<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"></head><body dir="auto"><div dir="auto">The success of Google's deep learning program in predicting protein folding is impressive. Maybe that is what he meant.</div><div dir="auto">https://www.nature.com/articles/d41586-020-03348-4</div><div dir="auto"><br></div><div dir="auto">-J.</div><div id="composer_signature" dir="auto"><div style="font-size:85%;color:#575757" dir="auto"><br></div></div><div><br></div><div align="left" dir="auto" style="font-size:100%;color:#000000"><div>-------- Original message --------</div><div>From: Steve Smith <sasmyth@swcp.com> </div><div>Date: 11/30/20 21:55 (GMT+01:00) </div><div>To: friam@redfish.com </div><div>Subject: Re: [FRIAM] New ways of understanding the world </div><div><br></div></div>
<p>Or a "model of nothing fit to everything we know: useful or
merely wrong?"<br>
</p>
<div class="moz-cite-prefix">On 11/30/20 1:41 PM, Jochen Fromm
wrote:<br>
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<div dir="auto">Chris Anderson, the editor in chief of Wired, asks
if a computer can find a theory of everything merely by learning
from data. Unfortunately most deep learning models are like a
black box which delivers good results but is hard to understand.
Would a theory of everything be a theory of nothing? It reminds
me of Russell Standish's book "theory of nothing".</div>
<a href="https://www.wired.com/2008/06/pb-theory/" class="moz-txt-link-freetext">https://www.wired.com/2008/06/pb-theory/</a>
<div dir="auto"><br>
</div>
<div dir="auto">-J.</div>
<div dir="auto"><br>
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