Reinforcement Learning Reflects Control Systems' Essence

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The intersection of reinforcement learning and large complex industrial systems highlights three essential components: objective functions, actions, and constraints. These elements serve as the key ingredients for applying reinforcement learning to real-world systems, emphasizing that both domains operate within measurable environments. This parallel underlines the critical nature of defining goals and decision-making processes while adhering to specific limitations.

After AlphaGo beat Lee Sedol, a young mechanical engineer at Google thought of another game reinforcement learning could win: energy optimization at data centers. Jim Gao convinced his bosses at the Google data center team to let him work with the DeepMind team to try. The initial pilot resulted in a 40% energy savings and led he and his co-founders to start Phaidra to turn this technology into a product.

Jim discusses the challenges of AI readiness in industrial settings and how we have to build on top of the control systems of the 70s and 80s to achieve the promise of the Fourth Industrial Revolution. He believes this new world of self-learning systems and self-improving infrastructure is a key factor in addressing global climate change.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Mentioned in this episode:

Mustafa Suleyman: Co-founder of DeepMind and Inflection AI and currently CEO of Microsoft AI, known to his friends as “Moose”
Joe Kava: Google VP of data centers who Jim sent his initial email to pitching the idea that would eventually become Phaidra
Constrained optimization: the class of problem that reinforcement learning can be applied to in real world systems
Vedavyas Panneershelvam: co-founder and CTO of Phaidra; one of the original engineers on the AlphaGo project
Katie Hoffman: co-founder, President and COO of Phaidra
Demis Hassabis: CEO of DeepMind