01/14",
"position": 34,
"query": "machine learning job market"
}
] |
|
Oracle AI for Fusion Applications
|
Oracle AI for Fusion Applications
|
https://www.oracle.com
|
[] |
Embedded AI capabilities in Oracle Fusion Cloud Applications ; Skills growth and career development, Job Seeker Analyst**, Assists job seekers in preparing for ...
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Recruiting Candidate email and text messages* Draft emails and text messages to one or more candidates regarding invitations to apply for jobs, next steps, task reminders, and other hiring topics.
Recruiting Candidate experience summary* Give candidates a summary of their best-fit capabilities and attributes for a position.
Recruiting Candidate Q&A* Enable candidates to get immediate answers in a conversational manner about the company, hiring department, benefits, and job requirements.
Recruiting Candidate score (job match snippet)* Present candidates with a score with comparative details on multiple criteria, such as skills, job history, and education, to help highlight how well they fit a job.
Recruiting Career site content* Create concise summaries for career sites of long-form company information and content on hiring topics, such as benefits, workplace environment, and company culture, to help attract and engage candidates.
Recruiting Compensation Guidelines Analyst** Helps provide insights and guidance on market trends and company policies around compensation for new hires and promotions.
Recruiting Employee Hiring Advisor** Helps provide hiring managers with insights and recommendations to streamline the recruitment process and assist in identifying the best candidates for open positions.
Recruiting Job applicant score* Present recruiters with a score to see how well candidate applications match job requisitions by comparing job requisition requirements with a candidate's education, experience, skills, and profile.
Recruiting Job category tiles and landing pages* Build career pages by job categories to deliver tailored candidate experiences and help drive candidate engagement.
Recruiting Job posting descriptions Create engaging job posting descriptions that distinctly convey a position's requirements and success criteria.
Recruiting Recommended jobs Alert candidates searching for career opportunities online to open roles for which they could be qualified based on an AI analysis of their experience.
Recruiting Similar candidates View candidates whose profiles are a strong match to the profile of the employee that has vacated a role.
Recruiting Similar jobs Alert candidates searching for career opportunities online to open jobs which have a skills/experience profile similar to the role they are searching for.
Recruiting Suggested candidates Hiring managers receive a list of candidates whose profile has a strong match to the profile of the employee that has vacated a role.
Recruiting Time to hire Hiring managers are provided with a forecast for how long it will take to fill an open role, making it possible to estimate the time the role will be vacant and helping them forecast staff costs at this time.
Skills growth and career development Career path recommendations Help employees understand their career growth possibilities by making it easy for them to discover different career options and AI-recommended roles.
Skills growth and career development Career Planning Guide** Helps guide employees in setting career goals and developing a roadmap to achieve them, offering advice on skill development and career progression.
Skills growth and career development Growth opportunities Give each employee AI-recommended growth and learning opportunities that automatically adapt to changes in their role and the business.
Skills growth and career development Internal Mobility Assistant** Helps employees navigate career advancement opportunities within the organization, providing guidance on open positions and required skills.
Skills growth and career development Job and position descriptions* Draft job and position descriptions to help inspire and motivate employees to explore career and growth opportunities.
Skills growth and career development Job Seeker Analyst** Assists job seekers in preparing for and finding employment, offering resume tips, interview coaching, and job search strategies.
Skills growth and career development Learning and Training Advisor** Helps recommend training and development opportunities to employees, supporting continuous learning and skill enhancement.
Skills growth and career development Learning descriptions* Create descriptions for learning requests, completed external learning, and self-paced learning items.
Skills growth and career development Role and capability guide descriptions* Draft descriptions for role and capability guides to help define role expectations and guide employees to attain the needed skills.
Skills growth and career development Skills management Detects, captures, and refreshes a tailored skills taxonomy for your business to provide skills recommendations across talent management processes such as recruiting, learning, and career development.
Skills growth and career development Skills recommendations Offer new skill recommendations to help employees grow and develop their careers.
Talent management Check-in discussion topics* Construct check-in discussion topics and suggest follow-up performance and development goals based on the check-in discussion.
Talent management Compensation plan instructions* Create compensation plan instructions based on individual plan data and configurations.
Talent management Development tips* Draft development tips for managers to help employees succeed and progress toward their career goals.
Talent management Feedback comments* Draft feedback comments to help improve employee performance and engagement.
Talent management Goal description* Suggest descriptions for individual and team goals to help foster greater alignment, productivity, and collaboration.
Talent management Market composite summary* Create explanations to help managers understand an employee's market composite.
Talent management Performance and Goals Assistant** Helps employees in setting and achieving performance goals, providing feedback and development plans to enhance job performance.
Talent management Performance review summary* Create a performance review summary by analyzing multiple data sources to help improve performance conversations and development suggestions.
Talent management Recognition comments* Draft recognition comments to acknowledge an employee’s success in a style matching your company’s culture.
Talent management Suggested successor Helps senior leaders find successors from across the organization by identifying personnel who have the skills needed for a particular role.
Talent management Talent Review Assistant** Aids in the talent review process, offering tools and insights to assist managers in evaluating employee performance and potential.
Talent management Team goal summaries* Create a summary of a team’s performance and development goals and progress.
HR and workforce management Benefits Analyst** Helps employees understand and maximize their benefits packages, offering personalized insights on medical, dental, and vision coverage based on individual needs.
HR and workforce management Collective Agreements Analyst** Assists employees in understanding collective bargaining agreements and their implications for employment terms.
HR and workforce management Compensation statement Analyst** Enables employees to ask potentially clarifying questions about their total compensation statement.
HR and workforce management Document type descriptions* Get suggestions for comments and descriptions for HR document records and types.
HR and workforce management Employee Contracts Analyst** Helps provide clarity and insights on employment contracts, helping enable employees to understand their terms and conditions.
HR and workforce management Leave and Absence Analyst** Helps employees navigate leave and absence policies, assisting with requests for time off and helping them understand their rights and benefits.
HR and workforce management Org Chart Analyst** Helps employees understand organizational structures, reporting lines, and department functions.
HR and workforce management Personal and Employment Details Assistant** Helps manage and update employees' personal and employment information. Helps provide suggestions on employee lifecycle events such as promotions, transfers, and relocations to help enable improved accuracy and improved adherence to company policies.
HR and workforce management Shift Scheduling Assistant** Assists in the creation and management of employee shift schedules, helping enable more optimal coverage and accommodate individual preferences.
HR and workforce management Timecard Assistant** Helps facilitate the accurate and timely submission of timecards, supporting proper tracking of hours worked. Assists in providing explanations of how pay is calculated based on hours worked.
Employee experience and onboarding About me* Draft the "About Me" content for an employee's Connections profile.
Employee experience and onboarding End of call (or chat) summary notes* Draft the wrap-up notes at the conclusion of a call or chat with an HR help desk agent.
Employee experience and onboarding Help desk request summary* Draft an overview of the issue, progress made, employee response, and planned next steps for a help desk request.
Employee experience and onboarding Knowledge article suggestion* Summarize a knowledge article suggestion while an employee creates a help desk request.
Employee experience and onboarding Knowledge article summary* Enables HR help desk agents to create a summary of a knowledge article to share with an employee.
Employee experience and onboarding Knowledgebase articles* Construct knowledgebase articles to help answer frequently asked questions faster and with less effort.
Employee experience and onboarding Manager survey analytics* Summarize survey responses in graph or chart form, display sentiments associated with each textual response, and view a summary of text responses.
Employee experience and onboarding Survey questions* Get suggestions for survey questions to help drive higher response rates.
Employee experience and onboarding New Hire Onboarding Assistant** Supports new employees through their initial days, offering information on company policies, culture, and key resources.
Employee experience and onboarding Perks and Awards Analyst** Helps inform employees about available perks and awards, helping them make the most of company-provided benefits and recognition programs. Helps to provide line managers with guidance on company policies around recognitions and awards.
Employee experience and onboarding Retirement and Pensions Analyst** Helps offer advice on retirement planning, including 401(k) and pension options, supporting employees as they prepare for their post-work years.
Employee experience and onboarding Tax Withholding Guide** Supports employees with guidance through W-4 elections to help enable the withholding of the correct amount of tax, prevent surprises at tax time, and manage cash flow throughout the year.
| 2023-01-14T00:00:00 |
https://www.oracle.com/id/applications/fusion-ai/
|
[
{
"date": "2023/01/14",
"position": 60,
"query": "AI job creation vs elimination"
}
] |
|
Artificial Intelligence in Healthcare: Benefits & Use Cases
|
Artificial Intelligence in Healthcare: Benefits & Use Cases
|
https://invozone.com
|
[] |
Researchers have created Eve, an AI 'robot scientist' aimed to make ... AI will bring value by automating or supplementing physicians' and staff's jobs.
|
In This Article:
Only by looking at how modern hospitals and healthcare systems manage medical data a doctor from the early twentieth century will be astounded by the changes in the medical system.
There was the good old-fashioned pen and paper back then, where doctors would normally write the symptoms, diagnoses, and treatments given to patients. So, how has it changed through the years? The answer is simple, artificial intelligence in healthcare happened.
This technology has progressed from a future promise to an inevitable reference point for innovation in the last several years. Not only does it make headlines daily, but the number of AI-related studies, research projects, university courses, and businesses has expanded tremendously.
Equally important, AI has also emerged as a powerhouse in the healthcare sector. According to a recent survey by Business Wire, the investments in artificial intelligence for healthcare will surpass 34 billion dollars by 2025! The COVID-19 pandemic has further enhanced the benefits of AI.
In this article, we will explore the role of AI in healthcare.
Why Is Artificial Intelligence Important In healthcare?
AI applications are autonomous, just like humans. They simulate human behavior in many ways, such as making decisions and performing tasks independently. There is an added benefit to using AI apps—they can work way faster than humans. Thus, AI healthcare use cases reduce the time and cost of medical tasks.
There can be numerous use cases of AI use cases in healthcare. These include the diagnosis of diseases, medical imaging, patient care, medication allocation, healthcare research, surgery, pandemic spread prediction, and many more. AI also aids in ensuring OIG exclusion compliance, enhancing the overall integrity of healthcare practices.
Especially the way it has played a role against COVID-19, we can see a shining future of artificial intelligence in the medical sector.
InvoZone has curated a collection of AI-related articles and Healthcare Trends that will urge you to create robust AI solutions. Explore to eliminate the indifferent mindset!
What Are The Benefits Of AI in Healthcare?
Artificial intelligence has found a permanent place in the healthcare industry because of its surreal abilities. Discover the benefits of artificial intelligence in healthcare that fueled its fire below:
Real-Time Data
Acquiring reliable information in a timely manner is a vital component in diagnosing and treating medical disorders. Doctors and other medical professionals use AI to accelerate and optimize important clinical decision-making by leveraging real-time and precise data.
More quick and realistic findings can lead to better preventative measures, cost savings, and patient wait times.
Real-time analytics can aid in the improvement of physician-patient relationships. Making crucial health data available via mobile devices can help patients participate in their treatments. Doctors and nurses can be notified of critical changes in patient statuses and crises via mobile notifications.
Streamline Tasks
Artificial intelligence in medicine has already altered healthcare procedures around the world. Appointment scheduling, clinical details translation, and patient history monitoring are examples of innovations.
AI is allowing healthcare institutions to automate time-consuming and delicate activities. Intelligent radiology equipment, for example, can discover crucial visual indicators, saving hours of intensive investigation. Similarly, EMS ePCR software leverages AI-driven automation to streamline emergency medical documentation, enabling first responders to capture and transmit critical patient data in real-time, improving both efficiency and patient outcomes. There are also automated systems for appointment scheduling, patient tracking, and care suggestions.
One specific duty that AI has simplified is insurance evaluation. AI is utilized to reduce the expenses associated with insurance claim denials by leveraging insurance claims analytics to detect and resolve inaccuracies and even streamline employment insurance maternity benefits. Health practitioners use AI to detect and fix incorrect claims before insurance companies refuse reimbursement.
Save Time And Resources
As more critical activities are automated, physicians have more time to examine patients and identify sickness and disease. AI is speeding up procedures to save valuable production hours.
Because time equals money in every industry, AI has the potential to save significant amounts of money. It eliminates administrative burdens, such as filing, evaluating, and settling accounts, which contribute to a large percentage of these wasteful expenditures.
Improve Research Quality
One cannot neglect the research domain when scribbling down artificial intelligence benefits in healthcare. AI enables researchers to collect vast amounts of data from various sources.
The capacity to rely on a vast and expanding corpus of knowledge paves the way for more effective examination of dangerous illnesses. In terms of real-time data, research can profit from the vast amount of accessible information.
Medical research organizations such as the Childhood Cancer Data Labare developing valuable tools to help medical practitioners explore large volumes of data.
AI has also been used to analyze and diagnose symptoms early in developing a disease. Telehealth systems are being adopted to follow patient progress, retrieve important diagnosis data, and contribute population data to shared networks.
Reduce Stress & Anxiety
More than half of primary care doctors are pressured by deadlines and other job factors. AI aids in the streamlining of procedures, the automation of activities, the quick sharing of data, and the organization of operations, all of which relieve medical personnel of the burden of juggling too many jobs.
What Are Examples Of Artificial Intelligence In Healthcare?
Let’s look at the top eight artificial intelligence use cases in healthcare.
Disease Diagnosis
In the list of applications of AI in healthcare, the top one is disease diagnosis. An adequate diagnosis of illnesses requires years of medical training. Even then, diagnostics is a difficult and time-consuming task.
With this in mind, the demand for expertise in many disciplines considerably outnumbers the available supply. As a result, it strains clinicians and frequently delays life-saving patient diagnoses.
Last year, scientists at Babylon, a worldwide tech business focused on digital health, discovered a novel approach to utilize machine learning to identify the illness. They created new AI symptom checkers in the hopes of reducing diagnosis errors in primary care. The new method solves the limitations of previous versions by incorporating causal reasoning into machine learning.
Moreover, researchers and application developers have worked on utilizing AI in early cancer detection. By using AI’s pattern recognition, doctors can diagnose cancer at an early stage. These tools can help to reduce cancer’s impact on patients, helping in better treatment and recovery.
Medical Imaging
Imaging is another well-known AI application in healthcare. It is also moving away from its primary focus—diagnosis—to playing an important role in therapy, particularly in the field of cancer.
Doctors are starting to rely on imaging to help them monitor tumors and the spread of cancer cells. It educates them on if medicines are functioning better and faster.
That new role for imaging is changing the sorts of treatments patients receive and dramatically increasing the information physicians receive about their functioning. Ultimately, they make better decisions about what treatment alternatives they require.
Top applications of AI in medical imaging include cardiovascular imaging, lung imaging, neurological imaging, and breast imaging. These applications not only help in the early diagnosis of diseases but also assist in continuous monitoring and adaptive treatment. Most medical imaging software involves deep learning solutions.
In simpler words, clinicians may discover diseases considerably faster, boosting early action by using artificial intelligence in medical imaging. To put an emphasis, Tulane University researchers showed that AI could identify and diagnose colorectal cancer as well as or better than pathologists by analyzing tissue images.
The goal of this study was to investigate if artificial intelligence can help pathologists keep up with the increased demand for their services.
Medical Research and Development
Scholars are leveraging AI to analyze and detect patterns in vast and complicated datasets in a timely manner; hoping to achieve precision within seconds.
It also aids them in searching the scientific literature for relevant research and merging various types of data; for example, as assistance in drug discovery and development.
The Institute of Cancer Studies' canSAR database combines genetic and clinical data from patients with information from scientific research, and it uses AI to identify new cancer treatment targets. Researchers have created Eve, an AI 'robot scientist' aimed to make drug development more efficient and cost-effective.
Another benefit of Ai in healthcare includes medical research. It assists in the matching of suitable individuals to clinical investigations.
For instance, in the case of the COVID-19 pandemic, AI has assisted biomedical scientists in the research and development of vaccination. CORD-19 is an open-source research dataset that has been used in more than 100,000 research papers to accelerate the pace of discovery.
Surgery
One of the fascinating examples of artificial intelligence in healthcare happens to be surgery. AI assists surgeons in performing life-critical operations with excellent precision. You might have watched a crazy video of a surgeon using an AI tool during an operation, right? That will be the norm soon.
Furthermore, AI can help in analyzing old data to find new surgical methodologies! This is an amazing favor of Artificial Intelligence for healthcare professionals.
Patient Care
There are numerous artificial intelligence applications in healthcare for patient care, such as patient support virtual assistants. These can provide virtual nursing and psychological help and answer quick questions.
Another similar application is AI-based telemedicine which assists patients virtually. Furthermore, the need for telemedicine has increased tremendously during COVID-19 as we want to avoid physical encounters.
AI researchers are using machine learning algorithms to study patients’ historical data. This way, healthcare professionals provide patients with customized care using pattern recognition.
Moreover, software development companies are integrating AI and IoT to provide smart health devices. Such devices can monitor the patient’s heart rate, diagnose potential issues, and alert healthcare providers in case of any issues.
Healthcare Business Management
AI has played a vital role in healthcare business management. This disruptive technology has automated many administrative tasks increasing ROI for the healthcare industry. I’ll give two examples:
First, healthcare apps can help doctors, nurses, and other providers save time on tasks. For instance, artificial intelligence examples in healthcare, such as voice-to-text transcriptions, could help order tests, prescribe medications, and write chart notes.
Second, Harrow Council is testing the IBM Watson Care Manager system in order to improve cost efficiency. It connects patients with a care provider that meets their requirements while staying within their care budget. It also creates personalized care plans and promises to provide insights into better use of care management resources.
Moreover, as workforce management mechanisms evolve, tools like Lindy.ai are optimizing healthcare efficiency, reducing administrative burden, and improving patient-provider interactions through AI-powered assistants
Pandemic Prediction
The Covid-19 pandemic has fuelled the need for artificial intelligence in healthcare. Everyone has faced and fought this pandemic in their capacity. Along with health workers and biochemists, AI researchers have been among the front-runners against coronavirus. They explored the practicality of artificial intelligence benefits in healthcare and yielded fruitful outcomes.
Machine learning models assist researchers and healthcare practitioners in analyzing large volumes of data to find interesting patterns. These patterns forecast the spread of COVID-19.
Such ML-based early warning systems are helping the general population, healthcare providers, and governments devise countermeasures against the virus.
Patient and Consumer-Facing Applications
There are presently several applications on the market that employ AI to provide personalized health evaluations and home care recommendations. The app Ada Health Companion uses AI to run a chatbot that integrates the user's symptoms with other data to suggest a probable diagnosis.
A comparable app, GP at Hand, created by Babylon Health, is presently being tested by a number of NHS surgeries in London.
Trends & Innovation AI Technology Changing the World of Voice Coaching Want to find out how AI fueled app Vocal Image is helping people with stroke to speak again? watch this episode Listen Now
Future Of Healthcare
Ai in healthcare is utilized or tested for a variety of objectives, including illness diagnosis, chronic condition management, health service delivery, and drug development.
Thus, make a place for your AI-powered ventures in the healthcare industry today because there will never be a right time; there’s only right now! And we’ll happily accompany you on this journey of the greater good as your healthcare software development services provider. Drop us an email, and we’ll get back to you in a day. Build An AI-Powered Healthcare App
Frequently Asked Questions
What problems can AI solve in healthcare?
There are many problems that artificial intelligence (AI) can potentially solve in healthcare, including
Analyzing medical images: AI algorithms can be trained to analyze medical images, such as X-rays and MRIs.
Predictive analytics: AI can be used to analyze large amounts of data, including electronic health records and medical research, to identify trends and predict outcomes.
Personalized medicine: AI can be used to create personalized treatment plans.
Administrative tasks: AI can be used to automate administrative tasks, such as scheduling appointments and processing insurance claims, freeing up time for healthcare providers to focus on patient care.
Drug discovery and development: AI can analyze chemical compounds and speed up the drug discovery process.
Assistive technologies: AI can be used to develop technologies, such as virtual assistants, that can help patients manage their health and connect with healthcare providers remotely.
What is the future of AI in healthcare?
The future of artificial intelligence (AI) in healthcare is likely to involve even more widespread use of AI technologies to assist with a variety of tasks, including diagnosis, treatment planning, drug discovery and development, and administrative tasks.
AI may also develop new assistive technologies and tools to help patients manage their health and connect with healthcare providers remotely.
How will AI change the healthcare industry?
AI will bring value by automating or supplementing physicians' and staff's jobs. Many monotonous processes will be totally automated, and we will be able to employ AI to help health professionals perform better at their professions and enhance patient outcomes.
How is AI transforming healthcare?
In the healthcare and life sciences industries, artificial intelligence is driving enormous improvement and innovation. AI is hastening progress in medication research and development. It enables better and faster diagnosis. It also allows for significantly more efficient company procedures.
That's significant, given how many of us stand to gain from it. Healthcare is definitely one of the domains where AI has the most potential.
| 2023-01-14T00:00:00 |
https://invozone.com/blog/artificial-intelligence-for-healthcare-top-seven-use-cases/
|
[
{
"date": "2023/01/14",
"position": 80,
"query": "AI job creation vs elimination"
}
] |
|
The Future of Work: A New Division of Labor
|
The Future of Work: A New Division of Labor
|
https://trainingmag.com
|
[
"Marcus Mossberger",
"Hr Strategy Director"
] |
In “The Work of the Future,” the authors suggest that “the future of AI is the future of work.” Embracing a collaborative relationship with technology ...
|
As we enter the new year, many of us attempt to emulate Nostradamus by forecasting what will happen in the months ahead. Unfortunately, few of us prove prescient as the future is hard to know. So instead of making predictions, I thought I would instead make three somewhat provocative proposals about what the future of work should look like by the end of the decade. These topics range from time to technology and transferability—and apply to all industries and all colors of collars. It will be up to you to decide if these recommendations are worth working toward, or just more prognostic rubbish.
1. Time: We should spend less time at work.
The remote work revolution was activated by the pandemic, but it will be preserved by Generations Y and Z, who have prioritized well-being over work. While certain vocal personalities such as JP Morgan CEO Jamie Dimon have made it clear that they are not fans of working from anywhere but the office, most business leaders have come to the realization that the genie is out of the bottle. That does not preclude a return to the workplace; rather, it means hybrid work arrangements (a blend of on-site and remote work) are likely the new normal.
If Dimon dislikes remote work, he is going to hate one of the newest trends: the four-day week. This does not refer to four 10-hour days mind you… this movement is all about eight-hour days for a total of 32 hours a week with the same pay and benefits. Pilots have been underway in more than two dozen countries, from the U.S. and UK to Japan and Australia, and the results have been overwhelmingly positive. Approximately 78 percent of employees who participated in these trials have reported being happier and less stressed, while the majority of the organizations said they had experienced no loss of productivity and some had even seen some improvement. Is it really possible to spend less time in the office, less time working in general, and still keep the wheels of commerce turning? Thanks to asynchronous, non-linear work and new technologies, the answer is, “Yes.”
2. Technology: We should embrace technology to become more human.
Prior to the pandemic, the hot topic in the world of work was how technology—notably artificial intelligence (AI) —was coming for our jobs.
There is growing evidence that new technologies could have significant social and material impacts on work, job quality, and ultimately wider society. It is time to embrace technology for what it is—a way to augment human talent to meet the needs of the organization while improving the quality of life for individuals. If AI can automate administrative, transactional tasks for doctors, lawyers, and accountants, it can unquestionably handle elements of your job. “Computers are good at the jobs we find hard, and bad at doing the ones we find easy,” says Michael Priddis, founder and CEO of Faethm by Pearson. As such, we can’t treat people like machines, or treat machines like people. The key will be to strike a balance that allows technology to augment humans while enabling people to reach their full potential. Human potential is virtually limitless, while the most sophisticated technology is only capable of doing what it was programmed to do. In “The Work of the Future,” the authors suggest that “the future of AI is the future of work.” Embracing a collaborative relationship with technology instead of an adversarial one is important today, and will be increasingly important tomorrow.
3. Transferability: We should hire for social skills and digital dexterity.
The average shelf life of skills in today’s world of work is shrinking as emerging technologies, evolving business models, and new social norms are changing faster than at any point in human history. That is why it has become critical to focus on building durable, transferable skills that will future-proof our ability to remain relevant despite the ongoing environmental changes happening around us. If we peer out into the distant future, we may not find anything that resembles what we consider a “job” today. But there are skills and capabilities that will continue to serve us well despite the changes happening all around us—just don’t call them soft.
If the promise of technology holds true and we find ourselves outsourcing administrivia to our robot colleagues, human-centered skills will take center stage. Historically referred to as “soft skills,” social and emotional intelligence, communication, active listening and even empathy more recently have been referred to as “power skills.” Beyond these interpersonal skills, there is also a growing need for advanced cognitive capabilities such as critical thinking, problem solving, and complex information processing and interpretation. And the one aptitude that may be the most important of all is adaptability.
Regardless of what industry you are in, digital dexterity has become fundamental for the modern workplace. Coined by analytics firm Gartner, the focus is on employees constantly upskilling their technical knowledge and capabilities as the pace of digital transformation continues to accelerate. Before you dismiss digital as the realm of IT, it is important to recognize the growing importance for these abilities in all disciplines of the business. “The demand for digital skills continues to surge in IT, but the fastest growth is occurring in nontraditional functions such as marketing, finance and sales,” says Kenneth Pyle, senior director, Advisory at Gartner. “And it’s not just happening in Silicon Valley tech companies but across all industries and regions.”
Helping People Reach Their Potential
Instead of attempting to predict the future, we all would be better served by producing the appropriate people practices that will allow individuals to reach their full potential. This involves a new mindset about how much we work, the type of work we do versus machines and how we future-proof ourselves. Time will tell what the future holds for humanity, but technology will continue to transform, and people with transferable skills will be the ones thriving regardless of what the new world of work becomes.
| 2023-01-14T00:00:00 |
2023/01/14
|
https://trainingmag.com/the-future-of-work-a-new-division-of-labor/
|
[
{
"date": "2023/01/14",
"position": 12,
"query": "future of work AI"
}
] |
Navigating the AI Revolution: How to Adapt and Thrive in the ...
|
Navigating the AI Revolution: How to Adapt and Thrive in the Future of Work
|
https://www.thinkerstrain.com
|
[] |
This article delves into how AI is transforming the future of work and what we can do to stay ahead of the curve.
|
Photo by DeepMind on Unsplash
It's an exciting time as we are on the cusp of an Artificial Intelligence (AI) revolution that promises to change the way we live and work. This article delves into how AI is transforming the future of work and what we can do to stay ahead of the curve. From identifying the industries most impacted by AI to exploring the ethical concerns surrounding its integration, this guide will help you navigate the AI revolution and position yourself for success.
It is no secret that Artificial Intelligence (AI) is rapidly changing our lives in countless ways, from the way we communicate to the way we work. In every aspect of our lives, AI is changing the way we think, the way we process information, and the way we interact with the world around us. But, as we face this technological revolution, the scary question is now inevitable: are we truly prepared for the future of work?
Governments, organizations, and individuals all have a role to play in this preparation. Governments must invest in retraining and upskilling programs to ensure citizens are equipped with the skills needed to succeed in the new economy. They must also invest in programs that help small and medium-sized businesses adopt AI so that they too can benefit from increased productivity and efficiency. We should remember what we learned from the pandemic. It is a grave concern that as the world faces an Artificial Intelligence (AI) revolution, our governments have failed to take adequate measures to prepare for its impact on the workforce. The recent global pandemic has served as a stark reminder of the dangers of being caught off guard and the detrimental effects of government inaction. It took months for governments to come up with a cohesive plan of action, and the damage had already been done. With AI set to transform the way we live and work, it is imperative that we learn from past mistakes and take proactive measures to ensure that we are not caught unprepared once again. And, it is crucial that governments invest in research and development to ensure that the technology is being used ethically and responsibly.
Photo by Fabian Blank on Unsplash
But, it is not only governments who have a responsibility in this preparation. As individuals, we must also take responsibility for investing in our own development. This means acquiring new skills, being open to change, and adapting to new technologies and ways of working. It is vital that we proactively address the ethical implications of AI in the workplace and build a more flexible and adaptable workforce.
As human beings, we naturally resist change. But in this age of rapid technological advancement, it is crucial that we change our mindset and become open to change. Evolution, as we know it, is happening at a slower pace than the changes to our life and environment after the industrial revolution. It is imperative that we speed up our adapting process, as AI is evolving at an astonishing rate. To be successful in the future, we must be willing to learn new skills and open our minds to new ways of thinking. According to a report by the World Economic Forum, some of the top in-demand skills for the future include complex problem-solving, critical thinking, creativity, and emotional intelligence.
AI will lead to many people losing their jobs and increased inequality. This perspective argues that as AI becomes more advanced, it will automate more tasks that were previously done by humans, which create the useless class . This will result in increased inequality as those who are able to adapt and acquire new skills will be able to find new job opportunities, while those who are unable to do so will be left behind. On the other hand there is also a possibility that with the help of AI governments can cover the life cost of the people who suffered and couldn't catch up with the speed of change.
As Generative Artificial Intelligence continues to integrate itself into the workforce, it is becoming increasingly apparent that it has the power to not only automate mind-numbing and repetitive tasks but also to create new job opportunities. However, as with any technological advancement, we should consider the potential drawbacks, and take proactive measures to prepare for the swift change in the nature of work.
Photo by Possessed Photography on Unsplash
As stated by the CEO of OpenAI, Sam Altman, while Generative AI will automate the boring tasks, it will also open doors for jobs in AI development, maintenance, and management. This will lead to job displacement and individuals will have to invest in retraining and upgrading their skills to adapt to the ever-changing nature of work.
A report by Forbes indicates that AI was expected to create 58 million new jobs by 2022. This means that even as certain jobs become obsolete, new job opportunities will become available. But, as we embrace the technological advancements of AI, it is important that we also consider the ethical implications of AI in the workplace and take steps to ensure that the technology is used responsibly and that workers are protected.
We need to actively work on developing the skills in our everyday lives. And, as we prepare for the future of work, we should stay informed about the latest developments in AI and the job market. By staying informed, we can make more informed decisions about our own careers and be better prepared for the changes that are coming. Imagine facing with a problem or situation in the future that you have never thought of and you have to make a quick decision.
As AI takes on more routine tasks, emotional intelligence will become an increasingly valuable asset in the workplace. individuals can prepare for this future by developing their emotional intelligence skills such as active listening, empathy, mindfulness, self-reflection, self-regulation, and social awareness. Emotional intelligence is becoming an essential skill in navigating the complex and ever-changing landscape of AI and technology. Leaders with high emotional intelligence will be better able to manage and motivate their teams, understand the emotions and needs of their employees, and foster a positive work culture. It's time for us to start investing in our emotional intelligence and prepare for the future of work. You can read my other article for some exercises to improve your EI.
Photo by Hello I'm Nik on Unsplash
As we move forward, remember the key to success in the future of work is adaptability. Those who are able to adapt and evolve with the changing times will be the ones who thrive. It is important that we stay informed and stay open to learning new skills and new ways of thinking. It is not enough to simply keep up with the times, we must actively work to stay ahead of them. Throughout human history, adaptability has been a crucial factor in our survival as a species. From the Stone Age to the Industrial Revolution, we have faced new changes and challenges in our environment and have had to adapt in order to survive and it is recorded in out DNA. We have had to learn new skills, adopt new technologies and ways of thinking, and evolve in order to thrive. The pace of change has been relatively slow, however, in comparison to the rapid technological advancements we are currently facing. The integration of Artificial Intelligence (AI) into our lives and workforces is one of the fastest and most profound changes we have ever had to adapt to.
In the words of James Baldwin, “Not everything that is faced can be changed, but nothing can be changed until it is faced.” It is time for us to face the reality of the future of work and take action to ensure that we are prepared for the changes that are coming.
| 2023-01-14T00:00:00 |
2023/01/14
|
https://www.thinkerstrain.com/post/navigating-the-ai-revolution-how-to-adapt-and-thrive-in-the-future-of-work
|
[
{
"date": "2023/01/14",
"position": 84,
"query": "future of work AI"
}
] |
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