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Andrej Karpathy (Tesla)
Andrej is currently Senior Director of AI at Tesla, and was formerly a Research Scientist at OpenAI. His educational materials about deep learning remain among the most popular.
Programming The Software 2.0 Stack
- Software 1.0 consists of explicit instructions to the computer written by a programmer.
- Software 2.0 can be written in much more abstract, human unfriendly language, such as the weights of a neural network.
- In software 2.0, we restrict the search to a continuous subset of the program space where the search process can be made efficient with back-propagation and stochastic gradient descent.
- If optimization is doing most of the coding, what are the humans doing?
- 2.0 programmers label data
- 1.0 programmers maintain the surrounding "dataset infrastructure":
- Visualize data
- Create and edit labels
- Bubble up likely mislabeled examples
- Suggest data to label
- Flag labeler disagreements
- Data labeling is highly iterative and non-trivial.
- Lane lines are different across the world.
- Cars have different shapes and sizes.
- Even traffic lights and traffic signs can be ambiguous.
- Label imbalances are very frequent.
- Data imbalances are very common.
⇒ Realistic datasets: high label and data imbalances, noisy labels, highly multi-task, semi-supervised, active.
- Show a full inventory and statistics of the current dataset.
- Create and edit annotation layers for any data point.
- Flag, escalate, and resolve discrepancies in multiple labels.
- Flag and escalate data points that are likely to be mislabeled.
- Display predictions on an arbitrary set of test data points.
- Auto-suggest data points that should be labeled.
⇒ Can we build GitHub for Software 2.0?