World Model · podcast knowledge graph

How Do AI Models Actually Think? - Laura Ruis

2025-01-20 · 78 min · 26 entities

episode page audio

Asserted relationships

  • → references Science concept
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://sciencedirect.com/science/article/abs/pii/0010027788900315
  • → references Articles website
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://nature.com/articles/s41586-023-06668-3
  • → references Html website
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    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://arxiv.org/html/2406.04268v1
  • → references Chinese Room person
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    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/chinese-room
  • 0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475
  • → references Wittgenstein concept
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/wittgenstein
  • → references Montague Semantics person
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/montague-semantics
  • → works at Cohere company
    0.50
    evidence rules-v4
    researcher at Cohere
  • → works at Cohere company
    0.50
    evidence rules-v4
    researcher at Cohere
  • → discusses Technology company
    0.40
    evidence rules-v4
    Feed category: Technology
  • → references tufalabs.ai website
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    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://tufalabs.ai/
  • → references Pricing website
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    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://centml.ai/pricing
  • → references Dreamcoder website
    0.38
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf

Entities found in this episode

websites 10

  • mentioned Articles website
    0.90
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://nature.com/articles/s41586-023-06668-3
  • mentioned Html website
    0.90
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://arxiv.org/html/2406.04268v1
  • references Articles website
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://nature.com/articles/s41586-023-06668-3
  • references Html website
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://arxiv.org/html/2406.04268v1
  • mentioned tufalabs.ai website
    0.45
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://tufalabs.ai/
  • mentioned Pricing website
    0.45
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://centml.ai/pricing
  • mentioned Dreamcoder website
    0.45
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf
  • references tufalabs.ai website
    0.38
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://tufalabs.ai/
  • references Pricing website
    0.38
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://centml.ai/pricing
  • references Dreamcoder website
    0.38
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf

companys 5

  • mentioned Cohere company
    0.62
    evidence rules-v4
    researcher at Cohere
  • mentioned Technology company
    0.50
    evidence rules-v4
    Feed category: Technology
  • works at Cohere company
    0.50
    evidence rules-v4
    researcher at Cohere
  • works at Cohere company
    0.50
    evidence rules-v4
    researcher at Cohere
  • discusses Technology company
    0.40
    evidence rules-v4
    Feed category: Technology

concepts 4

  • mentioned Science concept
    0.90
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://sciencedirect.com/science/article/abs/pii/0010027788900315
  • mentioned Wittgenstein concept
    0.90
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/wittgenstein
  • references Science concept
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://sciencedirect.com/science/article/abs/pii/0010027788900315
  • references Wittgenstein concept
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/wittgenstein

persons 4

  • mentioned Chinese Room person
    0.90
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/chinese-room
  • mentioned Montague Semantics person
    0.90
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/montague-semantics
  • references Chinese Room person
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/chinese-room
  • references Montague Semantics person
    0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://plato.stanford.edu/entries/montague-semantics

books 2

  • 0.90
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475
  • 0.77
    evidence rules-v4
    Link in episode "How Do AI Models Actually Think? - Laura Ruis": https://amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475

podcasts 1

Episode description as stored
Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabilities, and whether these models primarily rely on retrieval or develop procedural knowledge. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? Goto https://tufalabs.ai/ *** TOC 1. LLM Foundations and Learning 1.1 Scale and Learning in Language Models [00:00:00] 1.2 Procedural Knowledge vs Fact Retrieval [00:03:40] 1.3 Influence Functions and Model Analysis [00:07:40] 1.4 Role of Code in LLM Reasoning [00:11:10] 1.5 Semantic Understanding and Physical Grounding [00:19:30] 2. Reasoning Architectures and Measurement 2.1 Measuring Understanding and Reasoning in Language Models [00:23:10] 2.2 Formal vs Approximate Reasoning and Model Creativity [00:26:40] 2.3 Symbolic vs Subsymbolic Computation Debate [00:34:10] 2.4 Neural Network Architectures and Tensor Product Representations [00:40:50] 3. AI Agency and Risk Assessment 3.1 Agency and Goal-Directed Behavior in Language Models [00:45:10] 3.2 Defining and Measuring Agency in AI Systems [00:49:50] 3.3 Core Knowledge Systems and Agency Detection [00:54:40] 3.4 Language Models as Agent Models and Simulator Theory [01:03:20] 3.5 AI Safety and Societal Control Mechanisms [01:07:10] 3.6 Evolution of AI Capabilities and Emergent Risks [01:14:20] REFS: [00:01:10] Procedural Knowledge in Pretraining & LLM Reasoning Ruis et al., 2024 https://arxiv.org/abs/2411.12580 [00:03:50] EK-FAC Influence Functions in Large LMs Grosse et al., 2023 https://arxiv.org/abs/2308.03296 [00:13:05] Surfaces and Essences: Analogy as the Core of Cognition Hofstadter & Sander https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475 [00:13:45] Wittgenstein on Language Games https://plato.stanford.edu/entries/wittgenstein/ [00:14:30] Montague Semantics for Natural Language https://plato.stanford.edu/entries/montague-semantics/ [00:19:35] The Chinese Room Argument David Cole https://plato.stanford.edu/entries/chinese-room/ [00:19:55] ARC: Abstraction and Reasoning Corpus François Chollet https://arxiv.org/abs/1911.01547 [00:24:20] Systematic Generalization in Neural Nets Lake & Baroni, 2023 https://www.nature.com/articles/s41586-023-06668-3 [00:27:40] Open-Endedness & Creativity in AI Tim Rocktäschel https://arxiv.org/html/2406.04268v1 [00:30:50] Fodor & Pylyshyn on Connectionism https://www.sciencedirect.com/science/article/abs/pii/0010027788900315 [00:31:30] Tensor Product Representations Smolensky, 1990 https://www.sciencedirect.com/science/article/abs/pii/000437029090007M [00:35:50] DreamCoder: Wake-Sleep Program Synthesis Kevin Ellis et al. https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf [00:36:30] Compositional Generalization Benchmarks Ruis, Lake et al., 2022 https://arxiv.org/pdf/2202.10745 [00:40:30] RNNs & Tensor Products McCoy et al., 2018 https://arxiv.org/abs/1812.08718 [00:46:10] Formal Causal Definition of Agency Kenton et al. https://arxiv.org/pdf/2208.08345v2 [00:48:40] Agency in Language Models Sumers et al. https://arxiv.org/abs/2309.02427 [00:55:20] Heider & Simmel’s Moving Shapes Experiment https://www.nature.com/articles/s41598-024-65532-0 [01:00:40] Language Models as Agent Models Jacob Andreas, 2022 https://arxiv.org/abs/2212.01681 [01:13:35] Pragmatic Understanding in LLMs Ruis et al. https://arxiv.org/abs/2210.14986