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.
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.