[FRIAM] New ways of understanding the world

[Marcus Daniels]

I spent a fair amount of my youth disassembling boot procedures of various copy protection schemes.   There one is given a list of numbers that bootstrap an operating system and an application.  A small portion of that list of numbers is relevant to preventing that list of numbers from being copied from one media to another.   It wasn’t really necessary to have a theory of the application, generally, to understand how to change the numbers to make that list copyable.   If one had no theory of a computer instruction set or of an operating system, but was just given a disk and the goal of copying it to get the computer to do the same thing when the copied disk was put in to the disk drive instead of the original disk, it is possible to learn everything that is needed to learn which numbers to change.   No oscilloscope needed, no theory of solid state physics, etc.  Ok, maybe one reference manual.   Biology is the same, but without a concise reference manual.

From: Friam <friam-bounces at redfish.com> On Behalf Of thompnickson2 at gmail.com
Sent: Monday, November 30, 2020 1:25 PM
To: 'The Friday Morning Applied Complexity Coffee Group' <friam at redfish.com>
Subject: Re: [FRIAM] New ways of understanding the world

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
ThompNickSon2 at gmail.com<mailto:ThompNickSon2 at gmail.com>
https://wordpress.clarku.edu/nthompson/



From: Friam <friam-bounces at redfish.com<mailto: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<mailto: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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