Software 2.0 (2017)
To contribute my personal lowbrow dismissal, this is the guy in charge of computer vision at Tesla, where cars have had a habit of doing absolutely bonkers behavior that doesn’t make any sense, when faced with completely normal phenomenon like lanes that fork into two.<p>His single sentence caveat about how Neural Networks can fail in unintuitive and embarrassing ways is the understatement of the century. I’d like to add that Tesla still hasn’t solved that lane forking problem even eight years since it was first identified. I guess just throw more data at it, and eventually it will get better? At what point does the belief that things will get better with more data fed into the same algorithm become a religious creed?<p>Neural Networks are significant advances in the state of not just machine learning, but the world as a whole. But the caveat that we don’t really understand what they’re doing is the whole fucking problem. Until Neural Networks can take advantage of, constrain to, and augment human models, they don’t have a snowballs chance in hell at replacing the types of software we rely on the most.<p>Until then, you’ll just create a massively inefficient system where the neural network writes the software but you spend 10x on engineering your training datasets so that your brilliant neural network knows that it is better to commit to one of two lanes in a forked road than it is to crash into the concrete lane divider. Or to not be racist. Or to not go haywire because of a sticker on a stop sign.