Transformers and the Importance of Representations in Learning

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While transformers generate representations, not all representations are equal or correspond with reality. Transformers struggle with learning general purpose addition, only being able to learn modular addition. However, recent research shows that by structuring the data or network differently, transformers can learn the actual algorithm for addition, resulting in better representations.

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.

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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.