AI News, How do machines learnmeaning?

How do machines learnmeaning?

Computers consist of on/off switches and process meaningless symbols.

So the machine considers the word ship to be totally unrelated to the word boat: these two words would be as unrelated to one another as the words cat anddynamite.

has the advantage of precision — it’s useful in keyword search, for instance, if you know the document you want, and you know its exact title.

The key insight, made at the beginning of the information age, is to replace word meaning with something that machines can actually measure.

This means, for example, that the words ship and boat must represent related notions because they both occur often with the words stern, sail and sea, but almost never with glycerine or meow.

Two products are related to the extent that they tend to be purchased together, and two customers are similar to the extent that they buy similar products.

So the machine can learn which words are related by processing text, sentence by sentence, and seeing which words occur together.

These vectors live in a very high dimensional vector space, but we can use dimension reduction to make this representation more robust andtractable.

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