For MIT Professor Armando Solar-Lezama, one of the most common misunderstandings about AI is the notion that it can be dropped into existing human roles like a plug-and-play replacement.
In 2026, the hype for artificial intelligence (AI) agents is louder than ever before. These semi-autonomous programs can “think” and execute well-defined tasks in areas like customer service and software development, typically using language models (LMs). But fields like medical diagnosis and scientific discovery require them to inquire about a vast range of solutions in uncertain environments, which LMs struggle with.
A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81 percent. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.
David Clark, Senior Research Scientist at MIT CSAIL, helped design the system that connects nearly every computer on earth. As Chief Protocol Architect of the Internet from 1981 to 1989, he was there for the beginnings of the Internet. Calling the wave of AI technology an “echo” of what happened in the 80’s, Dr. Clark is cautioning, “ maybe we need to slow things down and think a bit.”
What happens when the team behind PlayStation meets the researchers pushing the boundaries of AI? You get The Nexus of Games and AI, a 12-part MIT Independent Activities Period (IAP) course, now available to stream.
AI models are proliferating fast. There’s Claude, ChatGPT, Gemini, Copilot, DeepSeek, Grok, Mistral, Llama, and many more emerging every day. But which ones to work with? And why? We asked MIT CSAIL faculty and students which AI tools they’re reaching for right now. The responses showed a variety of preferences, a clear winner in one area, and a word of caution about what goes into any public model’s memory.
Anthropic CEO Dario Amodei has said that AI could surpass “almost all humans at almost everything” shortly after 2027. While AI’s capabilities are certainly improving, such rapid progress might seem at odds with findings that show AI is still failing at 95%+ of remote freelance projects, and continues to struggle with hallucination, long term planning, and forms of abstract reasoning that humans find easy. But recent work from METR has found evidence that LLMs can gain capabilities in rapid surges — jumping from succeeding almost never to almost always in just a few years. If this is true across the economy, it could mean that workers could be blindsided by AI advances.
Designers, makers, and others often use 3D printing to rapidly prototype a range of functional objects, from movie props to medical devices. Accurate print previews are essential so users know a fabricated object will perform as expected.
