#75 - Emergence [Special Edition] with Dr. DANIELE GRATTAROLA
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
Trends in Graph Machine Learning with Michael Bronstein - #446
Today we’re back with the final episode of AI Rewind joined by Michael Bronstein, a professor at Imperial College London and the Head of Graph Machine Learning at Twitter.
In our conversation with Michael, we touch on his thoughts about the year in Machine Learning overall, including GPT-3 and Implicit Neural Representations, but spend a major chunk of time on the sub-field of Graph Machine Learning.
We talk through the application of Graph ML across domains like physics and bioinformatics, and the tools to look out for. Finally, we discuss what Michael thinks is in store for 2021, including graph ml applied to molecule discovery and non-human communication translation.
Graph ML Research at Twitter with Michael Bronstein - #394
Today we’re excited to be joined by return guest Michael Bronstein, Head of Graph Machine Learning at Twitter. In our conversation, we discuss the evolution of the graph machine learning space, his new role at Twitter, and some of the research challenges he’s faced, including scalability and working with dynamic graphs. Michael also dives into his work on differential graph modules for graph CNNs, and the various applications of this work.
Geometric Deep Learning with Joan Bruna & Michael Bronstein - TWiML Talk #90
This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. This time around I'm joined by Joan Bruna, Assistant Professor at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU, and Michael Bronstein, associate professor at Università della Svizzera italiana (Switzerland) and Tel Aviv University. Joan and Michael join me after their tutorial on Geometric Deep Learning on Graphs and Manifolds. In our conversation we dig pretty deeply into the ideas behind geometric deep learning and how we can use it in applications like 3D vision, sensor networks, drug design, biomedicine, and recommendation systems. This is definitely a Nerd Alert show, and one that will get your multi-dimensional neurons firing. Enjoy!
