Matej Zečević, an AI and causality researcher who co-organized the NeurIPS causal workshop, dives into the fascinating relationship between large language models (LLMs) and causality. He challenges the assumption that LLMs can genuinely understand causal structures, posing thought-provoking questions about their capabilities. Matej shares insights from his diverse journey, the role of transparency in AI, and emphasizes the importance of collaboration in advancing the field. His passion for literature and its influence on his work adds a delightful touch to the discussion.
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Matej's Journey to Causality
Matej Zečević’s journey started with wanting to be a hacker, moved to robotics, and then settled in causality sparked by Pearl's Book of Why.
His curiosity about intelligence and foundational questions drove his path through security, robotics, neuroscience, and causal inference.
insights INSIGHT
Key Gaps in Causal Research
Causality research spans causal discovery and inference, but lacks bridging these and philosophical inquiry.
Abstractions and connections to logic and symbolic AI are promising but underexplored areas in causal research today.
insights INSIGHT
Role of Abstractions in Causality
Abstractions in causality formalize how high-level and low-level causal variables relate, respecting interventions.
They help understand where causal variables come from and enable different levels of causal reasoning relevant in complex systems.
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This book provides a comprehensive introduction to causal inference, covering various methods and techniques for causal analysis. It delves into the fundamental concepts of causality, including directed acyclic graphs (DAGs) and causal diagrams. The book also explores advanced topics such as causal discovery, causal effects estimation, and causal mediation analysis. It is a valuable resource for researchers and practitioners in various fields who want to learn about causal inference.
The Book of Why
The New Science of Cause and Effect
Mel Foster
Dana Mackenzie Judea Pearl
Dana Mackenzie
Judea Pearl
In 'The Book of Why', Judea Pearl and Dana Mackenzie delve into the causal revolution, which has transformed the way we distinguish between correlation and causation. The book introduces causal diagrams, such as Directed Acyclic Graphs (DAGs), and explains how to predict the effects of interventions. It addresses fundamental questions about causality and its implications in fields like medicine, economics, and artificial intelligence. The authors also discuss the potential of causal inference in enabling computers to understand counterfactuals and engage in moral decision-making[2][4][5].
Thinking, Fast and Slow
Daniel Kahneman
In this book, Daniel Kahneman takes readers on a tour of the mind, explaining how the two systems of thought shape our judgments and decisions. System 1 is fast, automatic, and emotional, while System 2 is slower, effortful, and logical. Kahneman discusses the impact of cognitive biases, the difficulties of predicting future happiness, and the effects of overconfidence on corporate strategies. He offers practical insights into how to guard against mental glitches and how to benefit from slow thinking in both personal and business life. The book also explores the distinction between the 'experiencing self' and the 'remembering self' and their roles in our perception of happiness.
Video version of this episode available on YouTube Recorded on Aug 14, 2023 in Frankfurt, Germany
Are Large Language Models (LLMs) causal?
Some researchers have shown that advanced models like GPT-4 can perform very well on certain causal benchmarks.
At the same time, from the theoretical point of view it's highly unlikely that these models can learn causal structures. Is it possible that large language models are not causal, but talk causality?
In our conversation we explore this question from the point of view of the formalism proposed by Matej and his colleagues in their "Causal Parrots" paper.
We also discuss Matej's journey from the dream of becoming a hacker to a successful AI and then causality researcher. Ready to dive in?
Causal Bandits Podcast 
