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About threeyears ago, DeepMind, a company owned by Google that specializes in AI development, turned its attention to the ancient game of Go. Go had been the one game that had eluded all computer efforts to become world class, and even up until the announcement was deemed a goal that would not be attained for another decade! This was how large the difference was. When a public challenge and match was organized against the legendary player Lee Sedol, a South Korean whose track record had him in the ranks of the greatest ever, everyone thought it would be an interesting spectacle, but a certain win by the human. The question wasnt even whether the program AlphaGo would win or lose, but how much closer it was to the Holy Grail goal. The result was a crushing 4-1 victory, and a revolution in the Go world. In spite of a ton of second-guessing by the elite, who could not accept the loss, eventually they came to terms with the reality of AlphaGo, a machine that was among the very best, albeit not unbeatable. It had lost a game after all.

The saga did not end there. A year later a new updated version of AlphaGo was pitted against the world number oneof Go, Ke Jie, a young Chinese whose genius is not without parallels to Magnus Carlsen in chess. At the age of just 16 he won his first world title and by the age of 17 was the clear world number one. That had been in 2015, and now at age 19, he was even stronger. The new match was held in China itself, and even Ke Jie knew he was most likely a serious underdog. There were no illusions anymore. He played superbly but still lost by a perfect 3-0, a testimony to the amazing capabilities of the new AI.

Many chess players and pundits had wondered how it would do in the noble game of chess. There were serious doubts on just how successful it might be. Go is a huge and long game with a 19x19 grid, in which all pieces are the same, and not one moves. Calculating ahead as in chess is an exercise in futility so pattern recognition is king. Chess is very different. There is no questioning the value of knowledge and pattern recognition in chess, but the royal game is supremely tactical and a lot of knowledge can be compensated for by simply outcalculating the opponent. This has been true not only of computer chess, but humans as well.

However, there were some very startling results in the last few months that need to be understood. DeepMinds interest in Go did not end with that match against the number one. You might ask yourself what more there was to do after that? Beat him 20-0 and not just 3-0? No, of course not. However, the super Go program became an internal litmus test of a sorts. Its standard was unquestioned and quantified, so if one wanted to test a new self-learning AI, and how good it was, then throwing it at Go and seeing how it compared to the AlphaGo program would be a way to measure it.

A new AI was created called AlphaZero. It had several strikingly different changes. The first was that it was not shown tens of thousands of master games in Go to learn from, instead it was shown none. Not a single one. It was merely shown the rules, without any other information. The result was a shock. Within just three days its completely self-taught Go program was stronger than the version that had beat Lee Sedol, a result the previous AI had needed over a year to achieve. Within three weeks it was beating the strongest AlphaGo that had defeated Ke Jie. What is more: while the Lee Sedol version had used 48 highly specialized processors to create the program, this new version used only four!

Graph showing the relative evolution of AlphaZero : Source: DeepMind

Approaching chess might still seem unusual. After all, although DeepMind had already shown near revolutionary breakthroughs thanks to Go, that had been a game that had yet to be solved. Chess already had its Deep Blue 20 years ago, and today even a good smartphone can beat the world number one. What is there to prove exactly?

Garry Kasparov is seen chatting with Demis Hassabis, founder of DeepMind | Photo: Lennart Ootes

It needs to be remembered that Demis Hassabis, the founder of DeepMind has a profound chess connection of his own. He had been a chess prodigy in his own right, and at age 13 was the second highest rated player under 14 in the world, second only to Judit Polgar. He eventually left the chess track to pursue other things, like founding his own PC video game company at age 17, but the link is there. There was still a burning question on everyones mind: just how well would AlphaZero do if it was focused on chess? Would it just be very smart, but smashed by the number-crunching engines of today where a single ply is often the difference between winning or losing? Or would something special come of it?

Professor David Silver explains how AlphaZero was able to progress much quicker when it had to learn everything on its own as opposed to analzying large amounts of data. The efficiency of a principled algorithm was the most important factor.

On December 5 the DeepMind group published a new paper at the site of Cornell University called "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm", and the results were nothing short of staggering. AlphaZero had done more than just master the game, it had attained new heights in ways considered inconceivable. The test is in the pudding of course, so before going into some of the fascinating nitty-gritty details, lets cut to the chase. It played a match against the latest and greatest version of Stockfish, and won by an incredible score of 64 : 36, and not only that, AlphaZero had zero losses (28 wins and 72 draws)!

Stockfishneeds no introduction to ChessBase readers, but it's worth noting that the program was on a computer that was running nearly 900 times faster! Indeed, AlphaZero was calculating roughly 80 thousand positions per second, while Stockfish, running on a PC with 64 threads (likely a 32-core machine) was running at 70 million positions per second.To better understand how big a deficit that is, if another version of Stockfish were to run 900 times slower, this would be equivalent to roughly 8 moves less deep. How is this possible?

The paper "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" at Cornell University

The paper explains:

AlphaZero compensates for the lower number of evaluations by using its deep neural network to focus much more selectively on the most promising variations arguably a more human-like approach to search, as originally proposed by Shannon. Figure 2 shows the scalability of each player with respect to thinking time, measured on an Elo scale, relative to Stockfish or Elmo with 40ms thinking time. AlphaZeros MCTS scaled more effectively with thinking time than either Stockfish or Elmo, calling into question the widely held belief that alpha-beta search is inherently superior in these domains.

This diagram shows that the longer AlphaZero had to think, the more it improved compared to Stockfish

In other words, instead of a hybrid brute-force approach, which has been the core of chess engines today, it went in a completely different direction, opting for an extremely selective search that emulates how humans think. A top player may be able to outcalculate a weaker player in both consistency and depth, but it still remains a joke compared to what even the weakest computer programs are doing. It is the humans sheer knowledge and ability to filter out so many moves that allows them to reach the standard they do. Remember that although Garry Kasparov lost to Deep Blue it is not clear at all that it was genuinely stronger than him even then, and this was despite reaching speeds of 200 million positions per second. If AlphaZero is really able to use its understanding to not only compensate 900 times fewer moves, but surpass them, then we are looking at a major paradigm shift.

Since AlphaZero did not benefit from any chess knowledge, which means no games or opening theory, it also means it had to discover opening theory on its own. And do recall that this is the result of only 24 hours of self-learning. The team produced fascinating graphs showing the openings it discovered as well as the ones it gradually rejected as it grew stronger!

Professor David Silver, lead scientist behind AlphaZero, explains how AlphaZero learned openings in Go, and gradually began to discard some in favor of others as it improved. The same is seen in chess.

In the diagram above, we can see that in the early games, AlphaZero was quite enthusiastic about playing the French Defense, but after two hours (this so humiliating) began to play it less and less.

The Caro-Kann fared a good deal better, and held a prime spot in AlphaZero's opening choices until it also gradually filtered it out. So what openings did AlphaZero actually like or choose by the end of its learning process? The English Opening and the Queen's Gambit!

The paper also came accompanied by ten games to share the results. It needs to be said that these are very different from the usual fare of engine games. If Karpov had been a chess engine, he might have been called AlphaZero. There is a relentless positional boa constrictor approach that is simply unheard of. Modern chess engines are focused on activity, and have special safeguards to avoid blocked positions as they have no understanding of them and often find themselves in a dead end before they realize it. AlphaZero has no such prejudices or issues, and seems to thrive on snuffing out the opponents play. It is singularly impressive, and what is astonishing is how it is able to also find tactics that the engines seem blind to.

In this position from Game 5 of the ten published, this position arose after move 20...Kh8. The completely disjointed array of Blacks pieces is striking, and AlphaZero came up with the fantastic 21.Bg5!! After analyzing it and the consequences, there is no question this is the killer move here, and while my laptop cannot produce 70 million positions per second, I gave it to Houdini 6.02 with 9 million positions per second. It analyzed it for one full hour and was unable to find 21.Bg5!!

A screenshot of Houdini 6.02 after an hour of analysis

Here is another little gem of a shot, in which AlphaZero had completely stymied Stockfish positionally, and now wraps it up with some nice tactics.Look at this incredible sequence in game nine:

Here AlphaZero played the breathtaking 30. Bxg6!! The threat is obviously 30...fxg6 31. Qxe6+, but how do you continue after the game's 30...Bxg5 31. Qxg5 fxg6?

Here AlphaZero continued with 32. f5!! and after 32...Rg8 33. Qh6 Qf7 34. f6 obtained a deadly bind, and worked it into a win 20 moves later. Time to get a thesaurus for all the references synonymous of 'amazing'.

So where does this leave chess, and what does it mean in general? This is a game-changer, a term that is so often used and abused, and there is no other way of describing it. Deep Blue was a breakthrough moment, but its result was thanks to highly specialized hardware whose purpose was to play chess, nothing else. If one had tried to make it play Go, for example, it would have never worked. This completely open-ended AI able to learn from the least amount of information and take this to levels hitherto never imagined is not a threat to beat us at any number of activities, it is a promise to analyze problems such as disease, famine, and other problems in ways that might conceivably lead to genuine solutions.

For chess, this will likely lead to genuinely breakthrough engines following in these footsteps. That is what happened in Go. For years and years, Go programs had been more or less stuck where they were, unable to make any meaningful advances, and then came along AlphaGo. It wasn't because AlphaGo offered some inspiration to'try harder', it was because just as here, a paper was published detailing all the techniques and algorithms developed and used so that others might follow in their footsteps. And they did. Literally within a couple of months, new versions of top programs such as Crazy Stone, began offering updated engines with Deep Learning, which brought hundreds (plural) of Elo in improvement. This is no exaggeration.

Within a couple of months, the revolutionary techniques used to create AlphaGo began to appear in top PC programs of Go

The paper on chess offers similar information allowing anyone to do what they did. Obviously they won't have the benefit of the specialized TPUs, a processor designed especially for this deep learning training, but nor are they required to do so. It bears remembering that this was also done without the benefit of many of the specialized programming techniques and tricks in chess programming. Who is to say they cannot be combined for even greater results? Even the DeepMind team think it bears investigating:

"It is likely that some of these techniques could further improve the performance of AlphaZero; however, we have focused on a pure self-play reinforcement learning approach and leave these extensions for future research."

Replay the ten games between AlphaZero and Stockfish 8 (70 million NPS)

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The future is here AlphaZero learns chess | ChessBase

Chess changed forever today. And maybe the rest of the world did, too.

A little more than a year after AlphaGosensationally won against the top Go player, the artificial-intelligence programAlphaZero has obliterated the highest-rated chess engine.

Stockfish, which for most top players is their go-to preparation tool, and which won the 2016 TCEC Championshipand the2017 Chess.com Computer Chess Championship,didn't stand a chance. AlphaZero won the closed-door, 100-game match with 28 wins, 72 draws, and zero losses.

Oh, and it took AlphaZero only four hours to "learn" chess. Sorry humans, you had a good run.

That's right -- the programmers of AlphaZero, housed within the DeepMind division of Google, had it use a type of"machine learning,"specifically reinforcement learning. Put more plainly, AlphaZero was not "taught" the game in the traditional sense. That means no opening book, no endgame tables, and apparently no complicated algorithms dissecting minute differences between center pawns and side pawns.

Google headquarters in London from inside, with the DeepMind section on the eighth floor. | Photo: Maria Emelianova/Chess.com.

This would be akin to a robot being given access to thousands of metal bits and parts, but no knowledge of a combustion engine, then it experiments numerous times with every combination possible until it builds a Ferrari. That's all in less time that it takes to watch the "Lord of the Rings" trilogy. The program had four hours to play itself many, many times, thereby becoming its own teacher.

For now, the programming team is keeping quiet. They chose not to comment to Chess.com, pointing out the paper "is currently under review" but you can read the full paper here. Part of the research group isDemis Hassabis, a candidate master from England and co-founder of DeepMind (bought by Google in 2014). Hassabis, who played in the ProBiz event of the London Chess Classic, is currently at the Neural Information Processing Systems conference in California where he is a co-author of another paper on a different subject.

Demis Hassabis playing with Michael Adams at the ProBiz event at Google Headquarters London just a few days ago. | Photo: Maria Emelianova/Chess.com.

One person that did comment to Chess.com has quite a lot of first-hand experience playing chess computers. GM Garry Kasparov is not surprised that DeepMind branched out from Go to chess.

"It's a remarkable achievement, even if we should have expected it after AlphaGo," he told Chess.com. "It approaches the 'Type B,' human-like approach to machine chess dreamt of by Claude Shannon and Alan Turing instead of brute force."

One of the 10 selected games given in the paper.

Indeed, much like humans, AlphaZero searches fewer positions that its predecessors. The paper claims that it looks at "only" 80,000 positions per second, compared to Stockfish's 70 million per second.

GM Peter Heine Nielsen, the longtime second of World Champion GM Magnus Carlsen, is now on board with the FIDE president in one way: aliens. As he told Chess.com, "After reading the paper but especially seeing the games I thought, well, I always wondered how it would be if a superior species landed on earth and showed us how they play chess. I feel now I know."

Chess.com's interview with Nielsen on the AlphaZero news.

We also learned, unsurprisingly, that White is indeed the choice, even among the non-sentient. Of AlphaZero's 28 wins, 25 came from the white side (although +3=47-0 as Black against the 3400+ Stockfish isn't too bad either).

The machine also ramped up the frequency of openings it preferred. Sorry, King's Indian practitioners, your baby is not the chosen one. The French also tailed off in the program's enthusiasm over time, while the Queen's Gambit and especially the English Opening were well represented.

Frequency of openings over time employed by AlphaZero in its "learning" phase. Image sourced fromAlphaZero research paper.

What do you do if you are a thing that never tires and you just mastered a 1400-year-old game? You conquer another one. After the Stockfish match, AlphaZero then "trained" for only two hours and then beat the best Shogi-playing computer program "Elmo."

The ramifications for such an inventive way of learning are of course not limited to games.

"We have always assumed that chess required too much empirical knowledge for a machine to play so well from scratch, with no human knowledge added at all," Kasparov said. "Of course Ill be fascinated to see what we can learn about chess from AlphaZero, since that is the great promise of machine learning in generalmachines figuring out rules that humans cannot detect. But obviously the implications are wonderful far beyond chess and other games. The ability of a machine to replicate and surpass centuries of human knowledge in complex closed systems is a world-changing tool."

Garry Kasparov and Demis Hassabis together at the ProBiz event in London. | Photo: Maria Emelianova/Chess.com.

Chess.com interviewed eight of the 10 players participating in the London Chess Classicabout their thoughts on the match. A video compilation of their thoughts will be posted on the site later.

The player with most strident objections to the conditions of the match was GM Hikaru Nakamura. While a heated discussion is taking place online about processing power of the two sides, Nakamura thought that was a secondary issue.

The American called the match "dishonest" and pointed out that Stockfish's methodology requires it to have an openings book for optimal performance. While he doesn't think the ultimate winner would have changed, Nakamura thought the size of the winning score would be mitigated.

"I am pretty sure God himself could not beat Stockfish 75 percent of the time with White without certain handicaps," he said about the 25 wins and 25 draws AlphaZero scored with the white pieces.

GM Larry Kaufman, lead chess consultant on the Komodo program, hopes to see the new program's performance on home machines without the benefits of Google's own computers. He also echoed Nakamura's objections to Stockfish's lack of its standard opening knowledge.

"It is of course rather incredible, he said. "Although after I heard about the achievements of AlphaGo Zero in Go I was rather expecting something like this, especially since the team has a chess master, Demis Hassabis. What isn't yet clear is whether AlphaZero could play chess on normal PCs and if so how strong it would be. It may well be that the current dominance of minimax chess engines may be at an end, but it's too soon to say so. It should be pointed out that AlphaZero had effectively built its own opening book, so a fairer run would be against a top engine using a good opening book."

Whatever the merits of the match conditions, Nielsen is eager to see what other disciplines will be refined or mastered by this type of learning.

"[This is] actual artificial intelligence," he said. "It goes from having something that's relevant to chess to something that's gonna win Nobel Prizes or even bigger than Nobel Prizes. I think it's basically cool for us that they also decided to do four hours on chess because we get a lot of knowledge. We feel it's a great day for chess but of course it goes so much further."

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Google's AlphaZero Destroys Stockfish In 100-Game Match ...

Ding Liren is up to 3rd place in the Magnus CarlsenInvitational standings after a tense and well-played match came to an abruptend in the sudden death game. Its farce, not Armageddon, commented thewatching Alexander Grischuk as Maxime Vachier-Lagrave blundered and lost in just 16 moves.Meanwhile Anish Giris quest to win a game in the event goes on as he missed agreat chance before IanNepomniachtchi picked up the full 3 match points.

You can replay all the Magnus Carlsen Invitational gamesusing the selector below (click on a game to open it with computer analysis):

That meant Ian Nepomniachtchi was the days highest scorerfor winning without the need for Armageddon, while Ding Liren took two pointsand MVL one after the Armageddon game:

You can replay the live commentary with Tania Sachdev,Lawrence Trent, Jan Gustafsson, Peter Svidler, Alireza Firouzja and laterAlexander Grischuk, Fabiano Caruana and Ian Nepomniachtchi below:

And heres the aftershow with Pascal Charbonneau:

This was billed before it began as a heavyweight struggle,and it lived up to that reputation. The world numbers 3 and 5, or 3 and 2 ifyou took the rapid ratings, gave each other only glimmers of hope in the fourrapid games. Ding Liren perhaps came closest to breaking through with the whitepieces, but almost the greatest moment of peril in the first four games of thematch was when Ding disconnected in one of the games. Fortunately thistime the players resumed the game from the same position and times with aminimum of fuss.

That meant an Armageddon game between two players who hadlost their previous Armageddons - Ding Liren to Caruana,and MVL to Nepomniachtchi. If one thing seemed certain, it was that Maximecouldnt do worse than hed done in that game, when he was lost in 17 moves andresigned on move 20. But it turned out that was nothing!

Our commentators were already calling MVLs 4Bf5!? a blunder,while 12Nc2+? and 13Qxb2? were the final straws:

14.Ne5! is the only move that wins for White, but its absolutelycrushing, threatening mate on d7, among other things. 14b5 15.Qa5+ Ke8 16.Qc7 and Maxime threw in the towel rather thangive any spite checks:

It was a strange end to the match, but at least the breakbefore the Armageddon had given us time for an enjoyable intervention from the chessworlds most diplomatic player, Alexander Grischuk:

This match also got off to a quiet start, but in Game 2 IanNepomniachtchi showed that he meant business with 7.Qc2:

This quiet little move is not so innocent e.g. 70-0?8.Nxd5! Qxd5 9.Ng5! and Black could resign but the most noteworthy point isthat it seems to be a novelty. There was a time when no-one would "burn" anovelty in a rapid or blitz event, but this one is for serious stakes, and asFabiano Caruana said in a previous interview:

This is the only tournament that I do have for a long timeand it is also a tournament with many if not most of the best players in theworld so I do take it seriously and I really would love to do very well.

He added during Day 6, I don't know about other players,but I already showed all my Candidates ideas! referring to the virus-interruptedtournament in Yekaterinburg that will determine Magnus Carlsens next WorldChampionship challenger.

Anish wasnt going to fall into any simple traps, but he waseventually undone by a bold exchange sacrifice from his opponent:

25.Qxc7!? Bxf1 26.Kxf1 f6 27.Qxa7 and Nepos passed a-pawn eventually decided the game in Whites favour, though Giri could have done more to stop it.

That meant Anish needed to win one of the next two gamesand, in defiance of his critics, it looked as though he might make it in Game3. Critic-in-chief was that man Alexander Grischuk again, who didnt believe in the Dutchno. 1s instincts:

Of course for Anish it's very attractive to exchange queensand win a pawn - how can anything else be more attractive?

Giri, however, resisted the siren call of 27.Qxe4 Bxe428.Rxe6 and channelled his inner AlphaZero to go for 27.h6+! Kf7 28.Nf4! andonly swapped off queens when it gave him a clearly winning position. It was allgoing right until 34Rc4!, a fine double-purpose move:

The obvious threat is Rc1+ and Rh1 mate, which is not to betaken lightly, but it could have been parried by e.g. 35.f3! Instead 35.Rb1?allowed Nepo to change targets and go for the h7-knight instead with 35Rc8!and 36Rh8. In the end it was Nepo who was closer to a win.

In the final must-win game with the black pieces it wasnominally Giri who was pressing for a win, but he never came close and remainson 0 match points, tied with Alireza Firouzja:

There are still four rounds to go, a potential 12 matchpoints, for the players to improve their situation, but its approaching themust-win stage for Alireza as he takes on Fabiano Caruana tomorrow. Remember, the top four go forward to a knockout for the big prizes. The othermatch is another crowd-pleaser, as rapid world no. 1 Magnus Carlsen takes onrapid world no. 2 Maxime Vachier-Lagrave.

We hope you're enjoying the action...

...and if you think you can predict what will happen in Round 4 make sure to enter our Round 4 Fantasy Chess Contest.

Tune in again for all the Magnus Carlsen Invitational action from 15:30 CEST here on chess24.

See also:

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MCI Day 6: Ding Liren beats MVL in 16 moves - chess24

Making an appearance inLex Fridman's Artificial Intelligence Podcast, DeepMind'sDavid Silver gave lots of insights into the history of AlphaGo and AlphaZero and deep reinforcement learning in general.

Today, the finals of the Chess.com Computer Chess Championship (CCC) start between Stockfish and Lc0 (Leela Chess Zero). It's a clash between a conventional chess engine that implements an advanced alphabeta search (Stockfish) and a neural-network based engine (Lc0).

One could say that Leela Chess Zero is the open-source version of DeepMind's AlphaZero, which controversially crushed Stockfish in a 100-game match (andthen repeated the feat).

Even a few years on, the basic concept behind engines like AlphaZero and Leela Zero is breathtaking: learning to play chess just by reinforcement learning from repeated self-play. This idea, and its meaning for the wider world, was discussed in episode 86 of Lex Fridman's Artificial Intelligence Podcast, where Fridman hadDeepMind'sDavid Silver as a guest.

Silver leads the reinforcement learning research group at DeepMind and was lead researcher on AlphaGo and AlphaZero, and he was the co-lead on AlphaStar and MuZero. He did a lot of important work in reinforcement learning, defined as how agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

Silver explains: "The goal is clear: The agent has to take actions, those actions have some effect on the environment, and the environment gives back an observation to the agent saying: This is what you see or sense.One special thing it gives back is called the reward signal: how well it's doing in the environment. The reinforcement learning problem is to simply take actions over time so as to maximize that reward signal."

The first part of the podcast is mostly about the board game go and DeepMind's successful quest in building a system that can beat the best players in the worldsomething that had been achieved in many other board games much earlier, including chess. The story was also depicted in a motion picture.

While AlphaGo was still using human knowledge to some extent (in the form of patterns from games played by humans), the next step for DeepMind was to create a system that wasn't fed by such knowledge.Moving from go to chess, so from AlphaGo to AlphaZero, was an example of taking out initial knowledge and wanting to know how far you could go with self-play alone. The ultimate goal is to use algorithms in other systems and solve problems in the real world.

The first new version that was developed was a fully self-learning version of AlphaGo, without prior knowledge and with the same algorithm. It beat the originalAlphaGo 100-0.

It was then applied in chess (AlphaZero) and Japanese chess (shogi), and in both cases, it beat the best engines in the world.

"It worked out of the box. There's something beautiful about that principle. You can take an algorithm, and not twiddle anything, it just works," said Silver.

There's something beautiful about that principle. You can take an algorithm, and not twiddle anything, it just works.David Silver

In one of the most interesting parts of the podcast, Silver suggests that the (already incredibly strong) AlphaZero that crushed Stockfish can be even stronger and potentially crush its current version. To be fair, he starts by calling this a falsifiable hypothesis:

"If someone in the future was to take AlphaZero as an algorithm and run it with greater computational resources than we have available today, then I will predict that they would be able to beat the previous system 100-0. If they were then to do the same thing a couple of years later, that system would beat the previous system 100-0. That process would continue indefinitely throughout at least my human lifetime."

David Silver and Julian Schrittwieser in a photo from DeepMind's Twitter page prior to a Reddit AMA.

Earlier in the podcasts, Silver explained this mind-boggling idea of AlphaZero losing to a future generation that can benefit from bigger computer power and learn from itself even more:

"Whenever you have errors in a system, how can you remove all of these errors? The only way to address them in any complex system is to give the system the ability to correct its own errors. It must be able to correct them; it must be able to learn for itself when its doing something wrong and correct for it.And so it seems to me that the way to correct delusions was indeed to have more iterations of reinforcement learning. (...)

"Now if you take that same idea and trace it back all the way to the beginning, it should be able to take you from no knowledge, from a completely random starting point, all the way to the highest levels of knowledge that you can achieve in a domain."

There is already a new step for AlphaZero, which called MuZero. In this version, the algorithm, combined with tree-search, works without even learning the rules of a particular game. Perhaps unsurprisingly, it's performing superhumanly as well.

Why skip the step of feeding the rules? Because eventually DeepMind is working towards systems that can have meaning in the real world. And, as Silver notes, for that, we need toacknowledge that "The world is a really messy place, and no one gives us the rules."

Listen to the full podcast here.

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Creator David Silver On AlphaZero's (Infinite?) Strength - Chess.com

3/5/2020 As promised in the announcement of the release of Fat Fritz, the first update to the neural network has been released, stronger and more mature, and with it comes the brand new smaller and faster Fat Fritz for CPU neural network which will produce quality play even on a pure CPU setup. If you leave it analyzing the start position, it will say it likes the Sicilian Najdorf, which says a lot about its natural style. Read on to find out more!

If you havent yet updated your copy of Fat Fritz, now is the time to do it as it brings more thanminor enhancements or a few bug fixes. This update will bring the first major update to the Fat Fritz neural network, stronger than ever, as well as a new smaller one that is quite strong on a GPU, but also shines on even a plain CPU setup.

When you open Fritz 17, presuming you have Fat Fritz installed, you will be greeted with a message in the bottom right corner of your screen advising you there is an update available for Fat Fritz.

When you see this click on 'Update Fat Fritz'

Then you will be greeted with the update pane, and just need to click Next to get to it

When Fat Fritz was released with Fritz 17, updates were promised with the assurance it was still improving. Internally the version number of the release was v226, while this newest one is v471.

While thorough testing is always a challenge since resources are limited, a match against Leela 42850 at 1600 nodes per move over 1000 games yielded a positive result:

Score of Fat Fritz 471k vs Leela 42850: +260 -153 =587 [0.553]Elo difference: 37.32 +/- 13.79

1000 of 1000 games finished.

Also, in a match of 254 games at 3m +1s against Stockfish 11 in AlphaZero ratio conditions, this new version also came ahead by roughly 10 Elo.

Still, it isnt about Elo and never was, and the result is merely to say that you should enjoy strong competitive analysis. For one thing, it is eminently clear that while both Leela and Fat Fritz enjoy much of the same AlphaZero heritage,there are also distinct differences in style.

Perhaps one of the most obvious ways to highlight this is just the start position. If you let the engine run for a couple of minutes on decent hardware, it will tell you what it thinks is the best line of play for both White and Black based on its understanding of chess.

As such, I ran Leela 42850 with its core settings to see what it thought. After 2 million nodes it was adamant that perfect chess should take both players down the highly respected Berlin Defence of the Ruy Lopez.

Leela 42850 analysis:

info depth 19 seldepth 56 time 32675 nodes 2181544 score cp 23 hashfull 210 nps 75740 tbhits 0 pv e2e4 e7e5 g1f3 b8c6 f1b5 g8f6 e1g1 f6e4 d2d4 e4d6 b5c6 d7c6 d4e5 d6f5 d1d8 e8d8 h2h3

This is fine, but it is also very much a matter of taste.

Fat Fritz has a different outlook on chess as has already been pointed out in the past. At first it too will show a preference for the Ruy Lopez, though not the Berlin, but given a bit more time by 2.6 million nodes it will declare the best opening per its understanding of chess and calculations is the Sicillian Najdorf.

Within a couple of minutes this is its mainline:

info depth 16 seldepth 59 time 143945 nodes 7673855 score cp 28 wdl 380 336 284 hashfull 508 nps 54227 tbhits 0 pv e2e4 c7c5 g1f3 d7d6 b1c3 g8f6 d2d4 c5d4 f3d4 a7a6 f1e2 e7e5 d4b3 f8e7 e1g1 c8e6 c1e3 e8g8 f1e1 b8c6 h2h3 h7h6 e2f3 a8c8 d1d2 c6b8 a2a4 f6h7 a1d1 b8d7 f3e2 h7f6

From a purely analytical point of view it is quite interesting that it found 10.Re1! in the mainline. In a position where white scores 52.5% on average it picks a move that scores 58.3% / 58.9%.

Remember there is no right or wrong here, but it does help show the natural inclinations of each of these neural networks.

Even if chess is ultimately a draw, that doesnt mean there is only onepath, so while all roads may lead to Rome, they dont all need to pass through New Jersey.

Trying to find the ideal recipe of parameters for an engine can be daunting, and previously multiple attempts had been made with the well-know tuner called CLOP by Remi Coulom. Very recently a completely new tuner 'Bayes-Skopt' was designed byKarlson Pfannschmidt, a PhD student in Machine Learning in Paderborn University inGermany, who goes by the online nickname "Kiudee" (pronounced like the letters Q-D). It was used to find new improved values for Leela, which are now the new defaults.

His tuner is described as "A fully Bayesian implementation of sequential model-based optimization", a mouthful I know, and was set up with his kind help as it ran for over a week. It produces quite fascinating graphical imagery with its updated values. Here is what the final version looked like:

These values, slightly rounded, have been added as the new de facto defaults for Fat Fritz.

This is a completely new neural network trained from Fat Fritz games, but in a much smaller frame. Objectively it is not as strong as Fat Fritz, but it will run much faster, and above all it has the virtue of being quite decent on even a pure CPU machine. It wont challenge the likes of Stockfish, so lets get that out of the way, but in testing on quad-core machines (i.e. my i7 laptop) it defeats Fritz 16 by a healthy margin.

Note that this is not in the product description, soneedless to say, it is more nor less a gift to Fritz 17 owners.

Enjoy it!

More stories on Fat Fritz and Fritz 17...

Read more:
Fat Fritz 1.1 update and a small gift - Chessbase News