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MIT News - Machine learning
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Showing 51-100 of 107 entries
1 Dec
AquaCulture Shock program, in collaboration with MIT-Scandinavia MISTI, offers international internships for AI and autonomy in aquaculture
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases
25 Nov
BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.
AI supports the clean energy transition as it manages power grid operations, helps plan infrastructure investments, guides development of novel materials, and more.
19 Nov
MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.
Quantum chemist and School of Science Dean’s Postdoctoral Fellow Ernest Opoku is working on computational methods to study how electrons behave.
The virtual VideoCAD tool could boost designers’ productivity and help train engineers learning computer-aided design.
Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.
A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
21 Oct
How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.
To reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.
After being trained with this technique, vision-language models can better identify a unique item in a new scene.
Acting as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.
Co-founded by an MIT alumnus, Watershed Bio offers researchers who aren’t software engineers a way to run large-scale analyses to accelerate biology.
New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.
MIT researchers discovered a hidden atomic order that persists in metals even after extreme processing.
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
Incorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions.
MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.
Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.
AI system learns from many types of scientific information and runs experiments to discover new materials
25 Sep
The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
At the inaugural MIT Generative AI Impact Consortium Symposium, researchers and business leaders discussed potential advancements centered on this powerful technology.
MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models will perform based on smaller models in the same family.
MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.
DOE selects MIT to establish a Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions
10 Sep
The research center, sponsored by the DOE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
Professor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.
VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.
New research shows the natural variability in climate data can cause AI models to struggle at predicting local temperature and rainfall.
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.
Storage systems from Cloudian, co-founded by an MIT alumnus, are helping businesses feed data-hungry AI models and agents at scale.
Researchers created polymers that are more resistant to tearing by incorporating stress-responsive molecules identified by a machine-learning model.
This new approach could lead to enhanced AI models for drug and materials discovery.
Neural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.
ChemXploreML makes advanced chemical predictions easier and faster — without requiring deep programming skills.
24 Jul
Combining powerful imaging, perturbational screening, and machine learning, researchers uncover new human host factors that alter Ebola’s ability to infect.
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.
Language models follow changing situations using clever arithmetic, instead of sequential tracking. By controlling when these approaches are used, engineers could improve the systems’ capabilities.
The simulations matched results from an underground lab experiment in Switzerland, suggesting modeling could be used to validate the safety of nuclear disposal sites.
MIT engineers designed a versatile interface that allows users to teach robots new skills in intuitive ways.