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Category Archives: Chess Engines
Posted: November 17, 2019 at 2:21 pm
Stockfish - UCI chess engineInteresting compiled bySFisGODRank-based outpostsIntroduce OutpostRank[RANK_NB] which contains a bonus according tothe rank of the outpost. We use it for the primary Outpost bonus.The values are based on the trends of the SPSA tuning run with somemanual tweaks.Passed STC:LLR: 2.96 (-2.94,2.94) [-1.50,4.50]Total: 27454 W: 6059 L: 5869 D: 15526 Elo +2.40
Passed LTC:LLR: 2.94 (-2.94,2.94) [0.00,3.50]Total: 57950 W: 9443 L: 9112 D: 39395 Elo +1.98
----------------------------The inspiration for this patch came from Stefan Geschwentner's attemptof modifying BishopPawns into a rank-based penalty. Michael Stemberasuggested that maybe the S(0, 0) ranks (3rd, 7th and also maybe 8th)can still be tuned. This would expand our definition of Outpost andOutpostRanks would be removed altogether. Special thanks to Mark Tenzerfor all the help and excellent suggestions.
Rating JCER=3257Stockfish 19111311 - download
Here is the original post:
Posted: at 2:21 pm
The final of theFIDE Grand Prix in Hamburgwill be decided in a tiebreak on Sunday.Jan-Krzysztof Dudawas under pressure twice but held the draw against Alexander Grischuk in both standard games.
Over the board Duda and Grischuk had played each other in only rapid and blitz, and you might also remember their epic Speed Chess match here on Chess.com, played 14 months ago and narrowly won by Duda. On Friday, they met for the first time in classical chess (or "standard" as FIDE now calls it).
In a Queen's Indian, Grischuk seemed to surprise his opponent when he used a recent idea from Ivan Cheparinovon move 13. Duda thought for more than 50 minutes for his next two moves.
Grischuk wouldn't be Grischuk if he played the remainder of the game with more time on the clock, so he spent 47 minutes for his next two moves! He did manage to get a stable, positional advantage but missed a chance in time trouble.
The second game was even more exciting, with Grischuk again getting the better chances out of the opening, this time as Black in a Queen's Gambit Declined. It was obvious that Duda hadn't expected this particular variation.
Grischuk found a great pawn sacrifice behind the board, and engines gave him a big advantage after Duda took it. Again both players spent a lot of time early in the game; they weredown to 20 minutes after just 13 moves.
It became extremely tactical, and with so little time Duda found a number of great defensive moves and somehow held his own once again.
"Maybe a better calculator like Maxime Vachier-Lagrave would have found something," said Grischuk, "but he would not get this position because he doesn't play the Queen's Gambit, which is the most aggressive opening."
The biggest chess fans will know what to do on their free Sunday: follow the tiebreak between these two great players. It will start15:00 CET, which is 9 a.m. Eastern and 6 a.m. Pacific. You can follow the gameshere as part of our live portal.
Follow this link:
Posted: at 2:21 pm
How do you improve and build upon the worlds longest lasting chess playing program series? If you have followed the news pages for the last weeks, you will have heard of Fat Fritzas a concept, and while it is indeed the show-stopping addition to Fritz 17, it is far from the only novelty.For one thing, a brand new Fritz 17 engine is now in placewith no need for a fancy graphics cardto reap the benefits.Developed by Frank Schneider, the author of a very strong private engine known as Gingko, the new Fritz 17 engine will not only surpass the previous Fritz 16, but also provide new analysis for those seeking yet another new look at things.
As far as the interface is concerned, the key concept is 'helping you learn'. A number of brand new functions have been created with the sole purpose of helping you learn and train your openings. Let's face it, while studying openings can be fun, memorizing them is not and is often the reason many players avoid openings they might prefer, because of the challenge that presents. Fritz 17 brings an assortment of tools to help you and make that process as painless as possible.
Created the most gorgeous 3D chess images in record time (click or tap to enlarge)
Finally, Fritz 17 and ChessBase will not be left behind in the wave of new 3D technology. Readers may have heard about the concept of Ray Tracing, the new buzzword by Nvidia on fantastic lighting in 3D models, even used to debunk moon landing conspiracy theories by demonstrating the accuracy of all lighting effects. Their new series of video cards was designed around this concept, and new games and 3D modeling have exploded with the steady inclusion of it. New in Fritz 17 is a powerful set of live ray tracing effects for the 3D chess boards, with a staggering amount of control on how and where they are lit. Even fancy camera effects such as background blurring are possible. However, these do require those fancy new video cards to be enabled.
In any previous version of Fritz, all the above would more than justify a new version and release, but the icing on the cake is of course Fat Fritz.
The pice de rsistance in Fritz 17 for many people will be the inclusion of Fat Fritz. Based on the AI technology by DeepMind that created AlphaZero, Fat Fritz is a new set of custom made neural network weights that work in the open-source project Leela Chess Zero. The Leela Chess Zero project is based on the Go program Leela Zero and was designed to reproduce AlphaZero on the PC. One of the key tenets is that it follows the zero philosophy which means it uses nothing except what it learns of its own accord.
The philosophy behind Fat Fritz has been to make it the strongest and most versatile neural network by including material from all sources with no such 'zero' restrictions, such as millions of the best games in history played by humans, games by the best engines including Stockfish, Rybka, Houdini, and more, endgame tablebases, openings, and so on. If it was deemed a possible source of improvement, zero or not, it was used. Even millions of exclusive self-play games were created, but tweaked to create content that was more aggressive and speculative to learn from and mold its style. The only material that was not used to train Fat Fritz, out of principle, was content from the Leela project itself, as this was developed by their community for their neural networks.
After over a year of development, thousands of hours of computer time and human effort, we feel this will enrich analysts and players with creative and unique moves, all of the highest quality, to explore openings and the middlegame. While there is no question that making sure the engine can bring the highest standard is vital, and worry not it is there, it would be quite uninteresting to present an analyst that was essentially exactly the same as Engine X, except 20 Elo better. Instead, a contrasting point of view, no less strong, is far more interesting, and of far greater use.
Over the past weeks, many games, matches, and articles we have waxed poetic on its virtues, but instead of just throwing more mind-numbing resultshowever goodthe following game speaks far louder than words on what sort of player it is.
This was a draw (yes, a draw, but what a draw!) between Leela and Fat Fritz in a game played without a book by Fat Fritz. It played the French defense, and took a line that is considered by theory to be quite bad for Black, and saved it with novel moves, and then gave up pawn after pawn to fight with dynamic play.
A Classical Guide to the French Defence
This DVD gives you the key to start out with the French Defence. GM Yannick Pelletier is a specialist of this opening, and believes that the most efficient way to understand its ideas, plans, and typical structures is to study classical lines.
For our Discount Day, everything is 25% off!
Important note: While Fat Fritz is already incredibly strong, its development is not over, and over the next months, owners of Fritz 17 will enjoy upgrades to the neural network.
Many fans of Leela have complained about the complications in the installation process and requested a solution by ChessBase. As such Leela will also come installed with its best neural network (and you can always use another of course) and configured for use on the spot. Hopefully this will help bring more users and admirers to their project as a result.
Readers will have seen that to use Fat Fritz and Leela properlyyou will need a solid graphics card. There really is no way to get around this. The neural network weights are a massive file with tens of millions of values that comprise its vast knowledge of the game, and while the calculations are all done on the CPU, the graphics card, thanks to its unique design, is used to read the neural network and feed the CPU the evaluation for every position it examines. Even though Fat Fritz might be running a thousand times slower than conventional engines, it will still providetop analysis, but without a graphics card the experience will be only useful as a demonstration, andwill be slower by a factor of 1000-2000.
If you wanted an excuse to get a fancy new graphics card, you can now tell your better half, "but honey, it's for chess!" Photo: Zotac
The recommended hardware is an Nvidia RTX 2060 or faster, but the better the card, the better the experience. Needless to say, a graphics card will also bring far more advantages than just running Fat Fritz, such as gaming, 3D graphics, video rendering, AI development, and more. On the upside, nearly any computer can use this and there is no need to buy an entirely new machine. The Fat Fritz in the Engine Cloud runs on a quite modest quad-core CPU (five years old), though bolstered by two fast RTX 2080 video cards.
One of the characteristics of the neural networks is that they learn and evaluate a position based on winning chances. Fat Fritz will break this down into not only the overall winning chances, but the expected wins, draws and losses. This is useful since saying a position has a 50% chance won't tell you if that means it expects 50% wins and 50% losses, or just 100% draws. Fritz 17 has been modified to allow users to see this pure output of the neural network.
There is a new engine output option Display win probabilities that is active by default.
When you run the engine you will also see the breakdown at the bottom. You can see the first is the overall win rate, followed by the details of the wins, draws and losses.
In Fritz 17 you will find several new opening management and training functions:
The new opening repertoire in Fritz 17 is called "My Moves". It is separate for White and Black. You add variations to your repertoire by clicking on a move anywhere in the program and marking it as "My Move". This will include the whole variation up to this move into your repertoire. Marking moves is the only way to store variations, but this also saves a lot of time from entering moves one by one, copying from a source.
Manage and drill your opening repertoire
Your "My Moves"repertoire is stored online. You can access it from any machine, and also from a web browser.But the goodies don't stop there. Imagine you are watching a live game in Playchess, whether a casual blitz, or a broadcast from a top GM tournament. Even then you can instantly check to see if a game played is following an opening from your repertoire.
In the LiveBook in Fritz (and via the web app as in the image below) the moves will be color coded, and you can see if the move is in your repertoire already. If it isn't, just click on it to add it.
Blue = This move is in your repertoire for White.Green = Move is in your repertoire for Black.Cyan = You play this move with both colors.* (an asterisk) = Marked as 'My Move'.** (to asterisks) = Marked as 'Important for me'.
Chess database software creates a seductive illusion. The well structured management of opening variations in your personal analysis creates the impression that one can reproduce them easily over the board. Of course pure opening research is fun. However for practical success you really have to be able to recall everything from memory. Fritz 17 introduces the Drill as an efficient and entertaining way to help with this.
When Drilling, you play your lines and Fritz answers in a way that you stay within your prepared repertoires. Moves are played according to their frequency in master games.
After some time, Fritz 17 detects which variations you know well and which you dont. Then the shakier systems are offered more frequently so that you can solidify your memory in an efficient way.
The drill dialog displays your Memory Score If you reproduce the correct move for a position five times in a row, this position is counted as fully learned and scores one point. You have learned your repertoire 100% if you achieve this for any position.
Fritz 17 provides access to standard opening repertoires for nearly every prominent line in chess. Those repertoires are regularly updated to current theory and recent games on our server. You can either drill them with Free Drill or upload them to My Moves. Or you pick single lines by marking moves. Of course you may simply store them in your traditional databases. Open a list by calling Openings Standard Repertoires. Click on any entry and it will automatically load.
Standard Repertoires are available in four levels: Easy Club Tournament and Professional. This saves work: As a normal club player you dont have to extract the best moves from a deeply nested professional repertoire suitable only for master level players.
The best way to memorize opening variations is to execute moves physically. On the screen or even better on a board. Your brain will be more engaged in what you do. However, sometimes at the end of a long day you just might want to lean back, relax and leave mouse and keyboard untouched.
To satisfy this, Fritz 17 introduces the replay of variation trees. You can watch your repertoire being played out on the screen. Lines are picked randomly and repeated as often as you like. Just let it run, set the speed and watch.
Standard Repertoires and Full Tree Replay
Still, learning is not just memorization, and improving upon one's games is a key part of it all. Here too new functions will now go through your games,or games of your choice, to highlight mistakes, combinations, or sacrifices, and present them as ready-made exercises you can solve on your computer, or print out for yourself or your students.
You can print out the puzzles just like a book, with diagrams first and the solutions at the end.
Blitz games contain tactical errors. Errors usually happen at interesting positions, so you can learn from them. Since the last version of Fritz, games played on Playchess.com are analysed automatically, but that brought a dilemma:
Should one look at the analysis or play the next game?
The new function Blitz & Train helps with this: It creates training material from your online games, no matter whether they have been played today or last month. A few clicks and you can print exercise sheets from your own games. Click the tab 'Training' in the Playchess section of Fritz:
The option Sacrifices finds exercises with beautiful moves, whileBlunders asks for the best move in position where one side blundered.
With the arrival of the newest generation of video cards from Nvidia, Ray Tracing has now become a reality in real time. In anutshell the idea is that it is able to calculate and show light reflections from surface to surface in all of the incredibly complex relationships such as sunlight bouncing off a wall, which gives a lighter light, filtered through humid air, and so on. ChessBase has now introduced this added layer of realism to its 3D boards, with full control over every aspect if such is your desire.
The menu will now offer the option between the CPU built ray traced boards and the new GPU acceleration.
The options are enormous and even include fancy optical effects such as background blurring.
Needless to say, if you do not have a graphics card that can handle this, or do not want it, the classic 3D boards are still there for your enjoyment.
Fritz 17 represents one of the most ambitious and progressive versions ever, bringing to you tools that should help all players contend with modern problems. From the challenges of memorizing and studying complex opening theory, to easily reviewing games with a focus on improving your practical skills, to bringing what is bound to be an amazing addition to your arsenal of tools: Fat Fritz. In fact, Fritz 17 brings not one engine, but four: a brand new Fritz 17 engine, the new custom made neural network Fat Fritz with its penchant towards cutthroat chess, the well-established Leela with its very Karpovian approach to chess in a no-headache installation, and Stockfish itself.Plus a surprise in the near future!
Order Fritz 17 in the ChessBase Shop
Read more here:
Posted: November 3, 2019 at 2:48 pm
GM Larry Kaufman's bookKaufman's New Repertoire for Black and Whiteis now available in Europe in all formats, and in the United States as an e-book. It will soon be sold worldwide in all formats, including as an e-book from Forward Chess.
The book serves as a follow-up to the excellently received The Kaufman Repertoire for Black and White,but it is not a second edition. Kaufman has completely revised his repertoire from the White side and is now recommending 1.e4 instead of 1.d4the most impactful of many changes.
Kaufman's perspective is unique among chess authors. Kaufman is a graduate of MIT who has been involved in computer chess since the 1960s. Today, most chess players will be familiar with his work on first Rybka and now the Komodo chess engine.
Kaufman first became an IM in 1980 and a GM in 2008 when he won the World Senior Championship. He is currently a vital part of the Chess.com and Komodo team.
GM Larry Kaufman: My first complete repertoire for both colors was written in 2003 (The Chess Advantage in Black and White, Random House), and was partly done as a way to force myself to work out a complete repertoire that was both sound and compact enough to fit in one book, so that I could hope to remember most of it for my own tournament games. I reasoned that many others would also like such a book. The 2012 book was a completely new one for New in Chess, but with largely similar motivation, with the difference that now the engine I had been developing, Komodo, would play a major role.
The 2019 book was largely motivated by the MCTS [Monte Carlo Tree Search, a method for computer engines to choose which moves to investigate] revolution, because MCTS engines favor lines that are apt to work in over-the-board play against humans, not just ones intended for correspondence play vs. engines. The reason is that normal engines assume "perfect" play by the opponent, while MCTS merely assumes "good" play. My hope was that the analysis would be very different from books that use traditional engines. Of course there are good and bad moves in chess, so there is considerable overlap, but I expect people will see substantial differences from existing theory.
Why did you think the time was right to release the new repertoire?
Because Lc0 [a machine-learning chess engine that uses neural networks and MCTS] and Komodo MCTS had reached high-enough level to make an MCTS-based book logical. Also because the improvement in both hardware and software in seven years made the earlier book rather obsolete.
Who do you think will most benefit from this book?
The actual analysis is at a very high level, so is suitable for strong players, even for grandmasters. However the explanations of the moves are aimed at average amateur players, maybe in the 1200 to 1800 range. So I hope that players of a wide range of abilities can learn from this book, although they will learn different things. I suppose that players in the 1600 to 2000 range might get the most benefit, as they are not too strong to need the explanations but strong enough to utilize the advantages they may get from the lines.
Chess players today often suggest that opening theory is draining chess of its excitement. Do you agree?
Yes, I do agree, even though saying so won't help sales of the book! But if you are going to play competitive chess with reasonably strong players, whether over the board or online, you will get better results playing good openings that you know than playing poor ones or good ones that you don't know. I am a big advocate of reforms in chess, whether that means balloted openings, playing Fischer Random, Armageddon playoffs, special anti-draw rules, or whatever. But this book is written for chess as it is played currently.
How do you think opening preparation is different today from when you first became an IM and GM?
Well, that's two completely different questions, because I became an IM in 1980, before computers were of any use to chess players, but a GM in 2008, when I got my title by winning the World Senior Championship with the help of massive computer preparation both before the event and for every game. In 1980, preparation meant carrying around a couple books and reviewing the lines in the books that you wanted to play. Two different worlds!
Which major openings are you no longer recommending in the new repertoire, and which ones are you recommending now?
For White, it was a total switch from 1.d4 to 1.e4, motivated by some positive developments for 1.e4 and some negative ones for 1.d4. For Black, the main change is that the Breyer defense to the Spanish is now my backup line, with the Marshall becoming my main one, along with a chapter on the Moller that I suggest might be better for correspondence play than for over the board. No major change vs. 1.d4, although the games and analysis are heavily revised.
In analyzing the opening, in which aspects was Komodo superior, and in which aspects was Lc0 superior?
Lc0 was generally superior on my computer, because it has a very powerful, expensive GPU with 3,000 cores; Komodo just uses six of the eight CPUs. Lc0 has some weak points though, which Komodo patches up: Lc0 is rather blind to perpetual checks, relatively weak in evaluating many endgames, and lacks specialized chess knowledge that applies in infrequent situations. Also I would say that Komodo is generally superior when it is necessary to see a long, precise series of moves to justify the initial move choice. Lc0 will usually find good moves quicker with my GPU, but will be slow to admit that it is wrong.
Is Lc0 as strong as AlphaZero now?
If they both ran on the same or comparable hardware, I think they are close in strength. But even Chess.com doesn't have hardware like Google. Probably a five or 10-minute Lc0 think on my computer is comparable to a one-minute think of AlphaZero on Google's hardware.
Given your expertise with engines, have you any interest in competing in the International Correspondence Chess Federation?
The percentage of draws between top ICCF players is in the mid or high 90s, I'm told. Playing a game with such a draw percentage doesn't interest me.
How do you evaluate the future of Chess960 (Fischer Random) as a means of circumventing opening theory?
I am a big advocate of Chess960 (Fischer Random), especially after seeing the semifinals and finals of the World Fischer Random Chess Championship. I actually won the only U.S. open championship of the game ever held (I believe), about a decade ago. I don't find it interesting to watch two super-GMs reproduce 20+ moves of computer analysis in a broadcast standard chess game, but I try not to miss a single game of 960 live between the top players, knowing that they are thinking for themselves after just a couple moves. I only regret that there are hardly any live, over-the-board events that most players can join. I would love to see 960 take an equal footing with standard chess before I become too old to play.
Is the French Defense as bad as IM Danny Rensch says it is?
Normal chess engines think it's more or less as good as anything, but statistics and the neural-network engines rate it as inferior to the "big three" (1...e5, Sicilian, Caro.) I think that you need to know a lot to prove that the French is inferior, but at super-GM or correspondence level, it is just the fourth-best defense. But I owe my GM title and World Senior Championship to the French!
Is Bobby Fischer right that 1.e4 is "best by test"?
Yes, I agree with him on this (as well as on 960 and on the use of increment in chess, although I have a better claim to being the inventor of increment chess than he does!). But 1.d4 and 1.Nf3 are not far behind.
Which question do you wish you had been asked about the new repertoire?
"How can I write a technical opening book full of the latest games and novelties played and analyzed in 2019, when I'm old enough to have had a chess teacher (Harold M. Phillips) who played against Steinitz in 1894?"
As an active partner in Komodo, I have the technical knowledge needed to make best use of computers. I can afford the best practical hardware. Also I probably understand better than any other GM where the displayed scores are coming from, at least in the case of Komodo, and so I can explain the +.53 eval to the reader. I'm still an active tournament player, the oldest active American GM, about to turn 72. My parents lived to an average age of 100, so I'm pretty young still for an old man!
Posted: at 2:48 pm
The Halloween Gambit is surely the spookiest way to start a chess game. If you're a conservative chess player who doesn't like to give away pieces, it may indeed be "2 spooky 4 u."
Could this early knight sacrifice be a legitimate opening? No, probably not. But maybe.
Don't take my word for it. Let's see how the world's number-four player, Maxime Vachier-Lagrave, plays this scary gambit:
"I found my new opening," said MVL after the game. "In blitz its sort of a decent weapon."
The doubters out there may chalk up this win to the so-called "throwaway" competition playing Black...
...But this couldn't be further from the truth.
We know this thanks to a superhuman computer engine losing to the supernatural gambit in the Computer Chess Championship. Both of the beastly machines in this game are rated over 3000, with the machine-learning engine Winter coming out on top in this autumn-themed gambit.
Don't be too scared for Rubi, though. It struck back in the next game with a revenge-win using the other side of the gambit. Check out its spooktacular bishop sacrifice on f7 to go two pieces down early:
How can two ridiculously strong computers beat each other, each from the white side of this gambit? The only answer is that the Halloween Gambit is a powerful and uncanny attack, especially in blitz chess.
So how do you win with the Halloween Gambit? The gist of it seems to be, like most encounters in chess and life, to just wait for your opponent to make a mistake. Otherwise how could the power of fright alone overwhelm the material advantage of a knight for a pawn?
With the clock ticking down and two powerful center pawns chasing their knights to the back rank, many players panic when facing this gambit. I know it's hard to believe, but even I don't usually get checkmated by a pawn in 18 moves.
What's the history behind the Halloween Gambit? The opening was known by club players in the 1800s, but got its scary name relatively recently, in the late 1990s.
Steffen Jakob, a German chess player and computer programmer, became interested in the opening in 1996 and made a computer engine clone of Crafty devoted to playing the gambit. Jakob named the opening the Halloween Attack.
"Many players are shocked, the way they would be frightened by a Halloween mask, when they are mentally prepared for a boring Four Knights, and then they are faced with Nxe5," Jakob told the journalist Tim Krabbe in 2000.
The Halloween Gambit is so good in speed chess that even the author of "Pete's Pathetic Chess" can use it to winas long as the opponent isn't in the world's top four.
So how do you defend against this spine-chilling opening? It helps if you don't have a spine. Just look how calmly Stockfish, the GOAT computer engine, deals with the Halloween Gambit.
It does not even break a sweat swatting down the knight sacrifice, largely because it also lacks sweat glands.
In delivering checkmate, Stockfish used its king's knight to strike the final blowthe same king's knight that White sacrifices in the Halloween Gambit. Spooky! It's as if to say, "See? This knight is useful."
You can watch the world's top chess engines play this gambit in the Computer Chess Championship Halloween Gambit bonus, live now through Nov. 1.
Or even better, try out the Halloween Gambit in your own games...if you dare!
See the original post:
Posted: October 16, 2019 at 5:30 pm
An interview with Lennart Ootes
All photos: Lennart Ootes
You are a renowned chess photographer who visits about 25 top tournaments a year to make pictures of top players and amateurs and to support the transmission of these tournaments. With your work you shape the image of chess and chess players. Moreover, as you write on your recently launched website lennartootes.com you are also a strong amateur chess player with a rating of 2217. How did you get interested in chess and in chess photography?
I played chess from a young age. My dad is a passionate chess player and drove my brother Lars and me to many chess tournaments in the Netherlands. Till the age of 23 I was playing a lot of chess, made it up to 2200, but also lost 100 points once I spent less time on studying and playing.
My passion for chess photography is quite a coincidence. I have always been involved with chess tournaments in the Netherlands: I wrote tournament reports, organized events and operated DGT boards. Back in 2011, the Dutch chess website schaaksite.nl asked a friend and me to make video reports of youth tournaments. So we went to a camera shop and bought a Sony photo camera with video function. But it took another three years before I preferred the photo function over video. In 2014 I was hired to operate the DGT boards at the US Championships, but I also managed to take pictures as well, which ended up on websites like Chessbase, Chessvibes and Chess24. It motivated me to learn more about photography, I watched many hours of online tutorials and the rest is history. 🙂
What is it that fascinates you about chess players and the world of chess?
Chess is a sport and sport is emotion. I love to witness the crucial moments in a chess tournament and to capture the moments that tell the story.
I am very fortunate to get very close to the players, to see their hands shaking in time trouble and to hear their reaction after the game. I really have an amazing day at work when there are exciting games and some decisive results.
Maxime Vachier-Lagrave and Magnus Carlsen
Besides that, the chess world is a big family that shares so many great memories in so many different places.
How did you become a professional photographer? Did you take courses, did you teach yourself or did you learn by doing?
I have watched many hours of online photography and photo editing tutorials. EspeciallyLynda.com(which is now LinkedIn Learning) gave me a great basic understanding. But in the end I had to practice. To make many thousands of photos, edit the ones I like and throw away the ones that were not good enough. Chess is the only thing I do and it's easier to stand out in a niche market than to master the entire spectrum of photography.
Tell us a bit about the everyday life of a chess photographer when working at chess tournaments. Usually, at top tournaments photographers are only allowed to take pictures at the beginning of the game. What do you do to get the best pictures in that short period of time and what do you do the rest of the time to produce your pictures?
At classical events I take about 100-250 photos at the start of the round, then make a selection to reduce that to about 30 photos, edit them in the next 3 hours and come back to the playing hall when I expect some action: a potential tactical blow, time trouble, the end of a game or a player interview. I have to admit that I have the privilege to be house photographer at the Grand Chess Tour and some other tournaments, so I can get within the ropes and have more freedom to do my work compared to some colleagues.
Fabiano Caruana vs Magnus Carlsen
You seem to travel all year to take pictures from chess tournaments but I think officially you still live in Holland. Are you indeed living there or are you just travelling?
I am quite passionate about traveling, so I am happy you don't ask me about my CO2 footprint...
I live in Amsterdam together with some chess friends: Merijn van Delft, David Miedema and Nico Zwirs, who are all IMs and chess trainers. Amsterdam is an amazing city, but I am there only one week per month.I just came back from two days in Windhoek, Namibia, to join my girlfriend who flew there as a flight attendant. And after this I will go to the Isle of Man, three days at Hoogeveen, the Chess960 World Championships in Oslo and the Grand Chess Tour events in Romania, India and London almost back-to-back. I won't be home in the next two months. It's a lifestyle that I really enjoy.
Travelling might be exciting but isnt it boring to take pictures of chess players again and again two players who hardly move sitting opposite each other, looking grim most of the time?
When a game lasts for six hours, timing is actually very important. As a photographer I am looking for emotions or something interesting.
I have to agree that I don't get too excited when a social media manager asks me to take a normal photo of two players behind the board. But I have to say that the real magic happens on my laptop: I can crop the photo in different ways, make it black and white or pump up the contrast in a creative way. Editing photos is not a puzzle with only one correct outcome and that gives me a lot of pleasure.
Vladimir Kramnik vs Vishy Anand
How do find the motifs for your pictures, your inspiration?
I've had a wonderful time working at the Stedelijk Museum Amsterdam, the largest Modern and Contemporary Art Museum in the Netherlands, for 14 months. Russian avant-garde artists like Malevich and Kandinsky have made a deep impression on me with their colours, shapes and movement. I can really appreciate stadiums or interesting stage designs. From David Llada's photos it looked like theWomen's Grand Prix inSkolkovo had a great venue with interesting colours.I can only hope that more chess tournaments would learn from theatre and other sport events.
Do you have any idols or role-models in photography?
I don't consider photography as the ultimate art and I am not obsessively studying the greatest photographers of all time. Like chess, photography is very technical and has been extremely influenced by the computer. But I tend to check David Llada's photos when I'm carving for some inspiration.
And in chess?
When I was young I really enjoyed the games of original players like Shirov, Morozevich andVolokitin. But it is the rise of Magnus Carlsen in 2010-2013 that convinced me to try to make a living in the chess world.
Can you give us a very short crash course for budding chess photographers and reveal what mistakes one should avoid at all costs when taking pictures of chess players?
Chess photography is extremely hard. We work in low light conditions, our subjects do sit there for hours and we can only get close to them in the first few minutes. On my website you can find an article on how to get bright photos in a dark environment. Camera gear is important, but the biggest win can be made in editing photos.
The biggest mistake I see in chess photography is when the photo is not framed well. I see a lot of photos that are taken from a standing position, so you see more board than player. Cropping is also very important: what to you pick as your horizontal line, what part of the chess board to include. I tend to crop parts of the head to make a photo more intense, but that's a creative choice that depends per person.
If I had to review my own photos, I would say that I have trouble with the white balance (colour temperature) and that I publish too many photos that are just above average and nothing special. My biggest strength is to be at the right moment at the right time. That is something that comes with experience and some decent passion for the game.
(From left to right) Sergey Karjakin, Hikaru Nakamura, Maxime Vachier-Lagrave, Fabiano Caruana, Magnus Carlsen
Girl with curious hair
And what are the three most important rules or guidelines to follow?
Don't distract a player. And be nice to arbiters and security they can't make your day, only break it.
Hunt for the player's eyes. It's our primary way to get into the chess player's brain. Without eyes, it is hard to find the emotion of a player.
The Indian talent Praggnanandhaa Rameshbabu
Try to build a special feeling with the moments/persons you take a photo of. A photo is more special when someone plays an amazing game, or when something memorable happens.
You started your career as chess photographer in 2012. What are the most memorable moments of the last seven years chess wise and in regard to travelling?
The world championship in Chennai was my first memorable event. I was there on my own cost (like more events in 2012-2014) but experienced chess history and was really impressed by the Indian culture with its colours and smells. On the rest days we visited, for example, Anand's former high school and a place for disabled children that was supported by Vishy's donations.
The passion for the game in India is amazing. That also strikes me in countries like Croatia, Russia, Germany and the Netherlands. At the Grand Chess Tour in Zagreb, the spectators stayed in the playing hall for five straight hours and gave the players a huge applause at the end of every round.
Another amazing adventure was Timur Gareyev's world record blindfold chess in Las Vegas. The simul lasted for 19 hours, including a fire alarm caused by "raw food master" Joe who preferred to prepare some sausages for himself. Timur's opponents were exhausted after such a long game, while Timur was slowly peddling on his stationary bike. The next morning Timur was so hyperactive that he ran through a glass door of our apartment.
Timur Gareyev on his way to his record in blindfold chess
Are there any chess players that are particularly easy or particularly difficult to photograph?
It is hard to capture the eyes of Kasparov, Kramnik, Anand and Carlsen. Guess what they have in common (smiles).
But it is also satisfactory to take a good photo of these guys. Emotional players like Nepomniachtchi, Nakamura and Jobava usually deliver in front of the camera. Grischuk is a mystery. But maybe the most remarkable player is Daniel Naroditsky who pushed suffering to a next level.
Daniel Naroditsky (right) in despair as Hikaru Nakamura watches
On your website you publish about 30,000 of your photos. How does one navigate through such an enormous number of pictures and do you have any favourites pictures that you like particularly well?
For me it was quite a job to go through all these photos, to tag players and to rate them. In the "Highlights" sectionyou can find a selection of my favourite photo by theme. But you can also just search for a player.
In your work you are close to the top players. Did you form any friendships or do the players and you keep a professional distance?
The top players are incredibly friendly, especially when you take into account that they are sportsmen. There is no player I have daily contact with, but I created some special connection with Wesley So. We mainly do some silly trash talking, but its origin might come from the Chess960 blitz game that we played last year. I have to admit that Wesley played with time odds.
How does travelling to these top tournaments affect your chess: do you get better by watching all these top players analyse and play or are you too saturated with chess to play well yourself?
These top guys are beasts. I have nothing but respect for the games that they play, the amazing moves they make and the mistakes they have to allow in time trouble. I do feel inspired when I play an incidental league game after a top event, but it's not that I gain chess knowledge during these game. Honestly, it's easier to follow the games at home than in the playing hall.
As you write on your website you also co-produce live-transmissions from chess events and you are on the team of app developers who work for New in Chess. As you say, you are quite advanced with the latest tech developments and always looking for new ways to improve chess broadcasting.Now, if you had the necessary resources how would your ideal transmission of a chess tournament look like?
I think that there are too many round robin tournaments. Most sport events end with a big final, but in chess we keep on playing against the same players all the time.
In terms of broadcasting, there are many things to try. For example live commentary for chess fans rated under 1400 that works well in Norway and there are many chess fans that have never played a tournament game in their life. Or a well-produced 20-minute recap of the most interesting game of the day, where the two players tell the narrativethrough personal interviews before and after the game.
Another idea is to get more practical information from computer engines. Right now, the engines' only output is tocriticizethe top player with its evaluation and a long variation.I would love to get more human-like explanation from the engine: what is the essence of the position, how difficult is it to calculate or assess the top engine line for a human, are there possible tricks in a variation? I wish it could become a tool to understand and appreciate chess more than it does now.
To conclude: what are your plans for the future in regard to your own chess and in regard to chess photography and chess transmissions?
In am working on a small photo exhibition at the Max Euwe Centre in Amsterdam, so that's exciting. There are a lot of chess events in the rest of the year, so you will definitelysee a lot of new photos in the next months. But first I will co-produce the live commentary for the Isle of Man tournament I am not sure if I will bring my camera...
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Posted: at 5:30 pm
It didnt enthrall the world like a moon landing, didnt define a generation like Woodstock, didnt stun the experts like the Mets World Series win. But there was also a compelling world chess championship match during that packed year of 1969, one that is worth celebrating 50 years later.
As weve written here before, Boris Spassky is too often remembered today as the Russian guy Bobby Fischer beat. But Spassky in the mid-1960s was probably the strongest player in the world, with a flexible, harmonious style that set him apart from the great generation of Soviet grandmasters with whom he competed.
Armenian world champion Tigran Petrosian, whose quiet, positional style sometimes masked a unique, revolutionary approach to the game, narrowly held off Spassky in their first world title fight in 1966. But Spassky, 32, was at the peak of his powers in 1969, easily winning the right to a 24-game rematch and entering the Moscow match a betting favorite.
Given the suspicions and machinations that attended so many other world title matches before and after the two Petrosian-Spassky matches, it is nice to learn the two protagonists here genuinely liked and respected each other, despite or perhaps because of their deep stylistic differences at the chessboard.
Spassky may have entered the 1969 fight a touch overconfident, for he proceeded to lose the first game with the White pieces in 56 demoralizing moves. But he righted himself with wins in Games 4 and 5, and the play seesawed back and forth for weeks. With the match tied 8-8, Spassky finally broke Petrosians resistance with crucial wins in Game 17 and Game 19, the latter the one time in the match when the challengers famed attacking skills were on full display.
Petrosian, rightly considered one of the greatest defenders in chess history, here gets bulldozed in a classic Najdorf Sicilian battle. Blacks queenside counterplay never gets going, and with 15. g4!? Nxg4 (trying to gin up counterplay with 15b5?! 16. g5 hxg5 17. fxg5 Nh5 18. g6! is very strong for White) 16. Qg2 Nf6 17. Rg1 Bd7 18. f5!, White is already threatening tactical tricks such as 19. fxe6 fxe6 20. Nf5!.
White claims a powerful initiative with a second pawn sac: 19. Rfd1 Qd8?! (e5!? might be a better try, since Black survives on 20. Ne6!? [Nde2 preserves a small edge] fxe6 21. fxe6 Rxe6! [Bc6? 22. Rxf6!] 22. Bxe6 Bxe6 23. Rxf6 gxf6 24. Qg6 Bc4 25. Qxf6+ Kh7) 20. fxe6 fxe6 21. e5!, opening new lines while disrupting Blacks defense. Spasskys follow-up is impeccable: 21dxe5 22. Ne4! Nh5 (exd4 23. Rxf6! g5 [obviously not 23gxf6?? 24. Qg8 mate] 24. Qh3 Qe7 [the threat was 25. Rxh6+ Bxh6 26. Qxh6+ Kg8 27. Nxg5 28. Nxe6+ Kf7 29. Rf7 mate] 25. Rxg5 Qh7 26. Rfg6 hxg5 27. Nf6!, and if 27Qxh3, 28. Rg8 is mate) 23. Qg6! (the silicon engines at first think 23Nf4 saves things for Black, only to eventually hit on 24. Rxf4! exf4 [exd4 25. Rxf8+ Rxf8 26. Qxg7 mate] 25. Nf3! Qb6 26. Rg5! Rxc8 [hxg5 27. Nexg5 and mate next] 27. Nf6! and wins) exd4 24. Ng5!, and the champion resigns.
The threat is mate on h7, and theres no hope for Petrosian after 24hxg5 25. Qxh5+ Kg8 26. Qf7+ Kh7 27. Rf3 g4 [e5 28. Qh5 mate] 28. Rxg4 with checkmate in sight. Amazingly, Petrosian rallied from this debacle to win Game 20, but then lost again and two games later conceded the match, 12-10.
The most talked-about chess game of the week was a 47-move, midtournament draw at the FIDE Chess.com Grand Swiss tournament now underway on the Isle of Man. The reason: Belarus GM Vladislav Kovalev had Norwegian world champion Magnus Carlsen he of the 93-game unbeaten streak, whose last over-the-board loss at classical time controls came in the summer of 2018 on the ropes, with a slew of chances to deliver the knockout blow.
But its not easy to get the champ on the canvas and get him to stay.
We pick it up from todays diagram, where Carlsen as Black has been completely outplayed in a Rossolimo Sicilian: Kovalev dominates the center, has a monster passer on d7 and, after the just-played 29b6-b5, is winning a pawn to boot.
Kibitzers worldwide saw a number of winning paths, but White was facing a dogged superstar with just a few minutes to reach the 40-move time control. There followed 30. Nxc5!? (Rf5! c4 31. Re5!, centralizing the rook, was instantly winning) Bxc5 31. Qxc5 Kh7 32. Qd5 Qg5 33. Qe4+?! (and stronger here was 33. Rf5! Qe3 34. Rf7 a6 35. d4!, and Black has no perpetual check) Qg6 36. Qc7 Qg5 37. Qd6 Qg6 38. Qc7?! (Qd5! gets back on the winning track) Qg5 39. Qc6 Qe7, and with the passed pawn under control, Black has finally reached a defendable harbor.
After 40. Qxb5 Rxd7 41. Qf5+ g6 42. Qf8 Qxf8 43. Rxf8 Kg7 44. Ra8 Kf6 45. Kg2 Rxd3 46. Rxa7 Rd2+ 47. Kg1, Whites extra pawn means nothing with his king pinned on the back rank. A disappointed Kovalev agreed to the draw.
Spassky-Petrosian, Game 19, World Championship Match, Moscow, June 1969
1. e4 c5 2. Nf3 d6 3. d4 cxd4 4. Nxd4 Nf6 5. Nc3 a6 6. Bg5 Nbd7 7. Bc4 Qa5 8. Qd2 h6 9. Bxf6 Nxf6 10. O-O-O e6 11. Rhe1 Be7 12. f4 O-O 13. Bb3 Re8 14. Kb1 Bf8 15. g4 Nxg4 16. Qg2 Nf6 17. Rg1 Bd7 18. f5 Kh8 19. Rdf1 Qd8 20. fxe6 fxe6 21. e5 dxe5 22. Ne4 Nh5 23. Qg6 exd4 24. Ng5 Black resigns.
David R. Sands can be reached at 202/636-3178 or by email [emailprotected].
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By integrating continuous intelligence systems into business processes using real time and historical data, organizations can respond in near real time.
Oh, the many questions to ponder when it comes to extracting value from data.
What if you could analyze data as its created?
What if you could visualize your business?
What if you could better predict your customers needs?
What if you could gain insights from unstructured data like audio, text, or video?
What if you could automate immediate actions?
What if you always knew where your assets were, and where they would be?
What if you could update machine learning models continuously?
And what if you could do it all in real time?
Streaming analytics engines have the power and sophistication to answer the questions above in real time. As computing and networking costs have continued dropping year after year, sensors are monitoring nearly everything. Bluetooth, Wi-Fi, and 5G networks have enabled near-instantaneous delivery of huge volumes of data.
Deep learninguses artificial neural networks to recognize patterns in data. A human brainhas about 200 billion neurons, with about 32 trillion connections between them.Its these connections that enable people to recognize the pattern in speech,facial expressions, and so much more. Artificial neural networks have far fewerconnections, but as they continue to grow, they continue to improve inaccuracy.
These artificialneural networks have been applied to many areas, such as vision, speech,acoustics, natural language processing, medical image analysis, and board gamesranging from chess to go. In many of these situations, they have producedresults beyond top experts:
Whilethese high-profile grand challenges in computing were aimed at specific tasks,the experience gained has been applied to much broader areas.
Combiningall these forms of artificial intelligence with continuous intelligencedrawingfrom geospatial, real-time, and historical analyticscan further enhancebusiness ability to know where assets and people are at all times and helppredict what might occur next. One effort some years ago used anonymizedtelephone location data to predict with 95% accuracy where people would bebased on their past movements. Were all creatures of habit, going to work,school, synagogue, mosque, or church with great regularity, enabling thesekinds of predictions.
Addingrules engines and programmatic logic to AI, location data enables organizationsto automate many decisions that previously required human insights. Frompredictive maintenance based on actual driving conditions to decide the bestnext action to take with customers to improve loyalty, leading companies aredecreasing costs and improving revenues to become more successful.
By integrating continuousintelligence systems into business processes using real time and historicaldata, organizations can respond in near real time. Monitoring model drift andautomating model refresh and deployment enables the use of the most accurate AIto deliver on organizational improvements.
To learn more about these topics and get your questions answered, come hear me speak at IBMs Data and AI Forum on October 23, 2:15 pm3:00 pm. The Forum, which runs from October 21-24, 2019 in Miami, is the premier data and AI gathering of the year to learn how to drive smarter decisions, formulate more effective strategies and achieve better business outcomes with analytics.
I will discuss the role thatcontinuous intelligence plays in both AI and business and walk you through the benefits of using analytics to not only predict what will happen,but what to do about it.
See you there.
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Posted: August 6, 2017 at 3:42 am
Since the first industrial revolution, inventors have been driven by the idea that an automaton could mimic human intelligence.
There was even an attempt at a chess-playing automaton, the Mechanical Turk. This later turned out to be a hoax, as its inventor had someone sit inside the machine to make the supposedly intelligent chess moves against its human opponent.
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Just over two decades since the worlds first robot chess champion, Deep Blue, took its bow, artificial intelligence (AI) is breaking new ground technologically.
In March this year, AlphaGo from Googles DeepMind subsidiary proved that an AI could beat the best at the ancient game of Go an achievement many had predicted would take AI many years.
AlphaGos success suggests that the pace of AI technological advancement is accelerating. In time, it seems an AI will inevitably test what it means to be human.
There have been many heroic attempts at AI over the past 70 years, leading to several breakthroughs in AI and machine intelligence.
But beating world champion Garry Kasparov in a chess tournament, which is what IBM achieved with Deep Blue, is arguably more about the raw processing power of its hardware than the prowess of AI and logical reasoning.
In the UK, the first proper machine that was tasked with playing a game was the Hollerith Electronic Computer (HEC), which is currently on display at The National Museum of Computing (TNMOC) at Bletchley Park.
The machine was displayed to the public in 1953 at the Business Efficiency Exhibition in London. Raymond Bird, the electronics engineer who was tasked with developing the HEC, described the demonstration of the noughts and crosses game as a great success in showing the potential power of computers.
Andrew Herbert, chairman of TNMOC, says HEC became an instrument of the Cold War and used AI to help it achieve this. It was programmed to do automatic machine translation, he says. The computer was set up to convert written Russian into English the sort of demo that Microsoft often does today to show off the idea of a Star Trek-like Universal Translator.
In the 1950s, AI was also developed to support image recognition for the analysis of satellite photos during the Cold War. Again, the idea of learning to identify cats or whatever in a series of images is widely used today. AI was about clever pattern recognition, says Herbert.
But by the 1970s, AI scientists were attempting to second-guess how the human brain worked, he says. That is something neuroscientists still do not truly understand.
During the 1980s, Japan announced what it called the Fifth Generation computer initiative. This was the dawn of workstations and Japan saw that powerful computers could be made more intelligent. It led to the development of expert systems machines that could become domain experts in areas such as medical diagnosis, says Herbert.
Such expert systems captured a finite amount of information on a given subject domain, allowing less expert users to work on more complex problems without needing to have a specialist on hand.
With ubiquitous internet access, much more data became available, which led to what is now called machine learning. A big driver was search engine development by the likes of Bing, Google and AltaVista and, later, the recommendation engines all of which are based on pattern recognition technology.
The original man versus machine contest took place on 11 May 1997 when an IBM computer called Deep Blue defeated the reigning world chess champion, Garry Kasparov, grabbing the worlds attention and imagination. The six-game match lasted several days and ended with two wins for IBM, one for Kasparov and three draws.
But as with the Mechanical Turk of the 18th century, AI did not play much of a role in early logic game conquests.
Deep Blue was not a true AI because it analysed all possible chess moves using a brute force algorithm.
Primary Key Associates co-founder Andrew Lea has had an interest in AI for 35 years. His company uses the technology in data analytics to identify unknown knowns in datasets.
Lea says the reason why logical games such as chess and Go are strongly associated with AI is because they are closed domains. People were so much better than computers at playing these games, he says. Now we have the conundrum where computers are getting much better.
Lea wrote his first chess program for the BBC Model B, and recently developed a version for the Arduino microcomputer board. Writing good chess programs hasnt really increased our understanding of how people think, he says. I wrote a chess program 30 years ago. I remember writing chess on the BBC B microcomputer and its about how to make it smart on a small 8-bit computer. I think what makes AI is the ability to be smart and big, where big equals knowledge and experience.
For Lea, being smart is the opposite of brute force, where sheer computational power is thrown at the problem of identifying the best possible move for the robot chess player to make. Its about pattern recognition, knowing intuitively what you learnt from a previous game, and how this can make a difference in the current game, he says.
During the first decade of the new millennium, a step-change occurred as computational power increased to the point where neural networks and deep learning algorithms could be applied to AI.
Deep learning , to use IBMs definition, is based on the human brains decision-making process. By building multiple layers of abstraction, deep learning technology can solve complex semantic problems.
In 2011, IBM showcased its deep learning technology with the Watson computer, which beat two of the most successful human contestants on the long-running US TV game show Jeopardy!. The game show requires participants to provide a question in response to general knowledge clues. In the event, Watson marked a breakthrough in AI with its understanding of natural language and ability to make sense of vast amounts of written human knowledge.
Last March in Seoul, the Go-playing computer program AlphaGo, developed by Googles DeepMind division, defeated the best Go player of the last decade, Lee Sedol. AlphaGo won by resignation after 186 moves. Go is regarded as one of the hardest games for computers to master because of its sheer complexity. There are roughly 200 possible moves for a given turn compared with about 20 in chess, and more possible board configurations than the number of atoms in the universe.
People thought it would take 20 years for a computer to be able to beat a human at Go, but Elon Musk, CEO of Tesla and SpaceX, believes AlphaGo's mastery of Go shows just how quickly AI is evolving.
TNMOCs Herbert says: We are making great strides in enabling computers to perceive things, so we can build amazing applications that can mimic human behaviour, but it is not intelligence in the way of a human.
The risk to humanity that Musk fears is an AIs ability not only to outpace human intelligence, but to exploit an intelligent network in a way that could undermine society in order to achieve a seemingly benevolent objective.
Speaking at the National Governors Association on 15 July, Musk said: The pace of progress is remarkable. Now AlphaGo can play the top 50 Go players and crush them all.
There are now AI systems capable of learning without ever having being taught the fundamental principles or a basic understanding of the subject matter. You can see robots that can learn to walk from nothing within hours, which is way faster than any biological being, Musk told US state governors at the event.
One of the most recent breakthroughs came in June, when Facebook published research introducing dialog agents with the ability to negotiate. Similar to how people have differing goals, run into conflicts and then negotiate to come to an agreed-upon compromise, the researchers demonstrated that it is possible for dialog agents with differing goals implemented as end-to-end-trained neural networks to engage in start-to-finish negotiations with other bots or people while arriving at common decisions or outcomes, according to Facebooks blog.
While the ability to exhibit human-like negotiation tactics is certainly a big step forward, the Facebook bots gave a very public demonstration of an inherent risk in self-learning technology. They were switched off after they invented their own language for communicating a language that could not be understood by the human researchers.
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