"A new AI-infused classroom management tool with facial recognition capabilities has garnered attention recently with promises to take attendance, assess the emotional states of students, and monitor classes for distraction, boredom and confusion.
But the technology and similar developments have raised numerous legal, ethical, and civil liberties questions similar to those surrounding campus safety surveillance programs and test proctoring programs widely adopted during the COVID pandemic."
"Past scandals, bad photos, critical comments: the internet has a long memory. As the EU considers the 'right to be forgotten', we investigate the growing business of online reputation management - and learn how you can airbrush your own past"
""There is no substitute for in-person collaboration and connection," said Take-Two Interactive Software CEO Strauss Zelnick.
This is especially true for those who are in management roles in a firm, who find that actual management and mentoring is best done offline. The same goes for relationship-building, spontaneity, and creativity."
"If you've been job hunting recently, chances are you've interacted with a resume robot, a nickname for an Applicant Tracking System, or ATS. In its most basic form, an ATS acts like an online assistant, helping hiring managers write job descriptions, scan resumes and schedule interviews. As artificial intelligence advances, employers are increasingly relying on a combination of predictive analytics, machine learning and complex algorithms to sort through candidates, evaluate their skills and estimate their performance. Today, it's not uncommon for applicants to be rejected by a robot before they're connected with an actual human in human resources.
The job market is ripe for the explosion of AI recruitment tools. Hiring managers are coping with deflated HR budgets while confronting growing pools of applicants, a result of both the economic downturn and the post-pandemic expansion of remote work. As automated software makes pivotal decisions about our employment, usually without any oversight, it's posing fundamental questions about privacy, accountability and transparency."
"Over the next few months, Lipkin's assignments dwindled. Managers began referring to her as "Olivia/ChatGPT" on Slack. In April, she was let go without explanation, but when she found managers writing about how using ChatGPT was cheaper than paying a writer, the reason for her layoff seemed clear.
"Whenever people brought up ChatGPT, I felt insecure and anxious that it would replace me," she said. "Now I actually had proof that it was true, that those anxieties were warranted and now I was actually out of a job because of AI.""
"After I was diagnosed with attention deficit hyperactivity disorder (ADHD) in 2022, I started following Instagram accounts that could help me understand the condition. Reels and memes about being neurodivergent started to fill my feed, along with tips on how to manage ADHD in a relationship and other helpful advice. But within days, something else happened: my phone found out about my diagnosis.
All of a sudden, I was being served with ads for apps that claimed they could help me to manage my symptoms. There were quizzes to determine what type of ADHD I had: was I predominantly inattentive or impulsive, one asked. Did I definitely have it? Find out by taking this diagnostic test, another promised."
As our connectivity increases, we're seeing a growing number of cases of law enforcement using social media and other digital tools to help solve crimes. A study from Accenture, a global management and consulting agency, showed that 72 percent of respondent citizens believe that social media can help in crime investigations and in the prosecution of offenders.
"The performance of LipNet compares incredibly favorably to human lipreading experts on GRID corpus, the largest publicly-available sentence-level lipreading dataset. In fact, where human experts got just 52 percent, LipNet scored 93 percent. Its sentence-based approach to lip-reading also smashed the best previous attempt by a machine, which managed 79.6 percent accuracy on the same dataset."
""It's accelerating because there's a wide-open, unprotected landscape that people can go to," says Chris Carlson, vice president of product management at Qualys. "It's a gold rush to capture these devices for botnets.""
"Automated decision-making is appealing to businesses as it can save time and eliminate human emotional volatility.
"People have a bad day and it then colors their perception of the world and they make different decisions. In a hedge fund that's a big deal," he added."
"Scientists from University of Washington have been struggling for the past decade to decipher the complex structure of an enzyme that exhibits behavior similar to that of an enzyme key in the development of AIDS from an HIV infection, and which might hold a critical role in building a cure for the disease. Gamers playing spatial game Foldit have managed to collectively determine the enzyme's structure in ten days."
"Big data is the advance in data management technology, which allows for an increase in the scale and manipulation of a company's data. It allows companies to know more about their customers, products and their own infrastructure. More recently, people have become increasingly focused on the monetization of that data."
""I am in the camp that is concerned about super intelligence," Gates wrote. "First, the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though, the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don't understand why some people are not concerned.
"Graduated top of your class in Ghana and managed to land a place on an MBA at Oxford? Then you're heading for a salary high enough to make repaying your student loan a cinch - especially if half your Facebook followers are from McKinsey. At least, this is the kind of calculation being made by private funders stepping into the increasingly complex and competitive student loans market."
"Going from small data analytics to Big Data analytics or to predictive and prescriptive analytics is trickier. Expanding in both dimensions is human capital intensive, requiring talented data scientists. A McKinsey report (2011) estimates that by 2018, there will be a shortage of 140,000 to 190,000 workers with "deep analytical" experience and a further 1.5 million data-literate managers in the US. Technology giants such as Google, Facebook and Amazon, and large investment banks and top hedge funds can afford such employees, however even now the competition is fierce, as is evidenced by the ongoing talent war in Silicon Valley. The data scientist is indeed a sexy job in the 21st century."
"The algorithmic manager seems to watch everything you do. Ride-hailing platforms track a variety of personalized statistics, including ride acceptance rates, cancellation rates, hours spent logged in to the app and trips completed. And they display selected statistics to individual drivers as motivating tools, like "You're in the top 10 percent of partners!"
Uber uses the accelerometer in drivers' phones along with GPS and gyroscope to give them safe driving reports, tracking their performance in granular detail. One driver posted to a forum that a grade of 210 out of 247 "smooth accelerations" earned a "Great work!" from the boss."
"About two years ago the retail team lost another key task: negotiating with major brands and manufacturers the terms of popular sales on the site called "Lightning Deals." Common during the holidays as well as Mother's Day and Father's Day, they help move lots of inventory in a short period.
Now, instead of calling their vendor manager at Amazon, the makers of handbags, smartphone accessories and other products simply logged into an Amazon portal that would determine if Amazon liked the deal being offered and the quantity it was willing to buy. No small talk. No give and take. Thousands of Amazon man hours spent forecasting demand, planning marketing strategies and negotiating deals was now handled by software, a major leap in efficiency."
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Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs.
The downside of this powerful approach is that it means machine learning classifiers can be confounded by confusing, out-of-context elements in a scene, as is demonstrated in The Elephant in the Room, a paper from a trio of Toronto-based computer science academics."