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alt="The “Diffusion Forcing” method can sort through noisy data and reliably predict the next steps in a task, helping a robot complete manipulation tasks, for example. In one experiment, it helped a robotic arm rearrange toy fruits into target spots on circular mats despite starting from random positions and visual distractions (Credits: Mike Grimmett/MIT CSAIL)."
CSAIL article

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

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?

 

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Figure 1: Schematic overview of the framework for on-road evaluation of explanations in automated vehicles (Credit: MIT CSAIL and GIST).
CSAIL article

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.

Andrew Lo
Charles E. and Susan T. Harris Professor, CSAIL Principal Investigator
AI & Machine Learning
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alt="Using graph neural networks (GNNs) allows points to “communicate” and self-optimize for better uniformity. Their approach helps optimize point placement to handle complex, multi-dimensional problems necessary for accurate simulations (Image: Alex Shipps/MIT CSAIL)."
CSAIL article

Imagine you’re tasked with sending a team of football players onto a field to assess the condition of the grass (a likely task for them, of course). If you pick their positions randomly, they might cluster together in some areas while completely neglecting others. But if you give them a strategy, like spreading out uniformly across the field, you might get a far more accurate picture of the grass condition.

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alt="The “Faces in Things” dataset is a comprehensive, human-labeled collection of over 5,000 pareidolic images. The research team trained face-detection algorithms to see faces in these pictures, giving insight into how humans learned to recognize faces within their surroundings (Credits: Alex Shipps/MIT CSAIL)."
CSAIL article

In 1994, Florida jewelry designer Diana Duyser discovered what she believed to be the Virgin Mary’s image in a grilled cheese sandwich, which she preserved and later auctioned for $28,000. But how much do we really understand about pareidolia, the phenomenon of seeing faces and patterns in objects when they aren’t really there?