AI 'Nobel' winners
How Microsoft lured Inflection AI
Are AI Monopolies Here to Stay?
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AI 'Nobel' winners
In 1977, Andrew Barto, as a researcher at the University of Massachusetts, Amherst, began exploring a new theory that neurons behaved like hedonists. The basic idea was that the human brain was driven by billions of nerve cells that were each trying to maximize pleasure and minimize pain. A year later, he was joined by another young researcher, Richard Sutton. Together, they worked to explain human intelligence using this simple concept and applied it to artificial intelligence. The result was “reinforcement learning,” a way for A.I. systems to learn from the digital equivalent of pleasure and pain.
On Wednesday, the Association for Computing Machinery, the world’s largest society of computing professionals, announced that Dr. Barto and Dr. Sutton had won this year’s Turing Award for their work on reinforcement learning. The Turing Award, which was introduced in 1966, is often called the Nobel Prize of computing. The two scientists will share the $1 million prize that comes with the award. Over the past decade, reinforcement learning has played a vital role in the rise of artificial intelligence, including breakthrough technologies such as Google’s AlphaGo and OpenAI’s ChatGPT. The techniques that powered these systems were rooted in the work of Dr. Barto and Dr. Sutton. “They are the undisputed pioneers of reinforcement learning,” said Oren Etzioni, a professor emeritus of computer science at the University of Washington and founding chief executive of the Allen Institute for Artificial Intelligence. “They generated the key ideas — and they wrote the book on the subject.”
Their book, “Reinforcement Learning: An Introduction,” which was published in 1998, remains the definitive exploration of an idea that many experts say is only beginning to realize its potential. Psychologists have long studied the ways that humans and animals learn from their experiences. In the 1940s, the pioneering British computer scientist Alan Turing suggested that machines could learn in much the same way. But it was Dr. Barto and Dr. Sutton who began exploring the mathematics of how this might work, building on a theory that A. Harry Klopf, a computer scientist working for the government, had proposed. Dr. Barto went on to build a lab at UMass Amherst dedicated to the idea, while Dr. Sutton founded a similar kind of lab at the University of Alberta in Canada. “It is kind of an obvious idea when you’re talking about humans and animals,” said Dr. Sutton....
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How Microsoft lured Inflection AI’s staff
Reid Hoffman sat down with Mustafa Suleyman in the fall of 2023 to talk about the uncertain future of their startup, Inflection AI. From the moment they’d founded it 18 months earlier, there had seemed something can’t-miss about Inflection. Hoffman was perhaps the best-connected person in Silicon Valley. Suleyman, who co-founded DeepMind and sold it to Google for $650 million in 2014, was a star in his own right, as was Inflection’s other co-founder, Karén Simonyan, a top researcher in the field. Inflection’s product, a chatbot named Pi, had quickly attracted millions of monthly users by communicating a sense of emotional intelligence. Inflection’s founders defined success as expanding that user base into hundreds of millions, and ultimately billions, of people.
Still, Hoffman and Suleyman calculated that they would need to raise $2 billion more merely to fund their ambitions through 2024. And beyond that? Four billion? Six billion? Ten billion? The figures were staggering, yet somehow they still seemed inadequate for the task ahead. The fear as 2023 turned into 2024 was that the same behemoths that dominated tech in the 2010s—Google, Facebook, Microsoft, Apple, a few others—would ultimately dominate artificial intelligence, blocking the birth of a new generation of tech powerhouses. “The economics of AI have changed the equation for startups in the Valley,” Hoffman says. Once Inflection’s founders came to that conclusion, the company’s fate was more or less sealed.
If you looked at what the large companies would be doing, Google and Microsoft and potentially others, you knew they were going to create next-level models every 12 to 18 months,” Hoffman says. “When we started this business, we had no idea that people were going to open-source the absolute frontier,” Suleyman says. With such alternatives, there was a risk that models like the one Inflection was building “were fundamentally a commodity.” As best Suleyman could remember, Microsoft CEO Satya Nadella first floated the idea that he join Microsoft in December, a month or two after his sobering conversation with Hoffman, and then pursued the idea in a lunch during the World Economic Forum in mid-January in Davos, Switzerland. Microsoft had missed search and missed mobile, and Nadella told Suleyman that he had no intention of letting Microsoft be an also-ran in AI. Another factor, no doubt, was the fallout from OpenAI’s November 2023 boardroom coup—a haunting moment for the Microsoft CEO given that the company’s AI strategy to that point hinged entirely on that one startup.
He says to me, ‘Come and you’ll have every resource you need to build what you’re building except inside Microsoft,’” Suleyman remembers. Over the next several weeks, Suleyman made several trips to have dinner with Nadella in Microsoft’s hometown of Redmond, Washington. Microsoft buying Inflection outright was likely to be complicated or even impossible. Such a transaction would require approval from the Federal Trade Commission. Instead, Microsoft decided to hire Suleyman and Simonyan, along with any Inflection employee who wanted to join them. He would be the CEO of a new division called Microsoft AI, which would spearhead all consumer AI initiatives, including Bing, Edge and the Copilot chatbot. Simonyan would serve as chief scientist, as he had at Inflection. “Everybody here is going to get offers,” Suleyman said....
Read on Businessweek
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Are AI Monopolies Here to Stay?
Every time you type a question into ChatGPT, you are, probably without knowing it, making several monopolies richer. Actually, it’s no different if you use one of ChatGPT’s many competitors. Nearly all of them use chips from Nvidia Corp., which sells around 92% of the particular components — called artificial intelligence accelerators — that make chatbots function. Nvidia relies on a trio of partners to produce its semiconductors: South Korea’s SK Hynix Inc., Taiwan Semiconductor Manufacturing Co. and ASML Holding NV of the Netherlands. Each supplier has a market position almost as fortified as Nvidia’s, or even more so.
In technology, it’s long been accepted that important innovations can lead to companies dominating their markets and then staying on top for years by exploiting the laws of scale. It happened with mainframe and personal computers, web browsers, search engines, social networks and mobile software. When some of those earlier monopolies ended, it was largely because rivals brought them down rather than that government regulators took them apart, Standard Oil-style. It’s possible that AI will have its “iPhone moment,” when a new invention renders companies at the top of their market obsolete almost overnight. It’s also conceivable that AI simply won’t have the world-changing economic impact that the industry promises, ending the gold rush. Never has so much money been riding on the outcome. Together, Nvidia and its three critical partners had a combined market value of more than $4 trillion as of mid-March. Nvidia alone accounted for 6% of the S&P 500 Index of leading US stocks. TSMC and ASML have become the most valuable companies in their home countries.
For decades, Nvidia was known for gaming, not AI. It designs graphics processing units, or GPUs — components that render realistic images in video games such as Call of Duty. The GPUs use a technique known as parallel computing, in which multiple processors solve many computational problems simultaneously, and much faster than a traditional computer. A little over a decade ago, some enterprising researchers discovered these chips were well suited for deep learning, a type of computing that works much like the human brain and became the foundation for today’s ChatGPT boom. Nvidia Chief Executive Officer Jensen Huang made an early bet on some of those researchers, delivering a set of chips costing $129,000 to nonprofit startup OpenAI in 2016, when it was a small lab. “At first, it was almost an accident,” said Jason Furman, an economic policy professor at Harvard University. “Then they shrewdly capitalized on that accident.”. Actually Nvidia has stats for seeing which new uses of their chips gain critical mass of users to go big....