Agentic AI systems are “designed to pursue complex goals with autonomy and predictability” (MIT Technology Review). Agentic AI models enable productivity by taking goal-directed actions, making contextual decisions, and adjusting plans based on changing conditions with minimal human oversight.
More than seven years ago, cybersecurity researchers were thoroughly rattled by the discovery of Meltdown and Spectre, two major security vulnerabilities uncovered in the microprocessors found in virtually every computer on the planet.
20 years ago in a pre-ChatGPT world, a fake-paper generator created by 3 MIT kids fooled a major conference so badly that they had to completely reconfigure their reviewing practices.
A computation has two main constraints: the amount of memory a computation requires and how long it takes to do that calculation. If a task requires a certain number of steps, at worst the computer will need to access its memory for each one, meaning it'll require the same number of memory slots.
While early language models could only process text, contemporary large language models now perform highly diverse tasks on different types of data. For instance, LLMs can understand many languages, generate computer code, solve math problems, or answer questions about images and audio.
Proteins are the workhorses that keep our cells running, and there are many thousands of types of proteins in our cells, each performing a specialized function. Researchers have long known that the structure of a protein determines what it can do.
Not sure what to think about DeepSeek R1, the most recent large language model (LLM) making waves in the global tech community? Faculty from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are here to help!
In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.
Creating realistic 3D models for applications like virtual reality, filmmaking, and engineering design can be a cumbersome process requiring lots of manual trial and error.
Regina Barzilay, School of Engineering Distinguished Professor for AI and Health at MIT, CSAIL Principal Investigator, and Jameel Clinic AI Faculty Lead, has been awarded the 2025 Frances E. Allen Medal from the Institute of Electrical and Electronics Engineers (IEEE). Barzilay’s award recognizes the impact of her machine-learning algorithms on medicine and natural language processing.
