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Personalization features can make LLMs more agreeable
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
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https://news.mit.edu/rss/topic/artificial-intelligence2
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
By minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.
A new method developed at MIT could root out vulnerabilities and improve LLM safety and performance.
By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
The approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design.
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
From early motion-sensing platforms to environmental monitoring, the professor and head of the Program in Media Arts and Sciences has turned decades of cross-disciplinary research into real-world impact.
Assistant Professor Matthew Jones is working to decode molecular processes on the genetic, epigenetic, and microenvironment levels to anticipate how and when tumors evolve to resist treatment.
A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
MIT computer science students design AI chatbots to help young users become more social, and socially confident.
Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.
Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.
Academia-industry relationship is an early-stage accelerator, supporting professional progress and research.
At MIT, former U.S. ambassador to China Nicholas Burns highlights climate change as an area for diplomatic engagement, while exploring areas including China's emphasis on STEM education.
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.
Jointly led by the MIT Morningside Academy for Design, MIT Schwarzman College of Computing, and the Hasso Plattner Institute in Potsdam, the hub will foster a dynamic community where computing, creativity, and human-centered innovation meet.
Conference speakers discussed the unfolding trajectory of AI and the benefits of shaping technology to meet people’s needs.
Operations research expert Dimitris Bertsimas delivered the annual Killian Lecture, providing a look at the past and future of his work.
Sojun Park, a postdoc at the Center for International Studies, has learned much from his research on intellectual property as well as his interactions with students and mentors at MIT.
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
By moving their hands and fingers, users can direct a robot to play piano or shoot a basketball, or they can manipulate objects in a virtual environment.
MIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.
This new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.
An AI model generates novel proteins based on how they vibrate and move, opening new possibilities for dynamic biomaterials and adaptive therapeutics.
Mariano Salcedo ’25, a master’s student in the new Music Technology and Computation Graduate Program, is designing an AI to visualize and express music and other sounds.
A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.
By quickly generating aesthetically accurate previews of fabricated objects, the VisiPrint system could make prototyping faster and less wasteful.
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Dean Price, assistant professor in the Department of Nuclear Science and Engineering, sees a bright future for nuclear power, and believes AI can help us realize that vision.
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
Startup accelerator program grows to over 30 companies, almost half of them with MIT pedigrees.
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
As the NC Ethics of Technology Postdoctoral Fellow, Michal Masny is advancing dialogue, teaching, and research into the social and ethical dimensions of new computing technologies.
As the School of Humanities, Arts, and Social Sciences marks 75 years, Dean Agustín Rayo reflects on how AI is reshaping higher education and why SHASS disciplines continue to be central to MIT’s mission.
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
Founded by Tristan Bepler PhD ’20 and former MIT professor Tim Lu PhD ’07, OpenProtein.AI offers researchers open-source models and other tools for protein engineering.
The associate professors of EECS and chemistry, respectively, are honored for exceptional contributions to teaching, research, and service at MIT.
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.