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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."

Follow this link:
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:

Read the original:
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.

View post:
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

Garry Kasparov dominated chess until he was beaten by an IBM supercomputer called Deep Blue in 1997. The event made man loses to computer headlines the world over. Kasparov recently returned to the ballroom of the New York hotel where he was defeated for a debate with AI experts. Wireds Will Knight was there for a revealing interview with perhaps the greatest human chess player the world has ever known.

I was the first knowledge worker whose job was threatened by a machine, says Kasparov, something he foresees coming for us all.

Every technology destroys jobs before creating jobs. When you look at the statistics, only 4 percent of jobs in the US require human creativity. That means 96 percent of jobs, I call them zombie jobs. Theyre dead, they just dont know it. For several decades we have been training people to act like computers, and now we are complaining that these jobs are in danger. Of course they are.

Experts say only about 14 percent of US jobs are at risk of replacement by AI and robots. Nevertheless, Kasparov has some advice for us zombies looking to re-skill.

There are different machines, and it is the role of a human and understand exactly what this machine will need to do its best. ... I describe the human role as being shepherds.

Kasparov, for example, helps Alphabets DeepMind division understand potential weaknesses with AlphaZeros chess play.

The interview also yielded this gem of a quote from Kasparov:

People say, oh, we need to make ethical AI. What nonsense. Humans still have the monopoly on evil. The problem is not AI. The problem is humans using new technologies to harm other humans.

Its a fascinating read and one that should be done in its entirety, if only to find out why Kasparov thinks AI is making chess more interesting, even though humanity doesnt stand a chance of beating it.

See the article here:
Chess champion Garry Kasparov who was replaced by AI says most US jobs are next - The Verge