Continual learning versus task fine-tuning

Dive into the realities of AI-assisted coding, the origins of modern fine-tuning, and the cognitive science behind machine learning with fast.ai founder Jeremy Howard. In this episode, we unpack why AI might be turning software engineering into a slot machine and how to maintain true technical intuition in the age of large language models.

GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg)

Jeremy Howard is a renowned data scientist, researcher, entrepreneur, and educator. As the co-founder of fast.ai, former President of Kaggle, and the creator of ULMFiT, Jeremy has spent decades democratizing deep learning. His pioneering work laid the foundation for modern transfer learning and the pre-training and fine-tuning paradigm that powers today's language models.

Key Topics and Main Insights Discussed:

- The Origins of ULMFiT and Fine-Tuning

- The Vibe Coding Illusion and Software Engineering

- Cognitive Science, Friction, and Learning

- The Future of Developers

RESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izk

Jeremy Howard:

https://x.com/jeremyphoward

https://www.answer.ai/

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TIMESTAMPS (fixed):

00:00:00 Introduction & GTC Sponsor

00:04:30 ULMFiT & The Birth of Fine-Tuning

00:12:00 Intuition & The Mechanics of Learning

00:18:30 Abstraction Hierarchies & AI Creativity

00:23:00 Claude Code & The Interpolation Illusion

00:27:30 Coding vs. Software Engineering

00:30:00 Cosplaying Intelligence: Dennett vs. Searle

00:36:30 Automation, Radiology & Desirable Difficulty

00:42:30 Organizational Knowledge & The Slope

00:48:00 Vibe Coding as a Slot Machine

00:54:00 The Erosion of Control in Software

01:01:00 Interactive Programming & REPL Environments

01:05:00 The Notebook Debate & Exploratory Science

01:17:30 AI Existential Risk & Power Centralization

01:24:20 Current Risks, Privacy & Enfeeblement

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REFERENCES:

Blog Post:

[00:03:00] fast.ai Blog: Self-Supervised Learning

https://www.fast.ai/posts/2020-01-13-self_supervised.html

[00:13:30] DeepMind Blog: Gemini Deep Think

https://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/

[00:19:30] Modular Blog: Claude C Compiler analysis

https://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software

[00:19:45] Anthropic Engineering Blog: Building C Compiler

https://www.anthropic.com/engineering/building-c-compiler

[00:48:00] Cursor Blog: Scaling Agents

https://cursor.com/blog/scaling-agents

[01:05:15] fast.ai Blog: NB Dev Merged Driver

https://www.fast.ai/posts/2022-08-25-jupyter-git.html

[01:17:30] Jeremy Howard: Response to AI Risk Letter

https://www.normaltech.ai/p/is-avoiding-extinction-from-ai-really

Book:

[00:08:30] M. Chirimuuta: The Brain Abstracted

https://mitpress.mit.edu/9780262548045/the-brain-abstracted/

[00:30:00] Daniel Dennett: Consciousness Explained

https://www.amazon.com/Consciousness-Explained-Daniel-C-Dennett/dp/0316180661

[00:42:30] Cesar Hidalgo: Infinite Alphabet / Laws of Knowledge

https://www.amazon.com/Infinite-Alphabet-Laws-Knowledge/dp/0241655676

Archive Article:

[00:13:45] MLST Archive: Why Creativity Cannot Be Interpolated

https://archive.mlst.ai/read/why-creativity-cannot-be-interpolated

Research Study:

[00:24:30] METR Study: AI OS Development

https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

Paper:

[00:24:45] Fred Brooks: No Silver Bullet

https://www.cs.unc.edu/techreports/86-020.pdf

[00:30:15] John Searle: Minds, Brains, and Programs

https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC644B47A4299C637C89772FACC2706A