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<p class="MsoNormal">The crucial missing piece there is the ability to run experiments. If all that ever happens is that the machine hears “There was massive election fraud!” then it will likely conclude that there was.<o:p></o:p></p>
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<p class="MsoNormal"><b>From:</b> Friam <friam-bounces@redfish.com> <b>On Behalf Of
</b>Jochen Fromm<br>
<b>Sent:</b> Monday, November 30, 2020 12:41 PM<br>
<b>To:</b> The Friday Morning Applied Complexity Coffee Group <friam@redfish.com><br>
<b>Subject:</b> [FRIAM] New ways of understanding the world<o:p></o:p></p>
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<p class="MsoNormal">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".<o:p></o:p></p>
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<p class="MsoNormal"><a href="https://www.wired.com/2008/06/pb-theory/">https://www.wired.com/2008/06/pb-theory/</a><o:p></o:p></p>
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<p class="MsoNormal">-J.<o:p></o:p></p>
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