I am not saying that it doesn't use something that efectively is a probability calculation anywhere in the system, but that that isn't the main or the most important "engine".
I agree with your comment on "first learning thousands of other concepts". But that is a ton of (hierarchical) concepts that have to bo stored somehow and isn't then the storage/retrieval itself maybe more cruicial to the whole function than any special algo we just haven't figured out yet? (I mention this in my "wild" speculation on "models") :)
Re:Probabilistic AI can't be AI
by Houshalter on Sunday February 09, 2014 @02:23PM
(#7206709)
Perhaps but my understanding is that, at least for vision, the brain learns by trying to predict nearby neurons as well as the future, thus learning an internal model of the world.
Algorithms that do something like this are called Deep Learning and have recently proven extremely effective at machine vision and other tasks. They don't all work by prediction per se but they do learn to compress the input down to a smaller representation of features that can be used to recreate it and are very related to prediction.
I still disagree that the brain doesn't use probability at all or that we shouldn't focus on it in AI research.
>There are more external signs that we don't do probability. Statistical methods generally need a big learning set to learn anything. A small child doesn't have to see a set of 500 cartoon elephants in different poses to recognize elephants from then on in various different cartoons and in real life *)
No but they do see hundreds of hours of visual feed from their eyes from which they learn high level features. Learning an elephant from one example wouldn't be possible without first learning thousands of other concepts such as how to detect edges, shapes, 3d objects, the properties of animals, etc.