I love Go and the mysterious exponential expansion of humans and their
creations -- while in 3 hours I just dashed off a methanol missive, my
online game for 17 years now...

5 sweeteners harm human cells in vitro -- Armorel Diane van Eyk 2014.10.15
-- check for similar harm from methanol -- made by ADH1 enzyme in 20 human
cells into formaldehyde, the WC Monte paradigm: Rich Murray 2016.03.14
http://rmforall.blogspot.com/2016/03/5-sweeteners-harm-human-cells-in-vitro.html


Drug Chem Toxicol. 2015;38(3):318-27.
doi: 10.3109/01480545.2014.966381.
Epub 2014 Oct 15.
The effect of five artificial sweeteners on Caco-2, HT-29 and HEK-293 cells.
van Eyk AD 1.

1 Division of Pharmacology, Department of Pharmacy and Pharmacology,
University of the Witwatersrand , Parktown , South Africa.

http://www.tandfonline.com/doi/abs/10.3109/01480545.2014.966381

Taylor & Francis Group

Armorel Diane van Eyk ...


!! Rich Murray



On Mon, Mar 14, 2016 at 5:16 PM, Roger Critchlow <[email protected]> wrote:

> You can learn about the science of ridiculously complicated neural
> networks through a free Udacity course from Google,
>
>   https://www.udacity.com/course/deep-learning--ud730
>
> it won't explain alphago's networks but will explain the general
> architecture (watts towers?) and the google Tensor Flow toolkit.
>
> I ended up skipping the exercises because of tool problems, but the video
> lectures give a pretty good overview of how to build inscrutable computer
> programs for several different classes of inscrutability.  The discussion
> of how to feed images into deep learning networks probably covers a lot of
> the techniques used in alphago.
>
> -- rec --
>
>
> On Mon, Mar 14, 2016 at 6:52 PM, Russ Abbott <[email protected]>
> wrote:
>
>> You can get most articles through Sci-Hub
>> <https://plus.google.com/u/0/+RussAbbott1/posts/5YGik2SsyDV>. The Nature
>> piece is available here
>> <http://sci-hub.io/http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html>.
>> Amazing!
>>
>> -- Russ
>>
>> On Mon, Mar 14, 2016 at 3:38 PM Robert J. Cordingley <
>> [email protected]> wrote:
>>
>>> 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]> <[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.
>>>
>>> -- rec --
>>>
>>>
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>>> --
>>> Cirrillian
>>> Web Design & Development
>>> Santa Fe, NMhttp://cirrillian.com281-989-6272 (cell)
>>> Member Design Corps of Santa Fe
>>>
>>>
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>>> --
>>> Cirrillian
>>> Web Design & Development
>>> Santa Fe, NMhttp://cirrillian.com281-989-6272 (cell)
>>> Member Design Corps of Santa Fe
>>>
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