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This is interesting stuff. I recall that at one time Google seemed to be heading in somewhat similar directions with Google Sets (now sadly gone -- I miss it).

This is interesting stuff. I recall that at one time Google seemed to be heading in somewhat similar directions with Google Sets (now sadly gone -- I miss it). I know that the author is looking squarely at use cases along the lines of a recommendation engine that would replace a human expert. But personally, I think it might be more interesting to examine things the algorithm can do that humans would find difficult or unintuitive. Sure, king - man + woman = queen is a very significant achievement; it's also obvious, to a human. Now, what can this algorithm come up with that is worthwhile, but that I would not find so obvious? A couple of little comments: > The algorithm eventually sees so many examples that it can infer the gender of a single word, .... Do we really want to say that? Perhaps we should say that the algorithm is eventually able to make inferences that people would make based on knowledge of the gender of words -- which is not quite the same thing. (And again, I ask: what useful inferences can the algorithm make that humans would not make so quickly?) > Despite the impressive results that come with word vectorization, no NLP technique is perfect. Take care that your system is robust to results that a computer deems relevant but an expert human wouldn't. It should be noted that that "no NLP technique is perfect" idea applies to the NLP techniques used by human brains.

Retra 11y ago
It sounds like you are concerned that these inferences are over-fitting the training. But it is important to note that once you have a highly trained data set -- maybe one that can't produce anything novel but does extremely well at doing what humans agree on -- then it is probably very easy to relax your process to produce novel results through iteration. At least then you know you've built a robust enough system to get things right in a sterile setting. On the other hand, demanding novelty from a system that can't even get the basics right isn't likely to give you useful novelties so much as it will give you random mish-mash. To find a local maximum, you'll want to overshoot your target slightly and then back up slowly.

Retra 11y ago

It sounds like you are concerned that these inferences are over-fitting the training. But it is important to note that once you have a highly trained data set -- maybe one that can't produce anything novel but does extremely well at doing what humans agree on -- then it is probably very easy to relax your process to produce novel results through iteration. At least then you know you've built a robust enough system to get things right in a sterile setting. On the other hand, demanding novelty from a system that can't even get the basics right isn't likely to give you useful novelties so much as it will give you random mish-mash. To find a local maximum, you'll want to overshoot your target slightly and then back up slowly.