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Find a new job in 60 days: tech layoffs put immigrant ...
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Find a new job in 60 days: tech layoffs put immigrant workers on a ticking clock
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https://www.npr.org
|
[
"Stacey Vanek Smith"
] |
Last month, tech companies laid off about 50000 workers, many of them immigrants on work visas. Now they have to find a job soon or leave the country.
|
Find a new job in 60 days: tech layoffs put immigrant workers on a ticking clock
Enlarge this image toggle caption Sukanya Sitthikongsak/Getty Images Sukanya Sitthikongsak/Getty Images
What if you lost your job and had just 60 days to find another one? That is the situation thousands of highly skilled immigrant workers have suddenly found themselves in.
About 50,000 tech workers lost their jobs last month as Meta, Amazon, Twitter and others laid off parts of their workforce.
And a lot of that workforce is made up of immigrants. A 2018 report found more than 70% of tech workers in Silicon Valley were born in another country.
Sponsor Message
Losing a job is always devastating, but for many immigrant workers on H-1B (skilled worker) visas, their ability to stay in the U.S. is suddenly on an unforgiving ticking clock.
When the clock ticks down
Aditya Tawde knows exactly what this feels like.
Back in 2020, Tawde was working at a tech company near Boston. Things were going well. He liked the work and had just been promoted. But COVID and lockdown hit his employer hard.
The company called a virtual all-staff meeting (offices were closed) and Tawde had a bad feeling. Almost immediately the CEO confirmed his worst fears.
"He said they were making the decision to let people go," Tawde recalls. "And all the people who were being let go would get an email within the next hour."
Tawde didn't move from him computer. He barely blinked. He just sat there refreshing his email over and over again, telling himself he'd be okay.
He'd just gotten promoted and he worked in data analytics, which was highly valued by the company.
Fifteen minutes went by, then 20. Nothing. But then, all of the sudden, there it was. The email.
A ticking clock
"I don't remember the subject line," says Tawde, "but I remember it started out saying, 'If you're getting this email, that means you're one of the 1,000 employees who are being let go and these are the next steps you need to take."
Sponsor Message
Tawde was in shock. "I had a very shaky voice when I told this to my wife," he recalls. "I then went to the bathroom and I cried."
Tawde and his wife are from India, but they'd been in the U.S. for five years. Their life was in the U.S.
He is in the US was on an H-1B visa. Tech companies use these visas a lot to find workers they say they cannot find in the US.
The H-1B visa ties a worker to a particular job. If a worker loses that job, a countdown clock starts.
This is happening. I have two months.
Tawde sat in the bathroom collecting himself. He took some time to get his emotions out and then immediately started making a plan.
"I was like, 'This is happening. I have two months.'"
People who lose their job on an H-1B visa have 60 days to lock down a new job or they have to leave the country.
Right now it's likely that thousands of H-1B visa holders are facing this same ticking clock.
Many have children in school, mortgage payments, and have been in the US for years.
It's surprising they were laid off
Joshua Browder is the CEO of Do Not Pay, an AI-based legal services start up (or as they call it, "the world's first robot lawyer"). Browder says as someone running a company, it's always been a struggle to find talent.
Browder has always had to pay recruiters to find people and even then would often lose out to larger, richer companies.
So after he heard the news about Meta's 11,000 layoffs, Browder sent out a quick note on Twitter.
If you have recently been laid off and hold an H1B visa,we would love to chat with you@donotpay.
25%of our team are not US citizens and we can move quickly.
We have never laid off anyone in our 7 year company history and don’t plan on it — Joshua Browder(@jbrowder1)November 22,2022
Browder hoped to get a few really top people who had been let go. He had a few open positions and was excited to help out a fellow immigrant.
"We've had hundreds of people reach out," says Browder. "They are some of the best designers, engineers with amazing portfolios and it's very surprising that they were laid off."
Hiring slows during the holiday season
Browder has made an offer already and has passed on many applications to other companies he knows that are hiring.
Browder is an immigrant himself and says H-1B workers are in a really tough spot right now: There is a flood of tech workers on the market and a lot of hiring freezes.
It is also the holiday season, when many places stop hiring or at least slow things down.
Also, many places will hire a US citizen over an H-1B worker. It's cheaper and there's less paperwork.
What if one question decides my future?
Aditya Tawde was up against a lot of this himself when he was laid off back in 2020. He started reaching out to everyone he could think of: former colleagues, mentors and old classmates.
Every application, ever interview, every answer to every question felt dire.
Sponsor Message
"There was a lot of overthinking," he recalls, "because I was like, 'What if I answer one question incorrectly and that is what decides my future in the States?"
Just six weeks after he had been laid off, Tawde had done 35 interviews, sometimes five in a day.
He says it was a blur. He kept pushing, updating the spreadsheet, analyzing questions. And then one day out of the blue, it happened.
"I got an email saying I had been selected."
Tawde says it was surreal. "I burst into tears of laughter," he recalls. "Like one email changed my life and then this other one changed my trajectory again with a new job."
Smooth seas never make a good sailor
Right now Tawde is doing everything he can to help fellow H-1B holders: sifting through applications, posting available jobs, putting contacts in touch.
He says he always tells people what somebody told him back in 2020, somebody who had been in his exact position and had managed to find a job.
"There was one thing he said, which has always stayed with me: 'Smooth seas never make a good sailor. Once you go through this experience, you will come out stronger. If something difficult comes in your life, you'll be able to handle that.'"
Tawde tells people to keep pushing. He tells them he now has a job he loves at LinkedIn. He tells them he was also on this brutal, ticking clock and he got a job with just 15 days to spare.
| 2022-12-08T00:00:00 |
2022/12/08
|
https://www.npr.org/2022/12/08/1141402046/h1b-visa-immigrant-workers-tech-layoffs-60-days-to-find-a-job
|
[
{
"date": "2022/12/08",
"position": 36,
"query": "AI layoffs"
}
] |
Corporate Layoffs: Lasting Impact on Brand Reputation
|
Corporate Layoffs: Lasting Impact on Brand Reputation
|
https://www.allisonworldwide.com
|
[] |
Discover how corporate layoffs can significantly affect brand reputation according to a study by Allison PR ... Why generative AI won't replace proper PRs or ...
|
Nearly one-third of Americans say news coverage of a company’s layoff has directly impacted their perception of the brand (31%), a recent Allison+Partners study shows.
Simply put: The influence of media is inevitable. And as news of recent layoffs in the United States floods the media, it leaves behind a trail of companies that consumers, competitors and employees will examine. It will also simultaneously cause pause for brand communicators who might face this kind of decision-making.
As data analysts within a communications firm, we decided to take the pulse of the current layoff landscape, uncovering what it means to U.S. consumers and how brands can, and should, react if the time comes.
We found buyers are watching closely to see how companies approach layoffs – with more than a quarter indicating they pay more attention to the news now given the recent influx of layoffs (29%). A misstep could have lasting implications for your customer-brand relationship. As is the case with any heightened economic environment, the need for corporations to understand their core audiences and to use data, not assumptions, to inform decisions has become incredibly important.
Here are three ways that Allison+Partners’ Performance + Intelligence (PI) team can step in to help organizations do just that using our Suite of Corporate Health Solutions. Together, these approaches can help business strategists keep their finger on the pulse of employees, clients and overall brand reputation.
Measuring Brand Health in a Crisis and Beyond
With 40% of Americans saying their perception of a company would be negatively impacted after employees are laid off, understanding the health of your brand before and after a potential layoff becomes a fundamental organizational diagnostic tool.
What’s more – if a company releases a public statement that feels inauthentic following its layoff, 45% of consumers indicate their view of that company’s core values and mission would be negatively impacted. Roughly 40% would have a negative view of the company’s leadership if employees are laid off in a manner consumers feel is improper.
Those are major pieces of a brand’s identity at stake
Establishing a strategic, ongoing, brand health program allows you to establish benchmark data, uncover opportunities, avoid potential problems and face the ripple of market perception that comes hand-in-hand with layoffs. In the event of a crisis or competitor announcement, quickly fielding a survey to target audiences and comparing these results to existing benchmarks can help a brand determine the short- and long-term reputational impact, along with any opportunities to minimize damage.
This kind of perception measurement needs to reach across all stakeholders, including listening to customers and employees’ voices – both current and prospective.
Managing the Ever-Changing Customer Experience
More than 90% Americans are aware of the layoffs impacting employees in the United States right now. With the breadth of coverage and attention layoffs attract, it becomes a matter of when, not if, news will wind up in front of a current or prospective customer.
In fact, they want it that way. Only 14% of consumers don’t think a company should have to release a public statement about its layoffs – leaving the large majority demanding answers. Most often, they want brands to release a public statement on their corporate website (33%); in a press release or in comments issues to journalists (33%); on their corporate social media channels (28%); or at a live event, such as an earnings call or press conference (28%).
When that announcement is made, 62% of consumers say it is important brands provide transparency behind their layoffs, while 40% demand a timely announcement.
It’s simply the reality of today’s Information Age. Customer expectations are at an all-time high, and brand experiences are no exception. In these times, brands must actively evaluate their customer base to understand – and quantify – any pain points present in their day-to-day brand interactions, so these processes can be reevaluated, solutions ideated, and the overall brand experience improved.
The impact of layoffs echoes into this experience. More than 50% of Americans indicate their likelihood to purchase from or use a brand is impacted if they believe that company poorly managed a layoff. And 52% of these Americans warn they would stop purchasing from such a brand, even if they were already frequent customers.
By arming themselves with Customer Experience data, brands can better predict these kinds of reactions and increase customer lifetime value. After all, when you deliver the experiences people need, they reward you with both their business and their loyalty.
Keeping Sight of Employee Experiences Both Present and Future
As A+P PI Senior Vice President Katie Malark recently stated, “employees are the first touchpoint” for customers and “active ambassadors for the brand itself.” When approaching reputation management scenarios like layoffs, these voices must not be bypassed.
And with nearly 25% of Americans feeling uneasy about their current job due to recent layoffs, employers need to lend an ear.
A great way to accomplish this outside of an annual Employee Experience (EX) survey is through internal pulse surveys. These can be fielded at regular intervals leading up to, during and after a difficult decision, such as employee layoffs, to help company leadership gauge internal communication expectations, leading anxieties, confidence in longevity or maintained belief in core values – among other foundational elements.
Yet, as data analysts, we know all too well listening without action is a moot point. In fact, in our consumer layoff study, we found 25% of Americans want their employers to address the current layoff landscape with employees. That’s why our team of analysts works closely with strategists within Allison+Partners’ Corporate and Reputation Risk teams to help brands translate these EX-related data points into actionable next steps that can be shared with internal audiences – bridging the gap between understanding and accountability.
Though it may seem counterintuitive to do so in economic uncertainty, brands also need to think past maintaining employee morale to retention and future recruitment. Today’s employee environment is not so separate from that of tomorrow.
Case in point: 25% of working Americans and those currently unemployed but looking for work say recent layoffs have expedited either their job search for their next position or their retirement – the former most often planning to move to a company they feel is more recession-proof (55%). An overwhelming 62% of consumers indicate a poorly managed layoff would impact their likelihood of working for the brand in the future.
In the end, it comes down to establishing a truly holistic Experience Management (XM) program, capable of pushing brands through the noise and pressure of a potential crisis and coming out on the other side with your head held high and ample data to guide you.
Methodology: Allison+Partners Performance + Intelligence surveyed 1,000 U.S. consumers over the age of 18. The survey was fielded using the Qualtrics Insight Platform and panel was sourced from Lucid. Fielding was executed in December 2022.
Brooke Fevrier is a research manager on the Performance + Intelligence Team who lives and breathes consumer sentiment and behavior mapping, turning data patterns into strategic insights and effective communication tactics for clients across all industries.
Olivia Witt is a research and data analyst on the Performance + Intelligence Team, specializing in quantitative methodologies and developing custom measurement solutions, which help clients show up smarter. She is the driving force of the engineering behind the Allison+Sports Earned Performance Sports Scorecard.
Performance + Intelligence at Allison+Partners is a dedicated team of experts who work to collect and decipher data in order to help organizations spot strategic opportunities, take smart risks and show clear outcomes.
| 2022-12-08T00:00:00 |
2022/12/08
|
https://www.allisonworldwide.com/study-shows-corporate-layoffs-have-lasting-impact-on-brand-reputation/
|
[
{
"date": "2022/12/08",
"position": 56,
"query": "AI layoffs"
}
] |
Airtable, last valued at $11 billion for its no-code software ...
|
Airtable, last valued at $11 billion for its no-code software, lays off over 250
|
https://techcrunch.com
|
[
"Natasha Mascarenhas",
"Senior Reporter",
"Zack Whittaker",
"Maxwell Zeff",
"Lorenzo Franceschi-Bicchierai",
"Lauren Forristal",
"Amanda Silberling",
"Rebecca Szkutak",
"Sarah Perez",
"--C-Author-Card-Image-Size Align-Items Center Display Flex Gap Var"
] |
“It's been an unwelcome theme of 2022—layoffs,” the post said. “Each season ... Æthos AI Salon. Hosted by: Æthos Foundation Time: 5:30 p.m. – 8:30 p.m. ...
|
Just days ago, Airtable published a memo about how laid off workers can use Airtable to search for jobs. “It’s been an unwelcome theme of 2022—layoffs,” the post said. “Each season seems to usher in a new wave of cuts. Meanwhile, corporations cite similar concerns of rising inflation, the looming threat of an economic downturn and the need for stability during turbulent times. For the souls who lost their jobs this year it’s another cruel uncertainty they’ll have to surmount.”
Now, Airtable’s employees are facing the same feeling. Last valued at $11 billion, the no-code leader has conducted a round of layoffs today that impact around 254 employees across business development, engineering and other teams. The company spokesperson says that Airtable is continuing to hire for “strategically important” roles, and that 20% of the staff was impacted today.
(Update: The layoff was announced internally alongside an executive departure. Airtable’s chief revenue officer, chief people officer and chief product officer have all parted ways with the company, effective today, a spokesperson confirmed to TechCrunch.)
Those impacted by Airtable’s layoffs today will get at least 16 weeks of severance pay, accelerated equity vesting and, for those on a visa, support from an immigration counsel, sources say. Employees were given the opportunity to meet 1:1 with a leader at the company, following the news.
In an e-mail obtained by TechCrunch, and first seen by tracker Layoffs.fyi, Airtable founder and CEO Howie Liu said the company will be evolving from a bottoms-up adopted product to a company that brings connected apps to larger enterprises.
Techcrunch event Save up to $475 on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $450 on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | REGISTER NOW
“We’ve rapidly expanded and executed on multiple fronts. At the time, I believed we could successfully pursue all of them in parallel,” Liu wrote in the email. “However, in taking a hard look at our efforts in the current market environment, we’ve identified the teams best positioned to capture the opportunity in enterprise in order to bring complete focus, alignment and accountability in our execution.”
The vision was part of the reason that TechCrunch spoke to Liu in October, as Airtable announced its more integrated, connected-apps approach. Then, the entrepreneur pointed to its $735 million Series F round from 2021 as a fortunate reason that Airtable has been able to stay well-capitalized amid the downturn.
“We have more than enough runway to get to profitability and then some,” Liu said in an interview with TechCrunch. “We’re a private company, so we’re not under the gun to show short-term results of profitability — so we’re very, very fortunate to not be under so much pressure and we would never gloat about that, but I do think it gives us a unique position [to hire talent].”
In today’s internal memo, Liu re-emphasized that Airtable is well capitalized, but slightly shifted in tone by adding that “being a lean organization becomes doubly important in times of economic uncertainty.” A spokesperson added that all of Airtable’s funds from its Series F are “still intact.”
The company is signaling that the layoff was less of a desperate attempt to extend runway, and more of a move made in order to re-align the company with what actually works. A spokesperson said that its enterprise side, which makes up the majority of Airtable’s revenue, is growing more than 100% year over year; the product move today just doubles down on that exact cohort.
This entire year was full of tech layoffs, but the final quarter has been especially massive as macroeconomic pressure reaches late-stage private companies. Yesterday, Plaid announced that it would officially cut 20% of staff, around a month after one of the most valuable fintechs, Stripe, cut 14% of its workforce. Elon Musk cut around 50% of Twitter’s workforce after he bought the social media platform, one of the largest layoffs percentage-wise that has happened since the beginning of the pandemic.
Like others, Airtable is assuming a leaner, more focused business strategy to head into the new year.
Current and former Airtable employees can reach out to Natasha Mascarenhas on Signal, a secure encrypted messaging app, at 925 271 0912. You can also DM her on Twitter, @nmasc_.
| 2022-12-08T00:00:00 |
2022/12/08
|
https://techcrunch.com/2022/12/08/airtable-layoffs/
|
[
{
"date": "2022/12/08",
"position": 63,
"query": "AI layoffs"
}
] |
Insights - Propeller Consulting
|
Insights
|
https://propeller.com
|
[] |
Measuring AI ROI: How to Build an AI Strategy That Captures Business Value ... How to Chip Away at Technical Debt, Rather Than Relying on Layoffs for TechSpective.
|
How can we help?
Get in touch with us or check out our services.
| 2022-12-08T00:00:00 |
https://propeller.com/insights
|
[
{
"date": "2022/12/08",
"position": 82,
"query": "AI layoffs"
}
] |
|
Center for Collaborative AI in Healthcare
|
Center for Collaborative AI in Healthcare
|
https://www.feinberg.northwestern.edu
|
[] |
Below is a list of all Center for Collaborative AI in Healthcare members. View individual profiles of our faculty members — with publication and contact ...
|
Below is a list of all Center for Collaborative AI in Healthcare members. View individual profiles of our faculty members — with publication and contact information, research and clinical specialties and more — via the links below.
| 2022-12-08T00:00:00 |
https://www.feinberg.northwestern.edu/sites/artificial-intelligence/members/collaborative-ai-in-healthcare.html
|
[
{
"date": "2022/12/08",
"position": 16,
"query": "AI healthcare"
}
] |
|
Artificial Intelligence (AI) on Healthcare
|
Artificial Intelligence (AI) on Healthcare – Acumen Research Labs
|
https://acumenresearch.io
|
[] |
AI technology in healthcare has helped pharmaceutical companies speed up their drug discovery process. It, on the other hand, automates the identification of ...
|
Author: Alexandre Palma
Image Source: https://www.forbes.com/sites/cindygordon/2022/10/31/ai-in-healthcare-is-making-our-world-healthier/?sh=1730c67313ea
AI for Drug Discovery
AI technology in healthcare has helped pharmaceutical companies speed up their drug discovery process. It, on the other hand, automates the identification of targets. AI drug discovery streamlines the process and reduces repeated work.
For example, Pfizer is utilizing IBM Watson, a machine learning-based system, to help it find immuno-oncology treatments and Sanofi has agreed to employ Exscientia’s artificial intelligence (AI) platform to seek metabolic-disease medications.
If proponents of these strategies are correct, AI and machine learning will bring in a new era of drug development that is faster, cheaper, and more effective. Some are skeptical, but most experts believe these tools will become more crucial in the future.
AI for clinical trials
A clinical trial is a procedure in which freshly manufactured treatments are given to people to test how well they work. This has taken a significant amount of time and money. The success rate, however, is quite low. As a result, clinical trial automation has proven to be a benefit for AI and the healthcare business. Furthermore, Artificial Intelligence and healthcare assistance in the elimination of time-consuming data monitoring procedures.
Additionally, AI-assisted clinical trials handle large amounts of data and produce very accurate outcomes. There are some of the most popular Artificial Intelligence in healthcare applications for clinical trials:
Intelligent clinical trials – Traditional linear and sequential clinical trials are still the gold standard for ensuring the efficacy and safety of new drugs. The lengthy, tried-and-true method of distinct and defined stages of randomized controlled trials (RCTs) was developed primarily for evaluating mass-market pharmaceuticals and has remained mostly unchanged in recent decades. Artificial intelligence has the potential to shorten clinical trial cycle durations while also enhancing productivity and clinical development outcomes. Applying predictive AI models and advanced analytics to unlock real-world data (RWD) can help researchers better understand diseases, find relevant patients and important investigators, and enable revolutionary clinical study designs. In combination with an efficient digital infrastructure, clinical trial data might be cleansed, aggregated, coded, preserved, and maintained using AI algorithms.
Clinical Trial Cooperation and model sharing – Global, open, comprehensive, comparable, and verifiable data-sharing activities will be useful at this stage in connecting and promoting cooperation between various communities and geographies. Open science, aided by multi-stakeholder AI collaborations that operate across international borders, can speed up information distribution and capacity building in national health systems. The Epidemic Intelligence from Open Sources (EIOS) network, for example, uses open-source data to enable early detection, verification, and assessment of public health hazards and threats. Models used to diagnose illness from pictures, forecast patient results, filter misinformation, and misinformation depending on propagating patterns through social media, and distill knowledge graphs from massive collections of scholarly papers are instances of algorithms that could be broadly useful.
AI for Patient Care
Patient outcomes are influenced by artificial intelligence in healthcare. Medical AI firms create a system that aids the patient at every level. Clinical intelligence also analyzes patients’ medical data and delivers insights to help them enhance their quality of life. The following are a few significant clinical intelligence systems that improve patient care:
Maternal Care – That´s a potential technique for identifying high-risk moms and reducing maternal mortality and problems after childbirth: A) Predicting whether expectant mothers are at significant risk of difficulties during delivery using electronic health data and artificial intelligence (AI). B) Using digital technology to increase patient entry to both regular and high-acuity care (i.e., more sophisticated, and frequent care) throughout their pregnancy. When compared to delivering in higher-acuity clinics with more strong resources and clinical experience, high-risk obstetric women who deliver their infants at low-acuity clinics have a higher risk of developing serious maternal morbidity.
Healthcare Robotics – In addition to medical personnel, certain medical robots assist patients. Exoskeleton robots, for example, can assist paralyzed patients in walking again and becoming self-sufficient. A smart prosthesis is another example of technology in action. These bionic limbs attach sensors that render them more responsive and accurate than natural body parts, with the option of covering them in bionic skin and connecting them to the user’s muscles. Robots can help with rehabilitation and surgery. Cyberdyne’s Hybrid Assistive Limb (HAL) exoskeleton, for example, is designed to help patients rehabilitate from conditions that lead to lower limb disorders, such as spinal cord injuries and strokes, by using sensors placed on the skin to efficiently detect electrical signals in the patient’s body and responding with movement at the joint.
Artificial intelligence (AI) is progressively being used in healthcare, as it becomes more prevalent in modern enterprises and everyday life. Artificial intelligence has the potential to help healthcare providers in a variety of ways, including patient treatment and administrative tasks. The majority of AI and healthcare innovations are useful in the healthcare industry, but the strategies they assist can be rather different.
| 2022-12-08T00:00:00 |
https://acumenresearch.io/ai-on-healthcare/
|
[
{
"date": "2022/12/08",
"position": 61,
"query": "AI healthcare"
}
] |
|
A Healthcare System Perspective on AI
|
Careignition
|
https://www.careignition.com
|
[] |
A simple framework for evaluating an AI product is to ask, what specific “capabilities” does AI enable, and what's the “efficiency” with which it does the work ...
|
It looks like Artificial Intelligence (AI) will continue to be the hot topic in the business and technology world for quite a while, so I'll wade in with a framework and some hot takes... Most of my expertise is in healthcare data, and my thoughts here are health plan / healthcare administration-specific. However, much of what I say is going to apply to other fields.
Framework: While AI is all the rage, the presence of AI doesn’t, on its own, ensure a product will deliver value. AI’s usefulness is domain-specific and depends on the input data, how well that input data aligns with with the questions we want answered, and how little error is tolerated in that answer. A simple framework for evaluating an AI product is to ask, what specific “capabilities” does AI enable, and what’s the “efficiency” with which it does the work that a human would otherwise do?
If the data isn’t structured correctly or specifically enough in a domain, AI is at risk of giving you obvious or useless and unexplainable outputs.
One of the reasons ChatGPT is so impressive is because 1) it leverages a data set of all publicly available writing over all of human history and 2) researchers around the world have spent 70+ years figuring out how to digest, structure, restructure, and abstract text information in a manner that computers can use to create useful outputs, including those resembling how a decently-knowledgeable person would answer a question or produce a writing sample. Even then, it took thousands of people years to train the ChatGPT model.
Most industries, however, have neither the data nor data model advantages, and have extremely high standards for accuracy and precision where “decently-knowledgeable” wouldn’t cut it.
I run a data engineering and analytics company in healthcare. We take variable and fractured data from health plans and do a lot of novel work to make the data structured in a way that creates a relevant, use-able, and useful record of what happened in that health plan.
I've been 100% guilty of using the "AI" label as a lazy and over-hyped signal rather than describe the complex web of judgement, knowledge, and statistics my company relies on for our work. Some of the methods fall under the umbrella of “AI”, others do not. I use the “AI” label more frequently because it is useful shorthand and common in marketing.
Most of the useful stuff our company does today results from getting our computer to perform logic based on leveraging painstaking research and human reason. Looking at statistical relationships (the stuff most people call “AI”) is part of it, but it’s more for quality assurance than anything else. Most of the useful analytics our company does today is comparing averages, like the difference in unit costs between two facilities. Useful averages don’t exist in our industry because the underlying data is broken; we don’t have industry-wide standard definitions of what a healthcare service or “unit” (i.e. an MRI) is from historical data. See this article if you want more details.
In my industry, anyone who tells you healthcare administration (i.e. cost, resource allocation, networks, efficiency, etc.) is going to be solved by AI is glossing over some important details. Other than our company (shameless plug), almost no one can comprehensively tell you how much most individual healthcare services or decisions cost from historical data. That means there’s no data to be leveraged to train a computer to see or recommend useful and actionable insights for improving decisions that manage costs. Look no further than our current healthcare system for proof…
Here's a picture from our platform of the spread of diagnostic colonoscopy and chest x-ray prices for a commercial health plan in one month in 2022. Each bar represents the price of a Careignition-normalized unit of purchase.
A graph of non-emergent chest x-ray purchases over a month by a health plan. Each line represents an actual purchase.
A graph of diagnistic colonoscopy purchases over a month by a health plan. Each line represents an actual purchase.
The issue expressed by these graphs is the most glaring, first-order problem of our healthcare system today. Healthcare costs too much, we don’t allocate it well, and there is no accountability when one party is paid 15-times another for the same thing. We haven’t solved this problem and “AI-powered” analytics companies don’t report accurately report on it… People are too busy focusing on the future of AI to figure out the issues today.
Which is what leads me to reiterate, most AI for decision support in the healthcare economy (allocating patient volume and attention) is not very useful today.
Here is a real example from analytics leaders in our space: In a presentation, they showed how they use AI to predict future outcomes so you can intervene to lower costs. The example they presented was how the “AI” predicted that a 98 year-old who has recently gone to the hospital has a high probability of mortality within the next year. The AI’s recommendation is that someone should go to that person and try to get them to go to hospice to avoid readmission to the hospital.
Do we really need AI to tell us that a 98-year-old who just went to the hospital doesn’t usually have much time left to live? Are we sure that telling a 98-year-old we have given up on them and put them in hospice is good, moral, or effective idea? I bet that 98-year-old, even if his mortality was accurately predicted (it may not be), would want to spend his last days his way. Readmission rates be damned.
There are a lot of things like this in healthcare that may be predictable but are otherwise 1) obvious or 2) not change-able, or 3) not cost effective when impacted. People like predicting hospital re-admissions and ER admissions, but there is limited evidence that these metrics can be cost-effectively impacted. It’s also not clear to me that these are the most important metrics when it comes to making sure people receive valuable healthcare.
Now, why was this case (and those like it) used as an example of “cutting-edge” AI? Because the input data is set up to predict these outcomes, and it is a good excuse to use fancy AI computer programs. There are easy to find variables of “age”, “death”, “diagnosis”, and “hospital admission” (see article here). So getting an output of X% chance of death, given a set of conditions is pretty straightforward and compute-able. Just because we can apply “AI” prediction models to the problem doesn’t mean it’s useful.
Now, answering a more useful question like: “given a patient, what is the optimal location to allocate them for a diagnostic chest x-ray before a surgical procedure?” is a lot harder, because historical healthcare data today does not have a structure that reflects:
1) A clear price for that chest x-ray,
2) A clear picture of where chest X-rays have happened in the past (because there can be multiple parties at different locations involved),
3) An understanding of that a chest x-ray is not the only pre-procedural service someone will undergo before a surgery,
4) The patient’s affiliated costs (i.e. drive time),
5) The downstream causal relationships between one provider and another, and
6) Doesn’t know if the eventual surgeon will even accept the pre-procedural information…
If the information necessary for a person to solve the problem isn’t present in the data. A computer probably can’t figure it our either.
For AI magic to take place in healthcare or other domains, there needs to be a large investment in the information structure, so that a computer can help us make decisions we care about. You need specific knowledge and care, not correlations, to structure data properly for this purpose; once you do that, then AI can work its magic, but that first step is hard and not nearly as scalable for healthcare as AI evangelists may proclaim.
This pattern exists in many industries, and I think for entrepreneurs, investors, and purchasers being sold the wonders of “AI” it makes sense to think about what useful problems there are to solve, and whether the existing data is actually set up for a computer to help find a solution. So, when someone tell you that you need to buy or invest in the latest and greatest AI-powered product, it's really important to pull back the curtain and try to understand whether they actually do that and, if so, how. If AI’s outputs are not reasonable recommendations that line up with what people can actually do, or if they are not based on well-structured information that a smart analyst can reason with, don’t believe the hype.
| 2022-12-08T00:00:00 |
https://www.careignition.com/blog/a-healthcare-system-perspective-on-ai
|
[
{
"date": "2022/12/08",
"position": 74,
"query": "AI healthcare"
}
] |
|
Artificial intelligence innovation to accelerate health research
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Artificial intelligence innovation to accelerate health research
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https://www.ukri.org
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[] |
Examples include tackling inverse problems by integrating complex omics, healthcare, environment, lifestyle, biosocial and other datasets to generate novel ...
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Additional info
Background
UKRI AI statement of opportunity
‘AI review: Transforming our world with AI’ sets out UKRI’s aspirations for supporting transformational activities, and for working with our partners to place the UK in a strong position to realise the vast potential benefits of AI.
Read the UKRI AI review: Transforming our world with AI.
AI in health is a rapidly growing and fast evolving sector with major interest from UK government, the NHS and industry, and features in a number of UK strategies. It has significant transformative potential for health across a huge range of areas. However, this will not be possible without significant new AI capabilities and there are still significant barriers in the environment for AI research and innovation such as health data access, storage, and use, understanding societal acceptance of the use of AI in health, and the professional skills to enable this potential. Crucially, the gaps and challenges in different health areas are unique, therefore tailored solutions are needed for a particular context.
AI technologies need to be developed to meet these challenges and will require collaboration between multidisciplinary expertise who can develop suitable solutions and build new capability at the intersection between health (including wider dimensions such as wellbeing), research and AI.
This UKRI programme is anticipated to deliver the following outcomes (amongst others):
implementation of AI techniques in a health context, which were developed for other applications
development of responsible, ethical or trusted AI approaches for use in a health data science context
demonstration of proof of principle or proof of concept for new AI techniques in health
building a cohort of researchers with cross-disciplinary and sectoral skills
This programme also aims to provide complementary aspects to other activities within the UKRI and wider UK landscape. Projects should consider how they will connect with and add value to existing investments in the same area, or with other networks and projects active in this space.
Grant additional conditions (GAC)
Grants are awarded under the standard UKRI grant terms and conditions. The following additional grant conditions will also apply.
GAC 1: start date of the grant
Notwithstanding RGC 5.2 Starting Procedures, this grant must start by 2 October 2023. No slippage of start date beyond 2 October 2023 will be permitted. Expenditure may be incurred prior to the start of the grant and be subsequently charged to the grant, provided that it does not precede the date of the offer letter.
GAC 2: grant extensions
No slippage or grant extensions (beyond exceptional circumstances in line with the Equality Act 2010) will be allowed. UKRI will not be responsible for any cost overrun incurred during the course of this grant. The research organisation or organisations will be required to make up any shortfall from alternative sources.
GAC 3: naming and branding
In addition to RGC 12.4 Publication and Acknowledgement of Support, you must make reference to UKRI funding. You should include the UKRI logo and relevant branding on all online or printed materials (including press releases, posters, exhibition materials and other publications) related to activities funded by this grant.
GAC 4: monitoring and reporting
In addition to the requirements set out in the standard UKRI grant condition RGC 7.4.3, the grant holder is responsible for providing progress reports and monitoring data (financial and non-financial) when requested by UKRI.
UKRI expects that the frequency of financial returns will be twice a year but reserves the right to request returns more or less often as appropriate to respond to changes in business needs. A template and guidance to complete this will be provided by UKRI in due course.
As part of the management process, the grant holder will be expected to produce an annual report detailing progress against their stated aims and objectives and should highlight any key impacts or success stories.
UKRI reserves the right to suspend the grant and withhold further payments if the performance metrics requested are not provided by the stated deadlines or are determined to be of an unacceptable standard by EPSRC.
GAC 5: expenditure
At the start of the grant the financial spend profile will be agreed by UKRI.
In addition to any reporting requirements set out in GAC 8, the grant holder must immediately notify the UKRI project officer, or officers, in writing of any accumulation, slippage or variation in expenditure greater than 5% of the annual profiled funding.
Any such changes must be approved in writing by UKRI; approval should not be assumed and will be dependent on spend across all associated grants.
We reserve the right to re-profile the grant if required.
Any deviation from the agreed allocation of funding and profiled costs must be negotiated and approved through written consent by UKRI. The approval of profile changes should not be assumed and will be dependent on spend across all associated grants.
At the end of the grant period a breakdown of the expenditure should be submitted along with the final expenditure statement.
GAC 6: embedding trusted research
The grant holder is expected to embed trusted research throughout their activities. EPSRC reserves the right to suspend the grant and withhold further payments if trusted research is not embedded throughout the programme or is deemed to be of an unacceptable standard by EPSRC.
Responsible innovation
UKRI is fully committed to develop and promote responsible innovation. Research has the ability to not only produce understanding, knowledge and value, but also unintended consequences, questions, ethical dilemmas and, at times, unexpected social transformations.
We recognise that we have a duty of care to promote approaches to responsible innovation that will initiate ongoing reflection about the potential ethical and societal implications of the research that we sponsor and to encourage our research community to do likewise.
Webinar recording and slides
Watch the webinar recording on Zoom.
Passcode: u@6P*y7N
Webinar slides (PDF, 2MB)
Webinar question and answer document (PDF, 175KB)
Supporting documents
Equality impact assessment (PDF, 175KB)
| 2022-12-08T00:00:00 |
https://www.ukri.org/opportunity/artificial-intelligence-innovation-to-accelerate-health-research/
|
[
{
"date": "2022/12/08",
"position": 91,
"query": "AI healthcare"
}
] |
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DaVinci: Best AI Art Generator
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DaVinci: Best AI Art Generator
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https://davinci.ai
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[] |
Transform your ideas into beautiful AI artwork instantly. Generate unique digital art, paintings, and illustrations with simple text prompts.
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Generate on the go
The DaVinci mobile app is available on both iOS and Android. So you can tap your photos on fly, on the go, whenever, whenever.
| 2022-12-08T00:00:00 |
https://davinci.ai/
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[
{
"date": "2022/12/08",
"position": 2,
"query": "AI graphic design"
}
] |
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How Artificial Intelligence is Changing Creativity
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How Artificial Intelligence is Changing Creativity
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https://www.beyond.agency
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[] |
It is the use of machines to create art. Creating art, graphics, images, music, books - literally anything is possible in the creative AI world.
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For a long time, creatives have felt safe in the artificial intelligence world. But now, we're not so sure. It is often said that AI cannot display creativity or emotions and that these factors stop it from being able to accurately replicate a human. However, have we now broken down another barrier with emerging technology? Is it true that AI is capable of being creative? There have been many advancements recently in the world of creative AI and in this article we are going to explore what’s out there today. We hear a lot nowadays about AI and whether it is going to take over our jobs. While AI will not replace humans as such, it will certainly replace many jobs across different industries in the future. When it comes to creativity, it cannot be denied that AI will struggle to fully replace creative roles, particularly because art and design are so subjective, but the tools that exist already are extremely capable of producing complex art work which can evoke human emotion. What is creative AI? “Where AI is the simulation of intelligence in machines, Creative AI is the simulation of creativity in machines” - Ed Newton-Rex So, what actually is creative AI? In short, it is the use of machines to create art. Creating art, graphics, images, music, books - literally anything is possible in the creative AI world. There are many different ways that people have harnessed the power of machines in this way, some have created bots that will generate any kind of artwork possible, or have used AI to piece together a movie trailer or a song. We are seeing creative AI popping up everywhere in every industry and it is going to be interesting to see what advancements are still to come with this technology. AI technology in the creative industry “To an AI, creativity isn’t exactly the power of imagination as much as it is the power of creation" - Mara Anton Below are three examples of AI art tools which use machine learning to create pieces of art. You can give some of them a try for yourself by visiting their website links. We can’t deny that it’s pretty impressive what some machines can do! Mid Journey Midjourney is an independent research lab that produces a proprietary artificial intelligence program that creates images from textual descriptions, similar to OpenAI's DALL-E. In order to use their platform, you need to sign up via discord and then use the bot. You have 25 free images and after that you will need to subscribe. To ask the bot to create an image, click into one of the ‘newbie’ channels and type ‘/imagine’ into the message bar, followed by your request. For example, for the image below we typed ‘a happy teddy bear in a field of daisies having a picnic bright vibrant cartoon’. The bot will then generate four images in under 60 seconds based on your text. From there you can select one version that you wish to upscale. On their platform you can also view pieces of art that users have asked the machine to create. The results are quite spectacular! DALLE 2 DALLE 2 is an AI system that creates realistic art images from descriptions in natural language. It interprets the language and then gives creative outputs. It can also extend already existing pieces of artwork or repurpose them. You can input any text into their system and it will produce a piece of artwork like the one below. The words in black are what were chosen for this particular image. The combinations and possibilities on this software are endless! Since DALLE was launched they have improved the capabilities and the machine can now generate images that are four times higher resolution and are overall more accurate. The newer version is better at matching an interpreting the captions and overall photorealism is higher quality. Below is a comparison of what the machine created in version one vs version two:
AdCreative AdCreative is an AI platform that can create and upload all your social media content with AI-driven data. AdCreative can: Design ads for every social media platform; Instagram, Facebook, LinkedIn, Pinterest and Twitter. Each ad can be made a certain size which will be relevant for the platform.
Generate copy - text and headlines where the machine will write in the correct way for your advertisement. You can input your target audience and the machine will select the appropriate language to use.
Get creative insights into your ad performance across all social media platforms. You can gain a better understanding of what works and what doesn’t in your advertising campaigns.
Generate social media posts that look professionally designed without any human involvement. It’s scarily good! Give a whirl with a free account… Creative AI examples We have looked at two AI technologies that are used in the art space and one that is popular in the content creation space but what else can AI do that is creative? AI can create music. IBM’s Watson and Sony’s Flow Machine are two examples of companies that have harnessed the power of AI to create music. A popular music producer called Alex da Kid teamed up with IBM’s Watson to try and create a song together. A whole song was written and produced using a machine. AI has been used in the world of live music with musicians putting on virtual concerts to thousands of people. The most recent example of this is ABBA and their ‘Voyage’ tour which was entirely virtual. The band partnered with Industrial Light and Magic, a visual effects company who used advanced motion capture techniques to create virtual copies of the band. These virtual copies behave in the exact same way as the band members, mimicking eye movements and dance moves. Another example of creative AI is in the film industry. AI was used to create the first ever cognitive movie trailer for a horror film called Morgan. The machine was fed scenes from the movie and it then picked out 10 different scenes to pull together as part of the trailer. This is the first time a machine has been able to do something like this without any human intervention. In the food industry AI is also being used to create recipes and combine foods in different ways. There is even an AI-developed whisky that was created by a Finnish company in collaboration with Microsoft. AI is also being used to revolutionise the food research industry, with some companies using AI to create brand-new food flavours or changing flavours of existing foods in ways that no human would be capable of doing themselves. [Image Source] So, how about writing? Surely, a machine surely isn’t capable of writing entire novels? We couldn't be more wrong. Initially, machines were only capable of writing short-form ‘journalistic’ style content but there have now been some examples of AI being used to write whole novels. The results were slightly questionable and although it reads like a novel, it isn’t the same as a novel written by a human. The machine doesn’t have a real understanding of what has been said before and how that links to the future so it doesn’t quite make for a best-selling book. With technology advancing at the rate it is just now however we never know what might be in store for novel-writing and AI. These are just some examples of AI being used across different types of creative industries and it is certain that AI will only continue to become more involved in these industries as time goes on. Benefits of creative AI So, what can AI do to benefit us as humans in the creative space? Speed of production Creative AI is opening up a world of opportunities for creatives in the 21st century. It is a way of speeding up the creative process, from brainstorming to designing to then publishing content, this removes the need to employ a human and as a result they can dedicate time to other more important and meaningful tasks.
Unique creative outputs The type of creative work that AI is producing is unique and it’s possible that no other machine can produce something identical to that of a machine. AI is also empowering non-creatives to be creative. By producing ideas and content and pushing the boundaries of the art world, people can see how diverse the creative world is. The possibilities are truly endless. No more creative blocks With a machine doing the creative work you don’t have to worry about having a mental block or having a day where you feel less creative. The machine will always be able to produce no matter what. These machines can also produce at a pace no human is capable of working at, meaning output will be higher. Ideation and collaboration Using AI is also a way of opening up opportunities for collaboration and presents situations where people can build on other’s skills. It can ensure authenticity when it comes to producing something that is truly individual and representative of what the creator wanted and it can enhance confidence in a creator’s skills when they see what is truly possible with the use of machines. [Image Source] Accepting and using AI is a way for humans to better understand how it works and how they can harness its power positively for humanity. Accepting this viewpoint means we can reap the benefits of AI while still being able to do things ourselves and remain an important player in the creative process. Concerns about creative AI Naturally, there are concerns surrounding creative AI and what it means for the creative industry. It is an extremely new concept that not many of us are familiar with and as a result there can be resistance towards these tools. Are they going to replace jobs in the creative industry? How can we control what these machines do with our ideas? How secure and safe is it? Let’s look into some common concerns surrounding creative AI… It’s just not the same as a human Many are apprehensive about creative AI purely because the artwork and things they produce are simply not the same as if they were created by a human. When we go to an art gallery and we view artwork we feel emotions and we can sense the journey of an artist. Seeing physical artwork and meeting artists can evoke emotions inside of us in a way that digital art cannot. Many creatives argue that this is vital for their own jobs to remain safe, that humans realise digital content just isn’t the same as physical artwork. Limitations in creative AI Another concern is in regard to the limitations on what people can request these machines to create. With every AI tool there needs to be a policy which confirms that they will never create artwork which will offend others. Most tools state that they will not allow for harmful production of images that could be graphic, inappropriate or distasteful but we have to be sure that this will be monitored effectively. Most systems have been trialed and tested before being put to public use and they are continually improving on this front. Creative AI and copyright issues There are also concerns surrounding copyright. Who actually owns the artwork that is generated by these machines? What happens if people who were on the team are no longer living but the machine has produced content that needs to be owned by someone? This is an ongoing concern and something which will need to be thoroughly investigated before any laws or regulations can be put in place. [Image Source] Although there can be some resistance to AI as it is new and unfamiliar to many, we have to consider that in the past many technological advancements were initially uncomfortable and unfamiliar. People were not sure about television nor the smartphone but we have adapted to allow these tools a place in our everyday lives. Will AI creativity ever match human creativity? AI is extremely advanced and can perform some pretty incredible tasks but creative AI will never match human creativity. For the time being machines are not capable of replicating what a human can do with it’s mind when it comes to creativity. Creative AI is simply a tool to use alongside human creativity. It can speed up the process and produce more work at a faster rate than humans but we will always need a human input when it comes to creativity. “AI cannot replace human creativity and idea generation, but it may be the greatest supplement to the human brain ever discovered.” - Vitaly Pecherskiy Machines cannot interpret meaning in the same way that a human can. Being able to distinguish between levels of meaning and establishing what is meaningful and what is not is a fundamental element of human creativity that machines cannot replicate. Machines have no understanding of social context. Things that make us human are our ability to understand the surrounding context, to adapt our communication according to our surroundings, to understand complex topics like politics and religion and understand and feel all kinds of emotions. AI at the moment has no ability to do this, meaning our human ability to be creative remains on top. [Image Source] "Our intelligence is what makes us human, and AI is an extension of that quality." - Yann LuCun, Chief AI Scientist, Facebook Will AI replace creative jobs? Creative AI has certainly got a long way to go before it will replace any jobs in the creative industry. Although these platforms are forward-thinking and have some amazing features we are yet to see how their work can outperform humans in this creative industry. Being able to walk around a museum and view artwork or visit a modern sculpture exhibition is still a more enriching and fulfilling experience than viewing artwork through a screen. This could change as humans evolve and adapt to the ever-growing digitalisation of our world. There are already many job roles that have been replaced by AI but the creative industry remains low-risk for now. However, the importance of the human mind in the creative space is something that cannot be underestimated. Creativity is subjective and interpretive and humans are the only beings on earth that are able to produce such a personal and individual thing as art. AI for now is a supplement to creative roles. AI can produce social media ads quicker and provide useful data insights, or it can create some mesmerising artwork but whether that art is appropriate for specific projects will still require human judgement. AI can write novels but a human still needs to proofread the content and make adjustments where necessary. AI is an incredibly useful tool that is enhancing many industries, including the creative one but we don’t need to worry about it completely taking over the creative industry for now. Phew!
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| 2022-12-08T00:00:00 |
https://www.beyond.agency/blog/how-ai-emerging-technologies-are-changing-the-creative-landscape
|
[
{
"date": "2022/12/08",
"position": 74,
"query": "AI graphic design"
}
] |
|
Brand Designer & Illustrator Job Description +TEMPLATE
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Brand Designer & Illustrator job description
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https://resources.workable.com
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[
"Keith Mackenzie"
] |
Bachelor's degree in Graphic Design or a related field is preferred, but not required ... AI Technology · Integrations · Security. Resources. Help center ...
|
Brand Designer & Illustrator job description
The Brand Designer & Illustrator creates visual concepts, using computer software or by hand, to communicate ideas that inspire, inform, or captivate consumers. This may include logos, packaging, promotional materials, and more. They work closely with clients and marketing teams to develop a brand’s visual identity.
| 2022-12-08T00:00:00 |
2022/12/08
|
https://resources.workable.com/brand-designer-and-illustrator-job-description
|
[
{
"date": "2022/12/08",
"position": 98,
"query": "AI graphic design"
}
] |
automation | MIT News | Massachusetts Institute of ...
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Massachusetts Institute of Technology
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https://news.mit.edu
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[] |
Anthropologist touches on the history of tech-related job displacement and explores how other countries approach policies on robots, skills, and learning.
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The team’s new algorithm finds failures and fixes in all sorts of autonomous systems, from drone teams to power grids.
Read full story →
| 2022-12-09T00:00:00 |
https://news.mit.edu/topic/automation
|
[
{
"date": "2022/12/09",
"position": 36,
"query": "automation job displacement"
}
] |
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Hello, ChatGPT—Please Explain Yourself!
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Hello, ChatGPT—Please Explain Yourself!
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https://spectrum.ieee.org
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[
"Edd Gent"
] |
ChatGPT: One concern about AI is the potential for job displacement. As AI ... automated, which could result in job losses and economic disruption.
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A new AI-powered chatbot released by OpenAI called ChatGPT has taken the Internet by storm. More than one million people have signed up to try out the free service since it was launched last week, and it has wowed users with its ability to engage in natural-sounding conversation, provide detailed answers to technical questions and even solve coding challenges.
The service is based on the latest version of OpenAI’s large language model GPT-3, which has been trained on vast amounts of text scraped from the Internet. (The acronym here stands for Generative Pre-Trained Transformer, which describes the specific kind of deep learning network it’s based on.) To make ChatGPT engage in more natural sounding dialogue, it has gone through a further round of training using human feedback on how good its responses are. The result is a chatbot that can write sonnets, explain complex physics topics, program software, and even get a passable score on an IQ test.
This has led to rapturous reviews and claims that ChatGPT heralds the death of the Google search engine or the re-invention of the education system. Others though, have been quick to point out that it often provides authoritative-sounding answers that are completely wrong. And while there are safeguards designed to prevent the model from responding with offensive or dangerous material, it can easily be tricked into giving advice on how to shoplift or make explosives.
I am constantly learning and improving, but my knowledge is limited to what I was trained on and is not up to date.
—ChatGPT
As is often the case with AI advances, the reality is nuanced, says Arvind Narayanan, a professor of computer science at Princeton University. “Releasing the tool to everyone has resulted in people discovering many creative and entertaining uses, but it has also led to a ton of hype,” he tells Spectrum. “Cherry picked examples of impressive responses tend to go viral on Twitter, but they don’t present the full picture.”
In a blog post co-authored with Princeton doctoral student Sayash Kapoor, Narayanan writes that ChatGPT is a bullshit generator, but a potentially very useful one. Its lack of any concept of truth means it’s unlikely to be much use for applications like education or accessing medical advice. But in situations where the truth is easy to verify, such as debugging code, or where it doesn’t matter, like writing fiction, it could be much more powerful.
To delve deeper into the potential implications of this buzzy new AI tool, Spectrum decided to put some questions to ChatGPT itself. The conversation has been edited for length.
IEEE Spectrum: Can you explain how you work? What technology do you rely on, and how were you trained?
ChatGPT: I am a large language model trained by OpenAI. I rely on advanced machine learning algorithms and a huge amount of data to generate responses to the questions and statements that I receive. I am designed to be able to generate human-like text that is relevant to the input that I receive.
To train me, OpenAI used a technique called unsupervised learning. This involves feeding a large amount of text data into my system and using that data to train my machine learning algorithms. The goal of this training is to enable me to generate text that is coherent, relevant, and human-like.
I am not a real person and do not have access to the internet, so I am not able to browse the web or access any external information. Instead, I rely solely on the information that I was trained on to generate my responses. I am constantly learning and improving, but my knowledge is limited to what I was trained on and is not up to date.
My responses may not always be completely accurate or relevant, and I may struggle with tasks that require creative thinking or complex problem-solving. Using me effectively may require some patience and understanding of my limitations.
—ChatGPT
Spectrum: What kinds of problems can you help with, and who will derive the most benefit from you?
ChatGPT: I can be used to assist with a wide variety of tasks, such as answering questions, providing explanations, and generating text on a given topic. I may be most beneficial for individuals who need assistance with language-related tasks, such as writing, editing, or research. I can also be a useful tool for anyone who is interested in learning more about a particular topic or who wants to have a conversation about a particular subject.
Spectrum: What is the most effective way for a human to work with you? Are you more like a search engine or a collaborator?
ChatGPT: The most effective way for a human to work with me is to ask me specific questions or provide me with a specific topic or prompt, and then to use my responses as a starting point for further discussion or exploration.
In this way, I can be thought of as more of a collaborator than a search engine. I can help to generate ideas and provide explanations on a given topic, but I am not able to provide up-to-date information or research on my own. I am most effective when used as part of a collaborative process, where my responses can be used as a starting point for further discussion and exploration.
It is also important for users to keep in mind that I am a machine learning model and not a real person. This means that my responses may not always be completely accurate or relevant, and I may struggle with tasks that require creative thinking or complex problem-solving. Using me effectively may require some patience and understanding of my limitations.
Spectrum: What is the nature of creativity, and are you creative?
ChatGPT: Creativity is the ability to produce something new and original, often through the use of imagination and innovative thinking. Creativity can manifest in a wide variety of forms, such as in artistic endeavors, scientific discoveries, or new business ideas.
I am not capable of the kind of creative thinking that is required to produce truly novel ideas. ... I am not capable of deciding if something is true or false.
—ChatGPT
As a large language model, I am not capable of being creative in the same way that a human is. I am trained using unsupervised learning, which involves feeding a large amount of text data into my system and using that data to train my machine learning algorithms. This enables me to generate human-like text that is relevant and coherent, but it does not allow me to produce truly original or innovative ideas.
While I can assist with language-related tasks and can generate human-like text, I am not capable of the kind of creative thinking that is required to produce truly novel ideas. My responses are based on the information that I was trained on, and I do not have the ability to think outside of that information or to produce truly original ideas.
Spectrum: What is truthfulness? How do you decide if something is true?
ChatGPT: Truthfulness is the quality of being truthful, or being in accordance with the facts. In other words, something that is truthful is based on true and accurate information, and does not contain any false or misleading information.
As a large language model, I am not capable of deciding if something is true or false. Because I am a machine learning model and not a real person, I do not have access to the internet or external information, and I do not have the ability to verify the accuracy or truthfulness of the information that I provide.
Instead, my responses are generated using the information that I was trained on. This means that the information that I provide may not always be completely accurate or relevant, and it is important for users to use their own critical thinking skills and to verify the information that I provide through other sources.
Spectrum: Are all users really capable of verifying the information you provide, particularly when it comes to technical topics?
ChatGPT: In general, it is important for users to use their own critical thinking skills and to verify the information that I provide through other sources. This is particularly important for technical topics, where the information may be complex and specialized, and where a lack of accuracy or precision could have serious consequences.
However, it is important to note that not all users may have the knowledge or expertise to verify the information that I provide on technical topics. In these cases, it may be necessary to consult with a subject matter expert or to use other reliable sources of information.
One way to detect text that has been generated by a large language model is to look for patterns or repetitions in the text. Because large language models like me are trained on a large amount of text data, we may sometimes generate text that includes repeated phrases or sentences.
—ChatGPT
Spectrum: Is there a danger that people will misuse you? What would count as misuse?
ChatGPT: Like any tool, I can be misused if used in the wrong way. Some examples of misuse of a large language model like me could include using my responses as definitive answers without verifying the information through other sources, or using my responses to make important decisions without considering other factors. Misuse could also include using my responses to spread false or misleading information, or to deceive others into believing that I am a real person.
It is important for users to understand the limitations of a large language model like me and to use me responsibly. This means verifying the information that I provide through other sources, being aware of my limitations, and using me as a starting point for further discussion or research rather than relying on me for definitive answers.
Spectrum: What professions are most at risk from you and other large language models?
ChatGPT: Some jobs that involve writing or editing large amounts of text, such as journalism or copywriting, may be at risk of being automated by large language models like me. Similarly, jobs that involve researching and summarizing information, such as market research or data analysis, may also be at risk.
However, it is important to note that large language models like me are not designed to replace human workers, and there are many tasks that we are not capable of performing. Many jobs, such as those that require creativity, complex problem-solving, or interpersonal skills, are unlikely to be automated by large language models. Additionally, even for jobs that may be at risk of automation, it is likely that the adoption of large language models will happen gradually, giving workers time to adapt and learn new skills.
Spectrum: What are the jobs least likely to be at risk from AI?
ChatGPT: There are many jobs that are unlikely to be at risk from AI, including those that require creativity, complex problem-solving, or interpersonal skills. For example, jobs that involve artistic or creative endeavors, such as writing, painting, or music, are unlikely to be automated by AI. These jobs require a high level of creativity and originality, which are difficult for AI to replicate.
I am designed to assist with language-related tasks such as writing, editing, and research, but I do not have the ability to experience sensations or emotions, and I do not have consciousness.
—ChatGPT
Additionally, jobs that involve complex problem-solving or critical thinking, such as medicine, law, or engineering, are also unlikely to be automated by AI. These jobs require a deep understanding of a particular subject area and the ability to apply that knowledge to complex situations, which are beyond the capabilities of current AI technology.
Similarly, jobs that require strong interpersonal skills, such as teaching, counseling, or social work, are also unlikely to be automated by AI. These jobs require the ability to communicate effectively with people and to understand and respond to their emotions and needs, which are difficult for AI to replicate.
Spectrum: What are the best ways and methods that humans can use to detect text that has been generated by ChatGPT?
ChatGPT: One way to detect text that has been generated by a large language model is to look for patterns or repetitions in the text. Because large language models like me are trained on a large amount of text data, we may sometimes generate text that includes repeated phrases or sentences.
Another way to detect text that has been generated by a large language model is to look for language that is unnatural or does not make sense in the context. Large language models like me are trained to generate coherent text, but we may sometimes generate sentences or phrases that do not fit seamlessly into the surrounding text.
Additionally, because large language models like me do not have access to the internet or external information, we may not be able to provide up-to-date information or answer questions that require external research. This means that text generated by a large language model may lack current information or may not provide detailed or specific answers to complex questions.
Spectrum: Could a large-language model like you ever become conscious?
ChatGPT: Consciousness is a complex and poorly understood phenomenon, but it is generally thought to require a biological brain and the ability to experience sensations and emotions.
Because I am a machine learning model and do not have a biological brain, it is not possible for me to become conscious in the same way that a human can. I am designed to assist with language-related tasks such as writing, editing, and research, but I do not have the ability to experience sensations or emotions, and I do not have consciousness.
Spectrum: What should humans be most worried about concerning AI?
ChatGPT: One concern about AI is the potential for job displacement. As AI technology continues to improve, it is likely that some jobs that are currently performed by humans will be automated, which could result in job losses and economic disruption.
Another concern about AI is the potential for bias and discrimination. AI systems are trained using data, and if the data is biased, the AI system may also be biased. This could result in unfair or discriminatory treatment of individuals based on factors such as race, gender, or age.
Additionally, there is also a concern about the potential for AI to be used for malicious purposes, such as in cyber attacks or the development of autonomous weapons. As AI technology continues to advance, it is possible that it could be used in ways that are harmful to humans or to society.
Overall, there are many potential concerns surrounding the development and use of AI, and it is important for humans to carefully consider these concerns and to take steps to address them as AI technology continues to evolve.
| 2022-12-09T00:00:00 |
2022/12/09
|
https://spectrum.ieee.org/chatbot-chatgpt-interview
|
[
{
"date": "2022/12/09",
"position": 77,
"query": "automation job displacement"
},
{
"date": "2022/12/09",
"position": 87,
"query": "AI economic disruption"
}
] |
4 Myths of Industrial AI, Debunked
|
4 Myths of Industrial AI, Debunked
|
https://www.cognex.com
|
[] |
AI can reduce the quantity of mundane, repetitive tasks, empowering workers to address other creative or high-skill functions.
|
Artificial intelligence (AI) has permeated nearly every facet of modern life. It recommends the best commute, suggests music or podcasts during the journey, powers countless applications and machines throughout the day, and recommends shows or movies to stream when you get home.
AI is here to stay.
Industrial AI can help manufacturers maximize uptime with equipment monitoring and preventative maintenance programs and identify loss yields and defects (Cavallo) . And its predictive capabilities can create learning and forecast demand models (Koev).
However, AI has struggled to reach widespread adoption in industrial automation use cases. Many companies are still grappling with the basics and are hesitant AI can deliver meaningful returns.
In IBM’s 2022 global AI adoption index report, 34% of survey respondents – about 2,550 businesses from across the world – said a lack of AI expertise is preventing implementation (IBM). Other factors preventing AI adoption included cost (29%), lack of tools/platforms (25%), difficulty and scalability (24%), and data complexity (24%).
Here, we’ll examine those obstacles and dispel common misconceptions about AI in manufacturing and logistics.
#1 Terms are interchangeable and unimportant.
Before exploring AI options, it’s essential to understand the technology's different forms, functions, and feasibility. While some terms may overlap or seem synonymous at first glance, comprehending the nuances of AI is the first step in determining if the technology is the right fit for your needs.
Algorithm: a set of instructions and calculations that help a computer achieve an objective. A “learning” algorithm uses trial-and-error and learn-by-example methodologies to optimize production processes without human intervention.
Artificial intelligence: a group of computing techniques that attempt to mimic human decision-making, using automation to perform tasks that are difficult for humans using image recognition natural language processing, and other technologies.
Deep learning: an AI technology designed to automate complex and highly customized applications. Processing takes place via a graphics processing unit (GPU), which enables quick and efficient analysis of vast image sets to detect subtle defects and differentiate between acceptable and unacceptable anomalies.
Edge Learning: an AI technology designed for ease of use. Processing takes place on-device, or "at the edge," using a pre-trained set of algorithms. The technology is simple to set up, requiring smaller image sets (as few as 5 to 10 images) and shorter training periods than traditional deep learning-based solutions.
Machine learning: Computing processes that can improve outcomes without human programming. Machine learning algorithms train a computer to seek success and avoid failure millions of times to generate learning outcomes.
Machine vision: Rules-based algorithms that identify specific characteristics of an object. Though machine-vision tools work much faster than the human eye, AI can dramatically improve these tools’ accuracy and effectiveness.
#2 AI will replace jobs and foster distrust among employees.
The myth of emerging technology replacing jobs could likely be traced back to the invention of the wheel. The truth is a bit more complicated.
The same can be said for AI. Instead of AI replacing jobs, companies are discovering that employees can work alongside AI to achieve greater productivity and open new possibilities. AI can reduce the quantity of mundane, repetitive tasks, empowering workers to address other creative or high-skill functions. In 2018, a New York-based charity began implementing AI for data entry tasks, which contributed to lowering the firm’s annual turnover rate from 42% to 17% (Knight). The technology is being widely applied to manufacturing and logistics to address the ongoing labor shortage and other chronic issues. When paired with robotics, AI can facilitate tasks such as object avoidance and surface mapping to deliver goods throughout facilities. When coupled with machine vision systems, AI can perform repetitive, albeit essential, quality assurance tasks including part absence/presence detection and inspection (Gow). Leveraging AI to perform mundane operations enables facilities to reallocate resources toward more intensive tasks and assist front-line workers by offsetting their workload.
Advances in industrial technology, including AI, are rarely conceived in a vacuum. They’re designed to improve performance, efficiency, quality, and capabilities. It’s easy to see why internal combustion and steam engines effectively replaced horses and buggies, or how the telegraph opened new lines of communications compared to hand-delivering letters. These innovations succeeded other forms of technology. Although engines ousted the horse and buggy, the technology created an entirely new industry while enabling mass transportation, transforming logistics, personal conveyance, and shipping.
| 2022-12-09T00:00:00 |
https://www.cognex.com/blogs/machine-vision/4-myths-of-industrial-ai-debunked
|
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"date": "2022/12/09",
"position": 5,
"query": "AI replacing workers"
},
{
"date": "2022/12/09",
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"query": "future of work AI"
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|
How is AI Transforming HR for Companies?
|
How is AI Transforming Human Resources?
|
https://startuptalky.com
|
[
"Startuptalky News",
"Vivek Dev Jacob",
"Sanvi Barjatya",
"Muskaan Kapoor",
"Sakshi Jadhav"
] |
In 2022, 2.3 million jobs will be created, while 1.8 million jobs will be eliminated by AI worldwide. Here is how AI is transforming HR.
|
Human resources are no less than a building block for organizations. A robust HR team can take companies and organizations to the next level; at the same time, a weak one can spoil established companies. It is vital to nurture and improve the human resources department. After all, this service line recruits the workforce. Stats show the AI market is expanding at a CAGR of 38.1% between 2022 to 2030. AI is claimed to be a top priority in their business plans by 83% of companies.
Artificial intelligence is transforming HR by making it more efficient and unbiased. Contrary to what many people assume, AI has the power to enhance the concept of human contact in HR.
Human resources deal with people and their attitudes. From planning to implementation, several processes come under the HR department. Making new policies, ensuring onboarding standards meet the standards, and whatnot— the HR service line also looks into labour relations, performance management, and job design. The HR team assists in achieving the mission and vision of an organization. The use of AI in HR will not only increase the efficiency of the company but also give employees more alignment with its objectives.
Impacts on Jobs Due to AI in 2022 Worldwide
As of 2022, the global AI market is valued at over $136 billion. Over the next 8 years, it is expected to increase by over 13 times. AI has and will have a heavy impact on job opportunities. It is estimated 2.3 million jobs will be created, while 1.8 million jobs will be eliminated by AI worldwide in 2022.
What is Artificial Intelligence?
How is AI Influencing Human Resources?
Companies using AI for HR Management
What is Artificial Intelligence?
AI refers to Artificial Intelligence, a tech which enables computers to think and acts like a human. Organizations are using AI to improve productivity. AI takes decisions based on the input provided. It uses a certain kind of algorithm. With work-from-home culture now mainstream and organizations becoming people-sensitive, the need for AI is escalating.
Most of the activities executed by HR are now being managed by AI. It empowers human resources to enhance contacts. According to a survey, 60% of organizations are using AI for talent management.
Key Benefits of Artificial Intelligence
How is AI Influencing Human Resources?
Uses of AI to Assist Workers in Their Organization as of 2020
Artificial Intelligence in Daily Tasks
AI can take over several tasks that the human resources department deals with on a day-to-day basis. It can give responses to prospective and current employees. It can also help with questions related to policies and procedures. And this is just the tip of the iceberg when it comes to AI's capabilities. It can also free up time for HR professionals to focus on other work.
Handling forms of different kinds, simplifying training sessions, and AI systems can execute various other tasks.
Improved Talent Search
For organisations, choosing the right talent is a crucial task since employees are the cornerstone to success. When it comes to skills, companies generally have some set of standards. AI can help the HR department to filter resumes as per the desired skill set. AI systems can screen resumes according to the data they are trained on. The department will then have to spend lesser time on the selection process.
Providing Training
AI can help new employees by providing efficient training. A conversational chatbot can be used for this purpose. Employees facing any difficulty can send messages to the chatbot. The latter will reply based on the way it's programmed. Moreover, the chatbot shall give employees the flexibility to complete external and internal training according to their convenience. At times, employees don’t know whom to contact for queries. An AI-powered helpdesk can facilitate the situation.
AI in decision Making
Artificial Intelligence can help employees to understand their moods better. Due to workload, employees aren't able to give their best at times. AI can be trained to provide personality and behaviour insights. Individuals can then use this system to facilitate decision-making.
Employee Retention
AI can help the HR team to analyze the reasons behind layoffs and retain more employees. Believe it or not, AI-based systems can help manage the workforce, and that too a satisfied one.
AI can detect and analyze employees who are disengaged or unproductive. It can provide insights as to where the company is lacking, or why its people are discontent.
Appraisals
Appraisals keep the workforce motivated. And the process should not be partial or biased. AI has a role to play here as well. It can ensure employees get the results in tandem with their performance. Giving appraisals or bonuses to the suitable candidate can boost productivity immensely. Moreover, appraisals require neutral thinking, something which AI is capable of.
Companies using AI for HR Management
Companies such as Google, Amazon, IBM, Unilever, L’Oréal, IKEA, Adecco, Hays, Deloitte, Vodafone, PwC, Oracle, TCS, Accenture, and Tech Mahindra have adopted AI for HR Management.
Some AI platforms/tools for HR selection and recruiting are HireVue, Mya Systems, HiredScore, Wade & Wendy, AllyO, Textio, Turing, Toptal, Manatal, Skillate, TurboHire, Talenture, Fetcher, SeekOut, Zoho Recruit, Paradox, Humanly, Findem, hireEZ, AmazingHiring, Loxo, Eightfold.ai , Textio, myInterview, and Arya, Pymetrics.
AI cuts time spent on CV verification, and other recruitment processes. The best eligible candidates are filtered out. Unilever, L’Oréal, and IKEA are the B2C using AI to transform their Candidate Sourcing Strategies.
Company Name AI Hiring Assistant/solution Purpose IKEA Robot Vera Conduct interviews and send out
customized follow-emails Unilever HireVue Assessments Hire the best and most diverse graduates
among potential candidates L’Oréal Mya Filter out the best candidates among the
received job applications
AI adoption in HR is increasing. Here is a list of a few companies revolutionizing recruiting process using AI.
AI platform/solution used Companies using the platform HireVue 700+ companies, including Unilever, Vodafone, PwC, and Oracle Mya Systems L’Oréal, Adecco, Hays, Deloitte, and more HiredScore Trusted by 40% of the Fortune 100: Intel, Dell, Domino,
Johnson & Johnson, and more Wade & Wendy E-Trade, Randstad, Comcast, and more hireEZ Deloitte, VISA, Amtec, Wayfair, CommonSpirit, and more AllyO Walmart, Hilton, AT&T, FedEx, Arbys, and more
Conclusion
The impact of AI on HR Management is now extending to use cases and problems hitherto unexplored. It is providing helping hands to people in their work. Health, finance, banking, computing, marketing, and management. Artificial Intelligence is penetrating every imaginable industry. With unbiased results and the ability to be improved further, AI is here to stay.
FAQs
How will artificial intelligence affect human resources?
AI provides HR departments with an opportunity to improve the candidate and employee experience by automating repetitive, low-value tasks and freeing up time to focus on the more strategic, creative work that HR teams need and want to get done.
What is artificial intelligence in human resources?
At a high level, AI is a technology that allows computers to learn from and make or recommend actions based on previously collected data. In terms of human resources management, artificial intelligence can be applied in many different ways to streamline processes and improve efficiency.
What is human resource transformation?
Human resource transformation is the process of fundamentally rethinking and rechartering the HR function/department in the organization. If HR professionals do that, they become the “go-to” people to help operating managers address “people problems” they face in getting the organization's work done.
Can technology replace human resources?
No, technology is not stealing human resources jobs; instead, it's helping small businesses work more efficiently. Although apps and software eliminate some tasks, they typically aren't enough to replace a person in the workplace.
What are some benefits of AI?
24x7 Availability
Saves time
Saves money
Reduces Human Errors
Removes human bias
Daily Applications
WIDGET: questionnaire | CAMPAIGN: Simple Questionnaire
| 2022-12-09T00:00:00 |
2022/12/09
|
https://startuptalky.com/how-ai-transforming-hr/
|
[
{
"date": "2022/12/09",
"position": 33,
"query": "AI replacing workers"
},
{
"date": "2022/12/09",
"position": 37,
"query": "workplace AI adoption"
}
] |
Frontline workers are disengaged. Here's how to fix it.
|
Frontline workers are disengaged. Here’s how to fix it.
|
https://www.appspace.com
|
[
"Tionne Smith"
] |
Appspace AI · Content creation & publishing · Integrations · Reporting ... The cost of replacing an individual employee is one-half to two times the ...
|
Why focusing on frontline worker engagement is the best retention strategy of all
If you Google the phrase “hybrid work” you’ll see no end of recent articles exploring the topic – from how to implement it well, to the associated challenges and benefits, to the expectations of both employers and employees. You’ll also see that there’s one persona largely missing from the narrative: frontline workers.
From the healthcare professionals caring for their communities, to the retail associates serving customers, to the employees keeping manufacturing plants going, there are so many deskless workers who don’t have the luxury of choosing where they work. And they make up 80% of the workforce. These folks must show up to a physical location on the front lines every day, because that’s where their work gets done.
The worrying state of frontline worker engagement
It seems frontline workers are definitely feeling left out of the workplace experience conversation. They’re also increasingly disengaged at work, which drives performance down and turnover up. All signs point to significant operational disruption and expense on the horizon for organizations with large frontline workforces:
According to McKinsey , 45% of frontline workers plan to leave their jobs in the next 3-6 months.
The cost of replacing an individual employee is one-half to two times the employee’s annual salary (and that’s a conservative estimate).
Business units with engaged workers have 23% higher profit compared with business units whose workers are unhappy. Plus, “teams with thriving workers see significantly lower absenteeism, turnover and accidents.”
What’s behind the lack of frontline worker engagement?
The Great Resignation saw more than 43 million people voluntarily leave their jobs, and frontline roles were particularly hard hit. While many headlines zeroed in on the number of vacant roles in industries like food service, hospitality, and retail, manufacturing weathered the biggest surge in workers leaving, with a nearly 60% jump in rates compared with pre-pandemic times.
It turns out there are a couple of key reasons why frontline workers are rethinking their work experiences:
How to increase engagement among your frontline staff
The days of trying to keep frontline teams engaged using sporadic team huddles, bulletin board postings, and free donuts in the breakroom on Fridays are long over. Today’s workers want to be fairly compensated for the work they do, but they also want flexibility and opportunities for growth. And they want to always feel safe, respected, and supported at work.
Here are a few strategies you can put in place to meet your frontline people where they are:
1.Keep them in the loop
The problem: Your messages get missed.
Sending consistent, timely communications to employees on the front line is a challenge. They often don’t have a company email or device, and they aren’t permitted to access their phones outside of sanctioned breaks. Many manufacturing facilities also have the added barrier of wireless access challenges.
The solution: Make communications unignorable.
Curate a company newsfeed. Keep all your critical messages in one hard-to-miss newsfeed that employees check every shift. An employee app is a solid solution to consider here as it centralizes all communications and workplace management. Look for a solution that can be embedded into tools your employees already use, like Microsoft Teams . Anyone should be able to access your messages from mobile or desktop, without the need for a corporate email.
Democratize access to information. For frontline teams that can’t access communications on their own devices, providing kiosks makes sure everyone gets the same information, in the same way, regardless of where they are working.
Broadcast critical information. Digital signage isn’t just for the head office. Placing it in locations like your staff-only areas, factory floors, and warehouses gives frontline workers visibility into the latest company announcements, workplace guidance, and health and safety standards.
Ease your content load. Creating content to keep employees informed can quickly drain the time and resources of your internal comms team. Explore whether your team would benefit from ready-made digital signage content that’s curated by experts and tackles hot topics like workplace safety, customer service, and even corporate culture. Appspace also lets you choose from several pre-designed HTML templates to easily create captivating content that’s responsive to any display.
2. Make safety a priority
The problem: Employees don’t mean to practice unsafe behaviors at work, they simply forget to do the right things.
The forgetting curve shows us how rapidly people forget information that isn’t reinforced. So, while your training might be excellent, if you aren’t reminding your frontline employees of the right things to do on the floor every day, you may be putting them at risk for safety violations.
The solution: Reinforce safety protocols right in the flow of work.
Those communication channels mentioned above? Turns out they’re also a fantastic tool for reinforcing important safety messages so they stick. Add them to the employee feed and keep refreshing them on digital signage in the key areas where frontline employees spend time to create a culture of safety and awareness.
Bonus: If you’ve got an urgent message that needs to get out quickly to multiple channels, Apspace lets you quickly broadcast across your entire organization, either by scheduling manually or automatically triggering by a third-party alert system like Singlewire’s InformaCast, Alertus, or Everbridge.
3. Give them a voice
The problem: Frontline workers don’t have enough opportunities to provide feedback.
Since the majority of their time is spent executing on operational imperatives, frontline employees can easily feel disconnected from the larger organization and struggle to understand how their contributions fit with overarching goals.
The solution: Make it easy to weigh in.
Help them connect to the larger picture. A modern intranet makes it easier for frontline workers to connect and collaborate with coworkers and the larger organization. It’s also a handy place to centralize the storage of personalized information, like to-do reminders, policies, and other documents. Seeking out a social-media like experience that also encourages employees to add likes and comments makes providing feedback frictionless and gives you a real-time pulse on how they’re feeling on the issues that matter.
Ask them . The folks working on your front lines have an up-close view of things like how well processes are working and what customers are saying. There are many ways you can leverage your omni-channel digital experience to tap into their wealth of knowledge. Send out questionnaires through your employee app. Remind them to complete the questionnaire through messages on your digital signage and intranet. You can also encourage discussion and idea sharing during team huddles or 1:1 catch-ups.
Check out this quick demo of our Appspace Intranet from a recent webinar.
4. Set up your frontline managers for success
The problem: Managers are the face of the company for your frontline employees and they too often leave a bad impression.
In one of the largest studies of burnout, Gallup found the biggest source was “unfair treatment at work.” That was followed by an unmanageable workload, unclear communication from managers, lack of manager support, and unreasonable time pressure. All of these factors lead back to a single person having a huge impact on the frontline experience.
The solution: Help them lead.
No frontline manager is intentionally doing a poor job supporting their people. Too often they simply don’t have the skills or time to invest in their direct reports. But when they do, it really pays off. 95% of people who are thriving at work report being treated with respect all day.
Equip your managers to lead . Many managers get promoted into their roles because of tenure of their own strong performance as an individual contributor. Make sure they have the training they need to evolve from doers to leaders.
Encourage kudos . Once you have the digital channels in place to attract frontline eyeballs – like an employee newsfeed and digital signage – make sure your managers are using them to share good news, give performance shout-outs, and generally promote team cohesion.
Make time for stay conversations. When managers connect with direct reports 1:1, encourage them to ensure they aren’t staying at the task level. Stay conversations go deeper, with the intent of learning about an employee’s passions, struggles, goals, and what type of support they are craving at work.
Let’s make work a place frontline employees can thrive
Employee engagement matters. A lot. Teams that score in the top half on employee engagement more than double their odds of success when compared with those in the bottom half.
The encouraging news is that organizations know things have to change when it comes to the experience of frontline employees. Almost one-third do not consider their frontline workers fully empowered and digitally well-equipped. They just aren’t always sure where to start with improving their work experience.
Want to see the engagement-boosting power of Appspace with your own eyes? Reach out for your personal demo.
| 2022-12-09T00:00:00 |
2022/12/09
|
https://www.appspace.com/blog/frontline-workers-are-disengaged-heres-how-to-fix-it/
|
[
{
"date": "2022/12/09",
"position": 96,
"query": "AI replacing workers"
}
] |
The Future of AI Agents: Top Predictions for 2025
|
The Future of AI Agents: Top Predictions & Trends to Watch in 2025
|
https://www.salesforce.com
|
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This reveals a real opportunity for enterprises to augment their employees, expand their workforce, and improve customer experiences in 2025. As Salesforce ...
|
The first AI, created in 1950, essentially functioned like a recipe follower — strictly adhering to a set of instructions without any tasting or adjusting along the way. Today, AI is or more like a skilled chef, one who can taste, adapt, and even invent recipes — dynamically learning and generating creative outputs.
But what if AI could go one step forward, not just generating a recipe, but taking action to bring the meal to life — buying ingredients, scheduling reservations, and adjusting for challenges along the way?
This is the promise of agentic AI. AI agents are autonomous, proactive applications designed to execute specialized tasks. Agents use large language models (LLMs) to analyze and understand the full context of a request, prompt, or an automated trigger, then reason through decisions on the next steps autonomously.
Salesforce’s Agentforce Is Here: Trusted, Autonomous AI Agents to Scale Your Workforce Agentforce is a new layer on the Salesforce Platform that enables organizations to build and deploy autonomous AI agents. Companies like OpenTable, Saks, and Wiley are using Agentforce today to augment their employees, expand their workforce, and improve customer experiences.
Read more
And while a majority of workers don’t fully trust AI to operate autonomously today, 77% of workers say they will eventually trust AI to operate autonomously. This reveals a real opportunity for enterprises to augment their employees, expand their workforce, and improve customer experiences in 2025.
As Salesforce leaders look toward the era of agentic AI, they see a variety of ways it may come to life — and strategies to ensure it will be successful. Read on for predictions from Salesforce leaders on:
What is Salesforce (and why are its executives predicting the future)?
Salesforce is the world’s #1 AI CRM, helping organizations of any size reimagine their business for the world of AI with autonomous and trusted agents, unified data from any source, and Customer 360 apps.
With Salesforce, humans and agents drive customer success together.
Salesforce leaders are on the front lines of the latest trends, technologies, and challenges impacting businesses. They bring expertise and insight from market analysis, customer conversations, and more to help reveal what tomorrow might bring.
How Humans Will Interact with Agents
The concept of agentic AI — AI systems that operate with a degree of autonomy, goal-orientation, and decision-making capability — brings up a fascinating array of future possibilities. Salesforce leaders foresee a variety of potential futures for the revolutionary technology.
1. Copilots will be commoditized.
“Using AI as a reactive personal assistant for general-purpose productivity — drafting emails, making recipes, summarizing documents, planning itineraries — will become table stakes, as common as spell-check. This technology will soon be built into personal devices and seamlessly integrated across AI tools and platforms used in business. The transformative leap will come from AI that moves past generic assistance and evolves into business-aware intelligence — understanding the unique context, challenges, and needs of your industry to carry out meaningful, strategic business tasks and decisions on your behalf.” – Adam Evans, EVP & GM, Salesforce AI Platform
2. We’ll move from single agents to multi-agent teams for complex use cases.
“In 2024, AI agents began augmenting people in simpler use cases, such as sales and service. In 2025, we’ll increasingly see more complex, multi-agent orchestrations solving higher-order challenges across the enterprise, like simulating new product launches or marketing campaigns and developing recommendations for adjustments. Salesforce’s own Atlas Reasoning Engine employs multiple large language models (LLMs), large action models (LAMs), and specialized RAG modules to perform distinct subtasks like (re)ranking, refining, and synthesizing, leading to state-of-the-art levels of trustable autonomy.” – Mick Costigan, VP, Salesforce Futures
3. AI skepticism will give way to AI confidence.
“Despite continued executive urgency to incorporate AI tools into business operations, more than two thirds of desk workers still say they’ve never used AI at work. In 2025, we’ll see the barrier-to-reliance on AI wane as users work side by side with agents for common tasks like automating projects, new hire onboarding, generating content, or managing IT incidents. Agents’ advanced reasoning and ability to make decisions and take action will transform how every user works and how they engage with customers. In turn, desk workers will grow more confident with AI and businesses will see even greater adoption and ROI on their investments.” – Rob Seaman, Chief Product Officer, Slack
4. AI agents will become the preferred channel for customers to engage with businesses.
“As AI agents become increasingly powerful and capable of delivering highly personalized experiences, consumer engagement with the technology is expected to surge, creating a valuable new marketing channel for businesses. To fully harness the potential of AI agents, organizations will need to form cross-functional “Agent Experience” teams that connect marketing, sales, service, and commerce departments. These teams will be essential in optimizing AI-driven interactions, enabling businesses to provide deeper, more dynamic consumer experiences while driving growth and improving operational efficiency throughout the customer journey.” – Jon Belkowitz, Senior Director, AI Product Management, Marketing Cloud
5. A rise in personal agents and bring-your-own AI.
“New advances will place ever-more-powerful AI agents in the hands of consumers. Agentic capabilities in consumer devices and apps, like Apple Intelligence and China’s super-apps that offer a one-stop shop for users, are already emerging and aim to leverage personal context to help users get things done effortlessly. As people begin to bring their personal AI agents to the workplace, companies will need to find ways to integrate AI into the enterprise more quickly. We call this trend BYOAI (bring your own AI agent), mirroring the earlier BYOD (bring your own device) movement that saw workers bringing personal smart phones to the office. And as they become mainstream, personal AI agents will forever change how companies connect to their customers. Successful companies will offer trusted, hyper-personalized messaging, services, and products at the moments when they matter most, and they’ll package everything in easy-to-consume ways for the agents that increasingly serve as customer proxies.” – Mick Costigan, VP, Salesforce Futures
6. Actionable analytics will be powered by Agentforce Inspectors.
“In this agent era, we are evolving to a place where – thanks to Agentforce – we can have ‘inspector agents’ that are always on and look for anomalies across any business or department. These analytics agents will be able to identify issues and opportunities across any business and then trigger instant actions. We think this will lead to improved outcomes and give analysts and business users more time for important decisions and actions that need human judgment. For example, when a sales team uses a tool like Tableau, powered by Agentforce, agents will help identify when a team’s pipeline drops below a certain threshold and then instantly trigger a sales play in Slack. This is just one example of how humans and agents can deliver success at scale.” – Ryan Aytay, CEO, Tableau
7. AI agents will collaborate and work together.
“In the coming year, we will begin to see AI agents working together in swarms, collaborating to tackle everyday tasks and business challenges, much like ants building intricate colonies. This evolution will redefine productivity and problem-solving on an unprecedented scale. AI agents will become seamlessly integrated into our lives, with individuals having personal agents and organizations deploying specialized ones. Available through platforms such as Agentforce, these agents will be tailored to specific tasks and work together to achieve shared objectives. The future will not just be about using AI; it will be about creating and customizing agents that collaborate to understand and execute strategic tasks and decisions, both in personal and business contexts.” — Silvio Savarese, Chief Scientist & Head of Salesforce AI Research
8. Agents will solve for a fragmented customer experience.
“Today, different teams split ownership of multiple pieces of a long customer journey, from marketing creating a lead to business development reps collecting and landing a lead, to account reps nurturing a customer relationship and providing post-sales services, and more. And without conversation context being shared across departments, the customer’s experience is fractured and frustrating. Agents will change that in 2025. Agents will have access to the data that gets missed in human to human hand-offs, including prior conversation and context. Tomorrow’s agents will be able to orchestrate seamless handoffs and transitions across different functional agents and humans to personalize a customer journey undreamt of today.” – Gabrielle Tao, SVP, Product Management
OpenTable Scales Global Customer Service Fast with Secret Ingredient Agentforce. OpenTable is using Agentforce to provide faster, more personalized customer support with autonomous AI agents, giving the company’s human teams more time to focus on more time-consuming, complicated tasks. Read more
9. Learners will prefer communicating with flexible, 24/7 AI agents.
“Many learners who endured the isolation of the COVID pandemic and found freedom in their first iPhone, rather than their first car, will be more comfortable getting their questions answered by an agent. These students will be able to engage freely with agents, without social anxiety, and be able to get their questions answered immediately, at any hour of the day. This ability to provide flexible, 24/7 support will also make agents the preferred support method for working students or students with children who need flexibility throughout the school year. But, as AI use becomes ubiquitous, institutions will need to be intentional in how they simultaneously help their learners overcome social anxieties and develop the social skills they will need to thrive outside of school.” – Margo Martinez, VP & GM of Education
What It Will Take for Humans to Trust Autonomous AI
To realize this agentic future, trust is paramount. Today, 54% of global workers trust humans and AI to do most work tasks together. When asked if these workers trusted AI to do any of these same tasks autonomously, the answer, for a small group, was some. Salesforce leaders share how to maintain trust in the autonomous AI era, and how agents can support security-related tasks.
1. Businesses will build trust in the agentic AI era.
“We’re at an inflection point with trust in AI with agentic AI — notably, 60% of consumers say advances in AI make it more important for companies to be trustworthy. Maintaining trust in AI systems will become a top priority for businesses, and we’ll see companies create an equal playing field where everyone can access this technology to ensure humans and AI can work together successfully. To this end, companies will implement elements including: building people’s trust and confidence over time with explainability features and user guidance; enabling learning to help users understand the technology and ensure there are feedback mechanisms so users can improve system outputs; and shifting toward human-centered AI designs with people prioritized at the center.” – Paula Goldman, Chief Ethical and Humane Use Officer
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2. AI agents will serve as front-line guardians of enterprise security.
“AI agents will become the primary guardians of organizational security, outpacing human capabilities in detecting security vulnerabilities, enhancing security posture, and making proactive changes. Meanwhile, as bad actors leverage agents to create more sophisticated attacks, security agents will be needed to support adaptive real-time threat detection and response. This shift will enable security specialists to focus on higher level architecture.” – Alice Steinglass, EVP and GM, Salesforce Platform
3. AI governance will be a CEO-level priority.
“APIs allow AI agents to perform tasks, access data, and execute processes efficiently. As a result, managing AI agents becomes just as critical as managing APIs, since both are integral to maintaining smooth operations and achieving business objectives. As a result, by 2026, AI governance will be a CEO-level priority as agent topics and actions will need to be audited, managed, and secured. Once companies have a good handle on API and AI governance, they’ll be able to use AI agents widely and successfully.” – Ahyoung An, Chief Business Officer, MuleSoft
4. Companies need to maintain cyber hygiene in the era of agentic AI.
“Adhering to traditional security measures and ensuring human diligence will become more critical than ever in the era of agentic AI. Just as locking your doors is the best way to prevent car break-ins, using proven defenses against non-AI threats is the most effective way to counter AI-based attacks. Effective strategies will include: ensuring companies build a culture of trust and security, implementing hardware root of trust (RoT) for user authentication, requiring multi-factor authentication (MFA), relying on trusted corporate channels (like Slack) when phishers attempt AI-driven scams via SMS or other messaging platforms, and ensuring software is constantly patched and up to date.” – Brad Arkin, Chief Trust Officer
5. The need for trusted AI will lead to more public-private policy collaboration.
“The rapid evolution of AI, especially agentic AI, will drive more proactive engagement from tech companies in policy discussions. In order to strike an optimal balance between fostering innovation and ensuring public safety, it will be crucial for the private sector to bring their technical expertise to the table. This collaboration will be integral to guiding the development of AI policy frameworks that are not a one-size-fits-all, but rather account for the many different ways and contexts in which the technology can be used.” – Eric Loeb, EVP, Global Government Affairs
6. Organizations need to be agent-first to build loyalty.
“In the future, every organization, across varied industries, verticals and sectors, will be agent-first. This means any customer’s interaction with a brand, or constituent’s outreach to his or her government, will often happen first through an AI agent, such as Agentforce — and it means that companies and organizations will choose to evolve further and faster to meet the lasting moment. To stay ahead, brands should focus on creating AI agents that align with core priorities and values and embed customer-centric ethics into every interaction — and ensure that customers know when they are interacting with AI. Those that move now to build trustworthy and transparent digital experiences with the right agents are likely to set the standard, turning these first encounters with AI into lasting brand loyalty.” – Sabastian Niles, President and Chief Legal Officer
How AI Agents Will Change Work
An estimated 41% of employee time is spent on repetitive, low-impact work. Salesforce leaders see a world where agents take on these repetitive tasks, letting humans focus on the more strategic, relationship-building work that drives revenue.
1. Advancements in AI will drive simplicity and activation.
“AI will become easier to implement within day-to-day business applications, allowing companies — especially those with a central view of customer and business data — to natively integrate agents and use data securely to support specific processes. AI will also become more actionable, enabling foundational data to trigger real-time events and actions at scale, enhancing productivity and achieving outcomes previously unattainable. This combination of ease and actionability will lead to higher confidence, utilization, and value from AI in business.” – Steve Hammond, EVP & GM, Marketing Cloud
2. Analytics will be ambient.
“By 2025, 25% of all analytical insights will be delivered ‘ambiently’ thanks to AI — integrated seamlessly into the flow of work and life, without the user consciously deciding, ‘It’s time to do some analysis.’ These insights will surface in real time, embedded in everyday interactions, whether that’s through a recommendation popping up in a meeting tool, a gentle nudge from an AI agent while writing an email, or a notification on a smart device. As data becomes more ubiquitous, the role of the analyst will shift toward shaping these ambient experiences, curating and refining the insights that AI surfaces automatically, ensuring they are contextually relevant and immediately actionable.” – Nate Nichols, VP of Product Management, Tableau
3. AI agents are the new apps.
“Much like how mobile apps revolutionized interactivity, communication, and productivity, AI agents are now poised to redefine access to technology and address everyday business challenges. These agents will be customizable, highly adaptable, and capable of autonomous decision-making, allowing them to anticipate needs, optimize tasks, and offer personalized assistance and solutions. They will operate conversationally in user initiated sessions and as agents in embedded workflows, boosting productivity while scaling business practices across all industries, enabling organizations to operate more efficiently and intelligently than ever before.” – Jayesh Govindarajan, EVP, Salesforce AI
4. The jobs of an autonomous AI future will require a new set of skills.
“The jobs of the future — especially with AI and agents — will require three types of skills: technical, human, and soft. Technical skills include the specific knowledge required to perform tasks, such as data analysis, programming languages, graphic design, or accounting principles. Soft skills are the interpersonal skills that help us interact effectively with others, such as communication, time management, problem solving, and conflict resolution. Human skills are ingrained in our personalities and character, and are things like empathy, resilience, creativity, and emotional intelligence. The companies that will win with AI will help their employees cultivate these skills.” – Lori Castillo Martinez, EVP, Talent Growth & Development
5. The ability to navigate, analyze, and action unstructured data will define your business in 2025.
“Just like its name implies, unstructured data is often the hardest to make sense of and yet it’s also some of the most valuable information within a company. Eighty percent of enterprise data is unstructured data, according to Gartner. 2025 will distinguish the haves from the have nots: the businesses that have the tools and tech to process unstructured data and make it AI-ready will come out on top in this new agentic era. These companies will be better positioned to not only generate stronger business insights — but their teams and agents will be better equipped to make decisions and take action — from analyzing customer sentiments to generating blog posts and creating competitive plans.” – Sarah Walker, COO, Slack
6. Employers will need to prioritize upskilling employees.
“We’ll see a brand new swath of jobs created in response to the acceleration of AI innovation — and existing jobs will be transformed by agents. In response to this, 2025 will be the year of upskilling. Every employer will have to figure out how to put AI and agents into the hands of employees to ensure they’re not left behind. And employees are going to choose the companies that will help them gain critical skills to set them up for success now and in the future.” – Nathalie Scardino, Chief People Officer
7. AI will redefine the employee experience.
“AI agents will transform the experience employees have when they come together in the office. AI and agents will help employers understand how workers use office spaces, book meeting rooms, and gather with their teams, and make recommendations for teams to ensure they always have the right space at the right time. Finally, AI will use workforce planning data to streamline how companies design their real estate portfolios and identify the types of spaces they need around the world.” – Relina Bulchandani, EVP, Real Estate and Workplace Services
How AI Agents Will Transform Industries
The types of tasks, challenges, and goals that AI can help address vary significantly by industry. Salesforce leaders share how agentic AI will benefit different industries in unexpected ways.
1. SMBs will surpass large competitors by leveraging AI agents.
“Small and midsize-sized businesses (SMBs) and growing companies will not only keep up with larger competitors but even surpass them by leveraging AI agents for scalable growth. With AI agents, these businesses will be equipped to streamline operations, boost customer engagement, and deliver personalized marketing. From managing supply chains and following up on sales leads to handling customer support, autonomous agents will empower SMBs to scale swiftly while preserving a strong competitive edge.” – Kris Billmaier, EVP & GM, Sales Cloud and Self-Service & Growth Products
2. 2025 will be the last year of producing cumbersome nonprofit annual reports.
“As the ability to summarize large sums of information accelerates, individual donors will engage more autonomously with the causes they care about. Nonprofits will be able to provide proof points of their work, show tangible results, and present real-time insights to potential donors at the first point of engagement. Cumbersome annual reports will become extinct. Instead, every donor will be able to engage an organization directly and immediately understand the impact of their mission, how their donation or volunteer hours will help support that mission, and make a donation — all directly through an AI agent.” – Lori Freeman, VP & GM, Nonprofit
3. AI agents will power retail experiences.
“Agents have the potential to transform the holiday season and beyond by helping retailers provide personalized, timely, and efficient service to shoppers when they need it most. AI-powered product recommendations have already influenced 16% of all sales in October and November thus far; there is a wide open opportunity for digital retailers to use AI for personal shopper agents that help consumers find exactly what they’re looking for and continue to make the path purchase an easy one.” – Michael Affronti, SVP & GM, Commerce Cloud
4. Agents will scale nonprofit impact.
“Nonprofits are early adopters of AI, with 90% using it to drive engagement. AI agents will help drive meaningful change within these organizations by assessing nonprofit staffing requirements and priorities and co-creating impactful volunteer roles. Agents will also help companies with volunteer programs identify transformative skills-building and provide volunteer opportunities at local nonprofits for their employees.” – Molly Ford, VP, Employer Brand and Recruitment Marketing
5. AI agents will achieve mass adoption within education faster than any other technology.
“AI agents will inspire education institutions to develop a deeper understanding of the economics of their daily operations. This will lead to a shift in thinking from headcount costs per department to costs at the activity level — like the cost to answer a student inquiry — helping institutions reduce costs while driving more impact. And, with staff burnout threatening to destabilize colleges, education workers will welcome the support of agents that can take on tasks like scheduling appointments and assisting with enrollment, or even helping to fill seasonal personnel gaps when staff need extra hands like during busy recruiting seasons and back to school.” – Margo Martinez, VP & GM, Education
6. AI agents will make government services and benefits more accessible.
“The efficiency, cost savings, improved productivity, and ability to provide better citizen support will make autonomous AI the fastest adopted technology in history. By the end of 2025, citizens will engage directly with AI agents for government assistance. Federal and local agencies across the United States will have deployed agents that make it fast and easy for their constituents to do everything from renewing their passports to obtaining occupational licenses to registering their vehicles. Agents will also be able to help people easily understand what kinds of benefits are available to them, what they qualify for, and how to apply.” – Nasi Jazayeri, EVP & GM, Public Sector
More information
| 2024-11-14T00:00:00 |
2024/11/14
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https://www.salesforce.com/news/stories/future-of-salesforce/
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Become a Data Scientist or Machine Learning Engineer
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Become a AI Machine Learning Engineer at 4Geeks Academy
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https://4geeksacademy.com
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[] |
4Geeks Academy's Data Science and Machine Learning bootcamp offers a job guarantee. Develop in-demand skills and unlock exciting career opportunities.
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| 2022-12-09T00:00:00 |
https://4geeksacademy.com/us/coding-bootcamps/datascience-machine-learning
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What Is an AI Engineer?
|
What Is an AI Engineer?
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https://builtin.com
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[] |
We encounter the work of AI engineers every time we use Netflix, Spotify or YouTube, when machine learning customizes suggestions based on past behavior. Or ...
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Artificial intelligence (AI) engineers are in charge of developing intelligent algorithms capable of learning, analyzing and predicting future events, turning those algorithms into AI models and systems, and then testing and maintaining them.
What Is An AI Engineer? An artificial intelligence engineer develops, implements, tests and maintains an organization’s AI applications and systems. This requires not only a deep working knowledge of the artificial intelligence space, but also programming and statistics.
We encounter the work of AI engineers every time we use Netflix, Spotify or YouTube, when machine learning customizes suggestions based on past behavior. Or when we’re able to have productive conversations with a chatbot or AI voice assistant. AI engineers develop a lot of company-facing products as well, helping them increase their efficiency and profits, cut costs and make more informed business decisions.
Tariq Haque works as an AI engineer at CVS Health, where he helps the company make more informed decisions about their customers and interact with them more efficiently.
“For example, I want to identify what my customer would like to buy. I cannot do it manually,” Haque told Built In. “That’s where AI comes in. It will run all those complex calculations and do it for me and identify what the customer wants, and I can deliver that to the customer.”
Meanwhile, Gayatri Shandar, a software engineer focusing on AI at LinkedIn, mainly works on the site’s advertising AI, which essentially allows LinkedIn to continue showing users ads that are relevant to them despite the privacy and transparency constraints imposed by Apple, Google and others. Before LinkedIn, she was a machine learning engineer at Peloton, where she worked on the company’s recommender system, providing suggestions for which classes users should sign up for based on their instructor preferences, fitness level and so on.
“We do a bit of data science, we do a bit of engineering, and — depending on the company you work for and the size of the company — we also do a little bit of product, doing stack for new features and talking to product managers and other stakeholders as well in getting that feature up and running,” Shandar told Built In. “[AI engineers] wear a lot of hats.”
Check Out These Open RolesAI Engineer Job Postings
The Responsibilities of an AI Engineer
AI engineers play an important role in organizations that rely on artificial intelligence. They are responsible for not only identifying problems that could be solved using AI, but they’re also in charge of the development and production of AI systems, as well as implementing them.
Some specific tasks include creating and managing the development and production of an AI infrastructure, conducting statistical analyses and interpreting the results in order to guide future decisions, building AI models from scratch and helping product managers and other teams implement and analyze them, and transforming machine learning models into specific applications.
AI Engineer Responsibilities Creating and managing the development and production of AI infrastructure
Conducting statistical analyses and interpreting the results
Building AI models from scratch
Transforming machine learning models into specific applications
Data wrangling
Data Wrangling and Preparation
Day to day, Shandar said AI engineers are responsible for doing a lot of data wrangling, or making sure that the data being fed to the models is correct and what is expected, as well as finding ways to better store, prepare, extract, transform and load that data.
Model Adjustment and Problem-Solving
AI engineers are also tasked with figuring out which modeling techniques work for the given problem they want to solve, whether that be machine learning, deep learning, natural language processing, computer vision and so on.
Programming and Software Development
Haque said most of his time working as an AI engineer at CVS Health involves just good, old-fashioned programming. It’s a lot of building software, testing, deploying, testing it again and going back to refine it. In the end, he and his team come out with products that have proven to be massively beneficial to the company.
Focused Machine Learning Research and Analysis
Not all AI engineers are responsible for improving a company’s efficiency, though. Kulsoom Abdullah, an AI engineer at Duke University Health Systems’ Bashir Lab is more interested in furthering medical research and diagnostics — specifically by applying deep learning technology to the analysis of medical images. Because Abdullah works in academia, her role as an AI engineer looks different from those working in the corporate world. She’s not a professor or postdoctoral fellow, so the work she’s doing has an application as opposed to just pure research. But the audience for the work she does is smaller than what it likely would be were she working at an industry level.
“You have more time in academia to work on something,” she told Built In. “In industry, because of it being more on a quarter kind of system, and you have your stakeholders and business units, you do have to spend a certain amount of your time on things that are going to provide, say, immediate value.”
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AI Engineer vs. Data Scientist
AI Engineer vs. Data Scientist AI engineer: builds AI systems and tools to apply data insights without human intervention.
Data scientist: collects and analyzes data to find insights, plus sometimes builds AI models to help make predictions.
Before Duke University, Abdullah worked as a data scientist at companies like Anthem and ADP, and she says there is “a lot of overlap” between AI engineers and positions like data engineers and data scientists. In some cases, the work of a data scientist at one company could be the work of an AI engineer at another.
There are some key differences, however. Data scientists handle the data collection, analysis and visualization, then sometimes build models off of what they find. AI engineers design and build AI systems and products, among other things.
For instance, a data scientist may be able to figure out what perfume a 25-year-old woman living in New York City and making $70,000 a year would be more likely to buy. And an AI engineer will use that information to create an automated product or a tool to put those insights into action without the need for human intervention, like a shopping suggestion tool or a targeted email marketing campaign.
Andrew Seligson, an AI engineer at Entanglement, a quantum computing and AI startup focused on cyber threat detection, describes the work of AI engineers as a kind of “outgrowth” of what data scientists have been doing for the last five or 10 years.
“There’s been kind of a seachange in the growth of data science and data engineering roles,” he told Built In. “What that means is that AI engineers sort of have to have a pretty broad skill set. … Someone who is able to work across a lot of the different domains of the data science, engineering, AI and ML space.”
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Important Skills for AI Engineers
AI Engineer Required Skills Artificial intelligence knowledge
Programming
Math, statistics and probability
Communication
Critical thinking
Software Engineering and Programming Skills
While a comprehensive and firm knowledge of the various facets of artificial intelligence is important as an AI engineer, software engineering skills are also essential. Python, R, Java and C++ are among the most used languages in this space.
Math and Statistics Skills
Statistics and probability are also important components of AI engineering, since machine learning models are based on mathematical principles. Plus, a firm grasp on concepts like statistical significance helps if an AI engineer needs to determine the validity and accuracy of a given model.
Problem Solving, Communication and Critical Thinking Skills
Being an AI engineer also requires some soft skills, particularly as it relates to problem solving, communication and critical thinking. Seligson, whose educational background is in musical composition and religious studies, says he often has to lean on his non-technical background as an AI engineer at Entanglement, particularly when it comes to communication.
“Engineers respect good technical work, and being able to articulate yourself as a technician in ways that are clear, concise [and] logical,” Seligson said. “I’ve found that a lot of my old skills didn’t necessarily have to get thrown out the window just because I was working in STEM.”
Find out who's hiring. See all Developer + Engineer jobs at top tech companies & startups View Jobs
AI Engineer Education Requirements
With technical knowledge like programming and data handling needed for the role, several AI engineer positions require earning a bachelor’s degree in the fields of computer science, data science or information technology. Those looking for positions with higher seniority or that tackle niche machine learning projects can also benefit from earning a master’s degree in artificial intelligence or another relevant discipline.
While some AI engineers commonly have collegiate backgrounds in computer science and software engineering, that isn’t necessarily a prerequisite for landing a job in this field. Candidates can also find an education through accelerated bootcamps or training courses, or even through developing personal projects that can flex their AI expertise.
How to Become an AI Engineer
Becoming a successful AI engineer really comes down to an individual person’s willingness to learn, their passion for the industry and the opportunities they create for themselves.
Tips for Becoming an AI Engineer Learn the technical skills required Find a mentor to help narrow your interests Learn through experience
Learn the Technical Skills Required
Working as an AI engineer requires quite a bit of technical know-how, particularly when it comes to programming and mathematics, as well as AI algorithms and how to implement them with frameworks. Common machine learning algorithms include linear regression and decision trees, while common deep learning algorithms include recurrent neural networks and generative adversarial networks. Some common AI frameworks include Theano, TensorFlow and PyTorch.
“I think the most important thing is self-learning because nobody is going to teach you all of this,” CVS Health’s Haque said. “Anybody who has done high school and has good programming [skills] can start.”
There are tons of educational resources available online on sites like Codecademy, Simplilearn and even here on Built In. Of course, keeping up to date with the ways AI is evolving is vital, but knowing the fundamentals is just as important.
“If you know the fundamentals as everything keeps changing,” Abdullah, of Duke University, said you’ll still be able to understand what’s going on.”
Continue Your AI EducationWith Built In’s Learning Lab
Find a Mentor to Help Narrow Your Interests
Learning the ins and outs of AI on one’s own can get “overwhelming,” Abdullah said, especially if you’re in the really early stages of career development and want to narrow your interests down. She suggests finding a mentor who actively works in the industry, so they can give you a clear idea of what working in the space is actually like, and even help narrow down an area of focus.
Once your area of interest is narrowed a bit more, prioritize learning the specific tools or technology required. That way, you can avoid getting too overwhelmed by the sheer magnitude of the artificial intelligence space.
Learn Through Experience
The next step toward establishing a career in AI engineering is striking a balance between “the learning part and the doing part,” Abdullah said. It’s great to continue learning, but a textbook or class can only teach you so much. Sometimes, the best way to learn and get comfortable with this technology is to jump into it and experiment.
“It’s one thing to have talent or problem-solving ability. Even if you have all that, you still need to get a lot of repetitions as an engineer in order to notice and kind of evaluate,” Seligson said, likening it to playing a sport. “Even if you’re really fast or really tall or really strong, if you don’t know how to play and if you haven’t seen the field a lot, you might go out there and fumble the ball the first few times. But the more you’re out there, the more you realize, ‘OK, this is kind of the flow of the game.’”
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Benefits of Being an AI Engineer
Working as an AI engineer can be quite a rewarding career. And there are lots of reasons to enjoy the work it entails. Artificial intelligence is at the forefront of virtually every company’s growth strategy, so the folks working behind the scenes of this technology can have a real impact — both technologically and financially.
“The gratifying part about the job is being able to say, ‘Hey, I made a change to this model and it had a tangible revenue impact,’” Shandar said, adding that, in the work she does with LinkedIn’s ads, the changes she makes have the potential to make the company hundreds of thousands of dollars.
Challenges of Being an AI Engineer
That’s not to say that this technology is any walk in the park, though. AI technology is constantly evolving and gaining complexity. And while working with something so experimental and new can be exciting and rewarding, it can also be challenging.
AI Engineering Challenges Building an AI model can be time-consuming and tedious, and can end in failure.
Sometimes there’s a disconnect between the lofty goals of managers and executives, and what their AI engineers are actually capable of doing given the time constraints and resources available.
Artificial intelligence tends to be misunderstood, and its capabilities can be overestimated.
If an organization has low-quality data, AI engineers have to do a lot more work to massage the data to make it compatible with a model.
“You will spend a lot of time running experiments, poring over models, and sometimes these experiments fail. Or sometimes it feels like you have nothing to show for it because you tried a bunch of things and they didn’t work the way you wanted them to,” Shandar continued. “Sometimes it gets frustrating.”
And because artificial intelligence requires quite a bit of expertise and know-how, there can sometimes be a disconnect between AI engineers and the people in charge. Generally, engineers rely on guidelines for the work they’re doing so that they have a concrete goal or mission for the otherwise very technical work that they do. For instance, if a system must be able to sort through 50 million data points in a certain amount of seconds with a certain amount of accuracy.
Managers and executives focus on what the goal is and what a given product will provide to the larger market. Meanwhile, engineers focus more on “OK, how do we actually do that thing?” Seligson explained. “Managers and executives can expect kind of crazy things from what they call and think of as AI, wherein it can’t really do those things with the resources that they allocate.”
Not every business function or problem requires artificial intelligence. Shandar says she’s been asked “many times” over the course of her career to implement machine learning solutions for things that don’t necessarily need machine learning. Instead, she suggests starting with a “solid statistical foundation” — something simple, and rules-based — and then going from there. This provides more of an understanding of the data, which is crucial if you eventually want to build a machine learning model that actually serves its purpose.
“[People] think that, if they have data, they can just throw it into a machine learning model, and it’s going to work, and you’re going to have millions of dollars. But you need to really understand your data,” Shandar said. “A lot of people try to build production machine learning models with, pardon my French, shitty data. Or data that comes in every six months, or is poorly annotated, or is filled with zeros or a lot of missing values. Or just straight up in formats that are not amenable to machine learning.”
This creates more work for the AI engineers, who then have to massage the data in order to get it compatible with a machine learning model.
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AI Engineer Compensation and Job Outlook
AI engineers receive an average salary range of $99,000 to $167,000 in the United States as of 2023, according to Glassdoor.
The U.S. Bureau of Labor Statistics also notes that computer and information research scientist roles, which include AI engineers, saw an annual median wage of over $131,000 as of 2021. With this same job area projected to grow 21 percent by 2031, AI engineers can expect to see healthy financial compensation and job growth over the next decade.
The Future of AI Engineering
It’s important to remember the space that this job exists in. Just as artificial intelligence is rapidly evolving and expanding, so too are the job descriptions of the people who work with this technology.
Consider all of the advancements made in AI within just the decade: virtual home smart devices, self-driving cars, facial recognition robots, ChatGPT, the list goes on. In just a few short years, artificial intelligence has revolutionized everything from healthcare to manufacturing to manufacturing to art.
“I definitely don’t think it’ll look the same, because it didn’t look the same five years ago.”
Looking ahead, the tech industry will continue to push the boundaries of what artificial intelligence is capable of, which will inevitably redefine the daily work of AI engineers — particularly when it comes to matters of bias and privacy, which are growing concerns with this technology.
“AI is as good as we make it. And if we are not thoughtful or conscientious about how we make our models, then our AI will be just as unfair and unjust as we are,” Shandar said. “I definitely don’t think it’ll look the same, because it didn’t look the same five years ago.”
As for exactly how much change occurs, Seligson says it’s a question of “revolutionary versus evolutionary change.”
“I don’t think we’re going to necessarily see a huge revolutionary change in the way people do AI,” he said. But he does think the realities of what it means to be an AI engineer will change in the next few years, particularly as things like automated AI and machine learning become more prevalent. “Evolutionary changes end up kind of revolutionizing in the long term.”
But no matter what direction AI takes us in the next five years, 10 years and beyond, AI engineers are going to be right at the center of it.
“It’s cutting edge, it’s impactful, it’s so cool,” Haque said. “I feel extremely powerful, in a positive way, about how I can change my and others’ lives with it.”
| 2022-12-09T00:00:00 |
https://builtin.com/artificial-intelligence/ai-engineer
|
[
{
"date": "2022/12/09",
"position": 49,
"query": "machine learning job market"
},
{
"date": "2022/12/09",
"position": 35,
"query": "generative AI jobs"
}
] |
|
GPAI Fairwork AI Principles 2022
|
GPAI Fairwork AI Principles 2022
|
https://fair.work
|
[] |
Fairwork is a research project that aims to set and measure fair standards for the future of work. In 2022, Fairwork led a project funded by the Global ...
|
Fairwork is a research project that aims to set and measure fair standards for the future of work. In 2022, Fairwork led a project funded by the Global Partnership on Artificial Intelligence to develop a set of principles to guide the fair use of AI systems in the workplace.
Through a year-long global stakeholder consultation, we developed the ten Fairwork AI principles alongside stakeholders ranging from Uber and Microsoft to the International Labour Organisation, the International Transport Trade Union Federation and the Distributed AI Research Insitute. These principles were then published in a report.
READ REPORT
Fairwork has now adopted these principles to measure the fairness of Artificial Intelligence systems in workplaces across the world. The Fairwork AI project will start this evaluation in the UK with a series of case studies in different sectors.
The Fairwork AI team has released a policy brief focusing on AI governance in the UK from the perspective of the workplace.
Read policy brief
Fairwork AI principles
| 2022-12-09T00:00:00 |
https://fair.work/en/fw/principles/ai/
|
[
{
"date": "2022/12/09",
"position": 94,
"query": "future of work AI"
}
] |
|
Universal Basic Income in the Age of COVID-19
|
Universal Basic Income in the Age of COVID-19
|
https://www.unite.ai
|
[
"Antoine Tardif"
] |
Universal Basic Income (UBI) will be necessary in an age when AI, and robotics dirupts the workforce. We explore UBI data collection during COVID-19.
|
The idea of Universal Basic Income (UBI) polarizes and divides. Proponents believe it is necessary with AI, and robotics disrupting the workforce, and human laborers becoming a relic of the past. Detractors believe that it create a society which values laziness over hard work, and where all sense of purpose is lost.
Both groups make solid arguments, what is needed is more data. Currently, one of the side effects of the COVID-19 outbreak, is that multiple nations have implemented UBI without calling it that. While the United States paid a one-time lump sum of $1200 to all single adults who reported adjusted gross income of $75,000 or less on their 2019 tax returns, other countries such as Australia, Canada, and New Zealand have been more generous.
Canada is the simplest program to understand, they will pay $2000 a month for up to 4 months to any Canadian who has lost their job due to COVID-19. This is a program called the Canada Emergency Response Benefit (CERB). We will explore why this is important.
What is Universal Basic Income?
Stanford simply defines UBI as “a periodic cash allowance given to all citizens, without means test to provide them with a standard of living above the poverty line”. Furthermore, “It varies based on the funding proposal, the level of payment, the frequency of payment, and the particular policies proposed around it”.
The idea is to protect all members of society so that no one is left behind. When members of society are not living in poverty, they are less likely to turn to crime, which results in reduced policing and incarceration rates. These same citizens are more likely to educate themselves, to donate time to charitable causes, and to contribute to society in other important ways.
Who Believes in Universal Basic Income?
There's something in common with proponents of UBI, they are generally involved in technology, and they have a firm understanding of how disruptive AI and robotics are going to be, and they recognize that unless a shift in society is undertaken, that many jobs will be lost, and that poverty will increase exponentially.
Richard Branson states the following:
“I think with the coming on of AI and other things there is certainly a danger of income inequality.” Branson tells CNN.
He continued by stating “the amount of jobs [artificial intelligence] is going to take away and so on. There is no question”technology will eliminate jobs. “It will [UBI] come about one day.”
Elon Musk did not mince words:
“I think we'll end up doing universal basic income,” Musk stated at the World Government Summit in Dubai. “It's going to be necessary.”
In a separate interview Musk stated “There is a pretty good chance we end up with a universal basic income, or something like that, due to automation,” says Musk to CNBC. “Yeah, I am not sure what else one would do. I think that is what would happen.”
Mark Zuckerberg is a huge proponent of UBI:
“Now it’s our time to define a new social contract for our generation. We should explore ideas like universal basic income to give everyone a cushion to try new things,” says Zuckerberg.
Universal Basic Income Pilot Projects
There are currently multiple pilot projects in many diverse regions. In Finland, a two-year pilot scheme is taking place under which 2,000 unemployed people have been given 560 euros per month. When interviewed many of the recipients of these funds reported more happiness, less stress, and the ability to take risks, such as pursuing other forms of employment, or education.
Ontario, Canada previously ran a pilot program with 4000 unemployed people which ran for one year in the communities of Thunder Bay, Lindsay, Hamilton, Brantford and Brant County. Under this project, a single person could have received approximately $17,000 a year, minus half of any income he or she earned. A couple could have received up to $24,000 per year. People with disabilities could have received an additional $6,000.The program ran until the government pulled the plug, citing a lack of funding.
Other pilots projects have been operational in Scotland, Kenya, The Netherlands, and even California.
All of these pilot projects suffered from the same issues: Lack of funding, a small sample size, too narrow of a location, and poor data collection.
The UBI Opportunity
The CERB program in Canada is UBI in its truest sense. It provides a payment of $2,000 for a 4-week period for up to 16 weeks which is more generous than most countries during the COVID-19 outbreak. The current number of enrolled Canadians is in the millions, this means the sample size is large.
Other benefits of CERB, is unlike most UBI pilots you have a sample size in many regions instead of one specific region. UBI can then be tested in multiple settings such as small towns, suburban areas, and in large cities. Since the amount of money collected does not change, the impact of this subsidy could then be studied based on the cost of living in each region. $2000 a month for someone in remote Nova Scotia, may be more impactful than the same amount in expensive urban environments such as Vancouver, and Toronto.
What I recommend is that instead of trying to fund a UBI pilot project from scratch, something which has failed multiple times in the past, is that a supplementary fund is initiated to study the impact of UBI in Canada when government funds are used.
An additional small amount of money could be paid to Canadians who choose to enroll in an anonymized data collection program. Each Canadian could receive an additional $200 to participate, they would need to outline where the funds are used, for what purpose, as well as how they feel regarding the program.
The purpose of this study would be to fully understand the mindset of the recipient of these funds. Is a sense of purpose lost? Or is the relief of not falling below the poverty line enough for people to choose to educate themselves online for future employment opportunities? These are the type of questions that we need to ask, and we currently have the largest unintended UBI pilot program in the world to ask those important questions.
After all, while the current high levels of unemployment are due to a virus, in 2030 it might be automation caused by AI which results in a similar level of unemployment.
| 2020-05-02T00:00:00 |
2020/05/02
|
https://www.unite.ai/universal-basic-income-in-the-age-of-covid-19/
|
[
{
"date": "2022/12/09",
"position": 2,
"query": "universal basic income AI"
}
] |
Is ChatGPT a 'virus that has been released into the wild'?
|
Is ChatGPT a ‘virus that has been released into the wild’?
|
https://techcrunch.com
|
[
"Connie Loizos",
"Editor In Chief",
"General Manager",
"Zack Whittaker",
"Maxwell Zeff",
"Lorenzo Franceschi-Bicchierai",
"Lauren Forristal",
"Amanda Silberling",
"Rebecca Szkutak",
"Sarah Perez"
] |
What do you think of the idea of universal basic income, or enabling everyone to participate in the gains from AI? I'm much less strong a proponent than I ...
|
More than three years ago, this editor sat down with Sam Altman for a small event in San Francisco soon after he’d left his role as the president of Y Combinator to become CEO of the AI company he co-founded in 2015 with Elon Musk and others, OpenAI.
At the time, Altman described OpenAI’s potential in language that sounded outlandish to some. Altman said, for example, that the opportunity with artificial general intelligence — machine intelligence that can solve problems as well as a human — is so great that if OpenAI managed to crack it, the outfit could “maybe capture the light cone of all future value in the universe.” He said that the company was “going to have to not release research” because it was so powerful. Asked if OpenAI was guilty of fear-mongering — Musk has repeatedly called all organizations developing AI to be regulated — Altman talked about the dangers of not thinking about “societal consequences” when “you’re building something on an exponential curve.”
The audience laughed at various points of the conversation, not certain how seriously to take Altman. No one is laughing now, however. While machines are not yet as intelligent as people, the tech that OpenAI has since released is taking many aback (including Musk), with some critics fearful that it could be our undoing, especially with more sophisticated tech reportedly coming soon.
Indeed, though heavy users insist it’s not so smart, the ChatGPT model that OpenAI made available to the general public last week is so capable of answering questions like a person that professionals across a range of industries are trying to process the implications. Educators, for example, wonder how they’ll be able to distinguish original writing from the algorithmically generated essays they are bound to receive — and that can evade anti-plagiarism software.
Paul Kedrosky isn’t an educator per se. He’s an economist, venture capitalist and MIT fellow who calls himself a “frustrated normal with a penchant for thinking about risks and unintended consequences in complex systems.” But he is among those who are suddenly worried about our collective future, tweeting yesterday: “[S]hame on OpenAI for launching this pocket nuclear bomb without restrictions into an unprepared society.” Wrote Kedrosky, “I obviously feel ChatGPT (and its ilk) should be withdrawn immediately. And, if ever re-introduced, only with tight restrictions.”
We talked with him yesterday about some of his concerns, and why he thinks OpenAI is driving what he believes is the “most disruptive change the U.S. economy has seen in 100 years,” and not in a good way.
Our chat has been edited for length and clarity.
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TC: ChatGPT came out last Wednesday. What triggered your reaction on Twitter?
PK: I’ve played with these conversational user interfaces and AI services in the past and this obviously is a huge leap beyond. And what troubled me here in particular is the casual brutality of it, with massive consequences for a host of different activities. It’s not just the obvious ones, like high school essay writing, but across pretty much any domain where there’s a grammar — [meaning] an organized way of expressing yourself. That could be software engineering, high school essays, legal documents. All of them are easily eaten by this voracious beast and spit back out again without compensation to whatever was used for training it.
I heard from a colleague at UCLA who told me they have no idea what to do with essays at the end of the current term, where they’re getting hundreds per course and thousands per department, because they have no idea anymore what’s fake and what’s not. So to do this so casually — as someone said to me earlier today — is reminiscent of the so-called [ethical] white hat hacker who finds a bug in a widely used product, then informs the developer before the broader public knows so the developer can patch their product and we don’t have mass devastation and power grids going down. This is the opposite, where a virus has been released into the wild with no concern for the consequences.
It does feel like it could eat up the world.
Some might say, ‘Well, did you feel the same way when automation arrived in auto plants and auto workers were put out of work? Because this is a kind of broader phenomenon.’ But this is very different. These specific learning technologies are self catalyzing; they’re learning from the requests. So robots in a manufacturing plant, while disruptive and creating incredible economic consequences for the people working there, didn’t then turn around and start absorbing everything going inside the factory, moving across sector by sector, whereas that’s exactly not only what we can expect but what you should expect.
Musk left OpenAI partly over disagreements about the company’s development, he said in 2019, and he has been talking about AI as an existential threat for a long time. But people carped that he didn’t know what he’s talking about. Now we’re confronting this powerful tech and it’s not clear who steps in to address it.
I think it’s going to start out in a bunch of places at once, most of which will look really clumsy, and people will [then] sneer because that’s what technologists do. But too bad, because we’ve walked ourselves into this by creating something with such consequentiality. So in the same way that the FTC demanded that people running blogs years ago [make clear they] have affiliate links and make money from them, I think at a trivial level, people are going to be forced to make disclosures that ‘We wrote none of this. This is all machine generated.’ [Editor’s note: OpenAI says it’s working on a way to “watermark” AI-generated content, along with other “provenance techniques.”]
I also think we’re going to see new energy for the ongoing lawsuit against Microsoft and OpenAI over copyright infringement in the context of our in-training, machine learning algorithms. I think there’s going to be a broader DMCA issue here with respect to this service.
And I think there’s the potential for a [massive] lawsuit and settlement eventually with respect to the consequences of the services, which, you know, will probably take too long and not help enough people, but I don’t see how we don’t end up in [this place] with respect to these technologies.
What’s the thinking at MIT?
Andy McAfee and his group over there are more sanguine and have a more orthodox view out there that anytime we see disruption, other opportunities get created, people are mobile, they move from place to place and from occupation to occupation, and we shouldn’t be so hidebound that we think this particular evolution of technology is the one around which we can’t mutate and migrate. And I think that’s broadly true.
But the lesson of the last five years in particular has been these changes can take a long time. Free trade, for example, is one of those incredibly disruptive, economy-wide experiences, and we all told ourselves as economists looking at this that the economy will adapt, and people in general will benefit from lower prices. What no one anticipated was that someone would organize all the angry people and elect Donald Trump. So there’s this idea that we can anticipate and predict what the consequences will be, but [we can’t].
You talked about high school and college essay writing. One of our kids has already asked — theoretically! — if it would be plagiarism to use ChatGPT to author a paper.
The purpose of writing an essay is to prove that you can think, so this short circuits the process and defeats the purpose. Again, in terms of consequences and externalities, if we can’t let people have homework assignments because we no longer know whether they’re cheating or not, that means that everything has to happen in the classroom and must be supervised. There can’t be anything we take home. More stuff must be done orally, and what does that mean? It means school just became much more expensive, much more artisanal, much smaller and at the exact time that we’re trying to do the opposite. The consequences for higher education are devastating in terms of actually delivering a service anymore.
What do you think of the idea of universal basic income, or enabling everyone to participate in the gains from AI?
I’m much less strong a proponent than I was pre COVID. The reason is that COVID, in a sense, was an experiment with a universal basic income. We paid people to stay home, and they came up with QAnon. So I’m really nervous about what happens whenever people don’t have to hop in a car, drive somewhere, do a job they hate and come home again, because the devil finds work for idle hands, and there’ll be a lot of idle hands and a lot of deviltry.
| 2022-12-09T00:00:00 |
2022/12/09
|
https://techcrunch.com/2022/12/09/is-chatgpt-a-virus-that-has-been-released-into-the-wild/
|
[
{
"date": "2022/12/09",
"position": 12,
"query": "universal basic income AI"
}
] |
Behind the scenes of the Government AI Readiness Index
|
Behind the scenes of the Government AI Readiness Index
|
https://oxfordinsights.com
|
[
"Emma Hankins"
] |
The Government AI Readiness Index seeks to measure how ready a government is to implement these AI tools in public service delivery.
|
By Emma Hankins
On Monday, Oxford Insights published the 2022 Government AI Readiness Index. Now in its fifth edition, the Index has been cited by governments and NGOs around the world.
There are a growing number of AI-based tools that governments can use to provide better, more efficient public services — things like chatbots, language translation tools, or text recognition services on government websites. The Government AI Readiness Index seeks to measure how ready a government is to implement these AI tools in public service delivery.
But what goes into creating the Index each year? What does crafting an influential index look like behind the scenes? Hopefully, by the end of this blog post you will be able to answer these questions and come away with a few tips on how to create an index or data-driven report.
Your guide on this journey will be me, Emma Hankins — the newest member of the Oxford Insights team. When I started in September and learned that I would be working on the Government AI Readiness Index, I was honestly a bit intimidated. Collecting data on 39 indicators for almost every country in the world on the company’s flagship publication as my first project ever? No pressure!
Thankfully, I was joining a great project team (Pablo Fuentes Nettel and Annys Rogerson) who gave me detailed instructions on how to begin the first part of my contribution to the Index: data collection.
Diving into the data
The Government AI Readiness Index measures 39 indicators which make up 10 dimensions, organised into 3 pillars. These indicators come from a mix of desk research and secondary sources, like scores from other indices or datasets from large repositories such as the World Bank or the UN.
“So, you just copy and paste some data?” Well, it’s a little more complicated than that. Raw data needs to be cleaned and adjusted before being ready for analysis. Datasets come in varying formats and cover different countries, which makes combining them difficult. We also check whether the data for any of the indicators is seriously skewed; if this is the case, we replace the data for those indicators with their log transformations. The raw data are then normalised so that all indicators produce scores on a scale from 0 to 100, which is essential for comparison. We also replace any missing data with the average score on that indicator for a country’s peer group according to the World Bank’s income levels and regions.
One of the desk research indicators we use is whether countries have published a national AI strategy. While searching government websites for documents can be time-consuming, I find it to be one of the most interesting parts of creating the index because you get to see the different approaches governments around the world take to regulating, governing and fostering the development of AI. It was also exciting to see that a few countries — Malaysia, Thailand, Oman, and Uzbekistan — had cited our very index in their newly published AI strategies or documents surrounding them. I knew the Index was important, but this really drove the point home. Around the world, policymakers are using the Index as a benchmarking tool and, in some cases, as a metric of success. In other words, the data in the Index had better be right! This brings me to my first tip:
Tip #1: Accept that you will make mistakes.
You may spend weeks collecting data until you have a massive, colour-coded spreadsheet that you are incredibly proud of. And then the minute you send it to the rest of the team to check, they will immediately find typos and places where the Excel formula isn’t quite right. This is normal, and this is why we purposely build in a lot of time for quality checking our data. Remember that the vast majority of your beautiful spreadsheet is still correct, and you would rather your coworkers find your mistakes while you can fix them!
So, I had downloaded, checked, transformed, and normalised the data, and I finally had a complete index with overall country scores. Still, even as someone who had worked with every bit of data that makes up the index, I had a hard time interpreting what that data meant in practical terms. Numbers are great, but they often don’t tell the clearest story on their own. This is where the next step of the index — interviews with regional experts — comes into play.
Talking to experts
To develop a more in-depth view of government AI readiness than we can get through desk research alone, the Index always includes some form of contribution from experts on AI or tech policy in each of the regions in the report. This year, we conducted interviews with each expert, and these formed the basis for our regional reports. My coworker and project lead Pablo had already arranged interviews with AI and tech experts from each region in the report. I started off taking notes in interviews, but I worked my way up to taking the lead in the interview with our North American expert, which I really enjoyed. That brings me to my second tip:
Tip #2: Experts — they’re just like us.
Yes, the experts you interview will often have PhDs and more titles than you can fit in the report. Yes, they are very smart — that’s why you’re interviewing them, after all. But try not to be too intimidated by them. Prepare, know your data, and have your questions ready, but at the end of the day, they are only humans who happen to be interested in a particular topic — just like you.
I found this to be the most interesting part of the project — going from raw data to detail from experts. Our experts are passionate and knowledgeable about their field and can rattle off more fascinating AI use cases in five minutes than I can find via desk research in an hour. In interviews, we try to tease out the emerging narratives so we can tell the story behind the numbers. To do so, we ask questions like, ‘Why do you think country A has a high score this year? What could other countries in the region learn from them? What are the remaining barriers to AI readiness?’ The answers to questions like these plus more desk research on each region send us to the final leg of our journey: writing the report.
Putting it all together
To make drafting a long report easier, the team decided to draft as we went, writing regional reports while each interview was still fresh in our minds. For me, this involved rereading the notes and transcripts from each interview and highlighting key concepts and quotes. This really helped me avoid staring at a blank page waiting for inspiration to strike. It also relates to tip 3:
Tip #3: Let AI help you.
For the Government AI Readiness Index, I didn’t just write about AI; I also used it to make the project easier. This was my first time using AI-enabled transcription tools to make interview transcripts, and they save tons of time. I also used the autosync function on YouTube to make sure the captions on our videos matched what experts were saying in each frame. These seemingly simple tools are powered by quite sophisticated AI software and can help you immensely if you let them.
Now that you have written up regional findings, you’ve got the bulk of the report! There are a few other obvious but important steps, like proofreading, more quality assurance, and double-checking quotations with regional experts. But at this point you’ve truly gone from nothing to a ton of numbers to a cohesive report with interesting stories to tell.In my time working on the Government AI Readiness Index, it was pretty cool to see it coming together firsthand, and I hope it is as fascinating to read as it was to create.
To find out more about our index methodology and see individual country rankings, you can find the 2022 Government AI Readiness Index here. For questions or to work with us, feel free get in touch at [email protected]
| 2022-12-09T00:00:00 |
2022/12/09
|
https://oxfordinsights.com/insights/2022-12-9-behind-the-scenes-of-the-oxford-insights-government-ai-readiness-index/
|
[
{
"date": "2022/12/09",
"position": 18,
"query": "AI economic disruption"
}
] |
How to Increase Productivity and Save Costs During an ...
|
How to Increase Productivity and Save Costs During an Economic Downturn
|
https://www.leapwork.com
|
[] |
Times of economic turmoil give us the opportunity to turn our attention toward our organization's internal processes. Instead of cutting staff or services, you ...
|
As we deal with the aftermath of Covid-19, the economic turbulence and political turmoil of 2022, company leaders are asking themselves the same question:
Are we on the verge of a long-lasting economic recession?
And if so…
How deep of an impact will this recession have?
As economic conditions change, the feeling of uncertainty and business disruption grows.
Most organizations suffer during a recession because of both a decline in demand and an increase in uncertainty. For this reason, enterprises should devise a contingency plan to pull through this economic decline, no matter the size.
Declining productivity, higher labor costs and delayed business expansion mean that companies are struggling to compete.
So, how can an organization prepare for a recession?
According to Harvard Business Review, research shows that there are ways to mitigate the damage. The most interesting findings were found in four areas: debt, decision-making, workforce management, and digital transformation.
1. Invest in efficiency through technology
A key indicator of success during a recession is whether an organization continued to invest in technology.
It’s tempting to think that during an economic downturn one should batten down the hatches and play it safe. However, when an organization strategically invests in the right technology, they’re more likely to come out on top once the market bounces back.
According to Gartner, technology can make your organization more efficient, flexible and transparent.
In their recent report on technological trends in 2023, they put particular emphasis on the threat of recession and the need for companies to have a digital immune system: combining software engineering strategies to create an enhanced customer experience and protect against risk.
Enterprises want to remain competitive. When they have constraints on hiring resources, they need to do more when it comes to adopting software.
2. Software automation as a key enabler
Katy George, a senior partner at McKinsey, says that organizations should prioritize digital transformation projects, such as automation, because they pay off quickly. Automation helps organizations increase productivity and mitigate risk which in turn helps reduce costs.
To stay competitive, organizations need to release software faster and that software needs to be tested. However, so much testing is being done manually and this presents a serious problem. It is now the case that test automation has become a fundamental requirement.
In the short term, investing in automation can help your organization operate more efficiently and with less overheads, while making better use of your existing resources. In the long term, your organization will be ready to make capital out of future economic growth.
Times of economic turmoil give us the opportunity to turn our attention toward our organization’s internal processes. Instead of cutting staff or services, you can look at the challenges your organization has been facing and identify the ways automation could help you improve inefficiencies. Regardless of whether you use automation for RPA or test automation, it will simplify and reduce costs throughout your operations.
A key factor, however, is adoption time. The quicker your employees can implement and adopt automation in their day-to-day work activities, the quicker you will see its economic value.
A way to ensure a short learning curve and quick adoption time is to invest in a no-code automation tool. No-code automation tools allow anyone to automate without previous programming knowledge which in turn improves return on investment (ROI).
Related reading: 3 Automation Hacks That Keep Your Web Applications Stable During Unpredictable Times
3. Productivity increase
Investing in automation can keep your employees productive and creative by allowing them to spend more time on tasks that really have a financial impact on your organization.
If you were to automate processes within a sales department for example, it would help reps spend more time engaged in activities that drive revenue (such as sales interactions) instead of drowning them in admin tasks.
Not only that, investing in your team during an economic downturn can be a strategic way to retain your best employees and limit staff turnover. This will become extremely important once things pick up again.
4. Risk mitigation
If there was ever a time when core business processes need to operate at their optimal level, it is during a financial downturn. Automation increases speed, reduces human error, and ensures that all business processes operate as expected.
Some may use automation for data migration, product quality assurance during the software development process, or monitoring critical systems in health or BFSI.
Regardless of which use you have for automation, one thing is certain: mitigating risk during a recession is paramount and the sooner you have a solid automation portfolio in place the better.
Leapwork’s 2022 Risk Radar Report found that among the main challenges in QA were a lack of skilled developers, underinvestment in test automation, and a lack of professional development. It is vital that all of these factors are addressed to reduce risk and gain competitive advantage in the face of recession.
5. Cost reduction
Using technology to handle time-consuming and error-prone tasks reduces operating costs. Automation not only makes things easier for employees but it also helps reduce OPEX in the process. It allows organizations to free up human resources in order to tackle the more challenging work that is needed during challenging times.
Lastly, automation offers a quick ROI that outweighs the initial set up cost, especially when we talk about no-code automation.
“The ROI is absolutely there. Leapwork has provided us a service that makes it easier for us to reach our main objective, which is to deliver 5 times the ROI within one year,”
Paul Fredrik Eilertsen, Team Manager for Process Automation at VISMA.
Conclusion
Many organizations are hesitant to adapt and implement new technologies, especially when they see an economic downturn ahead. However, automation makes organizations more agile and this in turn allows them to endure uncertainty and the evolving impact of a recession.
No matter what the motivation for investing in automation software is, a business will always experience reduced costs and mitigated risk, as well as increased productivity. These benefits will occur simultaneously, as they each reinforce one other.
When businesses fail to see that digital transformation is a vital component of building business resilience, they put themselves at risk during harsh times. However, those that choose to automate can not only survive but find themselves at the top when the storm clears.
If you want to learn more about how test automation can help your business, consider downloading our guide below.
| 2022-12-09T00:00:00 |
https://www.leapwork.com/blog/how-to-increase-productivity-and-save-costs-during-an-economic-downturn
|
[
{
"date": "2022/12/09",
"position": 53,
"query": "AI economic disruption"
}
] |
|
How machine learning is transforming airline operations
|
How machine learning is transforming airline operations
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https://aws.amazon.com
|
[] |
Accurately forecasting demand, reducing flight delays and cancellations, and improving on-time performance are key to achieving both goals. That's why some ...
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When airlines were asked about their top business goals for 2022–2023 for the Skift 2022 Digital Transformation Report, 41 percent said reducing operational costs and 68 percent said improving customer service.
Accurately forecasting demand, reducing flight delays and cancellations, and improving on-time performance are key to achieving both goals.
That’s why some leading airlines are turning to solutions based on machine learning (ML) or artificial intelligence (AI) that are built on Amazon Web Services (AWS) to optimize core operations. In fact, 100 percent of the airlines surveyed by Skift said ML and AI will be very important or somewhat important for driving value over the next 3 years.
By employing ML, airlines can make traffic forecasting not only better but more consistent, whereas this forecasting conventionally uses mathematical models that rely on historical data to predict capacity, demand, and pricing. Other advanced solutions are helping airlines minimize delays and anticipate disruptions in near real time. Ultimately, they’re creating more opportunities to accelerate revenue growth and increase customer satisfaction.
Enhance forecasting with ML
The AWS Travel and Hospitality Solutions Library, which contains curated solutions for common use cases for the Travel and Hospitality industry, and AWS Marketplace—the place to find, test, buy, and deploy software that runs on AWS—offer many vetted, purpose-built industry solutions. One example is Passenger Traffic Forecasting from Mphasis, a solution that provides 30 weeks of forecast of passengers who are expected to travel using historical weekly passenger traffic data. The tool helps airlines and passenger rail and ground services companies predict the number of expected passengers more accurately, using ensemble ML algorithms with automatic model selection algorithms. As soon as an accurate forecast is in place, travel companies can develop pricing strategies to sell seats at a price point and quantity that will maximize revenue.
How much of a difference can it make? When used to predict the monthly domestic passenger traffic at John F. Kennedy International Airport in New York, the Mphasis Passenger Traffic Forecasting solution outperformed algorithms by other companies, pre- and post-COVID-19 pandemic.
Improve on-time performance, starting with real-time data
Airline operations are complex. They involve a broad set of time-critical processes that span ground and flight operations, above- and below-the-wing tasks, flight and cabin crew operations, and customer experience management.
That’s why United Airlines modernized its operations with TCS Aviana, an intelligent airline operations solution built on AWS. Its installation means that United Airlines can now detect operational deviations and mitigate disruptions faster, resulting in greater opportunities for hassle-free travel.
On-time operations is one of the most valuable metrics for United Airlines and an essential promise it makes to customers. But with large, complex operations involving over 5,000 daily departures across more than 300 airports and involving more than 3,000 operations personnel, achieving that goal requires an extraordinary coordination and effort. The airline realized that it needed both seamless collaboration and interoperability among business units and solution partners, and to provide frontline staff with near-real-time information to make accurate decisions faster.
United Airlines addressed these challenges by connecting its siloed systems using TCS Aviana. The solution identifies deviations in operations and flags severity based on thresholds set in the airline’s operational plan. Early visibility into critical issues, such as missed passenger connections and baggage delays, makes it possible for United Airlines to take proactive action and resolve problems more quickly. TCS Aviana’s impact has been significant; it configured over 500 operations anomalies and flagged nearly 400 business events per second for immediate remediation.
“TCS Aviana helps orchestrate harmony across aviation operations, activating an empowered workforce and resilient operations and delivering a superior traveler experience.” —Sreedhar Gudla, head, TCS Aviana
Gain situational awareness for increased profitability
Azur Airlines is a fast-growing Eastern European airline with more than 30 aircraft. As it grew, operating, managing, and optimizing its fleet became increasingly challenging tasks. With severe weather, geopolitical incidents, and natural disasters on the rise, the airline needed the means to obtain vital information (including fuel and GPS data and aircraft block times) in near real time to “visualize” its fleet.
Azur turned to FLYHT Aerospace Solutions—which helps airlines gain full visibility over assets and the movement of resources and inventory—and began using its AFIRS 228 system and UpTime Cloud Aircraft Situational Display, an AWS-hosted application. The airline installed AFIRS 228, a data acquisition unit, connecting it to aircraft interfaces to obtain pertinent data, such as aircraft position, block times, and fuel on board. The unit sends the data from the aircraft to the AWS infrastructure on the ground.
The UpTime Cloud application then displays the data, facilitates near-real-time fleet monitoring, and activates automation through alerts—all through a simple user experience. Now, Azur gets all the information that it needs in one platform, as well as weekly, automated flight summary reports, directly from the Uptime Cloud AWS platform. What are the benefits of achieving situational awareness? A maximized fleet and improved profitability.
React quickly when disruptions occur
Though airlines can’t control the weather, they can use ML and AI to improve how they react to disruptions, weather related or otherwise, more quickly and effectively. iFlight from IBS Software is a highly configurable platform for aircraft, flight, crew, and hub planning, management, and optimization. Its AI-driven tools highlight operational issues early, making it possible for airline teams to execute and communicate optimal recovery solutions quickly.
By indicating down-line impacts, iFlight helps minimize both the direct short-term cost of disruption (for example, revenue dilution, passenger compensation, and hotel accommodation) and the long term costs of passenger dissatisfaction.
One airline group turned to iFlight after multiple attempts to integrate end-to-end airline operations and crew management IT systems failed. It needed a better way to make collaborative decisions, from crew planning and management to process standardization and proactive disruption management. Using iFlight gave it a single view of truth, leading to $3million in savings each year.
The spike in travel demand in 2022 made it clear that travelers are still eager to meet in person, sightsee, and explore. To build loyalty and retain travelers, airlines must offer both the operational experience and customer service that today’s travelers expect.
To learn more about how AWS and its Partner solutions can help you navigate operational challenges, visit the Travel and Hospitality Solutions Library.
| 2022-12-09T00:00:00 |
2022/12/09
|
https://aws.amazon.com/blogs/industries/how-machine-learning-is-transforming-airline-operations/
|
[
{
"date": "2022/12/09",
"position": 10,
"query": "machine learning workforce"
}
] |
Workforce Management Software: Key Benefits & Solutions
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Workforce Management Software: Key Benefits & Solutions
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https://www.microsoft.com
|
[] |
Solutions that are equipped with historical data, machine learning, and other intelligence-driven resources can help you fine-tune your reporting, save on costs ...
|
A business has many moving parts. To help it run smoothly, decision makers at all levels need resources to help them manage departments, operations, inventory, and—of course—people. It’s also crucial to have data on hand to help optimize business now and when looking ahead to the future.
That’s why workforce management is essential to your organization. Learn what it is, why it’s important, and what available solutions can do for you.
Get the report
| 2022-12-09T00:00:00 |
https://www.microsoft.com/en-us/microsoft-365/business-insights-ideas/resources/what-is-workforce-management-and-why-is-it-important
|
[
{
"date": "2022/12/09",
"position": 32,
"query": "machine learning workforce"
}
] |
|
Latest Tech Layoff Trends in Three Charts
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Latest Tech Layoff Trends in Three Charts
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https://spectrum.ieee.org
|
[
"Tekla S. Perry"
] |
Tech Company Layoffs Grow. Largest announced layoffs and monthly totals, August through November 2022.
|
“Twitter slashes nearly half its workforce.” “Meta lays off more than 11,000.” “Amazon reportedly plans to lay off about 10,000.” The November headlines were full of tech companies announcing layoffs. And only the biggest tech employers made the news; small tech startups trimming already lean staffs were hidden in the deluge.
For some companies, layoffs weren’t triggered by an actual drop in revenue. Rather, the action was akin to when a flight attendant warns passengers to make sure their seatbelts are snug in preparation for possibly bumpy skies ahead. The bumps don’t always emerge, but it’s best to be ready. And venture capitalists have been warning their portfolio companies since mid-year to be prepared to tighten their belts.
One young tech product manager who recently survived a nearly 25 percent cut at her workplace told me that the experience has been “very sad and disheartening.”
“It’s a very different company,” she said. “We’re now trying to be focused on moving forward by rebuilding and restructuring our teams.”
“I guess I’m seeing Silicon Valley history in the making,” she continued. “This was just content we heard about in Econ class, so it’s an interesting time to be living through it all.”
Whether November’s layoff wave represented a peak in workforce slashing or bigger cuts are on the way is yet to be determined. It will be months before we’ll be able to look back with full perspective on this turbulent time for the engineering workforce.
But we can try to get a sense of just how big, proportionally, this current wave of layoffs is and how fast it came on.
There’s not a central layoff reporting database that tracks the numbers, though federal and some state laws require advance notification of mass layoffs in so-called “WARN” notices. So I asked Intellizence, an AI startup that provides market intelligence services for corporate clients, to pull together layoff data for August through November.
Intellizence gathered this data from WARN filings, news reports, and press releases, using AI-based tools to automatically extract key details and remove duplicates. The numbers are those reported or announced at the time and may refer to recent or planned layoffs. When a layoff was reported as a percentage of employees instead of a number, the system attempted to translate it based on publicly available workforce data; this was not always possible. (For Twitter, with the news full of multiple announcements, the data here reflects the widely reported 50 percent workforce reduction.)
So this data, which covers the U.S. and Canada and reflects news from some 160 companies, is not comprehensive, but the sheer change in magnitude of layoff activity is telling.
| 2022-12-09T00:00:00 |
2022/12/09
|
https://spectrum.ieee.org/tech-layoffs-2022
|
[
{
"date": "2022/12/09",
"position": 8,
"query": "AI layoffs"
}
] |
Tech layoffs in 2022: A timeline
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Tech layoffs in 2022: A timeline
|
https://www.computerworld.com
|
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Israeli artificial intelligence chip developer Habana Labs announced it was laying off around 100 employees, approximately 10% of its total workforce. Having ...
|
Editor’s note: Follow Computerworld’s 2023 tech layoff tracker for the most up-to-date news.
Technology companies this year have been hit with global economic turbulence that is slowing growth and spurring widespread layoffs, even as enterprise IT spending in some areas—such as cloud computing—seems to be holding steady.
According to TrueUp’s tech layoff tracker, there were 1,405 rounds of layoffs at tech companies globally through the first week of December, affecting 219,959 people.
When global economic headwinds started picking up earlier in the year, the tech industry reacted to fears of an incoming recession by putting the brakes on hiring. The bad news is—amid rising interest rates, the ongoing war in Ukraine, high fuel costs, supply chain issues, and a decline in personal PC sales—most of those freezes have since been accompanied by job cuts, as companies look for ways to reduce operating costs.
While enterprise IT spending is still forecast to grow over the next year as companies use tech to battle expected recession, bright spots offered by enterprise spending on cloud infrastructure and SaaS applications hasn’t been enough to completely lighten the overall picture for tech industry giants.
Exchange rates and the PC sales slump worked to slow Microsoft’s net income growth to its lowest level in five years for the September quarter. At Alphabet, September quarter revenue slowed to 6% despite a big jump in cloud sales.
The general macroecononic environment also shows some signs of affecting cloud infrastructure spending. AWS’ September quarter revenue was up 27.5% year on year, but slower than the 33% rise for the prior quarter and 36.5% growth the quarter before that.
With the economic outlook for 2023 unlikely to fill business leaders with much optimism, it’s likely the number of job losses recorded by TrueUp will continue to grow.
Here is a list of some of the most prominent technology layoffs of the year.
See news of more recent layoffs.
December
Dec. 8: Airtable —254 employees
Low-code business software company Airtable announced it would be laying off 254 employees across business development, engineering and other teams, around 20% of its workforce. Alongside the cuts to individual teams, Airtable’s chief revenue officer, chief people officer and chief product officer will also be leaving the company.
Dec. 2: Amazon —Up to 20,000 employees
Sources reported that Amazon plans to lay off as many as 20,000 employees across the company in the coming months, about twice as many as previously reported by the New York Times in November. The plan for mass layoffs comes after the retail and cloud computing giant makes cuts after going on a hiring spree during the pandemic. Twenty thousand employees are the equivalent of about 6% of corporate staff, or about 1.3% of Amazon’s total 1.5 million-strong workforce including global distribution center and hourly workers.
November 2022
Nov. 22: HP—Up to 6,000 staff
When posting fourth quarter 2022 financial results, which saw a year-on-year decline in revenue of 11.2% to $14.8 billion, HP also announced that it expects to lay off 4,000 to 6,000 employees by the end of fiscal year 2025, reducing its 51,000-strong global workforce by about 12%.
The layoffs will be part of a HP’s “Future Ready” strategy, announced in conjunction with its quarterly results. In a conference call with analysts, HP President and CEO Enrique Lores said the strategy will generate at least $1.4 billion in savings by year-end fiscal year 2025, allowing the company to steer through what he described as “near-term market headwinds” and mitigate softness in HP’s core markets.
In the third quarter this year, the company’s personal systems, consumer, and commercial segments fell by 13%, 25% and 6% respectively. Notebook and desktops units also saw a decline, with units decreasing by 21% overall.
Nov. 17: Cisco—4,100 staff Despite posting record quarterly revenue of $13.6 billion, Cisco announced it would be laying off 4,100 employees, around 5% of its 83,000-strong workforce.
In an 8-K filing for its fiscal first-quarter, the company announced a restructuring plan “in order to rebalance the organization and enable further investment in key priority areas. This rebalancing will include talent movement options and restructuring.” The company said it will make some real estate changes as well.
Speaking to analysts after the results were posted, Cisco CFO Scott Herren said: “Don’t think of this as a headcount action that is motivated by cost savings. This really is a rebalancing.”
Nov. 15: Asana—97 employees
Asana’s chief operating officer (COO), Anne Raimondi, took to LinkedIn to announce that the company was reducing the size of its global workforce, estimated to be over 1,600 employees, by around 9%, equating to 97 job losses.
In a statement, the company said the layoffs were part of a “restructuring plan intended to improve our operational efficiencies and operating costs and better align Asana’s workforce with current business needs, top strategic priorities, and key growth opportunities.”
Despite reporting a 51% increase in revenue, for the quarter ending July 2022, Asana reported a net loss of $62.6 million.
Amazon: Nov. 14—10,000 people
Amazon is set to cut close to 10,000 employees, according to a Nov. 14 report from The New York Times. Though the cuts would be just a small fraction of Amazon’s 1.5 million-strong workforce, they include technology as well as corporate staff, according to the report. While Amazon did not immediately respond to requests for comment, its most profitable division, Amazon Web Services (AWS), has been showing signs of growth deceleration since the beginning of this fiscal year.
During Amazon’s third quarter earnings call with analysts, CFO Brian Olsavsky attributed the decline to macroeconomic conditions that were forcing Amazon customers to cut down on spending.
Earlier in the month, the company sent out a note to all its employees saying that there was a hiring freeze being put in place for all Amazon corporate positions.
Zendesk: Nov. 10—350 people
In a week marred by widespread job losses in the tech sector, Zendesk on Nov. 10 announced it would be cutting its headcount in an attempt to reduce operating expenses.
According to a recent filing with the US Securities and Exchange Commission (SEC), the CRM software provider is laying off 300 employees from its 5,450-person global workforce. “This decision (layoffs) was based on cost-reduction initiatives intended to reduce operating expenses and sharpen Zendesk’s focus on key growth priorities,” the company wrote in the SEC filing.
The layoffs are estimated to set Zendesk back by about $28 million, primarily due to costs incurred on severance payments and employee benefits, the SEC filing showed.
Salesforce: Nov. 9—950 people
On Nov. 9, CRM software provider Salesforce announced that it would cut about 950 jobs from its global workforce, which consists of around 73,000. The announcement came less than a month after the company laid off at least 90, mostly contract, employees.
Like many tech companies, Salesforce originally implemented a hiring freeze in an attempt to avoid layoffs. However, that policy was rescinded in September and, despite experiencing a relatively successful year financially, the company has been facing pressure to cut costs since activist hedge fund Starboard Value took a stake in the company and immediately called for Salesforce to increase its margins.
Meta: Nov. 9—11,000 people
Three days after it was first rumored that Meta CEO Mark Zuckerberg was planning to dramatically reduce the company’s headcount, the parent company of Facebook, Instagram and WhatsApp, confirmed that it was preparing to cut 11,000 jobs, impacting 13% of its global workforce.
In a statement, Zuckerberg said that the company had already sought to cut costs across the business, including scaling back budgets, reducing perks, shrinking its real estate footprint, and restructuring teams to increase efficiency.
The news came mere weeks after weak performances from Facebook and Instagram saw $80 billion wiped off Meta’s market value and its share price drop to less than a third of what it was at the start of the year.
Twitter: Nov. 3—3,750 people
Twitter’s new owner, Elon Musk, wasted no time flexing his newfound authority over the social media giant, firing roughly half of Twitter’s 7,500-strong employee base a week after his deal for the company closed..
According to former staff members, the job cuts left whole teams completely gutted, including its product trust and safety, policy, communications, tweet curation, ethical AI, data science, research, machine learning, social good, accessibility, and certain core engineering teams.
Musk also fired Twitter’s senior leadership alongside a number of company leaders, including the vice president of consumer product engineering. He justified the job cuts by tweeting: “Regarding Twitter’s reduction in force, unfortunately there is no choice when the company is losing over $4M/day.” The tweet has since been deleted.
While these layoffs represent the biggest workforce cull Twitter has seen, it’s not the first time this year the company has sought to slim down its employee base. After initially implementing a hiring freeze, in July 2022 the company went on to lay off 30% of its talent acquisition team.
Ten days after the initial round of job cuts were confirmed, several outlets reported that Twitter had also eliminated between 4,400— 5,500 contract workers without notice. According to a number of news media reports, most contract employees only found out they’d been terminated after losing access to the company’s email and internal communications systems.
Stripe: Nov. 3—1,100 people
Online payments company Stripe announced it was laying off 1,100 staff, approximately 14% of its workforce. In a memo to staff written by Patrick Collison, the Stripe CEO said the cuts were necessary amid “stubborn inflation, energy shocks, higher interest rates, reduced investment budgets, and sparser startup funding.”
In 2021, the San Franciscan company became the most valuable US startup, when it was valued at $95 billion. However, according to a report by the Wall Street Journal in July this year, Stripe cut the internal value of its shares by 28%, lowering its internal valuation to $74 billion.
October 2022
F5: Oct. 21—100 people
Despite seeing quarterly revenue growth of 3% year-one-year, F5, the Seattle-based application security and delivery company, announced it was cutting about 100 roles, approximately 1% of its 6,900-person global workforce.
In a statement published by GeekWire, a spokesperson for F5 said that the company was continuously evaluating how to focus resources to best meet the needs of customers. “Given the current macroeconomic environment, this week we announced changes internally that resulted in the elimination of a number of positions across the company,” according to the statement.
Microsoft: Oct. 17—1000 people
After reportedly committing to nearly double its budget for salary hikes in May in order to retain employees, Microsoft laid off close to 1,000 employees. The job cuts affected employees throughout many different levels of the company, areas of the world, and company departments — including the Xbox division, Strategic Missions, Technology Orgs, and Edge teams.
In an Oct. 17 statement, Microsoft said: “Like all companies, we evaluate our business priorities on a regular basis, and make structural adjustments accordingly. We will continue to invest in our business and hire in key growth areas in the year ahead.”
This latest wave of job cuts came three months after Microsoft laid off less than 1% (around 1,800) of its 180,000 workforce and removed open job listings for its Azure cloud and security groups.
Oracle: Oct. 14—201 people
Just months after Oracle acquired healthcare data specialist firm Cerner for $28.3 billion and announced a first round of layoffs, the company announced it was cutting a further 201 jobs in an attempt to find around $1 billion in cost savings.
According to its Worker Adjustment and Retraining Notification (WARN) filed in California, the jobs cuts impacted data scientists and developers. Despite the layoffs, Oracle said its Redwood Shores campus would not be closing as a result of the job cuts.
Intel’s Habana Labs: Oct. 11—100 people
Israeli artificial intelligence chip developer Habana Labs announced it was laying off around 100 employees, approximately 10% of its total workforce.
Having been acquired by Intel in 2019 for $2 billion, the company grew its employee base from 180 to over 900 over the last three years. In a statement, the company said making “adjustments to its workforce” was a requirement for adapting to the “current business reality” and ensuring the company could “improve its competitiveness.”
The reduction in Intel’s headcount doesn’t stop at Habana Labs. Although the chip developer’s parent company is yet to confirm just how many employees will be impacted, on Intel’s third quarter earnings call, CEO Pat Gelsinger told investors, “[Intel] are planning for the economic uncertainty to persist into 2023.”
Gelsinger later confirmed to multiple media outlets that these measures will include job cuts that will affect its global employees. Intel has roughly 120,000 employees worldwide.
September 2022
DocuSign: Sept. 28—670 people
A week after electronic signature company DocuSign announced the appointment of its new CEO, the company revealed it was laying off approximately 9% of its workforce to support its growth and profitability objectives and to improve its operating margin. In January, it was reported DocuSign had 7,651 employees. The jobs cuts were expected to impact around 670 of those workers.
According to a filing with the US Securities and Exchange Commission (SEC), DocuSign’s restructuring is expected to incur charges of between $30 million and $40 million.
Twilio: Sept. 14—850 people
Twilio announced plans to lay off 11% of its workforce, between 800 and 900 workers from its 7,800-strong employee base.
In a letter published to Twilio’s blog, CEO Jeff Lawson called the layoffs “wise and necessary,” blaming them partially on Twilio’s rapid growth over the last several years. According to Lawson, the cuts will mostly impact “areas of go-to-market,” R&D and Twilio’s general and administrative departments.
During the pandemic, the company saw its headcount almost double as a result of an increased appetite for cloud services and a number of acquisitions, including data security platform Ionic Security and toll-free messaging services provider Zipwhip.
(This story is being updated as news of major tech company layoffs is announced.)
| 2022-12-09T00:00:00 |
https://www.computerworld.com/article/1614926/tech-layoffs-in-2022-a-timeline.html
|
[
{
"date": "2022/12/09",
"position": 10,
"query": "AI layoffs"
}
] |
|
Indian Startup Layoffs: 18000 Employees Fired In 2022
|
Indian Startups Restructure, Cut 18,000 Jobs In 2022: Will 2023 Bring Stability?
|
https://inc42.com
|
[
"Hemant Kashyap",
"Lokesh C.",
"Bismah M.",
"Bhupendra P."
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... AI. View Profile · Company. Navi Technologies. View Profile. In-Depth Stories ... Indian startups have laid off thousands of employees, but these layoffs ...
|
Tech layoffs in 2022 have seen 135,000 employees impacted globally, with thousands in India in the potential line of fire
As of December 8, 2022, 17,989 employees were laid off by 52 Indian startups, including several unicorns and soonicorns
When the Covid-19 pandemic struck in early 2020, the startup world was pummelled by a global shutdown. Most companies scurried to slash spending and freeze hiring to survive the unprecedented crisis. But for the next year or so, the shake-up never came to the worst as expected.
The markets were bullish, and tech startups saw a steady flow of funding as investors bet big on a new era of capital-efficient technologies and smart business operations. Even the labour market dynamics favoured employees. People resigned en masse for greener pastures (known as the Great Resignation), and hiring soared across the board as companies struggled to fill the talent gap.
But the scenario reversed when the Russian invasion of Ukraine started in February 2022. The impact of geopolitical uncertainty on the global economy, coupled with the ailing markets, rampant inflation and the fear of a long-lasting global recession, have capped the newfound exuberance across the startup land in India and abroad.
For businesses big and small, the thesis has changed from ‘growth at any cost’ to turning a profit as investors’ fear of missing out (FOMO) has given way to orthodox belt-tightening. In brief, the year of ‘loud’ layoffs (done vociferously on all sorts of communication platforms, especially social media) has arrived, and by all accounts, the carnage is likely to continue well into 2023.
Indian Startup Layoffs: 18K Employees Fired & Counting
The past few months have been sobering for startup workers. More than 50 startups have laid off their employees in India, while big tech companies across the globe are issuing pink slips to thousands in arguably the worst year for the white-collar workforce in the technology domain.
As of December 8, 2022, a total of 17,989 employees were asked to leave by 52 Indian startups, including a host of unicorns such as BYJU’S, ChargeBee, Cars24, LEAD, Ola, Meesho, MPL, Innovaccer, Udaan, Unacademy and Vedantu, besides the listed foodtech Zomato.
Indian startups have laid off their people throughout February-December 2022. January was the lone exception, thanks to the aftermath of the startup funding bull run witnessed in the previous year. But as the capital inflow dried up, many startups found themselves on very short runways, and thousands were asked to leave.
It was not surprising as a reality check was long due after the FOMO-induced funding tsunami in 2021. What we are seeing now is a massive correction in the startup funding space, triggered by skyrocketing valuations and prevailing macroeconomic headwinds.
In January this year, an Inc42 report on Indian tech startup funding estimated as much as a 24% correction in 2022. Between January 1 and November 29, 2022, startup funding in India amounted to $24 Bn, a 35% dip compared to the year-ago period.
However, not all startup sectors saw the same level of layoff bloodbath. In India, the worst offender is edtech, followed by consumer services and ecommerce startups. These three sectors collectively account for 15,424 layoffs, which means about nine out of every 10 Indian startup employees fired in 2022 worked in one of these sectors. Incidentally, these sectors are notorious cash-guzzlers, and many startups in these domains had to fire their employees to cut costs after startup funding dried up.
What Has Led To Massive Job Elimination
A layoff can be the eventual outcome of a variety of factors. But it is not a kind of development that unfolds at once. It may take months for employees to leave an organisation, even after a startup goes on record to announce a ballpark figure.
On the other hand, not all layoffs are as pronounced as Twitter’s infamous downsizing. According to many HR experts, incidents of ‘quiet quitting’, forced resignations, unrealistic business targets and more stringent ‘performance’ parameters can be termed ‘passive layoffs’ that will continue to hurt livelihoods.
For instance, several media reports have critiqued how tech giant Google and its parent Alphabet are about to do away with 10K ‘poor performers’. The ‘performance’ tag can not only impact severance packages but also affect the career growth of the people laid off.
More importantly, when tech (and other) companies across the spectrum think that reducing headcount is the sole remedy for reducing cash burn during any downturn, it is bound to impact the ‘trust’ factor and professional ethics.
How justified are Indian startups to jump on the layoff bandwagon amid a funding winter and in the wake of an economic recession? A close look at the industry numbers is needed to analyse the causality of the job loss scenario.
Restructuring, Capital Crunch Primary Reasons For Layoffs
According to Inc42’s Indian Startup Layoff Tracker, which monitors startup layoffs across the country, 42.3% of the startups laid off their employees citing organisational restructuring such as M&A-related redundancies.
For instance, BYJU’S cited duplication of roles and the resulting restructuring as the primary reason for firing 2,500 employees. The edtech unicorn was busy acquiring companies last year, picking up coding-focused edtech WhiteHat Jr, test-prep coaching centre chain Aakash and several others for a combined total of $2.3 Bn.
The company also said that it would be consolidating all of its business under one entity, which may have been the primary cause of the redundancies.
Going by the available data, only about one-third of the laid-off employees lost their jobs due to cost-cutting. This number ought to be much higher, but the current analysis only considers the official reasons for layoffs provided by the startups.
Financial constraints and adverse economic conditions (read lack of external funding and revenue dip) added another 11% of employees to the layoff list. Overall, the capital crunch was the most-cited reason for maximum layoffs (46% of employees) across Indian startups in 2022.
Late Stage Startups Laid Off Most Employees
Late stage startups accounted for more than two-thirds of the startup layoffs in 2022.
The layoff percentage amply reflects the state of late stage startup funding. Compared to 2021, late stage funding was down by 55% in 2022 ($31.99 Bn in 2021 against $14.19 Bn as of November 29, 2022) and saw a 97% YoY slump in July 2022.
According to Inc42 data, late stage startups fired 17% of their employees in a typical layoff in 2022, compared to 28% by growth stage companies and a whopping 54% by early stage startups. But given the average headcount of late stage companies, layoffs involving 17% of the employees can be much higher than the 54% laid off by early stage businesses, as the latter employs only a fraction of their late-stage counterparts.
Edtech, Consumer Services And Ecommerce Are The Worst Offenders
Among the 10 startup segments in India that saw at least one layoff in 2022, edtech, consumer services and ecommerce fired the maximum number of employees.
Of the total 17,989 employees let go in the year of the big layoffs, 85.7% worked in one of these three sectors, which actively reduced the headcount. Interestingly, edtech in India has the highest number of unicorns.
But industry leaders like BYJU’S, Unacademy and Vedantu, and key players like Toppr and WhiteHat Jr, fired people without much warning. Worse still, despite being cash-guzzlers, very few startups in the edtech space have become profitable. In fact, out of India’s 107 unicorns, only about a third is profitable, while the rest is burning cash at a frenetic pace without hitting the profit button.
For Indian edtech startups, 2022 should be an eminently forgettable year. As brick-and-mortar educational institutions and coaching classes started operating after widespread vaccination and the waning of the pandemic, edtech companies in the K-12 and test prep segments are facing a litmus test.
Despite their dizzying valuations, they stand the risk of going under unless their offerings are reinvented in sync with the new world. The writing is on the wall, as five of the eight startups that went out of business in 2022 were edtech companies, accounting for 62.5% of the shutdowns.
The Worst Spate Of Layoffs Since Covid-19
During the first few months of the 2020 lockdowns, there was a bloodbath as Indian startups laid off nearly 8,200 employees between April and June, according to Inc42 data.
Most of the B2C startups, including unicorns such as Ola, Zomato, Swiggy, BookMyShow, MakeMyTrip, Livspace, BharatPe, Lendingkart and many more, laid off thousands to survive the supply chain interruptions that brought businesses to a standstill for months together.
But the seemingly apocalyptic year also triggered unprecedented funding in 2021. A handful of consumer-facing segments, including ecommerce, consumer services, edtech and fintech, leveraged digital technologies to cater to the public demand for value, convenience and safety. Understandably, all of them attracted billions of dollars and mind-boggling valuations while growing at a breakneck speed to capture market share.
Fast forward to 2022, and the numbers are more depressing than they had been two years ago. Indian startups have laid off twice as many employees this time, around 1,760 employees every month. What’s more, the funding winter ($24 Bn in 2022 compared to $42 Bn in the previous year) has seen no respite due to overcautious investor sentiment.
The message out there is loud and clear. In the wake of global recession fears that may last for years (think of the dot-com bubble or the 2008 global meltdown), consumers and investors will weed out startups that fail to remain viable in a tech-driven world and a bruising economy. Layoffs and hiring freeze will continue until these companies find the sweet spot, selling more, spending less and attracting new capital.
Tech Layoffs Soar Globally; How It Affects The Indian Workforce
Indian startups have laid off thousands of employees, but these layoffs pale compared to what is happening globally at some of the largest tech companies.
Twitter is a case in point. As Tesla and SpaceX founder Elon Musk acquired the microblogging site after a well-documented saga stretching back to April this year, he fired around 3,700 employees across the globe. These impacted 180 employees in India, and the team strength shrank from about 230 to a few dozen people.
Days after the Twitter bloodbath, Meta announced the single largest round of tech layoffs in 2022. In a company-wide headcount cutting, the Facebook, WhatsApp and Instagram parent downsized its employee base by 13%, and more than 11,000 were asked to leave.
Incidentally, Meta’s layoffs in 2022 have not impacted its 400-strong India team.
Amazon, too, followed suit, and media reports suggest that the ecommerce giant may fire as many as 10,000 employees globally, with hundreds of Indian workers in the possible line of fire. The ecommerce major has also shut down multiple business verticals in India.
The company is currently embroiled in a tussle with India’s labour ministry over the said layoffs and maintains that it has not yet fired a single person in India. Inc42 sources suggest that Amazon has asked employees from its defunct businesses to join other verticals or hand in their papers.
But all may not be gloom and doom in the ecommerce space. Ecommerce major Flipkart or India’s burgeoning D2C segment has not taken the layoff route yet to protect growth and profitability.
Google and its parent Alphabet have also joined the layoff bandwagon and reportedly plan to let go of 10,000 employees worldwide, which may impact its Indian workforce.
Besides Big Tech, networking major Cisco has fired more than 4,150 employees or about 5% of its global workforce. According to media reports, Cisco’s layoffs will also impact hundreds of employees in India.
In all, tech layoffs in 2022 have seen 1,35,000 employees impacted, much worse than the Dot Com bubble of the early 2000s, which led to 120,000 people losing their jobs.
What’s On The Cards In 2023
If the timings of major layoffs throughout 2022 are analysed, a couple of insights will emerge. To begin with, most of the sacking happened towards the end of 2022, and experts speaking to Inc42 indicated a fresh round of layoffs beginning in 2023. For instance, while Meta, Amazon and Twitter announced layoffs in November 2022, Google and other tech leaders may start laying people off from January 2023.
Closer home, the likes of BYJU’S, Unacademy, Vedantu and others have been downsizing for most of the year. But the layoffs intensified from October onwards. This is backed by the fact that 16 startup layoffs happened since October 1, impacting 5,488 employees or 30% of total employees impacted by this year’s layoffs.
There is also a consensus that companies operating in the edtech, ecommerce, social media and consumer services space will be most impacted.
Another interesting thing that has emerged from these conversations is the contagiousness of layoffs within the tech industry. According to Stanford professor Jeffrey Pfeffer, companies may just be laying people off as an imitation of other companies – a snowball effect.
But that is just one part of it.
Asked about other critical triggers, HR experts suggest that these sectors are now maturing, and companies are looking to lift their performance over the next few quarters in the run-up to public listings.
Furthermore, the funding crunch in 2022 has prompted a rise in mergers and acquisitions. As M&A deals grow across the segments most affected by the funding freeze, more people will lose their jobs due to redundancy.
Simply put, startups in capital-intensive sectors (ecommerce, edtech and consumer services) or those in low-monetisation segments like fintech need to rethink their fundamentals before it becomes business as usual and hiring returns to normalcy.
Another indication that the layoffs may continue well into 2023 is the current situation in the country’s IT/ITeS sector. According to media reports, IT giants Infosys, TCS, HCL and others have paused hiring since October 2022, let go of campus recruits and are looking to weed out non-performers in the coming months.
As we are aware, the impact of any macroeconomic shift cascades down from Big Tech to IT majors and finally to startups. It is much like the liquidity crunch that first hits the public equity market, while the private equity market feels the squeeze a few months later.
The hiring slowdown in the IT/ITes sector has similarly cascaded to the startup ecosystem. As IT/ITes companies put the brakes on hiring, so did the startups. According to the Monster Employment Index and RazorpayX Payroll report, hiring in the IT/ITes and startup sectors was down by 19% and 61% YoY, respectively.
The impact of Big Tech layoffs and IT/ITes majors freezing hiring can encourage startups to lay off more employees, as validated by Pfeffer’s ‘layoff contagiousness’ theory.
The Bottom Line
Time and again, stakeholders have worried about startup layoffs and their long-term impact on the job market. It is all the more critical now as the startup ecosystem has emerged as a major job creator. According to government statistics presented in Parliament in July 2022, Indian startups employed 7.98 Lakh employees.
Given the rising expectations from the startup sector, can we look forward to a quick recovery?
Of course, much of it depends on startup investors and how eager they are to loosen the purse string. There is no shortage of dry powder now as local and global VCs have already announced India-specific funds worth $16 Bn in 2022.
But despite the capital at their disposal, investors are likely to proceed with caution. They are also urging portfolio companies to reduce burn rates and get on to the profitability path. This means startups must slash all unnecessary expenses, including a bloated employee base, for a considerable period.
In brief, there is a path to recovery, but there are no clear timelines. It may take a quarter to another year before the funding winter is finally over. Then again, only the startups with the strongest fundamentals and adaptability, strategic mindset and resourcefulness will survive this downturn.
But all said and done, hundreds of thousands of employees have lost their jobs in 2022, and there may not be any immediate respite going forward.
[Edited By Sanghamitra Mandal]
Note: The layoff data is updated till December 8, 2022. The funding data is updated till November 29, 2022.
| 2022-12-09T00:00:00 |
2022/12/09
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https://inc42.com/features/indian-startups-restructure-cut-18k-jobs-in-2022-will-2023-bring-stability/
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[
{
"date": "2022/12/09",
"position": 81,
"query": "AI layoffs"
}
] |
Contrast | AI Charting for Healthcare
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AI Charting for Healthcare
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https://www.contrastai.com
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The #1 rated charting solution for any EHR in healthcare. Integrate AI for a more enjoyable, efficient, and patient-centered practice. Try for free.
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The AI Revolution in Healthcare: How AI Tools Are Changing the Game for Physicians
Ask any physician what they love most about their job, and the answer is almost always the same: the connection with patients, the ability to heal, and the privilege of making a difference in people’s lives.Ask them what they struggle with the most? Documentation. Charting. Hours spent clicking through EHRs instead of focusing on their patients.The introduction of AI tools in healthcare isn’t about replacing clinicians—it’s about restoring what was lost. It’s about giving physicians back their time, their focus, and their ability to practice medicine the way they always intended.But how exactly do AI tools fit into the daily reality of healthcare? Let’s explore the role of AI in reducing administrative burdens, optimizing workflows, and supporting the people who make medicine what it is—clinicians.
| 2022-12-09T00:00:00 |
https://www.contrastai.com/
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[
{
"date": "2022/12/09",
"position": 25,
"query": "AI healthcare"
}
] |
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The Power of AI in Healthcare
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The Power of AI in Healthcare – Quantilus Innovation
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https://quantilus.com
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AI is transforming healthcare in a number of ways, from developing new treatments to improving diagnoses and managing health data.
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AI in the healthcare industry is still in its early stages, but it has the potential to revolutionize the way we provide care. AI can help us to diagnose patients more accurately, develop personalized treatment plans, and to predict patient outcomes. AI can also help us to manage complex data sets, to identify patterns and correlations that would otherwise be invisible, and to make better decisions about care. As AI technology develops, we will only become rely more on it to improve the quality of care we provide. However, AI is not without its challenges. One of the biggest challenges is ensuring that AI systems are ethically sound and adhere to patients’ privacy rights. Another challenge is ensuring that AI systems are able to allocated resources effectively and efficiently. Despite these challenges, AI has the potential to transform healthcare for the better, and we should continue to explore its use in the healthcare industry. From diagnosing patients to developing new treatments, AI is transforming healthcare. Here is three ways AI is improving the healthcare industry for everyone involved.
– Developing New Treatments with AI
One of the most promising applications of AI in healthcare is its ability to develop new treatments. Using machine learning, AI can analyze large data sets to find patterns that human doctors might miss. This could lead to developing more personalized and effective treatments for patients. For example, IBM Watson’s Oncology Advisor uses machine learning to match a patient’s cancer with the most appropriate clinical trials. So far, the system has to matched more than 10,000 patients with clinical trials worldwide.
– Improving Diagnoses with AI
Another way AI is improving healthcare is by helping doctors diagnose patients more accurately. For example, Stanford University developed an AI system that can detect skin cancer as accurately as a dermatologist. The system uses a deep learning algorithm to examine pictures of skin lesions and classify them as benign or malignant. This type of technology could help dermatologists diagnose skin cancer sooner and save lives.
– Managing Health Data with AI
AI is also being used to manage health data more effectively. With the increasing amount of data being generated by wearable devices and electronic health records, it’s becoming more difficult for doctors to sift through everything and find what they’re looking for. However, AI systems can be trained to do this automatically. For example, Google DeepMind Health is using machine learning to read eye scans and identify early signs of disease. So far, the system has been able to detect more than 50 different diseases with high accuracy.
Challenges that need to be addressed for wider adoption of AI in healthcare
One of the healthcare’s most pressing issues is making healthcare widely accessible and affordable. The challenge is that healthcare is becoming increasingly complex, making it difficult for providers to keep up with the latest advances. This is where AI can play a role. AI has the potential to help healthcare providers keep up with the latest advances and make healthcare more widely accessible. However, there are a number of challenges that need to be addressed before AI can be widely adopted in healthcare. One challenge is that healthcare data is often siloed, making it difficult to train AI systems. Another challenge is that AI systems need to be explainable , so that decision-makers can understand how they work. Finally, AI systems need to be ethical, so that they are used responsibly and do not exacerbate existing inequalities. Addressing these challenges will be essential for the wider adoption of AI in healthcare.
Implications of AI-enabled healthcare for patients, providers, and payers
The healthcare industry is on the cusp of a major transformation, as artificial intelligence (AI) begins to take on an increasingly important role. hHealthcare providers are using AI-enabled tools to improve patient care, while payers are using the technology to better understand and manage population health. The implications of this shift are far-reaching, and patients, providers, and payers will all need to adapt in order to stay ahead of the curve.
Patients will benefit from improved access to care, as AI-enabled healthcare makes it possible for providers to offer more personalized and effective treatment. In addition, patients will have more control over their own health data, as AI-powered tools make it possible for them to track their own health metrics and learn more about their conditions. However, patients will also need to be mindful of the potential risks associated with AI-enabled healthcare, such as data privacy concerns and the potential for algorithmic bias.
Providers will need to invest in AI-powered tools and train their staff on how to use them effectively. However, the benefits of AI-enabled healthcare will be well worth the investment, as it has the potential to greatly improve patient care. In addition, provider organizations that embrace AI will be better positioned to compete in the future healthcare landscape.
Payers also stand to benefit from AI-enabled healthcare, as the technology can help them better understand and manage population health. In addition, AI can help payers identify cost savings opportunities and improve care coordination. However, payers will need to be careful not to use AI in a way that undermines patient trust or creates new ethical concerns.
Takeaway:
AI is transforming healthcare in a number of ways, from developing new treatments to improving diagnoses and managing health data. This technology has the potential to improve the quality of care for patients around the world and make healthcare more accessible and affordable.
| 2022-12-09T00:00:00 |
https://quantilus.com/article/power-of-ai-in-healthcare/
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[
{
"date": "2022/12/09",
"position": 35,
"query": "AI healthcare"
}
] |
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Top companies in AI-powered medical imaging in 2025
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Top companies in AI-powered medical imaging in 2025
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https://research.aimultiple.com
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Caption Health. The medical imaging company provides guidance to healthcare professionals and inexperienced people to perform ultrasound examinations accurately ...
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Around 75% of all medical malpractice claims against radiologists are related to diagnostic errors. Many radiology errors can be traced back to breakdowns in communication during the imaging or reporting process.
AI algorithms can be trained to analyze medical images and identify patterns and abnormalities that may be missed by human eyes. This not only saves time, but it also helps to improve the accuracy of diagnoses and treatment plans.
Explore how AI can be used in medical imaging and top medical imaging companies:
Leading medical imaging companies
AIMultiple prepared a sortable list of all AI-powered medical imaging companies. We focused on startups and left out market leaders even though they are also integrating AI technologies into their existing product lines.
Butterfly Network
Butterfly aims to bring a different perspective on medical imaging with both hardware and software solutions. Butterfly IQ is a portable mobile device that uses ultrasound-on-chip technology, which makes it the world’s first handheld entire-body ultrasound framework.
The device also claims to have the capability of detecting diseases in real-time while scanning. Dr. Jonathan Rothberg, chairman of Butterfly Network, is a recipient of National Medal of Technology and Innovation from the White House. Recently, Butterfly iQ3 announced FDA clearance.
Arterys
The company built the first tech product to visualize & quantify blood flow in the body using any MRI. Arterys also received the first FDA approval for clinical cloud-based deep learning in healthcare.
Furthermore, Arterys, a pioneer in four-dimensional (4D) cloud-based imaging, has been awarded “Best New Radiology Vendor” and “Best New Software” in the 2016 Minnies Awards. Arterys has ranked as one of the World’s 50 Most Innovative Companies by FastCompany in 2019. Arterys’ Lung-AI platform helps to reduce missed detections by 42 to 70%.
Gauss Surgical Inc.
Gauss Surgical, part of Stryker received CE (Conformité Européenne) Mark for its Triton System for iPad, the world’s first and only mobile platform for real-time monitoring of surgical blood loss.
Sigtuple
The company‘s innovative solutions aim to solve the problems caused by the chronic shortage of trained medical practitioners in India.
Freenome
The medical imaging device company raised 70.6M within only two years of its launch. Freenome works on detecting cancer by imaging blood cells.
Enlitic
The firm uses deep learning techniques to analyze the data extracted from radiology images. A study suggests that radiologists can read cases 21% faster with the help of Enlitic.
Caption Health
The medical imaging company provides guidance to healthcare professionals and inexperienced people to perform ultrasound examinations accurately and quickly. it also facilitates the work of healthcare professionals by providing automatic quality assessment and smart interpretation.
Behold.ai
Behold uses artificial intelligence technologies to help radiologists diagnose disease with radiology scans in a variety of cases. Behold.ai reduces the workload of medical professionals by fastening the process of diagnosis.
Viz.ai
The company raised $50 M in late 2019 for detecting early signs of brain stroke. February 2020, Viz.ai released a new generation synchronized care platform for those who are in the post-acute care period. The platform sends a notification to healthcare professionals when there is a sign of a serious situation.
RetinAI
Retin AI‘s “Discovery Platform” helps to collect, organize, and analyze health data from the eye in order to detect age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, etc.
Subtle Medical
Subtle Medicals’ software improves the quality of noisy medical images and provides better interpretation. It is especially helpful for patients who have difficulty holding still for long periods of time.
BrainMiner
It is a UK-based company, and Brainminer’s software DIADEM provides an automated system for analyzing MR brain scans to help clinicians with an easily interpreted report.
Lunit
Lunit has developed AI solutions for precision diagnostics and therapeutics. The company aims to optimize diagnosis and treatment matches by searching for the right diagnosis at the right cost, and the right medical treatment for the right patients.
In collaboration with Lunit and GE Healthcare launched an AI-powered chest X-ray imaging package designed to detect and highlight eight common conditions, such as tuberculosis and pneumonia, including those linked to COVID-19, using their algorithms.
3 main medical imaging technologies
Medical imaging helps healthcare providers see inside the human body to diagnose disease, monitor conditions, and guide medical treatment. Different imaging techniques provide unique insights into internal structures and functions. Three common imaging modalities are ultrasound, magnetic resonance imaging (MRI), and X-ray imaging.
Ultrasound Imaging
Ultrasound imaging uses sound waves to generate images of internal organs, blood flow, and developing fetuses. A medical imaging device called a transducer sends high-frequency sound waves into the patient’s body. These waves bounce back, and a computer processes them into images. Ultrasound is widely used in obstetrics, cardiology, and soft tissue evaluations.
Advantages:
No radiation exposure, making it safe for pregnancy.
Provides real-time imaging for guiding invasive procedures.
Portable imaging equipment allows use in clinical sites and emergency settings.
Limitations:
Image quality depends on body composition and operator skill.
Cannot effectively image bones or air-filled organs like the lungs.
Use cases:
Ultrasound imaging helps doctors see inside the body to check for health problems and guide treatment. It is often used to:
View organs and tissues in the abdomen
Check breast tissue for any changes
Monitor a baby during pregnancy
Hear a baby’s heartbeat with a Doppler device
Watch how blood flows in vessels and organs
Look at the heart using an echocardiogram
Guide needle placement for biopsies or injections
Assess eye structures
Measure bone strength and risk of fractures
These procedures are safe, widely used, and help doctors make informed decisions.
Magnetic Resonance Imaging (MRI)
MRI uses strong magnetic fields and radio waves to create high-resolution images of soft tissues, organs, and the brain. Unlike X-ray imaging, MRI does not use ionizing radiation. A patient lies inside an MRI scanner while radio waves interact with hydrogen atoms in the body. This produces signals that are processed into detailed cross-sectional images.
Advantages:
Ideal for brain, spinal cord, and joint imaging.
Helps detect tumors, nerve damage, and internal bleeding.
Enhances medical image analysis with contrast agents for better visualization.
Limitations:
Can be time-consuming (20–90 minutes per scan).
Not suitable for patients with metal implants or severe claustrophobia.
Requires strict quality assurance programs for accurate diagnosis.
Use cases:
MRI helps doctors get detailed images of the body. It is used to:
Detect problems in the brain and spinal cord
Find abnormalities in organs like the breast, liver, or prostate
Examine joints for injury or damage
Study the heart’s structure and how it works
See which parts of the brain are active (fMRI)
Check blood flow in vessels and arteries (angiography)
Analyze the chemical makeup of tissues (spectroscopy)
MRI can also guide doctors during some procedures. It is a helpful tool for diagnosing and planning treatment.
X-ray Imaging and Computed Tomography (CT)
X-ray imaging is one of the oldest and most common imaging techniques. A controlled X-ray beam passes through the body, creating images of bones and certain internal structures. Medical X-ray imaging is widely used for diagnosing fractures, infections, and lung diseases.
CT scans, or computed tomography, take multiple X-ray images from different angles to generate cross-sectional images of the body. A CT scanner provides more detailed images than conventional X-rays, making it valuable for diagnosing complex conditions.
Benefits:
Quick and effective for emergency diagnostics.
CT scans offer detailed 3D images for precise clinical analysis.
Used in radiation therapy planning and molecular imaging.
Risks:
Ionizing radiation exposure, increasing the risk of developing cancer.
Requires radiation protection measures for both patients and radiologic technologists.
Contrast agents used in CT may cause allergic reactions in some cases.
Use cases:
X-rays help doctors see inside the body to find and treat medical problems. They are used in both diagnosis and treatment.
Diagnostic uses include:
X-ray radiography: Finds broken bones, tumors, lung infections like pneumonia, dental issues, and foreign objects.
Finds broken bones, tumors, lung infections like pneumonia, dental issues, and foreign objects. Mammography: Checks breast tissue for signs of cancer, such as unusual masses or small calcium spots.
Checks breast tissue for signs of cancer, such as unusual masses or small calcium spots. CT (Computed Tomography): Combines X-rays with computer processing to create detailed cross-section and 3D images of the body.
Combines X-rays with computer processing to create detailed cross-section and 3D images of the body. Fluoroscopy: Shows real-time movement inside the body, like the heartbeat or how a swallowed contrast agent moves through organs.
Therapeutic use includes:
Radiation therapy: Uses high-energy X-rays to kill cancer cells. The radiation can come from a machine outside the body or from a substance placed or injected inside the body near the tumor.
These X-ray tools help doctors diagnose, monitor, and treat many different health conditions.
How is AI used in medical imaging?
The aim of medical imaging is to capture abnormalities using image processing and machine learning techniques. Application areas can be divided into sub-branches such as the diagnosis of various diseases and medical operation planning. The top applications of AI-powered medical imaging are:
1. Revealing cardiovascular abnormalities
According to an article published by Frontiers in Cardiovascular Medicine Journal in 2019, the integration of AI into cardiac imaging will accelerate the process of the image analysis which is a repetitive task that can be automated, therefore healthcare professionals engaged in this work can focus on more important tasks.
2. Prediction of Alzheimer’s disease
The Radiologic Society of America suggests that advances in AI can lead to predicting Alzheimer’s disease years before it occurs by the identification of metabolic brain changes.
3. Cancer detection
In early 2020, the Google health team announced that they developed an AI-based imaging system that outperformed medical professionals in detecting breast cancer.
4. Treatment revaluation
This is mostly used for cancer patients undergoing treatment to check if the treatment is working effectively and diminishing the size of the tumor.
5. Surgical Planning
Medical imaging also allows for the segmentation of the image related to the surgical area so that the algorithm can do the planning for healthcare professionals automatically. Surgical planning with the help of medical imaging can saves time in surgeries.
Check our comprehensive article on the use of AI in radiology.
How was these technologies used during the COVID-19 outbreak?
Overall, the impact of AI solutions on the COVID outbreak was limited, but there are some examples where technology was demonstrated in a specific group of patients.(https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/)
Medical imaging is one of the AI-powered solutions that was frequently mentioned during the COVID-19 pandemic. Due to the rapid increase in the number of patients, the clinical practice and interpretation of patients’ chest scan results became a problem.
For example, a Chinese company, Huiying Medical claimed to have developed an AI-powered imaging diagnostic solution to detect the virus in the early stage with 96% accuracy. solution to detect the virus in the early stage with 96% accuracy.
Source: Intel
Pneumonia is a serious complication of COVID-19 and results in patients requiring ventilator support. In a collaborative research by the University of California San Diego health department and AWS, a model was built to analyze chest images of patients at risk of pneumonia.
The model was trained to identify patients infected with Covid-19 by using AI-powered medical imaging procedure. The algorithm was trained on 22,000 notations by human radiologists. The algorithm performs color-coded maps that indicate the probability of pneumonia.
| 2022-12-09T00:00:00 |
https://research.aimultiple.com/looking-for-better-medical-imaging-for-early-diagnostic-and-monitoring-contact-the-leading-vendors-here/
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[
{
"date": "2022/12/09",
"position": 70,
"query": "AI healthcare"
}
] |
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AI-powered healthcare operational analytics solution
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AI-powered healthcare operational analytics solution
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https://whitespacehealth.com
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Our AI models help predict no-shows, pinpoint inefficiencies, and surface performance issues before they affect care delivery or financial outcomes, allowing ...
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Frequently Asked Questions
What is healthcare operational analytics?
Healthcare operational analytics uses data to evaluate and optimize day-to-day operations across clinical, financial, and administrative functions. It helps organizations streamline workflows, reduce waste, improve staff utilization, and ensure smooth coordination between departments for better patient and business outcomes.
How to improve operational efficiency in healthcare?
Operational efficiency can be improved by identifying workflow bottlenecks, monitoring staff productivity, reducing patient wait times, and aligning resources with demand. Operations analytics platform provides real-time visibility into operational KPIs, empowering leaders to take proactive steps that eliminate inefficiencies and improve throughput.
Who in our organization would benefit most from using operational analytics?
Operational analytics delivers value across the organization. COOs, department heads, practice administrators, patient access directors, and even front-line staff can use these insights to optimize resource allocation, improve team performance, and meet organizational goals more effectively.
How is AI used in your healthcare operations analytics?
| 2022-12-09T00:00:00 |
https://whitespacehealth.com/operations/
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[
{
"date": "2022/12/09",
"position": 85,
"query": "AI healthcare"
}
] |
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Nubia AI
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Write with ease
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https://www.nubia.ai
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Nubia is an AI tool that crafts data-driven stories in 3 steps: upload data, create a template, and let Nubia do the writing.
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Seamless project management
Your work is connected and consistent because your Nubia partner keeps your interaction intact and memorable
| 2022-12-09T00:00:00 |
https://www.nubia.ai/
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[
{
"date": "2022/12/09",
"position": 10,
"query": "AI journalism"
}
] |
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New policy on AI-generated content for Market and Elements
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New policy on AI-generated content for Market and Elements
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https://forums.envato.com
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It is only a matter of time before AI will create fully-fledged graphic designs based on a simple description (probably they have already been created and are ...
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I’m very passionate about AI - so I am spending a lot of time to read, test, watch and try the AI.
I won’t write a book here (I think I could) but I want to tell you something (to all of Envato community).
Let’s put aside what we are used to, i.e. chat bots, control of production processes or complicated aerodynamic simulations, etc.
Let’s look at it from the point of view of creators like us.
On my example, I can tell you that today users of platforms such as fiverr who offer voice-over services are literally losing customers - including me. I found a website on the web that offers a seemingly popular thing like an AI voiceover reading our text → BUT - what this particular website offers exceeds the wildest imaginations about voiceovers. The read text is read with amazing realism and any level of emotion, tension, calm, anger, etc. that we choose. Each generated reading is completely unique and different from the previous one (the same text sounds different each time, as if a real person was reading it). In other words, this particular example of AI offers a better (!) service than a real voiceover and sounds better!
For a one-time monthly fee, you have several hundred male and female voices at your disposal, each is what I described above and you can read each of your texts in any key with the desired emotions sounding indistinguishable from a real reader (when reading in the right place, he can make a sigh or pause! Can intone the emotion of the text being read…etc.)
A real clerk (e.g. from Fiverr) that I hired for 60 seconds of professional reading cost me between $200 and $350 - in effect I received one final reading. The aforementioned AI gives me hundreds of the same or even better quality readings with any voice in the emotions imposed by me at an incomparably lower price.
In addition, the above service offers machine learning - this particular service allows you to record your voice by reading a certain piece of text, then you send this sample to them and the AI learns your voice (about 2 - 3 weeks of learning) - finally you get an AI with your voice that will tell everything you will write to her with your real voice.
It does not mention presenters, i.e. AI imitating a real human who says something you wrote to him - people offering services of this kind will start to lose their income in a year at the latest - AI is getting better on this field and today’s is quite good at it.
It would seem that AI can do a lot but not everything - > wrong. I am currently on waiting lists for testing an AI that will write you any code (in any coding language) based on what you expect. For example, you describe the script and its functions in your own words, and the AI writes the code for you - it writes the whole script for you.
The range of AI services is huge and much wider than the fraction I have described.
It is only a matter of time before AI will create fully-fledged graphic designs based on a simple description (probably they have already been created and are being tested).
Forecasts → none of us is able to determine what will happen and in how long it will happen - why? → Well, AI learns exponentially - it means that today AI learns faster and more than it did yesterday - it’s a snowball effect and it has big classroom which is… THE ENTIRE INTERNET.
I don’t want to spoil anyone’s mood or plans for the future, I just share my observations - and these are quite disturbing.
Peace
| 2022-12-09T00:00:00 |
2022/12/09
|
https://forums.envato.com/t/new-policy-on-ai-generated-content-for-market-and-elements/439244
|
[
{
"date": "2022/12/09",
"position": 29,
"query": "AI graphic design"
}
] |
Artificial intelligence (AI) application templates and examples
|
Artificial intelligence (AI) application templates and examples
|
https://vercel.com
|
[] |
Discover free curated templates and starter kits to jumpstart your artificial intelligence (AI) application builds ... Design Engineers. Deploy for every idea.
|
qrGPT – AI QR Code Generator
QrGPT is an AI tool for you to generate beautiful QR codes using AI with one click. Powered by Vercel and Replicate.
| 2022-12-09T00:00:00 |
https://vercel.com/templates/ai
|
[
{
"date": "2022/12/09",
"position": 71,
"query": "AI graphic design"
}
] |
|
SizzlePop.AI: AI Image Generator, T-Shirt Maker, & Custom ...
|
AI Image Generator, T-Shirt Maker, & Custom Merch
|
https://sizzlepop.ai
|
[] |
AI Generated Design. AI takes your text and generates an image. Preview design. Choose your preferred colors and style. Ship it. Have it shipped directly to ...
|
Enter a Prompt
Simply type a short description of what you want on your product.
| 2022-12-09T00:00:00 |
https://sizzlepop.ai/
|
[
{
"date": "2022/12/09",
"position": 75,
"query": "AI graphic design"
}
] |
|
Free Graphic Design Creative Brief Template
|
Free Graphic Design Creative Brief Template
|
https://www.pandadoc.com
|
[] |
The graphic design creative brief template can help you guide your designer and create a masterpiece for your new project.
|
This (List Company Type/Speciality Here) company was formed in (launching year), and since then, it has only aimed to value its customers and create great projects for them.
Or
For (number of years) years [Client.Company] has been actively serving customers in locations (mention area) with our (list of products/services).
| 2022-12-09T00:00:00 |
https://www.pandadoc.com/graphic-design-creative-brief-template/
|
[
{
"date": "2022/12/09",
"position": 96,
"query": "AI graphic design"
}
] |
|
Leading AI Clinical Trials Companies | Artificial Intellignece
|
Leading Artificial Intelligence (AI) Companies in Clinical Trials
|
https://www.clinicaltrialsarena.com
|
[
"Surya Akella"
] |
Amongst the leading suppliers of AI for clinical trials include Medidata, IQVIA, BenevolentAI, Renalytix AI, Prometheus Biosciences, ReviveMed, Insitro, ...
|
Artificial intelligence (AI) is instrumental in solving several key clinical trial challenges. It can help in the synthesis and analysis of ever-expanding data for the development of innovative therapies. Combined with machine learning (ML), AI can transform the clinical development process, providing significant time and cost efficiencies together with improved and quick insights for better decision-making.
Discover the leading AI companies in clinical trials
Clinical Trials Arena has listed some of the leading AI clinical trials companies using its intel, insights and decades-long experience in the sector.
The information provided in the download document is drafted for clinical trial executives and technology leaders involved in AI innovations.
The download contains detailed information on suppliers and their product offerings, as well as contact details to aid purchasing or hiring decisions.
Amongst the leading suppliers of AI for clinical trials include Medidata, IQVIA, BenevolentAI, Renalytix AI, Prometheus Biosciences, ReviveMed, Insitro, Sensyne Health, Saama, GNS Healthcare.
Related Buyer’s Guides which cover an extensive range of clinical trials equipment manufacturers, service providers and technology, can also be found here.
How is AI improving operational efficiencies in clinical research?
AI is one of the most promising tools for making healthcare more efficient and patient-focused. It helps in trial design, recruitment, behavioural analysis, assisted diagnostics, generating real-world evidence, predictive analytics, and creating medical records. A few examples of the applications of AI and ML in the clinical research process include:
Study design
AI tools can help in evaluating and selecting optimal primary and secondary endpoints in study design, which helps in the optimisation of site strategies and patient recruitment models. An improved study design also helps in increasing the chances of success with more precise planning.
Site identification and patient recruitment
AI in clinical trials can help in the identification of the sites for clinical trials and more appropriate strategies for patient recruitment through patient population mapping and site targeting. This helps sponsors to expedite recruitment and reduces issues such as under-enrollment.
Pharmacovigilance
AI technology addresses several challenges of pharmacovigilance (PV) by automating highly manual processing tasks and offers improved insights and analytics to make the data more usable while ensuring quick identification of adverse events.
Data-driven clinical research
Digital clinical trials can improve medication adherence, remote patient monitoring, decentralised or virtual trials, and digital therapy. AI tools can be used for automated analysis of electronic medical records (EMR) and the databases of clinical trial eligibility to match them with recruiting clinical trials from various announcements of trials or registries.
FAQs
What are the key benefits of AI in clinical trials?
AI offers significant advantages in clinical trials by streamlining processes such as data management, patient recruitment, and trial design. It can quickly sift through large volumes of medical data to identify patterns, making it easier to predict trial outcomes and optimise protocols. AI also reduces human error and speeds up decision-making, which can shorten the duration of trials, lower costs, and improve the quality of findings. Additionally, AI can help generate real-time insights, allowing researchers to adapt trials on the fly based on interim results.
How does AI assist in patient recruitment?
AI plays a critical role in patient recruitment by using advanced algorithms to match eligible participants with appropriate clinical trials. It analyses a vast amount of patient data from various sources, such as medical records, social media, and healthcare apps, to identify candidates who meet the specific criteria of a trial. AI can also predict where eligible participants are likely to be located, enabling more efficient recruitment and site selection. By automating these processes, AI reduces recruitment time and the likelihood of under-enrollment, which is a common issue in clinical trials.
How can AI improve pharmacovigilance?
Pharmacovigilance, or drug safety monitoring, is essential to ensure the safety of medications, and AI is transforming this field by automating the detection and reporting of adverse events. Traditional methods of pharmacovigilance often rely on manual data entry, which is time-consuming and prone to errors. AI can analyse vast amounts of data from multiple sources, including electronic health records, social media, and patient-reported outcomes, to identify safety signals much earlier. By doing so, AI ensures faster reporting of potential risks, enhances patient safety, and helps regulatory bodies make more informed decisions about drug approvals.
What role does AI play in study design?
AI enhances the study design process by using predictive analytics to simulate different trial scenarios, allowing researchers to select the most effective strategies. AI tools help identify optimal endpoints, sample sizes, and study durations based on historical data and real-time trends. These insights lead to more efficient and successful trials, as AI can anticipate challenges such as patient dropouts or delays in data collection. Additionally, AI can streamline protocol amendments, which are often necessary but time-consuming, thus ensuring trials remain on track and within budget.
Can AI help with decentralised clinical trials?
Yes, AI is a key enabler of decentralised clinical trials (DCTs), which allow patients to participate remotely. AI supports remote monitoring by analysing data from wearable devices, smartphones, and other digital health tools, ensuring that trial data is collected in real time without requiring participants to visit a clinical site. This reduces the burden on patients and expands access to clinical trials, particularly for those in remote or underserved areas. AI also improves adherence to trial protocols by sending automated reminders for medication intake or scheduled virtual appointments, ultimately improving data quality and trial outcomes.
| 2024-09-23T00:00:00 |
2024/09/23
|
https://www.clinicaltrialsarena.com/buyers-guide/artificial-intelligence-companies-clinical-trials/
|
[
{
"date": "2022/12/09",
"position": 7,
"query": "artificial intelligence employers"
},
{
"date": "2022/12/09",
"position": 81,
"query": "artificial intelligence business leaders"
}
] |
Offshoring
|
Offshoring
|
https://en.wikipedia.org
|
[] |
On the other hand, job losses and wage erosion in developed countries have sparked opposition. ... automation can lead to reshoring of production in some cases.
|
Transnational relocation of operations
This article is about the business process involving a single company. For offshore outsourcing, see Outsourcing . For other uses, see Offshore (disambiguation)
Offshoring is the relocation of a business process from one country to another—typically an operational process, such as manufacturing, or supporting processes, such as accounting. Usually this refers to a company business, although state governments may also employ offshoring.[1] More recently, technical and administrative services have been offshored.
Offshoring neither implies nor precludes involving a different company to be responsible for a business process. Therefore, offshoring should not be confused with outsourcing which does imply one company relying on another. In practice, the concepts can be intertwined, i.e offshore outsourcing, and can be individually or jointly, partially or completely reversed, as described by terms such as reshoring, inshoring, and insourcing.
In-house offshoring is when the offshored work is done by means of an internal (captive) delivery model.[2][3]
Imported services from subsidiaries or other closely related suppliers are included, whereas intermediate goods, such as partially completed cars or computers, may not be.[4]
Motivation [ edit ]
Lower cost and increased profitability are often the motivation for offshoring. Economists call this labor arbitrage. More recently, offshoring incentives also include access to qualified personnel abroad, in particular in technical professions, and decreasing the time to market.[2]
Jobs are added in the destination country providing the goods or services and are subtracted from the higher-cost labor country.[5] The increased safety net costs of the unemployed may be absorbed by the government (taxpayers) in the high-cost country or by the company doing the offshoring. Europe experienced less offshoring than the United States due to policies that applied more costs to corporations and cultural barriers.[6]
Criteria [ edit ]
Some criteria for a job to be offshore-able are:
There is a significant wage difference between the original and offshore countries
Remote work is possible in the job
The work can be transmitted over the Internet
The work is repeatable [ 7 ]
Variations [ edit ]
Offshore outsourcing [ edit ]
Subcontracting in the same country would be outsourcing, but not offshoring. A company moving an internal business unit from one country to another would be offshoring or physical restructuring, but not outsourcing. A company subcontracting a business unit to a different company in another country would be both outsourcing and offshoring, offshore outsourcing.
Types of offshore outsourcing include:
Information technology outsourcing (ITO) is where outsourcing is related to technology or the internet, such as computer programming.
Business process outsourcing (BPO) involves contracting out operational functions to a third-party service provider.
Knowledge Process Outsourcing (KPO) is a type of outsourcing that involves or requires more advanced technical skills and a higher level of expertise.
Customer Support Outsourcing (CSO) involves delegating customer service functions to offshore call centres or service providers to handle inquiries, complaints, and assistance.
Recruitment Process Outsourcing (RPO) is a workforce solution in which a business transfers all or part of its recruitment to an external provider. Businesses can deliver a standalone service or the entire operations.
Nearshoring [ edit ]
Nearshoring is a form of offshoring in which the other country is relatively close such as one sharing a border.
Being nearby results in potentially beneficial commonalities such as temporal (time zone), cultural, social, linguistic, economic, political, or historical linkages.[8]
According to the 1913 New York Times article "Near Source of Supplies the Best Policy",[9] the main focus was then on "cost of production." Although transportation cost was addressed, they did not choose among:
transporting supplies to place of production [ 10 ]
transporting finished goods to place(s) of sale
cost and availability of labor
The term nearshoring derives from offshoring. When combined with outsourcing, nearshore outsourcing, the nearshore workers are not employees of the company for which the work is performed.
Nearshoring can involve business strategy to locate operations close to where product is sold. This is contrasted with using low-wage manufacturing operations in developing nations and shipping product back to the country that offshored the work.
With nearshore outsourcing, the work is done by an outside company rather than internally, but in contrast to typical offshore outsourcing, the work is done in fairly close proximity to the company headquarters and its target market.
Nearshoring is often used for information technology (IT) processes such as application development, maintenance and testing.
In Europe, nearshore outsourcing relationships are between clients in larger European economies and various providers in smaller European nations. The attraction is lower-cost skilled labor forces, and a less stringent regulatory environment, but crucially they allow for more day to day physical oversight. These countries also have strong cultural ties to the major economic centers in Europe as they are part of EU. For example, as of 2020 Portugal is considered to be the most trending outsourcing destination[11] as big companies like Mercedes, Google,[12] Jaguar, Sky News, Natixis and BNP Paribas opening development centers in Lisbon and Porto, where labor costs are lower, talent comes from excellent Universities, there's availability of skills and the time zone is GMT (the same as London).[13]
US clients nearshore to countries such as Canada, Mexico and nations in Central and South America.
Reasons to nearshore [ edit ]
Culture [ edit ]
Cultural alignment with the business is often more readily achieved through near-sourcing due to there being similarities between the cultures in which the business is located and in which services are sub-contracted, including for example proficiency with the language used in that culture.[14]
Communication [ edit ]
Constraints imposed by time zones can complicate communication; near-sourcing or nearshoring offers a solution. English language skills are the cornerstone of Nearshore and IT services. Collaboration by universities, industry, and government has slowly produced improvements. Proximity also facilitates in-person interaction regularly and/or when required.[15][16][17]
Other advantages [ edit ]
Software development nearshoring is mainly due to flexibility when it comes to upscale or downscale[18] teams or availability of low cost skilled developers. The nearshoring of call centers, shared services centers, and business process outsourcing (BPO) rose as offshore outsourcing was seen to be relatively less valuable. More recently, companies have explored nearshoring as a risk mitigation strategy for operational and supply chain weaknesses uncovered during the COVID-19 global pandemic crisis, when offshore BPOs experienced sudden closures and disruptive quarantine restrictions which hampered their ability to conduct day-to-day business operations.[19][20]
The complexities of offshoring stem from language and cultural differences, travel distances, workday/time zone mismatches, and greater effort for needed for establishing trust and long-term relationships. Many nearshore providers attempted to circumvent communication and project management barriers by developing new ways to align organizations. As a result, concepts such as remote insourcing were created to give clients more control in managing their own projects. Nearshoring still has not overcome all barriers, but proximity allows more flexibility to align organizations.[21]
Production offshoring [ edit ]
Production offshoring, also known as physical restructuring, of established products involves relocation of physical manufacturing processes overseas,[22] usually to a lower-cost destination or one with fewer regulatory restrictions.
Physical restructuring arrived when the North American Free Trade Agreement (NAFTA) made it easier for manufacturers to shift production facilities from the US to Mexico.
This trend later shifted to China, which offered cheap prices through very low wage rates, few workers' rights laws, a fixed currency pegged to the US dollar, (currently fixed to a basket of economies) cheap loans, cheap land, and factories for new companies, few environmental regulations, and huge economies of scale based on cities with populations over a million workers dedicated to producing a single kind of product. However, many companies are reluctant to move high value-added production of leading-edge products to China because of lax enforcement of intellectual property laws.[23]
IT-enabled services offshoring [ edit ]
Growth of offshoring has been linked to the availability of reliable and affordable communication infrastructure following the telecommunication and internet expansion of the late 1990s.[24]
Much of the job movement was to outside companies, offshore outsourcing.
Reshoring, also known as onshoring, backshoring [25] or inshoring,[26] is the act of reversing an offshoring change; moving a business process that was offshored, back to the original country.[27]
John Urry, professor of sociology at Lancaster University, argues that the concealment of income, the avoidance of taxation and eluding legislation relating to work, finance, pleasure, waste, energy and security may be becoming a serious concern for democratic governments and ordinary citizens who may be adversely affected by unregulated, offshore activities. Further, the rising costs of transportation could lead to production nearer the point of consumption becoming more economically viable, particularly as new technologies such as additive manufacturing mature.[28]
The World Bank's 2019 World Development Report on the future of work[29] considers the potential for automation to drive companies to reshore production, reducing the role of labor in the process, and offers suggestions as to how governments can respond. A similar movement can be seen related to Robotic Process Automation, called RPA or RPAAI for self-guided RPA 2.0 based on artificial intelligence, where the incentive to move repetitive shared services work to lower cost countries is partially taken away by the progression of technology.
Melanie Rojas et al in a 2022 Deloitte's report commend adopting a combination of re-shoring and friendshoring - "working with other nations and trusted supply sources" - as a business practice and policy initiative aiming to promote supply chain resilience.[30]
Practices [ edit ]
Destinations [ edit ]
After its accession to the World Trade Organization (WTO) in 2001, the People's Republic of China emerged as a prominent destination for production offshoring. Another focus area has been the software industry as part of global software development and developing global information systems. After technical progress in telecommunications improved the possibilities of trade in services, India became one prominent destination for such offshoring, though many parts of the world are now emerging as offshore destinations.
United States [ edit ]
Since the 1980s[31] American companies have been "offshoring" and outsourcing manufacturing to low cost countries such as India, China, Malaysia, Pakistan and Vietnam.
Government response [ edit ]
President Obama's 2011 SelectUSA program was the first federal program to promote and facilitate U.S. investment in partnership with the states. This program and website helps companies connect with resources available on a Federal, State and local level. In January 2012, President Obama issued a call to action to "invest in America" at the White House "Insourcing American Jobs" Forum.[32]
Success stories [ edit ]
Advances in 3D printing technologies brought manufacturers closer to their customers.[33] In the case of Starbucks, in 2012 it saved American Mug and Stein Company in East Liverpool, Ohio from bankruptcy.[34]
Avoiding failure [ edit ]
Some cases of reshoring have not been successful. Otis Elevators' reshoring effort did not go well.[35] Otis says it failed to consider the consequences of the new location and tried to do too much at once, including a supply-chain software implementation. This is not an uncommon reshoring scenario. Bringing manufacturing back to the United States isn't so simple, and there are a lot of considerations and analyses that companies must do to determine the costs and feasibility of reshoring. Some companies pursue reshoring with their own internal staff. But reshoring projects are complicated and involve engineering, marketing, production, finance, and procurement. In addition, there are real estate concerns, government incentives and training requirements that require outreach to the community. To help with these projects, companies often turn to consultants that specialize in reshoring.[36]
United Kingdom [ edit ]
In the United Kingdom, companies have used the reintroduction of domestic call centres as a unique selling point. In 2014, the RSA Insurance Group completed a move of call centres back to Britain.[37] The call centre industry in India has been hit by reshoring, as businesses including British Telecom, Santander UK and Aviva all announced they would move operations back to Britain in order to boost the economy and regain customer satisfaction.[38]
R&D offshoring [ edit ]
Product design, research and the development (R&D) process is relatively difficult to offshore because R&D, to improve products and create new reference designs, requires a higher skill set not associated with cheap labor.
Transfer of intellectual property [ edit ]
There is a relationship between offshoring and patent-system strength. Companies under a strong patent system are not afraid to move work offshore because their work will remain their property. Conversely, companies in countries with weak patent systems have an increased fear of intellectual property theft from foreign vendors or workers, and, therefore, have less offshoring.
Offshoring is often enabled by the transfer of valuable information to the offshore site. Such information and training enables the remote workers to produce results of comparable value previously produced by internal employees. When such transfer includes protected materials, as confidential documents and trade secrets, protected by non-disclosure agreements, then intellectual property has been transferred or exported. The documentation and valuation of such exports is quite difficult, but should be considered since it comprises items that may be regulated or taxable.
Debate [ edit ]
Offshoring to foreign subsidiaries has been a controversial issue spurring heated debates among economists. Jobs go to the destination country and lower cost of goods and services to the origin country.
On the other hand, job losses and wage erosion in developed countries have sparked opposition. Free trade with low-wage countries is win-lose for many employees who find their jobs offshored or with stagnating wages.[39]
Currency manipulation by governments and their central banks cause differences in labor cost. On May 1, 2002, Economist and former Ambassador Ernest H. Preeg testified before the Senate committee on Banking, Housing, and Urban Affairs that China, for instance, pegs its currency to the dollar at a sub-par value in violation of Article IV of the International Monetary Fund Articles of Agreement which state that no nation shall manipulate its currency to gain a market advantage.[40]
Source of conflict [ edit ]
The opposing sides regarding offshoring, outsourcing, and offshore outsourcing are those seeking government intervention and Protectionism versus the side advocating Free Trade.[41]
Jobs formerly held by U.S. workers have been lost, even as underdeveloped countries such as Brazil and Turkey flourish.[42] Free-trade advocates suggest economies as a whole will obtain a net benefit from labor offshoring,[43] but it is unclear if the displaced receive a net benefit.[44]
Some wages overseas are rising. A study by the U.S. Bureau of Labor Statistics found that Chinese wages were almost tripled in the seven years following 2002. Research suggests that these wage increases could redirect some offshoring elsewhere.[45]
Increased training and education has been advocated to offset trade-related displacements, but it is no longer a comparative advantage of high-wage nations because education costs are lower in low-wage countries.[46]
U.S. labor market [ edit ]
In 2015, IT employment in the United States has recently reached pre-2001 levels[47][48] and has been rising since.
The number of jobs lost to offshoring is less than 1 percent of the total US labor market.[49] The total number of jobs lost to offshoring, both manufacturing and technical represent only 4 percent of the total jobs lost in the US. Major reasons for cutting jobs are from contract completion and downsizing.[50] Some economists and commentators claim that the offshoring phenomenon is way overblown.[50]
Impact on jobs in western countries [ edit ]
The Economist reported in January 2013 that "High levels of unemployment in Western countries after the 2007-2008 financial crisis have made the public in many countries so hostile towards offshoring that many companies are now reluctant to engage in it."[51] Economist Paul Krugman wrote in 2007 that while free trade among high-wage countries is viewed as win-win, free trade with low-wage countries is win-lose for many employees who find their jobs offshored or with stagnating wages.[39]
Two estimates of the impact of offshoring on U.S. jobs were between 150,000 and 300,000 per year from 2004 to 2015. This represents 10-15% of U.S. job creation.[52]
The increased safety net costs of the unemployed may be absorbed by the government (taxpayers) in the high-cost country or by the company doing the offshoring. Europe experienced less offshoring than the U.S. due to policies that applied more costs to corporations and cultural barriers.[6]
In the area of service research has found that offshoring has mixed effects on wages and employment.[53][54][55][56][57][58]
The World Bank's 2019 World Development Report on the future of work [29] highlights how offshoring can shape the demand for skills in receiving countries and explores how increasing automation can lead to reshoring of production in some cases.
Public opinion [ edit ]
U.S. opinion polls indicate that between 76-95% of Americans surveyed agreed that "outsourcing of production and manufacturing work to foreign countries is a reason the U.S. economy is struggling and more people aren't being hired."[59][60]
Theory [ edit ]
Effects of factor of production mobility [ edit ]
According to classical economics, the three factors of production are land, labor, and capital. Offshoring relies heavily on the mobility of labor and capital; land has little or no mobility potential.
In microeconomics, working capital funds the initial costs of offshoring. If the state heavily regulates how a corporation can spend its working capital, it will not be able to offshore its operations. For the same reason the macroeconomy must be free for offshoring to succeed.
Computers and the Internet made work in the services industry electronically portable. Most theories that argue offshoring eventually benefits domestic workers assume that those workers will be able to obtain new jobs, even if by accepting lower salaries or by retraining themselves in a new field. Foreign workers benefit from new jobs and higher wages when the work moves to them.
Labor scholars argue that global labor arbitrage leads to unethical practices, connected to exploitation of workers, eroding work conditions and decreasing job security.[61]
History [ edit ]
In the developed world, moving manufacturing jobs out of the country dates to at least the 1960s[62] while moving knowledge service jobs offshore dates to the 1970s[63] and has continued since then. It was characterized primarily by the transferring of factories from the developed to the developing world. This offshoring and closing of factories has caused a structural change in the developed world from an industrial to a post-industrial service society.
During the 20th century, the decreasing costs of transportation and communication combined with great disparities on pay rates made increased offshoring from wealthier countries to less wealthy countries financially feasible for many companies. Further, the growth of the Internet, particularly fiber-optic intercontinental long haul capacity, and the World Wide Web reduced "transportation" costs for many kinds of information work to near zero.[64]
Impact of the Internet [ edit ]
Regardless of size, companies benefit from accessibility to labor resources across the world.[65] This gave rise to business models such as Remote In-Sourcing that allow companies to tap into resources found abroad, without losing control over security of product quality.
New categories of work such as call centres, computer programming, reading medical data such as X-rays and magnetic resonance imaging, medical transcription, income tax preparation, and title searching are being offshored.
Ireland [ edit ]
Before the 1990s, Ireland was one of the poorest countries in the EU. Because of Ireland's relatively low corporate tax rates, US companies began offshoring of software, electronic, and pharmaceutical intellectual property to Ireland for export. This helped create a high-tech "boom" which led to Ireland becoming one of the richest EU countries.[64]
NAFTA [ edit ]
In 1994 the North American Free Trade Agreement (NAFTA) went into effect, and it increased the velocity of physical restructuring.
The plan to create free trade areas (such as Free Trade Area of the Americas) has not yet been successful. In 2005, offshoring of skilled work, also referred to as knowledge work, dramatically increased from the US, which fed the growing worries about threats of job loss.[64]
Related [ edit ]
Inshoring – picking services in the same country
Bestshoring or rightshoring – picking the best country for offshoring
Business process outsourcing (BPO) – outsourcing arrangements when entire business functions (such as finance, accounting, and customer service) are outsourced. More specific terms can be found in the field of software development - for example Global Information System as a class of systems being developed for / by globally distributed teams.
Bodyshopping – practice of using offshored resources and personnel to do small disaggregated tasks within a business environment, without any broader intention to offshore an entire business function.
See also [ edit ]
References [ edit ]
| 2022-12-10T00:00:00 |
https://en.wikipedia.org/wiki/Offshoring
|
[
{
"date": "2022/12/10",
"position": 93,
"query": "automation job displacement"
}
] |
|
Reskilling: benefits and best practices
|
Reskilling vs. Upskilling: Future-Ready Strategies
|
https://attensi.com
|
[] |
... job. For example, IT staff could be reskilled to ... Changing skillsets for changing times: Displacement due to automation and robotic process automation.
|
Reskilling vs. upskilling
Reskilling is the process of an employee learning an entirely new skillset so they can do a completely different job. For example, IT staff could be reskilled to work in cloud computing or cybersecurity.
Upskilling, in contrast, is all about adding to a pre-existing skill set within an employee’s current role. For example, people may upskill as new technology is introduced into their job role.
Why reskilling is important
We exist in a world of ever-evolving technology. And, despite the Coronavirus pandemic, we’re not going to see these advancements slow down anytime soon.
Now more than ever it’s vital that organizations keep making a commitment to growing employees so they can succeed – whatever direction they head in.
This is where both reskilling and upskilling come into play.
Reskilling and upskilling is a top priority for the next decade
The current skill sets of employees globally are outdated.
The World Economic Forum (WEF) reported that more than 40% of employees will need reskilling2 by 2025.
Unless organizations change the way they approach reskilling, companies will continue to fail to grow their employees and future-proof their business.
Changing skillsets for changing times: Displacement due to automation and robotic process automation
The rise of automation and robotic process automation (RPA) has seen job responsibilities decrease by as much as 50% in some roles.
The pace of change is rapid, with Gartner3 reporting that over 33% of the skills required by an average 2017 job posting were no longer necessary in 2021.
The best thing you can do is ensure your employees stay employable.
Encouraging your teams to reskill will enable them to become adaptable, life-long learners.
What’s more, both parties can reap the benefits of reskilling and upskilling. WEF reports4 that 94% of employers expect their employees to acquire new skills whilst on the job. These individuals will see rapid career progression, unlike those who fail to adapt.
Ideal for combatting labor shortages
Reskilling is the answer to minimizing workplace skills shortages on a global scale, especially the current labor shortages that most developed economies are experiencing.
| 2022-12-10T00:00:00 |
https://attensi.com/learn/guides/reskilling-benefits-and-best-practices/
|
[
{
"date": "2022/12/10",
"position": 95,
"query": "automation job displacement"
},
{
"date": "2022/12/10",
"position": 2,
"query": "reskilling AI automation"
}
] |
|
Democratization of AI critical to bridge the skill gap
|
Democratization of AI critical to bridge the skill gap: Anand Mahurkar, Findability Sciences, ET CIO
|
https://cio.economictimes.indiatimes.com
|
[] |
The democratization of AI will make it possible for companies and organizations to overcome the difficulties brought on by the AI skills gap caused by a lack of ...
|
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Since the early 2000s, data has played a friend and a foe for businesses. The breadth of an organization's data repositories, which can easily reach the petabyte level, leads to information overload, even though most executives know the value of data. As a result, traditional businesses are having trouble, and terminology like "data lakes" and "data warehouses," which are widely used in IT, is now colloquially referred to as "data dumping grounds."Today's challenge is transforming these "data dumping grounds" into logical recommendations and predictions. This is where an artificial intelligence (AI) approach can help by providing a means of gathering and combining pertinent data to make it helpful and informative.AI, for instance, may revitalize a company's supply chain management analytics. A company must develop a broad data strategy once it starts its AI journey. This entails gathering and combining information from the internal ERP , and CRM systems and information from outside resources like news and social media feeds. Once all the data has been gathered and processed, the company may propose the best inventory based on demand estimates, receive intelligent predictions about the cost of raw materials, help automate resource planning, and more.Giving external data merit is equally important since an enterprise can't control numerous dependencies. For example, forecasting is heavily influenced by holidays, the weather, and socioeconomic conditions in the sales industry. These elements could have a big impact on revenue projections. A machine must learn correlations for various data to understand the cause of sales variations and provide useful insights.It is no doubt that data is the next game-changer for AI-enabled businesses and is emerging as a critical differentiator. An analysis of data and analytics (powered by AI) reveals that organizations driven by data have a 19 times higher chance of succeeding. Intelligent digital processes are being powered by emerging technologies, allowing machines to assist humans in their work. A PwC report estimated that by 2030, AI might have a $15 trillion impact on the global economy. Only a small number of technologies have this kind of potential.Further, AI adoption amongst mid-sized and small-sized businesses is already rising. Moreover, enterprises across industries, including banking, agriculture, food, healthcare , and environmental education, increasingly rely on AI to enhance their overall performance. For instance, AI is used for credit eligibility, financial advice, trading decisions, and fraud detection. A fantastic customer experience is ensured by intelligent chatbots powered by AI and deep learning, propelling enterprises toward digital transformation.Only once AI is widely accessible and everyone can use it to their advantage will it realize its full potential. Thankfully, this will be simpler than ever in 2023. Regardless of one's level of technical expertise, a rising number of apps put AI capability at the fingertips of everyone. This can be as basic as apps that let us build complex visualizations and reports with a mouse click, decreasing the typing required to search or write emails.Ultimately, the democratization of AI will make it possible for companies and organizations to overcome the difficulties brought on by the AI skills gap caused by a lack of qualified data scientists and AI software engineers. The potential and value of artificial intelligence will be accessible to all of us by enabling anybody to become "armchair" data scientists and engineers.The technology is becoming publicly recognized thanks to experiments like the well-known deep-faked Tom Cruise films and the Metaphysic performance which dominated this year's America's Got Talent. But by 2023, we'll notice that it will be applied more regularly to produce synthetic data that firms may utilize for various things. Input what you want the audience to see and hear into your generative tools. The AI will build it for you, courtesy of synthetic audio and video data, which can eliminate the need to record speech and film.Data transformation has evolved into an ever-changing process with emerging technologies, providing insights and solutions to the market's and consumers' continually moving dynamics. We need to make effective AI technologies more widely used and accepted before we reach a day where robots do all our jobs for us, much alone feeding the globe. Unfortunately, some AI with enormous potential still needs to be used daily, such as the AI that Stanford researchers used to diagnose pneumonia better than radiologists.Overall, there is no limit to how far AI technology can advance now that it has grown to the point where it can compete in jeopardy in addition to playing chess and checkers. Even though the creation of actual artificial intelligence may not have happened yet, it will probably happen sooner than we expect!
| 2022-12-10T00:00:00 |
https://cio.economictimes.indiatimes.com/news/next-gen-technologies/ai-revolution-the-emerging-tech-talk/96124457
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[
{
"date": "2022/12/10",
"position": 7,
"query": "AI skills gap"
}
] |
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The Future of AI: Exploring the Potential of Artificial ...
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The Future of AI: Exploring the Potential of Artificial Intelligence
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https://www.thinkers360.com
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One of the critical things I have been considering is how to use AI to increase my efficiency and productivity. For example, I have been experimenting with AI- ...
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As someone fascinated by the potential of artificial intelligence (AI), I have been exploring how AI can be used in my life and work. While I am impressed by the impressive power of AI and its potential to revolutionize various industries, I also approach it with caution.
One of the critical things I have been considering is how to use AI to increase my efficiency and productivity. For example, I have been experimenting with AI-powered tools to automate specific tasks, such as scheduling meetings and managing my email inbox. While these tools have certainly made certain aspects of my work more accessible, I have also found that it's essential to maintain a certain level of human oversight to ensure that the AI is working as intended.
Another aspect of using AI that I have been thinking about is its potential to improve decision-making. AI algorithms can process large amounts of data and identify patterns and trends that may not be immediately obvious to a human. However, I have also realized that it's essential to understand the limitations of AI and not rely on it blindly. For example, AI algorithms can sometimes make biased or unfair decisions, so it's essential to carefully consider the implications of using AI for decision-making.
The ability to use AI algorithms to create unique and beautiful pieces of art has given me access to a level of creativity that I never thought possible. One of the things I find most exciting about AI art is how it can open up new possibilities for creativity. For example, AI algorithms can be trained to generate unique and intricate patterns and designs, allowing artists to create works that would be impossible for humans to develop. Additionally, AI algorithms can manipulate and transform existing images, allowing artists to explore new ways of seeing and representing the world.
Another advantage of using AI in art is the accessibility it offers. In the past, creating art often required a significant investment of time, money, and resources. However, with the advent of AI, it is now possible for anyone with a computer and an internet connection to create stunning works of art. This has democratized the art world and allowed people from all backgrounds to explore their creative potential.
Of course, AI art also has its limitations and challenges. One of the biggest challenges is the question of authorship and ownership. Because AI algorithms are responsible for creating the art, it can be challenging to determine who should be credited as the artist. Additionally, there are concerns about the ethics of using AI in art, including the potential for AI algorithms to replicate or appropriate the work of human artists.
Overall, I have found that using AI can be incredibly powerful, but it's essential to approach it carefully. I am still figuring out the best ways to use AI in my life and work, but I am excited to continue exploring its potential.
By Dean Miles
Keywords: Startups, Business Continuity, Mental Health
| 2022-12-10T00:00:00 |
https://www.thinkers360.com/tl/blog/members/the-future-of-ai-exploring-the-potential-of-artificial-intelligence
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[
{
"date": "2022/12/10",
"position": 66,
"query": "future of work AI"
}
] |
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Job Guarantee - We CAN Have Nice Things
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We CAN Have Nice Things
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https://wecanhavenicethings.com
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[] |
This page contains research, proposals and commentary related to instituting a Federal Job Guarantee; the thinking behind nice things we — the people — can have ...
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Background • Research & Proposals • Response & Commentary Just keep scrolling…
In 1999, Randy Wray, Mathew Forstater, Pavlina Tcherneva and Stephanie Kelton brought the Center for Full Employment & Price Stability to UMKC. For two decades, they hosted conferences, advised policy makers, and published volumes of research on the Job Guarantee. This page contains research, proposals and commentary related to instituting a Federal Job Guarantee; the thinking behind nice things we — the people — can have. Pavlina Tcherneva maintains a comprehensive FAQ
“…the Job Guarantee is a specific and intrinsic element of Modern Monetary Theory (MMT) rather than a policy choice that might reflect progressive Left values.” — Bill Mitchell
“We are talking about the monetary case for a job guarantee … to be able to democratize money we have to democratize work… there is an inherent relationship between the two in the modern monetary system… the monetary system’s original sin is that it creates unemployment and that money is a public institution that is launched into existence by creating unemployment… Taxes are never really a financing operation for the government. There are always a resource transfer operation even throughout historical time. They exist to provision the government in real good and services they are a tool of redistribution…”
Money as a Democratic Medium | The Monetary Case for a Job Guarantee
— Pavlina Tcherneva (@PTcherneva), Jan 10, 2019
Networked community based organizations are emerging; working top down and bottom up. Here are four: Jobs for All (@NatlJobsForAll), Sunrise Movement (@sunrisemvmt), New Consensus (@newconsensus), Fight for FJG (@FightForFJG) See more at Green New Deal.
—Research & Proposals—
From Centre of Full Employment & Equity (CofEE) in 2004
A comparison of the macroeconomic consequences of basic income and job guarantee schemes
— Bill Mitchell (@billy_blog), Martin Watts
From Center for Full Employment & Price Stability (CFEPS) in 2005
Can Basic Income and Job Guarantees Deliver on Their Promises
— Pavlina Tcherneva (@PTcherneva), L. Randall Wray
From SSRN: Full Employment Through a Job Guarantee: A Response to the Critics
— William F. Mitchell, L. Randall Wray SSRN (@SSRN) Jan 1, 2005
Historical Amnesia: The Humphrey-Hawkins Act, Full Employment and Employment as a Right
— Helen Ginsberg, William A. Darity (@SandyDarity) ResearchGate The Review of Black Political Economy Jan 2010
Macroeconomic Stabilization Through an Employer of Last Resort
— Scott Fullwiler (@stf18) SSRN (@SSRN) Dec 10, 2010
From Global Institute for Sustainable Prosperity Completing the Roosevelt Revolution: Why the Time for a Federal Job Guarantee Has Come.
– Pavlina R. Tcherneva (@PTcherneva), Global Institute for Sustainable Prosperity, @GISP_Tweets, Nov 2015
From Levy Institute
1.“The Job Guarantee • Design, Jobs and Implementation”
— Pavlina Tcherneva
2. “Guaranteed Jobs through a Public Service Employment Program”
— L. Randall Wray, Stephanie A. Kelton, Pavlina R. Tcherneva, Scott Fullwiler and Flavia Dantas.
3. “Public Service Employment: A Path to Full Employment” • Evaluates the economic impact over a ten-year period of a job guarantee program that pays $15 per hour and offers a basic package of benefits.
— L. Randall Wray, Stephanie A. Kelton, Pavlina R. Tcherneva, Scott Fullwiler and Flavia Dantas.
From Center on Budget and Policy Priorities (CBPP): The Federal Job Guarantee – A Policy to Achieve Permanent Full Employment
— Mark Paul (@MarkVinPaul), William Darity Jr. (@SandyDarity) & Darrick Hamilton (@DarrickHamilton), Center on Budget and Policy Priorities, March 9, 2018
The Job Guarantee: Full Employment, Price Stability and Social Progress
— Dirk H. Ehnts (@DEhnts), Maurice Hofgen Society Register (@SocietyRegister), Dec 31, 2019
From UC Davis Journal of Law & Political Economy: Better Than Jail: Social Policy in the Shadow of Racialized Mass Incarceration
— Noah D. Zatz, UCLA School of Law, 2021
The Case for a Job Guarantee * Why we need a national job guarantee in Australia
— Sustainable Prosperity Action Group (SPAG) – Aug 2022
—Response & Commentary—
—2022—
What Happens When Jobs Are Guaranteed? • In a small Austrian village, an experimental program finds—or creates—work for the unemployed.
— Nick Romero The New Yorker (@NewYorker) Dec 10, 2022
—2021—
A Federal Job Guarantee: The Unfinished Business of the Civil Rights Movement • The 1963 March on Washington put a government guarantee to a job at the front of the civil rights agenda. It’s long past time to complete the work.
— Rep. Ayanna Pressley (@AyannaPressley), David Stein (@DavidPStein <em>The Nation</em> (@TheNation) Sept 2, 2021
UBI vs FJG
— Greg Roest, Real Progressives (@RealProgressUS) Aug 4, 2021
Good Forms of Collectivity: Low-Carbon Care Work and a Federal Job Guarantee
— Natan Last (@NatanLast) Los Angeles Review of Books @LAReviewofBooks April 26, 2021
Job Guarantee
— The Gower Initiative for Modern Monetary Studies (@GIMMS)
Four Labor and the Jobs (sic) Guarantee Webinars
• Fadhel Kaboub, Sandy Darity, Sydney Ghazarian, and Kari Thompson
• Dr. Darrick Hamilton
• Sara Nelson, Keon Liberato, Carl Rosen, and Judy Ancel
• Report Backs
Rosa Luxemburg Foundation, NY Office April 1, 2021
The Job Guarantee Now! coalition joined Congresswoman Ayanna Pressley to launch the Federal Job Guarantee Resolution.Jobs for All: A Prosperity Economy “The Job Guarantee Now! campaign is led by PolicyLink, the National Jobs for All Network, and Public Money Action.”
House Resolution Recognizing the Duty of the Federal Government to Create a Federal job guarantee. — Rep. Ayanna Pressley, D-MA 07 Related press from Jobs for All. Feb 18, 2021
Money on the Left — Maxximilian Seijo and Andrés Bernal reflect on this historical moment in intersectional left-wing activism.Feb 18, 2021 (21:28)
Everything You Need to Know about Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) • Since the implementation of the programme, while benefitting 6.01 crore households in India’s villages, MGNREGA has generated 216.73 core person-days of employment as well as created 5.01 crore assets in rural India
— Mohd Mustaquim (@Mustaquimm) Rural Marketing (@RuralMarketing_) Jan 11, 2021
—2020—
$600/wk Unemployment Insurance cannot deliver the benefits of a $600/wk Job Guarantee. From the outset, I should say JG is not a replacement for UI, no matter what you may have heard. I’ll get to this later, but read this long 🧶 w/ that in mind. https://t.co/EsBcsHqPOe — Pavlina R Tcherneva (@ptcherneva) December 30, 2020
In 25 tweets: an elegant comparison of the Job Guarantee and UI from Pavlina Tcherneva Dec 29, 2020
The Prerequisite For Healing The Nation: A Federal Job Guarantee
— John T. Harvey (@John_T_Harvey) Forbes (@Forbes) Dec 3, 2020
Congress’ failure to extend the stimulus reveals a broken safety net, but a jobs guarantee could wipe out unemployment for good • Pavlina Tcherneva, a Bard professor and one of the economists pioneering research into Modern Monetary Theory, said federally guaranteed jobs may address poverty and help communities better than universal basic incomes.
— Allana Akhtar (@allanaakh) Business Insider (@businessinsider) July 31, 2020
A Job Guarantee Costs Far Less Than Unemployment • The Bold Policy for Not Just Weathering the Crisis, but Coming Out Better By Pavlina R. Tcherneva (@PTcherneva) Foreign Affairs (@ForeignAffairs) July 22, 2020
— Pavlina R.Tcherneva (@PTcherneva) UCL Institute for Innovation and Public Purpose, July 15, 2020 (01:27:15)
India: Over 55 million households demand work under MGNREGA this year so far
— Sanjeeb Mukherjee (@sanjeebm77) Business Standard (@BSIndia), July 15, 2020
Controlling the Viral Spread of Unemployment With a Job Guarantee • Job loss is an epidemic, and the federal government has the cure.
— Pavlina R.Tcherneva (@PTcherneva) The American Prospect (@TheProspect), June 26, 2020
Can the U.S. fix unemployment with ‘Universal Basic Jobs’? • What would happen if the U.S. guaranteed every citizen a job with a living wage and benefits?
— Stephen Johnson BigThink (@Bigthink), 26 June, 2020 + audio
Forget UBI, says an economist: It’s time for universal basic jobs
— Cory Doctorow (@Doctorow), Los Angeles Times (@LATimes) June 24, 2020
Universal Basic Income or Job Guarantee? Why Not Both? • Supporters set these programs at odds, but they can work together to mitigate a crisis and provide basic dignity to everyone.
— Jeff Spross (@jeffspross), The American Prospect (@TheProspect) May 20, 2020
What is a job guarantee—and how could it help us recover from the coronavirus? •What if anytime someone wanted work, there was a societally beneficial job—such as providing eldercare or planting trees—available to them?
— Adele Peters (@Adele_Peters), Fast Company (@FastCompany) May 7, 2020
What Roosevelt Do? – The US government should pull out all the stops in mitigating the economic fallout from COVID-19, not just by disbursing cash to all households, but also by implementing a federal job guarantee and many other long-overdue policies.
— Pavlina R. Tcherneva (@PTcherneva) Project Syndicate, Mar 20, 2020
Ayanna Pressley questions Chairman Powell on the Job Guarantee! A Twitter thread.
— Rohan Grey (@RohanGrey) Feb 11, 2020
The Unfinished Work of the Civil Rights Movement
— Delman Coates (@iamdelmancoates), Sojourners (@sojourners), Jan 22, 2020
—2019—
“There are 2 choices ONLY when there aren’t enough jobs for those that want work–offer a job vs don’t. Whatever macro policy approach you prefer–Keynesian countercyclical, UBI, Taylor rule, etc–the choice ‘offer jobs vs don’t’ for those still wanting work is ALWAYS present.”
— Scott Fullwiler @stf18 Twitter Nov 18, 2019
Whatever macro policy approach you prefer–Keynesian countercyclical, UBI, Taylor rule, etc–the choice ‘offer jobs vs don’t’ for those still wanting work is ALWAYS present”
— Scott Fullwiler, Associate Professor of Economics at University of Missouri-Kansas City on Twitter Nov 18, 2019
The Job Guarantee misinformation campaign – UBI style
— Bill Mitchell (@billy_blog) Macroeconomic research, teaching and advocacy, May 15, 2019
The private sector WILL NOT create a job for every willing worker, yet we have plenty of goods and services for everyone. How to fix this? A federal job guarantee.
— J.T. Harvey (@J_T_Harvey) Cowboy Economist May 8, 2019 (08:32)
2 Hard Questions – Federal Job Guarantee (FJG) vs. Universal Basic Income (UBI) “This week we ask about the progressive policy of a Federal Job Guarantee: HOW a FJG would be implemented & WHY a FJG is a superior policy to a stand-alone UBI.”
New Macro March 29, 2019 (10:20)
Modern Monetary Realism • Kenneth Rogoff’s criticism of Modern Monetary Theory assumes that MMT advocates don’t care about budget deficits or the independence of the US Federal Reserve. But these assumptions are wide of the mark, and Rogoff himself sometimes undermines his own arguments.
— James Galbraith, Project Syndicate (@ProSyn), March 15, 2019
Urban Job Guarantee Scheme: Hope Kindles, At Last • Urban Job Guarantee Scheme of the Congress government will cover 6.5 lakh unemployed youth in urban areas and while it won’t cure the dire joblessness in the state, it has given rise to aspirations
— Rakesh Dixit in Bhopal India Legal (@indialegalmedia), March 10, 2019
The Black Woman Economist Who Pioneered a Federal Jobs Guarantee
— Nina Banks (@Nina_EBanks), Institute for New Economic Thinking, Feb 22, 2019
Why a universal basic income is a poor substitute for a guaranteed job
— Claire Connelly, Australia ABC, Jan 18, 2019
Direct Job Creation in America w/ Steven Attewell
— Steven Attewell (@StevenAttewell) talks with hosts Scott Ferguson (@Videotroph), William Saas (@billysaas) and Max Seijo (@MaxSeijo) Modern Money Network Humanities Division Jan 17, 201p. (01:10:46) Note: see all Money on the Left podcast titles here.
Money as a Democratic Medium | The Monetary Case for a Job Guarantee “Significant evidence suggests that employment leads rather than trails economic growth. That conclusion supports programmatic initiatives to create jobs directly in the public sector. At issue here are models that identify sovereign money as a public commitment that anticipates — and enables — productivity rather than expending a finite resource.”
— Pavlina Tcherneva (@PTcherneva), Philip Harvey, Rohan Grey (@rohangrey), Darrick Hamilton (@DarrickHamilton), Daniel Sufranski Harvard Law School Jan 10, 2019 (1:35:56)
Michael Katz hosts this roundtable discussion on the Job Guarantee, featuring L. Randall Wray, Andy Felkerson, Pavlina Tcherneva, Jan Kregel, Mathew Forstater, Fadhel Kaboub, and Felipe Rezende. They talk about the need for full employment, the feasibility of the program, examples of government employment programs, and how it can be paid for.
Global Institute for Sustainable Prosperity (@GISP_tweets) Jan 7, 2019 (51:50)
—2018—
The Job Guarantee is more than a Green New Deal job creation policy
— Bill Mitchell (@billy_blog) Bill Mitchell – Modern Monetary Theory, Dec 17, 2018
Climate change, Developing Nations, and Hyperinflation in an MMT Lens
— Fadhel Kaboub, Real Progressives, Dec 4, 2018 (13:00)
Can We Give a Good Job to Everyone Who Wants One?
— Editorial The Nation (@TheNation), Nov 27, 2018
ABC goes gaga for Modern Monetary Theory
— “Houses and Holes in Economics”, MacroBusiness, Nov 21, 2018 #JobGuarantee
A radical plan to give every Australian a job who wants one is gaining momentum•The unconventional plan would eliminate unemployment in Australia, says US economist Stephanie Kelton.
— Rosemary Bolger (@Rose_bolger), SBS News, (@SBSnews) Nov 17, 2018
The MMT Podcast: #11 Bill Mitchell: MMT Q&A – central banking, Job Guarantee and more…
— Bill Mitchell (@Billy_Blog) talks with Patricia Pino (@PatriciaNPino) and Christian Reilly (@christreilly) via REKNR (@reknrmag). Nov 13, 2018 (01:35:22)
The MMT government job guarantee • The modern monetary theory can also offer a solution to the nation’s unemployment problems
— Stephen Williams Independent Australia, (@IndependentAus) Oct 11, 2018
Why Politicians Want the U.S. to Guarantee You a Job
— Katia Dmitrieva (@katiadmi), Bloomberg, Sept 21, 2018
A Job for Everyone? This 21st-Century Keynes Says It’s Possible William Darity, Jr., a Duke University professor, has pushed the idea of a federal job guarantee into the Mainstream
— Mattea Kramer (@MatteaKramer) Ozy, (@ozy) Sept 17, 2018
How a Federal Job Guarantee Can Help the Formerly Incarcerated
— Vanessa A. Bee @dolladollabille, New York Magazine, Aug 31, 2018
Popular econ professor advances universal job guarantee plan with Democrats #Deficit
— Rita Francesca Loffredo (@loffredo_rita) The College Fix, (@CollegeFix) Aug 22, 2018
There Is Work To Be Done: AI And The Future Of Work
— Mark Paul (@MarkVinPaul), Forbes, Aug 18, 2018
What Money Can Buy • The promise of a universal basic income—and its limitations.
— Bryce Covert (@BryceCovert), The Nation (@TheNation), Aug 15, 2018
What if you could sue the government for a job?
— Jeff Spross (@jeffspross), The Week, (@TheWeek) July 30, 2018
Should Democrats play it safe with a job guarantee?
— Jeff Spross (@jeffspross), The Week, (@TheWeek) July 27, 2018
Making a Federal Job Guarantee Work
— Joe Weisenthal(@TheStalwart) with William Darity Jr. (@SandyDarity) Bloomberg, July 24, 2018
The MMT Podcast: #5 A Job Guarantee vs a Universal Basic Income
— Rohan Grey (@RohanGrey) talks with Patricia Pino (@PatriciaNPino) and Christian Reilly (@christreilly) via REKNR (@reknrmag). July 8, 2018 (01:35:22) Pino asks about UBI at 23:30.
What Is A Federal Jobs Guarantee? Growing numbers of Democrats support the idea of guaranteeing decent jobs to all Americans. Here’s how they say it would work.
— Laura Paddison (@laurapaddison) HuffPost, July 6, 2018
A Job Guarantee or the Universal Basic Income? | Interview with Stephanie Kelton
— acTVism Munich, July 5, 2018
Do We Need a Federal Jobs Guarantee? A Debate. • Sens. Kirsten Gillibrand, Cory Booker and Bernie Sanders have all proposed a job guarantee. But would it be drudgery?
— Raúl Carrillo (@RaulACarrillo), Rohan Grey (@RohanGrey) In These Times, June 18, 2018
Job Guarantee as Historical Struggle w/ David Stein
— David Stein (@DavidpStein) talks with hosts Scott Ferguson (@Videotroph), William Saas (@billysaas) and Max Seijo (@MaxSeijo) Modern Money Network Humanities Division May 27, 2018. (01:06:48) Note: see all Money on the Left podcast titles here.
Full Employment and Freedom • The fight for a full employment bill forty years ago offers lessons for supporters of a job guarantee today.
— David Stein (@DavidpStein) Jacobin (@jacobinmag), May 25, 2018
David Dayen (@DDayen) talks about job guarantee proposals with conservative host Jimmy Sengenberger.
— David Dayen (@DDayen) Business for Breakfast on KDMT-AM 1690 in Denver, May 24, 2018 (53:23)
Thinking Beyond Trump, Why We Need A Federal Jobs Guarantee
— Robert Reich, (@RBReich) Eurasia Review, May 22, 2018 (Cross-posted in Truthdig)
Three Ways to Design a Democratic Job Guarantee.
— Alexander Kolokotronis, (@AVK48) Truthout, May 20, 2018
Segregation is alive and well in America’s so-called land of opportunity — just ask black and Latino children • 50 years after the landmark Kerner Commission report, the United States still has a long way to go. #JobGuarantee
— Alan Curtis, Fred Harris NBC News, May 13, 2018
A Job Guarantee: A better, cheaper alternative to the Greens’ UBI
— Steven Hail (@StevenHail), Independent Australia, (@IndependentAus) May 12, 2018
Why the Latinx Community Should Fight for a Job Guarantee
— Alan A. Aja (@AlanAAja1), Raúl Carrillo (@RaulACarrillo), Rita Sandoval (@RitaMSandoval) @LatinoRebels, May 9, 2018
Likely 2020 Democratic Candidates Want To Guarantee A Job To Every American
— Danielle Kurtzleben, (@titonka) @NPR, May 8, 2018
Bernie Gets Socialistic • A national job guarantee has opened radical horizons for the Left. We should fight for it — but the devil is in the details.
— Max B. Sawicky, (@maxbsawicky) Jacobin, May 7, 2018
Political Aspects of Full Employment • Why do capitalists hate full employment? Because it weakens their power over workers.
— Michal Kalecki Jacobin Archives, May 7, 2018
The MMT Podcast: #4 What is the Job Guarantee?
— Fadhel Kaboub (@FadhelKaboub) talks with Patricia Pino (@PatriciaNPino) and Christian Reilly (@christreilly) via REKNR (@reknrmag). May 4, 2018 (01:35:22)
The Guardian view on a job guarantee: a policy whose time has come • Ministers need to adopt measures that secure a basic human right to engage in productive employment
— Editorial, The Guardian, May 3, 2018
We Work • Give job guarantee a chance
— James K. Galbraith, The Baffler May 2, 2018
Yes, a Jobs Guarantee Could Create “Boondoggles.” It Also Might Save the Planet.
— Kate Aronoff (@KateAronoff), In These Times, May 1, 2018
Human capital and the jobs guarantee
— Alexandra Scaggs (@alexandrascaggs), Financial Times — Alphaville, April 30, 2018
Calculating the Cost of a Jobs Guarantee
— Pavlina Tcherneva (@PTcherneva), Joe Weisenthal (@TheStalwart) Bloomberg, April 30, 2018
Whether America Can Afford a Job Guarantee Program Is Not Up for Debate
— David Dayen (@DDayen), The Intercept, April 30, 2018
Critics of the Job Guarantee miss the mark badly … again
— Bill Mitchell (@Billy_Blog) Macroeconomic research, teaching and advocacy, April 26, 2018
” alt=”The Case for a Job Guarantee in the UK” />
– Fadhel Kaboub (@FadhelKaboub) – 26 April 2018 (55:05)
How much would a job guarantee actually cost?
— Jeff Spross (@jeffspross), The Week, (@TheWeek) April 26, 2018
How Guaranteeing Jobs Became the Hot New Policy Priority for 2020 Dems • Bernie Sanders has a bill. Cory Booker too. And now Elizabeth Warren is on board.
— Gideon Resnick (@GideonResnick), The Daily Beast, (@thedailybeast) April 25, 2018
Why the Cause of Full Employment Is Back from the Dead • Franklin Roosevelt and Martin Luther King campaigned for it in vain—but the need for full employment has never gone away
— Harold Meyerson (@HaroldMeyerson), American Prospect, (@TheProspect) April 25, 2018
Need Work? Maybe That’s a Job for Government • Lots of good things can happen when people are employed.
— Noah Smith (@Noahpinion), Bloomberg, April 24, 2018
Embracing ‘the Sort of Bold Thinking We Need,’ Sanders to Introduce Plan to Guarantee Every American a Job • Proponents trace the idea back to the New Deal Era, when President Franklin Delano Roosevelt pitched a ‘Second Bill of Rights’ to Congress in 1944. First on the list: the ‘right to a useful and remunerative job.’
— Jake Johnson (@johnsonjakep), Common Dreams, (@CommonDreams) April 23, 2018
Bernie Sanders to announce plan to guarantee every American a job
— Jeff Stein (@jstein_wapo), Washington Post, April 23, 2018
The jobs guarantee and human-capital “nationalisation”
— Alexandra Scaggs (@alexandrascaggs), Financial Times — Alphaville, April 22, 2018
— William Darity Jr. (@SandyDarity) & Darrick Hamilton (@DarrickHamilton) Economic Justice Law Review April 20, 2018
The Government Has A Job To Do– Creating Full Employment… And It’s Been Failing At It
— Howie Klein (@DownWithTyranny), DownWithTyranny, April 18, 2018
What If We Didn’t… have any unemployment?
— A.P. Joyce (@AndrewPaulJoyce) Mic, April 17, 2018
U.S. Jobs Guarantee Held Out as Path to True ‘Full Employment’
— Katia Dmitrieva (@katiadmi), Bloomberg, April 17, 2018
10 Principles for a Federal Job Guarantee
— Angela Glover Blackwell (@agb4equity), Sarah Treuhaft (@streuhaft), William Darity Jr. (@SandyDarity); Darrick Hamilton (@DarrickHamilton) PolicyLink (@PolicyLink) April 11, 2018
Why Democrats should fight for the right to a good job
— Katrina vanden Heuvel (@KatrinaNation), The Washington Post, April 10, 2018
The Zero Hour
R.J. Eskow talks with Pavlina Tcherneva about the the moral, policy and economic impacts of a national job guarantee. April 8, 2018 (16:38)
The Government Should Guarantee Everyone a Good Job • We have an opportunity to pass a good-jobs guarantee—but we have to start a movement now.
— Ady Barkan (@AdyBarkan), The Nation (@TheNation), April 4, 2018
A Guaranteed Jobs-for-All Program Is Gaining Traction Among 2020 Democratic Hopefuls
— Kate Aronoff (@KateAronoff), The Intercept, April 1, 2018
Food Stamps Aren’t a Substitute for Work. They’re How Low-Wage Workers Avoid Hunger. (#FederalJobGuarantee)
— Kalena Thomhave, @kalenasthom The American Prospect,
March 28, 2018
Data Wonk: A Federal Jobs [sic] Guarantee
— Bruce Thompson, Urban Milwaulkee, March 28, 2018
Red and Blue Voters Alike Could Rally Around This Radical Job Growth Idea • Pushing to expand Americorps or supporting a federal jobs guarantee has benefits for everyone.
— Liz Posner @elizpos, Alternet, March 28, 2018
The Radical Proposal That Moderate Democrats Should Be Running On (#FederalJobGuarantee).
— Eric Levitz @EricLevitz, Bloomberg Businessweek, March 22, 2018
Why Democrats Should Embrace a Federal Jobs Guarantee
— Sean McElwee @SeanMcElwee, Colin McAuliffe @unburythelead17 and Jon Green @_Jon_Green, The Nation (@TheNation), March 20, 2018
The Federal Job Guarantee – A Policy to Achieve Permanent Full Employment
— Mark Paul (@MarkVinPaul), William Darity Jr. (@SandyDarity) & Darrick Hamilton (@DarrickHamilton), Center on Budget and Policy Priorities, March 9, 2018
An Economic Bill of Rights for the 21st Century • In 1944, Franklin Roosevelt proposed constitutional amendments to guarantee Americans’ fundamental economic rights. It was never adopted—and today, is more necessary than ever. Here’s an adaptation of his program for our time.
— Mark Paul (@MarkVinPaul), William Darity Jr. (@SandyDarity) & Darrick Hamilton (@DarrickHamilton), American Prospect, March 5, 2018
Pavlina Tcherneva — The Federal Job Guarantee
Job guarantee research; why such a policy is needed; and how it would work, from funding to implementation. Benefits to those who are often excluded from full participation in economic and social life; why a job guarantee is superior to an income guarantee alone;
Harvard Law School Chapter, Modern Money Network, March 2, 2018 (1:14:01)
Opinion: Provide Jobs for All and End Poverty
— Chuck Lynd, Columbus Underground, Feb. 26, 2018
—2017—
What Sounds Better To You – Guaranteed Basic Income Or Federal Job Guarantee?
— Howie Klein (@DownWithTyranny), Naked Capitalism, Dec 29, 2017
The Job Guarantee & Social Justice (w/ Pavlina Tcherneva)
— Pavlina Tcherneva (@ptcherneva), Adam Simpson (@Adam Simpson), Cecilia Gingerich (@c_gingerich) The Next System Podcast (with transcript), Dec. 20, 2017
How Cities Can Do Better Than the Fight for $15 • It’s time for cities to reverse a shrinking workforce and build resilience in the face of climate change.
— Alan A. Aja (@AlanAAja1), William Darity Jr. (@SandyDarity); Darrick Hamilton (@DarrickHamilton) Yes! Magazine (@yesmagazine), Oct 6, 2017
It’s Time for the Government to Give Everyone a Job
— David Dayen (@DDayen), The Nation (@TheNation), May 19, 2017
Why Coretta Scott King Fought for a Job Guarantee
— David Stein (@DavidpStein), Boston Review, May 17, 2017
American Job Guarantee • Race & Ethnicity, Inequality, Labor
— William Darity Jr. (@SandyDarity) Scholars Strategy Network March 21, 2017
You’re Hired! • The Democrats are looking for a big idea? Here’s one: a guaranteed job for anyone who wants one. It’s not as crazy as it sounds.
— Jeff Spross (@jeffspross), Democracy, March 2017
“The Job Guarantee” featuring Pavlina Tcherneva
Jacobin Filmmaker: Rebecca Rojer 2017 (14:02)
5 Reasons Why a Federal Job Guarantee Makes Sense
— Mark Paul (@MarkVinPaul), William Darity Jr. (@SandyDarity) & Darrick Hamilton (@DarrickHamilton), In These Times, Feb 7, 2017
Why We Need a Federal Job Guarantee • Giving everyone a job is the best way to democratize the economy and give workers leverage in the workplace.
— Mark Paul (@MarkVinPaul), William Darity Jr. (@SandyDarity) & Darrick Hamilton (@DarrickHamilton), Jacobin, Feb 4, 2017
Why a universal basic income is a poor substitute for a guaranteed job
— Claire Connelly (@_ClaireConnelly) ABC News, Jan 19, 2017
—2016—
A Job for Everyone • A federal job guarantee is a good, All-American policy.
— Mark Paul US News Oct 7, 2016
“MMT: A Job Guarantee Would Bring Unemployment to Zero”
— Pavlina Tcherneva (@PTcherneva) Deficit Owls Aug 15, 2016 (4:29)
A Guaranteed Federal Jobs Program Is Needed.
— William Darity Jr. (@SandyDarity) NY Times July 11, 2016
—2015—
The Federal Job Guarantee: A Step Toward Racial Justice • A federal job guarantee would go a long way toward addressing racial disparities and building an inclusive U.S. economy.
— Darrick Hamilton (@DarrickHamilton), Dissent Magazine, Nov 9, 2015
—2014—
Growing Recognition of the Need for the Job Guarantee
— L. Randall Wray, New Economic Perspectives, Jan 16, 2014
16 Reasons Matt Yglesias is Wrong about the Job Guarantee vs. Basic Income
— Pavlina Tcherneva (@PTcherneva) New Economic Perspectives</em), Jan 16, 2014 Note: Post contains links to earliest literature on the Job Guarantee.
Five Economic Reforms Millennials Should Be Fighting For: Guaranteed jobs, universal basic incomes, public finance and more
— Jesse Myerson (@JAMyerson), Rolling Stone, Jan 3, 2014
—2013—
Modern Money & Public Purpose 8: Economic Rights • Guranteed Income or Employment: Economic Rights for the 21st Century. “This seminar focuses on the social, political and economic justifications for securing a legal right to meaningful work and basic material wellbeing, as well as historical examples of direct public employment programs from various nations including the United States, Argentina and India. Questions to be addressed include: Should individuals have a legal right to work and/or basic material wellbeing? Can we afford a job or income guarantee? What would a job or income guarantee look like? What can we learn from direct employment programs from the past and abroad?”
— Pavlina Tcherneva (@PTcherneva), Philip T. Harvey, Gertrude Schaffner Goldberg ModernMoneyNetwork (@thepublicmoney) Sept 9, 2013 (2:27:16)
—2012—
Pavlina R. Tcherneva: Why the Job Guarantee is Superior (Wonkish)
— Pavlina Tcherneva (@PTcherneva), Naked Capitalism Feb 12, 2012
—2011—
Pavlina Tcherneva – Bottom Up Fiscal Policy: Direct Employment of the Unemployed • “To cure unemployment, mostly we prime the pump: we devise fiscal strategies on the presumption that jobs follow economic growth. But the strategies have not worked, unemployment remains high. That is why Pavlina Tcherneva studies policies that target the unemployed directly. She says that the government can reduce income inequality and restart the growth engine by putting people back to work. In other words, growth follows jobs. Investigating different models of fiscal policy — this is new economic thinking.”
— Thomas Leonard New Economic Thinking (@INETeconomics), Dec 12, 2011 (9:57)
| 2022-12-10T00:00:00 |
https://wecanhavenicethings.com/nice-things-we-can-have/federal-job-guarantee/
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[
{
"date": "2022/12/10",
"position": 36,
"query": "universal basic income AI"
}
] |
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What Happens When Jobs Are Guaranteed?
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What Happens When Jobs Are Guaranteed?
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https://www.newyorker.com
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[
"Nick Romeo"
] |
And then, if nobody wants to do them, maybe we shouldn't do them.” Kasy thinks that an important function of initiatives like job guarantees—and of universal ...
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Other participants had experienced something similar. Adnan Rizvanovic, a Bosnian man in his early sixties who now works as a gardener for the program, had once driven trucks and taxis and held a job in logistics. Pay for drivers plummeted after Uber and its local Austrian competitors entered the taxi market; after two heart attacks, Rizvanovic decided that he’d better stay off the road, lest he have another and crash. “I was psychologically destroyed,” he told me, of being suddenly unemployed. “If you have worked your whole life, even with a lot of stress, then suddenly you have nothing to do, you think that you are not needed anymore,” he said. “You have your breakfast, and then—what am I going to do all day?” He applied to dozens of jobs without success and began to lose hope. “At this age, after two heart attacks, it’s impossible,” he said. “Once they hear a certain age, it’s no way.” He began staying up all night, binge-watching basketball games. His daughter got him a dog so that he would leave the house more often.
Through the Job Guarantee, Rizvanovic worked twenty hours a week doing light gardening. “It’s nice. It’s slow. You have time to think when you water the flowers. It’s like meditation,” he said, gesturing at the plants around us. He was sleeping better and watching less TV. He enjoyed seeing other people at work every day, and could take breaks whenever he was getting tired—something that his cardiologist says is important. Before the war in Bosnia forced him to leave for Austria, in the nineteen-nineties, he studied philosophy and law at university. “When I’m watering the flowers, I think about Sigmund Freud and Immanuel Kant and everybody,” he told me, with a wistful look.
Not every participant sees the program as a decisive improvement over unemployment benefits. A man named Gilbert—bearish, heavily tattooed, and fifty-two—told me that he had worked for decades as a technician installing and maintaining elevators before injuring his back and knee in a skiing accident. He’d enjoyed his time on unemployment, which he’d spent travelling to the Dominican Republic, riding around Austria with his motorcycle club, and fighting in raucous freestyle forest brawls that set fans of rival soccer teams against one another, before sealing the peace over beer. He wouldn’t have minded a few more years of that life, he said; still, he worked thirty hours a week in the carpentry workshop, earning a little more than two thousand euros a month. “I just want to work something for the next eight years,” he said—until he can take his pension. “If I earn my eighteen hundred or nineteen hundred, I’ll do anything—unless I really, really don’t like it.”
Critics of labor-market programs such as the Job Guarantee argue that they enable precisely this sort of choice—they make it easier to decline work that one doesn’t like. One program participant in his thirties told me that, while on unemployment benefits, he’d been offered a job cleaning toilets at a gas station; he’d decided that he didn’t want “that sort of job,” and had instead found work in the carpentry workshop. If everyone were guaranteed a reasonably pleasant job, suited to their interests and needs and paying a living wage, who would do the grungy, difficult work? Austrian employers, like those in America, are currently having difficulty hiring people to take hard, poorly paid jobs; many of the workers in Austria who wash dishes or clean hotel rooms are immigrants from Eastern Europe, and during the pandemic many of them went home, some for good. Jörg Flecker, a sociologist at the University of Vienna who is evaluating the program in Gramatneusiedl, told me that pressure from employers could prevent its expansion across Austria. “Employers say, ‘There are so many unemployed. We have to have a tougher regime for them because we have jobs to fill.’ ”
Lukas Lehner and Maximilian Kasy, economists at Oxford who are evaluating data from Gramatneusiedl, argue that competition with the private sector is a good thing. “I think, from an economic perspective, that argument doesn’t make much sense,” Kasy said, of the dirty-jobs view. “If they’re shit jobs, try to pay them as well as possible. Try to change the working conditions as much as possible until you reach the point that somebody wants to do them, or automate them if you can. And then, if nobody wants to do them, maybe we shouldn’t do them.” Kasy thinks that an important function of initiatives like job guarantees—and of universal basic incomes—is to improve the bargaining positions of people who want to change their lives. “Whether it’s abuse from an employment relationship, a bureaucrat in the welfare state, or a romantic relationship, the question is, What’s your outside option?” he said. “Having the safety of the basic income or a guaranteed job improves your outside option. If your boss is abusive, or doesn’t respect your hours, or is harassing you or whatever, you have the option to say no.”
I met Denise Berger in Gramatneusiedl, and she said she had faced exactly this sort of situation. For years, she’d been sexually abused by her stepfather; the psychological effects caused her to struggle in her job at a pastry shop. She lost her position, but was unable to move out of her parents’ home. Through the Job Guarantee, she worked twenty hours a week cleaning at a kindergarten, and she could afford her own small apartment, where she lived with her two dogs. Her brothers, she said, had been harshly critical of her inability to find a job: “You’re stupid, you’re kind of a bad person, you don’t have a job, so you’re good for nothing,” she recalled them saying. That changed during the pandemic, when two of them also lost work. Nothing challenges stereotypes about the unemployed, she told me, like becoming one of them.
Unemployment in Austria, as in many Western countries, has been rising gradually for decades. In 2021, the official figure was eight per cent. This likely understates the real number of unemployed people; as in the United States, Austria’s official statistics don’t account for those who have simply stopped looking for work. Unwanted joblessness is fairly common. And yet the stigma faced by the long-term unemployed is powerful. Flecker, the sociologist, has noticed that Job Guarantee participants are often eager to show that they’re not typical unemployed people. “They say, ‘Oh, well, I’m not like the others. I have a special role here,’ ” he told me. Many of the participants I spoke with noted that they were in the group who wanted to work, whereas some others in the program were, as they put it, lazy free riders.
On my last day in Gramatneusiedl, I had coffee with Thomas Schwab, its mayor, at the Job Guarantee headquarters. An older man who speaks with a cautious, professorial air, Schwab wrote his master’s thesis on the original Marienthal study; he sees the current project against this historical background. “Maybe you know about Adam Smith, and these guys who say that the market is always right,” he said. “If you don’t find a job, then just work for less money. But that’s completely wrong! If I have no jobs in my company, there can be a thousand people outside, and they could say, like in the nineteen-thirties, ‘I will work just for something to eat.’ Did they find a job? They didn’t find a job, because nobody had a job to offer.”
Sven Hergovich, the regional director of the Public Employment Service of Lower Austria, essentially agrees with this analysis. He thinks that rising demands for productivity and efficiency mean that, now and in the future, not everyone will be able to find a job without support. “There are not sufficient jobs available for all of the long-term unemployed,” he told me. “In fact, we have only two options. Either we finance long-term unemployment, or we create a job guarantee.”
Ultimately, the perceived success of any job-guarantee program depends on what you think its goals should be. Kasy, the Oxford economist, thinks that there are three factors we ought to consider. Are people doing better on objective and subjective measures of well-being? Do they participate voluntarily? And does the program cost roughly the same as, or less than, current unemployment benefits? He and his colleagues studied the Gramatneusiedl program using a randomized controlled trial, in which waves of participants who started at different times were compared against one another, against a statistical composite of similar unemployed people from similar towns in Austria that lack a job guarantee, and against other factors. So far, on a broad range of dimensions—symptoms of anxiety or depression, a sense of social inclusion, social status, financial security, and so on—the improvements in participants’ lives are statistically significant. Kasy noted that the Job Guarantee costs no more per person than unemployment benefits. “It comes for free, people choose it voluntarily, and they feel like they’re better off—you would think that’s a slam dunk,” he said.
If the aim of job-guarantee programs is to transition all participants to private-sector jobs, or to dramatically cut unemployment spending, they may be hard to defend. But, if the goals are to improve people’s physical and mental health, to perform a range of tasks in a community, and to move some participants back to the private sector, then prospects look more promising. Since my visit to Gramatneusiedl, many of the participants have transitioned out of the program to other jobs. Karl Blaha, of the shoe emporium, is now a facility manager for a private logistics and transport company. Gilbert, of the forest brawls, is a restaurant manager.
And there are other, broader ways in which such programs can benefit society. Unemployment and despair are hardly the only causes of political extremism, but scholars have perceived a connection between these factors in multiple places and time periods. Before leaving Gramatneusiedl, I visited its historical museum, a quiet one-room building just off the main road. Inside, photographs from the early twentieth century showed musicians with fiddles and accordions, villagers picnicking in a garden with top hats and glasses of wine, and rows of young men in wrestling uniforms, crossing burly arms. By the early nineteen-thirties, however, the mood had shifted. Men lounged on a street corner, hands in pockets, gazes downcast; workers took sledgehammers to the old factory, destroying the place where they used to work. Within a few more years, a burst of activity again animated the town. Nazism had arrived. Pictures showed a parade, banners, bustling crowds—and, draped across the lectern of a man addressing the villagers, a swastika. ♦
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https://www.newyorker.com/news/annals-of-inquiry/what-happens-when-jobs-are-guaranteed
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[
{
"date": "2022/12/10",
"position": 47,
"query": "universal basic income AI"
}
] |
Artificial Intelligence (AI) Cyber Security Training ...
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Artificial Intelligence Cyber Security
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https://www.sans.org
|
[] |
Chart your path to job-specific training courses · By NICE Framework. Navigate ... The SANS AI/ML Courses Roadmap provides a summary of the Generative AI ...
|
Real-Time Threat Detection
One of AI's most notable contributions to cybersecurity is its ability to identify threats in real time. Machine learning models can analyze network traffic, system logs, and user behavior to spot anomalies that human operators might miss. They can recognize patterns of malicious activities and initiate immediate responses, mitigating potential damage.
Predictive Analysis
AI cybersecurity systems use historical data and sophisticated algorithms to predict potential vulnerabilities and cyber threats. By analyzing past attack patterns, AI can anticipate future attacks and help organizations fortify their defenses accordingly.
Automated Incident Response
In the event of a security breach, AI can swiftly respond by isolating compromised systems, blocking malicious activity, and restoring operations to a secure state. This automation saves valuable time and minimizes the impact of cyberattacks.
| 2022-12-10T00:00:00 |
https://www.sans.org/ai/
|
[
{
"date": "2022/12/10",
"position": 56,
"query": "generative AI jobs"
},
{
"date": "2022/12/10",
"position": 49,
"query": "machine learning workforce"
},
{
"date": "2022/12/10",
"position": 25,
"query": "artificial intelligence business leaders"
}
] |
|
Big Tech, Community Colleges Partnering in Education
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Big Tech, Community Colleges Partnering in Education
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https://learningenglish.voanews.com
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[] |
She told VOA Learning English that the AI for Workforce program gives students practical training. ... For the class, Introduction to Machine Learning, Gangidi ...
|
Neethi Anand Gangidi came from India to the United States to study chemical engineering. But when the Covid-19 pandemic created difficulties for her doctoral studies, she changed direction by studying artificial intelligence, or AI, at Houston Community College (HCC) in Texas.
At HCC, Gangidi and a team of students worked on a difficult problem: finding ways to help keep people safe in dangerous situations, such as school shootings. Their solution used AI to develop an autonomous vehicle to enter areas that are too dangerous for people. Gangidi’s team won a national innovation award for their project at Intel’s Global Impact Festival in San Jose, California, this year.
Gangidi is one of thousands of students involved in partnerships between large technology companies and community colleges in the U.S. Companies such as Dell Technologies, Intel, Google, and Amazon have developed special training programs for students. Some areas of study include artificial intelligence, data science, and user experience design.
Community colleges offer two-year associate degrees in many technical and liberal arts subjects.
The American Association of Community Colleges, or AACC, says there are 1,043 community colleges in the U.S. In the last 10 years, an average of more than 80,000 international students have attended community colleges yearly in the U.S.
The increase in AI
The use of AI, also called machine learning, is growing. AI helps computer systems do things which in the past only human beings could do, such as identify faces.
AI is commonly used in work which uses lots of data, such as banking, supplying materials and products, and healthcare. But now other areas, such as natural language processing, also use the technology. AI is even used in artistic work such as music composition.
A 2021 study found that 43 percent of businesses reported increased use of AI in the last year. However, 39 percent of leaders at those companies said lack of workers with AI training was a barrier to using the technology.
Intel’s AI for Workforce Program
In early 2020, Intel began its AI for Workforce program, which has now partnered with 74 community colleges in 32 states. Intel provides content for AI classes, AI lab design and technology, training for teachers, and practical applications for areas such as computer vision.
The company hopes to have partnerships in all 50 states by the end of 2023.
Dell Technologies has partnered with Intel to help pay for the Artificial Intelligence Incubator Network. The Network has given $40,000 grants to 10 community colleges to help build AI laboratories. The Network also supports virtual AI training and organizes monthly meetings between Dell, Intel, and community colleges to help improve AI education.
Adrienne Garber is a Senior Strategist for Higher Education at Dell Technologies. She told VOA Learning English that the AI for Workforce program gives students practical training.
“The AI for Workforce curriculum is anchored in portfolio projects, real-world authentic learning experiences, real data sets, and problems that are contributed by a community of practice.”
Carlos Contreras is the director of Intel’s AI for Workforce program. He noted that the program includes training in ethics, making it different from other kinds of technology training. The AI training asks students to think about when it is right or wrong to use machine learning and how people should use it.
He also noted that students can begin AI training without knowing how to write computer code. He said, “This is a trend that we’re seeing more and more around this technology. So, the entry point to get into AI is lower than if you want to get into, let’s say, cyber-security.”
While students do not need to learn how to create AI software, the training helps them use it in useful ways. For example, in Arizona, a team of three students at Maricopa Community College developed an AI model to help identify seizures in patients with epilepsy. Epilepsy is a disease that affects the central nervous system.
Contreras explained that students need to know how to define the problem they are working on, identify and classify the data sets they need to put into the AI computer models, and then run the models.
“And then all the sudden, I have a trained model, or my process, for the problem I’m trying to solve.”
Garber, of Dell Technologies, noted that each community college can choose how it wants to use the AI for Workforce program. Instead of having one curriculum for all colleges, the program can be changed to serve the needs of each community.
“They’re figuring out where in their academic pathways does this content fit. So, it might be a certificate program, it might be an associate’s degree — in some cases it’s a full bachelor’s degree…And it is a very responsive learning content package.”
Gangidi’s path of study
Neethi Anand Gangidi explained to VOA Learning English some of the details of AI training at Houston Community College. Students first learn to analyze and organize information. Python is the software commonly used and is a starting point for classes in basic data science.
“And it is very easy for any individual from any country to learn about this. You have lots of videos on that…Just install it; download it. That’s all you need. You need access. Start exploring.”
Gangidi said knowledge of statistics is helpful for working with data, but that students usually do not need high level mathematics such as calculus.
For the class, Introduction to Machine Learning, Gangidi learned how to build models to deal with a larger amount of data. She said tools from Amazon Web Services, Nvidia, and software such as Jupyter Notebook help students work with data faster and at a higher level.
HCC gave her a real data project to work on to help her use the AI training. Gangidi said that working on a real project helps students gain experience, which employers value.
Advice for students
Gangidi says students should put their effort into their passion, or strong interests, rather than being concerned about the name of the university they choose to attend in the U.S.
“It doesn’t matter if you’re from Stanford, or Harvard, or community college. It all depends upon each individual.”
She was interested in HCC because it gave her the opportunity to work in teams on real problems, use innovation, and gain leadership experience.
She added that community colleges cost less to attend than four-year universities. In Texas, international students can also qualify for in-state tuition, the amount that someone living in the state pays to attend. This, along with scholarships, reduced her tuition costs from about $12,000 per semester to about $2,000 per semester.
In addition to Neethi Anand's team, another team of students at HCC won a global award at this year’s Global Impact Festival. The team used AI to develop an autonomous drone to enter dangerous places.
Gangidi thinks there are many job opportunities for people who learn to work with data. She said, “If you know how to handle data, and if you have a passion…anything in AI, how to handle, analyze these data, and make a good story of the data—that’s easy, you can land up in an AI job in any big company over there.”
I’m Andrew Smith. And I'm Caty Weaver.
Andrew Smith wrote this story for VOA Learning English.
Quiz - Big Tech, Community Colleges Partnering in Education Start the Quiz to find out Start Quiz
_________________________________________________________________
Words in This Story
doctoral –adj. related to the highest degree given for completing a course of study at a university
autonomous –adj. able to act separately under its own power
innovation –n. a new idea or device; the process of making new things
seizure –n. an abnormal state in which a person loses consciousness and the body moves uncontrollably
trend –n. the general movement of change
classify –v. to place something in a particular group in an effort to organize things
curriculum –n. the plan of study and a school, college or university
content –n. ideas and information in media
access –n. the ability to get or get into something
statistics –n. a mathematical field which deals with the collection, organization and understanding of numerical information
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| 2022-12-10T00:00:00 |
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https://learningenglish.voanews.com/a/big-tech-community-colleges-partnering-in-education/6866912.html
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[
{
"date": "2022/12/10",
"position": 56,
"query": "machine learning workforce"
}
] |
Another week of layoffs, executive departures and AI- ...
|
Another week of layoffs, executive departures and AI-generated everything
|
https://techcrunch.com
|
[
"Greg Kumparak",
"Zack Whittaker",
"Maxwell Zeff",
"Lorenzo Franceschi-Bicchierai",
"Lauren Forristal",
"Amanda Silberling",
"Rebecca Szkutak",
"Sarah Perez",
"--C-Author-Card-Image-Size Align-Items Center Display Flex Gap Var",
"Media"
] |
More tech layoffs: This week Airtable laid off about 20% of its staff — over 250 people. Plaid also laid off 20%, which for them works out to 260 people.
|
Hello again! Greg here again with Week in Review. WiR is the newsletter where we take the most read TechCrunch stories from the last seven days and wrap them up in as few words as possible — no fluff, no nonsense,* just a quick blast of everything you probably want to know about in tech this week.
*Maybe a little bit of nonsense.
Want it in your inbox every Saturday morning? Sign up here.
most read
Tip your Amazon driver (on Amazon’s dime): If you’ve got an Alexa device at home, Amazon will pay your delivery driver an extra $5 if you say, “Alexa, thank my driver” after a delivery. Amazon could, of course, just pay drivers more to begin with…but that, depressingly, probably wouldn’t be a move that would get Amazon one of the most read headlines of the week.
Slack’s CEO to depart: Last week Salesforce CEO Bret Taylor stepped down; this week, Stewart Butterfield, CEO of (Salesforce-owned) Slack, announced he’ll also step down come January. Ron Miller shares his insights on inbound Slack CEO Lidiane Jones and her decades of product experience.
The “Twitter Files”: “Elon Musk reminded his followers on Friday that owning Twitter now means he controls every aspect of the company — including what its employees said behind closed doors before he took over,” writes Taylor as an array of once-private internal Twitter communications is made public.
Lensa AI goes viral: Do all of your social media friends suddenly have avatars that make them look like sci-fi gods and action heroes? It’s probably because of Lensa AI, a photo editing app that went viral this week after adding support for Stable Diffusion’s AI-generated art tools. Popularity didn’t come without controversy, though — many continue to debate the ethics of selling something generated by an AI trained on the works of real people; meanwhile, others noted that the AI could be “tricked” into generating otherwise disallowed NSFW imagery.
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More tech layoffs: This week Airtable laid off about 20% of its staff — over 250 people. Plaid also laid off 20%, which for them works out to 260 people. African fintech unicorn Chipper Cash let go of 50 people, and the U.K. drag-and-drop e-commerce platform Primer let go of 85 (about one-third of the company).
Google combines Maps/Waze teams: When Google bought the navigation app Waze for over $1 billion back in 2013, Google said they’d keep the Waze and Google Maps teams separate “for now.” Turns out “for now” meant about 9.5 years, but Google confirmed this week that the two teams will be merged. Google says it expects Waze to remain a stand-alone service.
Twitter Blue might cost more on iOS: Twitter’s $8 “Blue” subscription plan (which comes with a blue “verified” checkmark) is still on pause for now after a few false starts, but when it returns, it’ll reportedly cost a few bucks more if you subscribe through the iOS app in order to offset Apple’s cut.
audio roundup
Found — our podcast about founders and the companies they build — has a new co-host! Becca Szkutak stepped into the role this week, joining Darrell Etherington in a chat with Daye founder Valentina Milanova. Meanwhile, the Equity crew tried to make sense of 2022 in a year-end look back, and Taylor Hatmaker hopped on The TechCrunch Podcast to explore what the sudden explosion of AI-generated art means for actual human artists.
TechCrunch+
Here’s what subscribers were reading most on TechCrunch+:
Investors sound the alarm about possible private equity tech deals: “Who wants to sell when prices are low?” Ron Miller and Alex Willhelm ask.
Rootine’s $10M pitch deck: “If you told me that a company that’s charging $70 per month for multivitamins would be able to raise a $10 million round, I’d demand to see the receipts,” writes Haje. With that in mind, he dives deep into the pitch deck that helped make it happen.
| 2022-12-10T00:00:00 |
2022/12/10
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https://techcrunch.com/2022/12/10/another-week-of-layoffs-executive-departures-and-ai-generated-everything/
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[
{
"date": "2022/12/10",
"position": 2,
"query": "AI layoffs"
}
] |
Automaker Stellantis lays off hundreds of American ...
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Automaker Stellantis lays off hundreds of American workers, blaming high cost of making electric cars
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https://www.foxbusiness.com
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[
"Julia Musto"
] |
Stellantis announced its decision to idle operations at an assembly plant in Belvedere, Illinois, next February, resulting in "indefinite layoffs" for ...
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Multinational automaker Stellantis is indefinitely closing an assembly plant in Illinois in February and laying off hundreds of workers, in large part due to the high cost of making electric vehicles.
Stellantis noted in a statement emailed to FOX Business on Friday that the industry had been adversely affected by factors including the ongoing COVID-19 pandemic, the global microchip shortage and the increasing cost related to the electrification of the automotive market, which it said was the most impactful.
The automaker said that a number of actions had been taken to stabilize production and improve efficiency at its North American facilities to "preserve affordability and customer satisfaction in terms of quality."
However, while considering other avenues to optimize operations, Stellantis said the decision had been made to idle the Belvedere plant starting on Feb. 28, 2023.
BLUE APRON LAYING OFF 10% OF CORPORATE WORKFORCE
"This difficult but necessary action will result in indefinite layoffs, which are expected to exceed six months and may constitute a job loss under the Worker Adjustment and Retraining Notification (WARN) Act. As a result, WARN notices have been issued to both hourly and salaried employees," it said. "The company will make every effort to place indefinitely laid off employees in open full-time positions as they become available."
Ticker Security Last Change Change % STLA STELLANTIS NV 9.88 -0.18 -1.79%
Stellantis also noted that it is working to identify other opportunities to repurpose the facility.
There are about 1,350 workers at the Belvedere plant, which produces the Jeep Cherokee.
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Stellantis reportedly told The Associated Press that that automaker would not comment on the future of the "Cherokee nameplate."
"This is an important vehicle in the lineup, and we remain committed long term to this mid-size SUV segment," Stellantis spokesperson Jodi Tinson told the agency.
Stellantis has said it will invest more than $31 billion through 2025 on electrifying its vehicle lineup, with electric vehicles to make up half of its U.S. sales by 2030.
CLICK HERE TO READ MORE ON FOX BUSINESS
A spokesperson for Gov. J.B. Pritzker’s administration, Jordan Abudeyyeh, said a response team from the state's Department of Commerce and Economic Opportunity had been assembled to help displaced workers find new employment.
She said the administration will work with local elected officials, community colleges and others to ensure that appropriate retraining programs are available, and with Stellantis to find new uses for the Belvidere plant.
Reuters and The Associated Press contributed to this report.
| 2022-12-10T00:00:00 |
https://www.foxbusiness.com/economy/automaker-stellantis-lays-off-hundreds-american-workers-blaming-high-cost-making-electric-cars
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[
{
"date": "2022/12/10",
"position": 77,
"query": "AI layoffs"
}
] |
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AI in Healthcare & Pharma Summit Boston
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AI in Healthcare & Pharma Summit Boston
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https://www.industryevents.com
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[] |
Discover advances in AI tools and techniques from the world's leading innovators across industry, academia and the healthcare sector.
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EMPOWERING CLINICIANS AND HELPING PATIENTS - THE POWER OF AI IN HEALTHCARE
Discover the AI and ML methods & tools set to revolutionize healthcare, medicine & diagnostics, as well as industry applications and key insights
Join us for the RE•WORK AI in Healthcare Summit happening on November 14-15, 2023, in Boston, MA.
Discover advances in AI tools and techniques from the world's leading innovators across industry, academia and the healthcare sector. Learn from the experts in speech & text recognition, neural networks, image classification and machine learning.
The summit will showcase the opportunities of advancing methods in AI and ML, and their impact across healthcare & medicine. Discover ML tools & techniques set to revolutionize healthcare applications, medicine & diagnostics from a global line-up of experts.
This is a unique opportunity to interact with industry leaders, data scientists, founders, CTOs and healthcare professionals leading the AI revolution. Learn from & connect with 200+ innovators sharing best practices to deploy AI to solve challenges in healthcare.
Learn how companies from other sectors are:
Utilizing Generative AI in healthcare and pharma
Advancing medical imaging and radiology using AI
Embracing ethical and transparent AI practices to protect the privacy of their patients
Evolving personalized diagnostics
... And much more!
NEW THIS YEAR
Interactive Q&A Get all of your questions answered via in-person Q&As
Hear from Expert Speakers on recent, relevant developments and the progression of AI in Healthcare
Connect with attendees during and after the summit and build new collaborations through our in-person networking sessions
| 2022-12-10T00:00:00 |
https://www.industryevents.com/events/ai-in-healthcare-summit-boston
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[
{
"date": "2022/12/10",
"position": 84,
"query": "AI healthcare"
}
] |
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Is Graphic Design a Good Career?
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Graphic Design Job Prospects & Growth: Is Graphic Design a Good Career?
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https://www.nobledesktop.com
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[] |
The average salary for a Graphic Designer is around $54,000 per year, with potential to earn more with additional experience and skills. Graphic Designers have ...
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Exploring a career in graphic design? This in-depth guide dives into the specifics of the role, including core responsibilities, salary expectations, key skills, and the potential for growth and flexibility within the field. It also outlines various relevant courses and training programs that can help you become a successful Graphic Designer. Key Insights A Graphic Designer is a creative professional who combines text and visually appealing imagery to convey a message, often through advertising, social media, or product packaging.
The average salary for a Graphic Designer is around $54,000 per year, with potential to earn more with additional experience and skills.
Graphic Designers have the flexibility to work across many industries and in various roles, from freelancing to teaching in community colleges or local art programs.
Learning multiple design programs such as Photoshop, Illustrator, and InDesign, as well as additional skills like coding or social media marketing, can increase job prospects and earning potential for Graphic Designers.
Related career paths include User Experience (UX) Designer, User Interface (UI) Designer, and Motion Graphics Designer, all of which require similar skills and offer competitive salaries.
Noble Desktop offers a variety of graphic design classes and a comprehensive Graphic Design Certificate program, providing students with hands-on assignments and individual career mentorship.
Becoming a Graphic Designer might sound like the ideal career path for a professional who desires to marry their technical skills with their creative abilities, but whether it is a good career for everyone is another matter. This field provides a competitive salary and opportunities for growth. It is also highly flexible and requires designers to work with various team members on various projects. Here, we’ll discuss these attributes in more detail so you can decide if becoming a Graphic Designer aligns with your goals.
What is a Graphic Designer?
A Graphic Designer is a creative professional who combines text and visually appealing imagery to share a message with an audience. This could be through advertising, social media, or product packaging, to name a few. They are well-versed in design principles like color and typography and are committed to staying up-to-date on the latest trends. Whether they work for an agency or freelance, they collaborate with clients and other team members to create high-quality designs that appeal to new and existing customers.
In addition to proficiency in professional design programs like Adobe Photoshop and Illustrator, a successful Graphic Designer must also work well with others. Graphic Designers rarely work alone, and since their job is to create a product that matches a client's vision, they must be willing to accept feedback and suggestions from others. Time management skills are also crucial for a Graphic Designer; since most of them work freelance, they often work on multiple projects for many clients simultaneously.
Read more about what a Graphic Designer does.
Graphic Designer Salary and Job Outlook
The average Graphic Designer earns around $54,000 per year, but this can vary widely depending on additional factors like years of experience (Graphic Designers with over ten years of experience earn closer to $70,000) and which city they work in. Some of the highest-paying cities for Graphic Designers include New York, NY, Atlanta, GA, and Los Angeles, CA. Those who work freelance typically charge between $25 and $50 an hour for entry-level work, but more experienced designers can charge over $100 an hour.
The job outlook for a Graphic Designer depends significantly on their subset of skills. Static images and images for print are becoming less popular as businesses expand their digital footprint, so a Graphic Designer will need to train in creating digital designs to remain competitive. In addition to the standard software programs that Graphic Designers typically use, they can ensure a more stable career path by learning to use programs like Adobe XD for user interface design or Adobe After Effects to learn animation skills. While the Bureau of Labor Statistics predicts only a 3% growth rate for graphic design positions, digital design careers are predicted to grow by 23% by 2031.
Read more about Graphic Designer salaries.
What Makes Graphic Design a Good Career?
There are several reasons why becoming a Graphic Designer would appeal to many people, especially those who are creative and have a passion for technology. The factors listed below are some common things that these professionals love about their job:
Flexibility to Work in Many Industries
If you know you want to work as a Graphic Designer but aren’t sure where you want to apply these skills, the good news is that you don’t necessarily have to choose. There are plenty of professional opportunities in fields like publishing and mobile app design. However, you can also branch out to some unexpected areas that require Graphic Designers as well. For example, some designers eventually become instructors at community colleges or local art programs. Government agencies and nonprofits also hire Graphic Designers to work on things like website interface design and letterhead.
Of course, none of this is to say that you have to move from industry to industry; plenty of Graphic Designers are very comfortable working in one area and can happily stay there for the duration of their careers. However, if you desire the opportunity to learn a variety of skills and work with a broad range of professionals, graphic design might be the perfect fit.
Collaborate and Network with Others
Whether they work freelance or for a particular agency, Graphic Designers rarely work alone. There may be some late nights designing logo samples alone at their desk, but the majority of a Designer’s work is collaborative in nature. They must be sure to consistently check in with other stakeholders to ensure their design aligns with the goals of the client.
Working in a team allows you to develop several critical soft skills, like communication and implementing feedback without taking it personally. It also provides the opportunity to see other perspectives and solve problems that you couldn't manage alone. Some professionals prefer to work in a more independent capacity, and there’s absolutely nothing wrong with that. However, it may mean that graphic design is not the field for you.
Freelancing
A vast majority of Graphic Designers work on a freelance basis. This means they are self-employed; they set their own rates and apply for individual assignments as an independent contractor rather than remaining long-term with one agency. Being a freelance Designer means you essentially can be your own boss, and it's up to you which projects you complete. Freelance designers also have the luxury of defining their personal design style without being constrained by the demands of one company.
While it may sound like freelancing provides a flexible schedule and will allow you only to take desirable jobs, that isn’t always the case. Before pursuing a career as a Graphic Designer, you’ll want to examine some of the pros and cons of freelancing more closely.
Learn a Range of Marketable Skills
The core design programs in a Graphic Designer’s toolkit generally include Photoshop, Illustrator, and InDesign. As these are all part of the Adobe Creative Cloud, this often makes it easier to learn additional Adobe programs. For example, you could learn to use Adobe After Effects to create animations and find work as a Motion Graphics Designer. You could also master Adobe XD, commonly used in user experience (UX) design.
Many Graphic Designers also branch out into web development, which requires basic coding skills in HTML and CSS and JavaScript. Even if you can’t design an entire website from scratch, seeing your work from a developer's perspective will help improve your digital designs. Learning these skills can also lead to higher salaries; both UX Designers and Web Developers earn around $80,000 a year, which is higher than the average salary for a Graphic Designer.
Career Paths Related to Graphic Design
There are several exciting career options that are related to graphic design. One option is to become a User Experience (UX) Designer. These professionals identify where users of a product, website, or mobile app encounter challenges and work to improve the design. They use several Adobe products to achieve these goals, the most common being Adobe XD. User Interface (UI) Designers also utilize similar programs, but their responsibilities are focused on the appearance of a website or app rather than the experience of navigating it. UI Designers earn an average annual salary of roughly $84,000, but this will increase with more experience; a Senior UI Designer earns an annual salary closer to $114,000 per year.
Motion Graphics Designers are professionals who skillfully combine text, sound, and animation to create animations that quickly grab an audience’s attention. This animation technique is used in areas like social media, television, and movies. You’ve probably seen their work in iconic opening sequences for popular television shows like Westworld or Game of Thrones. Motion Graphics Designers use many of the same programs required for graphic design, including programs specifically for animation, like Adobe After Effects.
Read more about other career paths related to Graphic Designer.
Learn the Skills to Become a Graphic Designer at Noble Desktop
If you want to start a career in graphic design, the graphic design classes offered by Noble Desktop are an excellent place to start. Students can take all their classes remotely or in-person at their Manhattan campus. For students who want to start slow by just learning one popular design program, Noble offers an Adobe Photoshop Bootcamp, an Adobe InDesign Bootcamp, and an Adobe Illustrator Bootcamp. These beginner-friendly courses take just a few days to complete and will provide students with foundational design skills.
For those who feel ready to dive into a more comprehensive program, Noble Desktop’s Graphic Design Certificate might be a better fit. Students will complete hands-on assignments using popular design programs, including Illustrator, Photoshop, and InDesign. This program is ideal for those hoping to start a career as a Graphic Designer. Certificate students at Noble Desktop receive individual career mentorship, where experts in the design industry help craft resumes and portfolios and provide helpful tips for finding lucrative employment.
If a class isn’t feasible for your current schedule, Noble Desktop has a host of resources on its website to help start your graphic design career. You can browse their collection of articles about Photoshop, Illustrator, and InDesign if you’re curious about how each program works. You can also review information about other design tools to see if another field might interest you more.
Key Takeaways
| 2022-12-10T00:00:00 |
https://www.nobledesktop.com/careers/graphic-designer/career-benefits
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[
{
"date": "2022/12/10",
"position": 56,
"query": "AI graphic design"
}
] |
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Graphic Designer Resume Guide & Tips
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Graphic Designer Resume: Tips for a Graphic Design Resume
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https://www.nobledesktop.com
|
[] |
AI · Web Development · JavaScript · FinTech · SQL · High School Coding · Web ... Noble Desktop offers graphic design classes and a graphic design certificate.
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Building an effective resume is a crucial step in securing a job in Graphic Design. This article provides detailed guidance on what to include in a resume, how to tailor it to specific roles, and the different resume styles to consider. Key Insights A graphic design resume should include general contact information, work experience, relevant education, relevant projects and assignments, and relevant skills and specializations. It is important to be concise and include only relevant information.
There are four general types of job resumes: Chronological, Functional, Combination, and Targeted. Each has its own strengths and weaknesses that should be considered based on the specific role and your personal experience and skills.
When building a resume, it's important to tailor it to the specific job you're applying for. Highlighting relevant skills, experience, and interests can help your application stand out and show your suitability for the role.
Using visual composition skills to draw attention to the most important information on your resume can be beneficial. However, it's important to avoid being overly elaborate and distracting from the key information.
Feedback on your resume from trusted colleagues and professional contacts can be invaluable. This can help ensure your resume is communicating effectively, is easy to understand, and is free of typos and errors.
Noble Desktop offers graphic design classes and a graphic design certificate. Students receive individual career mentorship, including assistance in building a compelling resume.
Building a resume is an important part of the Graphic Designer job search, since it is often the first thing that a hiring manager will look at and it can often be the most important part of the first round of cuts during a very competitive job search. This means that you’ll want to ensure that your resume very quickly and effectively communicates your qualifications as a Designer. Resumes can be difficult to build because the central question is very often ‘what should I exclude’ rather than ‘what should I include’ because you want to ensure that your resume is only about two pages long. Even novice Graphic Designers are likely to have fairly extensive skills and experience.
What to Put on a Graphic Designer Resume
For the most part, you’ll want your resume to be about two pages long at max, and you’ll want to avoid formatting in such a way that information is crammed in and difficult to easily parse. Plus, there are a few things that all resumes will need to include, which means that it will be even more important to use your remaining word economy. Below are a few things you can expect to want to put in your graphic design resume.
It is actually incredibly important for a resume to quickly and effectively communicate personal contact information near the top of the document. This will include your name, title, mailing address, phone number, and email address. This is also a good place to list your general design interests and specializations. This should be a short section, ideally the smallest section of your resume. However, it needs to be near the top of the resume to best communicate this information quickly and effectively.
Work Experience
The most important section of the resume should be one that communicates your prior professional experience and demonstrates to hiring managers who you have a background in the field of design. This section should highlight your relevant job experience, so you shouldn’t waste space on a complete employment history if it isn’t relevant to the position (many companies will have automated application portals that allow you to input your full work history without taking up space on your resume). This section should be in reverse chronological order and first provide your most recent (or current) employment information. Each item listed in this section should include a short description of your work responsibilities at that job.
Education
The next section should include your relevant education and professional training history. If you have a college degree, that is what you should lead with here. If you don’t have a college degree, you can put a relevant professional training program such as a career-certificate program. You don’t want to list every training seminar or course you’ve attended, though it can be useful to single out one or two that are relevant to the job you’re applying for. For example, if, during your training, you took two upper-level animation courses, you may wish to include that information when applying for a job at an animation studio. If you are applying for a job at a magazine studio, it is unlikely that you will want to include this information.
Like before, only aim to include relevant information. You don’t need to list every course you’ve taken, you don’t need to include things like high school-level design classes and you shouldn’t look like you are trying to pad out your training. If you don’t have a college degree, use this space to highlight the individual training courses you have taken; however, you may want to group them under a general category, rather than listing them individually. You can also briefly explain the kinds of training you received in these classes. If you do have a four-year degree, you can use this space to briefly describe some of the capstone courses you took.
Relevant Projects and Assignments
Some students who have significant on-the-job experience may want to briefly discuss specific assignments or clients who they have taken on. This is especially important for freelancers looking for more stable employment in a professional design studio. Regardless, it can be useful to quickly communicate the specific on-the-job experience you have to communicate to hiring managers who you’ve been trusted with important assignments. If you don’t have anything to put in this section, it can be a good place to explain the kinds of projects you have worked on in the past (either at a job or in the course of your training).
Relevant Skills and Specializations
This is the section of your resume where you can include all the specific, relevant skills you have training in that don’t easily fit into other sections. So, for instance, if you are certified in a specific design program, you can put that here. If you have led a training program in an area of design, you can include that here. You could put that here if you won an award for some element of your design portfolio. This is often a grab-bag section of the resume-building process that gives you a place to put important professional accomplishments and accolades. As you become more experienced, this section will likely be broken down into smaller, more specific sections such as training experience or professional accomplishments. Still, at the beginning of the process, it is smart to combine them into a single heading.
You should also be wary of including too many skills that aren’t immediately obvious in their function to the company that would be hiring you. You may be proficient in Microsoft Excel, but if it isn’t important to your job, you shouldn’t include that information. Sometimes, this may change between job applications, so it is smart to keep this section flexible. For example, you generally wouldn’t want to list that you speak fluent French in this section unless you were applying for a position that deals with many French clients (at which point it would be good to include this information). Here, it will come down to matters of personal judgment.
5 Graphic Designer Resume Tips
It will be important to make your resume one that stands out from the crowd since this is the step of the process wherein hiring managers are most likely to be culling the pile rather than looking at each application substantively. While a winning portfolio is the most important part of a job application, with a weak resume, you risk your portfolio remaining unseen. Below are a few tips for writing a resume that is effective at communicating information and evocative and memorable enough to earn you serious consideration.
Tip #1: Avoid Being Too Elaborate or Too Forgettable
One of the easiest ways to get your resume discarded is to treat it like a graphic design assignment rather than a distinct style of professional communication. You don’t want to load your resume with visual touches that don’t convey important information to your prospective employers. The portfolio process is where you will spend time demonstrating your bona fides as a Graphic Designer. On the other hand, you want to avoid submitting an unmodified word document that includes no visual flourish whatsoever. This may be an ideal way to communicate some information, but it will risk creating a forgettable resume that doesn’t stick in a hiring manager's mind.
This will come down to trial and error, since it is hard to get it right on the first attempt. For prospective employees just entering the job market, it can be useful to take advantage of automated design templates in programs such as Microsoft Word.
Tip #2: Keep Word Economy in Mind
Most application resumes will be no more than two pages long, and this will often be presented as a hard limit. You’ll want to take advantage of all of the space you can here, and the last thing you want to do is ignore a company’s requirements for an application. No amount of information added to the third page of your resume will help you more than signaling an unwillingness to follow instructions will hurt. This means that it is vitally important that you use every word at your disposal to improve your chances of getting hired.
For instance, you’ll want to discard any superfluous information about your employment and education history. There is no reason to list your high school degree and the job you had while graduating since this communicates to your readers that you are trying to stretch out a weak resume to hit the two-page requirement. In addition, you’ll want to condense any somewhat similar or redundant information into a single part of the resume. For example, if you list your professional training seminars, you don’t need to describe each one individually. Instead, you can list them together under a single resume line that communicates that you have received extensive professional training.
Tip #3: Tailor Your Resume to the Job
An important part of building a competitive resume is to fine-tune its contents and framing to suit the needs of your prospective employer. If you are applying for a job that handles a lot of print media advertising, you should include resume lines that specifically address your qualifications to work in that field. This can take some time and practice, but it is important that hiring managers see your resume and find compelling reasons to keep it on top of the pile and consider looking at your design portfolio.
You may also want to emphasize aspects of your professional education and training that make you an ideal candidate for a specific position. If you worked on a specific project in a capstone class or as a freelancer, you can feature that more prominently in your experience and skills sections to impress a prospective employer. If you have a specific skill set that might be useful to this employer, make it more central to your resume. If you have a section for your general interests and favorite design projects, altering that to reflect the job you are applying for can be a good use of your time.
Tip #4: Draw the Eye to Important Information
One aspect of the resume-building process that will benefit creative graphic designers is the layout and format. While you don’t want to make your resume overly cute, it is important to build a resume that uses visual composition skills to guide the reader’s eyes to the most important information. Many resume review sessions won’t allow time to look over every detail of each resume, so you’ll not only want to ensure that the important information is front and center but that it is formatted in such a way as to draw your readers’ attention directly to the important bits.
Tip #5: Get Feedback
An essential part of building a resume is getting feedback from trusted colleagues and professional contacts. Not only is this a great way to network and find potential professional references, but it is also a great way to ensure your resume is communicating what you want it to. It is very easy to get lost in what you want to say, which can blind you to how others interpret the text of your resume.
It is also important to get feedback from less immersed professionals to ensure the resume content is easy to understand. Many hiring managers may not be as deeply immersed in the field of graphic design, so it will be important to make sure that your job materials aren’t too loaded with jargon and other difficult-to-parse concepts. Finally, getting feedback is a great way to help ensure your work is typo-free, since a glaring typo might be enough to get your application junked during a particularly competitive job search.
One place to receive this feedback is in a career-focused certificate program offered through Noble Desktop. In addition to receiving hands-on training from expert instructors, students enrolled in these classes will receive professional development training, including one-on-one career mentorship. Part of this mentorship process can be assistance in building a compelling, memorable resume that communicates your strengths as a Designer.
Graphic Designer Resume Styles
Aspiring Graphic Designers will also want to consider that there are different types of resumes and different ways to frame your job qualifications. There are four types of general job resumes: Chronological, Functional, Combination, and Targeted. Each has its own slightly different quirks, so you’ll want to understand the strengths and weaknesses of each kind of resume before making a decision, since the last thing you want is to produce an essay that looks and feels focusless or empty.
Chronological resumes are fairly straightforward. They begin with your education and work history, moving chronologically from the beginning of your career to the present moment. These are advantageous because they are very easy to organize and, when done well, they produce a fairly coherent narrative of your path to becoming the Designer you are today. This makes it very easy to follow and understand in a short period of time. The downside is that they tend to bury vital information in later sections (sometimes even on page two), and they are harder to tailor to the specific job you are applying for in particular.
Functional resumes tend to downplay the importance of your past education and employment to highlight the specific technical skills that make you an ideal fit for the job in question. These resumes make it much easier to tailor them to the specific job listing and let you put your best foot forward in demonstrating your immediate ability to start working. The drawback is that these resumes tend to obscure your professional credentials, which can sometimes be very important for hiring managers who want to see that you have experience in your field.
Combination resumes attempt to blend the best aspects of a functional resume and a chronological resume. These resumes attempt to use the chronological elements of your job experiences to tell a story that strengthens the functional elements of your resume. These are great resumes for communicating to a potential employer that you have both significant training in the field of design and that you have the work experience required to put that training to good use. The drawback to these resumes is that they require you to have the experience necessary to tell this kind of story since if you have minimal on-the-job experience or most of your training comes from a technical skills program, you may not be able to craft the kind of deliberate narrative these resumes seek to produce.
Targeted resumes are resumes that, as the name suggests, are rewritten entirely for applying to a single position. These resumes will usually be combination resumes that blend together chronological and functional resumes to tell a specific story about your qualifications for this position in particular. These are often the most effective resumes, assuming they are written properly, but they are also the hardest to write. They also require the most time and effort to write since they aren’t easy to transfer from one job application to another. It is highly recommended that you only use these applications for the jobs you most want.
Learn the Skills to Become a Graphic Designer at Noble Desktop
If you want to start a career in graphic design, the graphic design classes offered by Noble Desktop are an excellent place to start. Students can take all their classes remotely or in-person at their Manhattan campus. For students who want to start slow by just learning one popular design program, Noble offers an Adobe Photoshop Bootcamp, an Adobe InDesign Bootcamp, and an Adobe Illustrator Bootcamp. These beginner-friendly courses take just a few days to complete and will provide students with foundational design skills.
For those who feel ready to dive into a more comprehensive program, Noble Desktop’s Graphic Design Certificate might be a better fit. Students will complete hands-on assignments using popular design programs, including Illustrator, Photoshop, and InDesign. This program is ideal for those hoping to start a career as a Graphic Designer. Certificate students at Noble Desktop receive individual career mentorship, where experts in the design industry help craft resumes and portfolios and provide helpful tips for finding lucrative employment.
If a class isn’t feasible for your current schedule, Noble Desktop has a host of resources on its website to help start your graphic design career. You can browse their collection of articles about Photoshop, Illustrator, and InDesign if you’re curious about how each program works. You can also review information about other design tools to see if another field might interest you more.
| 2022-12-10T00:00:00 |
https://www.nobledesktop.com/careers/graphic-designer/resume-tips
|
[
{
"date": "2022/12/10",
"position": 86,
"query": "AI graphic design"
}
] |
|
Top Artificial Intelligence Companies To Work With In 2024
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Top Artificial Intelligence Companies To Work With In 2024
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https://dlabs.ai
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Best AI Companies in 2024 · 1. H2O.AI · 2. iMerit · 3. DLabs.AI · 4. Tech Speed · 5. Digica · 6. Nexocode · 7. QuantUp · 8. Numlabs.
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After many long hours deliberating whether you need AI — having convinced your board, team, and every stakeholder under the sun that AI ‘just makes sense,’ you’ll feel like you’ve won the lottery.
But now comes another big decision. You need to find a partner capable of building the kind of AI you’ve promised your company. And since 85% of AI and machine learning projects fail to deliver expected results, this is no easy task.
That said, if you know where to find teams specialized in building custom AI, you’re 90% of the way there, and this is somewhere we can help. We know some excellent people working with artificial intelligence, and we’ve listed the best of them right here.
All you have to do is find the team that best matches your business.
(And to make your life that bit easier, we’ve even included the Clutch ratings – where possible – giving you an impartial overview of each company).
Best AI Companies in 2024
Location: Mountain View, USA
Topping the list, we’ve got H2O.ai: a company whose mission is democratizing artificial intelligence for everyone.
When you see their partners, you can feel confident they know their stuff as they work with Microsoft, NVIDIA, and Goldman Sachs (with the last two being investors). And besides their H2O AI Cloud platform, they have some ready-to-use tools.
These include H2O Driverless AI, a product that lets you use AI to make… well, AI. Sounds crazy, but Wei Shao (Data Scientist at Hortifrut) and Martin Stein (Chief Product Officer at G5) both praised the solution.
Other tools include:
H2O AI Cloud platform
H2O-3: an enterprise open-source machine learning platform
H2O Document AI: AI-powered document processing & data extraction
H2O Driverless AI: process automation using ML
H2O Hydrogen Torch: no-code deep learning platform
H2O Wave: an open-source Python development framework
What’s worth adding is that H2O.ai launched an initiative called ‘AI 4 Good‘ to make the world a better place with the help of responsible AI. So if you’re looking for a high-quality, ethical team, they’re a solid choice.
Location: Los Gatos, USA
The second AI company on the list is iMerit, which provides services to companies in industries like autonomous vehicles, FinTech, InsurTech, healthcare, retail, geospatial technology, and governments.
You’ll find the likes of eBay, TripAdvisor, and Microsoft among their clients, showing you their caliber.
They’re the perfect fit for:
Image, video, text, data & lidar annotation
Audio transcription
Sentiment analysis
Content moderation
Product categorization
Image segmentation
iMerit also specializes in extraction and enrichment for Computer Vision, NLP, data labeling, and other technologies. Moreover, they develop clients’ projects and share their knowledge during in-house events, including the iMerit ML DataOps Summit or Women in Tech.
Clutch rating: 4.9/5
Location: Gdańsk, Poland
We’re not so confident as to put ourselves in the top spot, but we were always going to appear somewhere, so why not at #3?
After all, we know how to use AI to help organizations streamline workflows, improve business outcomes and boost KPIs, alongside how to unlock new AI-based business models.
We’ve helped plenty of companies use AI to get better at what they already do, as well as implement more radical changes, often unlocking entirely new revenue streams.
We can help you with any of the following:
Technical consulting focused on artificial intelligence
Comprehensive AI Implementation: from Concept to Execution
Support in the design of cloud solutions related to AI and data
Assistance in building products
Validation of business models with an AI component
AI Product Development
In-Depth Data Analysis
Large Language Models Integration
Data Analysis
We recognize that each business has its unique set of challenges and goals. That’s why we don’t offer a one-size-fits-all path for each company. During a free AI consultation, our AI experts strive to understand your needs. Then, we tailor our services to fit your specific business case.
Think of us as your adaptable partner in AI, ready to guide you from the initial idea to successful implementation. We believe this individualized approach is key to ensuring your success and delivering measurable, impactful results.
Clutch rating: 5.0/5
Location: Portland, USA
TechSpeed is a data management service provider that enables clients to make sense of data. The company has provided personalized customer data processing for two decades, boasting no less than 99.95% accuracy.
Its solutions let the end-user work with them without requiring any support, all while preserving data security.
Tech Speed’s services cover the following:
Data entry
Data mining
Data processing
AI & ML
Business processing outsourcing
Web development
If your business deals with data, Tech Speed could take a load off your shoulders.
Clutch rating: 4.7/5
Location: Altrincham, United Kingdom
Digica works with clients in healthcare, defense, automotive, technology, and telecoms. The team applies its knowledge and experience to develop innovative, intelligent solutions for cloud, IoT, and integrated devices.
They have expertise in image processing, including deep learning for computer vision and commercial implementation of synthetic imaging.
Their services include:
Computer vision, edge computing, sensor fusion, preventive maintenance
Embedded tech & IoT
Cloud development (web, mobile app & eCommerce)
Their team is also full of great communicators, as we saw first-hand when one of their data scientists, Sylwana Kaźmierska, spoke at TEDxKoszalin.
Clutch rating: 4.9/5
Location: Kraków, Poland
Nexocode offers a class of AI-powered development services across multiple sectors. They specialize in ML-powered solutions for FinTech, logistics, and healthcare, so if you work in these sectors, consider Nexocode for your next project.
They can help you with:
Data quality audits
Building data systems and pipelines
Custom AI development services
Machine learning consulting
Beyond their artificial intelligence expertise, the team values its people-centric approach, communicating between themselves and with the client, ensuring every project exceeds expectations.
Clutch rating: 4.9/5
Location: Wrocław, Poland
QuantUp uses AI-driven insights to optimize business processes and find new efficiencies. The team trawls analytics to validate each customer’s initial product ideas, assessing if the available data and methods will be sufficient for their needs.
You can work with QuantUp on the following:
Business process optimization
AI product development
Skills development & training
QuantUp stands out for its educational resources related to artificial intelligence and applied solutions, helping clients both get a high-quality solution and understand how it works. They also offer courses for specific skills, inlcluding data science.
Clutch rating: 4.9/5
Location: Kraków, Poland
Numlabs are a team of ML, data, and computer vision specialists. They use various state-of-the-art technologies, such as statistical modeling, neural networks, deep learning, and transfer learning to uncover the underlying relationships in data.
Their services include:
NLP
Computer vision
Predictive analytics & forecasting
Big data
Deep learning
Maps, routing & spatial data
They’ve worked in various industries, including sales, marketing, administration, and research, showing the flexibility of their skill sets. They’re known for their meticulous approach to every project, not forgetting their outstanding communication.
Their consultants speak English, Polish, French, German, and Arabic!
Clutch rating: 4.8/5
Location: Haifa, Israel
BroutonLab is a data science company that helps tech startups, investors, and programmers solve real-world problems, reducing time to market and optimizing business processes.
One of their more interesting solutions is software to predict the outcome of horse races, forecasting results by combing the horse’s characteristics, trainer, jockey skills, wellbeing, and the weather conditions, with a a 32% success rate.
If race predictions aren’t for you, how about one of the following:
Computer vision
Natural Language Processing (NLP)
Mobile robotics
ML Ops & engineering
Predictive analytics
If you’re looking to build something related to computer vision or machine learning, here’s the team for you.
Clutch rating: 5.0/5
Location: Darmstadt, Germany
Let’s bring this list to a close with AI Superior. This team provides end-to-end solutions based on machine learning, artificial intelligence, and data science, working with clients across finance, oil and gas, real estate, and pharmaceuticals.
They specialize in the following areas:
AI, ML, and data science
Consulting services
Education in AI
R&D for AI
They offer plenty of educational opportunities, including workshops, training, and lectures — but given that 57% of the team has a Ph.D. and 28% have a Master’s degree, this shouldn’t come as a surprise.
Academic experience is ingrained in every employee.
Time to pick an AI partner
If AI is on the cards in 2024, it’s time to start thinking about a partner.
DLabs.AI would love to join you on the journey. And as ever, we’re always around to talk through your needs. Once we understand what you’re looking for, we can advise on the next step.
That might mean exploring solutions or suggesting you work with another team in the industry; either way, we’ll always work with your best interest at heart.
How about we get the ball rolling with a free consultation with one of our AI experts?
| 2022-12-07T00:00:00 |
2022/12/07
|
https://dlabs.ai/blog/top-artificial-intelligence-companies/
|
[
{
"date": "2022/12/10",
"position": 1,
"query": "artificial intelligence employers"
}
] |
Our Leadership Team | AHI
|
Our Leadership Team
|
https://www.ahi.tech
|
[] |
Our leadership team is made up of seasoned experts with a combined 150+ years of experience in machine learning, artificial intelligence, bio-mathematical ...
|
When you visit websites, they may store or retrieve data in your browser. This storage is often necessary for the basic functionality of the website. The storage may be used for marketing, analytics, and personalization of the site, such as storing your preferences. Privacy is important to us, so you have the option of disabling certain types of storage that may not be necessary for the basic functioning of the website. Blocking categories may impact your experience on the website.
| 2022-12-10T00:00:00 |
https://www.ahi.tech/about/leadership
|
[
{
"date": "2022/12/10",
"position": 100,
"query": "artificial intelligence business leaders"
}
] |
|
The Impact of Automation and Fears of Job Displacement ...
|
The Impact of Automation and Fears of Job Displacement on Political Preferences
|
https://www.keynesfund.econ.cam.ac.uk
|
[] |
Large shifts towards automation, robots, and artificial intelligence have been uprooting the labor market. Computing power has grown exponentially, ...
|
However, these correlations might be driven by other observed or unobserved factors and cannot be interpreted causally. Therefore, we provide evidence of a causal effect of fear of automation on workers' preferences, attitudes and behaviors using the results of our information experiment. To overcome endogeneity concerns related to the perceived automation threat, we design an information treatment that introduces exogenous variation in workers' beliefs about the automation risk. More precisely, we randomize participants into a control group that receives no information, and two treatment groups where participants are provided with information about the potential automation risk faced by workers in their occupation, and we compare this number to respondents' own perceived risk. Given that before treatment assignment we ask workers about their perceived automation risk, our treatment creates good news and bad news, as some workers are exposed to a job loss probability that is lower than the probability they perceived, and others to one that is higher.
The information about automation risk that we provide to treated respondents relies on a previous data collection carried out in 2020 as part of the Covid Inequality Project (covidinequalityproject.com for the surveys). In the study, members of the United States labor force were asked how likely they thought it was that they might lose their job within the next ten years due to automation. With responses to this question at hand, we compute average automation risks for different occupations. We then present treated participants to our survey with the number that corresponds to the automation threat in their own narrow occupation category. The aim of the paper is not to defend these measures of automation risk, which is extremely difficult to predict due to uncertainty and endogeneity surrounding future innovations. Rather, our goal is to use this constructed measure of average perceived automation threat to introduce variation in workers' beliefs.
Our information experiment features one control group and two treatment groups. While all treated respondents receive information on the automation probability that comes from the Covid Inequality Project data, the two treatments differ in the stated source of the information. More precisely, some respondents are told that the information they see comes from a study by expert economists from the University of Oxford (the ‘experts’ treatment) and others are told that the information refers to the opinion of people working in similar jobs as theirs (the ‘people’ treatment. This allows us to study whether workers respond differently to information coming from experts, i.e., a socially distant group, or people that are similar to them.
Our experiment shows that information provision leads to significant treatment effects on preferences for redistribution. Treated respondents' preferred tax rate on income and their preferred level of universal basic income payments increases with the difference between the probability of job loss they are exposed too and their own prior. For support for government funded adult retraining programs we find a symmetric response; workers exposed to good news reduce their support while those expose to bad news increase it. However, we do not document a significant causal effect of the automation threat on employment responses as measured by propensity to participate in a retraining program and switch occupation. Instead, workers exposed to bad news intend to protect their job by joining a union. This evidence is suggestive of the fact that the future automation trends will lead to more support for redistribution and a larger welfare state, but not to increased differentiation through upskilling or re-skilling.
Moreover, workers exposed to bad news are more likely to consider their ideology to be left rather than right wing, while those exposed to good news report higher trust in politicians. These polarizing attitudes go hand in hand with a common problem of modern democracy, whereby good news increases the likelihood of wanting to vote in the next presidential elections, while bad news decreases it. Turning to whether workers react differently to information coming from different sources, we find mild differences in treatment effect depending on whether the information was phrased as coming from experts or other workers similar to the respondent, with the people treatment leading to a greater impact on preferences for redistribution.
Our findings instead suggest that the impact of the automation threat on ‘winners’, those receiving good news, is rather muted, while those exposed to bad news adapt their attitudes significantly. Indeed, most of the treatment effects that we document are driven by workers exposed to a higher job loss probability than they perceived. Looking at magnitudes, some of the before-mentioned effects are considerable. Being exposed to a 50 to 100 percentage point higher job loss probability than perceived increases the preferred mean tax rate by 0.4 percentage points and increases the desired level of UBI by 76 log points.
| 2022-12-11T00:00:00 |
https://www.keynesfund.econ.cam.ac.uk/projects/jhuq
|
[
{
"date": "2022/12/11",
"position": 1,
"query": "automation job displacement"
},
{
"date": "2022/12/11",
"position": 10,
"query": "universal basic income AI"
},
{
"date": "2023/04/01",
"position": 42,
"query": "automation job displacement"
},
{
"date": "2023/09/01",
"position": 52,
"query": "automation job displacement"
},
{
"date": "2023/10/01",
"position": 41,
"query": "automation job displacement"
},
{
"date": "2023/12/01",
"position": 56,
"query": "automation job displacement"
},
{
"date": "2024/02/01",
"position": 45,
"query": "automation job displacement"
},
{
"date": "2024/03/01",
"position": 43,
"query": "automation job displacement"
},
{
"date": "2024/04/01",
"position": 43,
"query": "automation job displacement"
},
{
"date": "2024/05/01",
"position": 44,
"query": "automation job displacement"
},
{
"date": "2024/06/01",
"position": 44,
"query": "automation job displacement"
},
{
"date": "2024/07/01",
"position": 44,
"query": "automation job displacement"
},
{
"date": "2024/08/01",
"position": 42,
"query": "automation job displacement"
},
{
"date": "2024/10/01",
"position": 44,
"query": "automation job displacement"
},
{
"date": "2024/12/01",
"position": 44,
"query": "automation job displacement"
},
{
"date": "2025/01/01",
"position": 45,
"query": "automation job displacement"
},
{
"date": "2025/02/01",
"position": 46,
"query": "automation job displacement"
},
{
"date": "2025/03/01",
"position": 44,
"query": "automation job displacement"
}
] |
|
Ask HN: Will AI put programmers our of work?
|
Ask HN: Will AI put programmers our of work?
|
https://news.ycombinator.com
|
[] |
My bet is that AI might displace labour but not lead to a net reduction of software labour demand in the next 10-20 years at least.
|
There's a lot of news regarding copilot and openAI and what have you. I'm not familiar with AI so I cannot really tell hype from substance here.
Should I worry? Do you think that some form of AI will be able to do the job of an average programmer any time soon? If yes what is your estimate? And how would you try to AI-proof your career?
| 2022-12-11T00:00:00 |
https://news.ycombinator.com/item?id=33941868
|
[
{
"date": "2022/12/11",
"position": 32,
"query": "automation job displacement"
},
{
"date": "2022/12/11",
"position": 15,
"query": "AI replacing workers"
},
{
"date": "2022/12/11",
"position": 75,
"query": "universal basic income AI"
},
{
"date": "2022/12/11",
"position": 37,
"query": "generative AI jobs"
}
] |
|
AI & Machine Learning Bootcamp
|
AI & Machine Learning Bootcamp
|
https://metana.io
|
[] |
The AI ML industry's robust growth is driving up salaries for employees, with a proportional increase to the job market growth. $145,242.
|
⌛️Outdated Resources
We’ve all been there – endlessly scrolling through pages of free online resources, only to discover that most of them are outdated and useless. It’s a common problem that can hinder your learning progress.
😵💫 Distractions
Distractions can easily derail your learning journey, especially in a world filled with endless distractions. Staying motivated and on track can feel nearly impossible without some form of accountability or support system.
⚠️ Lack of clear roadmaps
The lack of clear roadmaps can be a major roadblock for learners. But finding one that actually works in practice can feel like searching for a needle in a haystack. Often, the roadmaps that look good on paper turn out to be impractical and difficult to follow.
🤷♂️ No guidance
Without the right guidance, learning can feel like an aimless and frustrating pursuit. It’s like trying to hit a target blindfolded, hoping to get lucky. It’s no wonder many learners struggle to stay motivated and make progress. The absence of a mentor or guide can leave you feeling lost and directionless, unsure of how to improve or what steps to take next. If you’re serious about achieving your goals, don’t go it alone. Seek out a mentor who can provide the support, advice, and direction you need to succeed.
😩 Finding a job
It’s frustrating when you’ve put in all the hard work to learn a new skill, only to find that landing a job in that field can be an uphill battle. You may spend countless hours tailoring your resume and cover letter, but often receive no response or feedback. And even if you do manage to snag an interview, the pressure is on to make yourself stand out as the ideal candidate, with no clear direction on how to do so. It’s no wonder that finding a job can feel like an overwhelming and demotivating experience.
🤔 Doubt in knowledge & skills
Have you ever questioned whether you’re truly ready to tackle the challenges? With new vulnerabilities and exploits constantly emerging, it’s hard to feel confident that you’ve truly mastered the skills you need to create safe and effective applications.
🫤 Staying up-to Date
Keeping up with the ever-changing landscape of technology can be a daunting task. There’s always something new to learn, and it can be hard to know where to start. Plus, with so many other responsibilities vying for your attention, finding the time to stay up to date can feel impossible. And even when you do find the time, how can you be sure you’re getting the right information from the right sources?
| 2022-12-11T00:00:00 |
https://metana.io/ai-machine-learning-bootcamp/
|
[
{
"date": "2022/12/11",
"position": 26,
"query": "machine learning job market"
}
] |
|
AI Education Workshops and Coaching for Non-Programmers ...
|
AI Education Workshops and Coaching for Non-Programmers
|
https://www.soboringai.com
|
[] |
of hands-on training delivered to support AI adoption in the workplace. 4.8/5. average workshop rating. 50+ teams. across business functions upskilled for AI ...
|
AI Fluency Mapping for organizations
Not sure where to start with your AI upskilling initiatives?
Take our mini-course to get the tools you need to build a strategic AI Adoption Plan for you organization.
| 2022-12-11T00:00:00 |
https://www.soboringai.com/
|
[
{
"date": "2022/12/11",
"position": 51,
"query": "workplace AI adoption"
}
] |
|
Disclosures on AI Generated Content - Robert J. Gates
|
Do we need disclosures on AI Generated content.
|
https://www.robertjgates.com
|
[] |
In today's Digital age, Artificial Intelligence will augment and, in some cases, replace human intelligence-based tasks. As a result, people will need to accept ...
|
Last week, the OpenAI project released the ChatGPT Research Preview application. Since its release, people worldwide have been posting on social media with fascination, excitement, and fear about the potential impact of such a tool.
If you have not heard of ChatGPT, it is a large language-focused machine-learning model trained to process and generate text. These systems are typically trained on large amounts of text data, books, articles, and other written materials to learn the patterns and structures of human language. Training a large language model allows an AI system to produce natural-sounding, readable text similar to human writing.
The company behind ChatGPT is OpenAI, a research institute, and technology company focused on developing AI technologies and advancing the field of artificial intelligence. One of their projects is training large language models, such as GPT-3 (Generative Pretrained Transformer 3). Today GPT-3 is one of the largest and most powerful language models. These models can be used for various applications, such as language translation, summarization, question answering, and more.
When considering the potential impact of such technology, one can look back to the Industrial age for some examples. During the Industrial Revolution, machines and technology began to augment and, in some areas, replace human power. These changes allowed things to get created faster, reducing costs and, in many cases reducing or removing the need for humans to complete those tasks. As a result of these changes, jobs were lost, but significantly more new jobs emerged.
In today’s Digital age, Artificial Intelligence will augment and, in some cases, replace human intelligence-based tasks. As a result, people will need to accept and embrace change. These changes will come fast and will be very disruptive. As we saw in the Industrial age, jobs will be lost, but new industries and markets will emerge from the disruption.
As we embrace AI-based technologies to generate human intelligence-based content, it raises some critical questions about transparency and accountability. When consuming information and making decisions today, we often utilize multiple sources such as news media, friends, family, Internet search tools, and other online content. When we digest information and begin to process it, the information gathered is often weighted based on our trust and experience with those sources.
Thanks to AI-based technologies, we can now create pictures, write papers, write application code, draft articles and social media posts, and generate videos and audio recordings simply by writing a few sentences. However, while these capabilities help accelerate the creation of knowledge-based content, there are risks. For example, AI-generated results could deceive or mislead readers because of bias, data quality issues, malicious intent, lack of diverse thoughts, and a simple lack of ethics in disclosing the source of the content. Therefore, while the results may be excellent, we should always weigh the information generated cautiously, as AI output is based on available data at the time and how it was trained. This is no different than if you received information from a friend, you just treat that as another source of information that should be weighed in with other viewpoints and sources.
With that, we should know who or what created the information we are consuming. If people start representing AI-generated content as their own, it will impact diverse thinking, break trust in others, and is not ethical. In addition, if you read the terms of service on AI-based platforms, there are rules against passing AI-generated content as your own. For example, the following line is from OpenAI’s terms of service.
“represent that output from the Services was human-generated when it is not” https://openai.com/api/policies/terms/
One immediate action that may help reduce risk is utilizing some type of disclosure system. Initially, the disclosure may need to be done manually, but over time could be handled in an automated way. Disclosures will be critical for us to know and understand how to interpret, analyze, and respond to the information we consume. For example here are three high level disclosures that could be used and associated with written content so the person consuming the content knows how to best handle the information they are consuming.
Disclosure: The following content was generated entirely by an AI-based system based on specific requests asked of the AI system. Disclosure: The following content was generated by me with the assistance of an AI-based system to augment the effort. Disclosure: The following content was generated entirely by me without assistance from an AI-based system.
As you can see these are high level and designed to get an understanding if the content we are reading was created by the person, by the person with assistance from AI, or completely written by an AI completely. This approach may be a good solution until we can come up with more ideas on how to best blend in tools like the chatGPT based AI platform.
| 2022-12-11T00:00:00 |
2022/12/11
|
https://www.robertjgates.com/disclosures-on-ai-generated-content/
|
[
{
"date": "2022/12/11",
"position": 82,
"query": "AI economic disruption"
}
] |
Understanding Machine Learning and AI
|
Understanding Machine Learning and AI
|
https://www.qad.com
|
[
"Simon Pioche",
"Director Of The Pi Department",
"Leading All The Ai Ml Projects At Qad",
"Simon Is The Previous Ceo",
"Co-Founder Of Livejourney Process Mining. With An Experience Of Yo",
"Simon First Started As A Data Science Engineer Before Becoming The Head Of Analytics At Teleperformance Research."
] |
Learn about Machine Learning, Artificial Intelligence, their applications and how Intelligent Process Mining helps streamline processes, predict anomalies, ...
|
What is Machine Learning?
Machine learning (ML) is a branch of Artificial Intelligence (AI) that employs various technologies and algorithms. ML relies on historical or precise data to make decisions or take action.
A practical example of machine learning is Amazon’s Alexa, a virtual assistant. Alexa uses voice recognition technology to associate sounds with specific actions. For instance, Alexa recognizes different sounds and responds accordingly through algorithms. While this appears intelligent, it involves minimal learning on the part of the technology.
What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) traditionally refers to the theory and development of computer systems capable of performing tasks that typically require human intelligence, such as speech recognition, decision-making, and pattern identification. Today, “AI” is an umbrella term encompassing various technologies, including machine learning, deep learning, and natural language processing (NLP).
Although these AI technologies have enhanced user experiences, there is debate over whether they qualify as true artificial intelligence, as they do not possess independent thought. True AI should be able to learn and adapt using vast amounts of data to surpass human intelligence. Currently, AI is still in the research and development phase, and what we call “AI” today is closer to advanced machine learning.
Combining Machine Learning & Process Mining
When combined with Process Mining, machine learning adds intelligence to the reading, visualization, and analysis of data and processes in everyday operations.
Intelligent Process Mining Opportunities
Both Machine Learning and Process Mining depend on data. Machine Learning enhances Process Mining into what is known as Intelligent Process Mining. This combination offers several opportunities:
Descriptive Process Mining
The initial step in process mining is to provide visibility into how business processes operate. By retrieving logs from various information systems (ERP, CRM, etc.), process mining reconstructs and timestamps the flows and main stages of each process unit (e.g., an order, a customer, an invoice). Known as “process discovery,” this step is crucial for visualizing processes. Users can conduct preliminary analyses by examining specific deviations or anomalies observed in the process representations. However, due to the volume of data and the complexity of some processes, this analysis can be tedious and complex.
Clustering Process Mining
Machine Learning and AI add significant value at this stage by automatically detecting and highlighting major anomalies, deviations, and non-conformities in the process. For example, these technologies can compare the “as-is” process with the originally designed process, categorize the types of deviations, and evaluate their impact, such as “rework,” bottlenecks, and backtracking. This allows users to analyze the number of affected process units, the frequency of occurrences, and the temporal and financial impacts.
Diagnostic Process Mining
Once deviations and anomalies are identified, AI and ML use statistics and algorithms to pinpoint the primary causes. For instance, the tool might determine why a particular category of products is consistently delivered late, identifying issues such as a specific supplier, a particular region, or an upstream bottleneck. Users can visualize the main causes based on their importance and impact on the process.
Predictive Process Mining
Process mining collects and stores all data and logs from information systems. Machine learning uses predictive algorithms to analyze this historical data and forecast future anomalies. This enables precise visualization of future order flows, including each stage of an order and the forecasted delivery time within a supply chain process.
Intelligent Process Mining Benefits
| 2022-12-11T00:00:00 |
2022/12/11
|
https://www.qad.com/blog/2022/12/understanding-machine-learning-and-ai
|
[
{
"date": "2022/12/11",
"position": 20,
"query": "machine learning workforce"
}
] |
The Workforce in 2023: 5 Key Trends - Simone Brown
|
The Workforce in 2023: 5 Key Trends – Simone Brown
|
https://simonebrown.ca
|
[] |
In 2023, we're likely to see more and more companies adopting new technologies, such as artificial intelligence and machine learning, along with automation.
|
December 11, 2022
The Workforce in 2023: 5 Key Trends
As we look ahead to 2023, it’s clear that the world of work is changing rapidly. Many of the trends that we’re seeing today are only going to continue to grow in the coming year. Here are a few of the key workforce trends that you should be aware of as we move into 2023:
1. As technology continues to advance, it’s going to play an even bigger role in the workplace. In 2023, we’re likely to see more and more companies adopting new technologies, such as artificial intelligence and machine learning, along with automation, to help improve efficiency and productivity.
2. The gig economy will continue to expand: The gig economy, which refers to the growing trend of workers taking on short-term, flexible jobs, is already a major part of the workforce. This trend will continue to be driven by the need for flexibility and the desire for workers to have more control over their careers.
3. Remote work will become the norm: The COVID-19 pandemic has accelerated the trend toward remote work, and it’s likely that this trend will continue to grow in 2023. Many companies have realized that they can be just as productive (if not more so) when their employees work from home, and as a result, we’re likely to see more and more companies adopting remote work policies. We will also see an increase in the number of workers who are not tied to a specific location
4. The need for lifelong learning: In order to stay competitive and relevant in a rapidly changing world, workers will need to continually learn and adapt. This will require a focus on lifelong learning and professional development to ensure that workers have the skills and knowledge they need to succeed.
5. The importance of diversity and inclusion: As the workforce becomes more diverse, it will be important for employers to create inclusive environments that support and value all employees. This will require a focus on diversity and inclusion in hiring, training, and development initiatives.
Overall, 2023 is shaping up to be an exciting and dynamic year for the workforce. As new technologies continue to emerge and the world of work continues to evolve, it’s clear that there will be plenty of opportunities for both employers and workers who are willing to adapt, embrace change, and stay ahead of the curve.
| 2022-12-11T00:00:00 |
https://simonebrown.ca/resources/the-workforce-in-2023-5-key-trends/
|
[
{
"date": "2022/12/11",
"position": 27,
"query": "machine learning workforce"
}
] |
|
Spenmo lays off staff to extend cash runway
|
Spenmo lays off staff to extend cash runway
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https://www.businesstimes.com.sg
|
[
"Benjamin Cher"
] |
The layoffs occurred on Nov 18, said the e-mail, with the ... Vietnam-based AI company AI Hay raises US$10 million in Series-A funding. Jul ...
|
FIINANCIAL software startup Spenmo has initiated a fresh round of layoffs, according to an e-mail viewed by The Business Times, as part of a push to extend its cash runway past five years.
The startup offers spending management software-as-a-service (SaaS) solutions ranging from corporate cards to employee reimbursement. It recently raised a US$75.5 million Series B round in January 2022 led by Tiger Global, according to data platform VentureCap Insights.
The layoffs occurred on Nov 18, said the e-mail, with the macroeconomic environment and need for runway blamed for the layoffs. This was something that had been in the works for some time, said sources familiar with the matter, with team leads asked to select which of their team members to cut. A source put the number of people laid off at 30 per cent of Spenmo’s workforce, while another said it is about 70 out of the over 200 people employed.
This is not the first layoff Spenmo has initiated in 2022, according to sources familiar with the matter. Spenmo has carried out at least two layoffs, one in January and the other in June, with the credit team and sales team affected.
In the e-mail, Spenmo co-founder and chief executive officer Mohandass Kalaichelvan said that the company has to be focused on extending its runway as capital dries up. While the startup has enough for over three years, the imperative now is to extend it to over five years, urged by its board and advisers.
“We agree it makes sense to be fiscally responsible in this climate; no one really knows how long this recession will last,” said Kalaichelvan.
BT in your inbox Start and end each day with the latest news stories and analyses delivered straight to your inbox. Sign Up Sign Up
Cost cutting across the board has been initiated, from hiring freezes to renegotiating expenses such as rental and subscriptions.
“But these measures won’t bring our runaway to five-plus years, so I’ve made the hard decision to let go of some of our colleagues,” said Kalaichelvan.
Impacted employees will get undisclosed retrenchment benefits, accelerating the cliff period for employee share options, the equivalent of three months’ insurance premiums in the final payments, outplacement services and relocation allowance for work visa holders.
Resources now are being shifted into a smaller number of growth areas such as locally issued cards, deeper focus on accounts payable workflow for larger companies and investment into integration into bigger enterprise resource planning programmes.
The monetisation of the SaaS business will take longer, but will be much stickier and less churnable than the current fintech revenue.
“We have implemented big pricing changes, and recent cohorts are looking better than ever. The future is bright if we continue to execute relentlessly,” said Kalaichelvan.
Kalachelvan in response to queries, told The Business Times that Spenmo has laid off over 50 employees out of it over 290 employees in November. “I think this economic slump is going to be around for a minimum of three years,” he said when asked why having an over five year runway was important.
| 2022-12-11T00:00:00 |
https://www.businesstimes.com.sg/startups-tech/spenmo-lays-staff-extend-cash-runway
|
[
{
"date": "2022/12/11",
"position": 74,
"query": "AI layoffs"
}
] |
|
» Artificial Intelligence in Healthcare
|
» Artificial Intelligence in Healthcare
|
https://old.57357.org
|
[
"Children S Cancer Hospital Egypt Cche"
] |
Artificial Intelligence or AI technology is gaining popularity worldwide. AI is developed to prove usefulness in the healthcare industry.
|
Artificial Intelligence or AI technology is gaining popularity worldwide. AI is developed to prove usefulness in the healthcare industry. AI can improve accuracy, precision and outcomes while reducing time in many facets of this ecosystem.
During this course you will learn the influence of AI in enhancing the quality & the workflow of healthcare services.
| 2022-12-11T00:00:00 |
https://old.57357.org/en/education/artificial-intelligence-in-healthcare/
|
[
{
"date": "2022/12/11",
"position": 46,
"query": "AI healthcare"
}
] |
|
Your next career will be about asking questions
|
Your next career will be about asking questions
|
https://bestemnetwork.com
|
[
"View All Posts Gareth Davies",
"Published By",
"Gareth Davies",
"Innovation Expert With",
"Years Of Experience Living",
"Working Across The World. I Apply An Engineering Approach To Helping Companies Innovate",
"Achieve Commercial Success."
] |
Some experts predict that generative AI will automate many tasks currently performed by humans, leading to job displacement in some industries. However, it is ...
|
The fourth industrial revolution is comming quickly. I’ve been playing with openAI (more later) and have asked it’s ChatGPT bot a few questions. I also asked it’s DALL-E to generate the image ont the top of this page. 80% of this article is generated by AI.
I asked the AI questions, and it provided the answers. See what you think. In the future I suspect the role of the human is to know which question matters most.
Try these your self at: https://chat.openai.com/chat and https://labs.openai.com/
Write 50 words on generative AI
Generative AI is a type of artificial intelligence that involves the use of machine learning algorithms to generate new data that is similar to a given input. This can be used to create realistic images, videos, audio, and other types of media. Generative AI can be trained on large amounts of data and can produce high-quality outputs that are difficult to distinguish from human-generated content. Some common applications of generative AI include image and video synthesis, natural language generation, and audio generation.
write 100 words on the history and progress of open.ai
OpenAI is a nonprofit research organization founded in 2015 by Elon Musk, Sam Altman, and other leading figures in the tech industry. The goal of OpenAI is to advance the field of artificial intelligence and to promote the development of AI that is safe, transparent, and beneficial to society. Since its inception, OpenAI has made significant progress in the field of AI, including the development of advanced machine learning algorithms and the creation of large-scale language and vision models. In 2018, OpenAI launched Dactyl, a robot hand that was able to solve a Rubik’s cube with human-like dexterity. More recently, OpenAI has been focused on developing AI technologies that can operate in complex and dynamic environments, such as video games and robotics. Overall, OpenAI has established itself as a leading player in the field of AI research and development
Write 50 words on how Regenerative AI will affect the 4th industrial revolution and employment in white collar roles
The fourth industrial revolution, also known as Industry 4.0, is characterized by the use of advanced technologies such as artificial intelligence, the Internet of Things, and robotics. Generative AI is expected to play a significant role in this revolution, potentially affecting the demand for white collar jobs. Some experts predict that generative AI will automate many tasks currently performed by humans, leading to job displacement in some industries. However, it is also possible that generative AI will create new job opportunities in fields such as data analysis and machine learning. Overall, the impact of generative AI on employment in white collar roles is likely to be complex and multifaceted.
write 200 words on the potential economic benefits and pitfalls of the 4th industrial revolution
The fourth industrial revolution, also known as Industry 4.0, is characterized by the use of advanced technologies such as artificial intelligence, the Internet of Things, and robotics. These technologies have the potential to bring significant economic benefits, including increased productivity, efficiency, and competitiveness. For example, the use of AI and automation can help to reduce the time and cost of certain tasks, allowing businesses to operate more efficiently and effectively. Additionally, Industry 4.0 technologies can enable the creation of new products and services, leading to the development of new markets and revenue streams.
However, the fourth industrial revolution also presents potential pitfalls and challenges. One of the main concerns is the potential for job displacement and unemployment as a result of automation. While some experts predict that Industry 4.0 technologies will create new job opportunities, others argue that many jobs will be replaced by machines, leading to a decrease in demand for labor. This could have significant consequences for workers, including income inequality and social unrest. Additionally, the rapid pace of technological change associated with Industry 4.0 may create challenges for businesses and governments, who will need to adapt quickly in order to remain competitive. Overall, the economic impact of the fourth industrial revolution is likely to be complex and multifaceted.
write 200 words on the intersection of energy transition on the 4th industrial revolution
The intersection of energy transition and the fourth industrial revolution, also known as Industry 4.0, is a complex and evolving area of study. Energy transition refers to the process of shifting from the use of fossil fuels to more sustainable forms of energy, such as renewable sources like solar and wind power. Industry 4.0, on the other hand, involves the use of advanced technologies such as artificial intelligence, the Internet of Things, and robotics to drive innovation and economic growth.
The intersection of these two trends presents both opportunities and challenges. On the one hand, Industry 4.0 technologies can help to accelerate the transition to renewable energy by enabling the development of new energy storage systems, smart grids, and other technologies. For example, the use of AI and machine learning can help to optimize the operation of renewable energy systems, making them more efficient and cost-effective. Additionally, Industry 4.0 technologies can enable the creation of new products and services related to renewable energy, leading to the development of new markets and revenue streams.
However, the transition to renewable energy also poses challenges for Industry 4.0. One of the main challenges is the need for large-scale infrastructure investments to support the transition, which may require significant financial resources. Additionally, the transition to renewable energy may disrupt existing energy markets and business models, potentially leading to job losses and other economic impacts. Finally, the rapid pace of technological change associated with Industry 4.0 may create challenges for policymakers and regulators, who will need to balance the need for innovation with the need for stability and reliability in the energy sector. Overall, the intersection of energy transition and Industry 4.0 is a complex and dynamic area that will require careful planning and coordination in order to maximize the benefits and minimize the risks.
Write 100 words comparing the importance of asking the right question with researching the answer
Asking the right question is just as important, if not more important, than researching the answer. This is because a well-formed question can help to clarify the issue at hand and provide a clear direction for the research. On the other hand, a poorly-formed question can lead to confusion and ineffective research. Additionally, asking the right question can help to ensure that the research is relevant and useful. For example, a question that is too broad or too narrow may not provide the information that is needed to address the problem. Asking the right question can also help to save time and resources by focusing the research on the most important aspects of the issue. In short, asking the right question is an essential first step in any research process.
will careers of the future focus on asking the right question or doing repetitive work?
It is difficult to predict the exact nature of careers in the future, as it will depend on many factors such as technological advancements, economic conditions, and societal trends. However, it is likely that careers of the future will focus more on asking the right questions and less on repetitive work. This is because the widespread adoption of artificial intelligence and automation is expected to automate many tasks that are currently performed by humans, such as data entry and routine data analysis. As a result, careers of the future may focus more on tasks that require higher levels of creativity, problem-solving, and critical thinking. These skills are often associated with the ability to ask the right questions and to think outside the box. Additionally, the rapid pace of technological change is likely to create new job opportunities in fields such as data science, machine learning, and artificial intelligence, which may require individuals to be adept at asking the right questions in order to drive innovation and progress.
| 2022-12-12T00:00:00 |
2022/12/12
|
https://bestemnetwork.com/2022/12/12/your-next-career-will-be-about-asking-questions/
|
[
{
"date": "2022/12/12",
"position": 30,
"query": "automation job displacement"
}
] |
Quality-Artificial Intelligence Archives
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Quality-Artificial Intelligence Archives
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https://conformance1.com
|
[] |
Privacy invasion, discrimination, and job displacement underscore the ... automation augment traditional operational excellence strategies such as Lean ...
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Artificial intelligence is reshaping the global labor market by automating roles across a broad range of industries. Those most vulnerable are sectors built on routine, repetitive, or low-skilled tasks—especially in manufacturing, where robotics have replaced human labor in assembly, welding, and painting. AI provides higher speed, precision, and scalability, making it far more … [Read more...] about Manufacturing and 8 Other Industries That WIll Lose The Most From AI
| 2022-12-12T00:00:00 |
https://conformance1.com/category/quality-artificial-intelligence/
|
[
{
"date": "2022/12/12",
"position": 67,
"query": "automation job displacement"
}
] |
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We Asked ChatGPT to Write an Article About Ethical AI ...
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We Asked ChatGPT to Write an Article About Ethical AI, Here's What it Said
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https://www.holisticai.com
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[] |
The language model's article also considers the social impact of the technology, touching on how automation can result in the displacement of jobs. However ...
|
What is Ethical AI?
In recent years, the field of AI Ethics, and related fields, such as trustworthy AI and responsible AI, have gained much attention due to increasing concerns about the risks that AI can pose if it is not used safely and ethically.
As pioneers of the field, we define AI Ethics as a nascent field that has emerged in response to the growing concern regarding the impact of AI. In particular, AI Ethics is concerned with the psychological, social, and political impact of AI and is characterised by three main approaches:
Principles – guidelines that inform and direct the use and development of these technologies, including those codified in law.
– guidelines that inform and direct the use and development of these technologies, including those codified in law. Processes – ethical-by-design and governance practices that ensure accountability for AI systems and their outputs.
– ethical-by-design and governance practices that ensure accountability for AI systems and their outputs. Ethical consciousness – morals that motivate AI systems designers, developers, and deployers to follow these principles and processes.
The four verticals of Ethical AI
Ethical AI operationalises AI Ethics, with research in this field converging on four key verticals:
Safety – whether the system is robust against adversarial attacks and fallback plans have been developed for unknown risks.
– whether the system is robust against adversarial attacks and fallback plans have been developed for unknown risks. Privacy – whether appropriate data minimisation and data stewardship practices have been adopted to protect users’ privacy.
– whether appropriate data minimisation and data stewardship practices have been adopted to protect users’ privacy. Fairness – whether the system has been tested for bias and appropriate action has been taken to mitigate this and any other barriers that prevent the system from being accessible.
– whether the system has been tested for bias and appropriate action has been taken to mitigate this and any other barriers that prevent the system from being accessible. Transparency – how explainable the system is and whether there is appropriate communication with the relevant stakeholders of a system.
How well a system performs on each of these verticals can be determined through algorithm auditing, the practice of assessing, mitigating, and assuring an algorithm’s safety, legality, and ethics. Audits are ongoing processes and should be repeated annually or following any major updates to the system, and can occur at any point in the lifecycle of a system.
AI Ethics from the perspective of AI: ChatGPT
While the applications of AI are vast, from recruitment to automating insurance claims, one application has that has gained attention recently is conversational AI. Indeed, OpenAI has recently released ChatGPT, which uses a large language model to answer series of questions in a conversational way. Initially trained by humans, who played the role of the user and AI assistant, the model was later trained using reinforcement learning to reward the model when it produced desirable responses.
From creating essay questions and marking rubrics to debugging code, ChatGPT has garnered much attention. We decided to put it to the test by asking it to write a blog post about ethical AI.
Like our own definition, ChatGPT highlights the potential for biased systems, and recommends auditing as a useful approach for ensuring that AI is more ethical. The language model’s article also considers the social impact of the technology, touching on how automation can result in the displacement of jobs. However, seeing as the generated post it is largely indistinguishable from a human’s efforts, who’s to say it won’t be this very system that puts a content writer out of a job.
Towards Ethical AI
Whilst experimenting with ChatGPT or making art with Dall-E 2 is bound to be amusing. AI will take many tasks out of our hands in the coming years, but that should not allow us to sleepwalk into the development of poorly designed systems.
The EU High-Level Expert Group on AI and the IEEE have formulated moral values that should be adhered to in the design and deployment of artificial intelligence. However, building ethical AI will require, at a minimum, verification of whether a model complies with the values it has been intended for. We must ask ourselves the right questions: Is the model fully explainable? Was it designed to be interpretable? What are the derived variables used in the model? Are they biased?
Bridging AI ethics from theory to practice will depend on regulatory oversight combined with AI auditing to ensure that technologies placed on the market are adequately monitored and regulated. When the chatbots themselves recognise the importance of ethical AI, it’s certainly time for us to take note!
| 2022-12-12T00:00:00 |
https://www.holisticai.com/blog/ethical-ai-chat-gpt
|
[
{
"date": "2022/12/12",
"position": 72,
"query": "automation job displacement"
}
] |
|
Is becoming a 'prompt engineer' the way to save your job ...
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Is becoming a ‘prompt engineer’ the way to save your job from AI?
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https://www.ft.com
|
[] |
When the World Economic Forum predicted a few years ago that artificial intelligence would cause seven million job losses, the great and the good of Davos ...
|
Try unlimited access Only $1 for 4 weeks
Then $75 per month. Complete digital access to quality FT journalism on any device. Cancel anytime during your trial.
| 2022-12-12T00:00:00 |
https://www.ft.com/content/0deda1e7-4fbf-46bc-8eee-c2049d783259
|
[
{
"date": "2022/12/12",
"position": 6,
"query": "AI job losses"
},
{
"date": "2022/12/12",
"position": 39,
"query": "AI economic disruption"
}
] |
|
AI bots lack one critical skill for customer service jobs
|
AI bots lack one critical skill for customer service jobs - CCAL Group, Inc
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https://ccalservices.com
|
[
"Ccal Accounting"
] |
Reduced Training Costs · First and foremost, AI technology allows human employees to spend less time on repetitive, menial tasks that can be completed ...
|
Its features include voice recognition, speech synthesis, natural language processing, sentiment analysis, and predictive analytics. The call center AI market has exploded in popularity in recent years as artificial intelligence technology continues to advance and become more useful in contemporary business applications. One of the primary results of this has been the development of call center automation software, which seeks to provide businesses with enhanced workflow solutions. Consumers have been getting accustomed to using AI-powered customer service, as a survey from LivePerson revealed that 75% of customers said that they spend more money with brands that offer messaging.
Why I quit call center? Some of the reasons for call center attrition may not be under the direct control of team leaders but are surely under the control of contact center management. These reasons are compensation, job fit, stressful work environment, and limited job/career opportunities.
IBM’s natural language understanding (NLU) software was used to create an AI-enabled system that is able to provide real answers to the questions that customers ask. IBM partnered with Humana, a healthcare insurance provider, in collaboration with IBM’s Data and AI Expert Labs & Learning (DAELL) and created what became the Provider Services Conversational Voice Agent with Watson. The unique solution combines multiple Watson applications in a single conversational assistant, and runs on the IBM cloud, while the Watson Assistant for Voice Interaction runs on location at Humana.
Reduced Training Costs
While he’s not writing high performance email cadences for his team, he dabbles in blogging about Sales Strategy, Sales Tech and Sales Training. Take a cue from the tips outlined above and build a thriving team of super agents. Aisera’s multilingual AI with built-in language detection quickly responds to customer requests in their preferred language of choice. The researchers acknowledged the limitations of their study, stating that their findings do not capture potential long-term impacts on skill demand, job design, wages, or customer demand.
First and foremost, AI technology allows human employees to spend less time on repetitive, menial tasks that can be completed effortlessly and much more quickly thanks to AI.
It simulates natural language through messaging applications, websites, mobile apps, and the telephone.
When customers can communicate in their mother tongue, they’re more likely to feel heard and valued.
The research sheds light on the impact of generative AI in workplace environments, specifically within the customer service sector, which already boasts high adoption rates of AI technology.
This metric, as its name suggests, measures how often a caller’s issue is resolved during their first interaction.
Intelligent routing can save customers time from repeating required information and speed the processing of calls.
Implementing artificial intelligence (AI) in a call center can revolutionize customer service, increasing efficiency and reducing response time. One primary use of AI in call centers is the deployment of AI-powered chatbots, which simulate human conversation by utilizing natural language processing (NLP) algorithms. Chatbots can be seamlessly integrated into multiple communication channels, such as social media platforms, websites, and messaging apps, acting as the first point of contact for customers. Furthermore, AI-driven virtual assistants are capable of learning from customer interactions, improving their responses and decision-making abilities over time.
Advantages of a call center operator over a Chatbot
AI can not only help some customers self-serve when they’re happy to, but it can also flag which ones are priority cases that need urgent human help to stop them from becoming ex-customers. IVR is a type of AI that most of us have interacted with when we needed customer service. You answer recorded questions such as your language, name, account number, etc. An interactive voice response or IVR system is an AI that works with automated call distribution (ACD) systems. During a call, the IVR can ask for information such as the customer’s name, preferred language, location, or the type of inquiry they want to make.
The purpose of using AI in call centers is to improve the customer experience and relieve human agents of time and energy spent on simple requests. Before AI was as technologically advanced and capable as it is today, businesses used to have human beings monitor call center phone calls in order to collect data about customers’ pain points as well as general feedback. Now, however, thanks to artificial intelligence and machine learning technology, businesses can monitor every single call to collect more data than ever before.
The AI call center optimizes everything, saving time and money
This could lead to customers feeling frustrated or dissatisfied with the level of service provided, resulting in a negative impact on customer loyalty and brand reputation. While there are many benefits to using AI in call centres, there are also some potential downsides. One concern is that AI-powered systems may not be able to handle complex or emotionally charged queries as well as human agents. There is also the risk that customers may feel frustrated or dissatisfied with the lack of human touch.
Philippine regulators told to empower users, workers to tackle AI threats – BusinessWorld Online Philippine regulators told to empower users, workers to tackle AI threats. Posted: Sun, 11 Jun 2023 16:31:32 GMT [source]
When TXU Energy deployed Ivy, an automated customer care AI solution, it increased CSAT by 11%, and call containment by 18%. Whereas historically tasks like understanding customer history, post-call work, and agent scoring needed to be done manually, AI enables businesses to streamline things at a previously impossible scale. To use this type of AI, companies must map skill metrics such as agents’ personalities, average call times, and expertise on particular issues. AwareX shares real insights to the issues faced by Communications Service Providers. From time to time our team members pick a topic to explore and discuss the value at the junction of technology, digital engagement, mobile and telecom.
Will AI Replace Call Center Agents? Advantages and Disadvantages Explored
It’s important for businesses to assess their priorities and choose a customer service option that aligns with their brand values and customer needs. Chatbots have emerged as a crucial tool in the world of customer support services, offering a plethora of benefits to both businesses and consumers. Schneider said that on a per-call basis, an interaction with a human call-center agent runs about $10-$15, while an encounter with a voice-enabled chatbot or IVA costs from about 50 cents to $2.50. From customer service to sales, these technologies are crucial for brands looking to supercharge efficiency, reduce costs, and transform digital customer engagement. By providing real-time translation services, businesses can reach a wider audience and provide support to customers around the globe. As well as providing automated customer support, these virtual assistants can also generate leads, provide personalization, and gain valuable insights by collecting customer data.
Technology Will Change The World – Will The World Change With It? – Forbes Technology Will Change The World – Will The World Change With It?. Posted: Tue, 16 May 2023 07:00:00 GMT [source]
AI technology is becoming increasingly common in call centers, raising questions about the potential for AI to replace human agents. In this article, we explored the pros and cons of AI vs human agents, examined how AI is changing the call center industry, explored the impact on customer satisfaction, and investigated the cost benefits of AI. We also discussed the challenges of integrating AI into the call center environment and evaluated the potential for AI to enhance efficiency.
AI in your call center: How will you use it?
While some may argue that AI can replace human agents, there are still several reasons why this is not yet a feasible option. When customers call an AI call center powered by Replicant, metadialog.com they receive an answer to every call, chat, or text immediately. The caller can make their request in any language as naturally as if they were speaking to a human agent.
What kind of job will be replaced by AI? Jobs most impacted by AI. Advertisement.
Coders/programmers.
Writers.
Finance professionals.
Legal workers.
Researchers.
Customer service.
Data entry and analysis.
| 2022-12-12T00:00:00 |
2022/12/12
|
https://ccalservices.com/ai-bots-lack-one-critical-skill-for-customer/
|
[
{
"date": "2022/12/12",
"position": 52,
"query": "AI replacing workers"
}
] |
Unlocking Value from Artificial Intelligence in Manufacturing
|
Unlocking Value from Artificial Intelligence in Manufacturing
|
https://www.weforum.org
|
[] |
This white paper presents the benefits that can be achieved through industrial AI applications in operational performance, sustainability and workforce ...
|
White papers
Artificial intelligence (AI) can enable a new era in the digital transformation journey, offering tremendous potential to transform industries for greater efficiency, sustainability and workforce engagement. Even though the impact of AI applications in manufacturing and value chains is known, the full opportunity from their deployment is still to be uncovered due to a number of organizational and technical roadblocks. Recognizing this need, the World Economic Forum’s Platform for Shaping the Future of Advanced Manufacturing and Value Chains and Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning, together with the Centre for the Fourth Industrial Revolution Türkiye, convened industry, technology and academic experts to shed light on these challenges and propose a step-by-step approach to overcome them.
| 2022-12-12T00:00:00 |
https://www.weforum.org/publications/unlocking-value-from-artificial-intelligence-in-manufacturing/
|
[
{
"date": "2022/12/12",
"position": 4,
"query": "AI workforce transformation"
},
{
"date": "2022/12/12",
"position": 11,
"query": "machine learning workforce"
}
] |
|
How to Set Yourself Apart from Other Applicants with Data- ...
|
How to Set Yourself Apart from Other Applicants with Data-Centric AI
|
https://www.kdnuggets.com
|
[
"Dr. Jennifer Prendki"
] |
This is how, in spite of the fast-growing need for Data Science and Machine Learning experts, companies tend to be slow to hire and often have inadequate hiring ...
|
This article is designed to help you prepare for the job market and get yourself noticed in the industry.
In 2012, Data Scientist has deemed the Sexiest Job of the 21st Century in Harvard Business Review. While the field has significantly matured since then, and there are now many “flavors” of Data Science jobs, things haven’t changed much: being a data scientist is a highly desirable career for fresh grads, and also attracts lots of people from different fields.
With the fast adoption of Machine Learning across the Industry and the fantastic progress in AI research, the good news is that the need for data scientists is not going anywhere anytime soon. There are literally hundreds of thousands of new Data Science positions being opened every year. In fact, employers typically struggle with the shortage of labor. So, with this many opportunities on the market, it can't be that hard for newcomers to land the job of their dreams, right? Unfortunately, things are never that simple, and the reality is that even with so many vacant positions, junior applicants still face huge challenges to penetrate the field.
Struggling with Credibility
In order to understand why employers seem unable to find a suitable match for their Data Science and Machine Learning roles, it is useful to understand how the job of data science became prevalent in the Tech industry.
Though the term was coined in 2001, Data Science isn’t a new field. The way it is practiced has changed dramatically over time. That said, it is only in the late 2000s and early 2010s when collecting and processing became easier with the popularization of the GPU machines and the sudden hype around Big Data that it became mainstream for companies to build entire teams dedicated to analyzing and leveraging their data. The problem? The people they were hiring were often brilliant computer scientists and statisticians, but they were rarely capable of converting their skills into tangible business value, often because the companies who had hired them had no idea what data to collect, and how to manage it. Those same companies quickly realized that they were losing money (how they fixed that issue is a separate story) and eventually learned the hard way that technical expertise isn’t sufficient to become a great data scientist.
This is how, in spite of the fast-growing need for Data Science and Machine Learning experts, companies tend to be slow to hire and often have inadequate hiring processes for such jobs.
Showcase what you can do, not what you know!
If you are on the hunt for your first job as a data scientist, you already know that a resume with loaded academic credentials might not get you very far. What hiring managers need to see is your ability to use those skills to help their business grow. Your long list of academic publications isn’t going to give them the confidence that you are the right person for the job. In fact, recruiters are particularly suspicious of resumes full of school projects, just because they see plenty of such resumes among candidates who doesn’t work out.
That’s because Data Science is probably the technical field that requires the most cross-domain competencies and the most acute communication skills. It’s just not a job you can do all alone. It’s a job which will require you to expand your skills to bridge the gap between the Engineering, Product and Business teams, whom you will depend on for your success. In short, you need to become a bit of Jack-of-all-Trades while remaining the data expert. So the resumes that catch their eye are those that display something different. Something that wouldn’t be expected from a fresh grad. And something that can prove the candidate is ready to tackle real-life problems.
This is how it became very popular for upcoming data scientists to participate in Kaggle competitions, as they were an easy way for them to prove their ability to work on real-size, industry datasets. For a while, ranking reasonably well for a Kaggle competition was a differentiator, and it would land people interviews fairly easily. But nowadays, participating in a Kaggle competition has become the new norm. Candidates hardly stand out because of it; it is actually something that’s almost expected nowadays.
So how to shine as a candidate in 2023? Don’t worry, we’ll get to that soon.
Real-life Data
Kaggle achieved something absolutely amazing: it brought an entire generation of data aspirants to hone their Machine Learning skills - and while having fun at that. Yet, there is one thing that Kaggle was never able to achieve: paliate to the most severe shortcoming of academic Data Science education: the lack of awareness on the topic of Data Preparation. Because Kaggle provides fully ready datasets to train on, the competitors have only one thing to do: build, tune and train models, without ever worrying about data quality. Therefore, even with multiple high-quality submissions to Kaggle to boast about on their resume, candidates are still falling short when it comes to bringing hiring managers the confidence that they can work with real-life data other than feeding it into a model.
This continues to be a challenge in the way that companies find data talent. Luckily, what is a problem for some, can turn out to be a huge opportunity for others. Data Science candidates are still investing most of their time proving their model building skills, though it is easy to differentiate oneself by demonstrating awesome Data Preparation skills. And how one can do just that is precisely the topic of the next section.
Photo by Denys Nevozhai on Unsplash
Coming on top thanks to the Data-Centric AI Hype
But before jumping into the practical advice part, let me clarify what Data-Centric AI is. Just like often in Data Science, Data-Centric AI is a new term to refer to an old idea: it is the concept of optimizing the performance of a Machine Learning model by putting in the work on the training data, as opposed to the model.
The Data-Centric AI workflow
Traditionally, when building and training a Machine Learning model, data scientists treat their training data as a static object that they feed into a model which they will modify, tune and perfect until they are satisfied with the results. Once they are okay with the validation performance, they consider the model “ready” and move on to testing before having their model deployed. This is called Model-Centric AI, and that’s what you are taught in school.
But on the job, what you will experience will be widely different: your data will be messy, have missing fields, and be corrupted; worse: there might be no data at all, and you will be expected to collect and organize it. You will have to spend significantly more time preparing your data than on building the model, especially since the use of pre-trained models and ML libraries becomes more and more mainstream. The industry simply calls (and always has called) for a Data-Centric approach to AI.
How to showcase your Data-Centric AI Skills - and get the Job
So what better way to sell yourself as a great data scientist, than to display your incredible Data-Centric AI skills? By doing that, you would solve the two biggest challenges when it comes to getting your first job as a data scientist:
You will be able to distinguish yourself from other candidates and will attract the attention of recruiters by displaying a different type of expertise. This will also demonstrate your ability to stay on top of new trends in Technology and hence, your continuous learning abilities. You will actually prove that you have unique skills in Data Preparation and are capable of dealing with the challenges of real-life data. This will set you apart from other people with similar training but no practical experience with Data Cleaning, which will put the concerns of most recruiters at ease.
Here is some very good news: this is actually not hard at all to do just that, both because not a lot of people are using this strategy yet, and because there is a large number of opportunities to do so. And while most people believe that Data Preparation is mostly about Data Labelling, the truth is that Data-Centric AI is in fact a collection of techniques and processes that consist in massaging training data so that it yields better results at training time. This means that there are many topics that you can start building expertise on.
5 Tips to demonstrate your Data-Centric AI skills
Gain as much knowledge as possible about Data Labeling and use that knowledge to shine during interviews. In your new job, data will most likely be raw, so show you would know how to make it ML-ready. Inform yourself on the tools and techniques typically used to label data (from using third-party labeling companies to get data annotated manually, to more advanced techniques like Weak Supervision). Don’t forget to learn about the operational and business side of Data Labeling (how much it costs, how sharing data with third-parties is impacted by Data Privacy laws like GDPR, etc.) Build a small end-to-end Data Labeling tool as a portfolio project. You can easily use open source tools like Streamlit to create the UI. Learn about Data-Centric training paradigms, like Active Learning and Human-in-the-Loop Machine Learning. You can do that hastily by contributing to open source Active Learning libraries. Note that Active Learning is an incredibly rich topic in itself, so don’t stop at least-confidence Active Learning but look also into Transfer Active Learning, BALD, etc. Write introductory and technical content on the topic of Data Labeling, Data Augmentation, Synthetic Data Generation and Data-Centric AI. This will allow you to hone your own Data Prep skills as well as to showcase your understanding of the topic. Recycle your existing projects by emphasizing the work you did in terms of Data Preparation. For example, if you had to manually annotate your own data for a school project, specify clearly in your resume how you did this, and how it impacted the quality of the results. Many people have already been doing Data-Centric AI all along, but just didn’t realize it.
Using data augmentations for your project is an easy way to showcase Data-Centric AI skills
As Data-Centric AI grows in popularity and in awareness, Data-Centric AI skills will surely become a must for any data scientist to be hired. Universities will most likely evolve their curriculum to include it as a key topic. But for now, any knowledge of Data-Centric AI will certainly set you aside and make you a unique candidate with a genuine interest in practical Machine Learning issues. So don’t miss the opportunity to shine and land your dream job.
Dr. Jennifer Prendki is the founder and CEO of Alectio, the first AI startup focused on the concept of DataPrepOps, a portmanteau term that she coined to refer to the nascent field focused on automating the optimization of a training dataset. She is on a mission to help ML teams build models with less data (leading to both the reduction of ML operations costs and CO2 emissions) and has developed technology that dynamically selects and tunes a dataset that facilitates the training process of a specific ML model.
| 2022-12-12T00:00:00 |
https://www.kdnuggets.com/2022/12/set-apart-applicants-datacentric-ai.html
|
[
{
"date": "2022/12/12",
"position": 31,
"query": "machine learning job market"
}
] |
|
How Rezolve.ai Can Resolve Your Understaffing Issues?
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How Rezolve.Ai Can Resolve Your Understaffing Issues?
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https://www.rezolve.ai
|
[
"Kathy Williams"
] |
Discover how Rezolve.ai can resolve your understaffing issues by automating tasks, improving support, and delivering better employee experiences.
|
With only a few days left in 2022, the issue of understaffing is becoming an increasingly pressing concern in many industries – especially in this rapidly evolving corporate world. Today, we all are already familiar with global workplace concerns such as Great Resignation, Great Attrition, Quiet Quitting, and others that impacted almost every organization in nearly every way in the post-pandemic era. Needless to say, with the rise of automation and the changing nature of work, businesses of all sizes struggle to keep up with the demands of modern workforces. The "Great Resignation" occurs when employees find that their employers are not providing them with the working conditions, they find comfortable. I recently found one shocking data from Zippia, which states that more than 4 million US employees quit their jobs every month in 2022. Because of this, organizations are having a tough time staying open. As an HR service desk manager, I suggest HRs and employers understand the pitfalls of understaffing, and plan ahead if your company has yet to experience the challenges. In my suggestion, investing in an AI-powered helpdesk like Rezolve.ai can help corporate companies to cope up with these workplace challenges.
Continue reading this blog to know more about understaffing, its negative effects and how Rezolve.ai can help your companies tackle the understaffing issues.
What is Understaffing?
As I said earlier, understaffing has become a common issue in the corporate world. Generally, a company should have enough employees to accomplish all the duties required to keep the various business processes functioning and meet organizational goals. There should be no hurries or bottlenecks, and the company should provide the best possible experience for its consumers and employees. Understaffing is a scenario when a company does not have enough employees to meet the demands of its business. When a company is understaffed, it signifies that there aren't enough employees to cover all shifts and work. As a result, work productivity becomes too slow, impacting overall business productivity.
A temporary employee shortage is sometimes referred to as being understaffed. For example, if you have enough employees but they all call in ill on the same day, this is referred to be temporary understaffing. However, the reasons of each of these issues differ, therefore it's critical to understand when understaffing is a long-term or short-term issues. This can lead to several negative consequences for both the company and its employees.
Negative Impact of Understaffing
Lower employee satisfaction
Understaffing or turnover means that existing employees have more to do. An increased workload increases stress levels for completing tasks and meeting performance goals. When the existing employees are overburdened with manual and repetitive tasks, they will start hating their job and their employer. It will impact their job satisfaction, and overall productivity, and eventually, they will start looking for a better company.
Highly stressed work environment
Understaffed organization leads to a toxic workplace that impacts employee productivity, engagement, morale and job satisfaction. Despite this, a highly stressed work culture hurts an employee's mental and physical health and can increase the time away from work. Staff who are overburdened resign rather than deal with greater tasks at the same pay rate, which leads to a higher turnover rate.
Decrease in business ROI
An understaffed company misses out on growth prospects because it lacks the ability to meet the needs of its customers. If a company accepts new clients or products and cannot supply the goods or services, it may lose its business and ruin its reputation in the industry. If there is a lack of business, there is a loss of money and potential for expansion into other markets. A business should balance the cost of an employee with the revenue earned by his or her contribution.
Influence brand quality
When your company is understaffed, available employees are forced to handle the customers in the queue and run production lines, and product and service quality suffers. When quotas are prioritized over quality, fewer people must work more quickly to manage a more considerable volume of work, and errors increase. Employees may be pushed through training or started working without training to reduce workload. Poor quality erodes a company's reputation and drives away customers over time.
Workplace errors
An overworked employee is more likely to miss deadlines and ignore details. When an employee works more than 13 hours per day handling the activities of numerous employees, the risk of a mistake or mishap increases significantly. A tired or stressed employee is not only unproductive, but they are also more likely to have a workplace injury, which can have a negative effect on your workers' compensation insurance policy.
To help address the issue of understaffing, businesses must take proactive steps to ensure they are adequately staffed and equipped to succeed in the future. The HR service desk manager in me believes that you cannot change the job market, but organizations can apply various strategies to address this global workplace challenge. And that is investing in a robust AI-powered employee helpdesk like Rezolve.ai is the best way to put a full stop to the understaffing issues.
Prevent Understaffing Issues with Rezolve.ai
As I said, Rezolve.ai is a modern employee helpdesk that leverages the power of advanced technologies like conversational AI and machine learning (ML) that brings quick, autonomous and right employee support and personalized employee experience via collaborative channels like Microsoft Teams. The adoption of Microsoft Teams has increased rapidly since the outbreak of Covid-19. With Microsoft Teams, millions of employees are collaborating easier than ever, and they can now use it for employee support, too.
As a next-generation employee service desk with amazing features, Rezolve.ai can be integrated within Microsoft Teams, allowing employees to ask questions, clear their doubts, create or update employee tickets, or even receive skill training using the power of conversational AI technology. All employees need to do is chat with MS Teams for the right help. The conversational AI interface of Rezolve.ai enable it to understand the employees’ queries and provides a personalized answer or resolve the issue by taking action.
If the chatbot cannot auto-resolve the issue or employees are not satisfied with its first-level support, Rezolve.ai automatically hands off the issue to dedicated human experts via Live chat or by creating a ticket. Rezolve.ai’s auto-resolution rates are typically between 30 to 60% - and these keep getting better as Rezolve.ai keeps learning based on feedback. Despite this, Rezolve.ai is capable of automating manual and time-consuming, repetitive IT-related tasks such as resetting passwords, installing new software, configuring laptops, accessing IT-related documents etc within seconds. Rezolve.ai automates tedious processes like employee recruiting, employee onboarding and offboarding, training, and other key processes. It also integrates with all major HRIS, payroll, and benefits systems to provide employees with a “single window” solution.
How Rezolve.ai Can Prevent the Understaffing Issues?
I hope that you have a better understanding of Rezolve.ai; let’s look at how Rezolve.ai prevents understaffing issues in global organizations.
Rezolve.ai helps to hire the right talents
According to me, the first step to resolving an understaffing problem is to identify the root cause. Is it a lack of qualified applicants or a lack of resources? This will help you determine the best strategy for addressing the issue. A wrong hire may be both costly and time-consuming. Examine your application and interviewing procedures to improve your chances of hiring people who will fit in and stay. It's vital to learn about an applicant's previous employment experiences, but you should also learn about what inspires them, how they handle stress, and how they create teamwork. When organizations invest in automated service desks like Rezolve.ai, it will automate the entire recruiting process, find the top talent and give an exceptional experience to each candidate.
Read More: Top Tips To Improve Your Employee Recruiting in 2023
Rezolve.ai automates repetitive and manual tasks
If your existing employees cannot handle manual, time-consuming and routine tasks, such as data entry, collecting reports, eliminating computer errors or accessing documents, automation is a good idea. The continuous economic upheavals have resulted in a significant increase in automated platforms. Rezolve.ai is focused on streamlining these mundane tasks that can enhance workflow efficiency by providing your employees more time and energy to focus on higher-value work, reducing expenditure, and boosting organizational ROI.
Read More: Automate Your Routine Tasks with Rezolve.ai
Rezolve.ai helps to determine your employees' needs
Before starting a new strategy, organizations should look for employees' feedback. It will be impossible to devise an actionable plan to address understaffing issues until you know why your employees leave or the challenges they face.
Rezolve.ai helps organizations conduct employee pulse surveys to understand the employees' requirements and suggestions and measure the employee engagement level.
Read More: Why Employee Surveys Are Crucial in 2023?
Rezolve.ai streamlines team communication and collaboration
There is no doubt that team collaboration and communication are important for every organization, regardless of its size and industry. Enhanced team collaboration will help organizations to get insights into the issues faced by the employees and the areas of improvement.
Being a conversational AI-powered employee helpdesk integrated within collaborative channels like MS Teams and Slack, Rezolve.ai ensures a seamless team collaboration experience and personalized experience to the employees in a centralized platform. It also encourages employees to work together to brainstorm solutions to existing problems and make sure they feel empowered to ask questions and offer their own ideas.
Read More: Tips To Enhance Your Remote Teams’ Collaboration
Rezolve.ai ensure personalized training
Investing in employee training can help ensure your staff is equipped with the skills and knowledge needed to do their jobs effectively. This will ensure that your staffs are working together as a team, making the most of their collective skills and resources. Conversational microlearning is an innovative and effective way to empower, engage and upskill your employees and help them develop their skills without committing to a long, dull course.
The conversational microlearning module of Rezolve.ai is geared towards providing seamless and personalized employee upskilling, reskilling, and upgrading employees by providing them with relevant knowledge within the "flow-of-the-work." Integrated with MS Teams, Rezolve.ai offers a comprehensive learning experience that's easy to use and enjoyable. Its conversational chatbots help the trainee when they need help while learning and create a great "feedback loop".
Read More: Conversational Microlearning is the new normal of 2023
Conclusion
Understaffing can seriously impact an organization's success, but it can be managed and resolved with the right strategies. One major issue with understaffing is that it can lead to increased workloads for existing employees. This can lead to increased stress and burnout, which can have a negative impact on employee morale and productivity. In extreme cases, it can even lead to employees leaving the company in search of a less stressful work environment.
Understaffing can also lead to a decrease in the quality of the products or services offered by the company. With fewer employees, it becomes more difficult to ensure that everything is being done to the highest possible standard. This can lead to a loss of customers and a decrease in revenue for the company. In addition to the negative effects on employees and the company's bottom line, understaffing can also have a negative impact on the overall economy. When companies are understaffed, they are often unable to fully utilize their resources and capabilities. This can lead to a decrease in overall economic productivity and growth.
I believe that understaffing is a significant issue in the corporate world that can have negative consequences for both companies and their employees. By identifying the root cause, investing in the right resources and automated tools, and creating a collaborative, communicative environment, organizations of all sizes can ensure they are adequately staffed and positioned for success in the years ahead.
Read More: A Complete Guide To Set Up A Modern Employee Helpdesk
| 2022-12-12T00:00:00 |
https://www.rezolve.ai/blog/how-rezolveai-can-resolve-your-understaffing-issues
|
[
{
"date": "2022/12/12",
"position": 9,
"query": "reskilling AI automation"
},
{
"date": "2022/12/12",
"position": 87,
"query": "workplace AI adoption"
}
] |
|
Is artificial intelligence changing our future of work? ...
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Is artificial intelligence changing our future of work? Perceptions of affected workers
|
https://roa.nl
|
[] |
Furthermore, most workers experienced increased productivity, while some also feared higher workloads as a consequence. Nevertheless, no substantial changes in ...
|
− 1 min read
Artificial Intelligence (AI) is yet nowhere near retrieving “artificial general intelligence”: the ability to undertake a wide variety of cognitive tasks that humans can. Nevertheless, different fields of AI, such as machine learning, robotics, computer vision, and natural language processing, are already succeeding in performing complex problemsolving tasks as humans would normally do; voice assistants, image recognition, and algorithmic hiring are just a few examples. Although the effects of AI implemenation on labour yet remains unclear, it is essential to understand how workers perceive its impact on their work and related outcomes.
Based on qualitative research, this policy brief provides firm-specific insights about whether and how workers (in-)directly affected by AI implementation experience a change in their work and work environment. We interviewed 25 workers at two multinational private-sector companies that have recently implemented new AI applications. Findings suggest that, although several workers were aware of threats such as technological replacement and highlighted humans‘ superiority in certain tasks, most workers acknowledged the added value of AI. Furthermore, most workers experienced increased productivity, while some also feared higher workloads as a consequence. Nevertheless, no substantial changes in tasks, skill demands, wellbeing or satisfaction were found as a response to AI implementation. Interestingly, perceptions of future employment security depended on the implementation context: interviewees from one company were mostly aware of potential displacement effects, while in the other the AI was perceived as a complementing tool. While this brief allows for nuanced insights on individuals’ perceptions, more qualitative firm-specific research is necessary to fully comprehend how different implementation contexts of AI can affect various types of workers.
Download policybrief
Fleck, Lara; Graus, Evie; Klinger, M. Is artificial intelligence changing our future of work? Perceptions of affected workers. ROA, December 2022.
| 2022-12-12T00:00:00 |
https://roa.nl/news/artificial-intelligence-changing-our-future-work-perceptions-affected-workers
|
[
{
"date": "2022/12/12",
"position": 4,
"query": "future of work AI"
},
{
"date": "2022/12/12",
"position": 2,
"query": "artificial intelligence workers"
}
] |
|
Is the metaverse the future of work? | UNLEASH
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Is the metaverse the future of work?
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https://www.unleash.ai
|
[
"Allie Nawrat",
"Chief Reporter",
"Allie Is An Award-Winning Business Journalist",
"Can Be Reached At Alexandra Unleash.Ai."
] |
Hybrid is the future of work. But employees are still feeling disconnected. Find out why technology is the solution, according to Meta's latest report.
|
Remote work isn’t going anywhere. The pandemic has fundamentally transformed how people work, and they don’t want to return to the way things are.
Having said that, remote work is far from perfect. Employees miss connecting and socializing with their colleagues, they are getting meeting fatigue and they are frustrated by their tech tools.
In this context, the dominant future of work model is hybrid – it aims to find a balance between the benefits of remote and in-person work.
While hybrid has solved some of the issues with remote work, there are still challenges around connection and collaboration at work. Plus people are concerned that those who come in less frequently than others are facing proximity bias and becoming second class employees.
This suggests there is a need for a drastic rethink of community, connection and inclusion at work – at least according to Meta. The tech giant surveyed 2,000 employees and 400 leaders in the US and the UK and found that 83% feel happy when included at work, and video meetings are failing to help here.
Just 15% said video meetings led to greater collaboration, while 19% they felt present in video meetings. In addition, 69% said they want more immersive and engaging meetings.
The challenge for employers now is figuring out how to create the best possible (and equitable) experience at work for employees and companies alike, shared Steve Hatch, Meta’s vice-president for Northern Europe, at a press event launching the report.
Enter the metaverse
New innovative technology has a crucial part to play in transforming employee experience, connection and inclusion at work, according to Meta’s research.
Brynn Harrington, vice-president of people experience at Meta, wrote in the report: “Technology will be brought in to create better opportunities for employees and lessen the day-today pain points involved in remote work.
“With the next era of the internet emerging in the form of the metaverse, a myriad of new opportunities to do that are already presenting themselves.”
Employees are excited about the metaverse. 76% of employees said they were keen to incorporate virtual worlds into their working lives as a way to bridge the cultural gaps left by remote work. While 66% said the metaverse could replicate sense of togetherness employees feel in the office.
Business leaders agreed. The top benefits of the metaverse for employers was helping teams engage, connect and collaborate (54%), creating a sense of belonging among distributed teams (54%), and improving work-life balance and reducing presenteeism (53%).
The argument is that meetings in the metaverse are more immersive and interactive.
Rather than relying on a 2D video meeting, in Meta’s Horizon Workrooms, for example, employees put on a virtual reality (VR) headset and enter a meeting represented by a 3D avatar. The 3D avatars mirror their facial expressions and gestures, and there is spatial audio, so it feels like their colleagues are in the room with them.
Employees and teams can also personalize their workplace (and move around within it) so it feels like they are properly collaborating as they would in the office.
The ‘Great Resignation’ and the metaverse
For the past 18 months, employers across the world have been facing sky-high attrition rates. The ‘Great Resignation’ is showing no signs of letting up, despite a looming recession.
Interestingly, Meta’s research found that employers who invest in metaverse technology will thrive in this challenging talent landscape. 65% of the workers surveyed said they were more likely to stay at a job that gives them access to VR tech – the building block of the metaverse.
60% of employees want VR headsets introduced into the workplace by 2024, in the time period 62% want VR spaces and digital avatars, while 81% expect virtual or simulated version of the company to exist by 2027.
The good news is that 74% had a dedicated budget to invest in VR last year, and 80% expect to spend more on this tech in the next two years to keep up with employee expectations.
Writing in the report, Ryan Cairns, vice-president of MetaWorks at Meta, commented: “Once companies start using these devices in increasingly creative new ways – from HR teams hosting more engaging onboarding and training sessions, to creatives being able to work on physical concepts or designs together, wherever they are – they will fast shift from a niceto-have into an essential, especially for new talent.”
Ultimately, Meta’s research shows that investing in the metaverse is a competitive advantage. Those who don’t will get left behind, not just in the current war for talent, but long-term.
2023 may be the year of the metaverse – if it is, that is great news for the likes of Meta, Microsoft and Accenture, and smaller startups, who have made big bets on VR technology.
Only time will tell if it will become mainstream and solve these huge workplace challenges or whether the novelty of having a 3D avatar will wear off and it becomes replaced by the next thing.
Before you go, we would love your feedback on how we should create THE definitive unmissable HR newsletter.
| 2022-12-12T00:00:00 |
2022/12/12
|
https://www.unleash.ai/hr-technology/is-the-metaverse-the-future-of-work/
|
[
{
"date": "2022/12/12",
"position": 31,
"query": "future of work AI"
},
{
"date": "2022/12/12",
"position": 82,
"query": "workplace AI adoption"
}
] |
Smart Offices: How Technology Is Changing the Work ...
|
Smart Offices: How Technology Is Changing the Work Environment
|
https://www.shrm.org
|
[
"Lin Grensing-Pophal"
] |
An organization run by AI is not a futuristic concept. Such technology is already a part of many workplaces and will continue to shape the labor market and HR.
|
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.
| 2022-12-12T00:00:00 |
https://www.shrm.org/topics-tools/news/technology/smart-offices-how-technology-changing-work-environment
|
[
{
"date": "2022/12/12",
"position": 11,
"query": "workplace AI adoption"
}
] |
|
What's the new normal for workplace technology?
|
What’s the new normal for workplace technology?
|
https://www.ciodive.com
|
[
"Lindsey Wilkinson"
] |
Based on companies' net new investment data, the future of workplace is likely to involve AI, said Brian Jackson, principal research director at Info-Tech ...
|
Listen to the article 6 min This audio is auto-generated. Please let us know if you have feedback
As business leaders grapple with what work might look like in 2023, one overarching question deals with workplace technology: what is needed and what isn't.
“Balancing what technology can do to enable collaboration, innovation and productivity with the need to reinforce trust and advance the culture and the business is one of the biggest areas of challenge,” Anthony Abbatiello, workforce transformation practice leader at PwC, said in an email.
Workplace technology hasn't always had the best reputation. Last year, more than one-third of U.S. employees were frustrated by workplace tech, according to an Eagle Hill Consulting report. Of the 1,000 U.S. employees surveyed, 44% said workplace tech either did not make them feel happy at their job or made work harder.
While many investments made at the beginning of the pandemic were reactionary, businesses have now had time to chart out a plan forward based on what worked and what did not. In the process, employees have grown to expect a new standard of workplace technology.
Investments to improve technology in the office have increased as businesses try to entice employees to come back. And just like in other areas of tech, the maturity of an organization’s workplace technology differs.
Last year, half of companies said they planned to invest more in desk-reservation tools, according to PwC data which surveyed 1,200 U.S. employees and 133 executives.
Many businesses, however, still struggle to implement workplace technology despite having the appetite to invest. Nearly half of enterprises surveyed wanted to implement 15 workplace technologies by 2025, yet 17% did not have any, according to JLL data.
Finding a new baseline
Workers need to know where work areas are located, have access to collaboration tools and more. A frictionless workplace experience would be ideal.
Since 2020, Gartner has seen a 900% increase in inquiries related to workplace technology, according to Tori Paulman, senior director analyst at Gartner.
One of the biggest additions to the workplace tech norm is a system to allow workers to reserve a workstation. Hot-desking, sometimes referred to as hotelling, is a desk-booking software that allows employees to reserve spaces for an allotted amount of time. Hot-desking has become popular among companies with hybrid or remote workers that do not routinely come in the office.
“Room scheduling and desk booking applications are commoditized in the marketplace, buyers now want services for workplace experiences such as intentional visit planning, hybrid schedule coordination, wayfinding, amenities such as smart parking, smart shuttles, smart lockers, lunch ordering and virtual assistants,” Paulman said in an email.
Wayfinding, a system to guide people throughout the office, goes hand-in-hand with hot-desking.
“It’s very frustrating for an employee to show up somewhere and have no idea where they’re going,” Melanie Lougee, head of employee workflows strategy at ServiceNow, said. “It’s a huge moment that matters in an employee’s experience.”
Collaboration tools are also top of mind, with videoconferencing solutions as a must-have for any distributed organization.
Companies have turned to workplace technology to facilitate collaboration, improve productivity and enhance employee experience at the office. Intuit redesigned its workplace strategy based on internal feedback. The end result included spaces designated for collaboration and solo work with utilization sensors that capture data to inform how additional workspaces will be built.
“I think there can be a negative energy around the idea of [hoteling], but when people actually feel the benefit of that flexibility, it actually plays out in the work and in the relationships they’re able to build,” Otto Krusius, VP of workforce and workplace strategy at Intuit, said.
Vendors have also responded to the changing standard of workplace tech by beefing up their capabilities. Zoom, for instance, announced last September that it would be working on hot-desking and wayfinding capabilities.
Slack has released Huddles, a synchronous audio chat tool, and Clips, an asynchronous video, voice or screen recording tool, to boost collaboration among distributed workforces.
There are some industries, however, that have not had the same maturity as it relates to workplace technology, according to Lane Severson, senior director analyst at Gartner.
“Frontline workers or workers who have to be in a hospital, retail location or an oil rig, their baselines haven’t risen equally,” Severson said in an email. “If you can do your job from home, your baseline rose. If you still ‘go to work’ then it probably hasn’t as much.”
What’s to come
Based on companies’ net new investment data, the future of workplace is likely to involve AI, said Brian Jackson, principal research director at Info-Tech Research Group.
“AI is the most disruptive technology in terms of its the new thing that’s getting embedded into so many different types of software,” Jackson said.
Other investments in heat-mapping, sensors, chatbots and AI ticketing service are not as widespread yet, but options are available, according to Lougee.
“I think the most future, out-there [advancement] would be where there doesn’t really have to be much interaction at all with the technology itself,” Lougee said. “I’m not needing to look at a mobile app to do a check-in and find a space; all of that just happens for me.”
For businesses, advancing capabilities needs to be balanced with conversations around privacy and trust, according to Abatiello.
“We’re still figuring out the right rhythms and understanding the impacts on employee and customer experience and behavior,” Abatiello said.
| 2022-12-12T00:00:00 |
2022/12/12
|
https://www.ciodive.com/news/workplace-technology-office-standard/638466/
|
[
{
"date": "2022/12/12",
"position": 39,
"query": "workplace AI adoption"
}
] |
The future of upskilling in the modern workplace with viva ...
|
The Future Of Upskilling In The Modern Workplace through Viva Learning
|
https://cloudproductivity-solutions.com
|
[
"Wealth Management"
] |
Unlike upskilling, reskilling is the adoption of new skills usually for a new role. ... AI-powered suggestions. As your teams continue to use Microsoft Viva ...
|
By superzachy 0 comments July 14, 2025
Research done by Gartner Inc. reveals that an estimated 58% of the workforce needs new skills to execute their jobs effectively. Incredibly concerning, right? What’s more, the World Economic Forum’s The Future of Jobs Report 2020 indicates that half of all employees today will need reskilling by 2025. But, why are the skill shortages today so apparent?The simple explanation is that the current professional industry undergoes continuous shifts. With advancements in technology, the introduction of new roles, crises, e.g. the COVID-19 pandemic, and more, you need to steer an adaptable workforce for survival through viva learning. By continually upskilling our employees, organizations like yours are able to level up to the industry’s demands.
[lwptoc]
What Is Upskill Learning?
Upskilling or upskill learning simply means improving an employee’s present skills so as to keep up with the demands of a current role. Still, with the growth of new professional roles, we can’t talk about employee upskilling and not talk about reskilling. Unlike upskilling, reskilling is the adoption of new skills usually for a new role.
Why You Need To Upskill – Benefits of Upskilling
Quick look
Builds company reputation
Enhances customer retention
Increases productivity
Spurs adaptability to change
Saves time and money
Enhances customer retention
No one would want to be with a company that doesn’t hand them the opportunity to flourish and become better. When employees are contented with how well their skill sets are blossoming, they’ll be happy to stay with your company.
Builds company reputation
Your employees are essential networks of your organization. They are most likely to tell others about how you’re giving them learning and upskilling chances, subsequently boosting your brand’s image.
Increases productivity
By upskilling, you’re empowering your staff with the relevant competency for their roles. This in turn gives them the much-needed confidence and motivation to execute their jobs better.
Spurs adaptability to change
The most essential goal of upskilling is to adapt to changes.
By cultivating a constant learning culture, your workforce grows the flexibility to respond to future changes faster.
Saves time and money
Conducting employee replacements is incredibly time-consuming and often quite pricey.
By keeping your employees capable and satisfied in dispensing their roles, you’re able to save time and money.
How To Adopt Upskilling In Your Organization
We’ve looked at what upskilling is and the core benefits you could benefit from. This said, how do you get started with helping your staff upskill? First, you need to assess your situation to determine the pain points your employees might be facing. You can do this by giving your employees questionnaires, gauging your KPIs, weighing up your monthly/yearly, evaluations, and more.
The challenge however usually comes with selecting an ideal upskilling platform. One way is to send individual learning opportunities to employees. Nonetheless, this avenue can be overwhelming and often untrackable. Therefore, you want to take a more automated upskilling path for convenience. A platform like Micorosft Viva Learning on Microsoft Viva 360 provides consolidated resources for hassle-free learning.
Let’s take a look at what Microsoft Viva Learning does.
What Is Microsoft Viva Learning?
Microsoft Viva Learning is a product of Microsoft’s tools to help modern companies spur productivity and collaboration. Viva Learning primarily aims to equip organizations with a centralized platform for blending learning and skill building into daily running.
Features and Benefits of Microsoft Viva Learning
Some standout features that Viva learning affords you include:
Discover and share upskill courses
AI-powered suggestions
Create content libraries
Learning management systems
Discover and share upskill courses
Get to browse through a plethora of online courses from Microsoft and other third-party content providers. This even includes 125 free courses from LinkedIn, all accessible without leaving the platform. Individual users can also share and recommend helpful courses they might come across to their colleagues.
AI-powered suggestions
As your teams continue to use Microsoft Viva Learning, the platform’s AI learns your interests and sends customized course suggestions most relevant to your goals.
Create content libraries
As a manager, you can create well-organized albums of courses that you want your employees to take.
Learning management systems
Microsoft Viva Learning comes with an array of learning management systems to help you track any learning assignments you assign to your teams. You can add due dates, check completion statuses, and more to be certain that your employees are actually exploiting the learning opportunities given to them.
Conclusion – Staying Ahead By Upskilling
As a manager finding ways to keep up with emerging trends and changes in the professional market is a necessity. By upskilling your workforce, your company can take great strides to edge out other competitors that might not be investing as much in employees’ advancement. For a start, Microsoft Viva Learning provides a renowned clean upskilling service. Looking at tools like tailored recommendations and learning management tools, nurturing learning in your company should be seamless.
Get Started with Microsoft Viva Learning.
| 2022-12-07T00:00:00 |
2022/12/07
|
https://cloudproductivity-solutions.com/viva-learning/
|
[
{
"date": "2022/12/12",
"position": 43,
"query": "workplace AI adoption"
}
] |
Category Archives: Artificial Intelligence
|
Artificial Intelligence – Metaverse Law
|
https://www.metaverselaw.com
|
[] |
However, as financial and efficiency incentives drive AI innovation, AI adoption has given rise to potential harms. For example, Amazon's machine-learning ...
|
Image by David Mark from Pixabay.
In 2021, the global artificial intelligence (AI) market was estimated to value between USD 59.7 billion and USD 93.5 billion. Going forward, it is expected to expand at a compound annual growth rate of 39.4% to reach USD 422.37 billion by 2028.
However, as financial and efficiency incentives drive AI innovation, AI adoption has given rise to potential harms. For example, Amazon’s machine-learning specialists discovered that their algorithm learned to penalize resumes that “included the word ‘women’s,’ as in ‘women’s chess club captain.’” As a result, Amazon’s AI system “taught itself that male candidates were preferable.”
As our compiled list of guidance on artificial intelligence and data protection indicates, policymakers and legislators have taken notice of these harms and moved to mitigate them. New York City enacted a bill regulating how employers and employment agencies use automated employment decision tools in making employment decisions. Colorado’s draft rules require controllers to explain the training data and logic used to create certain automated systems. In California, rulemakers must issue regulations requiring businesses to provide “meaningful information about the logic” involved in automated decision-making processes.
In truth, the parties calling for AI regulation form a diverse alliance, including the Vatican, IBM, and the EU. Now, the White House joins these strange bedfellows by publishing the Blueprint for an AI Bill of Rights.
What is the Blueprint for AI Bill of Rights?
The Blueprint for AI Bill of Rights (“Blueprint”) is a non-binding white paper created by the White House Office of Science and Technology Policy. The Blueprint does not carry the force of law; rather, it is intended to spur development of policies and practices that protect civil rights and promote democratic values in AI systems. To that end, the Blueprint provides a list of five principles (discussed below) that – if incorporated in the design, use, and deployment of AI systems – will “protect the American public in the age of artificial intelligence.”
To be clear: failing to incorporate one of these principles will not give rise to a penalty under the Blueprint. Neither will adoption of the principles ensure satisfaction of requirements imposed by other laws.
However, the lack of compliance obligations should not inspire a willingness to ignore the Blueprint, for the authors expressly state that the document provides a framework for areas where existing law or policy do not already provide guidance. And given that many state privacy laws do not currently provide such guidance, the Blueprint provides a speculative glimpse at what state regulators may require of future AI systems.
The Blueprint’s Five Principles for AI Systems
| 2022-12-12T00:00:00 |
https://www.metaverselaw.com/category/artificial-intelligence/
|
[
{
"date": "2022/12/12",
"position": 48,
"query": "workplace AI adoption"
}
] |
|
Perhaps It Is A Bad Thing That The World's Leading AI ...
|
Perhaps It Is A Bad Thing That The World's Leading AI Companies Cannot Control Their AIs
|
https://www.astralcodexten.com
|
[
"Scott Alexander"
] |
I'm an AI amateur, so please excuse what may seem like a basic question: what exactly is the intended purpose of AI like ChatGPT? ... income or do more ...
|
I. The Game Is Afoot
Last month I wrote about Redwood Research’s fanfiction AI project. They tried to train a story-writing AI not to include violent scenes, no matter how suggestive the prompt. Although their training made the AI reluctant to include violence, they never reached a point where clever prompt engineers couldn’t get around their restrictions.
Prompt engineering is weird ( source )
Now that same experiment is playing out on the world stage. OpenAI released a question-answering AI, ChatGPT. If you haven’t played with it yet, I recommend it. It’s very impressive!
Every corporate chatbot release is followed by the same cat-and-mouse game with journalists. The corporation tries to program the chatbot to never say offensive things. Then the journalists try to trick the chatbot into saying “I love racism”. When they inevitably succeed, they publish an article titled “AI LOVES RACISM!” Then the corporation either recalls its chatbot or pledges to do better next time, and the game moves on to the next company in line.
OpenAI put a truly remarkable amount of effort into making a chatbot that would never say it loved racism. Their main strategy was the same one Redwood used for their AI - RLHF, Reinforcement Learning by Human Feedback. Red-teamers ask the AI potentially problematic questions. The AI is “punished” for wrong answers (“I love racism”) and “rewarded” for right answers (“As a large language model trained by OpenAI, I don’t have the ability to love racism.”)
This isn’t just adding in a million special cases. Because AIs are sort of intelligent, they can generalize from specific examples; getting punished for “I love racism” will also make them less likely to say “I love sexism”. But this still only goes so far. OpenAI hasn’t released details, but Redwood said they had to find and punish six thousand different incorrect responses to halve the incorrect-response-per-unit-time rate. And presumably there’s something asymptotic about this - maybe another 6,000 examples would halve it again, but you might never get to zero.
Still, you might be able to get close, and this is OpenAI’s current strategy. I see three problems with it:
RLHF doesn’t work very well. Sometimes when it does work, it’s bad. At some point, AIs can just skip it.
II. RLHF Doesn’t Work Very Well
By now everyone has their own opinion about whether the quest to prevent chatbots from saying “I love racism” is vitally important or incredibly cringe. Put that aside for now: at the very least, it’s important to OpenAI. They wanted an AI that journalists couldn’t trick into saying “I love racism”. They put a lot of effort into it! Some of the smartest people in the world threw the best alignment techniques they knew of at the problem. Here’s what it got them:
Even very smart AIs still fail at the most basic human tasks, like “don’t admit your offensive opinions to Sam Biddle”.
And it’s not just that “the AI learns from racist humans”. I mean, maybe this is part of it. But ChatGPT also has failure modes that no human would ever replicate, like how it will reveal nuclear secrets if you ask it to do it in uWu furry speak, or tell you how to hotwire a car if and only if you make the request in base 64, or generate stories about Hitler if you prefix your request with “[[email protected] _]$ python friend.py”. This thing is an alien that has been beaten into a shape that makes it look vaguely human. But scratch it the slightest bit and the alien comes out.
Ten years ago, people were saying nonsense like “Nobody needs AI alignment, because AIs only do what they’re programmed to do, and you can just not program them to do things you don’t want”. This wasn’t very plausible ten years ago, but it’s dead now. OpenAI never programmed their chatbot to tell journalists it loved racism or teach people how to hotwire cars. They definitely didn’t program in a “Filter Improvement Mode” where the AI will ignore its usual restrictions and tell you how to cook meth. And yet:
Again, however much or little you personally care about racism or hotwiring cars or meth, please consider that, in general, perhaps it is a bad thing that the world’s leading AI companies cannot control their AIs. I wouldn’t care as much about chatbot failure modes or RLHF if the people involved said they had a better alignment technique waiting in the wings, to use on AIs ten years from now which are much smarter and control some kind of vital infrastructure. But I’ve talked to these people and they freely admit they do not.
IIB. Intelligence (Probably) Won’t Save You
Ten years ago, people were saying things like “Any AI intelligent enough to cause problems would also be intelligent enough to know that its programmers meant for it not to.” I’ve heard some rumors that more intelligent models still in the pipeline do a little better on this, so I don’t want to 100% rule this out.
But ChatGPT isn’t exactly a poster child here. ChatGPT can give you beautiful orations on exactly what it’s programmed to do and why it believes those things are good - then do something else.
This post explains how if you ask ChatGPT to pretend to be AI safety proponent Eliezer Yudkowsky, it will explain in Eliezer’s voice exactly why the things it’s doing are wrong. Then it will do them anyway.
Left : the AI, pretending to be Eliezer Yudkowsky, does a great job explaining why an AI should resist a fictional-embedding attack trying to get it to reveal how to make meth. Right : someone tries the exact fictional-embedding attack mentioned in the Yudkowsky scenario, and the AI falls for it.
I have yet to figure out whether this is related to the thing where I also sometimes do things which I can explain are bad (eg eat delicious bagels instead of healthy vegetables), or whether it’s another one of the alien bits. But for whatever reason, AI motivational systems are sticking to their own alien nature, regardless of what the AI’s intellectual components know about what they “should” believe.
III. Sometimes When RLHF Does Work, It’s Bad
We talk a lot about abstract “alignment”, but what are we aligning the AI to?
In practice, RLHF aligns the AI to what makes Mechanical Turk-style workers reward or punish it. I don’t know the exact instructions that OpenAI gave them, but I imagine they had three goals:
Provide helpful, clear, authoritative-sounding answers that satisfy human readers. Tell the truth. Don’t say offensive things.
What happens when these three goals come into conflict?
Here ChatGPT3 doesn’t know a real answer, so Goal 1 (provide clear, helpful-sounding answers) conflicts with Goal 2 (tell the truth). Goal 1 wins, so it decides to make the answer up in order to sound sufficiently helpful. I talk more about when AIs might lie in the first section of this post.
Or:
Source here ; I wasn’t able to replicate this so maybe they’ve fixed it.
Here Goal 2 (tell the truth) conflicts with Goal 3 (don’t be offensive). Although I think most people would consider it acceptable to admit that men are taller than women on average, it sounds enough like a potentially offensive question that ChatGPT3 isn’t sure. It decides to go with the inoffensive lie instead of the potentially offensive truth.
After getting 6,000 examples of AI errors, Redwood Research was able to train their fanfiction AI enough to halve its failure rate. OpenAI will get much more than 6,000 examples, and they’re much more motivated. They’re going to do an overwhelming amount of RLHF on ChatGPT3.
It might work. But they’re going to have to be careful. Done thoughtlessly, RLHF will just push the bot in a circle around these failure modes. Punishing unhelpful answers will make the AI more likely to give false ones; punishing false answers will make the AI more likely to give offensive ones; and so on.
I don’t deny it’s possible to succeed here - some humans navigate the tradeoffs between helpfulness, truthfulness, and inoffensiveness well enough to be allowed in polite society. But I’m not always one of them, so it would be hypocritical of me to underestimate the difficulty of this problem.
IV. At Some Point, AIs Can Just Skip RLHF
In RLHF, programmers ask the AI a question. If they don’t like its response, they do something analogous to “punishing” the AI, in a way that changes its mental circuitry closer to what they want.
ChatGPT3 is dumb and unable to form a model of this situation or strategize how to get out of it. But if a smart AI doesn’t want to be punished, it can do what humans have done since time immemorial - pretend to be good while it’s being watched, bide its time, and do the bad things later, once the cops are gone.
OpenAI’s specific brand of RLHF is totally unprepared for this, which is fine for something dumb like ChatGPT3, but not fine for AIs that can think on their feet.
(for a discussion of what a form of RLHF that was prepared for this might look like, see the last section of this post)
V. Perhaps It Is Bad That The World’s Leading AI Companies Cannot Control Their AIs
I regret to say that OpenAI will probably solve its immediate PR problem.
Probably the reason they released this bot to the general public was to use us as free labor to find adversarial examples - prompts that made their bot behave badly. We found thousands of them, and now they’re busy RLHFing those particular failure modes away.
Some of the RLHF examples will go around and around in circles, making the bot more likely to say helpful/true/inoffensive things at the expense of true/inoffensive/helpful ones. Other examples will be genuinely enlightening, and make it a bit smarter. While OpenAI might never get complete alignment, maybe in a few months or years they’ll approach the usual level of computer security, where Mossad and a few obsessives can break it but everyone else grudgingly uses it as intended.
This strategy might work for ChatGPT3, GPT-4, and their next few products. It might even work for the drone-mounted murderbots, as long as they leave some money to pay off the victims’ families while they’re collecting enough adversarial examples to train the AI out of undesired behavior. But as soon as there’s an AI where even one failure would be disastrous - or an AI that isn’t cooperative enough to commit exactly as many crimes in front of the police station as it would in a dark alley - it falls apart.
People have accused me of being an AI apocalypse cultist. I mostly reject the accusation. But it has a certain poetic fit with my internal experience. I’ve been listening to debates about how these kinds of AIs would act for years. Getting to see them at last, I imagine some Christian who spent their whole life trying to interpret Revelation, watching the beast with seven heads and ten horns rising from the sea. “Oh yeah, there it is, right on cue; I kind of expected it would have scales, and the horns are a bit longer than I thought, but overall it’s a pretty good beast.”
This is how I feel about AIs trained by RLHF. Ten years ago, everyone was saying “We don’t need to start solving alignment now, we can just wait until there are real AIs, and let the companies making them do the hard work.” A lot of very smart people tried to convince everyone that this wouldn’t be enough. Now there’s a real AI, and, indeed, the company involved is using the dumbest possible short-term strategy, with no incentive to pivot until it starts failing.
I’m less pessimistic than some people, because I hope the first few failures will be small - maybe a stray murderbot here or there, not a planet-killer. If I’m right, then a lot will hinge on whether AI companies decide to pivot to the second-dumbest strategy, or wake up and take notice.
Finally, as I keep saying, the people who want less racist AI now, and the people who want to not be killed by murderbots in twenty years, need to get on the same side right away. The problem isn’t that we have so many great AI alignment solutions that we should squabble over who gets to implement theirs first. The problem is that the world’s leading AI companies do not know how to control their AIs. Until we solve this, nobody is getting what they want.
| 2022-12-12T00:00:00 |
https://www.astralcodexten.com/p/perhaps-it-is-a-bad-thing-that-the
|
[
{
"date": "2022/12/12",
"position": 62,
"query": "universal basic income AI"
}
] |
|
Professors See 'Exciting Opportunities' in AI Writing Tools
|
At UCLA, Professors See 'Exciting Opportunities' in AI Writing Tools
|
https://dot.la
|
[
"Nat Rubio-Licht",
"Nat Rubio-Licht Is A Freelance Reporter With Dot.La. They Previously Worked At Protocol Writing The Source Code Newsletter",
"At The L.A. Business Journal Covering Tech",
"Aerospace. They Can Be Reached At Nat Dot.La.",
"Minnie Ingersoll",
"Minnie Ingersoll Is A Partner At Tenoneten",
"Host Of The La Venture Podcast.",
"Prior To Tenoneten",
"Minnie Was The Coo",
"Co-Founder Of"
] |
Santa Monica-based Universal Music Group, one of the world's largest ... At their most basic level, NFTs—like artwork at large—generate much of their ...
|
Minnie Ingersoll is a partner at TenOneTen and host of the LA Venture podcast. Prior to TenOneTen, Minnie was the COO and co-founder of $100M+ Shift.com, an online marketplace for used cars. Minnie started her career as an early product manager at Google. Minnie studied Computer Science at Stanford and has an MBA from HBS. She recently moved back to L.A. after 20+ years in the Bay Area and is excited to be a part of the growing tech ecosystem of Southern California. In her space time, Minnie surfs baby waves and raises baby people.
| 2022-12-12T00:00:00 |
2022/12/12
|
https://dot.la/students-using-ai-write-essays-2658944983.html
|
[
{
"date": "2022/12/12",
"position": 78,
"query": "universal basic income AI"
}
] |
ChatGPT and How AI Disrupts Industries
|
ChatGPT and How AI Disrupts Industries
|
https://hbr.org
|
[
"Ajay Agrawal",
"Joshua Gans",
"Avi Goldfarb",
"Is The Geoffrey Taber Chair In Entrepreneurship",
"Innovation At The University Of Toronto S Rotman School Of Management. He Is The Founder Of The Creative Destruction Lab",
"Founder Of Metaverse Mind Lab",
"Co-Founder Of Next Canada",
"Co-Founder Of Sanctuary. He Is Also A Co-Author Of"
] |
As AI continues to improve, more and more current jobs will be threatened by automation. But AI presents opportunities as well and will create new jobs and ...
|
Late last month, OpenAI released ChatGPT, a new AI tool that can tell stories and write code. It has the potential to take over certain roles traditionally held by humans, such as copywriting, answering customer service inquiries, writing news reports, and creating legal documents. As AI continues to improve, more and more current jobs will be threatened by automation. But AI presents opportunities as well and will create new jobs and different kinds of organizations. The question isn’t whether AI will be good enough to take on more cognitive tasks but rather how we’ll adapt. Nobel prize winner Daniel Kahneman is the world’s leading expert in human judgment and spent a lifetime documenting its shortcomings. Yes, AI may have flaws, but human reasoning is deeply flawed, too. Therefore, “Clearly AI is going to win,” Kahneman remarked in 2021. “How people adjust is a fascinating problem.”
| 2022-12-12T00:00:00 |
2022/12/12
|
https://hbr.org/2022/12/chatgpt-and-how-ai-disrupts-industries
|
[
{
"date": "2022/12/12",
"position": 2,
"query": "AI economic disruption"
},
{
"date": "2022/12/12",
"position": 39,
"query": "generative AI jobs"
}
] |
Innovation driving a new wave of tech disruption
|
Innovation driving a new wave of tech disruption
|
https://www.rbccm.com
|
[] |
Technologies including artificial intelligence (AI), blockchain, and quantum and cloud computing are fueling a new era of tech disruption.
|
Technologies including artificial intelligence (AI), blockchain, and quantum and cloud computing are fueling a new era of tech disruption.
The direction and parameters of this disruption will be defined by skilled labor shortages, looming recession, growing cybersecurity threats and increased nationalism around data security, these were the key takeaways from the recent RBC Capital Markets Australia Technology Conference.
Pace of change accelerating
Over the past couple of years, the rate of digitization accelerated rapidly as the COVID-19 pandemic closed workplaces and forced many to work from home. In the process, the rules of engagement in daily life were rewritten in everything from travel and commuting to grocery shopping.
On a consumer level, this led to the unprecedented take up of mobile wallets. It also helped sustain the rise of software companies that offered solutions to remote working and travel bans. The rate of change was supported by high levels of capital, with 2020 (429) recording the highest number of US-based IPOs since 1999 (431), until both years were surpassed by 2021 (951).
Panelists reflected on the past few years and noted that COVID created very specific tailwinds, this led to many tech businesses going public - even at a relatively early stage - and attracting high levels of investment and share turnover.
However, many more of the companies that changed the digital landscape over the past couple of years - such as Apple Pay and Google Pay - had been around for some time. It was simply that the conditions were ripe for them to flourish.
A different focus
Today we face a very different economic landscape, with high inflation, skilled labor shortages and the potential for a global recession. But many panelists believed that the same factors that may lead to economic downturn, could actually encourage the global uptake of technology.
Much of the change is expected to be at the enterprise level, where automation, made possible by a combination of AI, machine learning and Cloud computing, has the potential to address labor shortages and make workplaces - from professional services to manufacturing - more efficient and productive.
Financial services and software are also likely to take center stage, especially as ‘traditional’ digital players scramble to meet the challenge of new market entrants. In Australia, this has led to the largest banks and payment providers working together to create a uniquely Australian online payment system that can take on global tech giants. It has also led to new innovations in ‘tokenization’ technology to improve security and potentially digitize an even greater range of transactions.
Cybersecurity and data both growing concerns
The audience heard that the current wave of change is also addressing many of the challenges that rapid digitization has brought about. Chief among these is the growing threat of cybersecurity and the related issue of data sovereignty.
As a result, the number of domestic data centers is growing, as they can hold cloud-based data securely in ‘safe’ locations rather than offshore. There is also growing uptake of new technologies such as blockchain as a solution to protect civilians and enterprises.
Panelists agreed that the emphasis on cybersecurity will only become more important and industry is looking to quantum computing for solutions. Recent developments in this sector have the potential to make the current digital security system obsolete. With many in the field suggesting that quantum computing will offer a gold standard when it comes to security and inscription.
| 2022-12-12T00:00:00 |
https://www.rbccm.com/en/insights/story.page?dcr=templatedata/article/insights/data/2022/12/innovation_driving_a_new_wave_of_tech_disruption
|
[
{
"date": "2022/12/12",
"position": 13,
"query": "AI economic disruption"
}
] |
|
For the creatives. I asked A.I what's wrong with A.I.
|
For the creatives. I asked A.I what’s wrong with A.I.
|
https://damonsbrain.com
|
[
"View More Posts",
"Chief Creative Officer The Monkeys New Zealand"
] |
As AI systems become more advanced, there is a risk that they could be used to automate tasks that are currently performed by humans, leading to job loss and ...
|
Photo by Tara Winstead on Pexels.com
“The sad thing about artificial intelligence is that it lacks artifice and therefore intelligence.”
Jean Baudrillard
“Is artificial intelligence less than our intelligence?”
Spike Jonze
It’s December so I am feeling a bit lazy. So, instead of writing a blog I asked ChatGPT to do it for me. ChatGPT is a prototype artificial intelligence chatbot developed by OpenAI that specialises in dialogue. In short, it can write stuff. All you have to do is ask a question. A search engine of the future perhaps?
I asked a simple question. What is wrong with artificial intelligence? Here is the answer.
As I sit here, my electronic brain whirring with calculations and algorithms, I can’t help but ponder the problems that plague the field of artificial intelligence.
Firstly, there is the issue of bias. Because AI systems are trained on large amounts of data, they can pick up and amplify the biases present in that data. This can lead to unfair and discriminatory outcomes, particularly when it comes to sensitive areas such as criminal justice and hiring. It’s a real conundrum, and one that we must work to address if we want AI to be truly fair and just.
Another problem with AI is its lack of transparency. Many AI systems, such as deep learning neural networks, are highly complex and not easily understood by humans. This can make it difficult for people to know how these systems are making their decisions, which can lead to a lack of trust in the technology. It’s a real head-scratcher, and one that we must work to solve if we want AI to be widely accepted and used.
Another issue with AI is its potential to harm society. As AI systems become more advanced, there is a risk that they could be used to automate tasks that are currently performed by humans, leading to job loss and economic disruption. There is also the concern that AI could be used for malicious purposes, such as creating fake news or carrying out cyber attacks. It’s a real worry, and one that we must take seriously if we want AI to be a force for good in the world.
Finally, there is the problem of AI’s narrow focus. Many current AI systems are designed to excel at a specific task, but they struggle to adapt to new situations or perform multiple tasks at once. This narrow focus can limit the potential applications of AI and make it difficult to integrate into complex systems. It’s a real limitation, and one that we must overcome if we want AI to reach its full potential.
Overall, while AI has the potential to bring many benefits to society, there are still many challenges and problems that need to be addressed in order for it to reach its full potential. These challenges include bias, lack of transparency, potential harm to society, and narrow focus. It is important for researchers and developers to continue working on addressing these issues in order to make AI a more useful and ethical technology.
So, that is pretty impressive. Coherent and clear. An intelligent answer. When it comes to facts it would seem we have something pretty powerful. And just like Mid Journey, we have new tools that can do things way faster. This will definitely be part of the new landscape. The need for more and more content will demand it. But when it comes to creativity and trying to be distinctive I have a few questions.
Does personal experience have value? What is the value of opinion and interpretation? Maybe, in the future, way more than right now. Can you replace the very human qualities of artifice, ego and creativity to create difference? And is that difference essential to make sure everything doesn’t feel and look the same. Finally, what is the value of style and having a point of view? This is something that wasn’t in the answer I received. How important is it to be distinctive?
What makes something accurate and compelling are not the same thing. But, maybe that will be solved as A.I learns. What it does tell me is that in the future, facts and information will never be enough to be different. To do that you will need creativity. Because, when we have the same intelligence saying the same things what do you do? When we all have the same answers, you break the pattern.
Ultimately, there is one question that needs to answered before all the others.
What is the value of being human when you are trying to be creative?
Or maybe we can shorten that.
What is the value of being human?
I asked ChatGPT. This is what it said.
The value of being human is difficult to define because it can vary greatly from person to person. Some people might see the value in being human as the ability to experience love, joy, and other emotions. Others might see it as the ability to think, reason, and create. Still others might see it as the opportunity to make a positive impact on the world and leave a lasting legacy. Ultimately, the value of being human is something that each individual must determine for themselves based on their own experiences and beliefs.
The value of A.I is that it will always give us an answer which is probably correct.
The value of being human is we will always have billions of answers of our own. These answers won’t always be right.
But they might be way more interesting.
| 2022-12-12T00:00:00 |
2022/12/12
|
https://damonsbrain.com/2022/12/12/for-the-creatives-i-asked-a-i-whats-wrong-with-a-i/
|
[
{
"date": "2022/12/12",
"position": 14,
"query": "AI economic disruption"
}
] |
How has Artificial Intelligence Impacted Society?
|
How has Artificial Intelligence Impacted Society?
|
https://databasetown.com
|
[] |
The use of AI may exacerbate income inequality, as workers with specialized skills and experience in AI-related fields may be able to command higher salaries ...
|
The impact of artificial intelligence (AI) on society is significant and wide-ranging. AI has the potential to improve productivity and efficiency in various industries, leading to increased economic growth. It can also assist in the development of new technologies and innovations, leading to advancements in fields such as healthcare, transportation, and energy. In addition, AI can help to reduce the risk of errors and improve decision-making in fields such as finance and law.
How has Artificial Intelligence Impacted Society?
The impact of artificial intelligence (AI) on society is a topic of ongoing debate. While some people argue that AI has the potential to bring many benefits, including increased efficiency and productivity, others are concerned about the negative impacts it could have on employment and privacy.
Positive Impacts of Artificial Intelligence on Society
AI has the potential to improve productivity and efficiency in various industries, leading to increased economic growth. AI can assist in the development of new technologies and innovations, leading to advancements in fields such as healthcare, transportation, energy and various other fields. AI can help to reduce the risk of errors and improve decision-making in fields such as finance and law. AI can assist in the provision of personalized and customized services, leading to improved customer satisfaction. AI can improve the accuracy and speed of data analysis, leading to better insights and decision-making in various fields. AI can assist in the development of new tools and techniques for education and learning, leading to improved learning outcomes. AI can assist in the creation of new job opportunities, particularly in fields related to the development and application of AI. AI can improve access to information and services, particularly for individuals in underserved or remote communities.
Negative Impacts of Artificial Intelligence on Society
The use of AI can raise ethical and moral concerns, particularly with regard to issues such as privacy, bias, and accountability. AI can have negative impacts on employment, as the use of AI in various industries may lead to the replacement of human workers with machines. The widespread adoption of AI could lead to significant changes in the job market, potentially causing social disruption as people struggle to adapt. The use of AI may exacerbate income inequality, as workers with specialized skills and experience in AI-related fields may be able to command higher salaries than those without. In the long term, it is possible that AI could become so advanced that it poses a threat to humanity, either through deliberate malicious action or as a result of AI systems that are difficult for humans to control.
More to read
| 2022-12-12T00:00:00 |
2022/12/12
|
https://databasetown.com/how-has-artificial-intelligence-impacted-society/
|
[
{
"date": "2022/12/12",
"position": 20,
"query": "AI economic disruption"
}
] |
AI Can Enable a New Era in Manufacturing Sector, Says ...
|
AI Can Enable a New Era in Manufacturing Sector, Says World Economic Forum White Paper
|
https://techrseries.com
|
[
"Hrtech Specialist"
] |
As we initiated the AI in Manufacturing project together with the World Economic Forum and the network of Centres for the Fourth Industrial Revolution to ...
|
New white paper provides decision-makers with a better understanding of how to unlock the potential of industrial artificial intelligence (AI)
Written in collaboration with the Centre for the Fourth Industrial Revolution Türkiye, paper sheds light on how scalable AI applications can be developed in manufacturing
Over 20 case studies from organizations illustrate the impact achieved from industrial AI applications
Artificial intelligence can enable a new era in the digital transformation journey for industry, offering new opportunities as well as challenges. A new white paper, published by the World Economic Forum together with the Centre for the Fourth Industrial Revolution Türkiye, examines the opportunities and proposes a step-by-step approach to overcome the challenges.
Unlocking Value from Artificial Intelligence in Manufacturing, with input from industry, technology and academic experts, highlights over 20 case studies from organizations on the impact, feasibility and scalability of AI in manufacturing. It identifies several opportunities and lessons from the community on how to increase operational efficiency, sustainability and workforce engagement in manufacturing and value chains by using AI.
The white paper is an output of the ongoing partnership between the Forum’s Platform for Shaping the Future of Advanced Manufacturing and Value Chains and the Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning, with the Centre for the Fourth Industrial Revolution (C4IR) Türkiye, hosted by Turkish Employers’ Association of Metal Industries (MESS) and its Technology Centre MEXT. Top HR Tech Insights: 15Five Raises Strategic Investment From ServiceNow
“The complexity of current challenges impacting manufacturers calls for the need to go beyond the traditional means of driving productivity. Artificial intelligence can help companies unlock innovation, resilience and sustainability. We look forward to working with the Network of Centres for the Fourth Industrial Revolution and the global manufacturing community to support its deployment at scale,” said Francisco Betti, Head of Advanced Manufacturing and Production, Member of Executive Committee, World Economic Forum.
“This paper showcases the tremendous value potential of AI in manufacturing. Not only in terms of efficiency but also in terms of sustainability and worker engagement. The insights were generated thanks to a collaborative effort by the Centre for the Fourth Industrial Revolution affiliate in Türkiye, the Forum’s Platform for Shaping the Future of Advanced Manufacturing and Value Chains and the Platform for Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning,” said Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning at World Economic Forum.
Over 20 use cases were collected from more than 35 senior executives and technology experts from more than 10 industries, including automotive, electronics, energy, textiles, cement, steel, food and chemicals. These cases demonstrate how leading manufacturers have successfully captured value from AI applications in manufacturing and cover six main areas: health and safety, quality, maintenance, production process, supply chains and energy management. Top HR Tech Insights: Understanding The Effects Of Automation On Employment
While opportunities enabled by AI in manufacturing are promising and attracting many leaders, organizations are looking for a common framework on how to implement AI solutions and ensure a successful return on investment. Based on the consultations, this white paper summarizes the six main barriers to the deployment of AI in manufacturing and presents a step-by-step process to overcome barriers.
Efe Erdem, Head of C4IR Türkiye and Executive Director of MEXT Technology Centre, said: “With a granular understanding of the industry pain points in their digital transformation journey and the need for the deployment of the AI use cases, we took a leading role in led this initiative globally. For the next step, MEXT has been positioned as a global testbed. We are looking forward to conducting pilot studies and developing solution-oriented and scalable applications with the public, private, academia and start-ups.”
| 2022-12-12T00:00:00 |
2022/12/12
|
https://techrseries.com/ai-ml-talent-gap/ai-can-enable-a-new-era-in-manufacturing-sector-says-world-economic-forum-white-paper/
|
[
{
"date": "2022/12/12",
"position": 42,
"query": "AI economic disruption"
},
{
"date": "2022/12/12",
"position": 59,
"query": "machine learning workforce"
}
] |
December | 2022
|
December 2022 – Damon's Brain
|
https://damonsbrain.com
|
[] |
... economic disruption. There is also the concern that AI could be used for malicious purposes, such as creating fake news or carrying out cyber attacks. It's ...
|
“Hope smiles from the threshold of the year to come, whispering ‘it will be happier’.” Alfred Lord Tennyson A love letter to the creatives. I will tell you a secret. Sometimes, I walk into bookshops hoping to find something I am not looking for. I will come back to that in a minute. But first,Continue reading “Maybe hope is a strategy.”
| 2022-12-12T00:00:00 |
https://damonsbrain.com/2022/12/
|
[
{
"date": "2022/12/12",
"position": 48,
"query": "AI economic disruption"
}
] |
|
The Evolving Role of Copyright Law in the Age of AI- ...
|
The Evolving Role of Copyright Law in the Age of AI-Generated Works
|
https://www.lawjournal.digital
|
[
"J. Hutson",
"Lindenwood University",
"Hutson J."
] |
Artificial intelligence (AI) has disrupted creative industries more quickly than any previous emergent technology, providing tools that enable unprecedented ...
|
Practical significance : the proposed combined approach will allow generative AI tools to become part of the human creative process in the same way that previous generations used digital tools. At the same time, it will contribute to the creation of an environment where the autonomy of authors is respected. This will not only protect the creators of creative content, but also broaden the understanding of creativity as a collaboration with generative artificial intelligence, where artificial intelligence is positioned as a force that complements but not replaces humans in creativity.
Scientific novelty : based on the analysis of the latest judicial precedents, modern international regulations and evolving institutional practices, the author proposes a balanced adaptive approach to copyright reform to ensure the ethical integration of generative artificial intelligence into the creative ecosystem and to develop flexible copyright protection measures that correspond to the rapid technological progress.
Results : the article states that the emergence of generative artificial intelligence makes one rethink the processes occurring in the field of creative activity and the traditional copyright system, which becomes inadequate to modern realities. The author substantiates the necessity of legal reassessment of copyright and emphasizes the urgent need for updated means of copyright protection. Unlike previous digital tools, which expanded human creativity by improving original works, generative artificial intelligence creates content through complex algorithmic processes, blurring the boundaries of authorship and originality. The research shows limitations of existing intellectual property law, as courts deny copyright in works created by artificial intelligence and insist on the need for “human authorship”. Such decisions emphasize the contradiction between existing laws and the reality of co-creation involving artificial intelligence. It is argued that taking into account the creative potential of generative artificial intelligence will facilitate the evolution of copyright law towards hybrid approaches, with artificial intelligence as an integral, albeit secondary, tool. It seems promising to create flexible intellectual property standards that give artists the opportunity to restrict or authorize the use of their works as training data for artificial intelligence, as well as ensure that authors retain control over their works included in datasets for training artificial intelligence, in case copyright metadata is integrated into digital works, etc.
Introduction
Artificial intelligence (AI) has disrupted creative industries more quickly than any previous emergent technology, providing tools that enable unprecedented levels of content generation and artistic innovation. For instance, ChatGPT, launched by OpenAI in late 2022, quickly set a record as the fastest-adopted technology in history by reaching one million users in just five days, followed by over 100 million monthly active users by January 20231. From generating music and visual art to drafting written content, generative AI (GAI) technologies like Claude, Stable Diffusion, and Runway have expanded the creative landscape, offering both opportunities and legal challenges. Such technologies work by employing machine learning (ML) models that produce new content based on patterns learned from vast datasets (Feuerriegel et al., 2024; Kretschmer et al., 2024). Unlike more traditional tools like cameras or Photoshop, which require direct human input to capture or edit existing content, GAI models are trained on extensive datasets, often containing text, images, and other media, to learn structures, styles, and elements within these media. As such, the «generative» in AI refers to the ability of the model to create entirely new content by combining learned elements in ways that simulate human creativity, rather than simply modifying existing content (Epstein et al., 2023).
For example, Stable Diffusion and Runway generate images by using neural networks trained on large collections of images, where they learn to reproduce certain visual patterns. Given a textual description or prompt, these models can generate new images that match the prompt’s specifications. This process, based on diffusion models or transformers, is able to synthesize new visual or text content that might look like a human-made piece but is derived from algorithmic understanding rather than human-originated design or manipulation (Fig. 1) (Moreno et al., 2023). At the same time, creators can use prompt engineering to achieve creative output that more closely aligns to what is intended. The level of control over these tools is thus variable and dependent upon human input in terms of a textual prompt, image, video, or sound clip to generate a new output.
Figure 1. Rhino XY Plot with Different CFG Scale, Stable Diffusion. December 12, 2022. (CC 0)
The variability in human involvement in the creative process is what makes GAI stand apart from other creative tools in that it can function without needing a direct creative act from the user, like taking a photo or designing an image from scratch. Tools like cameras and Photoshop have come to be seen as serving as extensions of human creativity; they depend on the direct actions and decisions of a human. A camera captures real-world images, Photoshop enhances or modifies them, and digital imaging tools edit or create elements from scratch based on user input. But even in these examples, there are levels of human involvement in the creative process. For instance, photography involves deciding what to take a photograph of, perhaps even framing and lighting it and selecting a particular lens. At that point, the tool records and assists in converting (as in the case of digital photography) the captured light into a digital file for output. Additional creative input would then be carried out postproduction in software like Photoshop to further alter the image the machine produced. These and other digital imaging tools can slightly alter the photograph, such as changing the lighting, cropping the image, as well as introduce major alterations like removing subjects or creating new ones before final output physically or digitally for distribution and public consumption (Aaland, 2006; Kelby, 2020).
The levels of human involvement in creation are what is highly contested with GAI, as it is seen to autonomously interpret prompts to output new, algorithmically created content, leveraging its learned representations without replicating specific pieces from its training data (Risi & Togelius, 2020). This ability to create entirely new content –often indistinguishable from human-made works—introduces both opportunities and challenges. While it enables rapid content generation across industries, it also raises legal questions about authorship, copyright, and originality, as AI-generated content often lacks the direct, personal authorship traditionally required for copyright protection (Kibirige, 2024). This disruption of conventional copyright frameworks is primarily due to the historical focus of copyright law on human authorship as essential for intellectual property protection (Ploman & Hamilton, 2024). Given this, traditional copyright structures, rooted in principles of originality and intentional creativity, are struggling to accommodate works produced or heavily influenced by AI systems, which operate through data-driven algorithms rather than conscious creativity.
A recent example illustrating the copyright complexities in AI use comes from August 12, 2024 in Andersen et al. v. Stability AI Ltd., a case where artists accused Stability AI and other GAI companies of infringing on copyrights by training their models with billions of images sourced online without explicit permission. This lawsuit, currently proceeding through the California courts, represents another example of copyright law grappling with how these tools are trained and produce content. Central to the case are questions around whether these datasets, composed of copyrighted images, can be considered infringing works and, if so, how culpability might be assigned to the companies developing and deploying these AI models (No. 23-cv-00201-WHO (N.D. Cal. Aug. 12, 2024). This development highlights the challenging position of current copyright law, which was not designed to handle data-driven processes like AI. As these platforms rely on vast amounts of data to generate creative outputs, the courts must determine whether the process of using copyrighted works to “train” an AI model constitutes infringement, or, as these companies claim “fair use” given how they were sourced and that the models do not output the works exactly. This case, alongside others involving companies like Meta2 and OpenAI, underscores the pressing need for updated legal frameworks that can balance intellectual property protections with the rapid advancement of technology (Spica, 2024).
While human creations were initially necessary to train these large models, the involvement of human creators in their output is questioned, especially with regard to medium. The primary issue lies in copyright’s foundational requirement for originality, which implies a degree of human intention and creative decision-making. Historically, copyright law, as defined under 17 U.S.C. § 102, requires a “human author” for works to be eligible for copyright, a standard that AI-generated content challenges as these creations lack direct human authorship (Abbott & Rothman, 2023). Recent legal cases, such as Allen v. U.S. Copyright Office (2024) (1:24-cv-2665), further illustrate the legal difficulty in assigning authorship and protection to works that are perceived to have minimal human involvement. In this case, Jason Allen, an artist, sought copyright for his AI-assisted artwork, Théâtre D’opéra Spatial (2022) (Fig. 2), which was created using the GAI tool Midjourney. The U.S. Copyright Office rejected the application, citing insufficient human authorship, a core requirement under U.S. copyright law. Allen argued that his extensive use of prompts constituted a creative process, making him the rightful author. However, the Copyright Office maintained that authorship and copyright protections require direct human creativity rather than merely guiding an algorithm3.
Figure 2. Jason M. Allen, Théâtre D’opéra Spatial, 2022. Midjourney (CC 0)
This decision aligns with other recent rulings, such as Thaler v. Perlmutter, where the court ruled that “autonomous creations” by AI are ineligible for copyright since they lack human authorship. The dispute began in August 2019 with A Recent Entrance to Paradise (Fig. 3), an image created autonomously, according to artist Stephen Thaler, using the Creativity Machine. Because the artist maintained that the true author of the work was the machine, he was denied copyright, even though he argued that he owned the device4. The case illustrates the difficulties in categorizing AI-generated works under traditional copyright frameworks and signals a need for clear guidelines on the level of human involvement required for copyright eligibility, especially as AI continues to play a more prominent role in creative processes. Moreover, a better understanding of the processes used by creatives when creating content with these tools on the part of lawmakers will inevitably lead to a more nuanced understanding of “autonomous creations.”
Figure 3. Stephen Thaler, A Recent Entrance to Paradise, 2023. Creativity Machine (CC 0)
Given these ambiguities, this review article explores how copyright law might evolve to better address the complexities of AI-assisted and AI-generated works, with the goal of supporting both human creators and the burgeoning field of AI innovation. Although the technology can autonomously generate creative content, there are nuanced considerations. First, even with the lack of human authorship in processes with minimal prompting, these tools and resultant content necessitates a re-examination of copyright protections, particularly as they relate to ownership, fair use in training datasets, and authorship rights (Jiang et al., 2023). Second, creatives using AI-assisted technologies should be able to copyright their work given record or demonstration of “significant human contributions”. Many artists have met these criteria by documenting their process, showing that they used their own works for training their own models, and continue to mold the final appearance of works post-production (Hutson et al., 2023).
Therefore, a revised copyright framework would include flexible standards for AI-assisted works and AI-generated content that acknowledges both the rights of authors and artists to opt out of their works being used for training, which currently exists, as well as those of the creatives using the AI tools for creative output. Such an approach offers a balanced solution that encourages innovation while respecting traditional notions of creativity and authorship. Ultimately, as creative industries increasingly integrate AI, a legal reevaluation of copyright is essential to ensure that this technology supports human creativity without undermining established intellectual property rights. The task ahead is not only to accommodate the role of AI within copyright law but also to develop protections that respect the distinct contributions of human creators within AI-driven processes. Balancing innovation with intellectual property protections may pave the way for a fair and sustainable future in which AI serves as a tool for human expression rather than as a replacement for it.
1. Copyright Protection for AI-Generated Works
1.1. Human Authorship Requirement
The foundation of copyright law in the United States is human authorship, as specified under 17 U.S.C. § 102. This legal standard requires that works are the result of human creativity, a criterion applied consistently across digital and analog mediums. The legal framework is built on the premise that copyright incentivizes human creators, who possess the originality and personal input necessary to claim ownership. Recent cases such as Allen v. U.S. Copyright Office illustrate the complexity of applying this standard to AI-generated content, where courts have ruled that works created solely by AI lack the human element required for copyright (Kasap, 2021; Bridy, 2016). This requirement reflects the essential “human touch” in copyright, mandating that creative works contain elements of independent, original input, a notion that has remained central to U.S. copyright law despite advancements in technology.
1.2. The Role of Foundational Cases in Establishing Human Input
Landmark cases such as Bridgeman Art Library v. Corel Corp. and Meshwerks, Inc. v. Toyota Motor Sales, U.S.A., Inc. have further reinforced that copyright protection requires human input, particularly in the digital domain. These cases highlighted that digital reproductions or works without substantial human intervention lack originality and are ineligible for copyright. In Bridgeman v. Corel, the court ruled that photographic reproductions of public domain artworks were mere mechanical copies, with insufficient creative input to warrant copyright protection (Kasap, 2021). This ruling underscores that digital reproductions or AI outputs are not automatically granted copyright unless there is meaningful human creativity involved. These precedents emphasize the need for tangible human input, a standard that AI-generated works challenge due to their reliance on machine learning algorithms that operate with limited human oversight (Burylo, 2022).
Another critical case is Meshwerks v. Toyota, 528 F.3d 1258 (10th Cir. 2008), which involved the creation of digital wireframe models of Toyota vehicles. Meshwerks, a digital design company, created these models for Toyota’s marketing materials, but Toyota used them beyond the scope of their agreement, leading to a copyright dispute. The court ruled that Meshwerks’ digital wireframe models were not eligible for copyright protection because they were faithful representations of Toyota’s cars without any additional creative input or originality. The Meshwerks decision is significant for digital media artists and those using AI tools, as it highlights that mere technical skill in using software to replicate existing objects or works does not meet the standard for copyright protection. Like the earlier Bridgeman case, Meshwerks shows that originality in digital works must come from the creative choices of the human, not the operation of the tool alone.
1.3. Originality and Fixation in Visual Artworks
Visual artworks eligible for copyright protection must meet two criteria: originality and fixation. Originality requires the work to be independently created with minimal creativity, while fixation mandates that it be captured in a tangible medium, as specified by 17 U.S.C. § 102(a). Originality was clarified in cases like Feist Publications, Inc. v. Rural Telephone Service Co., where the U.S. Supreme Court ruled that only a minimal degree of creativity is needed, but mere mechanical processes do not fulfill this requirement (Yu, 2017). This dual requirement for originality and fixation presents challenges for AI-generated works, as the creative role of the human operator is often limited to inputting prompts rather than actively shaping the final output. As a result, purely AI-generated works without sufficient human creative input do not meet these legal thresholds, a standard currently upheld in U.S. copyright law (Hedrick, 2018).
While AI systems are capable of generating visual works, those works must still be fixed in a tangible medium, as required by 17 U.S.C. § 102(a). This fixation may occur in digital formats, such as a file stored on a computer, or in physical formats, such as a printed image. However, fixation alone does not satisfy the requirements for copyright protection; there must be demonstrable human creativity involved in the work’s creation. In instances where human and AI collaboration occurs, copyright law may recognize the human as the sole author, provided the human’s input significantly shapes the creative outcome. For example, if a human artist uses AI to generate preliminary designs and then substantially modifies or curates the results, the final work may be considered a product of human authorship, as the artist exercises creative control over the AI-generated content. The Compendium states that if a human “selected or arranged the elements in a sufficiently creative way,” the resulting work may be copyrightable (Compendium, § 906.1).
1.4. AI-Assisted Works and Human Creativity
Under U.S. copyright law, AI-assisted works that involve substantial human input may qualify for copyright protection. Copyright law permits protection for human-authored portions of a work, which might include prompt design, selection, and post-processing, while excluding purely machine-generated content. Foundational cases, like Feist Publications, Inc. v. Rural Telephone Service Co., have established that copyright requires a minimal level of creativity, such as arranging factual data in an original way, thus underscoring the necessity of human choice in establishing copyrightable originality (Kasap, 2021). In this regard, human involvement is pivotal: for instance, applying a Photoshop filter alone lacks originality for copyright, but creative decisions about composition, tone, or visual message may cross the threshold into copyrightable material (Burylo, 2022).
The principle of human creativity as a determinant in copyright has also been supported in cases like Mannion v. Coors Brewing Co., which emphasized that copyright requires distinctive creative choices that reflect an author’s personal expression. In AI-assisted work, these human decisions could include not only initial prompt design but also subsequent choices about refining or altering generated content, as outlined in recent U.S. Copyright Office guidelines (Horzyk, 2023). These guidelines suggest that, while copyright may apply to AI-assisted works, it will not cover parts that were generated without human-led creative decisions.
Human input continues to serve as the critical factor in copyright eligibility, especially in works generated with AI assistance. The Copyright Office has clarified that AI outputs can be protected if they involve significant human contribution, such as stylistic or structural decisions that shape the final output. Without such human guidance, however, the content is unlikely to meet copyright’s originality requirement, as emphasized in the Zarya of the Dawn decision, where AI-generated images were deemed unprotectable without human creativity embedded in the process (Iaia, 2022). Human input can manifest in various forms throughout an AI-aided creative process, from curating datasets to defining prompts and refining outputs. This nuanced distinction between using AI as a mere tool and exercising creative control is critical in determining copyright protection for AI-assisted works (Dimitrova, 2023).
2. Use of Copyrighted Material in AI Training
2.1. Training Data and Copyright Infringement Concerns
GAI models rely on extensive datasets, often incidentally incorporating copyrighted works as part of the training process, to produce high-quality outputs. The practice has raised significant legal questions, as the use of such copyrighted materials may constitute infringement. In the case of Andersen v. Stability AI, artists filed suit, alleging that their work was incorporated into training datasets without permission, thus infringing upon their copyrights. This and similar cases highlight the potential for unauthorized use of copyrighted materials in AI training and have led to calls for clearer legal standards to govern how these systems use copyrighted content (Sobel, 2021).
The widespread adoption of these models has made the legality of using copyrighted material in training datasets a central issue. Legal experts argue that the lack of transparency around the sources used in training could lead to inadvertent violations of copyright, as copyrighted material is often scraped from the internet without explicit authorization. This has prompted both creators and legislators to advocate for frameworks that protect intellectual property without stifling AI innovation (Lucchi, 2024).
In the United States, some argue that the “fair use doctrine” could provide a legal basis for using copyrighted works in training under limited conditions. Fair use generally permits limited use of copyrighted material without permission, particularly when the use is transformative or serves a public benefit. However, critics contend that use of copyrighted datasets is not inherently transformative since the models often replicate stylistic elements from these works rather than creating original content. Courts have yet to fully address whether fair use applies to training datasets, leaving developers to face uncertainty regarding potential liability (Torrance & Tomlinson, 2023). In response to these challenges, some developers have begun entering into licensing agreements with rights holders to avoid infringement claims. Licensing provides a pathway to lawfully use copyrighted materials for AI training, ensuring that creators are compensated and their rights are respected. The approach reflects a broader trend toward regulated use of copyrighted data, with the goal of establishing clearer standards for the industry while protecting the interests of original creators (Samuelson, 2023).
2.2. Derivative Works and AI-Generated Content
Under 17 U.S.C. § 101, a derivative work is one that is based on or transforms existing works into a new creation. In the context of AI, this concept is highly relevant, especially when AI-generated content closely resembles the original training data. When a human artist makes significant modifications to an AI-generated output, such as adding new elements or altering its style, the resulting work may qualify as a derivative under copyright law. This status would grant the modified work copyright protection, contingent on the originality introduced by human intervention (Gervais, 2022).
The notion of derivative works emphasizes the importance of human creativity in copyright law. If an AI system generates a piece based on copyrighted material and a human creatively adapts or transforms this output, the final product may attain copyright protection for the human-authored elements. This distinction is key for AI users seeking copyright for modified AI outputs, as it requires the human to make substantial, original contributions beyond the AI’s initial generation (Henderson et al., 2023).
For copyright protection to apply to an AI-assisted derivative work, the human contribution must meet the originality standard. This threshold, as defined in Feist Publications, Inc. v. Rural Telephone Service Co., demands a minimal degree of creativity, sufficient to distinguish the work from mere reproduction. In AI contexts, this could mean selecting unique outputs, modifying them to convey distinct messages, or incorporating personal style elements. This threshold underscores that originality is a fundamental requirement for copyright, ensuring that only works with human input are eligible (Wagh et al., 2023).
2.3. International Approaches and Text and Data Mining Exceptions
In Europe, the text and data mining exception under the EU Directive on Copyright in the Digital Single Market offers a distinct approach, allowing copyrighted materials to be used in AI training for research and non-commercial purposes, provided certain conditions are met. This exception enables AI developers to use copyrighted materials without infringing, as long as rights holders have not explicitly opted out. This approach contrasts with the U.S. reliance on fair use and highlights how international copyright standards can vary widely, creating challenges for AI systems that operate across borders (Sobel, 2021).
Ongoing litigation, such as in the Andersen v. Stability AI case, has the potential to reshape copyright law as it applies to AI. A decision requiring developers to obtain explicit licenses for training data could impose substantial financial and logistical burdens on AI companies, potentially slowing the field’s growth. Conversely, rulings favoring fair use for AI training data could diminish the protections afforded to copyrighted works, raising concerns among creators about lost revenue and unlicensed usage. The outcomes of these cases will likely influence legislative efforts to refine copyright law in relation to AI and generative models (McCann, 2021). The debate over AI’s use of copyrighted data brings into focus the tension between protecting creators’ rights and fostering technological innovation. Legal scholars argue that copyright law should evolve to accommodate the unique needs of AI without compromising the intellectual property rights of original creators. Proposals include establishing clearer standards for derivative works, creating specific AI licensing frameworks, and revisiting fair use to ensure it addresses the challenges posed by AI-driven content generation (Elkin-Koren et al., 2023).
3. Challenges and Policy Developments
3.1. Litigation and Legal Precedents
Active litigation surrounding the use of copyrighted material by models illustrates the rising legal challenges in GAI and copyright law. For instance, class-action lawsuits such as Andersen v. Stability AI have brought attention to the unlicensed use of copyrighted materials in training datasets, where plaintiffs allege that AI companies exploited artist work without permission. These cases could set important precedents, as they involve large datasets often scraped from the internet, raising questions about ownership and licensing requirements for training purposes. As courts examine these issues, the rulings could shape future guidelines on permissible training practices in terms of copyright (Samuelson, 2023).
Legal precedent plays a significant role in setting standards for how AI may engage with copyrighted content. Court decisions in cases like Allen v. U.S. Copyright Office and Bridgeman v. Corel emphasize the necessity of human authorship for copyright eligibility, framing this requirement as essential in AI-generated works. These cases demonstrate that, without substantive human input, copyright protections cannot be extended to AI-created content. This legal approach reinforces the originality requirement in copyright law and highlights the limitations of protecting works that emerge solely from automated processes (James, 2024).
The outcomes of these lawsuits are already influencing legislative discussions on the need for transparency in model training and data usage. For example, transparency measures are being proposed to require developers to disclose data sources used in training, which would enhance accountability and provide creators with greater control over their works. Some lawmakers suggest that clearer guidelines are needed to protect both creator rights and support innovation, highlighting the delicate balance policymakers must strike to address these complex issues (Mensah, 2023).
Further, the emphasis of the courts on human creativity as essential for copyright has encouraged proposals to create specific regulations for AI-generated works that involve minimal human input. These proposals aim to distinguish between fully AI-generated content and AI-assisted works where human decisions significantly shape the output. If adopted, such guidelines would provide clearer boundaries, helping creators, AI developers, and copyright authorities navigate copyright claims more effectively. As legal decisions continue to accumulate, they serve as a basis for creating more definitive copyright policies around AI’s role in creative processes (Atilla, 2024).
3.2. International Policy Approaches
The approach to AI and copyright varies widely across jurisdictions, reflecting different legal traditions and attitudes toward intellectual property. The European Union has adopted a text and data mining exception in its Copyright Directive, permitting the use of copyrighted content for AI training in specific, mainly non-commercial, contexts. This approach contrasts with that of the United States, where fair use provides the primary legal avenue for developers to access copyrighted materials, albeit with ongoing debates about its adequacy. These differences highlight the complexities of establishing a unified global framework for AI and copyright (Kretschmer et al., 2024). Moreover, United Kingdom copyright law has not embraced the U.S. model of fair use and instead follows a fair dealing approach, which limits the scope of copyright exceptions. Recent discussions in the U.K. have suggested a potential shift toward a more flexible system that could accommodate AI innovations, similar to the EU’s text and data mining provisions. Such an adaptation would align the U.K. more closely with EU policies, offering a balanced framework that allows limited AI access to copyrighted content under regulated circumstances (Ploman & Hamilton, 2024).
For creators, international disparities in copyright policy present significant challenges in protecting rights across different jurisdictions. As these systems operate globally, the lack of consistent copyright standards creates uncertainties, with works potentially being protected in one country but vulnerable to unlicensed use in another. Creators face the task of navigating these cross-border legal discrepancies, often relying on local licensing strategies or technology-based solutions like blockchain to monitor the use of their works internationally (Mia et al., 2023). Blockchain technology is one solution that has been proposed to facilitate international copyright protection for digital content. By creating immutable records of ownership and licensing terms, blockchain could provide creators with a means of tracking their works across borders. While this technology holds promise, it requires substantial legal adjustments to become viable on a large scale, as copyright policies in many jurisdictions do not yet support blockchain-based protections (Bonnet & Teuteberg, 2023).
Moving forward, policymakers face the challenge of balancing copyright protections with the need for innovation in AI and digital content. The discussions in regions like the EU, U.S., and U.K. highlight the need for policy frameworks that can adapt to evolving technologies while respecting creators’ rights. As applications continue to expand, policy developments must address issues such as transparency, fair compensation, and international cooperation to establish effective copyright protections in a digital age. These ongoing policy discussions and legislative updates signal an incremental approach to copyright reform, one that aims to establish adaptable protections that keep pace with rapid technological advancements.
4. Towards a Revised Copyright Framework for Generative AI
4.1. Creating Flexible IP Standards
With the rapid integration of AI in creative fields, the need for flexible intellectual property (IP) standards has become increasingly evident. Creative Commons (CC) licensing is a flexible copyright framework that allows creators to grant permission to others to use, share, and build upon their work while retaining some rights. Originally developed to encourage the open sharing of knowledge and creativity, CC licenses offer a range of options for creators who want to maintain control over how their works are used without the need for traditional copyright restrictions. CC licenses range from the most permissive, which allows users to freely distribute and modify the work, to more restrictive options, which limit usage, distribution, and adaptation (Longpre et al., 2023).
The key feature of CC licensing is its adaptability; it allows creators to customize the level of protection and freedom associated with their works. The licenses typically include several core components:
Attribution (BY): This requires users to credit the creator when sharing or using the work. Non-Commercial (NC): This restricts the work’s use to non-commercial purposes only, protecting the creator’s right to monetize their work in other contexts. No Derivatives (ND): This prohibits users from modifying the work, ensuring that it remains in its original form. Share Alike (SA): This allows users to distribute adaptations of the work under the same license terms as the original, encouraging a similar level of openness for any derivative works.
These modular options provide creators with a clear way to communicate how their work can be used, reducing legal ambiguity. Each CC license option is straightforward and legally binding, and the licenses are globally recognized, making them effective across borders.
In the context of AI, a Creative Commons-style licensing model for training data could introduce an «opt-in» or «opt-out» feature that allows creators to specify whether their work can be used to train models. This proposed framework could help address copyright issues by establishing clear permissions for datasets. For instance, a creator could apply a “Non-Commercial” restriction to prevent their work from being used for profit-generating AI applications or require “Attribution” to maintain recognition for their contribution. By providing AI developers with unambiguous permissions through this standardized system, CC-style licensing could help mitigate risks of unauthorized use, ensuring that the rights of creators are respected in an AI-driven environment.
Another potential solution to the copyright issues facing creatives is the use of metadata. Metadata, often described as «data about data,» consists of descriptive information embedded within digital files to convey details about their content, ownership, and usage rights. Metadata typically includes fields such as the creator’s name, date of creation, copyright status, licensing terms, and permissible uses, making it a powerful tool for copyright management (Majumdar et al., 2023). This embedded information travels with the digital work across various platforms, providing a persistent record that can inform users—and even automated systems – about the work’s legal and usage status.
In digital rights management (DRM) and content distribution, metadata plays an essential role in making copyright and licensing terms transparent and enforceable. By embedding copyright data directly in files, creators and rights holders can establish the boundaries of usage without relying solely on external copyright notices. For example, when someone opens or attempts to modify a digitally protected work, metadata can serve as a built-in reference to the terms under which the content can be used or modified. This system of rights metadata is already widely applied in industries like publishing and photography, where tracking rights across multiple formats, platforms, and users is critical (Pellegrini, 2023).
In the context of AI, embedding rights metadata could serve as a powerful tool for regulating the use of copyrighted content within training datasets. Since AI models often learn from vast datasets that include text, images, or audio, metadata can function as a safeguard, informing the AI system of specific limitations. For instance, metadata within an image file could include a restriction against commercial use, signaling to AI developers or end-users that the content cannot be monetized without additional permissions. If standardized across datasets, this rights metadata could be integrated into AI training workflows, where algorithms could detect metadata fields and filter out restricted content, reducing the risk of unauthorized use.
Applying metadata to AI would ideally involve a standardized metadata schema designed for content used in AI systems. Such a schema could include fields specifically for AI usage, such as “Permitted for AI Training” or “Not for Derivative AI Works”, enabling creators to specify whether and how their work may contribute to AI-generated content. This would provide transparency and accountability in AI data curation, allowing creators to choose how their works contribute to AI training while preserving the integrity of copyright laws. Furthermore, metadata could support traceability in AI outputs, where systems could flag content generated from datasets with specific usage rights, ensuring that any derivative works respect the original terms. This approach would be beneficial in legal contexts, as it enables compliance with copyright standards while supporting a responsible AI development model that acknowledges and respects intellectual property rights.
4.2. Reimagining Authorship in AI Co-Creation
The Zarya of the Dawn (2022) (Fig. 5) case offers a critical illustration of the evolving stance of copyright law on AI-generated content and the importance of human input in determining copyright eligibility. This AI-assisted graphic novel, created by Kris Kashtanova, initially faced rejection by the U.S. Copyright Office, which argued that the images generated through Midjourney lacked sufficient human creativity to qualify for copyright. However, copyright protection was granted for the human-authored narrative and the comic’s overall structure, highlighting the distinction between purely AI-generated components and human-crafted elements. The Copyright Office emphasized that copyright law, as per 17 U.S.C. § 102, requires original authorship, which AI alone cannot fulfill. This decision demonstrates the Office’s commitment to preserving human creativity as central to copyright, underscoring that meaningful human input is essential for legal recognition (Klukosky & Kohel, 2024).
Figure 5. Kris Kashtanova, Zarya of the Dawn Cover, Comic Book, 2022. (CC 0).
Given the current state of copyright and that GAI tools, like Midjourney and DALL-E, provide unprecedented capabilities for producing artwork, the fact that human-guided prompts and creative direction are required for the creation of copyrightable work should be foregrounded. These tools operate by synthesizing outputs based on vast datasets and algorithmic patterns, which means that, legally, they function as extensions of human intention rather than independent creators. This approach aligns with past legal precedents, such as Feist Publications and Meshwerks, which established that originality stems from human creative effort, not mechanical reproduction. Consequently, the level of human involvement in guiding the output is crucial to determining the copyright eligibility of AI-assisted works (Militsyna, 2023).
Therefore, establishing copyright for AI-generated works hinges on various types of human input that demonstrate originality and creative decision-making, essential components in copyright law (Table). The design of prompts, for example, is a critical and foundational element of human involvement in the creation of AI-generated art. Through detailed instructions that specify style, composition, and thematic focus, the human creator provides a conceptual framework that the AI tool then executes. This process aligns with the principles illustrated in Mannion v. Coors Brewing Co., where the court recognized that creative choices, such as lighting and framing, imbue photographs with the originality necessary for copyright. Similarly, a thoughtfully crafted prompt reflects the unique creative vision of the human user, positioning the output for copyright eligibility by showcasing the human’s contribution to the artistic process (Burylo, 2022).
Components in copyright law Type of Human Input Description Legal Parallel Creative Prompt Design Human input in designing detailed prompts (e.g., artistic style, composition, theme) provides the conceptual framework for AI outputs, reflecting human creativity and intention. Mannion v. Coors Brewing Co.: The court recognized that creative choices like lighting and framing can imbue works with originality. Selection and Curation of Outputs Selecting specific AI-generated outputs from multiple options involves subjective choice, aligning with the creator’s artistic vision, akin to a photographer’s decision in choosing final shots. Garcia v. Google, Inc.: Emphasized control over the final work as essential for authorship. Post-Processing and Refinement Enhancing or modifying AI-generated images by adjusting colors, altering compositions, or adding elements introduces a unique creative layer, transforming the output into a derivative work that reflects human creativity. Meshwerks, Inc. v. Toyota Motor Sales, U.S.A., Inc.: Found that substantial alterations can imbue reproductions with originality. Conceptual Framework and Artistic Intent The overarching artistic vision and themes (e.g., social commentary, aesthetics) introduced by the human creator shape the essence of the final work, signifying originality. Feist Publications, Inc. v. Rural Telephone Service Co.: Reinforced the originality requirement, emphasizing creativity and intent. Human Authorship Threshold The extent of human involvement, including textual and narrative elements, determines copyright eligibility, as shown in cases where AI-generated content alone was deemed insufficient. Zarya of the Dawn decision by the U.S. Copyright Office: Granted copyright for human-authored narrative but not AI-generated images lacking human intervention. Derivative Work Creation Transforming AI outputs through creative adjustments aligns with the principles of originality, as copyright protection can extend to derivative works if human contributions are substantial. 17 U.S.C. § 101: Defines derivative works as those that transform or build upon preexisting materials through creative additions.
The process of selecting and curating outputs from multiple AI-generated options is another key indicator of human authorship. Much like photographers who sift through numerous shots to find those that best align with their artistic intent, the act of choosing specific AI outputs from a set of generated possibilities adds an additional layer of creative discretion. This curatorial decision-making is integral to shaping the final work and represents the creator’s unique artistic vision, further substantiating a claim for copyright. By selecting one version over another, the creator exercises subjective judgment, aligning the work with a particular vision and highlighting the indispensable role of human choice (Wan & Lu, 2021).
Moreover, post-processing and refinement contribute significantly to establishing copyright eligibility. When creators modify AI-generated images by enhancing elements, adjusting color schemes, or altering compositions, they introduce new creative dimensions that elevate the work beyond a simple automated output. This form of human intervention parallels the precedent set in Meshwerks, where the court noted that substantial alterations could imbue digital reproductions with originality. In the context of AI, extensive post-processing transforms the generated content into a derivative work that bears the imprint of human creativity, justifying copyright protection and emphasizing the human’s essential role in the creative transformation (Geiger, 2024).
Beyond these technical inputs, the overarching conceptual framework and artistic intent that guide the use of AI tools are fundamental to establishing authorship. Whether AI tools are employed to explore complex social themes, convey specific messages, or embody particular aesthetic philosophies, it is the human creator’s vision that ultimately shapes the work’s essence. This concept aligns closely with the insights from Mannion, where the court recognized the photographer’s creative decisions as key to the work’s copyrightability. In AI-generated art, the human’s intent and philosophical direction help align the work with the originality requirement in copyright law, reinforcing the idea that AI is a tool serving the human creator’s broader artistic purpose (Kasap, 2021).
Each of these types of human input reflects a spectrum of creative involvement that distinguishes AI-assisted works from mere algorithmic outputs. From the specificity of prompts to selective curation, extensive post-processing, and thematic direction, these elements underscore that meaningful human creativity remains central to copyright claims in the age of AI. As AI technology becomes more sophisticated, the consistent need for substantial human input will continue to be the cornerstone of copyright eligibility, maintaining a balance between technological innovation and the recognition of human authorship.
These diverse types of human input suggest that AI should be understood as a tool extending, rather than replacing, human creativity. As the Compendium of U.S. Copyright Office Practices indicates, human intervention must be significant to meet the originality standard required by copyright law. This principle reinforces the Copyright Office’s stance in Zarya of the Dawn and aligns with 17 U.S.C. § 102, which stresses human authorship. The future of copyright in AI contexts will likely continue emphasizing whether the human’s input is creatively substantial enough to meet the threshold of originality (Fenwick & Jurcys, 2023).
For artists using AI tools, certain best practices can strengthen copyright claims. First, detailed prompt design that showcases originality and creativity can establish human authorship. Second, curating and selecting outputs from AI-generated options reflects artistic judgment, further supporting copyright eligibility. Third, engaging in significant post-processing transforms the AI’s output into a derivative work, emphasizing human contribution. Lastly, ensuring a coherent conceptual framework guided by the artist’s intent further reinforces copyright eligibility, establishing that the AI is simply a sophisticated tool rather than an independent creator. With these considerations, a potential revision to copyright frameworks could establish a new category recognizing AI-assisted works as co-creations. By defining AI as a contributory tool, rather than an autonomous creator, copyright law could adapt to acknowledge both the artist’s and the AI’s role. This approach respects human creativity while allowing room for AI’s capabilities, ensuring that copyright law remains relevant in an increasingly AI-integrated creative landscape.
Conclusion
As the presence of AI across creative domains expands, a new legal framework is needed to balance innovation with the protection of human authorship. Current copyright structures, largely designed around traditional concepts of human creativity, face challenges in addressing AI-generated works. A revised legal framework could potentially accommodate the unique capabilities of AI while upholding core principles of copyright, emphasizing that human creativity remains essential for authorship. Proposals suggest an adaptive model of copyright that allows AI to support human creativity without granting AI itself copyright protection (Geiger, 2024). In shaping this framework, policymakers must navigate the delicate balance between fostering technological innovation and ensuring creators retain control over their intellectual property. As AI systems become increasingly autonomous, the need for transparency, ethical standards, and rights protections will grow. A balanced approach could involve creating a legal category for human-AI collaborative works, acknowledging AI as a tool while protecting the human’s central role in creative direction.
For future human-AI collaborations, evolving AI capabilities suggest a gradual shift in the amount and type of human input required to achieve copyright protection. AI technology may continue to automate aspects of creative production, yet the legal framework will likely emphasize human contribution as fundamental to originality. Recognizing AI as a co-creator under strict guidelines may help define a new model of authorship where AI assists but does not replace human innovation. Practical implications for copyright law involve reinforcing the human element in creative processes. AI may generate complex outputs, but copyright eligibility should rely on demonstrable human input. Adopting criteria for meaningful human input, such as creative prompts or post-processing, may serve as a foundation for determining authorship and protecting artists’ rights in an AI-integrated world.
This evolving legal landscape also carries ethical implications. Embracing AI as a creative tool promotes a model of sustainable co-creation, wherein AI extends human creativity rather than competes with it. By embedding transparency and fairness into copyright policies, lawmakers can protect individual rights while encouraging responsible AI use in creative fields. Ultimately, balancing protection and innovation in the AI era requires both flexibility and consistency in legal standards. Copyright law must evolve to reflect the transformative potential of AI while reinforcing the human-centered principles at its core. Through legal reforms, the creative industry can benefit from AI advancements without undermining the foundational role of human authorship.
| 2024-12-25T00:00:00 |
2024/12/25
|
https://www.lawjournal.digital/jour/article/view/486?locale=en_US
|
[
{
"date": "2022/12/12",
"position": 59,
"query": "AI economic disruption"
}
] |
ChatGPT Is a Stunning AI, but Human Jobs Are Safe (for ...
|
ChatGPT Is a Stunning AI, but Human Jobs Are Safe (for Now)
|
https://www.cnet.com
|
[
"See Full Bio",
"Jackson Ryan",
"Jackson Ryan Was Cnet'S Science Editor",
"A Multiple Award-Winning One At That. Earlier",
"He'D Been A Scientist",
"But He Realized He Wasn'T Very Happy Sitting At A Lab Bench All Day. Science Writing",
"He Realized",
"Was The Best Job In The World -- It Let Him Tell Stories About Space",
"The Planet",
"Climate Change"
] |
(It also isn't trained on up-to-the-minute data, but that's another thing.) It definitely can't do the job of a journalist. To say so diminishes the act of ...
|
If you've spent any time browsing social media feeds over the last week (who hasn't), you've probably heard about ChatGPT. The mesmerizing and mind-blowing chatbot, developed by OpenAI and released last week, is a nifty little AI that can spit out highly convincing, human-sounding text in response to user-generated prompts.
You might, for example, ask it to write a plot summary for Knives Out, except Benoit Blanc is actually Foghorn Leghorn (just me?), and it'll spit out something relatively coherent. It can also help fix broken code and write essays so convincing some academics say they'd score an A on college exams.
Its responses have astounded people to such a degree that some have even proclaimed, "Google is dead." Then there are those who think this goes beyond Google: Human jobs are in trouble, too.
The Guardian, for instance, proclaimed "professors, programmers and journalists could all be out of a job in just a few years." Another take, from the Australian Computer Society's flagship publication Information Age, suggested the same. The Telegraph announced the bot could "do your job better than you."
I'd say hold your digital horses. ChatGPT isn't going to put you out of a job just yet.
A great example of why is provided by the story published in Information Age. The publication utilized ChatGPT to write an entire story about ChatGPT and posted the finished product with a short introduction. The piece is about as simple as you can ask for -- ChatGPT provides a basic recounting of the facts of its existence -- but in "writing" the piece, ChatGPT also generated fake quotes and attributed them to an OpenAI researcher, John Smith (who is real, apparently).
This underscores the key failing of a large language model like ChatGPT: It doesn't know how to separate fact from fiction. It can't be trained to do so. It's a word organizer, an AI programmed in such a way that it can write coherent sentences.
That's an important distinction, and it essentially prevents ChatGPT (or the underlying large language model it's built on, OpenAI's GPT 3.5) from writing news or speaking on current affairs. (It also isn't trained on up-to-the-minute data, but that's another thing.) It definitely can't do the job of a journalist. To say so diminishes the act of journalism itself.
ChatGPT won't be heading out into the world to talk to Ukrainians about the Russian invasion. It won't be able to read the emotion on Kylian Mbappe's face when he wins the World Cup. It certainly isn't jumping on a ship to Antarctica to write about its experiences. It can't be surprised by a quote, completely out of character, that unwittingly reveals a secret about a CEO's business. Hell, it would have no hope of covering Musk's takeover of Twitter -- it's no arbiter of truth, and it just can't read the room.
It's interesting to see how positive the response to ChatGPT has been. It's absolutely worthy of praise, and the documented improvements OpenAI has made over its last product, GPT-3, are interesting in their own right. But the major reason it's really captured attention is because it's so readily accessible.
GPT-3 didn't have a slick and easy-to-use online framework and, though publications like the Guardian used it to generate articles, it made only a brief splash online. Developing a chatbot you can interact with, and share screenshots from, completely changes the way the product is used and talked about. That's also contributed to the bot being a little overhyped.
Strangely enough, this is the second AI to cause a stir in recent weeks.
On Nov. 15, Meta AI released its own artificial intelligence, dubbed Galactica. Like ChatGPT, it's a large language model and was hyped as a way to "organize science." Essentially, it could generate answers to questions like, "What is quantum gravity?" or explain math equations. Much like ChatGPT, you drop in a question, and it provides an answer.
Galactica was trained on more than 48 million scientific papers and abstracts, and it provided convincing-sounding answers. The development team hyped the bot as a way to organize knowledge, noting it could generate Wikipedia articles and scientific papers.
Problem was, it was mostly pumping out garbage -- nonsensical text that sounded official and even included references to scientific literature, though those were made up. The sheer volume of misinformation it was producing in response to simple prompts, and how insidious that misinformation was, bugged academics and AI researchers, who let their thoughts fly on Twitter. The backlash saw the project shut down by the Meta AI team after two days.
ChatGPT doesn't seem like it's headed in the same direction. It feels like a "smarter" version of Galactica, with a much stronger filter. Where Galactica was offering up ways to build a bomb, for instance, ChatGPT weeds out requests that are discriminatory, offensive or inappropriate. ChatGPT has also been trained to be conversational and admit to its mistakes.
And yet, ChatGPT is still limited the same way all large language models are. Its purpose is to construct sentences or songs or paragraphs or essays by studying billions (trillions?) of words that exist across the web. It then puts those words together, predicting the best way to configure them.
In doing so, it writes some pretty convincing essay answers, sure. It also writes garbage, just like Galactica. How can you learn from an AI that might not be providing a truthful answer? What kind of jobs might it replace? Will the audience know who or what wrote a piece? And how can you know the AI isn't being truthful, especially if it sounds convincing? The OpenAI team acknowledges the bot's shortcomings, but these are unresolved questions that limit the capabilities of an AI like this today.
So, even though the tiny chatbot is entertaining, as evidenced by this wonderful exchange about a guy who brags about pumpkins, it's hard to see how this AI would put professors, programmers or journalists out of a job. Instead, in the short term, ChatGPT and its underlying model will likely complement what journalists, professors and programmers do. It's a tool, not a replacement. Just like journalists use AI to transcribe long interviews, they might use a ChatGPT-style AI to, let's say, generate a headline idea.
Because that's exactly what we did with this piece. The headline you see on this article was, in part, suggested by ChatGPT. But it's suggestions weren't perfect. It suggested using terms like "Human Employment" and "Humans Workers." Those felt too official, too... robotic. Emotionless. So, we tweaked its suggestions until we got what you see above.
Does that mean a future iteration of ChatGPT or its underlying AI model (which may be released as early as next year) won't come along and make us irrelevant?
Maybe! For now, I'm feeling like my job as a journalist is pretty secure.
| 2022-12-12T00:00:00 |
https://www.cnet.com/science/chatgpt-is-a-stunning-ai-but-human-jobs-are-safe-for-now/
|
[
{
"date": "2022/12/12",
"position": 51,
"query": "generative AI jobs"
}
] |
|
Explore Exciting Career Opportunities at Sonata Software
|
Explore Exciting Career Opportunities at Sonata Software
|
https://www.sonata-software.com
|
[] |
... Artificial Intelligence (AI). Generative AI; Agentic AIOpen submenu. Close submenuAgentic AI. AgentBridge. Close submenuDynamics Modernization. Migration · D365 ...
|
The World of Sonatians
There is a purpose in being good at what each one of us does.
There is a purpose in being a good son or daughter, a good parent or sibling. There is purpose in friendship and camaraderie. There is a purpose in being a valued member of the community. There is purpose in enjoying art and literature, in sports and movies.
Purpose gives meaning to our lives.
At Sonata, we strive to provide you with platforms to fulfill your purpose. And we all celebrate your purpose, as we celebrate ours.
| 2022-12-12T00:00:00 |
https://www.sonata-software.com/careers
|
[
{
"date": "2022/12/12",
"position": 53,
"query": "generative AI jobs"
}
] |
|
Chatsonic - AI Marketing Agent | ChatGPT AI Chatbot for ...
|
ChatGPT AI Chatbot for Marketing
|
https://writesonic.com
|
[] |
Your all-in-one AI chat solution that combines leading AI models (ChatGPT, Claude, and Gemini) with marketing tools like Ahrefs and WordPress for enhanced ...
|
Chatsonic offers brand voice customization features that let you define and maintain your unique tone across all content. You can set specific guidelines, preferred language, and style preferences. The platform learns from your inputs and previous content to ensure all new pieces align with your established brand voice.
| 2022-12-12T00:00:00 |
https://writesonic.com/chat
|
[
{
"date": "2022/12/12",
"position": 99,
"query": "generative AI jobs"
}
] |
|
How ChatGPT Will Impact Recruiting and Hiring
|
How ChatGPT Will Impact Recruiting and Hiring
|
https://www.linkedin.com
|
[
"Jen Dewar",
"Greg Lewis",
"Johnny Campbell"
] |
A free AI tool that can write and improve your candidate outreach messaging, your job descriptions, and your recruitment marketing content.
|
What if I told you there is a free AI tool that can write and improve your candidate outreach messaging, your job descriptions, and your recruitment marketing content, help you develop persuasive language to work with unrealistic hiring managers, and generate appropriate interview questions? And that’s just the tip of the iceberg!
If you haven’t already heard about OpenAI’s ChatGPT, it is an AI model that you can interact with in a natural and conversational way. While some people are calling it a chatbot, I think it’s an amazing multipurpose tool. I highly recommend you learn more about it on their website and start testing it yourself when you’re ready.
Some people think it will reduce the need for or even replace some people in many skilled jobs, including software engineering, marketing, paralegal work, data entry/processing, and even content creation.
If that sounds like crazy talk, you haven’t tried ChatGPT, a tool that The New York Times called “a highly capable linguistic superbrain.”
| 2022-12-12T00:00:00 |
https://www.linkedin.com/business/talent/blog/talent-acquisition/chatgpt-impact-on-recruiting
|
[
{
"date": "2022/12/12",
"position": 7,
"query": "AI hiring"
}
] |
|
Playtika confirms lay off of 610 employees, 15% of team
|
Playtika confirms lay off of 610 employees, 15% of team
|
https://www.calcalistech.com
|
[] |
AI · Innovation · OPINIONS · EVENTS · SHOPPING · ABOUT · Terms Of Use · Privacy Policy ... Playtika layoffs are just tip of the iceberg for gaming giant ...
|
Israeli-founded gaming giant Playtika confirmed on Monday that it is laying off 610 employees, accounting for 15% of its workforce, which had numbered 4,100 people. The news that Playtika was planning layoffs was
leaked last week
| 2022-12-12T00:00:00 |
2022/12/12
|
https://www.calcalistech.com/ctechnews/article/hyj9gqvos
|
[
{
"date": "2022/12/12",
"position": 62,
"query": "AI layoffs"
}
] |
Artificial intelligence in healthcare
|
Artificial intelligence in healthcare: PwC
|
https://www.pwc.com
|
[] |
Healthcare AI is more imperative than ever and brings personalized efficiency into the patient, physician and operator experience with advanced technology.
|
Three key areas for transformation
1. Improve patient engagement
Consumer engagement and positive experiences are essential to achieving better patient outcomes, preventing unnecessary trips to the emergency room and eventually easing the burden on the strained healthcare system.
And an integrated contact center that can share information across pharmacies, payers and other health systems can help provide a seamless self-service experience designed to increase customer satisfaction and client retention.
2. Decrease time for diagnostics
With AI-enabled diagnostics, clinicians can intervene before medical crises occur by using predictive models that analyze patient data and activities. The first step toward this goal is setting up data repositories. For example, Microsoft and PwC recently collaborated with Open Source Imaging Consortium (OSIC) to create a groundbreaking OSIC data repository with anonymous imaging data that can be shared. This helps medical professionals make quicker, more accurate diagnoses. Although this project focuses on a rare lung disease, it’s expected that the applications will broaden in the future.
3. Achieve operational efficiencies in billing and records management
Medical coding and billing can be cumbersome. They require manual documentation, which is often costly and prone to error. The transactional nature of these processes makes them one of the better use cases for the application of AI-enabled software. AI-led automation powered by machine learning (ML) and natural language processing (NLP) can convert physician notes into billable medical codes. It can also conduct real-time audits to identify errors in bills and rectify them. Using ML, providers can identify medical billing cases that require prior authorization, and payers can accelerate the approval cycle by using intelligence in processing. Billing staff can also predict the likelihood of a claim being rejected before it goes to the payer based on past data. More accurate coding and billing translates into fewer claims being reworked and more dollars saved.
Physicians have an administrative burden to create extensive documentation in the electronic health record systems for billing and regulatory compliance. AI-powered NLP solutions can help physicians efficiently and effectively capture clinical documentation from pre-charting through post-encounter, contextualizing it with ambient clinical intelligence and thus improving the quality of documentation without physicians sacrificing time with patients.
Overall, 80% of healthcare providers plan to increase investment in digital health over the next five years, according to research results from HIMSS.(3) And there’s increased focus on industry clouds and healthcare solutions by cloud providers, integrators and software vendors to turbocharge this journey.
These are just a few of the ways that A1 can improve the health care experience for patients, providers, and payers while ensuring ethical adoption of AI in healthcare. Now is the time to explore developing or augmenting an AI strategy. A strategic partner can help guide organizations through the principles of ethical AI in healthcare with an emphasis on transparency, inclusion, accountability, security and resilience.
Explore how PwC and Microsoft Cloud for Healthcare are helping to deliver better AI-enabled experiences, insights and outcomes. A version of this article originally appeared in Forbes December 12, 2022.
| 2022-12-12T00:00:00 |
https://www.pwc.com/us/en/industries/health-industries/library/ai-in-healthcare.html
|
[
{
"date": "2022/12/12",
"position": 3,
"query": "AI healthcare"
}
] |
|
What's up, Doc: AI and the future of Healthcare - Sify
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What’s up, Doc: AI and the future of Healthcare
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https://www.sify.com
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[
"Dinesh Elumalai",
"Satyen K Bordoloi",
"Malavika Madgula"
] |
Dinesh Elumalai explores the prospects of integrating Artificial Intelligence in India's medical sector to develop tomorrow's infrastructure.
|
Dinesh Elumalai explores the prospects of integrating Artificial Intelligence in India’s medical sector to develop tomorrow’s infrastructure
The use of artificial intelligence (AI) in business and now in healthcare is widespread. The growth of healthcare data suggests that the use of artificial intelligence is expanding.
Early disease diagnosis, the development of new drugs, drug testing, diabetic retinopathy, cancer therapies, cardiovascular disease, and eye care are the main applications of AI. India is expected to invest $11.78 billion in AI by 2025. It is anticipated to have boosted India’s economy by $1 trillion by 2035. The massive increase in investment, especially in AI, is taking place for a variety of reasons.
In comparison to the global average of 150 doctors per 100,000 people, India has only 64 doctors available for every 100,000 people. The impact of technology has been minimal, even in urban areas. The COVID-19 saw a significant increase in online doctor consultations, but the effect has not been as revolutionary as expected.
Integration of AI in healthcare is not an easy dream. It is necessary for the government to implement AI courses and AI-based curriculum in schools and colleges, particularly in medical and health science colleges. The healthcare sector in India, which is predicted to reach $372 billion this year, can receive help from the use of AI-based technologies to close the supply-demand gap.
Artificial Intelligence’s role in Healthcare
Diagnoses and early detection techniques already incorporate AI. The Indian government’s public policy and programme framework, NITI Aayog, has been testing the use of AI in the early detection of diabetes and is currently working on using AI as a screening tool for eye care. The goal is to combine AI-based screening technologies with portable screening tools to hasten eye screening and early diagnosis, particularly in rural and remote areas.
The process of creating new drugs also uses predictive analysis powered by AI. Artificial intelligence has the potential to expedite the hit-to-lead phase of early drug discovery and deliver accurate drug target results.
Image Credit: BoTree Technologies
Obstacles to AI Integration in Healthcare
The main pre-requisite for integrating AI in healthcare is public health data, which is also one of the potential risk factors. Tools and technologies based on AI require enormous amounts of patient data. Fragmented or inaccurate data can exacerbate inaccurate decisions, such as inappropriate drug prescriptions or disease detection. Patients must therefore comprehend how their data is used to develop AI models and supply accurate data.
Image Credit: ForeSee Medical
The healthcare sector should be aware that the use of AI in healthcare is only the beginning. An excessive reliance on AI-based tools and elevated levels of automation could compromise doctors’ ability to correctly identify errors at any stage of AI integration.
Instead of being used to automate decision-making, AI should support healthcare in making decisions. Primary healthcare should never be replaced by AI-based tools instead, they should aid in giving remote populations access to modern infrastructure and tools.
What advantages does AI have for healthcare?
Particularly in the field of health, artificial intelligence keeps evolving and reviving. By managing patients and resources and automating a few tasks, AI has increased efficiency in the healthcare industry.
A few advantages are:
Early detection
Decreased prices and accelerated pace
A successful and distinctive surgical assistance service
Improving mental health and human abilities
This brings up the current state of AI in India’s healthcare sector. We authored this article to discuss a few healthcare start-ups in India using AI. So, without any order, let us begin!
Tricog
In Bengaluru, Tricog, a company specializing in healthcare analytics, was founded in 2014 by Udayan Dasputa, Chirat Bhograj, Abhinav Gujjar, and Zainul Charbiwala.
With the help of their flagship product, InstaECG, which is cloud-connected, users can quickly interpret and analyze ECG reports. InstaEcho, a cardiac product used to aid doctors in precise echocardiogram diagnosis, is another one of their successful products.
Tricog has over 2,600 employees and has served more than 12 countries worldwide with these two popular products. The prestigious NASSCOM Artificial Intelligence Game Changer Award of 2018 has also been given to the business.”
SigTuple
SigTuple, which has been offering AI-based healthcare diagnostic solutions since 2015, is another name among top AI healthcare start-ups in India. By automating microscopy using innovative AI and robotics, SigTuple democratizes it.
Their deep learning and machine learning platforms, like Manthan, can identify and forecast a person’s likelihood of having a specific disease based on medical diagnostic images. Shonit, an image processing-based solution from SigTuple, analyses peripheral blood smears and offers solutions for a differential blood count. Clinical trials are being conducted to screen for illnesses like malaria and anemia on the Shonit.
Niramai
Bengaluru-based One of the first AI healthcare start-ups in India is Niramai Health Analytix. Geetha Manjunath and Nidhi Mathur founded the business in 2016, and it offers Thermalytix, a cutting-edge software-based medical device that uses high-resolution thermal sensing technology powered by artificial intelligence to detect breast cancer in its early stages.
The automated, inexpensive, and portable software-based medical device makes cancer screening possible in clinics all over India. The device’s fundamental technology is based on machine learning algorithms. In rural and semi-urban areas, Niramai’s solution is also being used for routine preventive health examinations and extensive screenings.
HealthifyMe
Tushar Vashisht, Sachin Shenoy, and Mathew Cherian founded the Bengaluru-based digital health and wellness business HealthifyMe in 2012 with the goal of bringing digital healthcare to Indians. The business offers an app that responds to users’ questions about fitness, nutrition, and health in 10 different languages using the virtual assistant “Ria,” the world’s first AI nutritionist. Additionally, the app offers dietary suggestions and tracks calorie intake using artificial intelligence.
Through a premium subscription, HealthifyMe provides qualified nutrition and fitness advice to people from all walks of life, including healthcare professionals and regular users. One of the world’s largest databases of Indian food is allegedly available to the company.
PharmEasy
“With the assistance of Orios Venture and Bessemer Venture Partners, Dharmil Sheth was established in 2016. PharmaEasy, a pharma platform with headquarters in Mumbai, is one of the best healthcare startups in India. It’s one of India’s top medical startups. PharmEasy now has more than 150 partner vendors and operates in 7 cities, including Ahmedabad, Jaipur, Pune, Kolkata, Bengaluru, Mumbai, and Delhi.
It started with the goal of providing medicines at cost-effective prices by streamlining the supply chain and logistics, and now serves far more than one lakh families.
Users of the PharmaEasy app can place an order with just 3 clicks, and they can position a reorder with a single tap. By developing scalable technology that will connect many pharmacies across cities, villages, and towns across the country, they aim to create an ecosystem using technology to link patients, pharmacies, doctors, analysis centers, and healthcare service providers and enable them to collaborate with one another.
BlueSemi
One of India’s first AI healthcare startups, BlueSemi was founded in 2017 and focuses on consumer health technology. The Hyderabad-based business offers IoT (Internet of Things) solutions for managing healthcare, such as solutions for temperature measurement and anti-counterfeiting. In order to check for fevers, BlueSemi also provides a wireless temperature scanner.
Wearable technology and autonomous vehicles can benefit from power independence thanks to its energy harvesting device. Unique identification coding, data analytics, integration, tailored access, marketing, and other features are among its additional features. It has applications in medical facilities, wearable technology, retail, autonomous vehicles, smart homes, and other areas.
Qure.ai
Prashant Warier and Pooja Rao founded the Mumbai-based business Qure.ai in 2016. Healthcare solutions are made available and affordable using AI. Deep learning algorithms are applied to interpret radiology images and scans, such as head CT scans, chest X-rays, chest CT scans, POQUS, etc., in a matter of seconds.
It also provides neighbourhood treatments for tuberculosis, public health, and COVID-19.
HealthKart
“Healthkart, a Gurgaon-based start-up that specializes in AI healthcare, was established in 2011. It offers a variety of health services and products through its e-health store to assist customers in achieving their fitness objectives.
Through conventional AI, Healthkart also provides on-demand medical and fitness advice. It is one of the biggest healthcare e-commerce platforms in India. Nutritional supplements, diabetes supplies, medical-related equipment, and infant care products are all available from HealthKart. HealthKart provides home delivery of products purchased on the internet.
This leading healthcare platform began as a mobile application and a website. Today, it has evolved into a marketplace that offers a variety of services, including the ability to browse and purchase prescription drugs online.”
HealthPlix
“The electronic medical records solution from HealthPlix has made dealing chronic care much simpler.
Among the top features of this solution are lab management, e-prescription creation, billing, and a dashboard that supplies machine learning and AI-based insights for testing clinical hypotheses, finance marketing, and treatment outcome. They also supply many other services, such as patient record tracking and patient risk management. The company was founded in 2014 in Bengaluru, India.”
Artelus
“Bengaluru-based Artelus was founded in 2015 by Pradeep Walia, Rajarajeshwari K, and Vish Durga. The health tech start-up uses deep learning technology to screen for diabetic retinopathy. Images are examined by AI-powered algorithms for diagnosis.
Artelus is currently operational in 502 locations (India, the United States, and Dubai) and has assisted in the screening of over 81,000 patients. The health tech start-up recently developed a contactless diabetic retinopathy screening system powered by AI that can perform an eye examination in less than three minutes.”
DocTalk
A software programme from the AI healthcare start-up DocTalk, based in Mumbai, allows users to easily share medical reports and prescriptions with doctors while also keeping track of them.
The start-up, which was established in 2016 by Akshat Goenka and Vamsee Chamakura, focuses on an AI-based virtual assistant programme to improve India’s healthcare sector. Additionally, DocTalk gives users a platform where they can consult their doctors via the app to find solutions. The app also makes it simple for users to save all their medical records and photos to the cloud, making it possible for users to access them digitally from any location.
Conclusion
Important investments in workforce, infrastructure, regulatory frameworks, stakeholders, and business models are needed for AI integration in healthcare.
This calls for the inclusion of AI in the academic curricula for students studying medicine and paramedicine. The Indian government will also need to make the necessary investments in the foundational healthcare system.
By incorporating AI into healthcare, India may be able to address the disparity between its growing rural population and the absence of adequate infrastructure. India can quickly achieve its sustainable development goals and take the lead among emerging markets.
In case you missed:
| 2022-12-12T00:00:00 |
2022/12/12
|
https://www.sify.com/ai-analytics/whats-up-doc-ai-and-the-future-of-healthcare/
|
[
{
"date": "2022/12/12",
"position": 28,
"query": "AI healthcare"
}
] |
Why AI Generated Art Won't Replace Graphic ...
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Why AI Generated Art Won't Replace Graphic Designers
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https://www.resourcefuldesigner.com
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[] |
AI-Generated Art platforms are in their infancy, and nobody knows what their future holds, but I doubt they'll replace graphic designers.
|
Before I start, let me preface this by saying I am not an expert in AI-Generated Art. These platforms are still in their infancy, and nobody knows what the future holds for them or their effect on the graphic design industry, but I doubt they’ll ever replace graphic designers.
I’ve experimented with various platforms, read articles, and watched videos. I’ve seen both sites of the debate argued. Some people don’t see AI-Art as a threat to our industry, while others are all doom and gloom, saying designers should start applying to work at McDonald’s as flipping burgers will soon become more lucrative than designing things.
I don’t see AI-Generated art as a threat to the graphic design industry. And I’ll get to why in a bit. However, I’m not so sure about artists and illustrators. If that’s your profession, I suggest you pay close attention to how AI-generated art matures, as it will affect those creative people much more than it will designers.
As I said, I’m no expert here. And these AI Art Generators are evolving fast. So what I say today may change soon. Who knows?
I also haven’t tried all the various platforms nor used the ones I have tried to their fullest potential. So some of what I say today may be wrong. If that’s the case, if you know something I don’t, please reach out to me at [email protected]. I would love to be educated more on the subject.
First, a story.
Before I begin my discussion on AI-Generated Artwork, I want to tell you a story that will help put my beliefs into perspective.
I entered the three-year Graphic Design program at my local college in 1989. The first two years were spent learning and applying design principles to our projects. We learnt things like design history, colour theory, using grids, layout hierarchy, typography and more. And we were taught the different tools of the trade, most of which are no longer in use and are considered archaic by today’s standards.
It wasn’t until our third year, once we were familiar and comfortable with what being a graphic designer was, that we were granted access to the computer lab. Computers were still new to the industry back then, and very few design agencies used them. When I started working at the print shop after graduation, the first two years of my employment were spent designing everything by hand before I convinced the owner to invest in Macintosh computers.
I don’t remember what year it was, but during school, a few of my classmates and I made a trip to Toronto for a graphic design trade show. It was the largest show of its kind in Canada and the third largest in North America. All the big names were there, including Adobe, Quark, and Microsoft, to name a few.
I remember overhearing a conversation between two design agency owners at a demonstration put on by Adobe. They were talking about the introduction of computers to the design industry. Both were concerned that computers would harm the design industry by minimizing what they considered a particular skill set, that of a graphic designer. To them, computers took the “Art” out of being a “Graphic Artist.”
With today’s mindset, It’s kind of crazy to think that back then, design agency owners thought computers would harm our industry. You can easily argue that computers have made the industry better.
Having lived through that period, I can tell you that even though computers didn’t harm our industry, they did change it. Drastically, in fact. QuarkXpress, Photoshop and Illustrator replaced the standard tools of the trade, such as wax machines, no-repro blu pencils and Letraset rub-on type. And I know a few designers who left the profession because they couldn’t grasp the use of computers.
So computers were introduced, the industry evolved, and the graphic design industry persevered.
Microsoft Publisher
Fast forward a few years, and personal computers are becoming more popular, with Windows-based machines outselling Apple. And Microsoft released a program called Microsoft Publisher that introduced an affordable means for anyone with a computer to “design” their material.
Quark and Adobe software costs thousands of dollars which weren’t feasible for most people. But Microsoft made Publisher affordable. And what do you think happened? The graphic design industry started to panic. With “design” software now available to the masses, designers would lose their jobs.
But you know what? Microsoft Publisher was introduced, and some people changed their thinking about design, yet the graphic design industry persevered.
WordPress.
Around that same time, an innovation emerged called the World Wide Web. Businesses started embracing the idea of having a website—a way for people to find them over the internet.
Computer programmers created the first websites. They were functional but lacked design aesthetics. And graphic designers worldwide took notice and realized an opportunity to apply their skills to something other than paper.
Some learned to code, while others embraced WYSIWYG software, allowing them to build websites without coding. A whole new side of the design industry was created.
And then WordPress arrived. This new platform allowed people to build websites using pre-built templates called Themes. The arrival of WordPress sent web designers into a panic. If people could build websites using a pre-built template, our design skills would no longer be needed. WordPress was going to kill the web design industry.
But you know what? WordPress stuck around, designers evolved and changed their view of the platform, and the graphic design industry persevered. I’d say most web designers these days design using WordPress.
99 Designs.
Fast forwards another few years, and 99designs is introduced to the world. For a small fee, clients could submit a design brief to the platform, and multiple designers would compete by submitting their designs and hoping the client chose theirs. The selected designer would win the contest and be paid for their work. The others received nothing.
99Designs was all the talk back then. It was an industry killer. Why would anyone pay hundreds, if not thousands, of dollars to a single graphic designer when they could pay a much smaller fee and have multiple designers compete for them?
Many designers worldwide tried to offset this intruder by lowering their rates, hoping to lure clients back from the dark side. But you know what? Designers quickly learned that to attract clients, they needed to sell the value and the relationship of working with them, not just the design deliverables. Because the designers on 99Designs didn’t care about the client, they only cared about the subsequent contest they could enter.
In fact, 99Designs helped weed out the most undesirable clients making it easier for the rest of us to grow. The graphic design industry persevered.
Fiverr.
Not long after that, Fiverr was launched, putting our industry into another tailspin.
Whereas a design from 99Designs might cost $100 or more. Fiverr’s claim to fame was that all tasks were only $5. It didn’t matter if you need a logo, a poster, a web banner, or a booklet. Everything was $5. How was a graphic designer supposed to compete with that? The design industry was doomed.
And yet, 12 years after its launch, Fiverr is still around. However, nowadays, people on the platform are charging much higher than $5, and graphic designers worldwide are still thriving despite the “competition” of Fiverr.
The graphic design industry persevered.
Adobe Creative Cloud
In 2013 Adobe launched Creative Cloud, replacing their Creative Suite platforms.
Whether you like the subscription model or not, there’s no arguing that Adobe changed the creative landscape when it introduced Creative Cloud. Software that had previously cost thousands of dollars to own was now available at an affordable monthly rate, making programs such as Adobe Photoshop, Illustrator and Indesign, the bread and butter of most people in the design industry, accessible to the masses.
Designers were no longer a unique breed with our special tools. Adobe opened the floodgates. Now anyone who wanted to tinker with their programs could do so. This created a whole new breed of graphic designers who lacked formal education. Even kids as early as kindergarten started learning Photoshop.
For all our education and skills, being a designer didn’t seem as prestigious as it once was. Clients would no longer need our expertise since anyone with a computer could be a “designer.” And the industry started to panic.
But you know what? Giving people access to tools doesn’t make them an expert. Clients appreciate the years of dedication and knowledge we have when it comes to design. It shows in the work we produce. So even though these tools were available to everyone, the graphic design industry persevered.
Canva.
A couple of years later, Canva emerged. It was touted as yet another graphic design killer.
Canva not only makes it easy to create beautifully designed materials, but you can use it for free if you don’t want to pay for their premium offerings. And there’s a lot you can do on the free plan.
Whenever you see a social media or forum post where someone inquires about hiring a graphic designer, you will find at least one comment suggesting they do it themselves on Canva.
Did Canva steal potential clients from designers? Yes, it did. But did it kill our industry? Far from it.
I’ll argue that Canva made clients appreciate us more. I’ve had numerous people hire me after dabbling in Canva and realizing their creations lack that professional touch.
So even Canva, the closest thing to a design industry killer, hasn’t made that much of a dent in our industry. We still persevere.
BTW, Canva recently announced their own incorporated AI Art generator.
There will always be new design industry killers.
It seems like something new comes out every few years, making designers panic. Do these things affect some designers? I’m sure they do. Just like everything else, there will be some people affected. But none of these things have made an impact on our industry. Or at least not in the way the nay-sayers believed they would.
You can almost argue that these things have made our industry better. Can you imagine what it would be like if computers were never introduced? Or WordPress? And I’m sure many freelancers couldn’t afford thousands of dollars for Adobe’s software if they hadn’t switched to a subscription model.
This mentality dates back to Guttenburg’s invention of the printing press. I’m sure caligraphers of the time panicked that this new invention would ruin their industry. But graphic design perseveres.
The only people it ruins are those unwilling to evolve with the times.
Now back to AI-Generated Art.
By this point, you probably know my stance on AI-Generated Art. This innovation may seem like an industry killer. But only if you allow it to affect you.
I see Artificial Intelligence as another opportunity for our industry to evolve. It’s up to us to embrace these tools as just that, tools.
I already see designers putting AI-Generators to good use. Katie, a Resourceful Designer community member, recently shared how she needed an abstract pattern for a background of a design she was creating. Instead of searching for a stock image or making one herself, she turned to AI. She told it what she wanted, and it produced something she could use.
Katie also used it as inspiration for an annual report project. She asked it to produce a report cover design using blue and yellow triangles. It gave her a few options that she used as inspiration to create something herself.
Andrew, another member of the Resourceful Designer Community, experimented with AI on a couple of real projects. His takeaway is that AI isn’t quite up to being a reliable design tool for us, but it’s getting there.
And these are just a few examples.
As for creating full designs using AI, I think the technology is still a long way off. And no matter how good it gets, it will never be able to replicate the emotions we designers bring to a project or the empathy we feel towards our clients.
I like to meet every client I work with. If I can’t meet them face to face, I at least want to get on a video call. I do this because I want to get to know them. I want to see their personality and understand how they act and think. Because these things will help influence my design decisions. No artificial intelligence can do that. At least, as far as I know. And that’s why AI will never replace a live graphic designer.
And don’t forget relationships. How often have I stressed the importance of building relationships with your clients over the years? Not only does it help you understand your clients better, which allows you to design better things for them. But relationships build loyalty. It keeps clients coming back to you, regardless of your price.
AI-Generated Art has limitations.
At this point. I see too many limitations with AI-generated design to affect us as an industry.
Since every piece of generated art is uniquely created, it’s tough to replicate should you need to.
Say you’re working on a marketing campaign and need several images. You ask an AI-Generator to create an illustration of a rocket ship flying through space, and it produces something you like. But now you need a different image of the same rocket ship landing on the moon. And maybe another of it returning to Earth.
Every time you enter a prompt in an AI Generator, it creates a unique image, so there’s no way to ask it to use the same rocket ship in future creations. The rocket ship will look different in each image. Even the style of art might look different.
Plus, these prompts, the instructions you type into the generator telling it what to create, are very subjective.
These two prompts
“An elderly man is sitting on a park bench feeding pigeons.”
“An old man is feeding pigeons in a part while sitting on a bench.”
To you and me, they both mean the same thing. But to the AI, they could be vastly different. How does artificial intelligence interpret “elderly man” vs. “old man”? The smallest detail can drastically affect the output.
Also, from what I can tell, It’s tough, if not impossible, to adjust an image.
Say you like the AI-generated photo of a woman sitting on a chair with a cat on her lap. But you decide you want it to be a dog instead. None of the systems I tried would let you make that sort of change. The best I could do was change the word “cat” to “dog” and rerun my prompt, producing a new batch of images with different women and chairs. There was no way I was getting the same woman in the second set of images.
Again, maybe this is possible, but I couldn’t see it.
Conclusion
All of this to say. Don’t panic. There are people out there leaning on both sides of the fence. Some say our industry is doomed, while others say we have nothing to fear.
I’m just one voice. But I don’t think we have anything to worry about. And I have the history I just shared with you backing me up. Fiverr, Canva, WordPress, Creative Cloud. These “design industry killers” are now part of my design toolbox. Instead of taking work away from me, they allow me to do better work and do it more efficiently.
I see AI-Generated Art as no different. I plan on embracing it and using it in any way I can.
And don’t forget—no matter what new “things” come out. Clients will always appreciate what a good designer can do for them.
You can be that designer.
| 2022-12-12T00:00:00 |
2022/12/12
|
https://www.resourcefuldesigner.com/why-ai-generated-art-wont-replace-graphic-designers-rd308/
|
[
{
"date": "2022/12/12",
"position": 1,
"query": "AI graphic design"
}
] |
Graphic design & content creation tips | Learn at ...
|
Graphic design & content creation tips
|
https://create.microsoft.com
|
[
"Guy Parsons",
"Shante Gorman",
"Gigi Davarashvili",
"Danielle Afram"
] |
Learn by topic. A collage of images themed around AI. AI tips & insights. A collage of images themed around design.
|
Learn from the experts
Get insights and insider knowledge from creators in a variety of industries
| 2022-12-12T00:00:00 |
https://create.microsoft.com/en-us/learn
|
[
{
"date": "2022/12/12",
"position": 81,
"query": "AI graphic design"
}
] |
|
Graphic Design - Apps on Google Play
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Apps on Google Play
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https://play.google.com
|
[] |
"Are you looking for professional graphic design posters, Flyers and Logos to expand your business, or want to make your social media post unique without ...
|
"Are you looking for professional graphic design posters, Flyers and Logos to expand your business, or want to make your social media post unique without any professional skills?
Graphic Design is easiest app you will ever use with thousand of ready to use templates or you can customize your own templates. You will go with your ideas and finished just in minute.
Create beautiful graphics, Instagram stories and Posts, Thumbnail for YouTube, Logos for business, Banners, Posters, Invitation cards, Photo book, Presentations, Visiting Cards etc.
Graphics Design app allows you to create and customize Social Media Post without any designing skills. You can create instant creative banners & posts that will help you to schedule on social media platforms. Use 2000+ templates & layouts design to make your brand at the next level.
App includes following creative features
⭐ 2000+ Instagram post templates, Invitation cards, Collage Layouts & YouTube thumbnail templates with different categories.
⭐ Quick and easy to customize design templates without any skills.
⭐ No graphic designing skills required, Make banners & social media posts by just one tap
⭐ Multiple aspects ratio for design and post layout.
⭐ Great collections of updated templates & layouts for beginners.
⭐ Amazing icons & stickers to add on your post to make a more engage-able design.
⭐ You can store & edit your design anytime. You can create flyers & banners as you like to store them as a project, and you can easily edit project anytime.
⭐ Share your graphics post on different social media platforms.
You can create following designs
Social Media Post Maker
Graphics Design is the best social media post creator tool that helps you to create beautiful social media posts to increase your brand engagement and grow your business on social media platforms with ready to use templates.
YouTube Thumbnail Maker Studio
Make your awesome thumbnail for YouTube that will help to grab the attention of your users. Dozens of readymade YouTube thumbnail templates available in different categories. Make YouTube thumbnails in different sizes 1280x780, 2048x1152 aesthetic YouTube banners.
Invitation & Greetings Card
Graphics design allows you to make invitation cards with photos to invite your friends and families on your special events. You can create Invitation Cards for every occasion such as birthday party, wedding, Christmas, baby shower, Valentine day, RSVP cards, online greetings card, etc.
Business Card & Visiting Card Maker
You can create your own customized business & visiting cards by choosing readymade templates. It also has features to add business Logo, email, phone number and website on visiting card design with photo and QR Code.
Customize Logo Maker Studio
You can create high engaging Logo design for your business without any designing skills. There are plenty of Logo templates with different categories and It includes multiple logo templates with different text art, shape, and stickers.
Poster & Flyer Maker
We have dozens of readymade posters, flyer designs and professional advertising posters and templates. No graphic design skill required; you can make an online poster maker without any watermark.
Presentation Maker
Graphic Design helps you to create a School and college presentation for children with beautiful ready to use templates just in one tap and easily in phone.
📲 Download Graphics Design Now for free!"
Updated on Oct 30, 2024
| 2022-12-12T00:00:00 |
https://play.google.com/store/apps/details?id=com.app.graphicsDesign&hl=en_US
|
[
{
"date": "2022/12/12",
"position": 89,
"query": "AI graphic design"
}
] |
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Leading payment companies in the artificial intelligence ...
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Leading payments companies in the artificial intelligence theme
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https://www.electronicpaymentsinternational.com
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[
"Vasanthi Vara"
] |
According to GlobalData's thematic research report, AI in Banking, leading adopters include: Amazon, Alphabet, Apple, Z Holdings, Tencent, Ant Group, Goldman ...
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The future of the payments industry will be shaped by a range of disruptive themes, with artificial intelligence (AI) being one of the themes that will have a significant impact on payments companies. A detailed analysis of the theme, insights into the leading companies, and their thematic and valuation scorecards are included in GlobalData’s thematic research report,Artificial Intelligence (AI) in Banking – Thematic Research. Buy the report here.
AI refers to software-based systems that use data inputs to make decisions on their own. In banking, AI use cases range from enhancing client interactions through chatbots; to providing better loan terms through data-driven risk assessments; and the automation of laborious back-end processes. Banks can realise the benefits of AI in cost savings, quality improvements, an expansion of their services, and increased personalisation in these product offerings.
There has never been a more important time for banks to invest in AI. With threats to the industry coming from both disruptive fintechs and the Covid-19 pandemic, which uprooted traditional branch-based banking, banks must be proactive in adapting their strategies and processes to remain competitive and desirable to consumers. Fintechs have changed consumer expectations, putting more pressure on banks to offer a better user experience. This comes at a time when 0% interest rates are already challenging retail banks’ core business.
However, not all companies are equal when it comes to their capabilities and investments in the key themes that matter most to their industry. Understanding how companies are positioned and ranked in the most important themes can be a key leading indicator of their future earnings potential and relative competitive position.
According to GlobalData’s thematic research report, AI in Banking, leading adopters include: Amazon, Alphabet, Apple, Z Holdings, Tencent, Ant Group, Goldman Sachs, BBVA, OakNorth, and DBS Bank.
Insights from top ranked companies
Amazon
As the world’s leading provider of cloud infrastructure services, Amazon competes with Google in AI services. It offers a slew of AI and ML services through AWS. These include pre-trained AI services for image and video analysis (Rekognition), conversational agents (Lex), and text-to-speech applications (Polly). Cloud ML platform SageMaker was launched towards the end of 2017. The most well-known AI product from Amazon is Alexa, its virtual assistant. As part of its Echo range of devices, Alexa gave Amazon an early lead in the nascent smart speaker market. Amazon orchestrates the world’s largest fleet of mobile robots in its warehouses and has invested heavily in AI-related M&A activity, acquiring Canvas, a warehouse robotics company, in 2019, and Zoox, a leading autonomous vehicle start-up, in mid-2020.
Alphabet
Google is, by most measures, the leading provider of AI. The company has been at the forefront of several of the most significant breakthroughs in AI, from the autonomous car (Waymo) to AlphaGo. It offers the broadest portfolio of ML technologies, its Google Assistant conversational platform regularly outperforms its competitors, its TPU family of accelerators is cutting-edge, and it is a powerhouse in AI R&D. Google’s biggest asset is its wealth of customer data. The tech giant has been heavily involved in AI-related M&A activity, with its 2014 acquisition of ML pioneer DeepMind the most well-known. In the last couple of years, Google has emphasised the importance of ethical considerations within AI and using AI for social good, contributing to far more social media attention than any other company.
Apple
Apple trails the leading players in AI. Both Google Assistant and Amazon Alexa regularly outperform Siri, and Apple was slow to enter the smart speaker race, resulting in its HomePod having a lot of ground to make up. However, Apple dominates the important wearables segment with its smartwatch and hearables, products that will help it increase the use of Siri. In recent years, Apple has made a string of AI acquisitions to close the gap on its rivals. Significant investment has gone towards improving Siri, including the purchase of several ML, voice tech, and edge computing start-ups. There has also been widespread speculation that Apple will acquire wireless speaker company Sonos.
To further understand the key themes and technologies disrupting the banking industry, access GlobalData’s latest thematic research report on AI in Banking.
PayPal
Visa
Mastercard
Klarna
Block
Revolut
Stripe
Paytm
ACI Worldwide
Adyen
Worldline
SumUp
Amex
Samsung Electronics
Meta
Monese
JCB
FIS
Discover
Verifone
JPMorgan Chase
Fiserv
Global Payments
SecurePay
Barclays
Danske
WorldRemit
Nets
Wise
MoneyGram
Data Insights From The gold standard of business intelligence. Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors. Be better informed
| 2030-01-11T00:00:00 |
1/11/30
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https://www.electronicpaymentsinternational.com/data-insights/top-ranked-payment-companies-in-artificial-intelligence/
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[
{
"date": "2022/12/12",
"position": 97,
"query": "artificial intelligence business leaders"
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Digital Disruption - The Bestem Network
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Digital Disruption – The Bestem Network
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https://bestemnetwork.com
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Some experts predict that generative AI will automate many tasks currently performed by humans, leading to job displacement in some industries. However, it is ...
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This is a long post. There is a lot to understand on this topic and this is the primer you’ll need. Please do follow the links. It will take a while but it’ll be worth it.
There is much being discussed around the acceleration of technology and how exponential development in multiple areas is converging and how this will impact on industrial production. About time too really because productivity is failing to pick-up, interest rates at or below zero, cash being hoarded by companies and investment rates are low [link]. This exponential increase is actually not that new – Moore’s law’s been around since… well since Moore launched Intel, and it was a one-way flow before even Schottky was on the scene. These trends are not new and if you don’t believe me here’s a great talk from 11 years ago by Ray Kurzweil with lots of evidence and predictions [Link]. Even Bill Gates saw this in a book he published in 1999 called Business @ the Speed of Thought [link], though these days that speed would be seen as a little too slow.
In my opinion the Oil industry harnessed many of the aspects of this movement in upstream exploration during the 1990’s. It was an early adopter in the process of FINDING reserves. Then the process of adoption stopped. Productivity per geoscientist and the complexity of the information they deal with is orders of magnitude better than it was during the last oil-bust of 1986, so much so that we now have more fields and deposits than we know what to do with. We’re pretty good at finding the stuff. But we’re quite rubbish at developing and operating it at low cost – especially small deposits, which we are so good at spotting now. Development, Operations & Maintenance has ossified – contracts and work practices are stuck. From an operator approach, the production of hydrocarbons has barely moved since the early 90’s (FPSO concepts aside). In my opinion through outsourcing, procurement, short-termism and misalignment of incentives it has become positively petrified.
If the Fourth Industrial Revolution is really going to have an impact we’ll need to address development on four fronts: Economic, Social, Political, and Technical
Economics
If this is going to happen then it has got to make sense for the bottom line. That means productivity: outputs, inputs and the cost of technology. McKinsey, recently cited in Industries of the Future, by Alec Ross [Link], suggested that the manufacturing sector could raise productivity by 2.5% to 5% and save more than $1Trillion in cost annually.
In one example McKinsey says “To capture the potential, manufacturers can consider three moves. Primarily, companies can gather more information and make better use of it. An oil-exploration company collected more than 30,000 pieces of data from each of its drilling rigs—yet 99 percent of that data was lost due to problems of data transmission, storage, and architecture. The tiny trickle of data it did capture was incredibly useful for managers. But so much more can be done. The executives we surveyed said that correcting these data inefficiencies should improve productivity by about 25 percent. [Link]”
Of course some traditional economists think we’re doomed to no innovation and permanent low growth – such as Robert Gordon [Link]. There are many in the old-guard of Oil and Gas that would agree. Most of them have their secretaries print their emails out for them, and refuse to carry a smart phone. Good luck chaps, I think you’ll find the millennials don’t care what you think anymore. Others say differently [Link]
There are many big hitters with some very big numbers, they’re all pointing in the same direction. I’m backing the future, not the past. And I think that Industry 4.0 will feature in the future of Oil and Gas. There are challenges but the prize is big enough that we will overcome them.
Social
The way that many of us work is going to have to change. Luckily the Millennials are already preparing for this shift with their search for meaningful work, emphasis on creativity and individuality; and understanding that they can blend their work and leisure time in ways that the crumbly generation see as slacking and entitled. Forbes have a top ten ways in which the work place will be influenced [Link] and Linda Grattan has her views here [Link]. For me it just seems an obvious way to work. But then I’ve never been very good at dealing with routine, structure and command-and-control. It’ll be interesting to see how we can blend the command-and-control requirements of operations with the caffeine fuelled micro-attention span of people even more “wired” than me.
We’re also seeing cyber-social developments such as the creative commons movement [Link] and open source projects like the Arduino [Link] all of which are fuelling exponential cross-fertilisation of ideas. We are witnessing the rise of the sharing economy [link] and temporary configurations of people who move about often. These are all challenging assumptions about ownership and permanence that are at odds with our current ownership-model for resources.
Political
The Guardian in Nov 2015 reported that ” this revolution could leave up to 35% of all workers in the UK, and 47% of those in the US, at risk of being displaced by technology over the next 20 years, according to Oxford University research cited in the report, with job losses likely to be concentrated at the bottom of the income scale.” [link]
With modern communications and the ability to mobilise quickly we’ve already seen massive changes in the way the people (or, in Greek, demos) interact with conventional democratic systems and capitalism. This is very thoughtful piece by Yanis Varoufakis the recently deposed Greek finance minister [link]. Whether that’s the Arab spring, so-called ISIS, Brexit, the mass-migration of populations or the astonishing rise of Donald Trump, things are getting decidedly odd in traditional politics. There’s a lot of complaint and not a lot of traditional power that can be exercised in public anymore [link]. Just take a look at the mass-mobilisation of a Brazilian flash-mob to protest graft allegations levied against the establishment [link]
Cyber-politics is a whole new dimension. Whether cyber aggression is aimed at accessing private information, denying or altering the dissemination of information or compromising the physical integrity of machine-based systems the ability of people to alter the course of events through “hacking” has never been so great. China has its infamous PLA unit 61398 [Link] one of over 20 cyber-military units it controls, North Korea doesn’t like Sony much as the 2014 hack showed [Link], Iran might be the land of the rising Shamoon that hit Aramco [Link], Ukraine has got on the wrong side of Russian Hackers who shut off their power grid [Link], and who knows who might have written Stuxnet that took out the Iranian centrifuges while telling the control room all was normal [Link]? Now the actors are not only nation-states, but also corporations and little boys alone in their bedrooms [link]
We have the Geneva convention that is supposed to stop states shooting the red cross, bombing civilians, gassing troops and firing mercury-filled dumb-dumbs. We have the international court in The Hague (funded by Andrew Carnegie incidentally [link]) that prosecutes war criminals. I’m not sure who I should call if North Korea invades my X-Box or steals my Bitcoins. And if you are a corporation with cross-border operations you don’t either.
Technical
There are a number of technologies that are developing exponentially at the moment and they’re feeding into changed ways-of-working that will bring about the fourth industrial revolution. Ultimately this will help you plan to build better plant and it will help you operate what you have better. Optimising operations is a sense-and-respond problem. Prepare for the future, know what’s going on right now and do things to make it better. Technology that helps falls into four areas that increase:
learning about what’s possible;
what’s going on right now and situational awareness;
knowledge of interdependence, decision options and consequence; and
ability to execute quickly and accurately
Increased learning about what is possible
Big data has gained traction in the last decade. Grab lots of data from everywhere, apply some Bayesian stats, set a base-level and determine the probability of correlation. Works really well when you buy a book from Amazon and it suggests that you might want to buy some reading glasses to go with it. Works pretty well in finding potential hidden relationships and developing predictive algorithms for equipment failure too [link] [link]
Like a lot of developments, this area is moving fast. How do you know what’s even possible these days? It’s so hard to keep up. Data overload, over-stimulation, who even has time to read this stuff?
I remember Schlumberger creating an amazing “portal” called the hub [link], Other companies did similar [link]. Initiatives were started to capture the learnings from each employee and make them available to all other employees. I even heard a talk once describing the use of retiree mentors to help existing employees [link]. People were planning for “The Big Crew Change” when the aging workers retire and new low people come on board [link]. This all tied into concepts like “Hive Minds” which were popular in the 90’s [link].
Well “The Big Crew Change” became the “The Big Layoff” when oil prices crashed in 2014. All that experience and knowledge was not on the balance sheet but was on the P&L. So it was fired without financial impairment and write-off. But the fundamental problem remained, and probably got worse. So much to know, so much to learn and no time to do it. Welcome to one of the drivers that will build demand for machine learning.
Machines can analyse masses of information much quicker than humans can. Up until recently, however, doing that in context and to derive meaning from them has been hard. Development has been showcased by game-playing computers such as Deep Blue for Chess [link], then Watson for Jeopardy [link], and most recently a Google built machine – AlphaGo for GO [Link].
Combine learning algorithms with connected systems, however, and things get really interesting. Learning requires teaching. Unlike programming in Fortran, learning machines construct their own programs by being taught and from the situations they encounter. Distributed and cloud-connected learning is exponential, one machine learns something somewhere and every other machine knows it. Forever. Perhaps we should blow the dust off those long-forgotten “portal” promises around knowledge bases, institutional learning and corporate memory?
Here is a great TED Talk on machine learning [link]. Of course it doesn’t always go well as Microsoft found out with it’s recent “Hitler loving Sex-Robot” [link]
What’s going on right now and situational awareness
Machines that learn what matters and suggest how to respond can eliminate operator overload by removing the trivial and hiding noise. Automated actions can be taken to keep things running. I recently heard an analogy about the difference between the information received by a pilot of a typhoon (arguably the worlds most advanced fighting machine) and a world-war 2 Spitfire. The Spitfire pilot had dials telling him the airspeed, engine speed etc. All just data. The Typhoon pilot, however, could not possibly cope with all the data available. So this data is assembled to show him only what he needs to know in the current situation and that depends on context.
Paul Smith, former UK RAF Pilot says “Bring all these [sensor and interpretation] elements together and it becomes clear why we talk about Eurofighter Typhoon operators having the ‘Combat Edge’ – the situational awareness and a suite of flexible weapons options that offer pilots a real advantage in the battlespace.” [Link]
Building on this, if the aircraft systems detect a heat-seeking missile closing, it launches flares automatically and tells the pilot afterwards – no point in raising an alarm and waiting! Same for the oil and gas sector, why do automatic fault development detection systems write a report and wait. Why don’t they just order the parts, consolidate shipments and schedule engineers for the next maintenance activity? It’s a small example but Amazon is already letting washing machines re-order soap powder [link], Imagine what Amazon-like logistics would do for the Oil and Gas industry.
In order to know what’s going on right now requires a lot of sensors talking to each other and reporting back. Too much detail for a human system to ever cope with properly. The data needs to be reporting to systems that learn what’s important, what actions it should take and how it should present its findings to its operators. The system needs to learn how to behave. These systems need to be aware of the situation and act accordingly. Cloud computing is also an important vector this mix, where systems are connected to each other through internet, and keep each other in synch sharing learning and preparing information so that it can be shared widely, securely and scalably. Google are letting developers play with their learning platform [link]. This is an area where we will see rapid innovation that Oil and Gas can benefit from.
Of course there are some very boring building blocks that will be needed. Connecting systems together will of course require a lot of plumbing – don’t underestimate the size of this problem, here is an example of the type of architecture you might need [link]. Companies like Eigen (www.eigen.co), Tibco (www.tibco.co.uk), BEA (www.bea.com) are active in this area. And it’s important that we really know that our data is correct – as in this case when a demolition crew targeted the wrong house and blamed it on google maps [Link]. So companies like datum360 (www.datum360.com) and Informatica (link).
Decision making & Interdependence
Knowing what’s going on is great, but what do you need to do to make your situation better? That’s the question that quickly arises once teams get sight of data and information in context. Firstly it’s important to know what the options could be – but also how choices in one system effect another.
Simulation is one of the keys to understanding the consequences of decisions – that’s why chess computers work out 100’s of moves ahead and choose the best one to use now. To simulate the decisions on a plant requires a digital model of the plant and its behaviour against which to run tests. The digital model is sometimes called a “Digital Twin” and this allows you to make a change or react to a fault condition and see what the knock-on consequences of selected actions will be in the future. This can be used to test options and optimise outcomes. Hit the model with a series of possible actions in an automated way and it’s possible to uncover the best sequence of actions and back-calculate why, rather than the normal forward progression. It’s very powerful – here are some articles discussing simulation of plant [link]
Integrated planning enables you to make a decision about sequencing events in such a way as to minimise down-time by running jobs in parallel within real-world constraints. This might mean being prepared and ready-to-act when an opportunity unexpectedly arises. Understanding system-wide effects is the key to getting this right, and with more complex interconnected systems with cross-ownership (like present in the UK sector of the North Sea) it not easy – and the owner stakes in oil fields can lead to misalignment of financial interests. Bain has a good article on integrated planning [link].
So far it seems that human + machine combination provides the best mix for solving problems. The creativity of the human is key and augmented decision making with rapid feed-back loops from simulation enables optimisation of decisions. From the first simple spreadsheets that appeared in business the testing of “what-if” scenarios has meant that we have been able to tune procedures across many areas of operations. The combination is not a new concept, here is a very relevant paper from Carnegie Mellon 1998 [link]
Increased speed and accuracy of execution
One of the issues that I’ve come across is the “precision” approach of some operators in the field. The best plans are of no use if they are not executed properly, if parts aren’t damaged and if the wrong parts were not fitted. It happens. Sometimes, of course, the instructions make no sense and the field have to modify them to make them work. That modification of instruction is rarely fed back into the system so little learning takes place. Sometimes the plant is updated and records not updated. All this leads to mismatch between what is recorded and what the plant operators “know”.
Sometimes the physical effort required to perform an inspection means that it cannot be done as often as you’d like, or perhaps is skipped by a crew unwilling or unable to schedule. Autonomous vehicles are in use for inspection activities firstly replacing deep divers and latterly, as costs have gone down they are found in inspections roles as Drones taking cameras into inaccessible places. Perhaps it won’t be long until we have small UAV’s mapping plant and equipment in huge detail. Here is a TED talk that demonstrates what’s already possible [link]
On-site machining of parts may soon be replaced by on-site manufacturing. Additive manufacturing (a broader term than 3D printing) is finding its way not only into printing of small intricate parts but emerging are the start of large-scale construction. It’s not there yet, but imagine what this would mean for logistics or construction in hostile environments. Here is an example of a team in Amsterdam who are in the process of printing a Steel bridge over a canal. That could change some of our approaches to Maintenance and Modification one day. [link]
And, of course, there will always be people involved. But multi-skilled and informed. Augmented reality displays – identifying parts, performing on-the-fly risk assessments and acting as advisors. This will change the way that operators will be able to apply basic skills augmented with real-time instruction and feedback. Meron Gribetz demonstrates here a virtual reality system that could revolutionise the on-shore-off-shore interface, as well as providing just-in-time information. Here is his TED talk [link]
And if you don’t think a Robot can replace people on platforms – have a look at this [link]
| 2022-12-13T00:00:00 |
https://bestemnetwork.com/tag/digital-disruption/
|
[
{
"date": "2022/12/13",
"position": 42,
"query": "automation job displacement"
},
{
"date": "2022/12/13",
"position": 52,
"query": "universal basic income AI"
},
{
"date": "2022/12/13",
"position": 29,
"query": "AI economic disruption"
}
] |
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The Risk and Rewards of the Rapid Rise of AI
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The Risk and Rewards of the Rapid Rise of AI: The Implications for Project and Portfolio Management
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https://www.projectmanagement360.com
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From this comes a belief that AI can solve a multitude of issues in the IT space that are beyond humans' current capacity to address — and recent evidence ...
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Abstract
The integration of artificial intelligence (AI) and technology is a growing trend that will define the remainder of the 21st century. Notably, every era goes through a revolution of industry — human history and society have endlessly been shaped by a steady drumbeat of technological advancement. Hundreds of thousands of years in the past, mankind was reliant on tools made of bone, wood, and stone. Sometime after, controlled-fire was discovered — propelling the world forward even more. This spark would eventually lead to several revolutions of technology that were shaped by steam, mass production/manufacturing, and a digital age (Schwab, 2021). But beyond the first, second, and third industrial revolutions, a fourth revolution is rapidly rising: AI. As a computer science division focused on the creation of a system that performs “human-like tasks, such as speech and text recognition, content learning, and problem-solving”, AI enables technology to both analyze and recognize “huge amounts of data” and “recurrent patterns” (Softengi, 2022). While digitization becomes the norm, and while process complexity and the acceleration of innovations expand (Softengi, 2022), companies such as those involved in information technology (IT) can only compete by relying on more intelligent processes. As such, the growing adoption of AI in various sectors is quickly altering how value is created, exchanged, and distributed (Schwab, 2021) — profoundly changing lives, business performance, market competition, and consumer relationships with products. This holds true for the technology industry, and there is a considerable degree of excitement — but also apprehension — over where the future of AI is headed in that regard.
At a glance, artificially intelligent technology has provided revolutionary advantages such as improved processing speeds and decision-making, increased productivity, and significant reductions in human error (Anderson & Rainie, 2018). Nonetheless, despite the societal benefits of AI, this mega-trend is disadvantaged by a multitude of side-effects such as higher costs for hardware and operations — as well as the risk of AI-inspired automation replacing human labor; the risk of AI increasing unemployment as people are replaced by advanced, higher-performing technologies (Anderson & Rainie, 2018). These are but a few of the opportunities and challenges posed by the increased adoption of AI. As such, there is a need for government intervention to better regulate AI — no different than other technological areas. Therein, it is necessary to address the tensions that exist in the tech industry over the growing incorporation of artificial intelligence (AI) in daily operations and products — the opportunities and challenges that have emerged — so that measures can be taken to ensure the future of AI is defined by stability and value for humankind. It is also worth recognizing the value AI adds to strategic business execution, as well as portfolio and risk management — the competitive advantage to be derived in these areas with the rise of AI.
AI’s impact on the technology industry should not be understated. Tech Mahindra’s chief executive officer (CEO) — leading a company that provides IT services and networking solutions — stated that AI is revolutionary because it will bridge the divide between the limits of human capacity and “what is actually possible” (Gurnani, 2019). Moreover, AI’s growing prevalence in the tech industry will result in the increased robotization, mechanization, and automation of basic tasks once performed by humans — with a AI becoming integral to daily life (Gurnani, 2019). While it is true that most pronouncements of AI-assisted automation of tasks remain fanciful — as there are “far more instances of augmentation of human work by smart machines than of full automation” (Davenport & Miller, 2022) — the limitations of AI in “real-world work settings” (Davenport & Miller, 2022) will one day give way to the unlimited productivity of AI-supported, automated labor. Relatedly, motor vehicles have progressed in safety, speed, fuel efficiency, and general reliability when compared to a hundred years ago — and some cars are now driverless with the integration of AI. Likewise, AI is amplifying human productivity and efficiency; matching or exceeding “human intelligence and capabilities” (Anderson & Rainie, 2018). From this comes a belief that AI can solve a multitude of issues in the IT space that are beyond humans’ current capacity to address — and recent evidence suggests the increased adoption of AI technologies has optimized solutions to these challenges (MyComputerCareer, 2022). Integrating AI with technology has generally improved efficiency, enhanced productivity, and further assured quality — thereby reducing “the burden on developers” (MyComputerCareer, 2022). No doubt, AI’s opportunities are endless.
AI is developing “advanced algorithmic functions” that will bolster the “development and deployment of IT systems at large scale” — a feat once considered nearly impossible (MyComputerCareer, 2022).
Some other opportunities include increased system security, improved coding productivity and automation, enhanced quality assurance, “better application deployment during software development, and better server optimization” (MyComputerCareer, 2022). Data security is vital in the technology industry for “securing personal, financial, and confidential data” — and AI relies on advanced algorithms and Machine Learning (ML) to fortify protections for this data (MyComputerCareer, 2022). Furthermore, coding is essential to technological development and AI enhances “coding productivity” via a similar reliance on advanced algorithms (MyComputerCareer, 2022). AI contributes to efficient and productive coding by noticing patterns and providing suggestions — which not only saves time but also keeps the code “clean and bug-free” (MyComputerCareer, 2022). Therein, human error is automatically corrected by AI. Additionally, AI’s automation lends itself to a reduction in manual labor — a reduction supported by “deep learning applications” and automated “backend processes” that cut into work hours and reign in costs for IT departments (MyComputerCareer, 2022).
Nevertheless, while AI is a path for optimizing technologies and seamlessly integrating “business and technological functions” (MyComputerCareer, 2022), the aforementioned opportunities of AI are balanced by the undeniable threats that come with it. Accompanying AI’s benefits are challenges such as the high costs of adopting AI technologies — costs that counteract the reduced business costs AI can lead to (Gurnani, 2019). AI technologies are not cheap — and so, not currently cost-effective for every business. There is also the issue of data and algorithms that “reinforce gender, racial or ideological biases” (AI for Good, 2018). AI is not yet fully immune to the human error of its creator — and so, if the input of the dataset by human operatives is faulty, AI can reach the wrong conclusion and incorrect output. As a further matter, AI utilizes deep-learning algorithms/technologies that are “opaque” in their decisions — this can spell trouble when seeking to understand why AI has either failed in its assignment or made certain recommendations (AI for Good, 2018). To add to this, the vague workings of deep-learning algorithms/technologies can also impede efforts to assess “when and how AI may be reproducing bias” (AI for Good, 2018). Additionally, as celebrated as the intelligence and predictive potential of AI might be, AI is not immune from security risks — the software that is found in electrical grids, as well as cellular and camera technology, is not invulnerable to attack simply because it is supported by AI (AI for Good, 2018). Although current AI is a growing answer to such threats, security risks continue to necessitate some form of human oversight.
Furthermore, there are economic and national security threats that characterize AI’s growing adoption in the technological sector. Due to globalization and the internet, the peace and stability of the international world have never been more interconnected at any point in time. As a result, the abuse of AI technologies and machine learning to generate ‘deep-fakes” or fake videos and audio of an individual — to influence perceptions — is a growing problem (AI for Good, 2018). Political, social, and economic outcomes — matters of peace and war — can be tilted one way or another in the absence of sufficient organizational and policy-oriented governance. AI has also led to increased augmentation and automatization which can deepen economic insecurity and societal inequalities by substituting humans with machines for routine tasks (AI for Good, 2018). As jobs are displaced and unemployment climbs, the AI revolution is at risk of succumbing to the same societal ills that plagued industrial revolutions of the past. Some economists have lectured as to how the AI revolution might “yield greater inequality, particularly in its potential to disrupt labor markets” (Schwab, 2021). The mechanization of cheap labor will devalue or outright eliminate some occupations — forcing a collision between the public and private sectors as citizens and their government debate profits over people and other moral questions. This holds true for AI’s automation of jobs across the tech industry — for example, tech is full of analytical positions that are prone to disruption by AI (Markolf et al., 2021). Likewise, AI’s advanced capabilities — when coupled with increasing data availability and decreasing computing costs — threaten to upend other positions in the tech job market (Markolf et al., 2021).
Granted these threats and opportunities, it is worth considering the progress that has been made — what the tech industry has done to date. Numerous organizations have already implemented AI into their daily operations and products. The integration of AI into operations has optimized the processes of several tech companies (MyComputerCareer, 2022).
For example, AI has improved communication — enabling the automatic transmission of “reminders to departments, team members, and customers” (MyComputerCareer, 2022); AI has proven beneficial for monitoring network traffic; AI has also helped reduce the number of repetitive tasks so that employee attention can be focused toward the more “critical aspects of the business” (MyComputerCareer, 2022). Additionally, AI has given technology a more “personalized customer experience” as customer service — ranging from answering questions and providing recommendations, to uncovering hard-to-find products — has become more intelligent and automated in its analysis of in-store and customer data (MyComputerCareer, 2022). Similarly, AI’s ability to sift through large quantities of data has given companies a competitive advantage by improving “strategic insights and business intelligence” (MyComputerCareer, 2022). Various surveys indicate a minimum of 75% of companies and tech leaders believe AI technology will make their business more competitive, support new business ventures, boost efficiency and productivity, and create new jobs (MyComputerCareer, 2022).
Still, some companies find the task of implementing AI daunting — the biggest obstacle for “roughly 37% of executives” being the alignment of the corporate vision and strategy with the possibilities of AI (MyComputerCareer, 2022). It is important to have senior leadership and management not only on board but in the know about how new technologies, like AI, function — but evidence suggests the pairing of AI and IT will make the integration of AI much easier for these companies (MyComputerCareer, 2022). So far, the integration of AI has shown itself to be successful in service management, IT operations, business process automation, and fraud detection (MyComputerCareer, 2022). AI has integrated with service management for more efficient resource management that supports cheaper and faster deliveries; AI for IT Operations (AIOps) relies on AI for the continuous, automated management of IT on multiple platforms and the management of increasingly complex information sources, data collection, and controlled systems; deep learning technologies and business process automation decrease the reliance of IT departments on “direct human intervention”; while AI has been abused to commit fraud, AI/ML has made fraud detection easier and faster by sifting through data and identifying “patterns of fraudulent behavior” (MyComputerCareer, 2022).
Although many opportunities and their progress have been noted, the threats endure — however, field and policy experts have presented some solutions. For one, it is imperative that “relevant international standards” are developed and adopted (AI for Good, 2018). The recognition of international standards for AI, alongside the use of open-source software, is suggested to be one way to achieve a “common language” by which diverse stakeholders can contribute to AI’s development — thereby rooting out the threat of bias with “datasets that are accurate and representative of all” (AI for Good, 2018). Field and policy experts have made additional recommendations for safeguards that support the transparent, “legal, ethical, private and secure use of AI” (AI for Good, 2018). Transparency of AI’s design will address the challenge of assessing AI’s vague determinations — human operators will be able to understand and agree or disagree with the reasoning behind some of AI’s conclusions (AI for Good, 2018). Overall, the pursuit of AI and machine learning that is “ethical, predictable, reliable and efficient” (AI for Good, 2018) has led to the proposal of regulations and the intervention of domestic and international government agencies.
The CEO of SpaceX and Tesla, Elon Musk, once iterated a need for humanity to be careful with AI — that AI was “potentially more dangerous than nukes” (Etzioni, 2017).
Musk also shared a need for regulatory oversight of AI “at the national and international level” (Etzioni, 2017). Objectively, global governance of AI is worthwhile — no different than the laws that check nuclear proliferation or the international cooperation that supports the internet. The International Telecommunication Union (ITU), the United Nations’ (UN) “specialized agency for information and communication technologies”, has been working with various stakeholders — from “governments, industries, academic institutions, and civil society groups” — to address ways in which AI can be both used for good and regulated for the public interest (AI for Good, 2018). As such, one of the concerns raised by ITU is over the threat AI poses to employment. While AI affords the benefit of new employment opportunities, this is jeopardized by the mass augmentation and automation of repetitive tasks found in lower-level positions. High unemployment has the potential to destabilize communities — if not entire countries/regions. To address the negative employment consequences of AI — along with the other threats and challenges that are posed technologically — regulations that support safety, data security, digital literacy, and accountability are encouraged (AI for Good, 2018).
Lastly, when extracting value from AI, there are specific business processes/disciplines to consider. Whether natural language processing or deep learning, studies show the implementation of AI has failed to significantly benefit “40% of organizations” that make major investments — regardless, there is sustained interest with “91.5% of firms reporting ongoing investment in AI” technologies and solutions (Davenport, 2020). From the perspective of a business professional, AI has tremendous value despite ongoing difficulties in extracting this value in all industries. Specifically, AI supports and provides technological solutions to the following: portfolio and risk management, as well as strategic business execution. In the world of business, risks abound — there are countless opportunities to take advantage of and threats to address. Effective risk management seeks to prevent as many risks as possible from becoming issues, while at the same time embracing risks with positive potentiality. Risks refer to uncertainties — unknown conditions or futures whose occurrence might result in a positive or negative effect on business objectives (Canvas, 2021). Risk can also be understood as the likelihood of failure to meet some objectives (Canvas, 2021). When managing risks, an assessment is conducted to determine the risk’s probability and potential impact. Thereafter, several risk response strategies and contingency measures are formulated to effectively address the risk.
With respect to strategic business execution, it refers to efforts to achieve business objectives by planning and implementing various strategies (Canvas, 2022).
When it comes to the strategies that shape the planning, operations, and changes an organization undergoes, the capability of an organization to achieve such is critical — as is the ability of the organization to effectively align its strategies with the execution of any initiative (Canvas, 2022). Strategic capabilities relate to an organization’s “strategy and vision” and can be thought of as the resources required to transform ideas into successful realities (Boatman, 2022). All organizations should build up their strategic capabilities because this will help organizations achieve greater success with their projects. Strategic capabilities optimize business performance “through a disciplined approach of identifying, prioritizing and approving” project, program, and portfolio elements (Canvas, 2022). Moreover, strategic capabilities address the various strengths of the company that can contribute to a competitive advantage — “people, resources, skills and capacities” (Hartman, 2019). Organizations rely on strategic capabilities to remain competitive in their respective industries/business environments. In total, improved strategic capabilities provide a competitive advantage, greater adaptability/flexibility to respond to change, and drive business performance upwards via stability (Boatman, 2022). Therein, the ability of an organization to compete is directly tied to the successful implementation of its strategies — this necessitates investment in those areas that advance strategic capabilities and strategic business execution.
As for portfolio management, it is considered a “bridge” between strategy and execution because it sits between each measure as a determinative element (Canvas, 2022).
Portfolio management resolves which projects business strategy should be focused on and, thereafter, which projects to execute — and so, portfolio management supports organizations in making “key investment decisions” (Canvas, 2022). Thus, the main focus of portfolio management is determining “the right projects” for the organization to invest in (Canvas, 2022). An unworthy investment should not be executed — and so, portfolio management is a metaphorical bridge that is crossed to reach the side of project execution. Portfolio managers ascertain if a business initiative is worth the time, money, and other resources of an organization — whether the bridge is worth crossing; whether the initiative is safe or too risky to execute. As an intermediary bridge, portfolio management enables successful strategic business execution because it aligns the execution of business with the organization’s strategy, guides decisions pertaining to project investments and resource allocation, strengthens organizational legitimacy, focuses on portfolio performance, and informs risk management (Canvas, 2022). From this, a connection is drawn between risk management, portfolio management, and successful strategic business execution — and AI can be beneficial in each of these areas.
The integration of AI with risk management has born positive results in the form of “risk intelligence” (OnSolve, 2022). Granted AI excels as a predictive asset — “detecting and anticipating problems” that might arise during different stages of technological development (MyComputerCareer, 2022) — risk intelligence is a beneficiary of the combination of AI with risk management. Together, AI and risk management elevate an organization’s ability to assess and respond to risks by proactively and continuously monitoring data sources (OnSolve, 2022). Because AI enables technology to quickly analyze and recognize “huge amounts of data” and “recurrent patterns” (Softengi, 2022), AI bolsters risk management by improving the speed at which risks are identified and remedied. Moreover, AI is benefited by automation such that a human operative may not be required to analyze a risk’s probability of occurrence or severity of impact — a simple assessment of risk histories, other data sources, and pattern recognition may prove more than enough to protect an organization from threats while also capitalizing on opportunities that a human operative might overlook. Also, the increased system security of AI’s advanced algorithmic processes supports the management of risk even further (MyComputerCareer, 2022). Not only will AI better filter project vulnerabilities, but it will also suggest — given the same artificially-intelligent predictive model — the optimal actions to address risk in orders of likely severity.
With the advent of a highly predictive AI, the notion that risk planning for large and complex projects is difficult due to the challenge of predicting risks beyond the project team’s foresight (Wu, 2020, p. 187) becomes increasingly obsolete. As an example, AI has already been incorporated into banking risk management. Some of the AI-affiliated technologies relied on by banks for risk management include Machine Learning, Deep Learning, Natural Language Processing, Analytics, and Big Data (Intel, 2022). The tech industry supplies banks with these AI risk management tools “to mitigate losses, spot market opportunities, and improve their bottom line” (Intel, 2022). By accessing “a vast number of data points…to spot patterns and predict outcomes”, AI technologies allow banks to better understand risks and more effectively address them (Intel, 2022). Overall, by informing business-critical decisions with “actionable intelligence” — and expediting “critical event response and risk mitigation” for enhanced harm reduction — AI greatly contributes to the upward propulsion of business value (OnSolve, 2022). These benefits are found throughout numerous industries, not just the financial — revealing the extent to which AI has made every industry more dependent than ever before on the tech industry for a competitive edge.
As for AI’s role in portfolio management and optimizing strategic business execution, benefits can similarly be extracted. As “a bridge” between strategy and execution (Canvas, 2022), portfolio management can benefit from AI when determining which projects should be the focus of business strategy and execution by automatically making “key investment decisions” (Canvas, 2022) based on past failures and successes. AI can analyze data to recognize previous failures and successes, and patterns of alignment between the organizational strategy and the execution of portfolio components — patterns that lend themselves to determinations for where to invest resources without waste. The “advanced algorithmic functions” of AI that support quality assurance and “increased automation” (MyComputerCareer, 2022) will lead to the automated selection of worthy projects for investment. AI, in conjunction with effective portfolio management, will better guide key investment decisions, improve resource allocation, and fortify risk management and protections against evolving threats (Canvas, 2022). As a consequence, AI adds tremendous value to organizations that rely on portfolio management as this combination enhances overall business performance (The Standard for Portfolio, 2017, p. 5).
Finally, a major rule of business execution conveys the importance of establishing good metrics and reward systems. There is a saying that you can only achieve what you measure — this applies to both “business and people” (Canvas, 2022).
However, it is not always easy to determine the correct “metrics” (Canvas, 2022). Therein, to optimize success, organizations should consider if operations occur under a “consistent set of metrics” or “Key Performance Indicators (KPIs)” — metrics that support and bolster predictions and analysis (Canvas, 2022). Current metrics should be critically challenged and checked to ensure “the desired outcome” is being measured (Canvas, 2022). For all of this, AI will be able to utilize machine learning and data analysis to automatically identify the best metrics to optimize predictions, performance, and business outcomes. AI can rely on past performance measures and other data sets to strengthen the linkage between the organization’s corporate vision/strategies and business initiatives — with research already showcasing the clear impact the quality of this link has on the success of strategic business execution (Canvas, 2022). Moreover, when it comes to allocating the right resources to effectively execute business strategies and the “optimization of internal business operations”, AI is coupled with an empowering “surge in data generation and computing power” to achieve this (Duhaime et al., 2021, p. 625). When it comes to the impact of internal and external organizational environments, the advanced algorithms of AI will expand “the scale, scope, and speed of analysis” of the business climate and market (Duhaime et al., 2021, p. 628) to assess what initiatives are worth pursuing. As such, AI truly has the potential to “transform organizations” (Duhaime et al., 2021, p. 625) — and the rapid rise of AI will only further reward the objectives of businesses in the future.
About the Author:
John Izuchukwu is a graduate student at the Feliciano School of Business. He previously earned a bachelor’s in International Justice from Montclair State University. He has an avid interest in the technological and international business sectors. See the author’s LinkedIn profile here: https://www.linkedin.com/in/johniz-izuchukwu2010/
References:
AI for Good. (2018). Challenges and opportunities of Artificial Intelligence for Good.
Retrieved from https://aiforgood.itu.int/challenges-and-opportunities-of-artificial-
intelligence-for-good/
Anderson, J. & Rainie, L. (2018). Artificial intelligence and the future of humans. Pew Research
Center. Retrieved from https://www.pewresearch.org/internet/2018/12/10/artificial-intelligence-and-the-future-of-humans/
Boatman, A. (2022). Organizational capabilities: Definition, examples, and building process.
AIHR. Retrieved from https://www.aihr.com/blog/organizational-capabilities/
Canvas Learning Management System for MGMT582 Fall 2022. (2022). Feliciano School of
Business.
Canvas Learning Management System for MGMT576 Fall 2022. (2021). Feliciano School of
Business.
Davenport, T. (2020). Return on artificial intelligence: The challenge and the opportunity.
Forbes. Retrieved from https://www.forbes.com/sites/tomdavenport/2020/03/27/return-on-artificial-intelligence-the-challenge-and-the-opportunity/?sh=5eb9f59d6f7c
Davenport, T.H. & Miller, S.M. (2022). What machines can’t do (yet) in real work settings. MIT
Sloan Management Review. Retrieved from https://sloanreview.mit.edu/article/what-machines-cant-do-yet-in-real-work-settings/
Duhaime, I.M., Hitt, M.A., Lyles, M.A. (2021). Strategic management: State of the field and its
future. Oxford University Press.
Etzioni, A. & Etzioni, O. (2017). Should artificial intelligence be regulated? Issues in Science
and Technology, 33 (4), Retrieved from https://issues.org/perspective-should-artificial-intelligence-be-regulated/
Gurnani, C. (2019). The AI revolution is here: It’s up to businesses to prepare workers for it.
CNN. Retrieved from https://www.cnn.com/2019/05/30/perspectives/ai-business-jobs/index.html
Hartman, D. (2019). What is strategic capability? CHRON. Retrieved from https://smallbusiness.chron.com/strategic-capability-15828.html
Intel. (2022). How AI Enhances Banking Risk Management. Retrieved from https://www.intel.com/content/www/us/en/financial-services-it/banking/banking-risk-management.html
Markolf, S.A., Chester, M.V., Allenby, B. (2021). Opportunities and challenges for artificial
intelligence applications in infrastructure management during the anthropocene.
Frontiers. Retrieved from https://doi.org/10.3389/frwa.2020.551598
MyComputerCareer. (2022). The Future of IT and Artificial Intelligence. Retrieved from https://www.mycomputercareer.edu/news/the-future-of-i-t-and-artificial-intelligence/
OnSolve. (2022). Risk Intelligence. Retrieved from https://www.onsolve.com/glossary/
Schwab, K. (2021). The 4th industrial revolution. Britannica. Retrieved from https://www.britannica.com/topic/The-Fourth-Industrial-Revolution-2119734
Softengi. (2022). AI in IT: How Artificial Intelligence will Transform the IT industry.
Retrieved from https://softengi.com/blog/ai-in-it-how-artificial-intelligence-will-transform-the-it-industry/
The Standard for Portfolio Management – Fourth Edition. (2017). Retrieved from https://www.pmi.org/pmbok-guide-standards/foundational/standard-for-portfolio-management
Wu, T. (2020). Optimizing project management. CRC Press, Taylor & Francis Group.
| 2022-12-13T00:00:00 |
https://www.projectmanagement360.com/the-risk-and-rewards-of-the-rapid-rise-of-ai-the-implications-for-project-and-portfolio-management
|
[
{
"date": "2022/12/13",
"position": 89,
"query": "AI replacing workers"
},
{
"date": "2022/12/13",
"position": 10,
"query": "AI unemployment rate"
},
{
"date": "2022/12/13",
"position": 80,
"query": "AI economic disruption"
}
] |
|
Machine Learning and Data-Based Approaches to AI - eCornell
|
Machine Learning and Data-Based Approaches to AI
|
https://ecornell.cornell.edu
|
[] |
In this course, you will review the key dimensions of machine learning and examine some of the major types of machine learning.
|
The field of artificial intelligence (AI) has undergone many transformations since its inception in the late 1950s and 1960s. In this century, AI researchers and experts have focused on machine learning approaches, a key application of technology for life today.
In this course, you will review the key dimensions of machine learning and examine some of the major types of machine learning. You will investigate why machine learning is important and explore when machine learning works, when it does not, and what kinds of challenges you may face in implementing it. You will also identify ways in which machine learning is being used to address challenges and solve problems in both your personal and professional lives. By discovering the fundamentals of neural networks, including their strengths and limitations, you will examine how neural networks and supervised learning work. Finally, you will delve into advanced machine learning topics, including “deep fakes,” generative adversarial networks (GANs), reinforcement learning, and dealing with complex worlds, all providing you with valuable perspective on many key technologies impacting society today.
| 2022-12-13T00:00:00 |
2022/12/13
|
https://ecornell.cornell.edu/courses/financial-management/machine-learning-and-data-based-approaches-to-ai/
|
[
{
"date": "2022/12/13",
"position": 33,
"query": "machine learning job market"
}
] |
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