> The power of Ayasdi is its unique ability to automatically discover insights — regardless of complexity — without asking questions. Ayasdi’s customers can finally learn the answers to questions that they didn’t know to ask in the first place. Simply stated, Ayasdi is ‘digital serendipity’.”
It’s a bold statement, however by using algebraic topology Ayasdi has managed to totally remove the human element that goes into data mining — and, as such, all the human bias that goes with it.
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Don't companies with massive datasets already do this in some way? Google's system for piecing together what people "really want" seems such a large, open-ended inquiry that it seems some automated insight discovery must be done.
Also, wouldn't a machine that generates automated insights require another layer of meta-analysis to be able to sift through which insights are actually usable? Using the example of sports statistics, you could generate an infinite number of trivial relations between players and plays and teams...it seems that at some point, a human with real domain knowledge has to go in and program a filter system, which seems to be about the same amount of work as the inquiry-generation that this software automates.
Finally, why all the emphasis on visualization? Visualization helps to illustrate to humans the possibilities of investigation...for a machine that can supposedly ask (and answer) all the correct questions, isn't visualization merely eye candy?
It'd be great to see a concrete example of this software in action. Perhaps feed it the NFL play-by-play data that was posted on HN a few weeks ago and see if it can generate usable strategy.
It's a bit of an odd article, but my guess is that it's a gloss of a press release, and that kind of writing is par for the course for AI/ML press releases.
Gunnar Carlsson, the researcher mentioned, seems to work mainly on an approach related to manifold learning (roughly, finding lower-dimensional structure in high-dimensional data) that's based on algebraic topology. He co-ran a workshop on that last year at NIPS, one of the main machine-learning conferences: https://sites.google.com/site/nips2012topology/ . He's written some highly cited papers on the subject, though it'd be a bit of a stretch to claim he invented the area, since there have been workshops at least back to 2007, that one organized by a set of French researchers: http://topolearnnips2007.insa-rouen.fr/description.html
I would guess the part about removing the human element from data mining is putting an optimistic PR spin on the basic idea of automatically extracting lower-dimensional structure, which, if it works, should allow for less feature engineering. The emphasis on visualization makes sense in that light, if they're planning to sell it as a no-expertise-necessary system: feed it data and get interpretable-by-non-experts viz as output, with any complexity that would normally require "data scientists" being handled automatically.
It’s a bold statement, however by using algebraic topology Ayasdi has managed to totally remove the human element that goes into data mining — and, as such, all the human bias that goes with it.
---
Don't companies with massive datasets already do this in some way? Google's system for piecing together what people "really want" seems such a large, open-ended inquiry that it seems some automated insight discovery must be done.
Also, wouldn't a machine that generates automated insights require another layer of meta-analysis to be able to sift through which insights are actually usable? Using the example of sports statistics, you could generate an infinite number of trivial relations between players and plays and teams...it seems that at some point, a human with real domain knowledge has to go in and program a filter system, which seems to be about the same amount of work as the inquiry-generation that this software automates.
Finally, why all the emphasis on visualization? Visualization helps to illustrate to humans the possibilities of investigation...for a machine that can supposedly ask (and answer) all the correct questions, isn't visualization merely eye candy?
It'd be great to see a concrete example of this software in action. Perhaps feed it the NFL play-by-play data that was posted on HN a few weeks ago and see if it can generate usable strategy.