Prof. Arnold Barnett speaks with CNN reporter Jacopo Prisco about his forthcoming study examining the safety of air travel. “The main takeaway is that in the period between 2018 and 2022, the worldwide death risk per boarding was one in 13.4 million,” writes Prisco. “That means that if you picked a flight completely at random and just took it, your chance of dying in a plane crash or a terrorist act was about one in 13 million.”
Corey Jaskolski SM '02 founded Synthetaic, a software company that uses AI to “automate the analysis of large datasets, namely satellite imagery and video, not containing labels,” reports Kyle Wiggers for TechCrunch. “Synthetaic’s technology offers a transformative approach to AI model training and creation, addressing the critical needs of technical decision makers,” says Jaskolski.
Researchers from MIT and elsewhere have developed an AI model that is capable of identifying 3 ½ times more people who are at high-risk for developing pancreatic cancer than current standards, reports Felice J. Freyer for The Boston Globe. “This work has the potential to enlarge the group of pancreatic cancer patients who can benefit from screening from 10 percent to 35 percent,” explains Freyer. “The group hopes its model will eventually help detect risk of other hard-to-find cancers, like ovarian.”
Prof. Jessika Trancik speaks with Associated Press reporter Alexa St. John to discuss electric vehicle emissions and ownership costs. Trancik notes, “buyers should consider total cost of ownership, which for an EV is generally less than that of a gas-powered counterpart due to savings on maintenance and fuel.”
Axios reporter Ina Fried spotlights a new report by Prof. Emeritus Stuart Madnick that finds “2.6 billion personal records have been exposed in data breaches over the past two years and that number continues to grow.” Additionally, Madnick found that” “Data breaches in the US through the first nine months of the year are already 20% higher than for all of 2022.”
MIT researchers have “used an algorithm to sort through millions of genomes to find new, rare types of CRISPR systems that could eventually be adapted into genome-editing tools,” writes Sara Reardon for Nature. “We are just amazed at the diversity of CRISPR systems,” says Prof. Feng Zhang. “Doing this analysis kind of allows us to kill two birds with one stone: both study biology and also potentially find useful things.”
Writing for Fortune, Sloan research fellow Michael Schrage and his colleagues, explain how AI-enabled key performance indicators (KPIs) can help companies better understand and measure success. “Driving strategic alignment within their organization is an increasingly important priority for senior executives,” they write. “AI-enabled KPIs are powerful tools for achieving this. By getting their data right, using appropriate organizational constructs, and driving a cultural shift towards data-driven decision making, organizations can effectively govern the creation and deployment of AI-enabled KPIs."
Graduate student Shayne Longpre speaks with Washington Post reporter Nitasha Tiku about the ethical and legal implications surrounding language model datasets. Longpre says “the lack of proper documentation is a community-wide problem that stems from modern machine-learning practices.”
Curtis Northcutt SM '17, PhD '21, Jonas Mueller PhD '18, and Anish Athalye SB '17, SM '17, PhD '23 have co-founded Cleanlab, a startup aimed at fixing data problems in AI models, reports Alex Konrad for Forbes. “The reality is that every single solution that’s data-driven — and the world has never been more data-driven — is going to be affected by the quality of the data,” says Northcutt.
Writing for Nature, graduate student Jelle van der Hilst offers advice on determining whether the data resulting from an experiment is meaningful and useful. “Although in research it is crucial that you don’t fully trust your data until it has been triple-proven and peer-reviewed,” writes van der Hilst, “we do have to gain some operational confidence in our methods and results. Otherwise, crippled by self-doubt, we’d never bring any new research into the world.”
A new study by MIT researchers demonstrates how “machine-learning systems designed to spot someone breaking a policy rule—a dress code, for example—will be harsher or more lenient depending on minuscule-seeming differences in how humans annotated data that were used to train the system,” reports Ananya for Scientific American. “This is an important warning for a field where datasets are often used without close examination of labeling practices, and [it] underscores the need for caution in automated decision systems—particularly in contexts where compliance with societal rules is essential,” says Prof. Marzyeh Ghassemi.
Forbes reporter Rob Toews spotlights Prof. Daniela Rus, director of CSAIL, and research affiliate Ramin Hasani and their work with liquid neural networks. “The ‘liquid’ in the name refers to the fact that the model’s weights are probabilistic rather than constant, allowing them to vary fluidly depending on the inputs the model is exposed to,” writes Toews.
Principal Research Scientist Kalyan Veeramachaneni speaks with Fast Company reporter Sam Becker about his work in developing the Synthetic Data Vault, which is helpful for creating synthetic data sets, reports Sam Becker for Fast Company. “Fake data is randomly generated,” says Veeramachaneni. “While synthetic data is trying to create data from a machine learning model that looks very realistic.”
Studies by researchers at MIT have found “that shifting to electric vehicles delivers a 30% to 50% reduction in greenhouse gas emissions over combustion vehicles,” reports Tom Krisher for Associated Press. According to Prof. Jessika Trancik, “electric vehicles are cleaner over their lifetimes, even after taking into account the pollution caused by the mining of metals for batteries,” writes Krisher.
Prof. Daniela Rus, director of CSAIL, writes for Forbes about Prof. Dina Katabi’s work using insights from wireless systems to help glean information about patient health. “Incorporating continuous time data collection in healthcare using ambient WiFi detectable by machine learning promises an era where early and accurate diagnosis becomes the norm rather than the exception,” writes Rus.