Daily Archives: February 14, 2021

Chess has captured the imagination of humans for centuries due to its strategic beautyan objective, board-based testament to the power of mortal intuition. Twenty-five years ago Wednesday, though, human superiority on a chessboard was seriously threatened for the first time.

At a nondescript convention center in Philadelphia, a meticulously constructed supercomputer called Deep Blue faced off against Garry Kasparov for the first in a series of six games. Kasparov was world chess champion at the time and widely considered to be one of the greatest players in the history of chess. He did not expect to lose. It was perhaps understandable; 1996 was an age of fairly primitive computer beings. Personal computers were only just becoming a more affordable commodity (35 percent of U.S. households owned a computer in 1997, compared with 15 percent in 1990), the USB had just been released, and it would be another five years until Windows XP made its way onto the market.

But Deep Blue was no run-of-the-mill computer. It was a behemoth built with the sole intention of being very good at chess. And it fulfilled that mission. On Feb. 10, 1996, the reigning world chess champion lost a game to a computer for the first time in history. Kasparov would win the 1996 match four games to two, but in May 1997, an upgraded Deep Blue would defeat Kasparov 32.

The 96 match nonetheless demonstrated that the tide was starting to turn in the chess world, and the tide was deep, blue, and electronic. It introduced chess computers to the world, sparking conversations about a rise of automation in the famously romantic field.

Some version of computers had been playing chess even before the emergence of artificial intelligence as an official field in the 1950s. Alan Turing, the famous cryptographer, had developed a handwritten chess algorithm in 1950 called Turochamp. In 1957, Alex Bernstein, a researcher and chess enthusiast from the Bronx, created the first complete chess program with the help of a number of his IBM colleagues.

Computer chess changed in the 80s. says Jonathan Schaeffer, president of the International Computer Games Association and professor of computer science at the University of Alberta. That decade, pioneering American computer scientist Ken Thompson released a paper proving something that now seems intuitive: If your computer was faster, your chess program would perform better. Programs could thus analyze more and more moves per second, increasing their chances of finding the best move possible.

Accordingly, computer chess became about getting the fastest technology. When I started in the [computer chess] game, we were using a single computer. Then it became 16, then 210, and so on to chips and supercomputers, says Schaeffer. In 1988, students at Carnegie Mellon University developed a sophisticated chess computer called Deep Thought. In January of that year, Deep Thought became the first computer to beat a grandmaster in a regular tournament game when it triumphed over Bent Larsen, a Danish GM. The next year, IBM hired three of those Carnegie students, Feng-hsiung Hsu, Thomas Anantharaman, and Murray Campbell, with the express aim of building a chess computer to rival the world champion; they would be joined by Chung Jen-Tan, Joseph Hoane Jr., and Jerry Brody later in the project. In October 1989, Kasparov played two games against Deep Thought, winning both of them with ease.

The first match demonstrated that the tide was starting to turn in the chessworld.

The loss to Kasparov in 1989 demonstrated the amount of work that needed to be done, says Schaeffer, so they took it to the extreme. They went off for seven years and built new computer chips that were faster, building a system that was scaled up to not just four computer chips, but 500. They added more knowledge to it as well as a book of openings, and eventually the brain of chess grandmaster Joel Benjamin helped provide expertise. This was a very long project involving many, many people, and significant financial expense, but it paid off for IBM in the form of media clamor.

The 2,800-pound Deep Blue, complete with special-purpose chess computer chips, was the end product. It was capable of processing 200 million moves per second, or 199,999,997 more than Kasparov could manage, according to IBM. This produced a chess machine that was stronger than any of its automated predecessors, and the outside world was stunned at the eventual resulta human had been outdone by a machine in this game of intellect, wit, and judgment. At the 1997 match, Kasparov and Deep Blue would go toe-to-automated-toe in front of numerous television cameras and a large crowd.

But Kasparovs loss was not as devastating as casual observers might have expected. Computers had beaten grandmasters before; it was inevitable that someone of Kasparovs stature would fall too. And though Kasparovs loss certainly came earlier than expected, the competitive chess world continued to go about its business relatively unfettered.

I dont think it affected chess players too much, says Matthew Sadler, chess grandmaster and co-author of Game Changer, a book about modern chess engine AlphaZero, Firstly, Kasparov was probably stronger than Deep Blue at the time, despite the loss. Secondly, it didnt really inspire any chess players with its play.

It helped that Deep Blue, at the time, was the exception rather than the rulemachines of its strength werent widely available. In 2006, though, a chess computer called Deep Fritz beat thenworld champion Vladimir Kramnik. I think thats really when chess players sort of thought, Oh, my goodness, the machines really are getting stronger than us, says Sadler, when they were beating us not on supercomputers, but on relative commodity hardware.

The change here wasnt just that a computer could win, but that a computer could help human players win if incorporated into their training regimes effectively. Computers were adept at judging the quality of moves and positions accurately, particularly during opening sequences. Some found this easier than others. Sadler says: I think a lot of competitive players took a while to adjust to the new reality. For example, if you werent really computer-literate, and all of a sudden you found yourself in a world where having a computer really makes a difference, thats a difficult thing.

Despite initial resistance from certain parts of the community, the advantages that computers afforded chess players eventually made them impossible to ignore. Sam Shankland gained his international master title in 2008, right around when computers started to become a necessity. There was some backlash, but honestly, those people are mostly gone now, Shankland, now a grandmaster and 2018 U.S. chess champion, says. They either got tired of losing and quit chess or they got tired of losing and adapted.

The sheer wealth of knowledge chess players now had access to meant that determination was increasingly rewarded. I think that chess is essentially a subset of talent and hard work, says Shankland, and as training resources like computers become better and more accessible, talent tends to become less important compared to hard workwhich suits a workhorse like myself.

Such accessibility has also led to chess, once reserved for rich families who could afford tutors and other training, to become a markedly more democratized pursuit. Take India, for example, says Shankland. Apart from Vishy [Anand], they werent a particularly strong chess nation historically. Now, theyre clearly the fastest-growing country in the world in terms of rising stars, and I think a lot of that is down to training resources becoming more widely available.

The availability of advanced chess analysis at the flick of a smartphone has caused a bizarre balance of power in the media and a certain trepidation among top-level players, as Peter Heine Nielsen, coach of current world champion Magnus Carlsen, points out:

When I started working with Vishy Anand, at a postgame press conference the players would explain the games, and everybody would look at them with excitement and think, Wow, these guys are clever. Now, the player in the press conference is a bit nervous because they have only calculated themselves, while all the journalists have been using advanced technology. So they are afraid to say, I thought this wasnt a strong move in case theyre wrong.

So sometimes before a press conference I speak to Magnus and tell him the computer said this or that, just so he knows. The spectator-player dynamic has changed a lotsome of the mystery has gone.

However, while certain human aspects of the games have disappeared, recent developments have caused professional players to rethink what they know about their beloved board game. In 2017, a team of scientists at Google-owned DeepMind created AlphaZero, a self-learning neural network program that surpassed the strongest chess program after just four hours of playing against itself.

Before the computer boom, and before the neural network boom, we were thinking quite dogmatically, says Nielsen. After both occurred, we were forced to rewrite our own solutions. It led to the game becoming more exciting. Moreover, the two strongest chess enginesLeela (which is based on AlphaZero) and Stockfishare available online, which signifies a remarkably more distributive and collaborative approach to chess innovation than that which was pioneered by Deep Blue, a closed circuit.

Despite all their progress, there are still some goals to which innovators in the chess world can aspire. The next step is for engines to explain what theyre doing, says Sadler, so that the average player can understand why an engine says, No, trading that piece is a bad idea. The relationship remains one of reciprocity.

One thing is certain: Chess programs will remain the most important piece of a professional players preparatory arsenal. Not using a computer to do chess would be like not using a calculator to do math, says Nielsen, I like itbut it doesnt matter if I like it or not. Its the right way to do it.

Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society.

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The 25th anniversary of Deep Blue beating Garry Kasparov in a chess game. - Slate

Fat Fritz has revolutionised the world of chess engines. The AI engine is based on the famous AlphaZero algorithms: the extraordinary playing strength and the completely new analyses fascinated the chess world. Now Fat Fritz 2.0 has arrived!

Fat Fritz 2.0 is based on the Stockfish 12 engine, but uses a completely new neural network with better ratings and greater performance.

NNUE-Technology:

Fat Fritz 2.0 achieves top performance even without a high-end graphics card!

Fat Fritz 2.0 miles ahead!

In the comparative match over 1552 games Fat Fritz 2.0 clearly beats Stockfish 12 (286 wins with 99 losses, rest draws) and outperforms the previous top engine by over 40 Elo points (as of February 2021).

Incl. Fritz 17 user interface (64 Bit).

Runs on any current Windows PC or laptop.

What you receive:

ChessBase Premium-Membership (sixmonths) with access to the ChessBase Video Portal, Playchess, the Tactic-App, LiveDatabase, LiveBook etc. Database of around 1.5 Mio. Games, and much more

Order Fat Fritz 2 in the ChessBase Shop now(best by download, to get it immediately)

Or: how about trying it out on our engine cloud?

SYSTEM REQUIREMENTS:

Minimum: PC Core i3 or i5 / AMD FX or Ryzen 3, 2 GB RAM, Windows 7/8/8.1 64Bit, DirectX9, graphics card with 256 MB RAM, DVD-ROM drive, Windows Media Player 11 and Internet access.

Recommended: PC Core i7, i9 or AMD FX, Ryzen 7/9 and Windows 10 64-Bit, 4 GB RAM, Windows 10, DirectX10, graphics card with 512 MB RAM or more, Windows Media Player 11, DVD ROM drive and Internet access. System requirements for ChessBase Account: Internet access and up-to-date browser, e.g. Chrome, Safari. Runs on Windows, OS X, iOS, Android and Linux.

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Fat Fritz 2.0 - The new number 1 - Chessbase News

It has been an extraordinary year for everyone, but no less so in computer chess. You would be forgiven for thinking we had seen the last major revolution in the field, thanks to AlphaZero and all the works it inspired and spawned. The neural network technology they created was staggering and incredible, and while it is still being explored and worked on, a new development has come to the chess world thanks to chesss Japanese sibling, Shogi.

Although the AlphaZero neural network is extremely powerful overall, and consequently Leela and Fat Fritz, it is held back by the need to use a GPU (graphics processor) to read it, slowing it down by over a thousand times compared to traditional speed demons such as Stockfish and Komodo. That it could still compete with them on equal terms in spite of this handicap speaks volumes on just how powerful those neural networks are.

Still, this came at a price too. The nature of its search, averaging the various outcomes, good or bad, led to a strongly exploitative style, but could fail in technical endgames or positions where surgical precision was needed. The dream of combining a large and powerful neural network with the speed and accuracy of Stockfishs search seemed just that: a dream.

Enter NNUE.

As chess players we naturally think of AlphaZero as a chess engine development first and foremost, whilst acknowledgingGo was its originator. It is easy to forget that the AlphaZero paper described its applications to three strategy games, not two, the third being the Japanese chess game known as Shogi.

Unsurprisingly, the shogi world was no less shaken up by the AlphaZero paper, but instead of just trying to reproduce it, a Japanese programmer, Yu Nasu, developed and published agroundbreaking paperfora different neural network architecture known as NNUE, inspired by DeepMinds success, whichran at full strength exclusively ona computer processor with no need for avideo card.

Just asAlphaZero and the first edition of Fat Fritz, NNUEalso builds it own chess knowledge by learning from chess positions and evaluations. NNUE completely revolutionized Shogi programs, leading to leaps of well over 100 Elo!It was their developers who shared their work with the chess world by adapting their open source Shogi NNUE code to the open source programStockfish. Based on the success of Stockfish 12, the first chess engine to implement NNUE, numerous other engine authors have also added NNUE into their work, such as the recently released "Dragon" by the Komodo team.

Order Fat Fritz 2 in the ChessBase Shop now

Fat Fritz 2 is trained and developed using this NNUE technology, runninginside the open source Stockfish binary, butwith a completely new and different neural network that differs significantly from Stockfish's on a couple of key points:

After all, if an elite neural network could be trained from just Stockfish evaluations, imagine what it could do with the sophisticated knowledge of Fat Fritz!To this end, some four billion positions were generated on thousands of hours of the most powerful graphics cards to train it.

So what was the result of this larger neural network with deeper chess knowledge?Ina large test at 1 minuteplus 1 second, Fat Fritz 2 scored +42 Elo over Stockfish 12.Click to download the PGN games.

Score of Fat Fritz 2 vs Stockfish 12:

286 wins / 99 losses /1167 draws

Elo difference: 42.1 +/- 8.5, LOS: 100.0 %, DrawRatio: 75.2 %1552 of 1552 games finished.

Note: the reason for the odd number of games is that the suite used has 776 positions, which with reversed colours leads to 1552games.

Also, there is reason to believe that its endgame play is superior overall, though even if true, exceptions will no doubt be found. Here are a few examples:

This has often been a very tough one for engines, even when enjoying all the tablebases available. Using a modest sixcores to emulate laptop users or those with less than premium desktops, here are the results of Stockfish 12 and Fat Fritz 2:

Analysis by Stockfish 12

While taking 37 seconds might not seem like a big deal, most people will not spend that long with an engine on a position unless they already suspect there might be something to uncover.

Analysis by Fat Fritz 2

Five seconds! True, this is but a single position, yet there is a reason Shirov's move remains in modern test sets to this day.

Another curiosity actually comes from an article published a few months ago by GM Karsten Mueller. He had asked for the analysis of a development version of Fat Fritz 2, and in itone of his moves had been refuted badly... much to his delight. The reason for his pleasure was that it showed new engines might become even more valuable tools in complex endgames.

Here Fat Fritz 2gave a winningline starting with 52...e7.

This same position was then shown to Stockfish 12, which had access to 30 threads, 7-piece tablebases, and after nine billion nodes it declared 52.d6 was winning and its choice.

There doesn't seem to be much doubt with such a high evaluation, does there?

Was this a case of more than one move doing the job? 52.d6 was then shown to Fat Fritz 2, but it declared this move and line a draw! According to it,there was a blunder just five plies into Stockfish's 50-ply mainline.

Instead of 56.h3? as shown above, Fat Fritz 2's 56.Nb4! saves the game. Granted, none of this is absolute proof, but this sort of thing has been common enough to stand out.

Regardless, it is clear Fat Fritz 2 is an enormous step forward, and will offer fantastic analysis and pleasure to all who use it. Anyone who felt left out, due to the previous need to use a powerful video card to reap the benefits of a world-class neural network, can now rejoice asthis is no longer required.

Order Fat Fritz 2 in the ChessBase Shop now(best by download, to get it immediately)

No such work is the result of one person, whether directly or indirectly, and this is no less true of Fat Fritz 2.

Deepest thanks and gratitude go to my friend Daniel Uranga, a brilliant Argentinian programmer who helped realize and test every mad idea I came up with, and there were many. His help was invaluable and his skill has now been deservedly recognized by Amazon who recently hired him and brought him to the UK.

Also, warmest thanks to my friend Dietrich Kappe who is an endless source of useful scripts and ideas, and who helped take the project to the next level. His friendship and generosity cannot be overstated.

Finally, where would any of this be without the fabulous Stockfish team and their legion of contributors, as well as Yu Nasu for his groundbreaking NNUE work, and Tanuki (nodchip) for his translation of the Shogi code for use in chess.

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Fat Fritz 2: The Best of Both Worlds - Chessbase News

Renegade Game Studios revealed this week that they will be releasing Gravwell 2nd Edition sometime later this year. This version won't be like the last in a few ways. They've kept the gameplay as-is but they've added some awesome stuff, such as new artwork from artist Kwanchai Moriya, an increased player count so you can play with up to six people, and new ship models with unique powers for every ship in the game. Essentially, if you love the original by designer Corey Young, you still have it. But this one expands on it and allows for more insane plays with more people at the table. The game is set to be released sometime in June 2021 as of when we're writing this, but you can pre-order it on Renegade's website right now for $40.

In Gravwell you're among a flotilla of ships that survived passing through a black hole. The fabric of space and the general physics you've known have changed. You quickly discover that your engines are offline, but you can navigate crudely using your tractor and repulsor beams. By mining asteroids and collecting raw elements from space dust, you muster just enough force to move your spacecraft. You lock onto another object, perhaps a spacecraft, and creep toward it. But just as you're about to engage your tractor beam, its captain engages his beam, catapulting his ship past you. If you don't act quickly, you'll be pulled the wrong direction! It's a real mind-bender! We've updated this award-winning game with beautiful all-new art by artist Kwanchai Moriya. In addition to the game art, the player ships each feature a new and unique sculpt. With the ability to accommodate up to 6 players, and unique powers for each ship, Gravwell 2nd Edition has all the gameplay players loved in the original, and much more!

Gavin is the current Games Editor for Bleeding Cool. He has been a lifelong geek who can chat with you about comics, television, video games, and even pro wrestling. He can also teach you how to play Star Trek chess, be your Mercy on Overwatch, recommend random cool music, and goes rogue in D&D. He also enjoys hundreds of other geeky things that can't be covered in a single paragraph. Follow @TheGavinSheehan on Facebook, Twitter, Instagram, and Vero, for random pictures and musings.

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Gravwell 2nd Edition Will Be Coming Out Later This Year - Bleeding Cool News