Prof. David Autor speaks with Bloomberg’s Odd Lots podcast hosts Joe Weisenthal and Tracy Alloway about how AI could be leveraged to improve inequality, emphasizing the policy choices governments will need to make to ensure the technology is beneficial to humans. “Automation is not the primary source of how innovation improves our lives,” says Autor. “Many of the things we do with new tools is create new capabilities that we didn’t previously have.”
Prof. David Autor and Prof. Daron Acemoglu speak with New York Times columnist Peter Coy about the impact of AI on the workforce. Acemoglu and Autor are “optimistic about a continuing role for people in the labor market,” writes Coy. “An upper bound of the fraction of jobs that would be affected by A.I. and computer vision technologies within the next 10 years is less than 10 percent,” says Acemoglu.
MIT researchers have found that “when an AI tool for radiologists produced a wrong answer, doctors were more likely to come to the wrong conclusion in their diagnoses,” report Daniel Payne, Carmen Paun, Ruth Reader and Erin Schumaker for Politico. “The study explored the findings of 140 radiologists using AI to make diagnoses based on chest X-rays,” they write. “How AI affected care wasn’t dependent on the doctors’ levels of experience, specialty or performance. And lower-performing radiologists didn’t benefit more from AI assistance than their peers.”
Research Scientists Karthik Srinivasan and Robert Ajemian speak with The Economist’s Babbage podcast about the role of big data and specialized computer chips in the development of artificial intelligence. “I think right now, actually, the goal should be just to harness big data as much as we can,” says Ajemian. “It’s kind of this new tool, a new toy, that humanity has to play with and obviously we have to play with it responsibly. The architectures that they built today are not that different than the ones that were built in the 60s and the 70s and the 80s. The difference is back then they did not have big data and tremendous compute."
Boston Magazine spotlights MIT’s leading role in the AI revolution in the Greater Boston area. “With a $2 million grant from the Department of Defense, MIT’s Artificial Intelligence Lab combines with a new research group, Project MAC, to create what’s now known as the Computer Science and Artificial Intelligence Laboratory (CSAIL). Over the next three years, researchers lead groundbreaking machine-learning projects such as the creation of Eliza, a psychotherapy-based computer program that could process languages and establish emotional connections with users (a primordial chatbot, essentially).”
Washington Post columnist Eduardo Porter spotlights various MIT research efforts aimed at ensuring that AI is used to empower human workers rather than replace them. Prof. David Autor notes: “AI, if used well, can assist with restoring the middle-skill, middle-class heart of the U.S. labor market that has been hollowed out by automation and globalization.”
Harry Rein '15, MEng '16 and Chris Tinsley MBA '20 co-founded ShopMy, a marketing platform designed to connect content creators with brands and monetize their content, reports Laruen Forristal for TechCrunch. “ShopMy’s marketing platform equips creators with the tools they need to earn from their product recommendations, like building digital storefronts, accessing a catalog of millions of products, making commissionable links and chatting directly with companies via mobile app,” explains Forristal.
Prof. Pulkit Agrawal and graduate student Gabriel Margolis speak with The Economist’s Babbage podcast about the simulation research and technology used in developing intelligent machines. “Simulation is a digital twin of reality,” says Agrawal. “But simulation still doesn’t have data, it is a digital twin of the environment. So, what we do is something called reinforcement learning which is learning by trial and error which means that we can try out many different combinations.”
Birago Jones SM '12 and Karthik Dinakar SM '12, PhD '17 co-founded Pienso – an AI platform that “lets users build and deploy models without having to write code,” reports Kyle Wiggers for TechCrunch. “Pienso’s flexible, no-code interface allows teams to train models directly using their own company’s data,” says Jones. “This alleviates the privacy concerns of using … models, and also is more accurate, capturing the nuances of each individual company.”
Prof. Thomas Malone speaks with Poets & Quants for Executives reporter Alison Damast about the executive education course he teaches with Prof. Daniela Rus that aims to provide senior-level managers with a better sense of how AI works. “We are certainly not trying to teach people to understand the details of how to write AI programs, though some of those in the course may know that already,” Malone says. “What we are trying to do is give them a sense of when it is easy and when it is hard to use AI technology at various times for different kinds of business applications.”
Research Scientist Eva Ponce speaks with Fast Company to explain how AI will impact supply chains. “One of the most common reasons I have seen companies fail when implementing disruptive technologies like AI is when they are rushing, with a lack of clear vision,” says Ponce.
Mashable reporter Adele Walton spotlights Joy Buolamwini PhD '22 and her work in uncovering racial bias in digital technology. “Buolamwini created what she called the Aspire Mirror, which used face-tracking software to register the movements of the user and overlay them onto an aspirational figure,” explains Walton. “When she realised the facial recognition wouldn’t detect her until she was holding a white mask over her face, she was confronted face on with what she termed the ‘coded gaze.’ She soon founded the Algorithmic Justice League, which exists to prevent AI harms and increase accountability.”
Writing for Fast Company, Senior Lecturer Guadalupe Hayes-Mota '08, SM '16, MBA '16 shares methods to address the influence of AI in healthcare. “Despite these advances [of AI in healthcare], the full spectrum of AI’s potential remains largely untapped,” explains Hayes-Mota. “Systemic hurdles such as data privacy concerns, the absence of standardized data protocols, regulatory complexities, and ethical dilemmas are compounded by an inherent resistance to change within the healthcare profession. These barriers underscore the urgent need for transformative action from all stakeholders to fully harness AI’s capabilities.”
A new study conducted by researchers at MIT and elsewhere has found large language models (LLMs) can be used to predict the future as well as humans can, reports Chris Stokel-Walker for Fast Company. “Accurate forecasting of future events is very important to many aspects of human economic activity, especially within white collar occupations, such as those of law, business and policy,” says postdoctoral fellow Peter S. Park.
Prof. Daniela Rus, director of CSAIL, speaks with The Economist’s Babbage podcast about the history and future of artificial neural networks and their role in large language models. “The early artificial neuron was a very simple mathematical model,” says Rus. “The computation was discrete and very simple, essentially a step function. You’re either above or below a value.”