user11749: Answering this question, @SmallChess 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.


I was looking for a reference to this quote. Did someone make this
prediction in the past?
I have another, perhaps more fundamental question related to another part of @SmallChess' 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? 

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.)


user825: (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.

Dr Bob, the legendary engine programmer who wrote Crafty believed NN was too slow.

(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.

(3). I suspect it'd take weeks if not months. Not sure.

user4393: 
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.

The issue comes down to computing power.
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.

11749: <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>
 825: <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.stack.imgur.com/CcXhj.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/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.stack.imgur.com/BaZvZ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/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.stack.imgur.com/MD96n.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/MD96n.png" alt="enter image description here"></a></p>

<p>(3). I suspect it'd take weeks if not months. Not sure.</p>
 4393: <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>
