When OpenAI published details of the stunningly capable AI language model GPT-4, which powers ChatGPT, in March, its researchers filled 100 pages. They also left out a few important details--like anything substantial about how it was actually built or how it works. That was no accidental oversight, of course. OpenAI and other big companies are keen to keep the workings of their most prized algorithms shrouded in mystery, in part out of fear the technology might be misused but also from worries about giving competitors a leg up. A study released by researchers at Stanford University this week shows just how deep--and potentially dangerous--the secrecy is around GPT-4 and other cutting-edge AI systems.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. In the world of artificial intelligence (AI) and machine learning (ML), 2022 has arguably been the year of foundation models, or AI models trained on a massive scale. From GPT-3 to DALL-E, from BLOOM to Imagen -- another day, it seems, another large language model (LLM) or text-to-image model. But until now, there have been no AI benchmarks to provide a standardized way to evaluate these models, which have developed at a rapidly-accelerated pace over the past couple of years. Don't miss our new special issue: Zero trust: The new security paradigm.

Self-driving cars are trained to be overly cautious, but there may be situations where they need to make high-speed maneuvers to avoid a collision. Can these vehicles, festooned with tens of thousands of dollars worth of high-tech sensors and programmed to drive at grandma-speeds, handle these split-second decisions like a human? Engineers at Stanford University may have the answer. They created a neural network that can enable driverless cars to perform high-speed, low-friction maneuvers just as well as race car drivers. When they eventually arrive, driverless cars will need capabilities beyond those of humans, as 94 percent of crashes are attributable to human error.

Sorting a bunch of differently coloured toy trucks and action figures seems like child's play, right? Unfortunately this remains a challenging task in the world of machine learning. So why not have humans simply show the machines how to do it? This is the inspiration behind a new research project led by Stanford Artificial Intelligence Lab Director Fei-Fei Li and her husband, Stanford Associate Professor Silvio Savarese. The project introduces two new global platforms -- RoboTurk and Surreal -- designed to provide high-quality task demonstration data to help researchers working in robotic manipulation.

What if a machine could "see" what doctors can't? Soon, there may be a an artificial intelligence tool for diagnosing melanoma that can do just that. Researchers at the University of California and Stanford University are developing a scanner and an app for your smart phone that may provide earlier melanoma detection than a melanoma biopsy. Melanoma represents only 5% of skin cancers. But it causes the most skin cancer deaths.

For the month of January, we addressed the performance of deep learning algorithms for disease diagnosis, specifically focusing on the paper by the stanford group -- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. We continue to generate a large interest in the journal club, with 347 people registered, 150 of whom signed on January 24th 2018 to participate in the discussion. The paper has had 3 revisions and is available here https://arxiv.org/abs/1711.05225 . Like many deep learning papers that claim super human performance, the paper was widely circulated in the news media, several blog posts, on reddit and twitter. Please note that the findings of superhuman performance are increasingly being reported in medical AI papers. For example, this article denotes that "Medical AI May Be Better at Spotting Eye Disease Than Real Doctors" To help critique the ChexNet paper, we constituted a panel composed of the author team (most of the authors listed on the paper were kind enough to be in attendance -- thank you!), Dr. Luke(blog) and Dr. Paras (blog) who had critiqued the data used and Jeremy Howard (past president and chief scientist of Kaggle, a data analytics competition site, Ex-CEO of Enlitic, a healthcare imaging company, and the Current CEO of Fast.ai, a deep learning educational site) to provide insight to deep learning methodology.

The items in this collage were selected from the AI TOPICS Web site's "AI in the News" collection that can be found--complete with links to the item's source and related AI TOPICS pages--at www. Please note that: (1) an excerpt may not reflect the overall tenor of the item, nor contain all of the relevant information; and, (2) all items are offered "as is" and the fact that an item has been selected does not imply any endorsement whatsoever. "In a special collection of articles published beginning 1 July 2005, Science Magazine and its online companion sites celebrate the journal's 125th anniversary with a look forward -- at the most compelling puzzles and questions facing scientists today. A special, free news feature in Science explores 125 big questions that face scientific inquiry over the next quarter-century…." What Is the Biological Basis of Consciousness? "For centuries, debating the nature of consciousness was the exclusive purview of philosophers.

Somewhere in northern Virginia, a man dressed as a car seat seeks the answers to vital questions about how autonomous vehicles interact with the public. The fellow, who had nothing to say when confronted by a local NBC reporter on Tuesday, spends his days driving a silver Ford Transit Connect van around Arlington County. It requires a little skill to do this without moving one's arms, but this goofy endeavor is done in the name of science, and builds on work done in recent years by similarly costumed researchers at Stanford University. Car Seat Man is part of a Virginia Tech Transportation Institute study into human-vehicle interactions--information automakers and tech companies like Google will find invaluable as they loose thousands of self-driving cars onto the country's roads. The Institute confirmed that the guy inside that definitely-not-store-bought car seat costume and his shiny new van are part of its research effort.

When Stanford University's robotic Volkswagen Touareg, "Stanley," won the Grand Challenge last week, robot enthusiasts everywhere cheered. By completing a 210-kilometer course over difficult desert terrain in just under seven hours, Stanley set an unprecedented milestone for autonomous vehicles. Even more amazingly, four other teams' vehicles also completed the course, with slightly slower times. "It's kind of like if you had challenged people to fly across the Atlantic, and instead of one guy [making it], just Lindbergh, you had five guys flying across at the same time," says Sebastian Thrun, an associate professor of computer science at Stanford and the leader of the Stanford team. The Lindbergh analogy is apt.

Around the world, there are people who need help. But sometimes, populations of poverty-stricken people are difficult to find. That is why researches at Stanford have put together a program to find people who need help, from space. One of the many difficulties in dealing with poverty is simply not knowing where to send aid. In many poor countries, data on which areas need the most help are hard to get.