[Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

Hideki Kato hideki_katoh at ybb.ne.jp
Mon Feb 1 12:00:15 PST 2016


Ingo Althofer: <trinity-a297d40e-3cf2-45f1-8d38-13a5912b636c-1454339862588 at 3capp-gmx-bs72>: 
>Hi Hideki,
>
>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. 
>http://www.althofer.de/robot-play/frisbee-robot-go.jpg
>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
>remark already.)

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.

Hideki

>Ingo.
>
>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>



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