Our mission is to reverse engineer intelligence. The nature of intelligence, how the brain produces it, and how it can be engineered, is simultaneously the greatest open question in the natural sciences and the most important engineering challenge of our time. At MIT, we believe these challenges are interlocked aspects of the same grand mission. By bringing together diverse teams to work at the intersection of science and engineering, we aim for breakthroughs in both natural and artificial intelligence.
Quest Research Affiliates
MIT–IBM Watson AI Lab
The MIT–IBM Watson AI Lab aims to advance AI hardware, software, and algorithms related to deep learning and reasoning; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society. Co-led by Aude Oliva at MIT and David Cox at IBM, and chaired by Anantha Chandrakasan, Dean of the School of Engineering, and Dario Gil, vice president of AI and IBM Q at IBM Research, the Lab will invest $240 million in AI efforts over the next 10 years. Current funded projects are answering questions such as:
- How can advanced algorithms expand capabilities in machine learning and reasoning?
- How can quantum computing optimize machine-learning algorithms and other AI applications?
- How can AI ensure the security and privacy of medical data?
MIT-SenseTime Alliance on Artificial Intelligence
The MIT-SenseTime Alliance on Artificial Intelligence opens new avenues of discovery across MIT in areas such as computer vision, human-intelligence-inspired algorithms, medical imaging, and robotics. SenseTime, founded by MIT alumnus Xiao’ou Tang PhD ’96, specializes in computer vision and deep learning technologies. The MIT-SenseTime Alliance will provide funding for 27 projects involving 50 principal investigators from MIT departments and labs within all five schools. Current funded projects are answering questions such as:
- How can linguistic theory transform machine-learning algorithms to better approximate how people converse?
- How can artificial systems, like robots, learn common sense knowledge?
- How can tools in product design and systems architecture capitalize on strategies that combine human and machine intelligence?
MIT-Liberty Mutual Insurance Collaboration
The MIT-Liberty Mutual Insurance Collaboration represents a $25 million, five-year commitment to advance artificial intelligence research in computer vision, computer language understanding, machine learning fairness, data privacy and security, and risk-aware decision making, among other topics. Research topics under discussion include efforts to make decision-making algorithms transparent to customers and regulators, use computer vision to reduce crashes by identifying dangerous driving conditions and roadways, further protect the anonymity and security of personal data, use computer language understanding to analyze insurance claims and speed processing and compensation, and structure investment portfolios.
Center for Deployable Machine Learning
From self-driving cars to real-time language translation, technology once dismissed as science fiction is close to becoming reality. Or is it? The truth is, most AI systems aren’t ready for prime time. They’re prone to failure beyond the narrow settings in which they were designed, vulnerable to malicious interference and may carry unexpectedly high societal costs when scaled. To build next generation AI systems that are robust, trustworthy and socially responsible, we need a radically different approach. The new MIT Center for Deployable Machine Learning offers the breadth and depth of knowledge to help AI achieve its potential in science, industry and society. Led by Aleksander Madry, a professor at MIT’s Department of Electrical Engineering and Computer Science, the center brings together faculty experts in interpretability, security, computer vision and applied machine learning.