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.”
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.”
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.”
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.”
Prof. Philip Isola and Prof. Daniela Rus, director of CSAIL, speak with Associated Press reporter Matt O’Brien about AI generated images and videos. Rus says the computing resources required for AI video generation are “significantly higher than for still image generation” because “it involves processing and generating multiple frames for each second of video.”
Prof. Daniela Rus, director of CSAIL, speaks with Boston Globe reporter Evan Sellinger about her new book, “The Heart and the Chip: Our Bright Future With Robots,” in which she makes the case that in the future robots and humans will be able to team up to create a better world. “I want to highlight that machines don’t have to compete with humans, because we each have different strengths. Humans have wisdom. Machines have speed, can process large numbers, and can do many dull, dirty, and dangerous tasks,” Rus explains. “I see robots as helpers for our jobs. They’ll take on the routine, repetitive tasks, ensuring human workers focus on more complex and meaningful work.”
Prof. Stuart Madnick speaks with CNBC reporter Kevin Williams about how the rise of generative AI technologies could lead to cyberattacks on physical infrastructure. “If you cause a power plant to stop from a typical cyberattack, it will be back up and online pretty quickly,” Madnick explains, “but if hackers cause it to explode or burn down, you are not back online a day or two later; it will be weeks and months because a lot of the parts in these specialized systems are custom made.”
Prof. Skylar Tibbits speaks with Tech Briefs reporter Andrew Corselli about his team’s work developing a new “additive manufacturing technique that can print rapidly with liquid metal, producing large-scale parts like table legs and chair frames in a matter of minutes.” Of his advice for engineers aiming to bring their ideas to fruition, Tibbits emphasizes: “Work hard, fail a lot, keep trying, don’t give up, and have amazing people around you. We're a research lab, so our whole goal is to go from impossible to possible. So, we're allowed to fail; we're not limited by profitability or customer demand or economy.”
Prof. Charles Stewart III and Ben Adida PhD ’06 speak with Fast Company reporter Spenser Mestel about how to restore the public’s faith in voting technology. Adida discusses his work launching VotingWorks, a non-profit focused on building voting machines. VotingWorks is “unique among the legacy voting technology vendors," writes Mestel. “The group has disclosed everything, from its donors to the prices of its machines.”
Researchers at MIT and elsewhere have developed a machine-learning model that can identify which drugs should not be taken together, reports Politico. “The researchers built a model to measure how intestinal tissue absorbed certain commonly used drugs,” they write. “They then trained a machine-learning algorithm based on their new data and existing drug databases, teaching the new algorithm to predict which drugs would interact with which transporter proteins.”
MIT researchers have developed a new technique “that could allow most large language models (LLMs) like ChatGPT to retain memory and boost performance,” reports Tony Ho Tran for the Daily Beast. “The process is called StreamingLLM and it allows for chatbots to perform optimally even after a conversation goes on for more than 4 million words,” explains Tran.
Prof. David Rand speaks with VOA News about the potential impact of adding watermarks to AI generated materials. “My concern is if you label as AI-generated, everything that’s AI-generated regardless of whether it’s misleading or not, people essentially are going to stop really paying attention to it,” says Rand.