[FRIAM] All Your Them Are One Model To Us Learn

[Marcus Daniels]

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:friam-bounces at redfish.com] On Behalf Of Merle Lefkoff
Sent: Tuesday, June 20, 2017 9:56 AM
To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
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 <rec at elf.org<mailto:rec at elf.org>> 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

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