"> This isn't a new concept. It's just being applied to robots in a simulated world to then be applied in the real world" Using simulation evolutionary training is not a new concept either. Doing it for a more basic robot (the classic two-wheels-and-some-ultrasonic-sensors swarm demobot) was a first-year task a decade ago at uni.
Like most of the 'Deep Learning' field, this is all stuff that's very well trod fro ma research standpoint, but instead of a revolution in understanding to make it viable commercially, it's instead having monumental computational power thrown at it. Like computing in general, AI of today isn't 'smarter' than research AI of a decade ago, it's just very stupid very fast.
Hmm, I'm not so sure that's fair to say. One could make a similar argument that the progress in electronics throughout much of the 20th century was stuff that was all well-trod from a research standpoint, as it relied on 19th century physics; in the 20th century they just had the technology to throw at the theory. By the time they had to worry about quantum effects, well, the revolutionary theoretical work there had been completed decades beforehand, as it was done in the early 20th century. Or one could say that the Manhattan Project was not revolutionary because it was simply the application of the theories of nuclear reactions that had already been worked out.
The basic algorithms of deep learning aren't new, that's true. But the engineering of building these networks efficiently and of effectively interconnecting various networks to create a powerful system of networks seems to be happening now. For instance, AlphaGo played Lee Sidol in a go match in 2016 and played Ke Jie in another match in 2017. Go experts agree that the 2017 version was a lot stronger than the 2016 version. But the 2016 version was running on much more powerful hardware than the 2017 version. Yes, there had been more time for training, but someone from Deep Mind, I believe it was David Silver, said that the big difference that let them decrease the hardware necessary to run AlphaGo was the tuning of their networks.
Coming out with a safe, working self-driving vehicle seems to take a lot more than having enough data and the processing power to crunch it by using only things that were known 30 years ago. It seems to take quite a bit of engineering and additional know-how, just like it did to actually build a working nuclear bomb. There's a lot of problem-solving left to be done. After all, the Nazis had all the same theories as well as experts in them, yet they didn't have a working nuclear bomb by the end of the war.
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Manch - Tuesday, October 10, 2017 - link
Wish work would have let me attend this :|thesenate - Tuesday, October 10, 2017 - link
Is the GPU in Drive Pegasus based on GV100? Or is it based on some unannounced variant of Volta, like GV102?Ryan Smith - Tuesday, October 10, 2017 - link
Neither. Post-Volta.edzieba - Tuesday, October 10, 2017 - link
"> This isn't a new concept. It's just being applied to robots in a simulated world to then be applied in the real world"Using simulation evolutionary training is not a new concept either. Doing it for a more basic robot (the classic two-wheels-and-some-ultrasonic-sensors swarm demobot) was a first-year task a decade ago at uni.
Like most of the 'Deep Learning' field, this is all stuff that's very well trod fro ma research standpoint, but instead of a revolution in understanding to make it viable commercially, it's instead having monumental computational power thrown at it. Like computing in general, AI of today isn't 'smarter' than research AI of a decade ago, it's just very stupid very fast.
Yojimbo - Tuesday, October 10, 2017 - link
Hmm, I'm not so sure that's fair to say. One could make a similar argument that the progress in electronics throughout much of the 20th century was stuff that was all well-trod from a research standpoint, as it relied on 19th century physics; in the 20th century they just had the technology to throw at the theory. By the time they had to worry about quantum effects, well, the revolutionary theoretical work there had been completed decades beforehand, as it was done in the early 20th century. Or one could say that the Manhattan Project was not revolutionary because it was simply the application of the theories of nuclear reactions that had already been worked out.The basic algorithms of deep learning aren't new, that's true. But the engineering of building these networks efficiently and of effectively interconnecting various networks to create a powerful system of networks seems to be happening now. For instance, AlphaGo played Lee Sidol in a go match in 2016 and played Ke Jie in another match in 2017. Go experts agree that the 2017 version was a lot stronger than the 2016 version. But the 2016 version was running on much more powerful hardware than the 2017 version. Yes, there had been more time for training, but someone from Deep Mind, I believe it was David Silver, said that the big difference that let them decrease the hardware necessary to run AlphaGo was the tuning of their networks.
Coming out with a safe, working self-driving vehicle seems to take a lot more than having enough data and the processing power to crunch it by using only things that were known 30 years ago. It seems to take quite a bit of engineering and additional know-how, just like it did to actually build a working nuclear bomb. There's a lot of problem-solving left to be done. After all, the Nazis had all the same theories as well as experts in them, yet they didn't have a working nuclear bomb by the end of the war.
liarajames - Monday, October 16, 2017 - link
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Produk unggulan http://resellerpropolis.com/ yang sudah terbukti ampuh mengatasi berbagai jenis penyakit dan aman dikonsumsi tanpa efek samping dan tanpa bahan pengawet http://resellerpropolis.com/khasiat-qnc-jelly-gama... sudah memiliki sertifikasi halal MUI dan BPOM RI jadi sudah terjamin keaslian dan khasiat yang terkandung dalam QnC Jelly Gamat.obatkistaqnc - Thursday, October 18, 2018 - link
Pengobatan alami untuk kista telah terbukti dengan herbal QNC Jelly Gamat yang khasiatnya sudah banyak di buktikan langsung dengan kesaksian http://obatkistaqnc.com dari konsumen yang sudah mencobanya. Untuk melihat kesaksiannya dapat di cek si sini http://obatkistaqnc.blogspot.com/ dan khasiatnya juga sudah banyak sudah membuktikannya.