[Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search
3-Hirn-Verlag at gmx.de
Tue Feb 2 00:31:08 PST 2016
welcome, and thanks for your valuable hint on the Google-whitepaper.
Do/did you have/see any cross-relations between your research and
Gesendet: Dienstag, 02. Februar 2016 um 05:14 Uhr
Von: "George Dahl" <george.dahl at gmail.com>
An: computer-go <computer-go at computer-go.org>
Betreff: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search
If anything, the other great DCNN applications predate the application of these methods to Go. Deep neural nets (convnets and other types) have been successfully applied in computer vision, robotics, speech recognition, machine translation, natural language processing, and hosts of other areas. The first paragraph of the TensorFlow whitepaper (http://download.tensorflow.org/paper/whitepaper2015.pdf) even mentions dozens at Alphabet specifically.
Of course the future will hold even more exciting applications, but these techniques have been proven in many important problems long before they had success in Go and they are used by many different companies and research groups. Many example applications from the literature or at various companies used models trained on a single machine with GPUs.
On Mon, Feb 1, 2016 at 12:00 PM, Hideki Kato <hideki_katoh at ybb.ne.jp[hideki_katoh at ybb.ne.jp]> wrote:Ingo Althofer: <trinity-a297d40e-3cf2-45f1-8d38-13a5912b636c-1454339862588 at 3capp-gmx-bs72>:
>first of all congrats to the nice performance of Zen over the weekend!
>> Ingo and all,
>> Why you care AlphaGo and DCNN so much?
>I can speak only for myself. DCNNs may be not only applied to
>achieve better playing strength. One may use them to create
>playing styles, or bots for go variants.
>One of my favorites is robot frisbee go.
>Perhaps one can teach robots with DCNN to throw the disks better.
>And my expectation is: During 2016 we will see many more fantastic
>applications of DCNN, not only in Go. (Olivier had made a similar
Agree but one criticism. If such great DCNN applications all
need huge machine power like AlphaGo (upon execution, not
training), then the technology is hard to apply to many areas,
autos and robots, for examples. Are DCNN chips the only way to
reduce computational cost? I don't forecast other possibilities.
Much more economical methods should be developed anyway.
#Our brain consumes less than 100 watt.
>PS. Dietmar Wolz, my partner in space trajectory design, just told me
>that in his company they started woth deep learning...
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Hideki Kato <mailto:hideki_katoh at ybb.ne.jp[hideki_katoh at ybb.ne.jp]>
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