Access, for a fee, to the original Jan, 2016 Nature article on AlpahGo
is at
http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html.
The freely available abstract says it uses deep neural networks ('value
networks' and 'policy networks'), tree search and Monte Carlo
algorithms. Figures and tables with more information are also freely
available from
http://www.nature.com/nature/journal/v529/n7587/fig_tab/nature16961_ft.html
Robert C
On 3/13/16 8:53 PM, Steve Smith wrote:
Me, I'm still stuck in the 80's... most of what I know about GO
programs involves trying to solve them using cellular automata systems
based on the promise of hardware implementations and other esoteric
ways of doing CA computation... Tomasso Toffolli's custom CA
hardware was one promising thing that I think eventually fizzled as
was our own Jim Crutchfield's analog "video feedback" CA computing
concepts...
My own favorite which I went on to do some exploratory work in was the
"memoisation" work of Bill Gosper which involves generating hash
tables at each scale (say 3x3, 6x6, 12x12, 24x24) cell arrays such
that if "redundant" patterns occurred at any scale they could be
"looked up" instead of computed. In a 3x3 (9 cell) array, there are
naturally only 512 (2^9) hash indices so the computation at that level
is manageable by memoisation... while a 6x6 is 2^36 or roughly 64M
entries, not quite so tractable/trivial if the distribution of
possible configurations of binary CA were uniform... which
interesting GO configurations naturally are NOT. A slight
modification to this is that a binary CA is not sufficient since the
states of each cell can be White/Black/Empty... so the math changes to
4^9 and 4^26,etc...
Similar attempts were made for checkers and chess which as I remember,
the state space for Checkers is much larger than for Chess
(surprising?) but GO... much higher (larger board!) and the depth
(number of relevant moves ahead) also much higher!
I look forward to hearing what the current state of computer GO play
might look like as well!
- Steve
There were stories during the expert systems episode in the 80's that
some experts when debriefed in an attempt to identify their rules
went on to lose faith in their own expertise and to resign from the
field. Other anecdotes talked about how some experts weren't capable
of expressing their expertise - such knowledge, skills & experience
was referred to as 'compiled knowledge', accessible but not
expressible, much like Artificial Neural Networks are. Work
<http://www.sciencedirect.com/science/article/pii/0950705196819204>
to address this problem has been underway since the 90's. Perhaps
others here can provide an update?
Robert C
On 3/13/16 8:45 AM, Marcus Daniels wrote:
I think a deep neural network trained from self play has a subjective, and even
inscrutable inner representation. Imagine such techniques were applied to
public policy decisions or medical diagnosis. Without a linguistic component
that co-evolved to describe a taken action, one could be left with robot
savants that outperformed humans on crucial tasks and no one, including the
robot, would have any idea why.
Sent from my iPhone
On Mar 13, 2016, at 8:01 AM, Roger Critchlow<[email protected]> wrote:
I've been watching parts of the match between Lee Sedol and Alpha Go on the
youtube deepmind channel. It's quite good, they start off with a discussion of
the previous game, give running commentary during the game, and audibly gasp
when the progress of the game shocks them. The post match press conferences
are not to be missed, either. It's a completely trump free zone.
But you're looking at a full day's work for each game, 6 hours and 17 minutes
of video from last night's game which Lee Sedol won. I was too tired to stay
up and watch so I tuned into youtube this morning and watched the endgame.
Apparently I forwarded past the key move, #78, which a Chinese journalist, quoting a
Chinese commentator, called "a God's move". Lee Sedol replied that it was the
only move he had at the time, that he had thought it would be easier to make some profit,
but it was quite difficult.
So the same play is described as both creative genius and inevitable in the
space of a few sentences. Glad to know that some things will never change.
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Santa Fe, NM
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281-989-6272 (cell)
Member Design Corps of Santa Fe
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