There’s a delicate art to teaching robots, even when you’re preparing them for predictable environments like factories, where they’ll repeat the same tasks a little differently depending on the obstacles they face. Whether a human is suddenly in their way or there’s new clutter, the machine must closely mimic its operator’s actions by staying on a trajectory (or motion path).
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.
Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications.
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.
On January 30, student teams will present their projects to the class, guest lecturers, and invited colleagues. This capstone session will be followed by an after-party celebrating the close of Nexus II.
This event is part of the MIT Sloan Tech Summit 2026, the largest student-led event at the MIT Sloan School of Management. CSAIL Alliances is not organizing this event, but is pleased to offer support.
MIT researchers have developed a new method for designing 3D structures that can be transformed from a flat configuration into their curved, fully formed shape with only a single pull of a string.
Most languages use word position and sentence structure to extract meaning. For example, “The cat sat on the box,” is not the same as “The box was on the cat.” Over a long text, like a financial document or a novel, the syntax of these words likely evolves.
