Taking what they learned about artificial intelligence and machine learning (ML) conceptually this year, students from across the greater Boston area had the opportunity to apply their new skills to real-world industry projects as part of an experiential learning opportunity. offered through Innovating Technological AI at MIT.
Hosted by MIT Schwarzman College of Computing, Break Through Tech AI is a pilot program that aims to close the talent gap for women and underrepresented genders in computing fields by providing skills-based training, industry-relevant portfolios, and Mentoring for college students in regional metropolitan 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 to learning and application in the spaces we have,” says Alana Anderson. , Vice Dean for Diversity, Equity, and Inclusion at the MIT Schwarzman College of Computing.
The inaugural cohort of 33 college students from 18 greater 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. The students then broke into small groups in the fall to collaborate on six machine learning challenge projects brought to them by MathWorks, MIT-IBM Watson AI Lab, and Replicate. Students spent five hours or more each week meeting with their teams, teaching assistants, and project advisors, including once a month at MIT, while juggling their regular academic load with other daily activities and responsibilities.
The challenges gave college students the opportunity to help contribute to real projects that industry organizations are working on and put their machine learning skills to the test. Members of each organization also served as advisors to the project, providing encouragement and guidance to the teams throughout.
“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 director of the MIT-IBM Watson AI Lab at MIT. “These projects will be a complement to their machine learning portfolio that they can share as a working example when they are ready to apply for a job in AI.”
Over the course of 15 weeks, the teams delved into large-scale, real-world data sets to train, test, and evaluate machine learning models in a variety of contexts.
In December, students celebrated the fruits of their labor at a showcase event held at MIT where the six teams gave final presentations on their AI projects. The projects not only allowed students to develop their expertise in artificial intelligence and machine learning, but helped “improve their knowledge base and skills in presenting their work to both technical and non-technical audiences,” says Oliva.
For a traffic data analysis project, students trained in MATLAB, a numerical computing and programming 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 smart. It’s as smart as you make it and that’s exactly what we’re trying to do,” said Srishti Nautiyal, a student at Brandeis University, as he introduced his team’s project to the audience. With companies already making autonomous vehicles a reality, from planes to trucks, Nautiyal, a physics and math student, shared that his team was also highly motivated to consider the ethical issues of the technology in their model for passenger safety. , drivers and pedestrians. .
Using census data to train a model can be tricky because it is often messy and full of holes. In a project on algorithmic fairness for the MIT-IBM Watson AI Lab, the most difficult task for the team was having to clean up mountains of unorganized data so that they could still gain insight from it. The project, which aimed to create an equity demo applied on a real dataset to test and compare the effectiveness of different equity interventions and fair metrics learning techniques, could eventually serve as an educational resource for interested data scientists. in learning about fairness in AI and using it. in their work, as well as promoting the practice of evaluating the ethical implications of machine learning models in industry.
Other challenging projects included an ML-assisted whiteboard for non-technical people to interact with out-of-the-box machine learning models, and a sign language recognition model to help disabled people communicate with others. A team working on a visual language app set out to include more than 50 languages in their model to increase access 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, students persisted and demonstrated courage to cross the finish line on their projects. With final submissions marking the conclusion of the fall semester, students will return to MIT in the spring to continue their Break Through Tech AI journey to tackle another round of AI projects. This time, students will work with Google on new machine learning challenges that will further hone their AI skills to launch a successful AI career.
