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.

An increasingly common sight: robots walking down the street, surrounded by astounded onlookers. But these machines aren’t yet the do-it-all assistants you’d want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it’s labor-intensive and time-consuming to physically teach these machines so many actions across different settings. 

In his 1927 paper, “A law of comparative judgment,” the American psychologist L. L. Thurstone proposed that when people select one option among multiple alternatives, they are picking the one that has the highest value to them, even though they cannot assign a particular number to that choice.

To accelerate and refine decision-making in a fast-paced, global marketplace, enterprises may deploy generative artificial intelligence models to help summarize and interpret the charts that often fill market summaries and financial reports.

The multiplication of matrices and of higher-dimensional arrays called tensors lies at the heart of modern computing. Matrix or tensor multiplication is, in fact, the most common operation carried out in artificial intelligence applications, as well as in scientific simulations and computer graphics. 

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. 

To improve data center efficiency, multiple storage devices are often pooled together over a network so many applications can share them. But even with pooling, significant device capacity remains underutilized due to performance variability across the devices.

Each spring, river herring populations migrate from Massachusetts coastal waters to begin their annual journey up rivers and streams to freshwater spawning habitat. River herring have faced severe population declines over the past several decades, and their migration is extensively monitored across the region, primarily through traditional visual counting and volunteer-based programs.