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""An emotionally intelligent human does not usually claim they can accurately put a label on everything everyone says and tell you this person is currently feeling 80% angry, 18% fearful, and 2% sad," says Edward B Kang, an assistant professor at New York University writing about the intersection of AI and sound. "In fact, that sounds to me like the opposite of what an emotionally intelligent person would say." Adding to this is the notorious problem of AI bias. "Your algorithms are only as good as the training material," Barrett says. "And if your training material is biased in some way, then you are enshrining that bias in code.""

"This is a broader pattern we're seeing across other fields where LLMs are being deployed. Whether it's coding, writing, design, law, or medicine, the most effective AI users are people who already have deep domain expertise. Expertise isn't obsolete; it's more important than ever-because the value isn't just in producing outputs quickly. It's in being able to vet, steer, and improve those outputs. The future of computer science education isn't about teaching less. It's about teaching differently. We still need students who can understand how software works at a fundamental level. But we also need to train them to collaborate with AI-to become fluent in prompting, reviewing, debugging, and refining AI-generated outputs. Mastering this hybrid skillset will be critical not just for engineers, but for anyone hoping to thrive in a world where knowledge work is increasingly AI-augmented. Practically speaking, AI could dramatically lower the barrier to entry for students. When I was in high school, it would take months (if not years) of training in CS before you could create a game or app that was genuinely cool to people that aren't inherently curious and nerdy."

"O'Neill recounts an exercise to improve service to homeless families in New York City, in which data-analysis was used to identify risk-factors for long-term homelessness. The problem, O'Neill describes, was that many of the factors in the existing data on homelessness were entangled with things like race (and its proxies, like ZIP codes, which map extensively to race in heavily segregated cities like New York). Using data that reflects racism in the system to train a machine-learning algorithm whose conclusions can't be readily understood runs the risk of embedding that racism in a new set of policies, these ones scrubbed clean of the appearance of bias with the application of objective-seeming mathematics. "

"In the fall of 2020, gig workers in Venezuela posted a series of images to online forums where they gathered to talk shop. The photos were mundane, if sometimes intimate, household scenes captured from low angles-including some you really wouldn't want shared on the Internet. In one particularly revealing shot, a young woman in a lavender T-shirt sits on the toilet, her shorts pulled down to mid-thigh. The images were not taken by a person, but by development versions of iRobot's Roomba J7 series robot vacuum. They were then sent to Scale AI, a startup that contracts workers around the world to label audio, photo, and video data used to train artificial intelligence."

"How that effects systems of governance has yet to be fully explored, but there are cautions. "Algorithms are only as good as the data on which they are based, and the problem with current AI is that it was trained on data that was incomplete or unrepresentative and the risk of bias or unfairness is quite substantial," says West. The fairness and equity of algorithms are only as good as the data-programming that underlie them. "For the last few decades we've allowed the tech companies to decide, so we need better guardrails and to make sure the algorithms respect human values," West says. "We need more oversight.""

"The researchers found that the cost of the computational power required to train the models is doubling every nine months. This is a prodigious rate of growth-at this rate, the cost of the hardware and electricity needed to build cutting-edge AI systems alone would be in the billions by later this decade, without accounting for other costs such as employee compensation."

"When employees at meta started developing their flagship AI model, Llama 3, they faced a simple ethical question. The program would need to be trained on a huge amount of high-quality writing to be competitive with products such as ChatGPT, and acquiring all of that text legally could take time. Should they just pirate it instead?"

"computer programs can accelerate discovery in the sciences faster than scientists can, if the computer is trained to behave like one"

"Google is using romance novels to teach its artificial intelligence (AI) system to better understand how people communicate. Researchers at Google Brain, the company's AI-focused deep learning project, presented a paper earlier this month that detailed techniques they used to teach its AI to write fiction - and the results were unexpectedly haunting."

"London-born Stanford student Joshua Browder created DoNotPay initially to help people appeal against fines for unpaid parking tickets. Dubbed "the world's first robot lawyer", Browder later programmed it to deal with a wider range of legal issues, such as claiming for delayed flights and trains and payment protection insurance (PPI)."

"Train the Deep Learning Ahem Detector with two sets of audio files, "a negative sample with clean voice/sound" (minimum 3 minutes) and "a positive one with 'ahem' sounds concatenated" (minimum 10s) and it will detect "ahems" in any voice sample thereafter."

"An investigation into the death of a 19-year-old French woman who reportedly live-streamed herself taking her own life has opened in France. The woman, who had been using the smartphone app Periscope, reportedly jumped under a train at a station about 25 miles (40 km) south of Paris on Tuesday."

"The NSA boasts in training materials that the program, called XKeyscore, is its "widest-reaching" system for developing intelligence from the internet."

"While the Moocs are associated with high-status universities, Alison's focus is on the vast numbers of people around the world needing to improve their vocational skills and training."

"The researchers, Tero Karras, Samuli Laine, and Timo Aila, came up with a new way of constructing a generative adversarial network, or GAN. GANs employ two dueling neural networks to train a computer to learn the nature of a data set well enough to generate convincing fakes. When applied to images, this provides a way to generate often highly realistic fakery. The same Nvidia researchers have previously used the technique to create artificial celebrities (read our profile of the inventor of GANs, Ian Goodfellow)."

"This description is reminiscent of the controversy that erupted back in January 2018 when photojournalist A. M. Ahad shared a behind-the-scenes video of multiple photographers shooting photos of a young man who was leaning out of a train window and striking a prayerful pose." Seems you do not even need Photoshop - would policies have stopped this?

"His team trained a machine learning algorithm to spot words and phrases associated with bullying on social media site AskFM, which allows users to ask and answer questions. It managed to detect and block almost two-thirds of insults within almost 114,000 posts in English and was more accurate than a simple keyword search. Still, it did struggle with sarcastic remarks."

"The underlying theory is that art evolves "through small alterations to a known style that produce a new one," which, as Ian Bogost (previously) points out, is "a convenient take, given that any machine-learning technique has to base its work on a specific training set.""

"This month, in an open-access study published in PLOS One, a team reported the first brain implant system that lets patients use their thoughts to navigate an off-the-shelf Android tablet. Compared to previous generations, this system doesn't require training-for example, learning to type on a different, non-QWERTY keyboard-or specialized interface equipment. With just her thoughts, T6 was able to send emails, chat with other paralyzed patients in the trial, Google random questions, and even shop on Amazon. For the first time since she became paralyzed, T6 regained access to the entire commercially-available Google Play ecosystem and the digital world."

"So they trained a deep-learning neural net on tons of examples of deepfaked videos, and produced a model that's better than any previous automated technique at spotting hoaxery. (Their paper documenting the work is here.)"