New transistor’s superlative properties could have broad electronics applications
Ultrathin material whose properties “already meet or exceed industry standards” enables superfast switching, extreme durability.
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Ultrathin material whose properties “already meet or exceed industry standards” enables superfast switching, extreme durability.
Genomics and lab studies reveal numerous findings, including a key role for Reelin amid neuronal vulnerability, and for choline and antioxidants in sustaining cognition.
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
Increasing severity and duration of heat drives data collection and resiliency planning for the forthcoming Climate Resiliency and Adaptation Roadmap.
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
Members of the MIT community, supporters, and guests commemorate the opening of the new college headquarters.
PhD student Xinyi Zhang is developing computational tools for analyzing cells in the age of multimodal data.
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
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.