Orange Alert

Last week, Ayres Fan, a fellow student in the Stochastic Systems Group, who is close to finishing, was suggesting that I should write about my beloved Syracuse Orange in this space.  I was completely disillusioned after the first three weeks of the football season, but that changed on Saturday.  Let me not write about the alert sounded by the Orange to the college football world, but a different orange alert. 

This past week was career week at MIT and a lot of representatives from companies were in town.  One Course 6 undergrad, in order to stand out at the career fair, fashioned some LED-laden bling.  My officemate Emily, in fact, complimented this student on her piece of flair at the fair.  On Friday, we heard that she had been arrested at gunpoint at the airport as a suspected terrorist. 

Note to self: 'flying while brown' is already an issue for me, so I shouldn't compound things by carrying conspicuous electronics -- I should really hide any electronic items I have on my person. 

So how do intelligence analysts investigate whether someone is a terrorist or not? 

Also on Friday, I was invited by my friend Zennard Sun to a dinner and demo hosted by Palantir at the upscale Nine Zero Hotel in Boston.  Palantir is a fairly new company that is developing very powerful software platforms for analysts, both in the intelligence community and the financial industry.  The intelligence platform is a way to deal with data about links among people and to analyze whether a group of people forms a clique, whether two people are really the same person, how money flowed from leaders out to operatives and then whether this resulted in coordinated travel, etc.  Some screen shots are available here, but my verbal description and the screen shots do not do the product justice.  It was really, really impressive, and apparently completely unlike anything currently deployed. 

Palantir incorporates some aspects of machine learning into their product, but their goal is not to have one button that will do everything -- they want to use human capabilities as well.  One thing I have been discussing with Alan recently is the idea of machine learning for a purpose, possibly an unspecified or underspecified purpose at that.  The usual modus operandi in machine learning is maximum likelihood -- finding the most likely explanation given a model.  In discussions Alan has had with people like military commanders, what he has observed is that commanders do want the most likely explanation, but also the most dangerous explanation and the most unusual or weirdest explanation. 

Some recent work by Sujay Sanghavi and Vincent Tan looks at doing machine learning for a purpose, where the purpose is hypothesis testing.  They learn graphical models from data to minimize hypothesis testing decision error.  What do you do when the cost or utility function to optimize is not given a priori?  That is an interesting line of inquiry that I may look at in the years to come. 

I wonder whether Ayres will encounter Palantir's financial platform Hedgehog in the years to come after he starts his job at Goldman Sachs.