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Comment by fiatmoney | Hacker News Reader

By inference, this looks to be "just" integrating a deep semantic embedding (presumably neural network based) of the individual webpages as a signal into their existing ranking framework.

AI is a stretch, but it is cool.

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Tried to get at this in the article (author here), but it's using vectors (think word2vec and seq2seq) to distill meaning and embed words and phrases into a single space that the computer can then use to reason about. From my understanding this is all done on the query end of things, so it's basically letting them do better natural language processing. It also ties into Hinton's work on "Thought Vectors".

As some feedback to the author, the following sentence doesn't make sense: "Artificial intelligence sits at the extreme end of machine learning..." Machine Learning is a subfield of AI.

Thanks for the feedback. As a general news organization we struggle with definitions/scoping for stuff within AI as it's such a new area and we try to write for a broad, albeit informed, readership. I'll keep this in mind for future articles where we classify the two.

Natural language processing, inference, and machine learning are all AI-related techniques. Calling a system that uses all of them effectively an AI is a fair label.

> deep semantic embedding (presumably neural network based)

Probably not. My bet is that it is word2vec based

https://code.google.com/p/word2vec/

Hiya. They wouldn't explicitly confirm that it is word2vec, but everything we discussed indicated it's likely doing something roughly equivalent to word2vec, and is also doing similar conversions for sequences which is likely connected to Sequence to Sequence learning (PDF: http://papers.nips.cc/paper/5346-sequence-to-sequence-learni...). It also links to Geoff Hinton's stuff on Thought Vectors which implicitly involves word2vec.

word2vec is a broadly neural network based embedding.

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