Two of the trickiest qualities to balance in the world of machine learning are fairness and accuracy. Algorithms optimized for accuracy may unintentionally perpetuate bias against specific groups, while those prioritizing fairness may compromise accuracy by misclassifying some data points.
In the classic cartoon “The Jetsons,” Rosie the robotic maid seamlessly switches from vacuuming the house to cooking dinner to taking out the trash. But in real life, training a general-purpose robot remains a major challenge.
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.
In the current AI zeitgeist, sequence models have skyrocketed in popularity for their ability to analyze data and predict what to do next. For instance, you’ve likely used next-token prediction models like ChatGPT, which anticipate each word (token) in a sequence to form answers to users’ queries. There are also full-sequence diffusion models like Sora, which convert words into dazzling, realistic visuals by successively “denoising” an entire video sequence
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?
The Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT) Editorial Board has awarded MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Gwangju Institute of Science and Technology (GIST) researchers with a Distinguished Paper Award for their evaluation of visual explanations in autonomous vehicles’ decision-making.
For robots, simulation is a great teacher for learning long-horizon (multi-step) tasks — especially compared to how long it takes to collect real-world training data.
