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These sources present information from seminars hosted by the MIT Embodied Intelligence group (https://ei.csail.mit.edu/seminars.html), focusing on advancements in artificial intelligence, particularly for robotics and visual understanding. One seminar explores a method using generated data from simulations to train robots for real-world tasks, highlighting the challenge of generating diverse and controlled visual data. Another discusses adapting large language models (LLMs) to improve visual classification and understand longer videos, pointing out current limitations in handling detailed visual information and temporal sequences. The third source introduces a framework for multisensory AI, aiming to integrate diverse data modalities like language, vision, and sensor readings to create more versatile and robust AI systems capable of understanding human behavior and complex environments. Finally, the last source describes a framework using recursive reasoning within language and vision-language models to handle long-horizon tasks and understand complex video sequences. Collectively, the sources underscore the ongoing research efforts to build more capable, general-purpose AI through improved data utilization, model adaptation, multimodal integration, and advanced reasoning techniques.

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