Generative AI represents a seismic shift in the way we approach creative tasks. A comprehensive understanding of these technologies enables organizations to leverage the power of the technology to increase productivity, improve customer service, enhance user experiences, develop content, create synthetic data, enable new discoveries, and so much more!
Add to calendarAmerica/New_YorkDriving Innovation with Generative AI09/29/2025
Generative AI represents a seismic shift in the way we approach creative tasks. A comprehensive understanding of these technologies enables organizations to leverage the power of the technology to increase productivity, improve customer service, enhance user experiences, develop content, create synthetic data, enable new discoveries, and so much more!
Become an essential part of your organization’s generative AI journey by equipping yourself with the knowledge and skills necessary to navigate the intricate world of generative AI. This six-week course leverages industry case studies, hands-on work with generative AI tools, and the latest thinking from 12 faculty members from MIT CSAIL.
As artificial intelligence models become increasingly prevalent and are integrated into diverse sectors like health care, finance, education, transportation, and entertainment, understanding how they work under the hood is critical. Interpreting the mechanisms underlying AI models enables us to audit them for safety and biases, with the potential to deepen our understanding of the science behind intelligence itself.
Neural networks have made a seismic impact on how engineers design controllers for robots, catalyzing more adaptive and efficient machines. Still, these brain-like machine-learning systems are a double-edged sword: Their complexity makes them powerful, but it also makes it difficult to guarantee that a robot powered by a neural network will safely accomplish its task.
Because machine-learning models can give false predictions, researchers often equip them with the ability to tell a user how confident they are about a certain decision. This is especially important in high-stake settings, such as when models are used to help identify disease in medical images or filter job applications.
When it comes to artificial intelligence, appearances can be deceiving. The mystery surrounding the inner workings of large language models (LLMs) stems from their vast size, complex training methods, hard-to-predict behaviors, and elusive interpretability.
As a child, I often accompanied my mother to the grocery store. As she pulled out her card to pay, I heard the same phrase like clockwork: "Go bag the groceries." It was not my favorite task.
