Author: Department of Electrical Engineering and Computer Science
It was standing-room only in the Stata Center’s Kirsch Auditorium when some 300 attendees showed up for opening lectures for MIT’s intensive, student-designed course 6.S191 (Introduction to Deep Learning).
Nathan Rebello, a first-year graduate student in chemical engineering, was among those who were excited about the class, coordinated by Alexander Amini ’17 and Ava Soleimany ’16 during MIT’s Independent Activities Period (IAP) in January.
“I hope to go into either industry or academia and to apply deep learning techniques for the design of new materials,” Rebello says. He signed up for 6.S191 to learn more about deep learning with the intention of applying it to the design of bio-inspired polymeric materials, adding: “I also wanted to network with students and faculty to explore their ways of thinking on this topic.”
There were plenty of people available for networking. “We want the class to be open and accessible to the broader community,” says Soleimany, a MIT and Harvard University graduate student, who, with Amini, also served as an instructor for the course. “We welcome people from outside MIT. There were many students from surrounding universities in Boston and even specialized physicians from Mass General Hospital. We had people fly in from California and from outside the country, from Turkey and China, to attend the lectures.”
The for-credit course has been offered for the past three years. A subset of artificial intelligence (AI), deep learning focuses on building predictive models automatically from big data. Each class consisted of technical lectures followed by software labs where students could immediately apply what they had learned. Technical lectures spanned state-of-the-art techniques in deep learning, and included lectures on computer vision, reinforcement learning, and natural language processing given by Amini and Soleimany, as well as guest lectures by leading AI researchers from Google, IBM, and Nvidia.
“This year, we remade the software labs totally from scratch and collaborated very closely with the Google Brain team to reflect the newest version of the framework TensorFlow, the language which we were using for the labs,” says Amini, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory. “TensorFlow is the most popular machine learning and deep learning framework out there.”
One specific lab featured research that Amini and Soleimany recently published in the Association for the Advancement of Artificial Intelligence/Association for Computing Machinery Conference on Artificial Intelligence, Ethics, and Society. “The focus is on building facial detection systems and using deep learning to make them unbiased with respect to things like gender and race,” Soleimany says. “This is a really exciting piece of work, but it’s also really pragmatic work, because there’s been a lot of news recently on AI being biased towards certain underrepresented minorities. To have students not only understand why that bias might arise, but also try to use deep learning to actually remove some of that bias was really cool. It’s cutting-edge work.”
For final projects, 6.S191 students could either write a brief review of a new deep learning paper or present a three-minute oral proposal for a deep learning application, to be judged by industry representatives.
This year, some 20 groups comprising two to four people completed projects, competing for high-end graphics processing units (GPUs) provided by Nvidia, each worth more than $1,000, and AI home assistants provided by Google.
One winning team proposed using deep learning to detect deformation in 2-D materials on a micro scale or even smaller. A second group proposed using it to design new catalysts for chemistry applications. The final group proposed using deep learning to analyze the X-rays of scoliosis patients.
“We thought that these three projects stood out in terms of their immediate applications and that these teams would take the GPUs and really put them to use,” Soleimany says.
Rebello, who had a basic knowledge of neural nets and TensorFlow before he enrolled in the course, was on the team that presented “Advanced Scoliosis Detection with Deep Neural Nets.”
“Even though my teammates and I were from different disciplines, we pooled our knowledge and interests to propose the award-winning idea of a merger of convolutional neural networks with scoliosis detection, potentially enabling doctors to detect subtle abnormal features from X-rays in the early stages of scoliosis and classify the severity of the condition over time,” Rebello says.
“The project was a fun way to think outside of the box,” says another member of the winning team, Eric A. Magliarditi, a graduate student in aeronautics and astronautics. The third team member, Sandra Liu, who is studying for a master’s degree in mechanical engineering, said she had little knowledge of deep learning before the class but was eager to learn about its applications to soft robotics, her academic interest. “The highlights of the course were the labs,” she says. “In one, we got to complete the code for a neural net that could generate Irish folk songs. It was fun to be able to do ‘hands-on’ projects and also to learn more about real-life applications of deep learning.”
Magliarditi had a real-life interest in the topic the trio explored. “I had advanced scoliosis — I had surgery to fix it in 2014 — so this topic was extremely relevant and interesting to me,” he says. “I am not entirely sure if our idea could work, but it is something I want to investigate further because it has some interesting consequences if it were to work.”
Not every idea presented was so practical. “One project was an AI personal assistant,” says Amini. “And though it may be far-fetched, a full-fledged AI assistant, essentially a micro-drone the size of an insect that would fly around the house and keep track of your personal belongings, would be pretty amazing.”
Amini and Soleimany plan to teach the deep learning course again during IAP 2020. In the meantime, the lectures from the 2019 class can be found on the course website.