Genius Edward Witten, could he help to intensify artificial intelligence research?

[ThoughtCurvature]

Part A - Artificial Intelligence and human-kind, in 2 sentences.

Artificial Intelligence is unavoidably exceeding humans in cognitive tasks, and some projections observe human level brain power in artificial machines/software by at least 2020 (See Wikipedia: Exascale computing)

Artificial Intelligence is already solving many of human kind’s problems (eg-1: Ai beat human doctors at predicting heart attacks); artificial intelligence is helping to unravel the mysteries of the cosmos (as scientists aim to do) – (eg-2: Ai can already recreate Nobel prize winning level work), and so artificial general Intelligence likely need be human kind's last universally important invention.





Part B - Crucial difference between Edward and Tegmark


Edward Witten is quite the human being/physicist.

Max Tegmark is also, quite the human/cosmologist.

Both have phd physics degrees.


But the urgent difference?

(1) Max presents consciousness as a mathematical problem... Although Max Tegmark is not an artificial intelligence pioneer nor is officially trained as an artificial intelligence researcher, Max is already contributing important work, helping to organize the theory of deep learning (A hot paradigm in Artificial Intelligence now). 

A sample of Max's AI work: (See: "Why does deep and cheap learning work so well?")

Max describing consciousness as a mathematical problem: (See: "Consciousness is a mathematical pattern: Max Tegmark at TEDxCambridge 2014")


(2) Edward Witten believes we will never truly understand consciousness...  (See "Edward Witten on consciousness")





Part C - How components approached by Edward's genius applies in AI today

Edward Witten's work concerns some deep stuff on manifolds. (Sample: "Monopoles and Four-Manifolds")

In artificial intelligence, models are observed to be doing some form of manifold representation, especially in the euclidean regime. (And are already demonstrated to be strong candidates for 'disentangling problems' of which many problem spaces occur)

Here are some unavoidable examples where manifolds apply in machine learning:

1. Yoshua Bengio et al, “Deep Learning Book”, 2016 
   
2. Kihyuk Sohn, Yuting Zhang et al, “Learning to Disentangle Factors of Variation with Manifold Interaction”, 2014 
   
3. Yoshua Bengio et al, “Disentangling Factors of Variation via Generative Entangling”, 2012 
   
4. Christopher Olah, "Neural Networks, Manifolds, and Topology", 2014 
   

Some months ago, I had personally contacted Witten, advising him that his genius could apply in AI. 




QUESTION:

Why does Edward Witten allow his belief (as shown in the video above) to block himself from possibly considerably contributing to artificial intelligence, one of human-kind's most profound tools, even despite contrasting evidence that manifolds apply in machine learning?

[Edwardo Piet]

Get banned already, PGJ.