But sometimes they do provide comic relief. Amazon uses AI-driven algorithms to determine what items you might be interested in buying besides the ones you have looked at. A couple of years ago I got an email from Amazon indicating that "Because of your browsing history you might benefit from the discounts available to you by joining Amazon Mom.
But now they have gone to the other extreme. Typically a day or two after I have looked at an item I get an email from Amazon indicating that "Because of your recent browsing history you might be interested in After all, unless I made a mistake, why wouldn't I be interested in an item I chose to look at?
And if they have a better conceptual understanding of positions that's because their much superior computational capabilities allow them to determine it. I had earlier estimated that AlphaZero enjoyed about an 80X computational capability advantage over Stockfish in their matches.
Yet in the last 6 TCEC Superfinals LeelaC0 has only been able to defeat Stockfish 2 out of 6 times in spite of its much superior computational capability. And I don't know why you say that "we've" found that neural networks NN don't think as many moves deep as traditional engines. NN-based engines typically use a version of a Monte Carlso Tree Search MCTS which in its pure form estimates the scoring probability for candidate move from each position by conducting simulated playouts of many games, thus actually looking at the results of each combination of moves through the end of the simulated games.
And you can't look any deeper than that! Classic engines seldom get a chance to look to the end of the game unless they find a forced mate or reach positions where they can use tablebases. But the actual champion in getting superior results with shallow searches has to be Capablanca. When he was once asked how many moves deep he looked he supposedly replied "Only one. But it's always the best move. After all, the effort was made to gain publicity and sales for Google's Tensor Processing Units and Deep Mind's neural network-based reinforcement training for use in other applications.
What would they possibly gain by continuing the AlphaZero efforts? What could they have achieved that would have topped their results against Stockfish? And if a show is that good, I can probably remember it myself.
Yes, she's a very good player but she seems to talk like and AlphaZero groupie, with a great deal of anthropomorphizing and a "gee whiz" attitude full of gushing praise. Besides, she doesn't really know how AlphaZero works, even those who developed AlphaZero claim that they don't know the specifics of how AlphaZero's neural network works, a claim that I find very dubious since the values stored in each of the neural network nodes are recorded and the calculations performed to produce the results..
Sometimes she attributes very deep motivation to AlphaZero's moves and tries to rationalize them afterwards as I indicated in Hikaru Nakamura kibitz The fact that AlphaZero simply made a mistake did not even occur to her. Don't get me wrong, I like Anna Rudolf and video analyses in general. And I have even written to her to complement her. But, when it comes to AlphaZero, she becomes a cheerleader while losing all her objectivity and I don't think that anyone should take her comments in these videos too seriously.
Seems basically these learning engines work backward. But, no, these learning engines don't work backward; they "work forward" just like "standard evaluation based engines". I prefer to refer to the latter as "classic" chess engines because they operate in basically the same way as described in Shannon's classic paper "Programming a Computer for Playing Chess".
With many refinements, of course. In this paper, published in but written in , Shannon describes the overall structure of such a computer program, a strategy for choosing a move in any position using the minimax algorithm, the use of an evaluation function consisting of many factors to assess each position, the use of search tree pruning, and many other ideas that are commonly incorporating in today's engines and have been for some time.
So both classic and neural network-based engines work somewhat similarly. From any given position they identify the "best" candidate moves to be investigated further. From the positions arising from each of those candidate moves they generate another set of candidate moves and repeat the process. We are also releasing new chess games - including a top 20 selected by GM Matthew Sadler gmmds - that show off its dynamic playing style and we hope will inspire chess players of all levels around the world.
IM Anna Rudolf also made a video analysis of one of the sample games, calling it "AlphaZero's brilliancy. The new version of AlphaZero trained itself to play chess starting just from the rules of the game, using machine-learning techniques to continually update its neural networks.
According to DeepMind, 5, TPUs Google's tensor processing unit, an application-specific integrated circuit for article intelligence were used to generate the first set of self-play games, and then 16 TPUs were used to train the neural networks.
The total training time in chess was nine hours from scratch. According to DeepMind, it took the new AlphaZero just four hours of training to surpass Stockfish; by nine hours it was far ahead of the world-champion engine. Stockfish had a hash size of 32GB and used syzygy endgame tablebases. AlphaZero's results vs. Stockfish in the most popular human openings. Click on the image for a larger version. The sample games released were deemed impressive by chess professionals who were given preview access to them.
GM Robert Hess categorized the games as "immensely complicated. DeepMind itself noted the unique style of its creation in the journal article:. The AI company also emphasized the importance of using the same AlphaZero version in three different games, touting it as a breakthrough in overall game-playing intelligence:.
AlphaZero uses its neural networks to make extremely advanced evaluations of positions, which negates the need to look at over 70 million positions per second like Stockfish does. According to DeepMind, AlphaZero reached the benchmarks necessary to defeat Stockfish in a mere four hours. AlphaZero runs on custom hardware that some have referred to as a "Google Supercomputer"—although DeepMind has since clarified that AlphaZero ran on four tensor processing units TPUs in its matches. In December , DeepMind published a research paper that announced that AlphaZero had easily defeated Stockfish in a game match.
AlphaZero would go on to defeat Stockfish in a second match consisting of 1, games ; the results were published in a paper in late Unfortunately, AlphaZero is not available to the public in any form. The match results versus Stockfish and AlphaZero's incredible games have led to multiple open-source neural network chess projects being created.
Even Stockfish, the conventional brute-force king, has added neural networks. In DeepMind and AlphaZero continued to contribute to the chess world in the form of different chess variants. When DeepMind and the AlphaZero team speak, the chess world listens! From the moment it stepped onto the scene, AlphaZero has changed chess by spawning a new generation of neural network chess engines, by contributing to chess variants, and through its transcendent games.
As mentioned, AlphaZero defeated the world's strongest chess engine, Stockfish, in a one-sided game match in December scoring 28 wins, 72 draws, and zero losses. The public was given 10 example games from this match, and the chess world's reaction was borderline disbelief.
GM Peter Heine Nielsen likened watching AlphaZero's games to seeing a superior species landing on earth and showing us how to play chess:. It approaches the 'Type B,' human-like approach to machine chess dreamt of by Claude Shannon and Alan Turing instead of brute force.
GL: A huge amount! GL: How much interest and passion there is around this project. It's been fantastic to watch the community really get excited, and all the chess personalities who have tried playing it. AS: While testing the engine in Fritz, I noticed a number of very strange tactical oversights, even in fairly simple positions.
Is this normal, and if so, when would they be overcome? GL: Definitely normal, the network is still learning tactics, especially at low depths. Also, we are on a much smaller network than DeepMind used 10x instead of 20x Also, at the current rate of development, how long would it take to reach that level presuming similar hardware?
GL: From their paper, 44 million if I recall correctly, while we are at 8. However, we've also had some significant bugs during the training process, and have been learning a lot as we go. I wouldn't be surprised if our run took longer. GL: I've always loved the combination of distributed systems and chess. A few years back I also developed "fishtest", which helped take Stockfish to number one on the rating lists although these days, a wonderful community has taken it on.
My professional work is on developing autonomous cars, which is extremely fun and challenging. Also, you most definitely want a video card to run it properly. The faster the better, but anything beyond Intel integrated graphics should be a plus. If you have a discrete GPU graphics card , get the gpu-win. Many other programs rely on this library so you may have it already. It is vital this be done before trying to install it as a UCI engine.
Naturally, you can download any version really, even when it was rated a big fat zero. Right-click on the blue Network number and save. You then unzip it to the main Leela folder. Now you will have a very large file with a long name that looks like a secret code. Rename it to weights. This is a simple command that will have the computer run a full tuning of your computer to get best results with Leela.
It may take minutes, so be patient. Running the script will do a full tune of your graphics card to get optimal results. Please note this can improve performance to almost double even! The multiple computer chess world champion comes in a new and yet more powerful version.
When you want to update the neural network with the latest version, all you need to do is download the newest Network file, and rename it again. If you would like to contribute some of your computer time to help Leela, just double-click on the client.
It will start running self-play games, and send them to the site. To stop the process, just close the window. For more information, please see the Getting Started page.
If you are having trouble with Leela, or have any questions or suggestions, be sure to check out the official forum for it. Nf3 Nf6 3. Bg2 Bb7 5. Nc3 e6 6.
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