Andrew W. Lo, MIT Professor in the Sloan School of Management, CSAIL PI, and Faculty Co-Director of FinTechAI@CSAIL, says, "The question is: `Do large language models have our back?’ The answer is no, not yet.” 

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.

An algorithm flags a patient as high risk for sepsis; a risk score informs whether a woman receives additional cancer screening; a deterioration model triggers an alert that sends a care team to a bedside. 

As AI tools reshape what it means to write software, a hard question is emerging for educators: what do you teach the next generation of computer scientists in the world of AI?

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.

Imagine working at a warehouse or office sometime in the near future, and you’re asked to help a new trainee learn the basics of their job. The catch: It’s a robot. To teach them, you might want to play a game of “show and tell” — that is, physically showing how to do something a few different ways, while also explaining what you’re doing.