Students from across the Boston area took what they learned conceptually about artificial intelligence and machine learning (ML) this year and had the opportunity to test their new skills through an experiential learning opportunity offered by Break Through Tech real industrial projects apply AI at MIT.
Break Through Tech AI, hosted by MIT Schwarzman College of Computing, is a pilot program that aims to close the talent gap for women and underrepresented genders in computing fields by providing competency-based training, industry-relevant portfolios, and mentoring for undergraduate students in regional metro areas to position them more competitively for careers in data science, machine learning, and artificial intelligence.
“Programs like Break Through Tech AI give us the opportunity to connect with other students and other institutions, and allow us to bring MIT values of diversity, equity and inclusion into learning and application in the spaces we use entertain,” said Alana Anderson, associate dean of diversity, equity, and inclusion at MIT’s Schwarzman College of Computing.
The inaugural cohort of 33 students from 18 Boston-area schools, including Salem State University, Smith College and Brandeis University, began the free, 18-month program last summer with an eight-week, skills-based online course to learn the Basics of AI and machine learning. Students then split into small groups in the fall to collaborate on six machine learning challenge projects presented to them by MathWorks, MIT-IBM Watson AI Lab, and Replicate. Students dedicated five hours or more each week to meeting with their teams, teaching assistants, and project advisors, including a monthly meeting at MIT, while balancing their regular academic study load with other daily activities and responsibilities.
The challenges gave students the opportunity to contribute to current projects that industry organizations are working on and demonstrate their machine learning skills. Members from each organization also acted as project advisors, encouraging and guiding the teams throughout the project.
“Students gain industry experience by working closely with their project advisors,” says Aude Oliva, director of strategic industry engagement at MIT Schwarzman College of Computing and MIT director of the MIT-IBM Watson AI Lab. “These projects will be an add-on to their machine learning portfolio to share as a working example when they are ready to apply for a job in AI.”
Over the course of 15 weeks, teams delved into rich real-world datasets to train, test, and evaluate machine learning models in a variety of contexts.
In December, the students celebrated the fruits of their labor at a showcase event at MIT, where the six teams gave final presentations on their AI projects. The projects not only allowed the students to build their experience with AI and machine learning, but also helped “improve their knowledge base and their skills to present their work to both a technical and non-technical audience,” says Oliva.
For a traffic data analysis project, students were trained in MATLAB, a programming and numerical computing platform developed by MathWorks, to create a model that enables decision-making in autonomous driving by predicting future vehicle trajectories. “It is important to realize that AI is not that intelligent. It’s only as smart as you make it, and that’s what we were trying to do,” said Brandeis University graduate student Srishti Nautiyal as she introduced her team’s project to the audience. With companies already realizing autonomous vehicles ranging from airplanes to trucks, Nautiyal, a physics and mathematics student, shared that her team is also highly motivated to consider the technology’s ethical issues in their model for passenger, driver and pedestrian safety .
Using census data to train a model can be difficult as it is often messy and full of holes. In an algorithmic fairness project for the MIT-IBM Watson AI Lab, the team’s most challenging task was cleaning up mountains of disorganized data so they could still extract insights from it. The project – which aimed to demonstrate fairness applied to a real dataset to assess and compare the effectiveness of different fairness interventions and fair metric learning techniques – could eventually serve as an educational resource for data scientists interested in to learn more about fairness in AI and to use it in their work; and to promote the practice of assessing the ethical implications of machine learning models in industry.
Other Challenge projects included an ML-enabled whiteboard for non-technical people to interact with pre-built machine learning models, and a sign language recognition model designed to help people with disabilities communicate with others. A team working on a visual language app wanted to include over 50 languages in their model to improve accessibility for millions of visually impaired people around the world. According to the team, similar apps on the market currently only offer up to 23 languages.
Throughout the semester, the students persevered and showed courage to cross the finish line with their projects. With final presentations marking the conclusion of the fall semester, students will return to MIT in the spring to continue their Break Through Tech AI journey and tackle another round of AI projects. This time, students will work with Google on new machine learning challenges that will allow them to hone their AI skills even further to launch a successful career in AI.