The Myth of AI Breakthroughs - Cutting Through Hype in Neural Network Research

The Myth of AI Breakthroughs - Cutting Through Hype in Neural Network Research

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[] Adjunct Professor of Law

4 of 22

4 of 22

[] Adjunct Professor of Law

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The Myth of AI Breakthroughs - Cutting Through Hype in Neural Network Research

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  1. 1 [] Jonathan's preferred coffee
  2. 2 [] Takeaways
  3. 3 [] LM Avalanche Panel Surprise
  4. 4 [] Adjunct Professor of Law
  5. 5 [] Low facial recognition accuracy
  6. 6 [] Automated decision making human in the loop argument
  7. 7 [] Control vs. Outsourcing Concerns
  8. 8 [] perpetuallineup.org
  9. 9 [] Face Recognition Challenges
  10. 10 [] The lottery ticket hypothesis
  11. 11 [] Mosaic Role: Model Expertise
  12. 12 [] Expertise Integration in Training
  13. 13 [] SLURM opinions
  14. 14 [] GPU Affinity
  15. 15 [] Breakthroughs with QStar
  16. 16 [] Deciphering the noise advice
  17. 17 [] Real Conversations
  18. 18 [] How to cut through the noise
  19. 19 [] Research Iterations and Timelines
  20. 20 [] User Interests, Model Limits
  21. 21 [] Debugability
  22. 22 [] Wrap up

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