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Leela Zero is a free and open-source computer Go program released on 25 October 2017. It is developed by Belgian programmer Gian-Carlo Pascutto,[1][2][3] the author of chess engine Sjeng and Go engine Leela.[4][5]

Leela Zero's algorithm is based on DeepMind's 2017 paper about AlphaGo Zero.[3][6]Unlike the original Leela, which has a lot of human knowledge and heuristics programmed into it, the program code in Leela Zero only knows the basic rules and nothing more. The knowledge that makes Leela Zero a strong player is contained in a neural network, which is trained based on the results of previous games that the program played.[7]

Leela Zero is trained by a distributed effort, which is coordinated at the Leela Zero website. Members of the community provide computing resources by running the client, which generates self-play games and submits them to the server. The self-play games are used to train newer networks. Generally, over 500 clients have connected to the server to contribute resources.[7] The community has provided high quality code contributions as well.[7]

Leela Zero finished third at the BerryGenomics Cup World AI Go Tournament in Fuzhou, Fujian, China on 28 April 2018.[8] The New Yorker at the end of 2018 characterized Leela and Leela Zero as "the worlds most successful open-source Go engines".[9]

In early 2018, another team branched Leela Chess Zero from the same code base, also to verify the methods in the AlphaZero paper as applied to the game of chess. AlphaZero's use of Google TPUs was replaced by a crowd-sourcing infrastructure and the ability to use graphics card GPUs via the OpenCL library. Even so, it is expected to take a year of crowd-sourced training to make up for the dozen hours that AlphaZero was allowed to train for its chess match in the paper.[10]

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Leela Zero - Wikipedia

Neural network-based chess engine

Leela chess Zero (abbreviated as LcZero, Lc0) is a free, open-source, and neural networkbased chess engine and distributed computing project. Development has been spearheaded by programmer Gary Linscott, who is also a developer for the Stockfish chess engine. Leela chess Zero was adapted from the Leela Zero Go engine,[1] which in turn was based on Google's AlphaGo Zero project.[2] One of the purposes of Leela Chess Zero was to verify the methods in the AlphaZero paper as applied to the game of chess.

Like Leela Zero and AlphaGo Zero, Leela chess Zero starts with no intrinsic chess-specific knowledge other than the basic rules of the game.[1] Leela chess Zero then learns how to play chess by reinforcement learning from repeated self-play, using a distributed computing network coordinated at the Leela Chess Zero website.

As of 2020[update], Leela chess Zero had played over 300 million games against itself,[3] and is capable of play at a level that is comparable with Stockfish, the leading conventional chess program.[4][5]

The Leela chess Zero project was first announced on TalkChess.com on January 9, 2018.[1][6] This revealed Leela Chess Zero as the open-source, self-learning chess engine it would come to be known as, with a goal of creating a strong chess engine.[7] Within the first few months of training, Leela Chess Zero had already reached the Grandmaster level, surpassing the strength of early releases of Rybka, Stockfish, and Komodo, despite evaluating orders of magnitude fewer positions while using MCTS.

In December 2018, the AlphaZero team published a new paper in Science magazine revealing previously undisclosed details of the architecture and training parameters used for AlphaZero.[8] These changes were soon incorporated into Leela Chess Zero and increased both its strength and training efficiency.[9]

The work on Leela chess Zero has informed the similar AobaZero project for shogi.[10]

The engine has been rewritten and carefully iterated upon since its inception, and now runs on multiple backends, allowing it to effectively utilize different types of hardware, both CPU and GPU.[11]

The engine supports the Fischer Random Chess variant, and a network is being trained to test the viability of such a network as of May 2020.[11]

The method used by its designers to make Leela Chess Zero self-learn and play chess at above human level is reinforcement learning. This is a machine-learning algorithm, mirrored from AlphaZero used by the Leela chess Zero training binary to maximize reward through self-play.[1][8] As an open-source distributed computing project, volunteer users run Leela Chess Zero to play hundreds of millions of games which are fed to the reinforcement algorithm.[3] In order to contribute to the advancement of the Leela Chess Zero engine, the latest non-release candidate (non-rc) version of the Engine as well as the Client must be downloaded. The Client is needed to connect to the current server of Leela Chess Zero, where all of the information from the self-play chess games are stored, to obtain the latest network, generate self-play games, and upload the training data back to the server.[12]

In order to play against the Leela Chess Zero engine on a machine, 2 components are needed: the engine binary, and a network (The engine binary is distinct from the client, in that the client is used as a training platform for the engine). The network contains Leela Chess Zero's evaluation function that is needed to evaluate positions.[12] Older networks can also be downloaded and used by placing those networks in the folder with the Lc0 binary.

Self-play Elo is used to gauge relative network strength to look for anomalies and general changes in network strength, and can be used as a diagnostic tool when Lc0 undergoes significant changes. Through test match games that are played with minimal temperature-based variation, Lc0 engine clients test the most recent version against other recent versions of the same network's run, which is then sent the training server to create an overall Elo assessment.

Standard Elo formulae are used to calculate relative Elo strength between the two players. More recent Self-Play Elo calculations use match game results against multiple network versions to calculate a more accurate Elo value.

The Self-Play approach has several consequences on gauging Lc0 Elo:

Cumulative Self-Play Elo inflation can be compared with other runs to gauge the lack of generality in gauging strength with pure cumulative self-play elo. The Fischer Random Chess run Test 71.4 (named 714xxx nets), ranks at nearly 4000 cumulative self-play Elo 76 nets into its run (714076). The T60 (6xxxx) run 63000 net has a cumulative self-play Elo of around 2900. Pitting 714076 against net 63000 reveals 63000 clearly beats 714076 in head-to-head matches at most, if not all "fair" time controls. 4000 Elo >> 2900 elo, but the net with 2900 Elo is clearly beating the 4000 Elo net. This alone is enough to credit the claim that Cumulative self-play Elo is not an objective measure of strength, nor is it a measure which allows one to linearly compare Lc0 network strength to Human strength.

Setting up the engine to play a single node with ``--minibatch-size=1`` and ``go nodes 1`` for each played move creates deterministic play, and Self-Play Elo on such settings will always yield the same result between 2 of the same networks on the same start position--always win, always loss, or always draw. Self-play Elo is not reliable for determining strength in these deterministic circumstances.

In season 15 of the Top Chess Engine Championship, the engine AllieStein competed alongside Leela. AllieStein is a combination of two different spinoffs from Leela: Allie, which uses the same evaluation network as Leela, but has a unique search algorithm for exploring different lines of play, and Stein, an evaluation network which has been trained using supervised learning based on existing game data featuring other engines (as opposed to the unsupervised learning which Leela uses). While neither of these projects would be admitted to TCEC separately due to their similarity to Leela, the combination of Allie's search algorithm with the Stein network, called AllieStein, is unique enough to warrant it competing alongside mainstream Lc0. (The TCEC rules require that a neural network-based engine has at least 2 unique components out of 3 essential features: The code that evaluates a network, the network itself, and the search algorithm. While AllieStein uses the same code to evaluate its network as Lc0, since the other two components are fresh, AllieStein is considered a distinct engine.)[13]

In early 2021, the LcZero blog announced Ceres, a new chess engine that uses LcZero networks. It implements Monte Carlo tree search as well as many novel algorithmic improvement ideas. Initial Elo testing showed that Ceres is stronger than Lc0 with the same network. [14]

In April 2018, Leela chess Zero became the first neural network engine to enter the Top Chess Engine Championship (TCEC), during season 12 in the lowest division, division 4.[15] Leela did not perform well: in 28 games, it won one, drew two, and lost the remainder; its sole victory came from a position in which its opponent, Scorpio 2.82, crashed in three moves.[16] However, it improved quickly. In July 2018, Leela placed seventh out of eight competitors at the 2018 World Computer Chess Championship.[17] In August 2018, it won division 4 of TCEC season 13 with a record of 14 wins, 12 draws, and 2 losses.[18][19] In Division 3, Leela scored 16/28 points, finishing third behind Ethereal, which scored 22.5/28 points, and Arasan on tiebreak.[20][18]

By September 2018, Leela had become competitive with the strongest engines in the world. In the 2018 Chess.com Computer Chess Championship (CCCC),[21] Leela placed fifth out of 24 entrants. The top eight engines advanced to round 2, where Leela placed fourth.[22][23] Leela then won the 30-game match against Komodo to secure third place in the tournament.[24][25] Concurrently, Leela participated in the TCEC cup, a new event in which engines from different TCEC divisions can play matches against one another. Leela defeated higher-division engines Laser, Ethereal and Fire before finally being eliminated by Stockfish in the semi-finals.[26]

In October and November 2018, Leela participated in the Chess.com Computer Chess Championship Blitz Battle.[27] Leela finished third behind Stockfish and Komodo.[28]

In December 2018, Leela participated in season 14 of the Top Chess Engine Championship. Leela dominated divisions 3, 2, and 1, easily finishing first in all of them. In the premier division, Stockfish dominated while Houdini, Komodo and Leela competed for second place. It came down to a final-round game where Leela needed to hold Stockfish to a draw with black to finish second ahead of Komodo. It successfully managed this and therefore contested the superfinal against Stockfish. Whilst many expected Stockfish to win comfortably, Leela exceeded all expectations and scored several impressive wins, eventually losing the superfinal by the narrowest of margins in a 49.5-50.5 final score.[29]

In February 2019, Leela scored its first major tournament win when it defeated Houdini in the final of the second TCEC cup. Leela did not lose a game the entire tournament.[30][31] In April 2019, Leela won the Chess.com Computer Chess Championship 7: Blitz Bonanza, becoming the first neural-network project to take the title.[32]

In May 2019, Leela defended its TCEC cup title, this time defeating Stockfish in the final 5.5-4.5 (+2 =7 -1) after Stockfish blundered a 7-man tablebase draw.[33] Leela also won the Superfinal of season 15 of the Top Chess Engine Championship 53.5-46.5 (+14 -7 =79) versus Stockfish, including winning as both white and black in the same predetermined opening in games 61 and 62.[34][35]

Season 16 of TCEC saw Leela finish in 3rd place in premier division, missing qualification for the superfinal to Stockfish and new neural network engine AllieStein. Leela did not suffer any losses in the Premier division, the only engine to do so, and defeated Stockfish in one of the six games they played. However, Leela only managed to score 9 wins, while AllieStein and Stockfish both scored 14 wins. This inability to defeat weaker engines led to Leela finishing 3rd, half a point behind AllieStein and a point behind Stockfish.[36] In the fourth TCEC cup, Leela was seeded first as the defending champion, which placed it on the opposite half of the brackets as AllieStein and Stockfish. Leela was able to qualify for the finals, where it faced Stockfish. After seven draws, Stockfish won the eighth game to win the match.[37]

In Season 17 of TCEC, held in January-April 2020, Leela regained the championship by defeating Stockfish 52.5-47.5, scoring a remarkable 6 wins in the final 10 games, including winning as both white and black in the same predetermined opening in games 95 and 96.[38] It qualified for the superfinal again in Season 18, but this time was defeated by Stockfish 53.5-46.5.[39] In the TCEC Cup 6 final, Leela lost to AllieStein, finishing 2nd.[40]

Season 19 of TCEC saw Leela qualify for the superfinal again. This time it played against a new Stockfish version with support for NNUE, a neural networkbased evaluation function used primarily for the leaf nodes of the search tree. It defeated Leela convincingly with a final score of 54.5-45.5 (+18 -9 =73).[41][42]

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Leela Chess Zero - Wikipedia

Fall 2021: the season of pumpkins, pecan pies, and peachy new phones. Every year, right on cue, Apple, Samsung, Google, and others drop their latest releases. These fixtures in the consumer tech calendar no longer inspire the surprise and wonder of those heady early days. But behind all the marketing glitz, theres something remarkable going on.

Googles latest offering, the Pixel 6, is the first phone to have a separate chip dedicated to AI that sits alongside its standard processor. And the chip that runs the iPhone has for the last couple of years contained what Apple calls a neural engine, also dedicated to AI. Both chips are better suited to the types of computations involved in training and running machine-learning models on our devices, such as the AI that powers your camera. Almost without our noticing, AI has become part of our day-to-day lives. And its changing how we think about computing.

What does that mean? Well, computers havent changed much in 40 or 50 years. Theyre smaller and faster, but theyre still boxes with processors that run instructions from humans. AI changes that on at least three fronts: how computers are made, how theyre programmed, and how theyre used. Ultimately, it will change what they are for.

The core of computing is changing from number-crunching to decision-making, says Pradeep Dubey, director of the parallel computing lab at Intel. Or, as MIT CSAIL director Daniela Rus puts it, AI is freeing computers from their boxes.

The first change concerns how computersand the chips that control themare made. Traditional computing gains came as machines got faster at carrying out one calculation after another. For decades the world benefited from chip speed-ups that came with metronomic regularity as chipmakers kept up with Moores Law.

But the deep-learning models that make current AI applications work require a different approach: they need vast numbers of less precise calculations to be carried out all at the same time. That means a new type of chip is required: one that can move data around as quickly as possible, making sure its available when and where its needed. When deep learning exploded onto the scene a decade or so ago, there were already specialty computer chips available that were pretty good at this: graphics processing units, or GPUs, which were designed to display an entire screenful of pixels dozens of times a second.

Anything can become a computer. Indeed, most household objects, from toothbrushes to light switches to doorbells, already come in a smart version.

Now chipmakers like Intel and Arm and Nvidia, which supplied many of the first GPUs, are pivoting to make hardware tailored specifically for AI. Google and Facebook are also forcing their way into this industry for the first time, in a race to find an AI edge through hardware.

For example, the chip inside the Pixel 6 is a new mobile version of Googles tensor processing unit, or TPU. Unlike traditional chips, which are geared toward ultrafast, precise calculations, TPUs are designed for the high-volume but low-precision calculations required by neural networks. Google has used these chips in-house since 2015: they process peoples photos and natural-language search queries. Googles sister company DeepMind uses them to train its AIs.

In the last couple of years, Google has made TPUs available to other companies, and these chipsas well as similar ones being developed by othersare becoming the default inside the worlds data centers.

AI is even helping to design its own computing infrastructure. In 2020, Google used a reinforcement-learning algorithma type of AI that learns how to solve a task through trial and errorto design the layout of a new TPU. The AI eventually came up with strange new designs that no human would think ofbut they worked. This kind of AI could one day develop better, more efficient chips.

The second change concerns how computers are told what to do. For the past 40 years we have been programming computers; for the next 40 we will be training them, says Chris Bishop, head of Microsoft Research in the UK.

Traditionally, to get a computer to do something like recognize speech or identify objects in an image, programmers first had to come up with rules for the computer.

With machine learning, programmers no longer write rules. Instead, they create a neural network that learns those rules for itself. Its a fundamentally different way of thinking.

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How AI is reinventing what computers are - MIT Technology Review

In the movieTrue Grit, federal marshal Rooster Cogburn is asked if the gun that he brandished at a crime scene was loaded. Cogburn, played by John Wayne, dryly responds,A gun thats unloaded and cocked aint good for nothing.Something similar might be said of a Supreme Court docket, particularly when there is a Second Amendment case that could prove one of the most impactful decisions of the term.

The court will soon take upNew York State Rifle & Pistol Association Inc. v. Bruen, more than a decade after its last major gun rights decision. The case promises to be a showdown between the Supreme Court and lower courts, which have been chipping away at the high court's prior Second Amendment rulings.

In 2008, the Supreme Court handed down a landmark ruling inDistrict of Columbia v. Heller, recognizing the Second Amendment as encompassing an individual right to bear arms. Two years afterHeller, inMcDonald v. City of Chicago, the court ruled that this right applied against the states.

The new case concerns concealed-carry restrictions underN.Y. Penal Law 400.00(2)(f) that require a showing of proper cause. Lower courts have upheld the New York law, but there are ample constitutional concerns over its vague standard, such as showing that you are of good moral character. The case presents a single short, direct question whether New Yorks denial of petitioners applications for concealed-carry licenses for self-defense violated the Second Amendment.

The high court has been carefully waiting for just the right case to address states and cities that have sought to limit gun rights. Indeed, just this week, the courtturned down a challengeof a Wisconsin law imposing a lifetime ban on gun ownership for former felons, including cases involving nonviolent crimes. That and other cases seemedtailor-made for Justice Amy Coney Barrett, who wrote a strong defense of the Second Amendment in a similar case as an appellate judge.

It often is difficult to determine which side of the court supplied the votes to grant review in a case. That is not the situation here. The New York case was clearly accepted by conservative justices with a mind toward reversal of the U.S. Court of Appeals for the2nd Circuit.

The selection of a New York case is particularly poignant. Some of the justices were none too pleased with the Big Apple last year when city officials suddenly sought to withdraw a case on the courts docket.New York politicians had passed a law that many of us viewed as unconstitutional, with its imposition of burdensome limits on the transportation of lawful guns from homes. Those politicians publicly thumped their chests about going to the Supreme Court with the law and limiting the Second Amendment precedent; professing absolute confidence, they litigated the law, and, again, the2nd Circuit supported the dubious statute. The Supreme Court accepted the case for review and was expected to overturn the law until New York suddenly changed the law and then quietly sought to withdrawits case before any ruling.

The courtultimately dismissed the casebut did so over the objections of three dissenting justices. It was a rare instance in which the court resisted such a mootness ruling after a party sought to withdraw but, then, few litigants have had the temerity to do what New York did. Justices Samuel AlitoSamuel AlitoA politicized Supreme Court? That was the point Locked and Loaded: Supreme Court is ready for a showdown on the Second Amendment The Hill's Morning Report - Presented by Altria - Jan. 6 panel flexes its muscle MORE,Neil GorsuchNeil GorsuchLocked and Loaded: Supreme Court is ready for a showdown on the Second Amendment Justices weigh request for information on CIA's post-9/11 torture program Supreme Court declines to hear dispute over DC representation in Congress MOREandClarence ThomasClarence ThomasA politicized Supreme Court? That was the point Locked and Loaded: Supreme Court is ready for a showdown on the Second Amendment Two conservatives resign from Biden's Supreme Court commission MOREspecifically called out New York for manipulating the docket by withdrawing an unconstitutional law just before a final opinion. Justice Brett KavanaughBrett Michael KavanaughLocked and Loaded: Supreme Court is ready for a showdown on the Second Amendment Why Latinos need Supreme Court reform Feehery: A Republican Congress is needed to fight left's slide to autocracy MORE joined in the condemnation and added menacingly that some federal and state courts may not be properly applyingHellerandMcDonald. The Court should address that issue soon, perhaps in one of the several Second Amendment cases with petitions for certiorari now pending before the Court.

The court then did precisely that, by accepting a case with the very same plaintiffs:New York State Rifle & Pistol Association.On this occasion, however, the court is unlikely to tolerate another bait-and-switch by state officials trying to withdraw the case at the last minute.

If those four justices are still intent on pushing back on lower courts, they need only Chief Justice John Roberts or Barrett to hand down a major ruling in favor of gun rights.

The briefs filed in the case include groupssuch theCato Institute,which directly confronted the court about it being legally absent without leave on gun rights for more than a decade. Cato has argued that judicial inaction has contributed to the Second Amendments demise. Its no secret that many federal courts have engaged in systematic resistance toHellerandMcDonald.

Many point to the courts statement inHeller,which acknowledged that like most rights, the right secured by the Second Amendment is not unlimited. It then listed possible sensitive places for denying permits to former felons. Lower courts limiting gun rights have repeated those lines like a mantra, and the high court appears poised to bring clarity to that ambiguity.

Bruenhas many of the same elements asHeller, including a rich historical discussion of what gun ownership has meant through history. Notably, English subjects in the American colonies were the first to receive written guarantees of the right to bear arms for self-defense;settlers of the Virginia colony in 1607 and the New England colony in 1620 were subjects under royal charters recognizing that right. In England, the right to bear arms was formally declared in the 1689 Declaration of Rights that stated that the right to arms was among the subjects true, ancient and indubitable rights.

That history will weigh heavily in the court defining the right of people to carry weapons in self-defense outside of the home. In many ways,Bruenis the shot not taken last year inNew York State Rifle & Pistol Association Inc. v. City of New York.Now the same plaintiffs are back, and New York has supplied another perfect case for the expansion of gun rights. So if you are wondering ifBruenis loaded, at least four justices are likely to agree that a Second Amendment case thats unloaded and cocked aint good for nothing.

Jonathan Turley is the Shapiro Professor of Public Interest Law at George Washington University. You can find his updates on Twitter@JonathanTurley.

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Locked and Loaded: Supreme Court is ready for a showdown on the Second Amendment | TheHill - The Hill

By TCR Staff | October 20, 2021

U.S. District Judge Cynthia Bashant has been asked by several gun owners to issue a temporary restraining order blocking a novel ban on non-serialized, unfinished, untraceable firearms known as ghost guns signed into law by San Diego Mayor Todd Gloria law, which is set to go into effect Oct. 23, 2021, and may violate state law allowing gun owners to assemble their own weapons, reports the Courthouse News Service. The Second Amendment proponents are suggesting San Diegos ban criminalizes legal conduct rather than preventing untraceable weapons from getting into the wrong hands.

Attorneys for the gun owners argued San Diegos ordinance was an outlier in California, where those who want to DIY assemble their own firearms must apply for and receive a California Department of Justice-issued serial number to engrave or permanently affix to their firearm within 10 days of assembling it. According to the mayors office, in 2020 San Diego saw a 169 percent increase in the number of ghost guns retrieved and impounded compared to the previous year.

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Second Amendment Backers Want San Diego Ban on Ghost Guns Reversed - Crime Report