Large Action Models (LAMs) & Rabbits 🐇

Practical AI: Machine Learning, Data Science, LLM

Recently the release of the rabbit r1 device resulted in huge interest in both the device and “Large Action Models” (or LAMs). What is an LAM? Is this something new? Did these models come out of nowhere, or are they related to other things we are already using? Chris and Daniel dig into LAMs in this episode and discuss neuro-symbolic AI, AI tool usage, multimodal models, and more.

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Featuring:

• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• rabbit r1
• Salesforce blog on LAMs
• LangChain tools
• MM-LLMs: Recent Advances in MultiModal Large Language Models

Something missing or broken? PRs welcome!

Chapitres

1. Welcome to Practical AI (00:00:07)

2. Daniel is in Atlanta? (00:00:43)

3. Daniel's embarrassing moment (00:01:49)

4. New AI devices (00:02:48)

5. Where does privacy fit? (00:03:28)

6. We've been giving data for years (00:04:47)

7. Perception of humanity (00:06:59)

8. A look at the rabbit r1 (00:09:27)

9. Why hardware? (00:11:43)

10. Moving past our phones (00:13:32)

11. Sponsor: Read Write Own (00:14:46)

https://readwriteown.com/?utm_source=changelog&utm_medium=practicalai&utm_campaign=changelog

12. Explaining the LAM (00:15:54)

13. Different inputs for models (00:20:28)

14. Integrating external systems (00:23:10)

15. Structuring an unstructured world (00:26:06)

16. Sponsor: Shopify (00:29:59)

https://www.shopify.com/practicalai

17. Origins of LAMs (00:32:35)

18. How instruments could be used as inputs (00:38:35)

19. If this approach sticks (00:41:18)

20. Predictions on LAMs (00:45:00)

21. This has been fun! (00:46:14)

22. Outro (00:47:27)