Electrical Engineering & Computer Science (eecs)
Electrical Engineering & Computer Science (eecs)

MIT scientists build a system that can generate AI models for biology research

BioAutoMATED, an open-source, automated machine-learning platform, aims to help democratize artificial intelligence for research labs.

Educating national security leaders on artificial intelligence

Experts from MIT’s School of Engineering, Schwarzman College of Computing, and Sloan Executive Education educate national security leaders in AI fundamentals.

Researchers teach an AI to write better chart captions

A new dataset can help scientists develop automatic systems that generate richer, more descriptive captions for online charts.

Computer vision system marries image recognition and generation

MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single system.

MIT-Pillar AI Collective announces first seed grant recipients

Six teams conducting research in AI, data science, and machine learning receive funding for projects that have potential commercial applications.

Envisioning the future of computing

MIT students share ideas, aspirations, and vision for how advances in computing stand to transform society in a competition hosted by the Social and Ethical Responsibilities of Computing.

Novo Nordisk to support MIT postdocs working at the intersection of AI and life sciences

MIT-Novo Nordisk Artificial Intelligence Postdoctoral Fellows Program will support up to 10 postdocs annually over five years.

Bringing the social and ethical responsibilities of computing to the forefront

The inaugural SERC Symposium convened experts from multiple disciplines to explore the challenges and opportunities that arise with the broad applicability of computing in many aspects of society.

MIT researchers make language models scalable self-learners

The scientists used a natural language-based logical inference dataset to create smaller language models that outperformed much larger counterparts.

Scaling audio-visual learning without labels

A new multimodal technique blends major self-supervised learning methods to learn more similarly to humans.