On 20.10.2017 21:12, uurtamo . wrote:
do something like really careful experimental design across many dimensions
simultaneously (node weights) and several million experiments -- each of
which will require hundreds if not tens of thousands of games to find the
result of the change. Worse, there
I agree. Even on 19x19 you can use smaller searches. 400 iterations MCTS is
probably already a lot stronger than the raw network, especially if you are
expanding every node (very different from a normal program at 400
playouts!). Some tuning of these mini searches is important. Surely you
don't
> You can also start with 9x9 go. That way games are shorter, and you probably
> don't need 1600 network evaluations per move to do well.
Bonus points if you can have it play on goquest where many
of us can enjoy watching its progress, or even challenge it...
regards,
-John
The paper describes 20 and 40 block networks, but the section on comparison
says AlphaGo Zero uses 20 blocks. I think your protobuf describes a 40 block
network. That's a factor of two
If you only want pro strength rather than superhuman, you can train for half
their time.
Your time looks
Training of AlphaGo Zero has been done on thousands of TPUs, according to
this source:
https://www.reddit.com/r/baduk/comments/777ym4/alphago_zero_learning_from_scratch_deepmind/dokj1uz/?context=3
Maybe that should explain the difference in orders of magnitude that you
noticed?
On Fri, Oct 20,
On Fri, Oct 20, 2017, 21:48 Petr Baudis wrote:
> Few open questions I currently have, comments welcome:
>
> - there is no input representing the number of captures; is this
> information somehow implicit or can the learned winrate predictor
> never truly approximate the
I suggest scaling down the problem until some experience is gained.
You don't need the full-fledge 40-block network to get started. You can
probably get away with using only 20 blocks and maybe 128 features (from
256). That should save you about a factor of 8, plus you can use larger
This sounds like a nice idea that is a misguided project.
Keep in mind the number of weights to change, and the fact that "one factor
at a time" testing will tell you nearly nothing about the overall dynamics
in a system of tens of thousands of dimensions. So you're going to need to
do something
I reconstructed the full AlphaGo Zero network in Caffe:
https://sjeng.org/dl/zero.prototxt
I did some performance measurements, with what should be
state-of-the-art on consumer hardware:
GTX 1080 Ti
NVIDIA-Caffe + CUDA 9 + cuDNN 7
batch size = 8
Memory use is about ~2G. (It's much more for
On 20.10.2017 16:44, Hendrik Baier wrote:
Where is the respect and the appreciation for other people's
groundbreaking work without immediately having to make the discussion
about your own research, or otherwise derailing it into the irrelevant
or fantastical?
Instead of joining your
On 19-10-17 13:00, Aja Huang via Computer-go wrote:
> Hi Hiroshi,
>
> I think these are good questions. You can ask them at
> https://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/
It seems the question was indeed asked but not answered:
On 20.10.2017 15:07, adrian.b.rob...@gmail.com wrote:
1) Where is the semantic translation of the neural net to human theory
knowledge?
As far as (1), if we could do it, it would mean we could relate the
structures embedded in the net's weight patterns to some other domain --
The other domain
When I did something like this for Spanish checkers (training a neural
network to be the evaluation function in an alpha-beta search, without any
human knowledge), I solved the problem of adding game variety by using UCT
for the opening moves. That means that I kept a tree structure with the
On 19-10-17 13:23, Álvaro Begué wrote:
> Summing it all up, I get 22,837,864 parameters for the 20-block network
> and 46,461,544 parameters for the 40-block network.
>
> Does this seem correct?
My Caffe model file is 185887898 bytes / 32-bit floats = 46 471 974
So yes, that seems pretty close.
Where is the respect and the appreciation for other people's
groundbreaking work without immediately having to make the discussion
about your own research, or otherwise derailing it into the irrelevant
or fantastical?
Congratulations again to the AlphaGo team! Excellent work well
described, a
I tried to reimplement the system - in a simplified way, trying to
find the minimum that learns to play 5x5 in a few thousands of
self-plays. Turns out there are several components which are important
to avoid some obvious attractors (like the network predicting black
loses on every move from
Robert Jasiek writes:
> So there is a superstrong neural net.
>
> 1) Where is the semantic translation of the neural net to human theory
> knowledge?
>
> 2) Where is the analysis of the neural net's errors in decision-making?
>
> 3) Where is the world-wide discussion preventing
On Fri, Oct 20, 2017 at 12:06 AM, Robert Jasiek wrote:
>
> 3) Where is the world-wide discussion preventing a combination of AI and
> (nano-)robots, which self-replicate or permanently ensure energy access,
> from causing extinction of mankind?
>
You will find it if you Google
On 20.10.2017 09:38, Xavier Combelle wrote:
What is currently named nanorobot is simply hand assembled molecules
which have mechanical properties and need huge
framework to be able simply move.
Sure. But we must not wait until such a thing exists.
--
robert jasiek
You seems to lack of knowing what is really a nano robot in current term.
They are very far to have the possibility to self replicate them self
and far more being able to dissolve the planet by doing that.
What is currently named nanorobot is simply hand assembled molecules
which have mechanical
On 20.10.2017 07:10, Petri Pitkanen wrote:
>> 3) Where is the world-wide discussion preventing a combination of AI
>> and (nano-)robots, which self-replicate or permanently ensure energy
>> access, from causing extinction of mankind?
3) Would it be a bad thing? All thing considered, not just
1) There is no such thing and I do doubt if it ever will exist. Even humans
fail elaborate why they know certain things
2) If we are talking about new one. Very few people seen it playing so I
guess we lack the data. For the old we know it made errors, dunno if
analysis points why. Neural nets
22 matches
Mail list logo