Hierarchical and Continual RL with Doina Precup - #567

Hierarchical and Continual RL with Doina Precup - #567

Today we’re joined by Doina Precup, a research team lead at DeepMind Montreal, and a professor at McGill University. In our conversation with Doina, we discuss her recent research interests, including her work in hierarchical reinforcement learning, with the goal being agents learning abstract representations, especially over time. We also explore her work on reward specification for RL agents, where she hypothesizes that a reward signal in a complex environment could lead an agent to develop attributes of intuitive intelligence. We also dig into quite a few of her papers, including On the Expressivity of Markov Reward, which won a NeruIPS 2021 outstanding paper award. Finally, we discuss the analogy between hierarchical RL and CNNs, her work in continual RL, and her thoughts on the evolution of RL in the recent past and present, and the biggest challenges facing the field going forward. The complete show notes for this episode can be found at twimlai.com/go/567

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AI Engineering Pitfalls with Chip Huyen - #715

AI Engineering Pitfalls with Chip Huyen - #715

Today, we're joined by Chip Huyen, independent researcher and writer to discuss her new book, “AI Engineering.” We dig into the definition of AI engineering, its key differences from traditional machi...

21 Jan 202557min

Evolving MLOps Platforms for Generative AI and Agents with Abhijit Bose - #714

Evolving MLOps Platforms for Generative AI and Agents with Abhijit Bose - #714

Today, we're joined by Abhijit Bose, head of enterprise AI and ML platforms at Capital One to discuss the evolution of the company’s approach and insights on Generative AI and platform best practices....

13 Jan 202558min

Why Agents Are Stupid & What We Can Do About It with Dan Jeffries - #713

Why Agents Are Stupid & What We Can Do About It with Dan Jeffries - #713

Today, we're joined by Dan Jeffries, founder and CEO of Kentauros AI to discuss the challenges currently faced by those developing advanced AI agents. We dig into how Dan defines agents and distinguis...

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Automated Reasoning to Prevent LLM Hallucination with Byron Cook - #712

Automated Reasoning to Prevent LLM Hallucination with Byron Cook - #712

Today, we're joined by Byron Cook, VP and distinguished scientist in the Automated Reasoning Group at AWS to dig into the underlying technology behind the newly announced Automated Reasoning Checks fe...

9 Dec 202456min

AI at the Edge: Qualcomm AI Research at NeurIPS 2024 with Arash Behboodi - #711

AI at the Edge: Qualcomm AI Research at NeurIPS 2024 with Arash Behboodi - #711

Today, we're joined by Arash Behboodi, director of engineering at Qualcomm AI Research to discuss the papers and workshops Qualcomm will be presenting at this year’s NeurIPS conference. We dig into th...

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AI for Network Management with Shirley Wu - #710

AI for Network Management with Shirley Wu - #710

Today, we're joined by Shirley Wu, senior director of software engineering at Juniper Networks to discuss how machine learning and artificial intelligence are transforming network management. We explo...

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Why Your RAG System Is Broken, and How to Fix It with Jason Liu - #709

Why Your RAG System Is Broken, and How to Fix It with Jason Liu - #709

Today, we're joined by Jason Liu, freelance AI consultant, advisor, and creator of the Instructor library to discuss all things retrieval-augmented generation (RAG). We dig into the tactical and strat...

11 Nov 202458min

An Agentic Mixture of Experts for DevOps with Sunil Mallya - #708

An Agentic Mixture of Experts for DevOps with Sunil Mallya - #708

Today we're joined by Sunil Mallya, CTO and co-founder of Flip AI. We discuss Flip’s incident debugging system for DevOps, which was built using a custom mixture of experts (MoE) large language model ...

4 Nov 20241h 15min

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