Building on what they have learned conceptually about artificial intelligence and machine learning (ML) this year, students across the Greater Boston area will put their new skills into practice as part of the experiential learning opportunities offered through Break Through Tech. I had the opportunity to apply it to an industry project. AI at MIT.
Hosted by the MIT Schwarzman College of Computing, Break Through Tech AI bridges the talent gap between women and the underrepresented gender in computing by providing skill-based training, industry-relevant portfolios, and mentoring to undergraduate students. It is a pilot program aimed at filling the Place in a regional metropolitan area to make careers in data science, machine learning, and artificial intelligence more competitive.
“Programs like Break Through Tech AI give us the opportunity to connect with other students and institutions, and bring MIT’s values of diversity, equity, and inclusion to life in the spaces we own. and applications,” says Alana Anderson. Associate Dean of Diversity, Equity, and Inclusion at MIT Schwartzman College of Computing.
Last summer, the first cohort of 33 undergraduate students from 18 Greater Boston Area schools, including Salem State University, Smith College, and Brandeis University, enrolled in a free 18-year course, including an eight-week online skills-based course. We have started our monthly program. Fundamentals of AI and Machine Learning. Students then split into small groups in the fall to collaborate on six machine learning challenge projects presented by MathWorks, MIT-IBM Watson AI Lab, and Replicate. Students spent more than five hours each week meeting with their team, teaching assistants, and project advisors, juggling their normal academic course load with other day-to-day activities and responsibilities.
This assignment gave undergraduates the opportunity to contribute to a real-world project being worked on by an industry organization and test their machine learning skills. Members of each organization also acted as project advisors, providing encouragement and guidance to the entire team.
Aude Oliva, Director of Strategic Industry Engagements at MIT Schwartzman College of Computing and MIT Director of the MIT-IBM Watson AI Lab, said: “These projects will be add-ons to our machine learning portfolio that we can share as working examples when we are ready to apply for an AI job.”
Over 15 weeks, the team dug into large real-world datasets to train, test, and evaluate machine learning models in a variety of contexts.
At a showcase event held at MIT in December, students celebrated their achievements and six teams gave final presentations of their AI projects. The project not only allowed students to gain experience in AI and machine learning, but also “improved their knowledge base and skills in presenting their work to both technical and non-technical audiences.” ‘, says Oliva.
In a traffic data analysis project, students received training in MATLAB, a programming and numerical computing platform developed by The MathWorks, to create a model that predicts future vehicle trajectories to enable decision-making in autonomous driving. . “It is important to realize that AI is not very intelligent,” said Brandeis University student Srishti Nautiyal, while introducing his team’s project to the audience. Nautiyal, a physics and mathematics major with companies already enabling self-driving cars, says her team will consider the ethical issues of technology in models for the safety of passengers, drivers and pedestrians. He said he was very enthusiastic about
Training models using census data is often tricky and full of holes, so be careful. In the MIT-IBM Watson AI Lab project on Algorithmic Fairness, the most difficult task for the team is to clean up the vast amount of unorganized data so that insights can be derived from it. was. This project aims to create fairness demonstrations applied to real datasets to evaluate and compare the effectiveness of different fairness interventions and fairness metric learning techniques, and ultimately It can serve as an educational resource for data scientists interested in learning about and using AI fairness. It also aims to promote the practice of evaluating the ethical impact of machine learning models in the industry.
Other challenge projects included ML-assisted whiteboards for non-technical people to interact with ready-made machine learning models and sign language recognition models to help people with disabilities communicate with others. rice field. The team that worked on the Visual Language App set out to incorporate over 50 languages into their models and 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 showed persistence and grit to cross the finish line with their projects. With a final presentation marking the end of the fall semester, the student will return to MIT in the spring to continue his Break Through Tech AI journey and work on another round of his AI project. Now, students will be able to work with Google to tackle new machine learning challenges and further hone their AI skills with the aim of launching successful careers in AI.
