Tom Griffiths

Tom Griffiths, Princeton professor and author exploring computational cognitive science. He discusses whether mathematical principles can characterize thought. He traces logic from Aristotle and Boole to probabilistic reasoning. He covers Bayesian views, resource-rational heuristics, sampling strategies, inductive biases, and how human cognition compares with modern AI.

Tom Griffiths, Princeton professor bridging psychology and computer science, explores mathematical models of thought and AI. He discusses probabilistic versus symbolic approaches, how autoregressive training shapes LLM behavior, engineering inductive biases and meta-learning, and modeling curiosity as information-seeking. Short, clear dives into building and evaluating minds and machines.

In this engaging discussion, Tom Griffiths, a Princeton professor specializing in AI and cognitive science, dives into his book, The Laws of Thought. He explores how mathematics has historically shaped our understanding of both human and machine intelligence. Tom elaborates on three frameworks—rules, neural networks, and probability—that drive modern AI and connects these concepts to language. He emphasizes the unique human skills of judgment and metacognition while discussing the limits of large language models and the future of human-AI collaboration.

Praveen Murugesan, VP of Engineering at Samsara, on quantum computing for routing and more autonomous ops. Antje Barth, Amazon technical staff on developer-facing AI tooling and UI automation workflows. Tom Griffiths, cognitive scientist and author exploring human cognition shaping AI design. Will Falcon, CEO of Lightning AI and PyTorch Lightning creator on turning open source into a revenue-generating company.

Tom Griffiths, a Princeton computational cognitive scientist who studies the math behind mind and AI. He and Dave probe how AI reveals quirks of human thought. Short takes on inner speech, mitochondria as energy-driven processors, emotions as computational tools, resource-rational decision-making, and which AI styles avoid messing with your head.

Tom Griffiths, Princeton cognitive scientist and author of The Laws of Thought, explores how logic, neural networks, and probability form a trio for understanding cognition. He traces historical ideas, contrasts algorithms and implementations, and discusses resource-rationality, amortized inference, and how constraints shape both human minds and modern AI.

Tom Griffiths, Princeton psychologist and computer scientist studying mathematical laws of thought, and Igor Tolczynski, WorldQuant founder and coauthor focused on predictive AI. They trace AI’s deep historical roots and three mathematical traditions. They explore where prediction is already reshaping medicine, finance, and public policy. They discuss how AI reveals differences between human and machine thinking.

Tom Griffiths and Brian Christian, co-authors of 'Algorithms to Live By', explore how computer algorithms can solve everyday problems like choosing the right apartment or managing career choices. They discuss the 37 percent rule for effective decision-making and balance the efficiency of algorithms with human intuition. The duo highlights prioritization strategies to tackle procrastination and shares insights on optimizing life’s many choices, emphasizing the importance of understanding context and the emotional aspects of decision-making.

From ChatGPT to self-driving cars, AI is everywhere these days – but its rollout hasn’t always been entirely smooth. Tom Griffiths, PhD, a professor of psychology and computer science at Princeton University, talks about how artificial intelligence works, how AI differs from human cognition, how it’s changing the way science is done, and how studying human cognition can help researchers improve AI systems. For transcripts, links and more information, please visit the Speaking of Psychology Homepage. Learn more about your ad choices. Visit megaphone.fm/adchoices

Tom Griffiths, Princeton professor bridging psychology and computer science, and author of The Laws of Thought. He traces logic from Aristotle and Boole to modern neural nets. Short takes cover why transformers learn language, how human inductive biases differ from LLMs, and what constraints might give machines conscious-like phenomenology.