Neural networks, a type of machine-learning model, are being used to help humans complete a wide variety of tasks, from predicting if someone’s credit score is high enough to qualify for a loan to diagnosing whether a patient has a certain disease. But researchers still have only a limited understanding of how these models work. Whether a given model is optimal for certain task remains an open question. MIT researchers have found some answers. They conducted an analysis of neural networks and proved that they can be designed so they are “optimal,” meaning they minimize the probability of misclassifying borrowers […]

Posted by Peter Mattson, Senior Staff Engineer, ML Performance, and Praveen Paritosh, Senior Research Scientist, Google Research, Brain Team Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering safe self-driving cars to amplifying human productivity. To realize this potential, however, organizations need ML solutions to be reliable with ML solution development that is predictable and tractable. The key to both is a deeper understanding of ML data — how to engineer training datasets that produce high quality models and test datasets that deliver accurate indicators of how close we are to solving the target problem. The process of […]

Researchers at the McGovern Institute for Brain Research at MIT and the Broad Institute of MIT and Harvard have harnessed a natural bacterial system to develop a new protein delivery approach that works in human cells and animals. The technology, described today in Nature, can be programmed to deliver a variety of proteins, including ones for gene editing, to different cell types. The system could potentially be a safe and efficient way to deliver gene therapies and cancer therapies. Led by MIT Associate Professor Feng Zhang, who is a McGovern Institute investigator and Broad Institute core member, the team took […]