Can AI Think Before It Speaks? (Ep. 437)

The team breaks down Anthropic’s new research paper, Tracing the Thoughts of a Language Model, which offers rare insight into how large language models process information. Using a replacement model and attribution graphs, Anthropic tries to understand how Claude actually “thinks.” The show unpacks key findings, philosophical questions, and the implications for future AI design.

Key Points Discussed

Anthropic studied its smallest model, Haiku, using a tool called a replacement model to understand internal decision-making paths.

Attribution graphs show how specific features activate as the model forms an answer, with many features pulling from multilingual patterns.

The research shows Claude plans ahead more than expected. In poetry generation, it preselects rhyming words and builds toward them, rather than solving it at the end.

The paper challenges assumptions about LLMs being purely token-to-token predictors. Instead, they show signs of planning, contextual reasoning, and even a form of strategy.

Language-agnostic pathways were a surprise: Claude used words from various languages (including Chinese and Japanese) to form responses to English queries.

This multilingual feature behavior raised questions about how human brains might also use internal translation or conceptual bridges unconsciously.

The team likens the research to the invention of a microscope for AI cognition, revealing previously invisible structures in model thinking.

They discussed how growing an AI might be more like cultivating a tree or garden than programming a machine. Inputs, pruning, and training shapes each model uniquely.

Beth and Jyunmi highlighted the gap between proprietary research and open sharing, emphasizing the need for more transparent AI science.

The show closed by comparing this level of research to studying human cognition, and how AI could be used to better understand our own thinking.

Hashtags

#Anthropic #Claude3Haiku #AIresearch #AttributionGraphs #MultilingualAI #LLMthinking #LLMinterpretability #AIplanning #AIphilosophy #BlackBoxAI

Timestamps & Topics

00:00:00 🧠 Intro to Anthropic’s paper on model thinking

00:03:12 📊 Overview of attribution graphs and methodology

00:06:06 🌐 Multilingual pathways in Claude’s thought process

00:08:31 🧠 What is Claude “thinking” when answering?

00:12:30 🔁 Comparing Claude’s process to human cognition

00:18:11 🌍 Language as a flexible layer, not a barrier

00:25:45 📝 How Claude writes poetry by planning rhymes

00:28:23 🔬 Microscopic insights from AI interpretability

00:29:59 🤔 Emergent behaviors in intelligence models

00:33:22 🔒 Calls for more research transparency and sharing

00:35:35 🎶 Set-up and payoff in AI-generated rhyming

00:39:29 🌱 Growing vs programming AI as a development model

00:44:26 🍎 Analogies from agriculture and bonsai pruning

00:45:52 🌀 Cyclical learning between humans and AI

00:47:08 🎯 Constitutional AI and baked-in intention

00:53:10 📚 Recap of the paper’s key discoveries

00:55:07 🗣️ AI recognizing rhyme and sound without hearing

00:56:17 🔗 Invitation to join the DAS community Slack

00:57:26 📅 Preview of the week’s upcoming episodes

The Daily AI Show Co-Hosts: Andy Halliday, Beth Lyons, Brian Maucere, Eran Malloch, Jyunmi Hatcher, and Karl Yeh