1X

NEO is Starting to Learn on Its Own

Platform: YouTube
Length: 4:30
Spoken language: English
Views: 93.8K
Median cut: 11.80s
Face on screen: 70%
Dark frames: 12%

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Transcript of NEO is Starting to Learn on Its Own

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.

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

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