英文科技媒体分类 feed · MIT News · Machine learning

The system asks humans for feedback to increase trust in machine-generated designs for buildings and bridges.

MIT scientists developed a training method to generate models that simulate the effects of human activities on climate much more quickly than other approaches.

By learning how matter moves, the WorldParticle system could assist with developing soft robots, medicines, and more.

The new approach, known as FLEX, could lead to more intuitive, interactive household and assistive robots.

A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.

A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.

A model developed by researchers at MIT and Boston Children's Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.

New research can help AI models understand crystalline materials in 3D and reason about their properties.

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.

Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.

By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.

An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.

Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.

“InstructMesh” can generate designs for everyday objects that are easy to edit and fabricate for both experts and newcomers to 3D modeling.

Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.

In a study focusing on hiring decisions, MIT researchers found the use of a single algorithm by many firms could benefit job seekers in certain situations.

A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.

This patient-specific method, called xvr, helps doctors use X-rays for surgical navigation in fields such as orthopedics and neurosurgery.

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.

MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.

A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.

The senior lecturer, already director of the degree program, will now oversee all aspects of the center’s activities and operations.

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.

A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.

New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.

A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.

Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.

An expert in machine learning, statistics, and computation, Rakhlin succeeds Professor Ankur Moitra.

Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently.

Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.

MIT students designed, built, and tested a jet engine with AI copilots, assessing AI’s usefulness in developing high-performance aerospace systems.

“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.

Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.

MIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water.

MIT and Tec de Monterrey will expand FrED curriculum to universities across Mexico.

Computer scientist Phillip Isola cuts through the hype to explain how AI agents work and what the future might hold for this rapidly advancing technology.

Associate Professor Anna Huang delivers the keynote address, “In Search of Human-AI Resonance,” to a capacity crowd.

In a new Keller Gallery exhibition, Alexandros Haridis SM ’17, PhD ’22 traces centuries of ideas about aesthetic judgment and explores how design can make complex computational systems visible.

To help robots do chores in places like homes and factories, a new approach from MIT uses one language model to clarify users’ instructions, then another to ignore irrelevant info.

A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.

Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.

MIT researchers’ approach captures subtle atomic patterns, improving predictions of material properties.

A new spatial memory system for robots efficiently captures details about the objects they see while exploring their environment.

Using technology invented at MIT, Cartesian’s system for locating objects could also find uses in manufacturing, logistics, and robotics.