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Causal Inference with LLMs and Reinforcement Learning Agents?

Do LLMs have a world model?

Can they reason causally?

What's the connection between LLMs, reinforcement learning, and causality?

Andrew Lampinen, PhD (Google DeepMind) shares the insights from his research on LLMs, reinforcement learning, causal inference and generalizable agents.

We also discuss the nature of intelligence, rationality and how they play with evolutionary fitness.

Join us in the journey!

Recorded on Dec 1, 2023 in London, UK.

About The Guest
Andrew Lampinen, PhD is a Senior Research Scientist at Google DeepMind. He holds a PhD in PhD in Cognitive Psychology from Stanford University. He's interested in cognitive flexibility and generalization, and how these abilities are enabled by factors like language, memory, and embodiment.

Connect with Andrew:
- Andrew on Twitter/X
- Andrew's web page

About The Host
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4).

Connect with Alex:
- Alex on the Internet

Links
Papers
- Lampinen et al. (2023) - "Passive learning of active causal strategies in agents and language models" (https://arxiv.org/pdf/2305.16183.pdf)

- Dasgupta, Lampinen, et al. (2022) Language models show human-like content effects on reasoning tasks" (https://arxiv.org/abs/2207.07051)

- Santoro, Lampinen, et al. (2021) - "Symbolic behaviour in artificial intelligence" (https://www.researchgate.net/publication/349125191_Symbolic_Behaviour_in_Artificial_Intelligence)

- Webb et al. (2022) - “Emergent Analogical Reasoning in Large Language Models” (https://arxiv.org/abs/2212.09196) 

Books
- Tomasello (2019) - “Becoming Human” (ht

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