Q&A: What is agentic AI today, and what do we want it to be?
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.
3 Questions: Beyond data-driven aesthetics
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.
Improving the speed and energy-efficiency of AI agents
A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.
NSF renews support for MIT-led AI and physics institute, expanding a new model for discovery
IAIFI enters its second phase with increased funding, broader ambitions, and a growing community at the frontier of AI and fundamental physics.
MIT researchers teach AI models to interpret charts
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.
Justin Solomon appointed associate dean of engineering education
MIT faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.
For most US drivers, EVs offer emissions benefits and cost savings
When it comes to emissions, individual driving patterns matter as much as how “green” the regional electricity mix is, MIT researchers report.
Mapping the ocean with autonomous sensors
Founded by Ravi Pappu SM ’95, PhD ’01, Apeiron Labs is deploying low-cost ocean sensors to improve storm forecasts, detect endangered species, and more.
Method for stress-testing cloud computing algorithms helps avoid network failures
The “MetaEase” technique provides a heads-up to potential scenarios that could cause long wait-times or outages.
A faster way to estimate AI power consumption
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone
New dataset of 30,000-plus competition math problems from 47 countries gives AI researchers a harder test — and students worldwide a better training ground.
Teaching AI models to say “I’m not sure”
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
Professor Emeritus Jack Dennis, pioneering developer of dataflow models of computation, dies at 94
The influential first leader of the Computation Structures Group at MIT played a key role in the development of asynchronous computing.
New technique makes AI models leaner and faster while they’re still learning
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
Helping data centers deliver higher performance with less hardware
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.