Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and efficient at providing answers, clarifying concepts, and distilling information. But to establish trustworthiness of content generated by such models, how can we really know if a particular statement is factual, a hallucination, or just a plain misunderstanding?
When Nikola Tesla predicted we’d have handheld phones that could display videos, photographs, and more, his musings seemed like a distant dream. Nearly 100 years later, smartphones are like an extra appendage for many of us.
When you think about hands-free devices, you might picture Alexa and other voice-activated in-home assistants, Bluetooth earpieces, or asking Siri to make a phone call in your car. You might not imagine using your mouth to communicate with other devices like a computer or a phone remotely.
What does sustainable fashion design have in common with Tetris? For both, an intriguing puzzle awaits, where you must configure unique shapes in a way that fills up the available space.
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.
