While working toward her dissertation in computer science at MIT, Marzyeh Ghassemi wrote several papers on how machine-learning techniques from artificial intelligence could be applied to clinical data in order to predict patient outcomes. “It wasn’t until the end of my PhD work that one of my committee members asked: ‘Did you ever check to…
In fall 2020, MIT’s School of Engineering and Takeda Pharmaceuticals Company Limited launched the MIT-Takeda Program, a collaboration to support members of the MIT community working at the intersection of artificial intelligence and human health. Housed at the Abdul Latif Jameel Clinic for Machine Learning in Health, the collaboration aims to use artificial intelligence to…
Posted by Alexey Kurakin, Software Engineer and Roxana Geambasu, Visiting Faculty Researcher, Google Research Machine learning (ML) models are becoming increasingly valuable for improved performance across a variety of consumer products, from recommendations to automatic image classification. However, despite aggregating large amounts of data, in theory it is possible for models to encode characteristics of…
Antibodies, small proteins produced by the immune system, can attach to specific parts of a virus to neutralize it. As scientists continue to battle SARS-CoV-2, the virus that causes Covid-19, one possible weapon is a synthetic antibody that binds with the virus’ spike proteins to prevent the virus from entering a human cell. To develop…
A police officer is at the scene of a murder. No witnesses. No camera footage. No obvious suspects or motives. Just a bit of hair on the sleeve of the victim’s jacket. DNA from the cells of one strand is copied and compared against a database. No match comes back, and the case goes cold. …
Posted by Sungyong Seo, Software Engineer and Sercan O. Arik, Research Scientist, Google Research, Cloud AI Team Deep neural networks (DNNs) provide more accurate results as the size and coverage of their training data increases. While investing in high-quality and large-scale labeled datasets is one path to model improvement, another is leveraging prior knowledge, concisely…
Posted by Abhijit Guha Roy, Research Software Engineer and Jie Ren, Research Scientist, Google Research Deep machine learning (ML) systems have achieved considerable success in medical image analysis in recent years. One major contributing factor is access to abundant labeled datasets, which are used to train highly effective supervised deep learning models. However, in the…
OpenAI has built a new version of GPT-3, its game-changing language model, that it says does away with some of the most toxic issues that plagued its predecessor. The San Francisco-based lab says the updated model, called InstructGPT, is better at following the instructions of people using it—known as “alignment” in AI jargon—and thus produces less offensive…
The human brain is finely tuned not only to recognize particular sounds, but also to determine which direction they came from. By comparing differences in sounds that reach the right and left ear, the brain can estimate the location of a barking dog, wailing fire engine, or approaching car. MIT neuroscientists have now developed a…
Neural networks are sometimes called black boxes because, despite the fact that they can outperform humans on certain tasks, even the researchers who design them often don’t understand how or why they work so well. But if a neural network is used outside the lab, perhaps to classify medical images that could help diagnose heart…