Want to learn how to train an artificial intelligence model? Ask a friend
Source: Kim Martineau
The MIT Machine Intelligence Community began with a few friends meeting over pizza to discuss landmark papers in machine learning. Three years later, the undergraduate club boasts 500 members, an active Slack channel, and an impressive lineup of student-led reading groups and workshops meant to demystify machine learning and artificial intelligence (AI) generally. This year, MIC and MIT Quest for Intelligence joined forces to advance their common cause of making AI tools accessible to all.
Starting last fall, the MIT Quest opened its offices to MIC members and extended access to IBM and Google-donated cloud credits, providing a boost of computing power to students previously limited to running their AI models on desktop machines loaded with extra graphics processors. The MIT Quest and MIC are now collaborating on a host of projects, independently and through MIT’s Undergraduate Research Opportunities Program (UROP).
“We heard about their mission to spread machine learning to all undergrads and thought, ‘That’s what we’re trying to do — let’s do it together!” says Joshua Joseph, chief software engineer with the MIT Quest Bridge.
A makerspace for AI
U.S. Army ROTC students Ian Miller and Rishi Shah came to MIC for the free cloud credits, but stayed for the workshop on neural computing sticks. A compute stick allows mobile devices to do image processing on the fly, and when the cadets learned what one could do, they knew their idea for a portable computer vision system would work.
“Without that, we’d have to send images to a central place to do all this computing,” says Miller, a rising junior. “It would have been a logistical headache.”
Built in two months, for $200, their wallet-sized device is designed to plug into a tablet strapped to an Army soldier’s chest and scan the surrounding area for cars and people. With more training, they say, it could learn to spot cellphones and guns. In May, the cadets demo'd their device at MIT’s Soldier Design Competition and were invited by an Army sergeant to visit Fort Devens to continue working on it.
Machine Intelligence Community members and ROTC students Ian Miller and Rishi Shah present a portable computer vision system they built to help soldiers detect cars and people in their field of view.
The student-led MIT Machine Intelligence Community joined forces this year with the Quest for Intelligence to advance their common cause of making AI tools accessible to all. Here, rising senior Michael Silver sketches out a design for a container-based cloud service that would allow MIT undergraduates to log in and train an AI model from anywhere.
Machine Intelligence Community members and ROTC students Ian Miller and Rishi Shah present a portable computer vision system they built to help soldiers detect cars and people in their field of view.
The student-led MIT Machine Intelligence Community joined forces this year with the Quest for Intelligence to advance their common cause of making AI tools accessible to all. Here, rising senior Michael Silver sketches out a design for a container-based cloud service that would allow MIT undergraduates to log in and train an AI model from anywhere.
Rose Wang, a rising senior majoring in computer science, was also drawn to MIC by the free cloud credits, and a chance to work on projects with the Quest for Intelligence and with other students. This spring, she used IBM cloud credits to run a reinforcement learning model that’s part of her research with MIT Professor Jonathan How, training robot agents to cooperate on tasks that involve limited communication and information. She recently presented her results at a workshop at the International Conference on Machine Learning.
“It helped me try out different techniques without worrying about the compute bottleneck and running out of resources,” she says.
Improving AI access at MIT
The MIC has launched several AI projects of its own. The most ambitious is Monkey, a container-based, cloud-native service that would allow MIT undergraduates to log in and train an AI model from anywhere, tracking the training as it progresses and managing the credits allotted to each student. On a Friday afternoon in April, the team gathered in a Quest for Intelligence conference room as Michael Silver, a rising senior, sketched out the modules Monkey would need.
As Silver scrawled the words "Docker Image Build Service" on the board, the student assigned to research the module apologized. “I didn’t make much progress on it because I had three midterms!” he said.
The planning continued, with Steven Shriver, a software engineer with the Quest Bridge, interjecting bits of advice. The students had assumed the container service they planned to use, Docker, would be secure. It isn’t.
“Well, I guess we have another task here,” said Silver, adding the word “security” to the white board.
Later, the sketch would be turned into a design document and shared with the two UROP students helping to execute Monkey. The team hopes to launch sometime next year.
“The coding isn’t the difficult part,” says UROP student Amanda Li, a member of MIC Dev-Ops. “It’s the exploring the server side of machine learning — Docker, Google Cloud, and the API. The most important thing I’ve learned is how to efficiently design and pipeline a project as big as this.”
Silver knew he wanted to be an AI engineer in 2016, when the computer program AlphaGo defeated the world’s reigning Go champion. As a senior at Boston University Academy, Silver worked on natural language processing in the lab of MIT Professor Boris Katz, and has continued to work with Katz since coming to MIT. Seeking more coding experience, he left HackMIT, where he had been co-director, to join MIC Dev-Ops.
“A lot of students read about machine learning models, but have no idea how to train one,” he says. “Even if you know how to train one, you’d need to save up a few thousand dollars to buy the GPUs to do it. MIC lets students interested in machine learning reach that next level.”
Conceived by MIC members, a second project is focused on making AI research papers posted on arXiv easier to explore. Nearly 14,000 academic papers are uploaded each month to the site, and although papers are tagged by field, drilling into subtopics can be overwhelming.
Wang, for one, grew frustrated while doing a basic literature search on reinforcement learning. “You have a ton of data and no effective way of representing it to the user,” she says. “It would have been useful to see the papers in a larger context, and to explore by number of citations or their relevance to each other.”
A third MIC project focuses on crawling MIT’s hundreds of listservs for AI-related talks and events to populate a Google calendar. The tool will be closely patterned after an app Silver helped build during MIT’s Independent Activities Period in January. Called Dormsp.am, the app classifies listserv emails sent to MIT undergraduates and plugs them into a calendar-email client. Students can then search for events by day or by a color-coded topic, such as tech, food, or jobs. Once Dormsp.am launches, Silver will adapt it to search for and post AI-related events at MIT to an MIC calendar.
Silver says the team spent extra time on the user interface, taking a page from MIT Professor Daniel Jackson’s Software Studio class. “This is an app that can live or die on its usability, so the front end is really important,” he says.
Wang is now collaborating with Moin Nadeem, MIC’s outgoing president, to build the visualization tool. It’s exactly the kind of hands-on experience MIC was intended to provide, says Nadeem, a rising senior. “Students learn fundamental concepts in class but don’t know how to implement them,” he says. “I’m trying to build what freshman me would have liked to have had: a community of people excited to do interesting stuff with machine learning.”
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