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[identity profile] naath.livejournal.com Mon 2008-04-14 12:11
I'm taking an AI course at the OU at the moment... and, yes, one of the things I've been learning about is neural networks that are trained to classify things that are presented to them. And there's two ways of training - one has the "answers" with the training questions (like a parent saying "no, dear, the plant is not your dinner") and the other way doesn't and just leaves the AI to find similarities.

These sorts of things turn out to be quite good at, for example, picking out the concept of "noun" or "animate thing" when presented with suitable training data.

What I find hard when working with computers (and apparently people working in AI find hard too) is representing visual input in a "fuzzy" way, indeed I find doing any visual work on a computer without using a GUI to manipulate it very difficult to get from in-my-head to in-the-computer (I've been programming a simple GUI without a GUI GUI-editor for work and it HURTS MY BRAIN); and computers are bad at "recognise a handwritten letter" - although they are getting better.

I think most of how humans learn these things is being told off for doing it wrong and rewarded for doing it right - either by other people or in more direct ways (like getting ill from eating the wrong things) so we make fuzzy categories that are very similar to those that our parents and friends have - and then we get confused when our fuzzy categories turn out to be very unlike other people's when we argue with people on the internet (for example).

I think the difficulty with AI is not to give it a big database but rather to learn how to write code that allows the AI to *learn* effectively from sets of training data (or from interacting with trainers). We're pretty rubbish at understanding how humans learn; and I'm not sure that knowledge would transfer directly to computers anyway (because the storage medium is very different), but learning definitely seems to be key.
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