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Artificial intelligence has become so good at chess that its only competition now comes from other computer programs.Indeed, a human hasnt defeated a machine in a chess tournament in 15 years.

Its an impressive technical achievement, but that dominance has also made top-level chess less imaginative, as players now increasingly follow strategies produced by soulless algorithms.

But a newresearch papershows that AI could still make the game better for us puny humans.

The study authors developed a chess enginewith a difference. Unlike most of its predecessors, their system isnt designed to defeat humans. Instead, its programmed to play like them.

[Read: How this company leveraged AI to become the Netflix of Finland]

The researchers believe Maiacould make the game more fun to play. But it could also help us learn from the computer.

So chess becomes a place where we can try understanding human skill through the lens of super-intelligent AI, said study co-author Jon Kleinberg, a professor at Cornell University.

Their system called Maia is a customized version of AlphaZero, a program developed by research lab DeepMind to master chess, Shogi, and Go.

Instead of building Maia to win a game of chess, the model was trained on individualmoves made by humans. Studyco-author Ashton Anderson said this allowed the system to spot what players should work on:

Maia has algorithmically characterized which mistakes are typical of which levels, and therefore which mistakes people should work on and which mistakes they probably shouldnt, because they are still too difficult.

Maia matched the movesof humans more than 50% of the time, and its accuracy grew as the skill level increases.

The researchers said this prediction accuracy is higher than that of Stockfish, the reigning computer world chess champion.

Maia might not be capable of teaching people to conquer AI at chess but it could help beat their fellow humans.

You can read the study paper on the preprint server arXiv.

Published January 27, 2021 18:52 UTC

Original post:
This AI chess engine aims to help human players rather than defeat them - The Next Web

Another 127,000 new developers starred Facebook's projects in the past year.

Facebook's open-source platform has been growing steadily since it launched and is showing no sign of its popularity waning anytime soon: the past year has seen the project expand yet again, reaching close to 1.3 million followers on Github.

SEE: Managing AI and ML in the enterprise: Tech leaders increase project development and implementation (TechRepublic Premium)

That's another 127,000 new developers starring Facebook's projects on the open-source platform just in the last year, a testimony to "the growth of open source worldwide," according to Suraj Subramanian, developer advocate at Facebook, who compileda review of the social media giant's key achievementsin the open-source space in 2020.

The past year has seen Facebook open source expand yet again, reaching close to 1.3 million followers on Github.

For many years, Facebook has been sharing the company's creations with the wider developer community in a major open-source project.

Developers around the world can access the codebase for some of the company's major software and hardware tools in Github repositories. Subramanian confirmed that Facebook's portfolio of repositories has now grown to more than 700, with over 200 projects made public this year alone another increase from 2019, which saw 170 new repositories added to the portfolio.

SEE:The algorithms are watching us, but who is watching the algorithms?

Both Facebook engineers and independent developers around the world contributed to the community by tweaking Facebook's codebase almost 128,000 times in total, with about 15% of those changes carried out by participants external to the company. That marks a change from the previous year, when external contributors committed about a third of the total changes.

Both Facebook engineers and independent developers around the world contributed to the community by tweaking Facebook's codebase 128,000 times in total.

Subramanian added that 20 new projects were added to Facebook's PyTorch ecosystem, a Python-based machine-learning library that is mostly used for computer applications and natural language processing.

The past few months also saw many companies external to Facebook make use of the PyTorch library for a variety of use cases, ranging fromtraining robotic crop sprayersto identifying weeds as they move through a field, toimproving the training of surgeons. Pharmaceutical firm AstraZeneca also revealed that it is using PyTorch tostreamline the drug discovery process.

Among some of the key technologies that were open sourced by the social media company last year, Subramanian highlighted M2M-100, a multilingual machine translation model that cantranslate any pair of 100 languageswithout relying on English, and is thought to be more accurate than systems that require translating into English before coming up with a final translation in the target language.

Facebook also made its ReBel algorithmavailable to the public in 2020, which builds on the technology that underpins AlphaZero to beat humans at a wide range of games such as poker or Texas Hold'em, and constitutes, according to Subramanian, "a big step towards general AI."

Another one of Facebook's open-source projects that has garnered attention is React Native, a JavaScript code library that lets developers build user interfaces for native iOS and Android apps. Although the platform has existed for a long time, in early 2020 Facebook open-sourced a new React library called Recoil to provide developers with features like time-travel debugging, which are hard to achieve with React alone. In less than a year, Recoil has alreadysecured over 11,000 followers.

Original post:
Open source at Facebook: 700 repositories and 1.3 million followers - ZDNet

What happens when the pandemic finally ends and hundreds of millions of people whove spent an inordinate amount of time secluded are suddenly launched back into the rat race?

Things will likely never go back to normal, but eventually well find a way to occupy space together again and that could be difficult for people whove developed social anxiety or had setbacks in their treatment due to the unique nature of pandemic isolation.

We couldnt find any actual rats to ask how theyre coping with the race, but a team of laboratory mice might just have the answer: its dropping a bunch of acid and letting nature do its thing.

According to a team of researchers from McGill University, LSD (colloquially known as acid) makes people more social and capable of greater human empathy.

The team figured this out by giving lab mice LSD and then measuring their brain activity. The mice became more social while under the influence. And the positive effects of LSD were immediately nullified when the scientists used bursts of light to interrupt the chemical processes thus rendering the mice immediately sober.

The researchers work led to novel insight into how LSD causes a cascade effect of receptor and synapse activity that ultimately seems to kick-start neurotypical feelings of empathy and social inclination.

Due to the nature of the specific chemical reactions concurring in the brain upon the consumption of LSD, it would appear as though its a strong candidate for the potential treatment of myriad mental illnesses and for those with autism spectrum disorder.

Per the teams research paper:

These results indicate that LSD selectively enhances SB by potentiating mPFC excitatory transmission through 5-HT2A/AMPA receptors and mTOR signaling. The activation of 5-HT2A/AMPA/mTORC1 in the mPFC by psychedelic drugs should be explored for the treatment of mental diseases with SB impairments such as autism spectrum disorder and social anxiety disorder.

Quick take: Scientists have understood the effect LSD has on mood receptors in the brain for decades. Whats new here is that we now know how those interactions cause other interactions that create whats essentially a system for increasing empathy or decreasing social anxiety.

Recent research on LSD, cannabis, and psilocybin (shrooms) indicates each has myriad uses for combating and treating mental illness and other disorders related to neurotypical receptor and synapse regulation.

The McGill teams research on LSD, for example, indicates it could prove useful to fight the harmful effects of alcoholism where people are at increased risk of developingsocial anxiety due toaddiction, thus further isolating themselves from others.

This latest study is important in that it drives home what decades of research and millennia of anecdotal evidence already tells us: Some drugs have the potential to do good.

And if we could study them like rational humans instead of allowing politicians to make it almost impossible for researchers to conduct controlled, long term studies on so-called banned substances the world would be a better place.

If you think this is interesting, check out this piece on Neural from earlier today. Where the study in the article youve just read says LSD can amplify empathy and reduce social anxiety, this one shows how empathy happens in a theory of the mind that can be identified down to the single-neuron level.

Read next: Zuckerberg promises Facebook will show less political content from now on

Read more:
Scientists say dropping acid can help with social anxiety and alcoholism - The Next Web

In 2017 the chess world was shaken to its core when Stockfish (the world's strongest chess engine) was defeated in a one-sided match. It was not defeated by a human but by an unknown computer program that seemed to be otherworldlyAlphaZero.

Let's learn more about this powerful chess entity. Here is what you need to know about AlphaZero:

AlphaZero was developed by the artificial intelligence and research company DeepMind, which was acquired by Google. It is a computer program that reached a virtually unthinkable level of play using only reinforcement learning and self-play in order to train its neural networks. In other words, it was only given the rules of the game and then played against itself many millions of times (44 million games in the first nine hours, according to DeepMind).

AlphaZero uses its neural networks to make extremely advanced evaluations of positions, which negates the need to look at over 70 million positions per second (like Stockfish does). According to DeepMind, AlphaZero reached the benchmarks necessary to defeat Stockfish in a mere four hours.

AlphaZero runs on custom hardware that some have referred to as a "Google Supercomputer"although DeepMind has since clarified that AlphaZero ran on four tensor processing units (TPUs) in its matches.

In December 2017, DeepMind published a research paper that announced that AlphaZero had easily defeated Stockfish in a 100-game match. AlphaZero would go on to defeat Stockfish in a second match consisting of 1,000 games; the results were published in a paper in late 2018.

Unfortunately, AlphaZero is not available to the public in any form. The match results versus Stockfish and AlphaZero's incredible games have led to multiple open-source neural network chess projects being created. Leela Chess Zero, Leelenstein, Alliestein, and others try to emulate AlphaZero's learning and playing style. Even Stockfish, the conventional brute-force king, has added neural networks.

In 2020 DeepMind and AlphaZero continued to contribute to the chess world in the form of different chess variants. When DeepMind and the AlphaZero team speak, the chess world listens!

From the moment it stepped onto the scene, AlphaZero has changed chess by spawning a new generation of neural network chess engines, by contributing to chess variants, and through its transcendent games.

As mentioned, AlphaZero defeated the world's strongest chess engine, Stockfish, in a one-sided 100-game match in December 2017 (scoring 28 wins, 72 draws, and zero losses). The public was given 10 example games from this match, and the chess world's reaction was borderline disbelief.GM Peter Heine Nielsen likened watching AlphaZero's games to seeing a superior species landing on earth and showing us how to play chess:

Other grandmasters shared Nielsen's sentiment, including the legendary GM Garry Kasparov, who told Chess.com, "It's a remarkable achievement.... It approaches the 'Type B,' human-like approach to machine chess dreamt of by Claude Shannon and Alan Turing instead of brute force."

Others questioned the results because of the disparity of hardware used in the first match. Some also found it unfair that Stockfish was not allowed to use its opening book and its endgame tablebase.

GM Hikaru Nakamura stated: "I don't necessarily put a lot of credibility in the results simply because my understanding is that AlphaZero is basically using the Google supercomputer, and Stockfish doesn't run on that hardware; Stockfish was basically running on what would be my laptop."

Roughly one year after the first match, DeepMind published a new paper that announced an updated version of AlphaZero had defeated Stockfish in a 1,000-game match. This time, the current version of Stockfish (version 9 at the time) was used, Stockfish was able to use a strong opening book in many of the games, the time controls were adjusted (with Stockfish having large time advantages), and Stockfish was run on the same type of hardware used in the Top Chess Engine Championships (TCEC).

The results didn't change muchAlphaZero defeated Stockfish again with a score of 155 wins, 839 draws, and 6 losses.

In 2019 and 2020 GM Vladimir Kramnik was able to spend some time with AlphaZero and the DeepMind team to explore chess variants andco-wrote a paper with DeepMind about the exploration of new chess variants, including sideways pawns, no castling, torpedo chess (where pawns can always move forward one or more squares).

In September 2020 Chess.com hosted a roundtable discussion with Kramnik and members of the DeepMind team where they discussed variants and other topics. You can watch the full video here:

Many of these chess variants (and more) have been added to Chess.com. This article outlines the new chess variants and how to play them. If you want to check out any of these variants for yourself, simply head over to Chess.com/variants or hover your mouse over the "Play" button in the menu bar and select "Variants":

After you select "Variants," you are directed to the Chess Variants Page. All you have to do is select a variant and press "Play."

In this first game example, we see some of the magic that AlphaZero shocked the world with in the first match. AlphaZero gambits a pawn in the opening and immediately goes on the attack. After 19...Kxh6 Stockfish is up a piece, but the king is not safe, and the entire queenside is undeveloped:

AlphaZero keeps up the pressure, but its compensation for the piece is mostly unclear to us mortals. Only in hindsight can we tell that a couple of Black's pieces (most notably the a8-rook and queen's knight) will never really be part of the game. After 36.Qe6, the position has crystallized, and AlphaZero wins convincingly:

This second game example is from the second AlphaZero-Stockfish match. AlphaZero puts on a positional clinic and tortures Stockfish with the bishop pair in the endgame after 45. Bxe4. Here is the full game:

In the following video, GM Robert Hess covers this fantastic game in great detail:

You now know what AlphaZero is, what it has accomplished, and more. If you are interested in seeing what you can learn from AlphaZero's play, check out this great series of video lessons by Chess.com's IM Danny Rensch.

Original post:
AlphaZero - Chess Engines - Chess.com

As with Go, we are excited about AlphaZeros creative response to chess, which has been a grand challenge for artificial intelligence since the dawn of the computing age with early pioneers including Babbage, Turing, Shannon, and von Neumann all trying their hand at designing chess programs. But AlphaZero is about more than chess, shogi or Go. To create intelligent systems capable of solving a wide range of real-world problems we need them to be flexible and generalise to new situations. While there has been some progress towards this goal, it remains a major challenge in AI research with systems capable of mastering specific skills to a very high standard, but often failing when presented with even slightly modified tasks.

AlphaZeros ability to master three different complex games and potentially any perfect information game is an important step towards overcoming this problem. It demonstrates that a single algorithm can learn how to discover new knowledge in a range of settings. And, while it is still early days, AlphaZeros creative insights coupled with the encouraging results we see in other projects such as AlphaFold, give us confidence in our mission to create general purpose learning systems that will one day help us find novel solutions to some of the most important and complex scientific problems.

This work was done by David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis.

Read more from the original source:
AlphaZero: Shedding new light on chess, shogi, and Go ...