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278 - Austin Chadwick and Chris Lucian, Co-Hosts of the Mob Mentality Show

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Manage episode 525646190 series 2543897
Content provided by Jason Swett. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jason Swett or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode I talk with Austin Chadwick and Chris Lucian about AI and machine learning. We discuss why LLMs may not lead to AGI, the history of AI funding, the philosophy of induction versus explanation, and my robot project idea for building intelligence from sensory experience up.

Links:

  continue reading

Chapters

1. A Snail Mail Newsletter Pitch (00:00:00)

2. Guests Introduced And Backgrounds (00:03:26)

3. Career Shifts And Meraki Role (00:06:06)

4. What’s Wrong With LLMs (00:07:42)

5. Beyond Transformers: Wider ML Tools (00:10:40)

6. Hype, Skeptics, And The Middle Path (00:14:58)

7. Funding Cycles And AI Winters (00:18:36)

8. AlphaGo And Global Investment (00:21:13)

9. Thousand Brains And Embodied Models (00:23:17)

10. Senses, Abstraction, And Pruning (00:27:11)

11. Induction, Deduction, And Truth (00:31:12)

12. Few Shot Learning And Human Bias (00:35:29)

13. Firmware, Instincts, And Evolution (00:39:06)

14. Genetic Algorithms And Emergence (00:43:23)

15. Simulation Limits And Usefulness (00:46:05)

16. Closing Thoughts And Links (00:49:02)

283 episodes

Artwork
iconShare
 
Manage episode 525646190 series 2543897
Content provided by Jason Swett. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jason Swett or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode I talk with Austin Chadwick and Chris Lucian about AI and machine learning. We discuss why LLMs may not lead to AGI, the history of AI funding, the philosophy of induction versus explanation, and my robot project idea for building intelligence from sensory experience up.

Links:

  continue reading

Chapters

1. A Snail Mail Newsletter Pitch (00:00:00)

2. Guests Introduced And Backgrounds (00:03:26)

3. Career Shifts And Meraki Role (00:06:06)

4. What’s Wrong With LLMs (00:07:42)

5. Beyond Transformers: Wider ML Tools (00:10:40)

6. Hype, Skeptics, And The Middle Path (00:14:58)

7. Funding Cycles And AI Winters (00:18:36)

8. AlphaGo And Global Investment (00:21:13)

9. Thousand Brains And Embodied Models (00:23:17)

10. Senses, Abstraction, And Pruning (00:27:11)

11. Induction, Deduction, And Truth (00:31:12)

12. Few Shot Learning And Human Bias (00:35:29)

13. Firmware, Instincts, And Evolution (00:39:06)

14. Genetic Algorithms And Emergence (00:43:23)

15. Simulation Limits And Usefulness (00:46:05)

16. Closing Thoughts And Links (00:49:02)

283 episodes

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