 So I was a philosophy major by background and so what oh that explains it exactly exactly so the new data mix of that's right disciplines and like an intelligence analysis a lot of people are in liberal arts area that also do good math if you will and so those two skill sets allow for creativity and then computation to come together so for us what's underneath the covers if you will the fundamental difference is we look at a way a word is used in context a symbol so like toothbrush and you can figure out what toothbrush means without having an ontology of toothbrush you know being under hygiene or some other because look where do you really put toothbrush right is it a stick you know is it is it basically something that's under you know human hygiene so where do you fit it well it's known by how it's used so it tends to be associated with toothpaste and teeth and generally mornings and hopefully evenings if you're you know very hygienic so what the system does it looks at patterns of the way a word is used in context to ground a word in terms of its surrounding material and that's how a human baby does it too right I mean they're exposed to here's the taxonomy that came down from Aristotle and now we know what this means they're exposed to invariant patterns and what we do is we build that from the ground up which allows us to handle data that's traditionally defy knowledge engineering and that those algorithms have been now ported to work on Hadoop and work at scale and on some of the hardest data sets you know in the world that are inside certain agencies so it's is it of is it a sort of modern form of what I think of as classification which is a brute force miserable exercise it's sort of an automated approach toward drawing inferences for this large corpus of data is that fair description I think the way I would classify it is it's yeah it was a bad pun if it was one is as a clustering algorithm so you look at the similarity of things and you build hierarchical similarity from the bottom up so that you can make things relate to other things with no a priori set of categories right and so we do have the capability to do entity extraction and sort of pattern recognition type approaches classification approaches through training but we see that technology while we would say we're comparable to the best in the world at that especially one that runs on Hadoop we think that technology you know is very useful at the high level but it gets really hard at the details right because a massive human investment is necessary to apply that at low granularity categories so we try to do the the hard low-end stuff the bottom of the ontology wheel from the bottom up algorithmically through clustering and do the top down through classification yeah okay so now