Addressing the Promises and Challenges of AI

Estimated reading time: 6 minutes

For Institute Professor Robert Langer, another panelist in “Computing for the Marketplace,” AI holds great promise for early disease diagnoses. With enough medical data, for instance, AI models can identify biological “fingerprints” of certain diseases in patients. “Then, you can use AI to analyze those fingerprints and decide what … gives someone a risk of cancer,” he said. “You can do drug testing that way too. You can see [a patient has] a fingerprint that … shows you that a drug will treat the cancer for that person.”

But in the “Computing the Future” section, David Siegel, co-chair of Two Sigma Investments and founding advisor for the MIT Quest for Intelligence, addressed issues with data, which is at the heart of AI. With the aid of AI, Siegel has seen computers go from helpful assistants to “routinely making decisions for people” in business, health care, and other areas. While AI models can benefit the world, “there is a fear that we may move in a direction that’s far from an algorithmic utopia.”

Siegel drew parallels between AI and the popular satirical film “Dr. Strangelove,” in which an “algorithmic doomsday machine” threatens to destroy the world. AI algorithms must be made unbiased, safe, and secure, he said. That involves dedicated research in several important areas, at the MIT Schwarzman College of Computing and around the globe, “to avoid a Strangelove-like future.”

One important area is data bias and security. Data bias, for instance, leads to inaccurate and untrustworthy algorithms. And if researchers can guarantee the privacy of medical data, he added, patients may be more willing to contribute their records to medical research.

Siegel noted a real-world example where, due to privacy concerns, the Centers for Medicare and Medicaid Services years ago withheld patient records from a large research dataset being used to study substance misuse, which is responsible for tens of thousands of U.S. deaths annually. “That omission was a big loss for researchers and, by extension, patients,” he said. “We are missing the opportunity to solve pressing problems because of the lack of accessible data. … Without solutions, the algorithms that drive our world are at high risk of becoming data-compromised.”

Seeking Humanity in AI

In a panel discussion earlier in the day, “Computing: Reflections and the Path Forward,” Sherry Turkle, the Abby Rockefeller Mauzé Professor of the Social Studies of Science and Technology, called on people to avoid “friction free” technologies — which help people avoid stress of face-to-face interactions.

AI is now “deeply woven into this [friction-free] story,” she said, noting that there are apps that help users plan walking routes, for example, to avoid people they dislike. “But who said a life without conflict - makes for the good life?” she said.

She concluded with a “call to arms” for the new college to help people understand the consequences of the digital world where confrontation is avoided, social media are scrutinized, and personal data are sold and shared with companies and governments: “It’s time to reclaim our attention, our solitude, our privacy, and our democracy.”

Speaking in the same section, Patrick H. Winston, the Ford Professor of Engineering at MIT, concluded on an equally humanistic — and optimistic — message. After walking the audience through the history of AI at MIT, including his run as director of the Artificial Intelligence Laboratory from 1972 to 1997, he told the audience he was going to discuss the greatest computing innovation of all time.

“It’s us,” he said, “because nothing can think like we can. We don’t know how to make computers do it yet, but it’s something we should aspire to. … In the end, there’s no reason why computers can’t think like we [do] and can’t be ethical and moral like we aspire to be.”

• < Previous Page