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The most powerful chess engines of all time are all well-known to most chess players. If you are wondering which available engine is the strongest, then look no furtherStockfish is the king of chess engines.

Let's learn more about this mighty engine. Here is what you need to know about Stockfish:

Stockfish is the strongest chess engine available to the public and has been for a considerable amount of time. It is a free open-source engine that is currently developed by an entire community. Stockfish was based on a chess engine created by Tord Romstad in 2004 that was developed further by Marco Costalba in 2008. Joona Kiiski and Gary Linscott are also considered founders.

Stockfish is not only the most powerful available chess engine but is also extremely accessible. It is readily available on many platforms, including Windows, Mac OS X, Linux, iOS, and Android.

Stockfish's accomplishments are more impressive than those of any other chess engine. It has won eight Top Chess Engine Championships (TCEC) through 2020. Stockfish has also dominated Chess.com's Computer Chess Championship since 2018, winning the first six events and more.

Stockfish had firmly established itself as the strongest chess engine in the world before 2017, which is why the chess world was shaken to its core when it lost a one-sided match to a neural network computer program called AlphaZero. This loss to AlphaZero led to the development of other neural network projects (most notably Leela Chess Zero,Leelenstein, and Alliestein).

Although Stockfish has kept its spot atop the chess engine list, the neural network engines had been getting closer and closer to Stockfish's strength. In September 2020, Stockfish 12 was released, and it was announced that Stockfish had absorbed the Stockfish+NNUE project (NNUE stands for Efficiently Updatable Neural Network). What does this move mean? Well, now the raw power of the traditional brute-force Stockfish has been improved by the evaluation abilities of a neural network enginea mind-boggling combination!

As of October 2020, Stockfish is the highest-rated engine according to the computer chess rating list (CCRL) with a rating of 3514it is the only engine with a rating above 3500. According to the July 2020 Swedish Chess Computer Association (SSDF) rating list, Stockfish 9 is ranked #3, Stockfish 10 is ranked #2, and Stockfish 11 is ranked #1 with a rating of 3558. Taking the top three spots with three different versions is quite impressive.

According to this great video on the strongest chess engines of all time (based on the SSDF rating lists), Stockfish is the strongest engine of all timea sentiment that is widely shared in the chess community.

As mentioned, Stockfish has dominated the TCEC since it started participating. It has won eight TCEC championships and also has six second-place finishesit has placed first or second in every season it has participated since 2013 with only one exception. From 2018-2020 it won seven out of nine TCEC seasons ahead of Komodo, Leela Chess Zero, Shredder, Houdini, and other top-level engines.

Stockfish also won the 2014 TCEC Fischer Random tournament, the TCEC season 10 Rapid tournament, and three TCEC cups (in 2018, 2019, and 2020 respectively).

Chess.com's Computer Chess Championship has also been a common winning ground for Stockfish. It has won eight of the 13 events through 2020 and placed second in four others. Stockfish continues to defeat the neural network engines in most competitions.

The first game example is from the 2018 Stockfish-AlphaZero match. Stockfish wins quickly and easilycan you ask for more than defeating the strongest chess entity that the world has ever seen in a mere 22 moves?Stockfish sacrifices a pawn early in the opening and gains a large advantage after 13. Rd3. After 18. Rh4, all of Stockfish's pieces are active and developed, while all of AlphaZero's pieces are on the back rank (except for the queen):

The sacrifices with 19. Bc4! and 20. Nce4! are powerful and finish the game quickly.

In this second game example, we see Stockfish dispatch another famous chess engine that stood atop the chess engine world for years: Rybka. Stockfish gains a nice advantage out of the opening that it keeps throughout the game. The fireworks start with Stockfish's 28. Bxh6+!

Stockfish keeps up the pressure with an exchange sacrifice on move 31 and dominates the rest of the game after Rybka's 33...Kh7:

In this fantastic video by Chess.com's NM Sam Copeland, Stockfish+NNUE dismantles the neural network engine Stoofvlees:

Stockfish is the engine for analysis on Chess.com. It is very easy to use on this site in several ways. One is to go to Chess.com/analysis and load your PGN or FEN:

Another easy-to-use method of analyzing your games on Chess.com with Stockfish is to select "Analyze" after you complete a game in Live Chess.

Yet another way to analyze your games with Stockfish on Chess.com is with Chess.com's analysis board. Simply go to Live Chess and select the drop-down menu below the Tournaments tab:

After you select this menu, simply press "Analysis Board." Then you can analyze with Stockfish!

The Analysis Board is very easy to use and can help you with any phase of the game. This article explains how to use it.

In this video, Chess.com's IM Danny Rensch explains some of the Stockfish analysis features available on Chess.com:

You now know what Stockfish is, why it is important, how to analyze with Stockfish on Chess.com, and more. Head over to Chess.com/CCC to watch Stockfish and other top engines battling at any time on any day!

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Stockfish - Chess Engines - Chess.com

That headline might seem a bit churlish, given the tremendous amount of energy, investment, and hype in the AI space, as well as undeniable evidence of technological progress. After all, AI today can beat any human in games ranging from chess to Starcraft (DeepMinds AlphaZero and AlphaStar); it can write a B- college history essay in seconds with a few prompts (OpenAIs GPT-3); it can draw on-demand illustrations of surprising creativity and quality (OpenAIs DALL-E 2).

For AI proponents like Sam Altman, OpenAIs CEO, these advances herald an era where AI creative tools are going to be the biggest impact on creative work flows since the computer itself, as he tweeted last month. That may turn out to be true. But in the here and now, Im still left somewhat underwhelmed.

Not by what these AI tools can do, exactly. Typing a short prompt into DALL-E 2 and getting back, say, a medieval painting where the wifi isnt working feels close to magic. Still, human beings can write essays and human beings can draw illustrations, and while GPT-3 and DALL-E 2 can do those tasks faster, they cant really do them better. Theyre superhuman in velocity, not quality. (The exception in the above group is DeepMinds game-playing model, which really is superhuman just ask poor defeated Go master Lee Se-dol but until those AI skills can be employed in the much more complex real world, its mostly an interesting research project.)

So AI can be fascinating and cool and even be a little bit scary, but what it isnt yet is truly able to play a vital role in solving important problems something that can be seen in the fact that all of these advances have yet to boost Americas sluggish productivity numbers.

Thats why the recent news about AlphaFold, an AI model from DeepMind that can predict the three-dimensional structure of proteins, seems genuinely monumental heralding not just a new era in artificial intelligence but a new era in useful, important science.

For decades, molecular biologists have been trying to crack whats known as the protein-folding problem.

Proteins are the biological drivers of everything from viruses to human beings. They begin as strings of chemical compounds before they fold into unique 3D shapes. The nature of those shapes as much as the amino acids that make them up define what proteins can do, and how they can be used.

Predicting what shape a protein will take based on its amino acid sequence would allow biologists to better understand its function and how it relates to other molecular processes. Pharmaceuticals are often designed using protein structural information, and predicting protein folding could greatly accelerate drug discovery, among other areas of science.

However, the issue in the protein-folding problem is that identifying a proteins eventual structure has generally taken scientists years of strenuous lab work. What researchers needed was an AI algorithm that could quickly identify the eventual shape of a protein, just as computer vision systems today can identify human faces with astounding accuracy. Up until just a few years ago, the best computational biology approaches to protein-folding prediction were still far below the accuracy scientists could expect from experimental work.

Enter AlphaFold. Another product of DeepMind, the London-based AI company that was bought by Google (which later became Alphabet) in 2014, AlphaFold is an AI model designed to predict the three-dimensional structure of proteins. AlphaFold blew away the competition in a biennial protein-structure prediction challenge in late 2020, performing almost as well as gold-standard experimental work, but far faster.

AlphaFold predicts protein structures through a deep learning neural network that was trained on thousands of known proteins and their structures. The model used those known connections to learn to rapidly predict the shape of other proteins, in much the same way that other deep learning models can ingest vast quantities of data in the case of GPT-3, about 45 terabytes of text data to predict what comes next.

AlphaFold was recognized by the journal Science as 2021s Breakthrough of the Year, beating out candidates like Covid-19 antiviral pills and the application of CRISPR gene editing in the human body. One expert even wondered if AlphaFold would become the first AI to win a Nobel Prize.

The breakthroughs have kept coming.

Last week, DeepMind announced that researchers from around the world have used AlphaFold to predict the structures of some 200 million proteins from 1 million species, covering just about every protein known to human beings. All of that data is being made freely available on a database set up by DeepMind and its partner, the European Molecular Biology Laboratorys European Bioinformatics Institute.

Essentially you can think of it as covering the entire protein universe, DeepMind CEO Demis Hassabis said at a press briefing last week. We are at the beginning of a new era of digital biology.

The database basically works as a Google search for protein structures. Researchers can type in a known protein and get back its predicted structure, saving them weeks or more of work in the lab. The system is already being used to accelerate drug discovery, in part through an Alphabet sister company called Isomorphic Laboratories, while other researchers are tapping AlphaFold to identify enzymes that could break down plastics.

The sheer speed enabled by AlphaFold should also help cut the cost of research. Kathryn Tunyasuvunakool, a DeepMind research scientist, told reporters that AlphaFold required only about 10 to 20 seconds to make each protein prediction. That could be especially useful for researchers laboring on neglected diseases like leishmaniasis and Chagas disease, which are perennially underfunded because they mostly strike the desperately poor.

AlphaFold is the singular and momentous advance in life science that demonstrates the power of AI, tweeted Eric Topol, the director of the Scripps Research Translational Institute.

It may well be that AI models like GPT-3 that deal in general language are ultimately more influential than a more narrow application like AlphaFold. Language is still our greatest signal of intelligence and potentially even consciousness just witness the recent controversy over whether another advanced language model, Googles LaMDA, had become sentient.

But for all their advances, such models are still far from that level, and far even from being truly reliable for ordinary users. Companies like Apple and Amazon have labored to develop voice assistant AIs that are worthy of the name. Such models also struggle with bias and fairness, as Sigal Samuel wrote earlier this year, which is a problem to be solved with politics rather than technology.

DeepMinds AlphaFold model isnt without its risks. As Kelsey Piper wrote earlier this year about AI and its applications in biology, Any system that is powerful and accurate enough to identify drugs that are safe for humans is inherently a system that will also be good at identifying drugs that are incredibly dangerous for humans. An AI capable of predicting protein structures could theoretically be put to malign uses by someone looking to engineer biological weapons or toxins.

To its credit, DeepMind says it weighed the potential dangers of opening up its database to the public, consulting with more than 30 experts in biosecurity and ethics, and concluded that the benefits including in speeding the development of effective defenses against biological threats outweighed any risks. The accumulation of human knowledge is just a massive benefit, Ewen Birney, director of the European Bioinformatics Institute, told reporters at the press briefing. And the entities which could be risky are likely to be a very small handful.

AlphaFold which DeepMind has said is the most complex AI system it has ever built is a highly effective tool that can do things humans cant do easily. In the process, it can make those human biologists even more effective at their jobs. And in the age of Covid, those jobs are more important than ever, as is their new AI assistant.

A version of this story was initially published in the Future Perfect newsletter. Sign up here to subscribe!

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DeepMinds AlphaFold could be the future of science and AI - Vox.com

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[Event "FICGS__CHESS__WCH_STAGE_1_GROUP_M_02__000025"][Site "FICGS"][Date "2022.03.14"][Round "1"][White "Werner,Frank-Karl"][Black "DeBonis,Patrick"][Result "*"][WhiteElo "2256"][BlackElo "2182"]

1.g3 d5 2.Nf3 c6 3.Bg2 Bg4 4.O-O Nd7 5.h3 Bh5 6.d4 e6 7.c4 Be7 8.cxd5 exd5 9.Nc3 Bxf3 10.exf3 Ngf6 11.h4 O-O 12.Bh3 Nh5 13.Re1 Nb6 14.Bg4 Nf6 15.Bf5 g6 16.Bd3 Ne8 17.Kg2 Ng7 18.Bh6 Bf6 19.Ne2 Re8 20.Rh1 Nd7 21.Qc2 Nf8 22.Rad1 Qd6 23.Qd2 Nfe6 24.Bb1 a5 25.a3 a4 26.g4 Rad8 27.h5 Ng5 28.Rde1 Rxe2 29.Rxe2 N7e6 30.hxg6 hxg6 31.Rxe6 Nxe6 32.f4 Qe7 33.f5 gxf5 34.Qd3 Bg7 35.Be3 Re8 36.Rh5 Nf8 37.*

Although many say that it seems quite impossible to beat such a correspondence chess champion in a 12 games match nowadays, you'll probably find some tips in the previous answers by former champions & finalists. Always playing the best move according to the strongest chess engines may not be the solution. Ideas from the famous "Art of war" by Sun Tzu still can be used in this modern chess era, and maybe in other games as well like Go & poker holdem, now all dominated by machines.

Yen-Wei Huang is FICGS Go champion...After his win in the Go world championship final match, Yen-Wei shared his analysis on the games and his views around the world of Go (Weiqi, Baduk) and particularly computer Go in the forum.

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Will Google Deepmind envisage to make its so-called A.I. master Poker Holdem next? We'll probably have an answer within a few months. However, it seems that we're not so close to see an artificial consciousness that could be compared to the human one, that is probably our very last privilege. Meanwhile, let's play!

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Correspondence chess server, Go (weiqi) games online - FICGS

In 2021, Nikhil Kamath, founder of Zerodha, defeated five-time world champion Vishwanathan Anand in chess with the help of computers (he confessed later on) at a celebrity fundraiser. The controversy sparked discussions around the use of AI in the game of chess.

As India is all set to host the 44th edition of the Chess Olympiad in Mahabalipuram starting on July 28, lets look at how AI has impacted the game of chess.

The earliest mention of technology in chess can be traced back to the 18th century when Austrian empress Maria Theresa commissioned a chess-playing machine. Many players competed against the Mechanical Turk, thinking it was an automated machine. However, it turned out to be a scam. A human hidden inside the machine was operating it.

In the mid-1940s, British mathematician Alan Turing began theorising how a computer could play chess against a human. In 1949, Claude Shannon published a seminal paper describing a potential program to do exactly that. In 1950, Alan Turing created a program capable of playing chess. Soon after, the Dietrich Prinz and Bernstein chess program burst into the scene.

Computer chess appeared for the first time in the 1970s. MicroChess, the first commercial chess program for microcomputers, in 1976; Chess Challenger in 1977; and Sargon, which won the worlds first computer chess tournament for microcomputers, in 1978.

The robotic chess computers came about in the 1980s. Boris Handroid, Novag Robot Adversary and Milton Bradley Grandmaster are some examples. The most popular was Chessmaster 2000, which ruled the chess video and computer games industry for the next two decades.

As chess computers were gaining popularity in the 1980s, Gary Kasparov, the then world chess champion, claimed AI-driven chess engines could not defeat top-level chess grandmasters. However, in 1989 and 1996, Kasparov beat IBMs powerful chess engines, Deep Thought and Deep Blue.

Things started to change in the late 1990s. In 1997, Deep Blue defeated Kasparov. A year later, Kasparov came up with the idea of Cyborg chess or centaur chess, in which human and computer skills are combined to up the level of the game. The first cyborg chess was held in 1998.

In 2017, AlphaZero, a computer program developed by DeepMind, defeated the worlds strongest chess engine Stockfish. AlphaZero used the reinforcement learning technique in which the algorithm mimicked humans learning process to train its neural networks.

In 2018, TalkChess.com released Leela Chess Zero, developed by Gary Linscott (who also developed Stockfish). Without having any chess-specific knowledge, Leela Chess Zero learned the game based on deep reinforcement learning using an open-source implementation of AlphaZero.

In 2019, DeepMind came up with another algorithm based on reinforcement learning called MuZero.

Chess players use AI-driven chess engines to analyse their and competitors games. As a result, AI has helped in improving the quality of games.

Post pandemic a lot of chess competitions were moved online. In the European Online Chess Championship, as many as 80 participants were disqualified for cheating. FIDE, the international chess body, has approved an artificial intelligence-driven behaviour-tracking module for the FIDE Online Arena games. Chess.com, an internet chess server, uses a cheat detection system to assess the probability of a human player matching the moves of a chess engine or surpassing the games of some of the greatest chess players with the help of a statistical model. DeepMind is also working to develop a new cheat detection software.

AI has also brought down the cost and effort of training and helped develop new chess strategies.

AI has indeed changed the dynamics of the game. However, using AI in chess has raised a few issues. Computer chess engines have significantly improved gameplay. However, people have also raised concerns that players of this age depend too much on machine-driven analysis.

Even when it comes to detecting cheating, AI poses a few issues. First, there is a possibility a player might be wrongly red-flagged by AI. For example, a Chess.com player and grandmaster, Akshat Chandra, was banned after a win against Hikaru as his moves supposedly matched Komodo, a strong positional chess engine. Though Chandra has been proved innocent, his reputation took a hit.

Chess engines and deep learning-based neural networks present enormous possibilities. Moreover, the complex nature and the strategic orientation of the game have provided a ground for assessing any progress in the field of artificial intelligence. They (games) are the perfect platform to develop and test ideas for AI algorithms. Its very efficient to use games for AI development, as you can run thousands of experiments in parallel on computers in the cloud and often faster than real-time, and generate as much training data as your systems need to learn from. Conveniently, games also normally have a clear objective or score, so it is easy to measure the progress of the algorithms to see if they are incrementally improving over time, and therefore if the research is going in the right direction, said DeepMind cofounder Demis Hassabis.

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Chennai Chess Olympiad and AI - Analytics India Magazine

Melanie Mitchell, an AI researcher at the Santa Fe Institute, is also excited to see a whole new approach. We really havent seen this coming out of the deep-learning community so much, she says. She also agrees with LeCun that large language models cannot be the whole story. They lack memory and internal models of the world that are actually really important, she says.

Natasha Jaques, a researcher at Google Brain, thinks that language models should still play a role, however. Its odd for language to be entirely missing from LeCuns proposals, she says: We know that large language models are super effective and bake in a bunch of human knowledge.

Jaques, who works on ways to get AIs to share information and abilities with each other, points out that humans dont have to have direct experience of something to learn about it. We can change our behavior simply by being told something, such as not to touch a hot pan. How do I update this world model that Yann is proposing if I dont have language? she asks.

Theres another issue, too. If they were to work, LeCuns ideas would create a powerful technology that could be as transformative as the internet.And yethis proposal doesnt discuss how his models behavior and motivations would be controlled, or who would control them. This is a weird omission, says Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI expert at Boston Consulting Group.

We should think more about what it takes for AI to function well in a society, and that requires thinking about ethical behavior, amongst other things, says Gupta.

Yet Jaques notes that LeCuns proposals are still very much ideas rather than practical applications. Mitchell says the same: Theres certainly little risk of this becoming a human-level intelligence anytime soon.

LeCun would agree. His aim is to sow the seeds of a new approach in the hope that others build on it. This is something that is going to take a lot of effort from a lot of people, he says. Im putting this out there because I think ultimately this is the way to go. If nothing else, he wants to convince people that large language models and reinforcement learning are not the only ways forward.

I hate to see people wasting their time, he says.

See the article here:
Yann LeCun has a bold new vision for the future of AI - MIT Technology Review