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NEO is Starting to Learn on Its Own

Plattform
YouTube
Länge
4:30
Gesprochene Sprache
Englisch
Aufrufe
93.840
Median-Schnitt
11.80s
Gesicht im Bild
70%
Dunkle Bilder
12%
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Transkript von NEO is Starting to Learn on Its Own

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  1. 0:16Today we're releasing the 1x world model.
  2. 0:19NIO can now turn any prompt into autonomous action.
  3. 0:23With the latest update to the 1x world model, NIO learns from internet-scale data
  4. 0:27and applies it to the real world.
  5. 0:29By visualizing future actions with a video model grounded in physics,
  6. 0:34Neo can generalize to new tasks it's never seen before.
  7. 0:37This opens a new path for robotics learning,
  8. 0:41one where robots learn by teaching themselves,
  9. 0:43using the data they generated on their own,
  10. 0:45and grow in AI capabilities alongside the rapidly improving video modes.
  11. 0:55Our world model is pre-trained on web-scale video data that captures human interaction,
  12. 1:01and then post-trained on robot data to ground that understanding in real-world physics
  13. 1:06and Neo's humanoid embodiment. This allows Neo to visualize future actions and use a built-in
  14. 1:12inverse dynamics model to turn those visualizations into movement. Today, we'll use the world model
  15. 1:19as Neo's cognitive core to execute autonomous action sequences several
  16. 1:23seconds long. How it works is you'll give Neo a prompt with voice or text. For this
  17. 1:29example I'll use the world model desktop interface to illustrate how it comes
  18. 1:33together and start with something simple like having Neo pack this orange into
  19. 1:38the lunchbox.
  20. 1:45Once you prompt Neo, it'll take what it sees as input to generate a visualization of how
  21. 1:50to complete the request.
  22. 1:51As you can see on the screen below, Neo is visualizing how to perform this task using
  23. 1:56its world model.
  24. 1:58You can either have Neo automatically execute the generation or choose the best one to run
  25. 2:03from among several parallel generations.
  26. 2:10While NEO has never seen these objects before, there are still some examples of pick and
  27. 2:15place tasks in our dataset.
  28. 2:17So one might assume it's generalizing to a similar task to create this visualization.
  29. 2:23But this is not the case.
  30. 2:25With the world model, NEO can perform any task with any object, whether there is existing
  31. 2:30data or not.
  32. 2:34Not only has NEO never seen his toilet, but it has never performed a task anywhere near similar.
  33. 2:40Without any prior data on how to operate a toilet seat, the transfer of human knowledge
  34. 2:45becomes a lot more apparent. The world model is also really useful for handling unpredictable
  35. 2:56environments. Traditional AI models for robots are often overly sensitive to the
  36. 3:02surroundings, like changes in lighting or even random objects in the environment.
  37. 3:07NEO's world model, by contrast, applies human knowledge of how to navigate highly
  38. 3:13dynamic settings to handle even the most extreme changes in environment.
  39. 3:24Given a world model can generate just about anything you can imagine, there is no limit
  40. 3:30to a NEO can try and execute autonomously.
  41. 3:58We still have work to do to increase the length and success rate of each generated ability
  42. 4:05to make the world model more useful.
  43. 4:07But with the world model, NEO can create unlimited data of itself doing real-world tasks, creating
  44. 4:13a flywheel that enables NEO to teach itself anything, marking a true paradigm shift in
  45. 4:20AI and robotics.
45 Zeilen · 504 Wörter

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