<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"></head><body dir="auto"><div dir="auto">Dennis Overbye recently had a similar NY Times article. It claims that "in 10 years, machine-learning will be as essential to doing physics as knowing math". What do you think? </div><div dir="auto">https://www.nytimes.com/2020/11/23/science/artificial-intelligence-ai-physics-theory.html</div><div dir="auto"><br></div><div dir="auto">-J.</div><div><br></div><div align="left" dir="auto" style="font-size:100%;color:#000000"><div>-------- Original message --------</div><div>From: Jochen Fromm <jofr@cas-group.net> </div><div>Date: 11/30/20 21:42 (GMT+01:00) </div><div>To: The Friday Morning Applied Complexity Coffee Group <friam@redfish.com> </div><div>Subject: [FRIAM] New ways of understanding the world </div><div><br></div></div><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>https://www.wired.com/2008/06/pb-theory/<div dir="auto"><br></div><div dir="auto">-J.</div><div dir="auto"><br></div></body></html>