To achieve full self-driving, we need to recreate how humans drive, which involves using optical sensors and neural networks to interpret the road environment.

The implementation of self-driving technology is challenging; while the ideas are easy, the actual engineering work, such as optimizing compilers and reducing latency, is what makes it difficult.

The challenge remains to understand how autonomous linguistic systems can effectively bridge to perceptual systems, allowing for coherent communication and action in the world.

Initially, I thought the lane-keeping problem for self-driving cars was too difficult to solve, but it turned out to be manageable with the right approach.

I believe self-driving technology will eventually be fully solved as it is a bounded problem, unlike more complex scenarios.

In self-driving cars, supervised learning can help label the positions of other vehicles based on sensor readings.

For robotics to work effectively, it needs to understand the physical environment around it, which is a significant challenge that has historically held back progress.

Self-driving cars perform well in clear conditions but struggle in snow, which alters the road's contours and complicates navigation for the vehicles.

There's a psychological barrier regarding the acceptance of self-driving cars, as people are accustomed to the current level of road fatalities and may resist change despite the potential for improved safety.