Tag: MIT

US & Canada

MIT’s Lincoln Laboratory establishes Biotechnology and Human Systems Division; Spearheads AI and Biology Research

MIT Lincoln Laboratory has established a new research and development division, the Biotechnology and Human Systems Division.

AI Research

Meet Your AI-Generated Dream Anime Girl

Do you dream of Asuna Yuuki? Do you long to escape to a fantasy world with a beautiful anime partner? If so there’s a new artificial intelligence system just for you — the “Waifu Vending Machine” can create a highly customized anime companion in minutes.

AI Research

Global Minima Solution for Neural Networks?

New research from Carnegie Mellon University, Peking University and the Massachusetts Institute of Technology shows that global minima of deep neural networks can been achieved via gradient descent under certain conditions. The paper Gradient Descent Finds Global Minima of Deep Neural Networks was published November 12 on arXiv.

AI United States

MIT Is Opening a $1Bn AI College

The Massachusetts Institute of Technology (MIT) today announced they will invest US$1 billion into a new college for artificial intelligence. The MIT Stephen A. Schwarzman College of Computing will “constitute both a global center for computing research and education, and an intellectual foundry for powerful new AI tools.”

AI

Julia 1.0 Released

MIT-developed Julia has become one of the world’s fastest-growing programming languages. Last year it teamed with a supercomputer to catalogue 200 million astronomical objects within 15 minutes — one thousand times faster than the previous rate.

AI Research

SJTU & MIT Paper Reinvents Neural Architecture Search; Slashes Computational Resource Requirements

The dearth of AI talents capable of manually designing neural architecture such as AlexNet and ResNet has spurred research in automatic architecture design. Google’s Cloud AutoML is an example of a system that enables developers with limited machine learning expertise to train high quality models. The trade-off, however, is AutoML’s high computational costs.