Disagree with example. Human teenagers spend quite a few years learning object recognition and other skills necessary for driving before driving, and I’d bet at good odds that a end-to-end training run of a self-driving car network is shorter than even the driving lessons a teenager goes through to become proficient at a similar level to the car. Designing the training framework, no, but the comparator there is evolution’s millions of years so that doesn’t buy you much.
The end-to-end training run is not what makes learning slow. It’s the iterative reinforcement learning process of deploying in an environment, gathering data, training on that data, and then redeploying with a new data collection strategy, etc. It’s a mistake, I think, to focus only the narrow task of updating model weights and omit the critical task of iterative data collection (i.e., reinforcement learning).
Disagree with example. Human teenagers spend quite a few years learning object recognition and other skills necessary for driving before driving, and I’d bet at good odds that a end-to-end training run of a self-driving car network is shorter than even the driving lessons a teenager goes through to become proficient at a similar level to the car. Designing the training framework, no, but the comparator there is evolution’s millions of years so that doesn’t buy you much.
The end-to-end training run is not what makes learning slow. It’s the iterative reinforcement learning process of deploying in an environment, gathering data, training on that data, and then redeploying with a new data collection strategy, etc. It’s a mistake, I think, to focus only the narrow task of updating model weights and omit the critical task of iterative data collection (i.e., reinforcement learning).