Despite their impressive capabilities, large language models are far from perfect. These artificial intelligence models sometimes “hallucinate” by generating incorrect or unsupported information in response to a query.
Frontier AI Safety & Policy Panel: Where We're at & Where We're Headed – Perspectives from the UK
It's been around a year since chatbots became widespread and governments worldwide turned their attention to advanced AI safety and governance. In this event co-hosted by MIT CSAIL Alliances, the MIT-UK program and the UK government’s AI Safety Institute, we will discuss the current state of research and where we're headed. Questions to be answered include: How will we control and govern AI agents?
Multimaterial 3D printing enables makers to fabricate customized devices with multiple colors and varied textures. But the process can be time-consuming and wasteful because existing 3D printers must switch between multiple nozzles, often discarding one material before they can start depositing another.
AI systems are increasingly being deployed in safety-critical health care situations. Yet these models sometimes hallucinate incorrect information, make biased predictions, or fail for unexpected reasons, which could have serious consequences for patients and clinicians.
To the untrained eye, a medical image like an MRI or X-ray appears to be a murky collection of black-and-white blobs. It can be a struggle to decipher where one structure (like a tumor) ends and another begins.
As artificial intelligence agents become more advanced, it could become increasingly difficult to distinguish between AI-powered users and real humans on the internet. In a new white paper, researchers from MIT, OpenAI, Microsoft, and other tech companies and academic institutions propose the use of personhood credentials, a verification technique that enables someone to prove they are a real human online, while preserving their privacy.
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.
In sports training, practice is the key, but being able to emulate the techniques of professional athletes can take a player’s performance to the next level. AI-based personalized sports coaching assistants assist with this by utilizing published datasets. With cameras and sensors strategically placed on the athlete's body, these systems can track everything, including joint movement patterns, muscle activation levels, and gaze movements.
