The direct conversion of methane gas to liquid methanol at the site where it is extracted from the Earth holds enormous potential for addressing a number of significant environmental problems. Developing a catalyst for that conversion has been a critical focus for Associate Professor Heather Kulik and the lab she directs at MIT. As important…
imodels: A python package with cutting-edge techniques for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. Recent machine-learning advances have led to increasingly complex predictive models, often at the cost of interpretability. We often need interpretability, particularly in high-stakes applications such as medicine, biology, and political science (see here and here…
Posted by Chelsea Finn, Research Adviser and Eric Jang, Senior Research Scientist, Robotics at Google People can flexibly maneuver objects in their physical surroundings to accomplish various goals. One of the grand challenges in robotics is to successfully train robots to do the same, i.e., to develop a general-purpose robot capable of performing a multitude…
It’s one thing to know whether an individual customer is intrigued by a new mattress or considering a replacement for their sofa’s throw pillows; it’s another to know to how to move these people to go ahead and make a purchase. When deployed strategically, artificial intelligence (AI) can be a marketer’s trusted customer experience ally—transforming…
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…
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…
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…