Machine-learning algorithms are often referred to as a "black box." Once data are put into an algorithm, it's not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the "black box" problem, through a combination of data science and physics-based engineering. In class 2.C01 (Physical Systems Modeling and Design Using Machine Learning), Professor George Barbastathis demonstrates how mechanical engineers can use their unique knowledge of physical systems to keep algorithms in check and develop more accurate predictions.

Machine learning algorithms are often referred to as "black boxes." Once the data is put into the algorithm, it is not always possible to know exactly how the algorithm will reach the prediction. This can be especially frustrating when problems occur. MIT's new Mechanical Engineering (MechE) course teaches students how to combine data science and physics-based engineering to tackle the "black box" problem. In Class 2.C161 (Modeling and Designing Physical Systems Using Machine Learning), Professor George Barbastathis teaches how mechanical engineers use their unique knowledge of physical systems to check algorithms and create more accurate predictions.

In MIT 2.C161, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions. Machine-learning algorithms are often referred to as a "black box." Once data are put into an algorithm, it's not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical engineering (MechE) course at MIT teaches students how to tackle the "black box" problem, through a combination of data science and physics-based engineering.

Every day for the past few weeks, charts and graphs plotting the projected apex of COVID-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the COVID-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus. "Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology," explains Raj Dandekar, a Ph.D. candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).

Researchers at the Massachusetts Institute of Technology have developed an artificial intelligence (AI) system that can isolate small, nearly transparent imperfections in poorly lit images in order to reproduce objects. A blog post published by MIT News today describes a deep neural network -- layered mathematical functions loosely mimicking the behavior of neurons in the brain -- that can erase target artifacts from grainy images. George Barbastathis, professor of mechanical engineering at MIT, believes this might have applications in medicine. "In the lab, if you blast biological cells with light you burn them, and there is nothing left to image," he told MIT News. "When it comes to X-ray imaging, if you expose a patient to X-rays, you increase the danger they may get cancer. What we're doing here is -- you can get the same image quality but with a lower exposure to the patient. And in biology, you can reduce the damage to biological specimens when you want to sample them."