The Next Frontier in AI — Replacing Statistical Independence with Human Intuition
It takes a human about 20–30 hours to learn how to drive a car, while it takes tens of thousands of hours to train a neural network to achieve this same capability. Even after all of these years of training and despite of using the latest and greatest in processing and sensor technology, self-driving cars are still not deemed road-safe.
When humans learn how to drive, they already have a basic understanding of the world. They have a basic intuition of how to recognize a dangerous situation and they can fill in the blanks under incomplete information. These abilities are critical in many life-or-death situations that may not occur frequently, but when they occur there would be significant negative impact on the driver and their surroundings. We can train our learning model to recognize many of these situations, but there is an infinite number of them and even after millions of miles driven, the machine learning model will not have experienced anywhere near all of them. Why? Because deep learning models do not have an inherent understanding of how the world works. They do not know any laws of physics and neither do they know ethics or even liability laws. Everything they learn is based on statistical independenceof all input variables. Humans however operate by making implicit assumptions on how some of these input variables are correlated. The video shows a very basic situation of how neural networks learn by going through literally every iteration of a mistake. They even have to make these mistakes separately, when going left and right. This is statistical independence at its finest.
When you see a ball rolling on the road, you automatically watch out for children playing. When you see a car with a bunch of mattresses precariously strapped to its roof you change lanes and ideally pass that car very quickly, or when you are trying to get out of your parking spot after soccer practice you know that unless you actually inch forward a little, nobody will let you out and you will be there forever. But how abruptly or how far should you inch out?. This depends on a lot of factors that can even be dependent on the individual. For example, you recognize your friend’s car and you know that they will let you out, so you are more confident backing out. Or the other car flashes its headlights and the driver turned her head and nodded at you, which is also a clear sign amongst us humans that you can safely proceed. While you could teach deep learning models to recognize many of these clues, you could not teach them all of them, without the learning model fundamentally understanding the world.
I deliberately picked this heading as this is the “next frontier” in deep learning. We can have people sit down and come up with long lists of real-life situations, e.g.
