How did you make the assumption that I am “very dependant on Google” for my livelihood? I’m not sure I even know you, nor you know me.

Voting Google up in Hunch? Could you please clarify this? I don’t understand what you’re saying.

instead she has listened to moron-critics who spend hours a day staring into the navel of web2 for no reason whatsoever, and she felt she needed to gain their respect as a for-real tech leader and legit entrepreneur. in the end she’ll probably have blown the wad she got from flickr and divorcing whatshisass, with nothing to show for it but a spot on the wayback machine

Perhaps what I should be doing is asking Hunch if conversing with Steven Marlin is worth the effort.

Why is she dissing it? probably and partially for publicity because she thinks she’s come up with what she thinks is the next and better service in the same mould, but the truth is, only time will tell. And users.

Caterina’s compliant about the Yahoo Q&A is like democracy: it’s a good system until you involve the people (read: don’t understand what’s going on and vote for the wrong candidates) and linking that back to Answers, I’d agree only on a level because a couple of the categories allow silly questions to be asked, and with silly questions come silly answers, and in the modern search everything web 2.0 some of the silly Q&A will be found.

*disclosure* I regularly browse Yahoo!Answers. I do take it fairly seriously, and try to answer the questions in an intelligent way – as do most users, but occasionally if the question is that in need of a funny answer I’ll give one. The category of P&S (regular users will know what this is) is probably most to blame.

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Now, had you taken this test BEFORE you decided which car to buy, you would have gotten a different result.

To me it just gathers user demographics and then attempts to sell you something, likely working as an affiliate for larger companies.

The problem with Yahoo Answers is perhaps the fact that it relies on now old-skool contextual advertising, while Hunch works on CPA advertising, which can be more profitable.

But who will want to answer ten questions every time they search for something, especially considering Twitter is now the norm for time spent on a certain topic.

There is no need to bash Caterina. At least, she is out there doing something. I take my hat to entrepreneurs like her, like her idea or not, but people like her (in any domain) that keeps the world advancing. I criticized Caterina’s claim about Yahoo system (see my message at the very bottom of this thread), because I wanted to bring the readers attention here that Yahoo are already doing R&D is machine learning which is fact, but at least that I didn’t bash her.

Give respect to those who are out there developing something and risking their own money for freeloaders like you & me to use. You seem to bash every entrepreneurs who have been covered here at TC. You bashed Steven Wolfram and now Caterina Fake.

Have you developed a product that us readers here can check out if it is of superior to the products that you have frequently bashed here on this blog? If you do, then please let us readers know the link so we can check it out and see if it has no fault that deserves no bashing.

Fake described machine learning without her knowing that she did. I suspect that she has no background in it. Good on her for bringing her product to the market but one cannot hype up a product on the basis that is completely new (unbeknown to most internet readers), but actually I have no doubt that Yahoo researchers have already covered this area or currently developing it into their Q&A system. Why do I think this way? Well I have seen a few publications from Yahoo researchers that made it to various reputable computing peer review journals. What does this tell you? The R&D at Yahoo is alive and ongoing, so Fake’s claim is not right on the mark or at best it was an uninformed. A good example of this was an excellent paper from Yahoo Researcher, Sharad Goel who submitted a paper to the very popular machine learning journal NIPS (Neural Information Processing System) with :

Title:
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Predictive Indexing for Fast Search

Abstract:
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We tackle the computational problem of query-conditioned search. Given a machine-learned scoring rule and a query distribution, we build a predictive index by precomputing lists of potential results sorted based on an expected score of the result over future queries. The predictive index datastructure supports an anytime algorithm for approximate retrieval of the top elements. The general approach is applicable to webpage ranking, internet advertisement, and approximate nearest neighbor search. It is particularly effective in settings where standard techniques (e.g., inverted indices) are intractable. We experimentally find substantial improvement over existing methods for internet advertisement and approximate nearest neighbors.

There are many publications that have come out of Yahoo R&D, the one I cited above is just one of them.