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I feel like this relates to a discussion held during Nerd Hour at the end of last Friday’s vfriam. I was arguing that given, say, a string of numbers, and no information external to that string, that no AI could detect “order” unless it already possessed a theory of what order is. I found the discussion distressing because I thought the point was trivial but all the smart people in the conversation were arguing against me.
n
Nicholas Thompson
Emeritus Professor of Ethology and Psychology
Clark University
https://wordpress.clarku.edu/nthompson/
From: Friam <[hidden email]> On Behalf Of Jochen Fromm
Sent: Monday, November 30, 2020 3:15 PM
To: The Friday Morning Applied Complexity Coffee Group <[hidden email]>
Subject: Re: [FRIAM] New ways of understanding the world
The success of Google's deep learning program in predicting protein folding is impressive. Maybe that is what he meant.
-J.
-------- Original message --------
From: Steve Smith <[hidden email]>
Date: 11/30/20 21:55 (GMT+01:00)
To: [hidden email]
Subject: Re: [FRIAM] New ways of understanding the world
Or a "model of nothing fit to everything we know: useful or merely wrong?"
On 11/30/20 1:41 PM, Jochen Fromm wrote:
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".
https://www.wired.com/2008/06/pb-theory/
-J.
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