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GPT-6 Astra with Ben Davis

Platform
YouTube
Length
2:16
Spoken language
American English
Views
56.1K
Median cut
6.09s
Face on screen
90%
Dark frames
11%
OpenAI GPT-6 Astra with Ben Davis at 0:00wide presenter
OpenAIGPT-6 Astra with Ben Davis
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OpenAI GPT-6 Astra with Ben Davis at 0:02close presenter
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OpenAI GPT-6 Astra with Ben Davis at 0:25corner inset
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OpenAI GPT-6 Astra with Ben Davis at 2:14
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Transcript of GPT-6 Astra with Ben Davis

Transcribed by a speech model from the film's own audio, not written by the brand. Click any line to jump the film to that moment.

  1. 0:00I'm very, very impressed.
  2. 0:02It was able to solve one that we did not get.
  3. 0:04Actually, three that we did not get.
  4. 0:06As well as one puzzle that no one else in the world has solved.
  5. 0:10So it was the first one to get it.
  6. 0:11That's wild. I was very impressed.
  7. 0:13So you spent some time testing Astra.
  8. 0:15Yes.
  9. 0:16Like, what was the first moment where it really hit you
  10. 0:18how different this model was?
  11. 0:20I went to a big conference called DEF CON
  12. 0:22where there are these big puzzle challenges
  13. 0:24that me and my friends spent days trying to solve.
  14. 0:27I took some of the hardest problems from that challenge
  15. 0:31and I threw them at the new model Astra.
  16. 0:33It solved this one big puzzle,
  17. 0:36which was like a bunch of Rubik's cubes
  18. 0:38that were arranged in this weird three by four pattern
  19. 0:41that you had to deduce a message from.
  20. 0:43It was able to get that three out of three times.
  21. 0:45The one thing it did need is it got the official hint
  22. 0:48from the puzzle creators,
  23. 0:49but it's the same thing we got when we were doing it.
  24. 0:51But once you gave it that, it got the actual solution.
  25. 0:54Same thing with this other puzzle,
  26. 0:55which was this giant dress with tons of multicolored beads all over it
  27. 0:59that you had to, again, find a message from.
  28. 1:01This was a task that required it to be able to actually see
  29. 1:05and understand what's happening across tons of disparate images
  30. 1:08that were not like, we didn't really take the best pictures of this thing.
  31. 1:12But even so, it was able to figure it out.
  32. 1:14Ultimately got the answer once we got the main hint from the maintainers.
  33. 1:18It did it.
  34. 1:19So the model comes up with a theory.
  35. 1:20It sends off another agent to go test it, sees how it works.
  36. 1:23Then the research branches, so it has basically 10 slots
  37. 1:27that can fill with other agents to go do things.
  38. 1:30So these are all running in parallel.
  39. 1:32Main agent is just kind of orchestrating these together.
  40. 1:35If you're just starting from here
  41. 1:36and then you have to get a word here,
  42. 1:38there's no verification in between these two.
  43. 1:40So it's very easy for the model to get lost
  44. 1:42on bad assumptions and bad paths
  45. 1:44and just disappear into something that won't work.
  46. 1:46I found this model is much better at keeping itself on track,
  47. 1:49which is a lot of the reason why it's been able
  48. 1:51to keep on task and solve these problems.
  49. 1:53And then the other side of this is really, really think
  50. 1:58that the sub-agent workflows, the swarm workflows,
  51. 2:02all of these like putting many instances
  52. 2:05of this agent together to work on much bigger,
  53. 2:08more complex problems that you probably feel like
  54. 2:10are currently possible, it is now possible
  55. 2:13and it's worth trying.
55 lines · 479 words

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