31–37 of 37 posts
Would have like to hear at least one concrete exemple of startup actually doing that. Seems a bit theoretical at the moment, as big companies doesn't need to do that thanks to existing datasets, and I've never heard any startups using dozens (hundreds?) of contractors for this kind of job.
See http://blog.echen.me/2012/04/25/making-the-most-of-mechanica... for some examples.
I think he's really missing the point about the importance of self-play in Alpha Go. Human play provided a seed for training the system, but the thing that made it work was the fact that the computer could play an unlimited number of games against itself; the fact that Go is a game with clear rules made it possible to label a huge number of board positions without any human-derived training data at all. The human-der…
I think he's really missing the point about the importance of self-play in Alpha Go. Human play provided a seed for training the system, but the thing that made it work was the fact that the computer could play an unlimited number of games against itself; the fact that Go is a game with clear rules made it possible to label a huge number of board positions without any human-derived training data at all. The human-der…
Do you have first hand sources about this? I have now been hearing all and everything about what makes Alpha Go so great... First it was the hardware, then it was the use of the Monte-Carlo tree search with NN... And even more just 1 day ago https://news.ycombinator.com/item?id=11382954
http://www.theverge.com/2016/3/10/11192774/demis-hassabis-in...
(This is why I take exception to the claim in this blog post that the supervised training data was critical to success...)
Earlier quoted context omitted.
Do you have first hand sources about this? I have now been hearing all and everything about what makes Alpha Go so great... First it was the hardware, then it was the use of the Monte-Carlo tree search with NN... And even more just 1 day ago https://news.ycombinator.com/item?id=11382954
"the AlphaGo algorithm, this is something we’re going to try in the next few months — we think we could get rid of the supervised learning starting point and just do it completely from self-play, literally starting from nothing." http://www.theverge.com/2016/3/10/11192774/demis-hassabis-in... (This is why I take exception to the claim in this blog post that the supervised training data was critical to success...)
Earlier quoted context omitted.
Do you have first hand sources about this? I have now been hearing all and everything about what makes Alpha Go so great... First it was the hardware, then it was the use of the Monte-Carlo tree search with NN... And even more just 1 day ago https://news.ycombinator.com/item?id=11382954
"the AlphaGo algorithm, this is something we’re going to try in the next few months — we think we could get rid of the supervised learning starting point and just do it completely from self-play, literally starting from nothing." http://www.theverge.com/2016/3/10/11192774/demis-hassabis-in... (This is why I take exception to the claim in this blog post that the supervised training data was critical to success...)
I agree that it's something big. Training Alpha Go on itself means something bigger than "just" optimizing a statistical model on human data. I think recognition of logic elements to strategy planning are parts of what will make ML really close to IA. (With memory, and cleverness to learn) And are the next big steps.