Answers.Body: <ol>
  <li>Pretty much everybody. The reason being that it is STILL slower. The evaluation function for alphazero has WAY more variables so despite more computing power, alphazero can only calculate a fraction of number of positions than stockfish. However the evaluation function for alphazero is much much better due to self-learning and also takes forever to calculate.</li>
  </ol>
  
  <p><a href="https://arxiv.org/pdf/1712.01815.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1712.01815.pdf</a></p>
  
  <p>Consider this. Google used 5000 super computers to train the AI to play before the game. Now consider what happens if you give stockfish access to 5000 super computers to think for 4 hours and if it memorized all those lines.</p>
  
  <ol start="2">
  <li><p>The issue comes down to computing power.</p></li>
  <li><p>Not in your lifetime.</p></li>
  </ol>
  
  <p>It is a bit difficult to find Ghz to tflop conversions but consider this.</p>
  
  <p><a href="https://www.pugetsystems.com/labs/hpc/Intel-Core-i9-7900X-and-7980XE-Skylake-X-Linux-Linpack-Performance-1059/" rel="nofollow noreferrer">https://www.pugetsystems.com/labs/hpc/Intel-Core-i9-7900X-and-7980XE-Skylake-X-Linux-Linpack-Performance-1059/</a></p>
  
  <p>28 cores on 2.6Ghz gives about 1.123Tflops. So for 88 cores running at 2.2Ghz each is around 3Tflops. So your system gives around 3Tflops.</p>
  
  <p>Now I can't find any information on the first generation TPUS but the second generation TPUs have 180 Tflops each.</p>
  
  <p><a href="https://en.wikipedia.org/wiki/Tensor_processing_unit" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Tensor_processing_unit</a></p>
  
  <p>So 1 TPU = 60 of your systems. So 5000*60*4 = 1200000 hours = 136 years.</p>
  
  <p>Also should clearly show how unfair the alphazero vs stockfish match is.</p>
  
  <p>Edit: I made a mistake, you said 2 22 core systems. My calculations had 88 cores. So you would need to double the previous total to 272 years.</p>
  
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Answers.Text: Thanks @Matthew Liu. 5000 super computers, are you sure? In the Supplementary Materials, they mention that "Each MCTS was executed on a single machine with 4 first-generation TPUs". So, based on your computation this would mean roughly 960 hours instead of 1200000!
Answers.UserDisplayName: Kortchnoi
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Answers.Text: @Kortchnoi It clearly states in the paper they used 5000 first gen TPUs and 64 second gen TPUs. No idea about how good their first gen is but if its even half as good as their second gen it's still going to take somebodys lifetime to train on a normal machine. Also in the match the hardware alphazero was running on was literally over 100x faster than the one stockfish was on. Deepmind is trying to cover up that fact by saying alphazero evaluates way 1/1000 positions per second than stockfish, which is moot if alphazero's evaluation of the position is a million times better.
Answers.UserDisplayName: Matthew Liu
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Answers.Text: thanks for the comment. Right but this is stated in the working paper version (Dec 2017) not in the published and refereed one (Dec 2018). I don't know if they used a new protocol or corrected a potential mistake.
Answers.UserDisplayName: Kortchnoi
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Answers.Text: @Kortchnoi There were no serious changes made. Also I think you are mistaken about the hardware used for training and for the actual match vs stockfish. For training they used 5000 first gen tpus for training games and 64 second gen tpus. For the match they used 4 tpus against stockfish, which is literally over 100x more powerful than what stockfish was running on in that match. Yes they used 5000 super computers to train alphazero, not an impressive task when you can just throw money as a super rich company.
Answers.UserDisplayName: Matthew Liu
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Answers.Text: You are right! I was mistaken.
Answers.UserDisplayName: Kortchnoi
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Answers.Text: I don't see why you consider the alphazero vs stockfish match so unfair because of what went into training alphazero. Training it is one thing, actually playing is another, and I don't see why anyone should pull their hardware punches when it comes to training. Why should DeepMind prepare an alphazero that won't be effective for 136 years?
Answers.UserDisplayName: Allure
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Answers.Text: The second gen. TPUs are rated 45 TFLOPS each, according to the Wikipedia article you link to. The nVidia Tesla V100 (data center GPU) is capable of 125 TFLOPS for deep learning ([source](https://www.nvidia.com/en-us/data-center/tesla-v100/#specs)), so TPUs appear to be closer to graphics cards than supercomputers.
Answers.UserDisplayName: Dennis
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Answers.Text: @Dennis there are 4 cores in every tpu so every tpu has 180 tflops
Answers.UserDisplayName: Matthew Liu
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Answers.Text: *The TPUs are then arranged into 4-chip modules with a performance of 180 TFLOPS.* The module is rated 180 TFLOPS. Each of the four TPUs in a module is rated 45 TFLOPS.
Answers.UserDisplayName: Dennis
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Answers.Text: @Dennis Really how ignorant can you be? According to you your computer has 8 CPUs on it or however many cores it has. Have you considered using a google search instead of arguing on something you literally have no idea about? https://arstechnica.com/information-technology/2017/05/google-brings-45-teraflops-tensor-flow-processors-to-its-compute-cloud/ https://medium.com/syncedreview/googles-tpu-chip-goes-public-in-challenge-to-nvidia-s-gpu-78ced56776b5. Literally took me 2 seconds to find results from a google search tells you each TPU has a performance of 180 tflops.
Answers.UserDisplayName: Matthew Liu
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Answers.Text: @Allure It is incredibly unfair due to several factors. With 5000 super computers and 4 hours of thinking time, any modern engine would easily crush all the other engines by analyzing book openings and memorizing key lines. Not to mention DURING the match, the hardware alphazero was running on was over 100x better than the specs stockfish was running on. At that point even a poorly programmed engine would beat stockfish.
Answers.UserDisplayName: Matthew Liu
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Answers.Text: If your interpretation is correct (I still think the Wikipedia and Arstechnica articles say something else and at least [this guy](https://news.ycombinator.com/item?id=14363337) agrees with me), since [the first-gen card has only one processor](https://images.anandtech.com/doci/12195/google-tpu-board-2.png), it should be capable of less than 45 TFLOPS.
Answers.UserDisplayName: Dennis
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Answers.Text: And you're still calling what's comparable to a (very high-end) graphics card a "supercomputer". The [IBM Summit supercomputer](https://en.wikipedia.org/wiki/Summit_(supercomputer)) has 27,648 Tesla V100 GPUs. With each being capable of up to 125 TFLOPS for deep learning, that about 3,456,000 TFLOPS.
Answers.UserDisplayName: Dennis
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Answers.Text: @Dennis Google says their TPU has 180 tflops https://cloud.google.com/tpu/. Also I already said I wasn't able to find any info on their first gen TPU. A 2080 ti has 13.4 tflops so I guess it depends on your definition of supercomputer but just 1 of these TPUs are already much better than a top of the line gaming GPU.
Answers.UserDisplayName: Matthew Liu
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Answers.Text: Let us [continue this discussion in chat](https://chat.stackexchange.com/rooms/88313/discussion-between-dennis-and-matthew-liu).
Answers.UserDisplayName: Dennis
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Answers.UserDisplayName: Matthew Liu
Answers.Body: <p><strong>(1)</strong> Let's go back to 2016 when Google had just beaten the best Go player...</p>
  
  <p><a href="http://talkchess.com/forum3/viewtopic.php?f=2&amp;t=59072&amp;hilit=machine+learning+chess+slow+machine+learning&amp;start=50" rel="nofollow noreferrer">http://talkchess.com/forum3/viewtopic.php?f=2&amp;t=59072&amp;hilit=machine+learning+chess+slow+machine+learning&amp;start=50</a></p>
  
  <p>Milos who I believe is a PhD claimed machine learning had been tried "N" times.</p>
  
  <p><a href="https://i.sstatic.net/CcXhj.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/CcXhj.png" alt="enter image description here"></a></p>
  
  <p>Dr Bob, the legendary engine programmer who wrote Crafty believed NN was too slow.</p>
  
  <p><a href="https://i.sstatic.net/BaZvZ.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BaZvZ.png" alt="enter image description here"></a></p>
  
  <p><strong>(2)</strong></p>
  
  <ul>
  <li><a href="https://www.chessprogramming.org/NeuroChess" rel="nofollow noreferrer">https://www.chessprogramming.org/NeuroChess</a></li>
  <li><a href="https://arxiv.org/abs/1509.01549" rel="nofollow noreferrer">https://arxiv.org/abs/1509.01549</a></li>
  <li>Others I miss here...</li>
  </ul>
  
  <blockquote>
    <p>Is it possible to pin down precisely what they have done better than others? Could it be simply that they use a lot more of computing ressources than before?</p>
  </blockquote>
  
  <ul>
  <li>Google indeed had more computing resources but that's not all ...</li>
  <li>Google had a superior deep learning model</li>
  <li>Google adopted MCTS instead of pure alpha-beta</li>
  <li>Google developed the model from self-playing, not like learning from GM games</li>
  </ul>
  
  <p>This was the network used in NeuroChess. It's a shallow network in our modern standard. It's <strong>NOT</strong> a deep NN.</p>
  
  <p><a href="https://i.sstatic.net/MD96n.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MD96n.png" alt="enter image description here"></a></p>
  
  <p>(3). I suspect it'd take weeks if not months. Not sure.</p>
  
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Answers.UserDisplayName: SmallChess
Body: <p>Answering this <a href="https://chess.stackexchange.com/questions/23456/why-does-alphazero-evaluate-on-the-order-of-1000-times-less-moves-than-stockfish">question</a>, @SmallChess offered this answer:</p>
  
  <blockquote>
    <p>Before Google's chess journey, the chess engine community believed:
    AI chess will not beat classical programming because neural networks
    (or other models) run much slower.</p>
  </blockquote>
  
  <ol>
  <li><p>I was looking for a reference to this quote. Did someone make this
  prediction in the past?</p></li>
  <li><p>I have another, perhaps more fundamental question related to another part of @SmallChess' answer:</p></li>
  </ol>
  
  <blockquote>
    <p>AI machine learning is not new to chess. There were serious attempts
    before Google established. Unfortunately, nobody had the
    determination, skills and resources to do a good job.</p>
  </blockquote>
  
  <p>Is it possible to pin down precisely what they have done better than others? Could it be simply that they use a lot more of computing ressources than before? </p>
  
  <ol start="3">
  <li>Subsidiary question: For a layman, who doesn't know how to compare TPU<br>
      and CPU, instead of 4 hours, how long would Alphazero's training
      have taken with Stockfish's configuration? (44 threads on 44
      cores - two 2.2GHz Intel Xeon Broadwell CPUs with 22 cores instead of a single machine with 4 first-generation TPUs. See
  Supplementary Materials for    their <a href="http://science.sciencemag.org/content/362/6419/1140" rel="nofollow noreferrer">Science paper</a>.)</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-01-13T23:47:48.317Z
Comments.Text: You might be interested in the Leela Chess Zero project, which is attempting to duplicate AlphaZero using the information given in the papers. They've done a really good job, and Leela is almost as good as Stockfish now. You can read about the hardware they used as well on their website. http://lczero.org/
Comments.UserDisplayName: Allure
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-01-14T07:13:43.437Z
Comments.Text: Just a note-AI didn't beat classical engine-in the match classical engine had many features cut of running on poor hardware and made mistakes obvious even to same engine on a descent phone... That was marketing action to attract money; there are still 5-15 years until AI for real will be able to compete with classical chess engine; but the time will come-history repeats-same was Human Vs PC-top GM's for money or because of bad conditions(PC don't get tired, Kasparov played with no rest days) lost to PCs that were not able to beat human, but since ~2015 PC for real became stronger than Human.
Comments.UserDisplayName: Drako
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Comments.CreationDate: 2019-01-14T08:55:12.823Z
Comments.Text: @Drako have you read the Science paper and the supplementary material? They seemed to have improved a lot on the previous critics.
Comments.UserDisplayName: Kortchnoi
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-01-14T17:04:25.283Z
Comments.Text: I second the Leela Chess Zero project. Specifically to answer 2 though - Yes, they have a lot better hardware and resources at their disposal but because of the good hardware they were able to test the concept of "Zero". And it turns out that the AI in the longer run, learns to play better when it learns everything from scratch rather than learning from concepts that humans have learned from playing those games.
  
  They also created an architecture of Neural Network which is more general than just being good at 1 specific task, rather the architecture of the NN is generalized enough to learn..
Comments.UserDisplayName: stackErr
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-01-14T17:04:42.297Z
Comments.Text: ..game of Go or chess. And possibly more.
Comments.UserDisplayName: stackErr
Tags: <stockfish><programming><alphazero>
Title: AlphaZero and the revenge of the Artificial Intelligence
ThreadText: # AlphaZero and the revenge of the Artificial Intelligence
  
  (Asked by: username_0 on 2019-01-13)
  
  Answering this [question](https://chess.stackexchange.com/questions/23456/why-does-alphazero-evaluate-on-the-order-of-1000-times-less-moves-than-stockfish), @username_6 offered this answer:
  
  > Before Google's chess journey, the chess engine community believed:
  > AI chess will not beat classical programming because neural networks
  > (or other models) run much slower.
  
  1. I was looking for a reference to this quote. Did someone make this
     prediction in the past?
  2. I have another, perhaps more fundamental question related to another part of @username_6' answer:
  
  > AI machine learning is not new to chess. There were serious attempts
  > before Google established. Unfortunately, nobody had the
  > determination, skills and resources to do a good job.
  
  Is it possible to pin down precisely what they have done better than others? Could it be simply that they use a lot more of computing ressources than before?
  
  3. Subsidiary question: For a layman, who doesn't know how to compare TPU  
     and CPU, instead of 4 hours, how long would Alphazero's training
     have taken with Stockfish's configuration? (44 threads on 44
     cores - two 2.2GHz Intel Xeon Broadwell CPUs with 22 cores instead of a single machine with 4 first-generation TPUs. See
     Supplementary Materials for their [Science paper](http://science.sciencemag.org/content/362/6419/1140).)
  
  **Tags:** stockfish, programming, alphazero
  
  > **Comments:**
  > **username_1**: You might be interested in the Leela Chess Zero project, which is attempting to duplicate AlphaZero using the information given in the papers. They've done a really good job, and Leela is almost as good as Stockfish now. You can read about the hardware they used as well on their website. http://lczero.org/  
  > **username_2**: Just a note-AI didn't beat classical engine-in the match classical engine had many features cut of running on poor hardware and made mistakes obvious even to same engine on a descent phone... That was marketing action to attract money; there are still 5-15 years until AI for real will be able to compete with classical chess engine; but the time will come-history repeats-same was Human Vs PC-top GM's for money or because of bad conditions(PC don't get tired, Kasparov played with no rest days) lost to PCs that were not able to beat human, but since ~2015 PC for real became stronger than Human.  
  > **username_0**: @username_2 have you read the Science paper and the supplementary material? They seemed to have improved a lot on the previous critics.  
  > **username_3**: I second the Leela Chess Zero project. Specifically to answer 2 though - Yes, they have a lot better hardware and resources at their disposal but because of the good hardware they were able to test the concept of "Zero". And it turns out that the AI in the longer run, learns to play better when it learns everything from scratch rather than learning from concepts that humans have learned from playing those games.  They also created an architecture of Neural Network which is more general than just being good at 1 specific task, rather the architecture of the NN is generalized enough to learn..  
  > **username_3**: ..game of Go or chess. And possibly more.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_4 on 2019-01-14. Score: 2
  
  1. Pretty much everybody. The reason being that it is STILL slower. The evaluation function for alphazero has WAY more variables so despite more computing power, alphazero can only calculate a fraction of number of positions than stockfish. However the evaluation function for alphazero is much much better due to self-learning and also takes forever to calculate.
  
  <https://arxiv.org/pdf/1712.01815.pdf>
  
  Consider this. Google used 5000 super computers to train the AI to play before the game. Now consider what happens if you give stockfish access to 5000 super computers to think for 4 hours and if it memorized all those lines.
  
  2. The issue comes down to computing power.
  3. Not in your lifetime.
  
  It is a bit difficult to find Ghz to tflop conversions but consider this.
  
  <https://www.pugetsystems.com/labs/hpc/Intel-Core-i9-7900X-and-7980XE-Skylake-X-Linux-Linpack-Performance-1059/>
  
  28 cores on 2.6Ghz gives about 1.123Tflops. So for 88 cores running at 2.2Ghz each is around 3Tflops. So your system gives around 3Tflops.
  
  Now I can't find any information on the first generation TPUS but the second generation TPUs have 180 Tflops each.
  
  <https://en.wikipedia.org/wiki/Tensor_processing_unit>
  
  So 1 TPU = 60 of your systems. So 5000\*60\*4 = 1200000 hours = 136 years.
  
  Also should clearly show how unfair the alphazero vs stockfish match is.
  
  Edit: I made a mistake, you said 2 22 core systems. My calculations had 88 cores. So you would need to double the previous total to 272 years.
  
  > **Comments:**
  > **username_0**: Thanks @username_4. 5000 super computers, are you sure? In the Supplementary Materials, they mention that "Each MCTS was executed on a single machine with 4 first-generation TPUs". So, based on your computation this would mean roughly 960 hours instead of 1200000!  
  > **username_4**: @username_0 It clearly states in the paper they used 5000 first gen TPUs and 64 second gen TPUs. No idea about how good their first gen is but if its even half as good as their second gen it's still going to take somebodys lifetime to train on a normal machine. Also in the match the hardware alphazero was running on was literally over 100x faster than the one stockfish was on. Deepmind is trying to cover up that fact by saying alphazero evaluates way 1/1000 positions per second than stockfish, which is moot if alphazero's evaluation of the position is a million times better.  
  > **username_0**: thanks for the comment. Right but this is stated in the working paper version (Dec 2017) not in the published and refereed one (Dec 2018). I don't know if they used a new protocol or corrected a potential mistake.  
  > **username_4**: @username_0 There were no serious changes made. Also I think you are mistaken about the hardware used for training and for the actual match vs stockfish. For training they used 5000 first gen tpus for training games and 64 second gen tpus. For the match they used 4 tpus against stockfish, which is literally over 100x more powerful than what stockfish was running on in that match. Yes they used 5000 super computers to train alphazero, not an impressive task when you can just throw money as a super rich company.  
  > **username_0**: You are right! I was mistaken.  
  > **username_1**: I don't see why you consider the alphazero vs stockfish match so unfair because of what went into training alphazero. Training it is one thing, actually playing is another, and I don't see why anyone should pull their hardware punches when it comes to training. Why should DeepMind prepare an alphazero that won't be effective for 136 years?  
  > **username_5**: The second gen. TPUs are rated 45 TFLOPS each, according to the Wikipedia article you link to. The nVidia Tesla V100 (data center GPU) is capable of 125 TFLOPS for deep learning ([source](https://www.nvidia.com/en-us/data-center/tesla-v100/#specs)), so TPUs appear to be closer to graphics cards than supercomputers.  
  > **username_4**: @username_5 there are 4 cores in every tpu so every tpu has 180 tflops  
  > **username_5**: *The TPUs are then arranged into 4-chip modules with a performance of 180 TFLOPS.* The module is rated 180 TFLOPS. Each of the four TPUs in a module is rated 45 TFLOPS.  
  > **username_4**: @username_5 Really how ignorant can you be? According to you your computer has 8 CPUs on it or however many cores it has. Have you considered using a google search instead of arguing on something you literally have no idea about? https://arstechnica.com/information-technology/2017/05/google-brings-45-teraflops-tensor-flow-processors-to-its-compute-cloud/ https://medium.com/syncedreview/googles-tpu-chip-goes-public-in-challenge-to-nvidia-s-gpu-78ced56776b5. Literally took me 2 seconds to find results from a google search tells you each TPU has a performance of 180 tflops.  
  > **username_4**: @username_1 It is incredibly unfair due to several factors. With 5000 super computers and 4 hours of thinking time, any modern engine would easily crush all the other engines by analyzing book openings and memorizing key lines. Not to mention DURING the match, the hardware alphazero was running on was over 100x better than the specs stockfish was running on. At that point even a poorly programmed engine would beat stockfish.  
  > **username_5**: If your interpretation is correct (I still think the Wikipedia and Arstechnica articles say something else and at least [this guy](https://news.ycombinator.com/item?id=14363337) agrees with me), since [the first-gen card has only one processor](https://images.anandtech.com/doci/12195/google-tpu-board-2.png), it should be capable of less than 45 TFLOPS.  
  > **username_5**: And you're still calling what's comparable to a (very high-end) graphics card a "supercomputer". The [IBM Summit supercomputer](https://en.wikipedia.org/wiki/Summit_(supercomputer)) has 27,648 Tesla V100 GPUs. With each being capable of up to 125 TFLOPS for deep learning, that about 3,456,000 TFLOPS.  
  > **username_4**: @username_5 Google says their TPU has 180 tflops https://cloud.google.com/tpu/. Also I already said I wasn't able to find any info on their first gen TPU. A 2080 ti has 13.4 tflops so I guess it depends on your definition of supercomputer but just 1 of these TPUs are already much better than a top of the line gaming GPU.  
  > **username_5**: Let us [continue this discussion in chat](https://chat.stackexchange.com/rooms/88313/discussion-between-dennis-and-matthew-liu).  
  
  ---
  
  ### Answer by username_6 on 2019-01-14. Score: 1
  
  **(1)** Let's go back to 2016 when Google had just beaten the best Go player...
  
  <http://talkchess.com/forum3/viewtopic.php?f=2&t=59072&hilit=machine+learning+chess+slow+machine+learning&start=50>
  
  Milos who I believe is a PhD claimed machine learning had been tried "N" times.
  
  [![enter image description here](https://i.sstatic.net/CcXhj.png)](https://i.sstatic.net/CcXhj.png)
  
  Dr Bob, the legendary engine programmer who wrote Crafty believed NN was too slow.
  
  [![enter image description here](https://i.sstatic.net/BaZvZ.png)](https://i.sstatic.net/BaZvZ.png)
  
  **(2)**
  
  * <https://www.chessprogramming.org/NeuroChess>
  * <https://arxiv.org/abs/1509.01549>
  * Others I miss here...
  
  > Is it possible to pin down precisely what they have done better than others? Could it be simply that they use a lot more of computing ressources than before?
  
  * Google indeed had more computing resources but that's not all ...
  * Google had a superior deep learning model
  * Google adopted MCTS instead of pure alpha-beta
  * Google developed the model from self-playing, not like learning from GM games
  
  This was the network used in NeuroChess. It's a shallow network in our modern standard. It's **NOT** a deep NN.
  
  [![enter image description here](https://i.sstatic.net/MD96n.png)](https://i.sstatic.net/MD96n.png)
  
  (3). I suspect it'd take weeks if not months. Not sure.
  
  
  ---
  
  