When to trust an AI model
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
Staff members receive recognition for their exceptional support of the MIT community.
Ammonia could be a nearly carbon-free maritime fuel, but without new emissions regulations, its impact on air quality could significantly impact human health.
The IDEAS Social Innovation Challenge helps students hone their entrepreneurship skills to create viable ventures for public good.
Developed by MIT RAISE, the Day of AI curriculum empowers K-12 students to collaborate on local and global challenges using AI.
This new tool offers an easier way for people to analyze complex tabular data.
In a retrospective talk spanning multiple decades, Professor Al Oppenheim looked back over the birth of digital signal processing and shared his thoughts on the future of the field.
This tiny, biocompatible sensor may overcome one of the biggest hurdles that prevent the devices from being completely implanted.
Twelve faculty members have been granted tenure in six units across MIT’s School of Engineering.
These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.
Known for building connections between the social sciences, data science, and computation, the political science professor will lead IDSS into its next chapter.
This novel circuit architecture cancels out unwanted signals at the earliest opportunity.
MosaicML, co-founded by an MIT alumnus and a professor, made deep-learning models faster and more efficient. Its acquisition by Databricks broadened that mission.
The dedicated teacher and academic leader transformed research in computer architectures, parallel computing, and digital design, enabling faster and more efficient computation.