In this episode of Machine Learning Street Talk, Tim Scarfe, Yannic Kilcher and Connor Shorten interviewed Harri Valpola, CEO and Founder of Curious AI. We continued our discussion of System 1 and System 2 thinking in Deep Learning, as well as miscellaneous topics around Model-based Reinforcement Learning. Dr. Valpola describes some of the challenges of modelling industrial control processes such as water sewage filters and paper mills with the use of model-based RL. Dr. Valpola and his collaborators recently published “Regularizing Trajectory Optimization with Denoising Autoencoders” that addresses some of the concerns of planning algorithms that exploit inaccuracies in their world models!

00:00:00 Intro to Harri and Curious AI System1/System 2
00:04:50 Background on model-based RL challenges from Tim
00:06:26 Other interesting research papers on model-based RL from Connor
00:08:36 Intro to Curious AI recent NeurIPS paper on model-based RL and denoising autoencoders from Yannic
00:21:00 Main show kick off, system 1/2
00:31:50 Where does the simulator come from?
00:33:59 Evolutionary priors
00:37:17 Consciousness
00:40:37 How does one build a company like Curious AI?
00:46:42 Deep Q Networks
00:49:04 Planning and Model based RL
00:53:04 Learning good representations
00:55:55 Typical problem Curious AI might solve in industry
01:00:56 Exploration
01:08:00 Their paper - regularizing trajectory optimization with denoising
01:13:47 What is Epistemic uncertainty
01:16:44 How would Curious develop these models
01:18:00 Explainability and simulations
01:22:33 How system 2 works in humans
01:26:11 Planning
01:27:04 Advice for starting an AI company
01:31:31 Real world implementation of planning models
01:33:49 Publishing research and openness

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Regularizing Trajectory Optimization with Denoising Autoencoders: [ Ссылка ]
Pulp, Paper & Packaging: A Future Transformed through Deep Learning: [ Ссылка ]
Curious AI: [ Ссылка ]
Harri Valpola Publications: [ Ссылка ]
Some interesting papers around Model-Based RL:
GameGAN: [ Ссылка ]
Plan2Explore: [ Ссылка ]
World Models: [ Ссылка ]
MuZero: [ Ссылка ]
PlaNet: A Deep Planning Network for RL: [ Ссылка ]
Dreamer: Scalable RL using World Models: [ Ссылка ]
Model Based RL for Atari: [ Ссылка ]

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