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OpenAI

GPT-6 Astra with Ben Davis

प्लैटफ़ॉर्म
YouTube
अवधि
2:16
बोली जाने वाली भाषा
अमेरिकी अंग्रेज़ी
व्यूज़
56.1 हज़ार
औसत कट
6.09s
स्क्रीन पर चेहरा
90%
गहरे फ़्रेम
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 2:14
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GPT-6 Astra with Ben Davis की ट्रांसक्रिप्ट

फ़िल्म के अपने ऑडियो से एक स्पीच मॉडल द्वारा लिखा गया, ब्रांड द्वारा नहीं। उस पल पर जाने के लिए किसी भी पंक्ति पर क्लिक करें।

  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 पंक्तियाँ · 479 शब्द

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