Machine Learning Street Talk (MLST)

An emergent behavior or emergent property can appear when a number of simple entities operate in an environment, forming more complex behaviours as a collective. If emergence happens over disparate size scales, then the reason is usually a causal relation across different scales. Weak emergence describes new properties arising in systems as a result of the low-level interactions, these might be interactions between components of the system or the components and their environment. 

In our epic introduction we focus a lot on the concept of self-organisation, complex systems, cellular automata and strong vs weak emergence. In the main show we discuss this more in detail with Dr. Daniele Grattarola and cover his recent NeurIPS paper on learning graph cellular automata. 

YT version: https://youtu.be/MDt2e8XtUcA

Patreon: https://www.patreon.com/mlst

Discord: https://discord.gg/ESrGqhf5CB

Featuring;

Dr. Daniele Grattarola

Dr. Tim Scarfe

Dr. Keith Duggar

Prof. David Chalmers

Prof. Ken Stanley

Prof. Julian Togelius

Dr. Joscha Bach

David Ha

Dr. Pei Wang

[00:00:00] Special Edition Intro: Emergence and Cellular Automata

[00:49:02] Intro to Daniele and CAs

[00:57:23] Numerical analysis link with CA (PDEs)

[00:59:50] The representational dichotomy of discrete and continuous at different scales

[01:05:21] Universal computation in CAs

[01:10:27] Computational irreducibility 

[01:16:33] Is the universe discrete?

[01:20:49] Emergence but with the same computational principle

[01:23:10] How do you formalise the emergent phenomenon 

[01:25:44] Growing cellular automata

[01:33:53] Openeded and unbounded computation is required for this kind of behaviour

[01:37:31] Graph cellula automata

[01:43:40] Connection to protein folding

[01:46:24] Are CAs the best tool for the job?

[01:49:37] Where to go to find more information