The historical magazine had been published by the MIT Alumni Association, was more closely aligned with the interests of MIT alumni, and had a more intellectual tone and much smaller public circulation. In September 2005, it was changed, under its then editor-in-chief and publisher, Jason Pontin, to a form resembling the historical magazine.īefore the 1998 re-launch, the editor stated that 'nothing will be left of the old magazine except the name.' It was therefore necessary to distinguish between the modern and the historical Technology Review.

It was founded in 1899 as The Technology Review, and was re-launched without 'The' in its name on April 23, 1998, under then publisher R. MIT Technology Review is a bimonthly magazine wholly owned by the Massachusetts Institute of Technology, and editorially independent of the university. Read the full story on MIT Technology Review.Magazine about technology MIT Technology Review

Thus it is a poor tool for modeling continuous processes, especially ones that are measured irregularly over time. Its design requires it to learn from data with clear stages of observation. A traditional neural network struggles to handle this. But data from medical records is kind of messy: throughout your life, you might visit the doctor at different times for different reasons, generating a smattering of measurements at arbitrary intervals. Published in MIT Technology Review | by Karen Hao, DecemResearchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model continuous processes like changes in health.ĭavid Duvenaud was working on a project involving medical data when he hit upon a major shortcoming in AI.Īn AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient’s health over time.

Resources for Students, Faculty & Staffĭavid Duvenaud, an assistant professor at the Departments of Statistical Sciences and Computer Science, and his team borrowed equations from calculus to redesign the core machinery of deep learning.Ī radical new neural network design could overcome big challenges in AI.

Research Opportunities, Scholarships & Awards.

Techreview