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Weve long been used to playing games; artificial intelligence holds the promise of games that play along with us.

Artificial intelligence (AI for short) is undoubtedly one of the hottest topics of the last few years. From facial recognition to high-powered finance applications, it is quickly embedding itself throughout all the layers of our lives, and our societies.

Video gaming, a particularly tech-savvy domain, is no stranger to AI, either. So what can we expect to see in the future?

Maybe one of the most exciting prospects regarding the use of AI in our games is the possibilities it opens up in regards to interactions between the player and the software being played. AI systems can be deployed inside games to study and learn the patterns of individual players, and then deliver a tailored response to improve their experience. In other words, just like youre learning to play against the game, the game may be learning how to play against you.

One telling example is Monoliths use of AI elements in their Middle-Earth series. Dubbed Nemesis AI, this algorithm was designed to allow opponents throughout the game to learn the players particular combat patterns and style, as well as the instances when they fought. These opponents re-appear at various points throughout the game, recounting their encounters with the player and providing more difficult (and, developers hope, more entertaining) fights.

An arguably simpler but not less powerful example of AI in gaming is AI Dungeon: this text-based dungeon adventure uses GPT-3, OpenAIs natural language modeler, to create ongoing narratives for the players to enjoy.

Its easy to let the final product of the video game development process steal the spotlight. And although it all runs seamlessly on screen, there is a lot of work that goes into creating them. Any well-coded and well-thought-out game requires a lot of time, effort, and love to create which, in practical terms, translates into costs.

AI can help in this regard as well. Tools such as procedural generation can help automate some of the more time- and effort-intensive parts of game development, such as asset production. Knowing that more run-of-the-mill processes can be handled well by software helpers can free human artists and developers to focus on more important details of their games.

Automating asset production can also open the way to games that are completely new freshly-generated maps or characters, for example every time you play them.

For now, AI is still limited in the quality of writing it can output, which is definitely a limitation in this regard; after all, great games are always built on great ideas or great narratives.

Better graphics has long been a rallying cry of the gaming industry, and for good reason we all enjoy a good show. But AI can help push the limits of what is possible today in this regard.

For starters, machine learning can be used to develop completely new textures, on the fly, for almost no cost. With enough processing power, it can even be done in real-time, as a player journeys through their digital world. Lighting and reflections can also be handled more realistically and altered to be more fantastic by AI systems than simple scripted code.

Facial expressions are another area where AI can help. With enough data, an automated system can produce and animate very life-like human faces. This would also save us the trouble of recording and storing gigabytes worth of facial animations beforehand.

The most significant potential of AI systems in this area, however, is in interactivity. Although graphics today are quite sophisticated and we do not lack eye candy, interactivity is still limited to what a programmer can anticipate and code. AI systems can learn and adapt to players while they are immersed in the game, opening the way to some truly incredible graphical displays.

AI has already made its way into the world of gaming. The case of Alpha Go and Alpha Zero showcase just how powerful such systems can be in a game. And although video games have seen some AI implementation, there is still a long way to go.

For starters, AIs are only as good as the data you train them with and they need tons and tons of data. The gaming industry needs to produce, source, and store large quantities of reliable data in order to train their AIs before they can be used inside a game. Theres also the question of how exactly to code and train them, and what level of sophistication is best for software that is meant to be playable on most personal computers out there.

With that being said, there is no doubt that AI will continue to be mixed into our video games. Its very likely that in the not-so-distant future, the idea that such a game would not include AI would be considered quite brave and exotic.

See more here:
How AI is impacting the video game industry - ZME Science

In this Q&A, David Keene, chief marketing officer at Speechmatics, discusses the importance of diversity and inclusion in tackling AI bias, and the value of speech recognition tech

The speech recognition provider was found to be outperforming the likes of Google and Amazon in voice understanding.

This week, Cambridge-based AI speech recognition provider Speechmatics launched its Autonomous Speech Recognition software. The companys technology was found to outperform Amazon and Google in overall accuracy for African American voices (82.8% versus Googles 68.7% and Amazons 68.6%), based on datasets used in Stanfords Racial Disparities in Speech Recognition study. This equates to a 45% reduction in speech recognition errors the equivalent of three words in an average sentence and Speechmatics new software looks to deliver similar improvements in accuracy across accents, dialects, age, and other sociodemographic characteristics.

Up to now, speech recognition has been commonly misconceived due to the limited amount of labelled data available to train on. But in this Q&A, Speechmatics CMO David Keene explained to Information Age the value that the technology can bring, and the importance of diversity and inclusion in tech.

The innovation and adoption of AI technologies is gathering speed at an unprecedented pace. From government AI strategies to the NATO announcement today, this tech is going to be front and centre on the agenda for years to come. For AI technology to be truly useful to the world at large, however, it has to be globally representative. We cannot and must not build AI systems for an elite set of users. It is unethical but also doesnt make commercial sense.

Our machine learning breakthrough has taken a big step forward towards understanding every voice allowing us to plug in to the internet and train on millions of hours of publicly available data rather than smaller, biased labelled datasets. Next step in this journey is to work out how we can understand the digitally excluded those voices that are not commonplace on the internet in audio books, on podcasts and social media networks.

This article will explore why a lack of diversity in tech remains a problem for organisations, despite efforts being made to mitigate this. Read here

In an ideal world, your tech team would mirror the market you are selling to and we have to do better as a community going beyond the cookie cutter hiring process to find those people. That is going to take years and years to achieve though and there are things we can do in the meantime. Inclusion is a mindset and needs to be ingrained into the culture of the business and mapped to the bottom line.

Strength and innovation doesnt come from homogeneity. It is fascinating to see how much tech skews to the make-up of the tech team developing it. Male-heavy developer teams will build tech that works better for men. Tech teams based in Michigan will better understand voices from Michigan (I am looking at you Bing). We need to recognise that we naturally build for our own and make a conscious decision to test innovations with a much broader group of people.

Speech recognition technology is in the fabric of so much of what we do these days. From e-learning to voice assistants, courtroom transcriptions to driverless cars research varies but we are looking at a $30+ billion market within the next few years which is hugely exciting.

That growth is running alongside a macro-move to productivity requiring us to take low value tasks out of the supply chain driving automation and robotics. This all only works positively for wider society if these speech recognition systems understand all voices.

Take McDonalds as an example if they want to put in a speech recognition system to take orders in their drive-throughs that system HAS to understand all its customers. For that to happen the system needs to be trained to understand all voices which means going way beyond the bias labelled datasets that are often limited in terms of representation.

Over the last 18 months, weve seen some incredible dedication, transformations, and innovation from professionals and organisations alike especially in the tech sector. And our 2022 Awards, now in its eighth year, aims to highlight the growth, continuity and results of these incredible women, allies, and organisations.

View the categories and nominate yourself or a colleague/peer who deserves to be recognised and celebrated.

Automatic is when the machine is fed specific, usually biased human-labelled information to learn on. Autonomous means you plug it in and it learns unsupervised from all available data on the internet. In AI we call this learning on first principles rather than being rules-led. This is the general move that AI innovators are now trying to make. A similar comparison is IBMs Deep Blue vs Googles AlphaZero. Deep Blue was trained on human data from chess games played by people (specific biased data assuming humans know how to play chess). It was trained to beat a human. AlphaZero was trained from first principles to play a superhuman game of chess. We now have the technology breakthrough to do this for something more complex than a game with rules and that is, of course, speech recognition.

Excerpt from:
Q&A: How Speechmatics is leading the way in tackling AI bias and improving inclusion - Information Age

In October 2015, AlphaGo played its first match against the reigning three-time European Champion, Mr Fan Hui. AlphaGo won the first ever game against a Go professional with a score of 5-0.

AlphaGo then competed against legendary Go player Mr Lee Sedol, the winner of 18 world titles, who is widely considered the greatest player of the past decade. AlphaGo's 4-1 victory in Seoul, South Korea, on March 2016 was watched by over 200 million people worldwide. This landmark achievement was a decade ahead of its time.

Inventing winning movesThe game earned AlphaGo a 9 dan professional ranking, the highest certification. This was the first time a computer Go player had ever received the accolade. During the games, AlphaGo played several inventive winning moves, several of which - including move 37 in game two - were so surprising that they upended hundreds of years of wisdom. Players of all levels have extensively examined these moves ever since.

Playing the online MasterIn January 2017, we revealed an improved, online version of AlphaGo called Master. This online player achieved 60 straight wins in time-control games against top international players.

The Chinese summitFour months later, AlphaGo took part in the Future of Go Summit in China, the birthplace of Go. The five-day festival created an opportunity to explore the mysteries of Go in a spirit of mutual collaboration with the countrys top players. Designed to help unearth even more strategic moves, the summit included various game formats such as pair Go, team Go, and a match with the worlds number one player Ke Jie.

See more here:
AlphaGo | DeepMind

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