The speaker attended Tesla's AI Day and was impressed by the scale and complexity of their efforts in autonomous driving and robotics. They highlighted several key innovations, such as the use of vector space for prediction, fusion of camera sensor data, incorporation of time into the neural network architecture, and the use of neural networks as heuristics for planning.
Data annotation and the use of Tesla's Dojo computer for training were also mentioned as critical parts of the process. The speaker noted the potential of Tesla's system to improve over time through an iterative process of manual and automated labeling, retraining, and deployment.
The presentation also included a preview of Tesla's future plans, such as the development of a humanoid robot and the potential for Tesla's neural network architecture and data engine pipeline to be applied in various environments beyond autonomous driving.
The speaker concluded that the scale and complexity of Tesla's efforts suggest a promising future for AI and robotics.
1. The speaker was impressed by the scale of effort in the Tesla AI day presentation, which included autonomous driving and real-world robotics tasks.
2. The speaker believed that the tasks of autonomous driving and real-world robotics perception were significantly harder than commonly thought.
3. The speaker mentioned the high level of effort in data annotation, simulation, inference compute, and training compute required to solve these problems.
4. The presentation included the kind of effort required to solve the autonomous driving problem, including the neural network architecture and pipeline, autopilot compute hardware in the car, and dojo compute hardware for training the data and annotation.
5. The speaker highlighted the potential of the solution to extend beyond the car and the robot, to the humanoid form.
6. The speaker discussed the innovations in the neural network, including the prediction of vector space, the fusion of camera sensor data before the detections, and the use of video contacts to model not just vector space but time.
7. The speaker discussed the idea of using neural networks as heuristics in a similar way that neural networks were used as heuristics in the multicarlo tree search.
8. The speaker mentioned that the data and annotation were critical parts of making all of this work, including the manual labeling and the use of clips of data that includes video imu gps odometry, and so on.
9. The speaker discussed the innovation on the autopilot computer side, including the neural network compiler that optimizes latency and the testing and debugging tools for variants of candidate trained neural networks.
10. The speaker mentioned the continued innovation on the autopilot computer side, including the neural network compiler that optimizes latency and the testing and debugging tools for variants of candidate trained neural networks.
11. The speaker discussed the future deployment of the dojo computer, which is used for training and can essentially become an AI training as a service, directly taking on AWS and Google Cloud.
12. The speaker highlighted the applicability of the neural network architecture and data engine pipeline to much more than just roads and driving, mentioning its potential use in the home, in the factory, and by robots.
13. The speaker expressed excitement about the presentation of a humanoid Tesla bot and the potential of it to solve the problem of perception, movement, and object manipulation.