Sam Altman, one of the biggest names in tech, was sacked from ChatGPT-maker OpenAI without warning.

We've previously highlighted some of the most promising people in tech before they became household names. In 2002, the list included two young innovators named Larry Page and Sergey Brin of Google. A 23-year-old Mark Zuckerberg was on the list in 2007. In 2008 we featured Andrew Ng, who wrote an excellent essay for us this yea sharing his tips for aspiring innovators on trying, failing, and the future of AI. This year we've seen tech companies racing to release their hottest new AI systems, and often neglecting safety and ethics.

For more than 30 years, Geoffrey Hinton hovered at the edges of artificial intelligence research, an outsider clinging to a simple proposition: that computers could think like humans do--using intuition rather than rules. The idea had taken root in Hinton as a teenager when a friend described how a hologram works: innumerable beams of light bouncing off an object are recorded, and then those many representations are scattered over a huge database. Hinton, who comes from a somewhat eccentric, generations-deep family of overachieving scientists, immediately understood that the human brain worked like that, too--information in our brains is spread across a vast network of cells, linked by an endless map of neurons, firing and connecting and transmitting along a billion paths. He wondered: could a computer behave the same way? The answer, according to the academic mainstream, was a deafening no. Computers learned best by rules and logic, they said. And besides, Hinton's notion, called neural networks--which later became the groundwork for "deep learning" or "machine learning"--had already been disproven. In the late '50s, a Cornell scientist named Frank Rosenblatt had proposed the world's first neural network machine. It was called the Perceptron, and it had a simple objective--to recognize images. The goal was to show it a picture of an apple, and it would, at least in theory, spit out "apple." The Perceptron ran on an IBM mainframe, and it was ugly.

Most early adopters face AI skill gaps and are looking for expertise to boost their capabilities. According to Deloitte's global study of AI early adopters, 68 percent report a moderate-to-extreme AI skills gap.1 What are the "most wanted" roles to fill these gaps? According to AI early adopters, the top four most-needed positions are "AI builders" who are involved in creating AI solutions: Beyond these AI builders, adopters are seeking "AI translators" who bridge the divide between the business and technical staff--both at the front and back end of building AI solutions: When we compare companies with relatively little AI experience (they've built five or fewer production systems) with those possessing extensive AI experience (they've built 20 or more production systems), we observe an interesting shift in "most wanted" roles (see chart). Early on, AI researchers are the most sought-after, with about a third of the less-experienced rating them a top-two needed role. Business leaders rank near the bottom.

Most popular @KDnuggets tweets for Feb 14-20 were Most Retweeted: #NeuralNetwork #AI is simple. So... Stop pretending you are a genius https://t.co/EM0svl1p6r So... Stop pretending you are a genius https://t.co/EM0svl1p6r So... Stop pretending you are a genius https://t.co/EM0svl1p6r Most Clicked: The #AI superstars at Google, Facebook, Apple-they all studied under this guy https://t.co/sbV1GylS25

For more than 30 years, Geoffrey Hinton hovered at the edges of artificial intelligence research, an outsider clinging to a simple proposition: that computers could think like humans do--using intuition rather than rules. The idea had taken root in Hinton as a teenager when a friend described how a hologram works: innumerable beams of light bouncing off an object are recorded, and then those many representations are scattered over a huge database. Hinton, who comes from a somewhat eccentric, generations-deep family of overachieving scientists, immediately understood that the human brain worked like that, too--information in our brains is spread across a vast network of cells, linked by an endless map of neurons, firing and connecting and transmitting along a billion paths. He wondered: could a computer behave the same way? The answer, according to the academic mainstream, was a deafening no. Computers learned best by rules and logic, they said. And besides, Hinton's notion, called neural networks--which later became the groundwork for "deep learning" or "machine learning"--had already been disproven. In the late '50s, a Cornell scientist named Frank Rosenblatt had proposed the world's first neural network machine. It was called the Perceptron, and it had a simple objective--to recognize images. The goal was to show it a picture of an apple, and it would, at least in theory, spit out "apple." The Perceptron ran on an IBM mainframe, and it was ugly.

Uber is hiring Raquel Urtasun, a prominent artificial intelligence researcher at the University of Toronto, as the ride-hailing company aims to build a lab for driverless car research in the Canadian city, a hotbed for AI talent. Urtasun--an associate professor at the university who specializes in the computer vision software that allows driverless cars to view the world around them--will oversee the new venture. "We hope to draw from the region's impressive talent pool as we grow, helping the dozens of researchers we plan to hire stay connected to the Toronto-Waterloo Corridor," Travis Kalanick, Uber's embattled CEO, wrote in a blog post published this morning. The move resonates on multiple levels, given the ongoing legal attack against Uber's existing computer vision technology by Waymo--the driverless car company that grew out of Google--and the widespread controversy over Uber's allegedly misogynistic internal culture. Urtasun could help the company forge another much-needed path to the kind of AI that driverless cars will require.

Uber is hiring Raquel Urtasun, a prominent artificial intelligence researcher at the University of Toronto, as the ride-hailing company aims to build a lab for driverless car research in the Canadian city, a hotbed for AI talent. Ursasun--an associate professor at the university who specializes in the computer vision software that allows driverless cars to view the world around them--will oversee the new venture. "We hope to draw from the region's impressive talent pool as we grow, helping the dozens of researchers we plan to hire stay connected to the Toronto-Waterloo Corridor," Travis Kalanick, Uber's embattled CEO, wrote in a blog post published this morning. The move resonates on multiple levels, given the ongoing legal attack against Uber's existing computer vision technology by Waymo--the driverless car company that grew out of Google--and the widespread controversy over the Uber's allegedly misogynistic internal culture. Urtasun could help the company forge another much-needed path to the kind of AI that driverless cars will require.