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
