Mediaboss Marketing

Six new variants, including four from the recent AlphaZero paper,are now available for all Chess.com members to try. They can be found at Chess.com/variants.

The recent scientific paper from Google's DeepMind, co-written by 14th world chess champion Vladimir Kramnik, caused quite a stir. The nine variants that were tested bythe self-learning chess engine AlphaZero were discussed widely in the chess world.

Now, four of them can be played on Chess.com. Three other variants are now available on our site as well. Here's an overview, starting with the AlphaZero ones:

This is a variant that's easy to understand and possibly closest to regular chess: castling is not allowed, and that's it. As GM Magnus Carlsen remarked this weekend, he would have answered GM Hikaru Nakamura's Bongcloud 1.e4 e5 2.Ke2(which Naka played against GM Jeffery Xiong) with 2...Ke7 and after 3.Ke1 Ke8 you have No Castling chess.

But it's safer to play the variant itself, now available on our site so that you don't run the risk of playing that Bongcloud and getting an opponent that doesn't play along and smashes you instead.

Play No Castling chess here.

This variant was called Self-capture in the DeepMind paper. It basically allows you to take your own pieces as well, in addition to your opponent's pieces. You can imagine positions where it makes sense for a rook to take the pawn in front of it because opening a line could be (much) more valuable than that pawn.

Kramnik was very enthusiastic about this variant. He wrote:"I like this variation a lot, I would even go as far as to say that to me this is simply an improved version of regular chess.... Regardless of its relatively minor effect on the openings, self-captures add aesthetically beautiful motifs in the middlegames and provides additional options and winning motifs in endgames."

Play Capture Anything here.

On Friday, September 18, Chess.com hosted a round-table discussion with GM Vladimir Kramnik, IM Danny Rensch, and researchers of DeepMind discussing their latest paper in which AlphaZero explores chess variants. Here it is for replay:

This is possibly the most complicated variant of all: pawns are not only allowed to run forward but also sideways.As Kramnik wrote, "Even after having looked at how AlphaZero plays Pawnside chess, the principles of play remain somewhat mysteriousit is not entirely clear what each side should aim for. The patterns are very different, and this makes many moves visually appear very strange, as they would be mistakes in classical chess."

Play Sideways Pawns here.

Torpedo speeds up the game as here pawns can move by one or twosquares anywhere on the board. (In standard chess, only in the starting position are they allowed to move two squares.) Interestingly, en passant canconsequently happen anywhereon the board. But the biggest difference is that games become more tacticalcompared to standard chess. Watch those pawns.

Play Torpedo here.

Besides the four variants tested by AlphaZero, three other variants have been made available this week as well. Fog of War, also known as Dark Chess, has been the most popular so far. It is a variant where the main novelty is lack of information:you can only see the squares where your pieces or pawns can move and attack.

Play Fog of War here.

If you want to remove any information (instead of the board itself, which you'll need to play), you can try Blindfold chess. In addition to being a fun challenge, this could be a good way to train your visualization skills.

Playing blindfold chess has such a rich history that there's a separate Wikipedia page on it as well as a page in our Terms section. In the 1990s and early 2000s, the Melody Amber tournament had top grandmasters play blindfold chess behind a laptop for which special software was created. If the tournament were to be re-instated, they could just log into Chess.com.

Play Blindfold chess here.

Last but not least, Chess.com has added the variant Chaturanga. Speaking of history, this ancient Indian board game is in fact considered to be a common ancestor to chess.

So what are the rules? Well, for starters, the pieces have different names. The king is Raja, the queenFerz, the rook Ratha, the bishop Alfil, the knight Ashva, and the pawnBhata.The Ferz is much weaker is it can only move one square diagonally.The Alfil jumps two squares diagonally. Other than that, the pawns can only move one square and castling does not exist.

Play Chaturanga chess here.

This is not all. As some of Chess.com's programmers go pretty wild about variants, they are now working on a project called Custom variants, where members can mix and match rules to make their own variant.

That would be perfect for GM Levon Aronian, who already came up with something. He recently stated that he would like to try Capture Anything but limit captures to the heavy pieces. Soon, he might be able to create Aronian Chess on Chess.com.

Interested in trying out the new variants? Find them at Chess.com/variants.

See also:

See the original post:
AlphaZero (And Other!) Chess Variants Now Available For Everyone - Chess.com

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]

Here is the original post:
Leela Zero - Wikipedia