Hello Oliver,
2015-03-16 11:58 GMT+00:00 Oliver Lewis ojfle...@yahoo.co.uk:
It's impressive that the same network learned to play seven games with
just a win/lose signal. It's also interesting that both these teams are in
different parts of Google. I assume they are aware of each other's
The important thing is that the games don't have to be played perfectly: They
just need to be significantly better than your current model, so you can tweak
the model to learn from them.
Thats an important incite. I hadnt thought of that.
Maybe could combine with some concept of forgetting,
To be honest, what I really want is for it to self-learn,...
I wonder if even the world's most powerful AI (i.e. the human brain)
could self-learn go to, say, strong dan level? I.e. Give a boy genius a
go board, the rules, and two years, but don't give him any books, hints,
or the chance to play
The human brain is not the most powerful AI, because it fails the A test.
I suspect bootstrapping is not very hard. I have recently written a Spanish
checkers program starting with no knowledge and I got it to play top-human
level checkers within a few weeks.
You can build a database of games as
I was thinking about bootstrapping possibilities, and wondered whether
it would be possible to use a shallower mimic net for positional
evaluation playouts from a specific depth on after having generated
positions with a certain branching factor that typically allows the
actual pro move to be
On Wed, Dec 31, 2014 at 9:29 PM, Hugh Perkins hughperk...@gmail.com wrote:
- finally, started to get a signal, on the kgsgo data :-) Not a very strong
signal, but a signal :-) :
test accuracy: 364/1 3.64%
Up to 35.1% test accuracy for next-move-prediction task now, still 9%
lower than
