Deep Learning with SAS

I don’t know if you’ve seen SAS‘s campus. It’s a collection of enormous glass buildings. Abstract art greets you throughout the grounds. Outside the S-building stands a thirty-foot structure of red pipes bent at 90 and 45 degree angles and inside is what looks a bit like the cross-section of a cube. Looking out a window, one can just see the top of another big glass building over a copse of conifers.

As of the Friday before last, this is the organization that offers the funds that provide my stipend and pay for my tuition. Leaving the Leonardo project happened so subtly that my team and I all forgot to have some sort of commemoration ceremony. Last Friday I stood up from my desk shook my team leader’s hand, telling him it was “good working with him,” then he said we should arrange for one last team celebration. Our co-workers joked that this might be like the going-away parties in the Godfather, and I recommended we make sure to  eat at a popular, well-lit restaurant.

Now my way is paid to work on deep learning for language. Really, I couldn’t imagine a better fit to my interests. Of course I’m interested in Natural Language Processing, and my zeal for deep learning is such that I need to actively temper it to avoid poisoning conversations by implying to other researchers that all the techniques they’ve been using are outdated and soon to be obsolete. Now I get to work with a group of people to put my money where my mouth is and actually make something revolutionary, or at least useful.

Since we’re just starting, right now I’m reading papers about deep learning language techniques. I’ve found twenty-five papers over the last three years in the small set of conferences that I’ve checked. There’s an awful lot of interest in the domain of machine translation, but my favorite paper thus far has taken a sentiment analysis approach to identifying ideological biases in written text. With deep learning, it is able to understand that “the big lie of ‘the death tax'” is ideologically liberal, whereas an old-style system would take words two or so at a time and likely see “death tax” and think conservative.

I spoke with my new team leader, Brad, about using the big, fancy computer they’ve offered me for my personal research. He said that would not be a good idea, as it would complicate the ownership of whatever research I produced. “If you want a bigger computer,” he said, “I’ve just got one laying around that nobody’s using. I can get that to you within a week.”

Things are going pretty well.

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4 thoughts on “Deep Learning with SAS”

  1. That’s really interesting about the ideological slant detection. It seems like there situations will continue to arise where more and more advanced detection will be necessary. Sometimes different ideologies can lead to similar policy conclusions via different routes, and sometimes pejorative terms like “death tax” can be expropriated by their opponents or used in different contexts.

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