The Demis I know

The 2024 Nobel Prize for Chemistry, with a monetary award of 11 million Swedish kronor (approximately €950,000), was awarded to three scientists: David Baker of the University of Washington received one half of the prize "for computational protein design"; while Demis Hassabis and John Jumper, both from Google DeepMind in London, UK, jointly received the other half "for protein structure prediction."

The Nobel Prize web site says: The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.

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We reported on the September nomination of the three scientist in this report. On Monday the prizes were handed out at the Stockholm Concert Hall, in the presence of the Swedish royal family.

The three winners of the 2024 Nobel Prize for Chemistry.

Hassabis was a chess prodigy with a 2300 rating at the age of 13, just 35 Elo behind Judit Polgar. He was also an accomplished player in games, and at the age of 17 he programmed the bestselling game Theme Park. He founded DeepMind Technologies in London, which was acquired by Google for around $400 million in 2014. DeepMind developed AlphaGo, which achieved world class strength in Go, and AlphaZero, which did the same for chess. That was followed by AlphaFold, which revolutionized protein structure prediction. This breakthrough AI program allowed the two AI researchers to predict the structure of all known single proteins. AlphaFold facilitates the simulation of protein interactions, paving the way for the design of biologics, heralding a new era in therapeutic development.

ChessBase editor emeritus Frederic Friedel met Demis Hassabis for the first time in the mid 1990s, at a dinner in London, which he described for the German news magazine.

The following article is reproduced with kind permission of Spiegel Online, where it first appeared (in November 2018). The author was told to make the series personal, describe the development of chess programming not as an academic treatise but as a personal story of how he had experienced it. For some ChessBase readers a number of the passages will be familiar, since the stories have been told before on our pages. For that we apologize. For others this can serve as a roadmap through one of the great scientific endeavors of our time.

The Adventure of Chess Programming (3)

By Frederic Friedel

It was the mid 1990s. I was in London, accompanying World Chess Champion Garry Kasparov, as I often did, on one of his appearances. This time it was in Home House, a beautiful Georgian villa in Marylebone, and one evening we were joined at dinner by a former child prodigy in chess. He had reached master level (Elo 2300+) at the age of 13 and captained a number of English junior chess teams. He was also a world-class computer games player. It was an interesting encounter, with the lad enthusiastically describing a computer game he was developing. After he left I said to Garry: "That's a cocky young fellow!" "But very smart," Garry replied. And we left it at that.

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Twenty years later I read in the news that Google had purchased a company called DeepMind Technologies, for £400 million. DeepMind was a British artificial intelligence enterprise which had created neural network software that learned how to play early-gen video games like Pong and Space Invaders, all on its own. It was not hand-programmed to do this, but used methods that were very like those of a human player gaining proficiency in the game. The goal, DeepMind said, was "to create a general-purpose AI that can be useful and effective for almost anything." One of the founders of the company was Demis Hassabis.

Demis? Wait a minute, wasn't that the lad we had met in Home House? For a year I watched the progress the company made as a member of the Google family, and was especially fascinated to see how they solved a problem that had needled computer experts for decades: DeepMind created a program, AlphaGo, that learned to play the ancient game of Go, taking it all the way to master and then world championship level. The rules of Go are deceptively simple, but the branching factor makes it very hard for computers to calculate. In the first article of this series I described how in a 40-move game of chess there were 10^128 possible sequences of moves – vastly more than the number of atoms in the universe. Well, in Go there are 10^170 possible board configurations, which dwarfs the number of chess games to insignificance.

We followed the progress of AlphaGo closely on the news page of ChessBase, which shares with DeepMind an affinity for capitalising in the middle of names. The program used deep neural networks to study a very large number of games, developing its own understanding of what human play looks like. After that it honed its skills by playing different versions against itself while learning from its mistakes. This process, known as reinforcement learning, produced a master-level Go playing software.

More than twenty years after the first encounter Garry Kasparov discusses artificial Intelligence with Demis Hassabis in this 40-minute highly enlightening Google Talk.

At this stage I contacted Demis, who remembered our encounter in Home House and invited me to visit DeepMind in London. My counter-proposal: his team should come to Hamburg to see the assets we have for chess. ChessBase has over eight million high-class games, 100 thousand annotated by very strong players, 200 million chess positions in the cloud, with the evaluations of the world's most powerful computers attached to each of them, the largest and most up-to-date "live" openings book in the game, etc., etc. DeepMind could use this data to train a neural network for chess – more accurately: have the neural network train itself to play the game.

Demis was open to the idea and promised to consider it. What he did not tell me at the time was that they were already developing a chess engine that was unlike anything anyone had ever seen before. Traditional engines have their knowledge of the game of chess programmed into them, meticulously, one factor at a time. The DeepMind neural network took a radically different path: it was told the rules of the game, how the pieces move and the ultimate goal of checkmate. Nothing else. Using state-of-the-art techniques in artificial intelligence, the program, AlphaZero, played against itself, millions upon millions of times, identifying patterns of its own accord, and adjusting the values as it saw fit. In other words, it produced its own concepts and knowledge, using pattern recognition just as humans do, and improving as it learned. And it did this without the need for all the ChessBase data I was offering.

How was this possible? Initially the system played absurd games, where one side gives up three pieces for nothing, and the other side cannot win because it had lost four pieces. But with each iteration, with each 10,000 or so learning steps, it became stronger. Running on the latest proprietary hardware – for the technology savant: 5,000 first-generation and 64 second-generation TPUs – the program played 44 million games against itself and, in the process, rose to the level of world class chess strength. Nobody had told AlphaZero anything about strategy, nobody had explained that material was important, that queens were more valuable than bishops, that mobility mattered. It had worked everything out by itself, drawing its own conclusions – conclusions, incidentally, that no human being will ever be able to comprehend.

In the end AlphaZero played a test match against an open source engine named Stockfish, one of the top three or four brute force engines in the world. These programs all hover around 3500 points on the rating scale, which is at least 700 more than any human player. Stockfish ran on 64 processor threads and looked at 70 million positions per second; AlphaZero ran on a machine with four TPUs, looking at just 80,000 positions per second. It compensated for this thousand-fold disadvantage by selectively searching only the most promising variations – moves that in its self-play had proved to be effective in similar positions.

In the 100 games that were played against Stockfish, AlphaZero won 25 as white, three as black, and drew the remaining 72 games. All games were played without recourse to an openings book. In addition a series of twelve 100-game matches were played, starting from the 12 most popular human openings. AlphaZero won 290, drew 886 and lost 24 games. Some in the traditional computer chess community call the match conditions "unfair" (no opening books or only constrained openings), but I conclude that without doubt AlphaZero is the strongest entity that has ever played chess. And it had become this after studying the game, from scratch, all alone without any external advice, for a total of about nine hours.

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Fred 'n Demis – reunion after two decades, at the World Chess Championship in London, November 2018 | Photo by Christian Hesse, who also attended the Championship

Google and DeepMind were quite relaxed about the project and revealed the methods they used to all and gentry. One of the project managers even came to visit ChessBase in Hamburg and held a talk for half a dozen of our talented young programmers. They went away inspired, determined to learn more about this kind of computer intelligence.

• Read the full article here
• Also read: DeepMind founder motivates children