Table 3 suggests that there are general information processing features that 
translate across domains.   Table 4 suggests that becoming an expert in many 
things doesn’t make you (much) of a worse expert in any one thing.   Don’t pull 
your kid out of liberal arts college just yet?   And weirdly different domains 
too.   Perhaps there is some natural modularity that comes out of the 
contrasting training sets, even without the attentional mechanism?

From: Friam [mailto:[email protected]] On Behalf Of Merle Lefkoff
Sent: Tuesday, June 20, 2017 9:56 AM
To: The Friday Morning Applied Complexity Coffee Group <[email protected]>
Subject: Re: [FRIAM] All Your Them Are One Model To Us Learn

Wow, Roger.  I don't get all this, but tell me---does this change everything 
for modelers?

On Tue, Jun 20, 2017 at 9:39 AM, Roger Critchlow 
<[email protected]<mailto:[email protected]>> wrote:
Apropos my babbling about all patterns being patterns and all the mechanisms 
that recognize patterns being an incomprehensible jumble of mechanisms, Google 
shares One Model to Learn Them All, https://arxiv.org/abs/1706.05137, in which 
it turns out that throwing all the architectural elements from all kinds of 
deep learning into a single model ends up working pretty well.  Adding the 
recursive elements used to parse the linear sequences of elements in languages 
never hurts and mostly improves the performance of image classifiers and object 
recognizers.  Go figure.

Found from 
https://venturebeat.com/2017/06/19/google-advances-ai-with-one-model-to-learn-them-all/amp/

-- rec --


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--
Merle Lefkoff, Ph.D.
President, Center for Emergent Diplomacy
emergentdiplomacy.org<http://emergentdiplomacy.org>
Santa Fe, New Mexico, USA
Visiting Professor in Integrative Peacebuilding
Saint Paul University
Ottawa, Ontario, Canada

[email protected]<mailto:[email protected]>
mobile:  (303) 859-5609
skype:  merle.lelfkoff2
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