Venture capitalists gather to discuss

Surviving breast cancer changed the course of Regina Barzilay’s research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. That includes what treatments to recommend, but also whether a patient’s sample even warrants a cancer diagnosis, she explained at the Nov. 10 Machine Intelligence Summit, organized by MIT and venture capital firm Pillar.

“We do more machine learning when we decide on Amazon which lipstick you would buy,” said Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT. “But not if you were deciding whether you should get treated for cancer.”

Barzilay now studies how smarter computing can help patients. She wields the powerful predictive approach called machine learning, a technique that allows computers, given enough data and training, to pick out patterns on their own — sometimes even beyond what humans are capable of pinpointing.

Machine learning has long been vaunted in consumer contexts — Apple’s Siri can talk with us because machine learning enables her to understand natural human speech — yet the summit gave a glimpse of the approach’s much broader potential. Its reach could offer not only better Siris (e.g., Amazon’s “Alexa”), but improved health care and government policies.

Machine intelligence is “absolutely going to revolutionize our lives,” said Pillar co-founder Jamie Goldstein ’89. Goldstein and Anantha Chandrakasan, head of the MIT Department of Electrical Engineering and Computer Science (EECS) and the Vannevar Bush Professor of Electrical Engineering and Computer Science, organized the conference to bring together industry leaders, venture capitalists, students, and faculty from the Computer Science and Artificial Intelligence (CSAIL), Institute for Data, Systems, and Society (IDSS), and the Laboratory for Information and Decision Systems (LIDS) to discuss real-world problems and machine learning solutions.

Barzilay is already thinking along those lines. Her group’s work aims to help doctors and patients make more informed medical decisions with machine learning. She has a vision for the future patient in the oncologist’s office: “If you’re taking this treatment, [you’ll see] how your chances are going to be changed.”

Machine senses

Machine learning has already proven powerful. But Antonio Torralba, professor of electrical engineering and computer science, believes that machines can learn faster, and thereby do more. His team’s approach mimics the way humans learn in infancy. “We just start playing with things and seeing how they feel,” Torralba said. To illustrate, he showed the room a video of a baby turning over squeaky bubble wrap in her hands. Importantly, we notice the noises things make when we move them around, he said.