“There is a paradigm shift in the field,” says Barzilay, who has spearheaded 6.806/6.864 since 2004. “For the first time, we are incorporating deep learning methods in this course. Deep neural networks can actually learn representations and make semantic and linguistic connections in a very rich way.” “These are flexible machine learning tools with nearly limitless architectural variations,” says Jaakkola, who lectures on deep learning. “NLP offers an exciting mixture of algorithmic questions, complex combinatorial structures, representational questions, and high-dimensional statistical estimation.” The course received a makeover in the Fall 2015 semester. Its new format puts increasing emphasis on student-driven research, with two-thirds of class time and 50 percent of the grade focused on student projects, supported by close faculty interaction. Students have flocked to the revamped course; last semester enrollment rose above 100 students, requiring Kirsch Auditorium instead of the 32144 classroom. Memory and Understanding Surya Bhupatiraju, a junior in 6-3 and 18, recently decided Artificial Intelligence was his “calling” when he took 6.806/6.864. “It was a rather large milestone in my undergraduate career. Until this course, the extent of my NLP knowledge was understanding bag-of-word models and recurrent neural networks.” Bhupatiraju and fellow 6-3 junior Simanta Gautam developed their project “Non-Markovian Control Policies for Text-based Games using External Memory and Deep Reinforcement Learning,” building on the 2015 work of TA Karthik Narasimhan. Like Narasimhan, they were interested in applying deep reinforcement learning framework to textbased computer games. Their goal: to teach a machine to play games not only in the present tense, but informed by past experience. “We wanted to hook up memory networks to the model (and have it) extract certain memories that would seem most relevant or helpful (to) act,” says Bhupatiraju. For instance, the “player” might be told to go to a virtual room and repeat a previous task. Using a deep neural network, the machine could look back. “Though game performance in explicitly non-Markovian (memorybased) policies was only barely better than the baseline model. It showed that there’s a lot of potential to implement the full memory network module and use it to pick good memories,” says Bhupatiraju. Bhupatiraju says he and Gautam want to improve their model and “hopefully publish,” as well as work on original NLP problems. CRT Success One ambitious project came at the suggestion of MGH physician Charlotta Lindvall, MD, PhD, who hoped NLP could predict the viability of invasive Cardiac Resynchronization Therapy (CRT) for heart failure patients. Three undergraduates interested in medical data — 6-3 seniors Austin Freel, Josh Haimson, and 6-3 junior Michael Traub — worked with Lindvall on their project “Predicting the Effectiveness of www.eecs.mit.edu
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