[FRIAM] New ways of understanding the world

[thompnickson2 at gmail.com]

All, 

 

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

 <mailto:ThompNickSon2 at gmail.com> ThompNickSon2 at gmail.com

 <https://wordpress.clarku.edu/nthompson/> https://wordpress.clarku.edu/nthompson/

 

 

From: Friam <friam-bounces at redfish.com> On Behalf Of Jochen Fromm
Sent: Monday, November 30, 2020 3:15 PM
To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
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.

https://www.nature.com/articles/d41586-020-03348-4

 

-J.

 

 

-------- Original message --------

From: Steve Smith <sasmyth at swcp.com <mailto:sasmyth at swcp.com> > 

Date: 11/30/20 21:55 (GMT+01:00) 

To: friam at redfish.com <mailto:friam at redfish.com>  

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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