Tag: Laboratory for Information and Decision Systems (LIDS)

Personalization features can make LLMs more agreeable

Many of the latest large language models (LLMs) are designed to remember details from past conversations or store user profiles, enabling these models to personalize responses. But researchers from MIT and Penn State University found that, over long conversations, such personalization features often increase the likelihood an LLM will become overly agreeable or begin mirroring […]

Read More

Study: Platforms that rank the latest LLMs can be unreliable

A firm that wants to use a large language model (LLM) to summarize sales reports or triage customer inquiries can choose between hundreds of unique LLMs with dozens of model variations, each with slightly different performance. To narrow down the choice, companies often rely on LLM ranking platforms, which gather user feedback on model interactions […]

Read More

Why it’s critical to move beyond overly aggregated machine-learning metrics

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to test whenever a model is deployed in a new setting. “We demonstrate that even when you train models on large amounts of data, and choose […]

Read More

3 Questions: How AI could optimize the power grid

Artificial intelligence has captured headlines recently for its rapidly growing energy demands, and particularly the surging electricity usage of data centers that enable the training and deployment of the latest generative AI models. But it’s not all bad news — some AI tools have the potential to reduce some forms of energy consumption and enable cleaner grids. […]

Read More

New method improves the reliability of statistical estimations

Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county. They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships. Standard machine-learning methods excel at making predictions and sometimes provide […]

Read More

Prognostic tool could help clinicians identify high-risk cancer patients

Aggressive T-cell lymphoma is a rare and devastating form of blood cancer with a very low five-year survival rate. Patients often relapse after receiving initial therapy, making it especially challenging for clinicians to keep this destructive disease in check. In a new study, researchers from MIT, in collaboration with researchers involved in the PETAL consortium […]

Read More

A smarter way for large language models to think about hard problems

To make large language models (LLMs) more accurate when answering harder questions, researchers can let the model spend more time thinking about potential solutions. But common approaches that give LLMs this capability set a fixed computational budget for every problem, regardless of how complex it is. This means the LLM might waste computational resources on simpler […]

Read More

MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble. So far, aerial microrobots have only been […]

Read More

New control system teaches soft robots the art of staying safe

Imagine having a continuum soft robotic arm bend around a bunch of grapes or broccoli, adjusting its grip in real time as it lifts the object. Unlike traditional rigid robots that generally aim to avoid contact with the environment as much as possible and stay far away from humans for safety reasons, this arm senses […]

Read More

Researchers discover a shortcoming that makes LLMs less reliable

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study. Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks. The researchers found that models can mistakenly […]

Read More

Bigger datasets aren’t always better

Determining the least expensive path for a new subway line underneath a metropolis like New York City is a colossal planning challenge — involving thousands of potential routes through hundreds of city blocks, each with uncertain construction costs. Conventional wisdom suggests extensive field studies across many locations would be needed to determine the costs associated […]

Read More

Charting the future of AI, from safer answers to faster thinking

Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and […]

Read More

Teaching robots to map large environments

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain. Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but […]

Read More

A faster problem-solving tool that guarantees feasibility

Managing a power grid is like trying to solve an enormous puzzle. Grid operators must ensure the proper amount of power is flowing to the right areas at the exact time when it is needed, and they must do this in a way that minimizes costs without overloading physical infrastructure. Even more, they must solve […]

Read More

MIT Schwarzman College of Computing welcomes 11 new faculty for 2025

The MIT Schwarzman College of Computing welcomes 11 new faculty members in core computing and shared positions to the MIT community. They bring varied backgrounds and expertise spanning sustainable design, satellite remote sensing, decision theory, and the development of new algorithms for declarative artificial intelligence programming, among others. “I warmly welcome this talented group of […]

Read More

Fighting for the health of the planet with AI

For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching. Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the Department of Electrical Engineering and […]

Read More

New prediction model could improve the reliability of fusion power plants

Tokamaks are machines that are meant to hold and harness the power of the sun. These fusion machines use powerful magnets to contain a plasma hotter than the sun’s core and push the plasma’s atoms to fuse and release energy. If tokamaks can operate safely and efficiently, the machines could one day provide clean and […]

Read More

Accounting for uncertainty to help engineers design complex systems

Designing a complex electronic device like a delivery drone involves juggling many choices, such as selecting motors and batteries that minimize cost while maximizing the payload the drone can carry or the distance it can travel. Unraveling that conundrum is no easy task, but what happens if the designers don’t know the exact specifications of […]

Read More

3 Questions: The pros and cons of synthetic data in AI

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow […]

Read More

A new way to test how well AI systems classify text

Is this movie review a rave or a pan? Is this news story about business or technology? Is this online chatbot conversation veering off into giving financial advice? Is this online medical information site giving out misinformation? These kinds of automated conversations, whether they involve seeking a movie or restaurant review or getting information about […]

Read More

Eco-driving measures could significantly reduce vehicle emissions

Any motorist who has ever waited through multiple cycles for a traffic light to turn green knows how annoying signalized intersections can be. But sitting at intersections isn’t just a drag on drivers’ patience — unproductive vehicle idling could contribute as much as 15 percent of the carbon dioxide emissions from U.S. land transportation. A […]

Read More

Youssef Marzouk appointed associate dean of MIT Schwarzman College of Computing

Youssef Marzouk ’97, SM ’99, PhD ’04, the Breene M. Kerr (1951) Professor in the Department of Aeronautics and Astronautics (AeroAstro) at MIT, has been appointed associate dean of the MIT Schwarzman College of Computing, effective July 1. Marzouk, who has served as co-director of the Center for Computational Science and Engineering (CCSE) since 2018, […]

Read More

New algorithms enable efficient machine learning with symmetric data

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine-learning model might think it is a new data point. In computer science parlance, the molecule is “symmetric,” meaning the fundamental structure of that molecule remains the same if it undergoes certain […]

Read More

A new way to edit or generate images

AI image generation — which relies on neural networks to create new images from a variety of inputs, including text prompts — is projected to become a billion-dollar industry by the end of this decade. Even with today’s technology, if you wanted to make a fanciful picture of, say, a friend planting a flag on […]

Read More

This “smart coach” helps LLMs switch between text and code

Large language models (LLMs) excel at using textual reasoning to understand the context of a document and provide a logical answer about its contents. But these same LLMs often struggle to correctly answer even the simplest math problems. Textual reasoning is usually a less-than-ideal way to deliberate over computational or algorithmic tasks. While some LLMs […]

Read More

How to more efficiently study complex treatment interactions

MIT researchers have developed a new theoretical framework for studying the mechanisms of treatment interactions. Their approach allows scientists to efficiently estimate how combinations of treatments will affect a group of units, such as cells, enabling a researcher to perform fewer costly experiments while gathering more accurate data. As an example, to study how interconnected […]

Read More

Confronting the AI/energy conundrum

The explosive growth of AI-powered computing centers is creating an unprecedented surge in electricity demand that threatens to overwhelm power grids and derail climate goals. At the same time, artificial intelligence technologies could revolutionize energy systems, accelerating the transition to clean power. “We’re at a cusp of potentially gigantic change throughout the economy,” said William […]

Read More

LLMs factor in unrelated information when recommending medical treatments

A large language model (LLM) deployed to make treatment recommendations can be tripped up by nonclinical information in patient messages, like typos, extra white space, missing gender markers, or the use of uncertain, dramatic, and informal language, according to a study by MIT researchers. They found that making stylistic or grammatical changes to messages increases […]

Read More

Unpacking the bias of large language models

Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document or conversation, while neglecting the middle. This “position bias” means that, if a lawyer is using an LLM-powered virtual assistant to retrieve a certain phrase in a 30-page affidavit, the LLM is more likely to […]

Read More

Inroads to personalized AI trip planning

Travel agents help to provide end-to-end logistics — like transportation, accommodations, meals, and lodging — for businesspeople, vacationers, and everyone in between. For those looking to make their own arrangements, large language models (LLMs) seem like they would be a strong tool to employ for this task because of their ability to iteratively interact using […]

Read More