But it still seems like pattern recognition based on a training set only, which is, in my opinion, a task on a lever prior to intelligence, like what visual cortex does. It cannot make new reference between words to produce (infer) new antonyms and analogies (not presented in a training g set), which is intelligence.
The system isn't training on antonyms and analogies - it's training on wikipedia. It's learning the meaning (and multiple senses) for every word it can find.
The test they use to see if it actually learned what these words meant, in a limited sense, is to test it against a subset of verbal IQ tests (not what it was trained on!). You could ask it the antonym, synonym, or analogy for anything in English. This is an extension of word2vec / word embeddings.
That it beats the scores of college graduates impresses me.
"it's training on wikipedia. It's learning the meaning (and multiple senses) for every word it can find."
I don't think that is entirely correct. After cursory reading of the paper, my understanding is that they look up a list of word senses for each word in a dictionary (or multiple dictionaries). And then they try to learn something about each of those word senses from wikipedia (that is they create seperate word embeddings for each of those senses). So what they do not do is to learn what senses a word has. That is done by the humans who created the dictionaries.
What that means is that they cannot pick up new senses of words, which doesn't matter for answering IQ test questions because these questions rarely change and are typically based on well established word meanings.
Unfortunately it makes this approach less than ideal for things like understanding the news (something I'm working on), where new contexts of words keep popping up all the time.
Well, it is teaching a computer to do well on an "intelligence test" and ironically many of the ways that humans use to distinguish each other's intelligence tend to not measure the unique, flexible and adaptable properties of human intelligence and rather tend to be tests of more computer-like behavior in human - playing chess well was for a long time considered a measure of high intelligence, for example.
Personally I'm not sure this helps for generic AI (directly). But could serve as an autonomies piece to help the generic AI with linquesitics (especially on shaving CPU cycles)? A bit like you can use your visual memory for certain things?
