Separating Reasoning and Knowledge in AI Models

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Exploring the training of language models solely on logical deductions or pure math to create reasoning-focused models without worldly knowledge. Discusses different types of knowledge and the success of small transformers on logic puzzle tasks despite lacking knowledge about the world.

In this episode, Nathan chats with Josh Albrecht, CTO of Imbue. They discuss how to create agents for reasoning, reliability, and robustness. If you need an ecommerce platform, check out our sponsor Shopify: https://shopify.com/cognitive for a $1/month trial period.

We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.

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TIMESTAMPS:

(00:00:00) – Episode Preview

(00:07:14) – What does it mean to be a research company?

(00:10:25) – How is the reasoning landscape these days and how might it evolve?

(00:11:03) – Data quality is highly important

(00:21:15) – What’s the difference between good features and a good world model?

(00:27:31) – The impact of new modalities on reasoning

(00:29:15) – How much can reasoning and knowledge be separated?

(00:45:13) – Imbue demo and are they building their own LLMs or using others?

(00:49:37) – Does Imbue have a deal with Nvidia?

(00:57:48) – Carbs framework

(01:12:57) – Imbue’s involvement with policy and and AI safety

(01:16:23) – Takeaways from AI Safety Summit and Biden’s Order

This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at erik@turpentine.co.