MIT News·
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Read full articleIn this piece, we reflect on AIES 2025, and outline the conversations and presentations from a discussion session on LLMs in the context of clinical usage and human rights. This is a crosspost from the latest issue of AI Matters, published by the ACM SIAGI. This year’s conference on artificial intelligence, ethics and society (AIES) […]
When generative AI first moved from research labs into real-world business applications, enterprises made a tacit bargain: “Capability now, control later.” Feed your proprietary data into third-party AI models, and you will get powerful results. But your data passes through systems you do not own, under governance you do not set. The protections you rely…
MIT’s Sinan Aral has the data . . . and a warning. Outsourcing creativity to AI may be the most rational move leaders make, and the most dangerous one.
AEVS enhances trust and transparency in AI agent operations, crucial for scaling autonomous systems and ensuring accountability in complex networks. The post Fetch.AI launches AEVS for verifiable AI agent executions appeared first on Crypto Briefing.
Dimitris Bertsimas and Megan Mitchell discuss the motivation behind Universal Learning, and what sets the new MIT Open Learning educational initiative apart.
In new research from CMU, Oxford, MIT, and UCLA, having a GPT-powered assistant ripped away left people fumbling to solve math problems on their own.
AI agents are evolving into always-on autonomous systems that can remember, learn, and operate continuously across multiple platforms. OpenClaw, Hermes Agent, and Claude are leading this transformation, but each is taking a radically different approach that could define the future of AI automation.
The problem wasn’t just the perfectly polished, yet mediocre prose. It’s what’s lost when we surrender the struggle to translate thought into words I have been teaching fiction writing at MIT since 2017. Many of my students last wrote fiction in middle school, and very few have experienced a proper workshop, so at the start of every semester I offer these directions for writer and reader alike: Read the story at least twice. Mark what works and what doesn’t – underline great sentences, flag clunky syntax, gaps in logic and unrealistic dialogue. Ask yourself: does the story work? Why or why not? What could improve it? Answer in a signed letter to the author, attached to their story. Give your honest opinions. Remember that an effective peer review demands close reading of the text accompanied by a boldness of spirit. Continue reading...
