Ingo Althofer: <trinity-a297d40e-3cf2-45f1-8d38-13a5912b636c-1454339862588@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... >_______________________________________________ >Computer-go mailing list >Computer-go@computer-go.org >http://computer-go.org/mailman/listinfo/computer-go -- Hideki Kato <mailto:hideki_ka...@ybb.ne.jp> _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
