| >Using Heidegger as a starting point, he argued that the brain does not create internal representations of objects in the world. Rather, it learns how to see the world directly. That's not how vision works. There's a small high resolution foveated area that is scanned to look at regions of interest, while the peripheral vision does a far lower resolution perception of the rest of the field of view. Everything that our brain does is a model of the world, not a direct perception. If you're in a room, you can't look at all the corners at the same time, yet you know where they are, to a fair degree of precision, and can point to them with your eyes closed once you're oriented, even the corners behind you. >Deep learning experts essentially found a way (backpropagation, gradient descent, fast computers and lots of labeled or pre-categorized data) to create the rules automatically. The rules are in the form, if A then B, where A is a pattern and B a label or symbol representing a category. Not really, they are function approximators, very good ones. However... the next point is still true, none the less >The problem with expert systems is that they are brittle. Presented with a situation for which there is no rule, they fail catastrophically. Functions which work reliably over a given set of inputs, are only valid for inputs very near that range. They do fail when unexpected inputs are presented. This is evident to any parent upon reflection of their experience. This is why I've suggested many times, here and elsewhere, that Tesla needs a team to just make up unusual circumstances and aggressively try to get the self driving network to fail, and add that to the training data. >Why the Brain Does Not Model the World Just pain wrong, in so many ways. It's not an exact model of the world, but the model we have is good enough to predict things that effect our survival, which is all that really matters to evolution. Surprise and humor are what happen when a model of the world meets an unexpected input. >A New AI Paradigm Will leave Deep Learning in the Dust
Discrete signal timing should be the main focus of AI research, in our opinion. It is the key to generalization. Spike timing might generate a better model of the human brain, but that doesn't imply it will make better function approximators. If it can some how reduce the computational complexity of training, I'm all for it. Eventually, with enough time and training, a neural network learns how to efficiently represent the world, and anticipate what's next. It's a question of training data and computation. Consider it takes a human 16 years to be considered adequate to the task of learning to drive, and you get a rough approximation of the complexity involved. Eventually, full self driving will happen, and it's likely to all be neural nets doing the job. |