Smarter training of neural networks
MIT CSAIL project shows the neural nets we typically train contain smaller “subnetworks” that can learn just as well, and often faster.
Putting vision models to the test
Study shows that artificial neural networks can be used to drive brain activity.
MIT Solve announces $1.25 million in funding for 2019 Solver teams
Prizes from GM, the Patrick J. McGovern Foundation, Vodafone Americas Foundation, and others will be awarded to Solver teams selected from four Global Challenges.
Liberty Mutual Insurance joins MIT's Quest for Intelligence
Company announces $25 million, five-year collaboration.
For better deep neural network vision, just add feedback (loops)
The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects.
3Q: Assessing MIT’s computing infrastructure needs
In planning for the MIT Schwarzman College of Computing, working group is exploring needs across all parts of the Institute.
Improving security as artificial intelligence moves to smartphones
Researchers unveil a tool for making compressed deep learning models less vulnerable to attack.
Forums address MIT’s plans to reshape its computer science education
Working group discussions focus on how to design the MIT Schwarzman College of Computing to best serve the community and society.
3Q: Setting academic parameters for the MIT Schwarzman College of Computing
Working Group on Curricula and Degrees co-chairs discuss their progress toward establishing credentials and courses for the college.
Vivienne Sze wins Edgerton Faculty Award
EECS faculty member is recognized for technical innovation, educational excellence, and efforts to advance women and underrepresented minorities in her field.
3Q: Structuring the MIT Schwarzman College of Computing
Co-chairs of the Organizational Structure working group discuss their goals and progress.
3 Questions: Provost Martin Schmidt on building a new college
Working groups of faculty, students, staff are meeting regularly to develop ideas for the MIT Schwarzman College of Computing.
The future of agriculture is computerized
Machine learning can reveal optimal growing conditions to maximize taste and other features.
Teaching machines to reason about what they see
Researchers combine statistical and symbolic artificial intelligence techniques to speed learning and improve transparency.