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How a medical database developed at MIT evolved into a global standard of data-sharing

ai, AI (Artificial Intelligence), Artificial Intelligence, data, Electrical engineering and computer science (EECS), Harvard-MIT Health Sciences and Technology, Health care, Health sciences and technology, history, History of MIT, History of science, Information systems, Institute for Medical Engineering and Science (IMES), Internet, Medicine, Research Laboratory of Electronics, School of Engineering

Before the advancement of scientific data storage and collaboration via the cloud, medical investigators seeking health research breakthroughs had to overcome significant obstacles to collaboration and key clinical data gathering. Data were siloed and difficult to distribute, so those looking to undertake research had no option but to gather them themselves. This not only made research […]

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Cómo anticipar el burnout y construir organizaciones públicas más saludables

Advanced Analytics, ai, AI (Artificial Intelligence), AI Ethics, Analytics, analytics platform, Artificial Intelligence, Customer Experience, data, Data Analytics, digital transformation, Real-Time Analytics, Trustworthy AI, 全般

El bienestar laboral se ha consolidado como una prioridad estratégica para las organizaciones, tanto públicas como privadas. La Organización Mundial de la Salud define el burnout como un síndrome derivado del estrés crónico mal gestionado en el trabajo, caracterizado por agotamiento, distanciamiento mental de la actividad profesional y una disminución […] The post Cómo anticipar

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3 Questions: Neural transparency and the future of AI design

ai, AI (Artificial Intelligence), Algorithms, Apps, Artificial Intelligence, Computer science and technology, data, ethics, Faculty, Human-computer interaction, Interview, Machine Learning, Media Lab, Mental health, Research, Safety, School of Architecture and Planning, Technology and society

Millions of people are now designing their own personalized artificial intelligence companions, yet most have little idea how those creations will actually behave. In a new paper, MIT Media Lab Assistant Professor Pat Pataranutaporn and his graduate student researchers Anthony Baez and Sheer Karny introduce “neural transparency,” a tool that lets everyday users glimpse inside

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Helping AI models to meet the real world

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Business and management, Computer science and technology, data, Electrical engineering and computer science (EECS), Faculty, IDSS, Innovation and Entrepreneurship (I&E), Laboratory for Information and Decision Systems (LIDS), MIT intellectual property, MIT Schwarzman College of Computing, Profile, Research, School of Engineering, Startups

Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools. Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with the

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Jesse Thaler named director of the Laboratory for Nuclear Science

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Professor Jesse Thaler has been named director of the MIT Laboratory for Nuclear Science (LNS), effective Aug. 1. He succeeds Professor Bolek Wyslouch, who directed LNS for the past decade. Thaler is a theoretical particle physicist who combines techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. “In his research,

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Q&A: What is agentic AI today, and what do we want it to be?

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Human-computer interaction, Interview, Machine Learning, MIT Schwarzman College of Computing, School of Engineering

The deployment of automated software systems called AI agents has recently exploded. A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon. To understand the fundamentals and potential impacts of these

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3 Questions: Beyond data-driven aesthetics

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“Beyond Data-Driven Aesthetics,” by MIT Architecture alumnus and researcher Alexandros Haridis, on view at the MIT Keller Gallery through June 30, examines 20th- and 21st-century efforts to transform computing into a medium for creative production and aesthetic judgment in architecture and the applied arts. Drawing on philosophy, mathematics, computer science, and design computation, the exhibition

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HP accelerates enterprise workflows with OpenAI Frontier

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HP has scaled its OpenAI Frontier integration across global operations to optimise enterprise workflows and accelerate output. The hardware manufacturer initiated testing of the platform in February 2026. Early pilot programs yielded verified operational gains in software engineering and cybersecurity remediation. Expanding these initial trials into an enterprise-wide operating model requires connecting access protocols, contextual

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Improving the speed and energy-efficiency of AI agents

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), Electronics, Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering, Software, Sustainability

Agentic workflows are artificial intelligence-powered software systems that chain together multiple models and external tools to tackle complicated tasks, like analyzing a video and answering questions about it.But the way these highly fragmented systems are designed and deployed often causes inefficiencies that can lead to wasted computation, energy, and cost. To improve efficiency, researchers from MIT

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NSF renews support for MIT-led AI and physics institute, expanding a new model for discovery

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The MIT-led Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) has received renewed support from the National Science Foundation (NSF) for an additional five years, increasing annual funding from $4 million to $4.98 million. The renewal marks a new phase for IAIFI, which has spent its first five years building a research model and an

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