Karpathy's presentation is really good. Watch it later if you get some time. The key points are
- Telsa is using fleet for learning (Fleet learning). This has 3 components,
a. Trigger infrasture that collects the kind of data telsa is looking for training. The rational here is we don't need massive amounts of same kind of data but needs right kind of data for training neural networks correctly.
b. Data Engine which learns from these examples.
c. Shadow mode where they deploy the learned model and test how its doing in real world and iterate this process.
- Drivers are themselves acting as labelers and tesla is in a unique position to take advantage of this.
- People drive vision only. Visual recognition is essential for autonomy. Lidar has much less information than vision, for example to identify the thing on the road is a plastic bag, lidar provides few points. But vision gives more data for telling this.
- Telsa is using fleet for learning (Fleet learning). This has 3 components, a. Trigger infrasture that collects the kind of data telsa is looking for training. The rational here is we don't need massive amounts of same kind of data but needs right kind of data for training neural networks correctly. b. Data Engine which learns from these examples. c. Shadow mode where they deploy the learned model and test how its doing in real world and iterate this process.
- Drivers are themselves acting as labelers and tesla is in a unique position to take advantage of this.
- People drive vision only. Visual recognition is essential for autonomy. Lidar has much less information than vision, for example to identify the thing on the road is a plastic bag, lidar provides few points. But vision gives more data for telling this.