The Limitations of Deep Learning
This article is a bit misleading. I believe NNs are a lot like the human brain. But just the lowest level of our brain. What psychologists might call &quot;procedural knowledge&quot;.<p>Example: learning to ride a bike. You have no idea how you do it. You can&#x27;t explain it in words. It requires tons of trial and error. You can give a bike to a physicist that has a perfect deep understanding of the laws of physics. And they won&#x27;t be any better at riding than a kid.<p>And after you learn to ride, change the bike. Take one where the handle is inversed. And turning it right turns the wheel left. No matter how good you are at riding a normal bike, no matter how easy it seems it should be, it&#x27;s very hard. Requires relearning how to ride basically from scratch. And when you are done, you will even have trouble going back to a normal bike. This sounds familiar to the problems of deep reinforcement learning, right?<p>If you use only the parts of the brain you use to ride a bike, would you be able to do any of the tasks described in the article? E.g. learn to guide spacecraft trajectories with little training, through purely analog controls and muscle memory? Can you even sort a list in your head without the use of pencil and paper?<p>Similarly recognizing a toothbrush as a baseball bat isn&#x27;t as bizarre as you think. Most NNs get one pass over an image. Imagine you were flashed that image for just a millisecond. And given no time to process it. No time to even scan it with your eyes! You certain you wouldn&#x27;t make any mistakes?<p>But we can augment NNs with attention, with feedback to lower layers from higher layers, and other tricks that might make them more like human vision. It&#x27;s just very expensive.<p>And that&#x27;s another limitation. Our largest networks are incredibly tiny compared to the human brain. It&#x27;s amazing they can do anything at all. It&#x27;s unrealistic to expect them to be flawless.