> Even if you have a lot of hardware, it's *hard* to
make it add value, as anyone who tried to run MCTS on a cluster could
testify - it's not just a matter of throwing it at the problem, and the
challenges aren't just engineering-related either.
For those of us who don't know, could you talk a lit
> I'd propose these as the major technical points to consider when
> bringing a Go program (or a new one) to an Alpha-Go analog:
> ...
> * Are RL Policy Networks essential? ...
Figure 4b was really interesting (see also Extended Tables 7 and 9): any
2 of their 3 components, on a single machin
I think the first goal was and is to find a pathway that clearly works to
reach into the upper echelons of human strength, even if the first version
used a huge amount of resources. Once found, then the approach can be
explored for efficiencies from both directions, top down (take this away
and see
On Thu, Jan 28, 2016 at 10:29:29AM -0600, Jim O'Flaherty wrote:
> I think the first goal was and is to find a pathway that clearly works to
> reach into the upper echelons of human strength, even if the first version
> used a huge amount of resources. Once found, then the approach can be
> explored
Hi!
Since I didn't say that yet, congratulations to DeepMind!
(I guess I'm a bit disappointed that no really new ML models had to be
invented for this though, I was wondering e.g. about capsule networks or
training simple iterative evaluation subroutines (for semeai etc.) by
NTM-based appro
