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What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
HAI Weekly Seminar
The world we live in is inherently compositional: just like a sentence is built upon phrases and words, a visual scene comprises a collection of interacting objects and entities, which in turn are derived from the sum of their parts. This compositionality plays a critical role in our ability to understand the world, organize the acquired knowledge through a rich set of concepts, and easily adapt them to novel situations and environments. Essentially, it is considered one of the fundamental building blocks of human intelligence. How to incorporate such compositionality into AI models? How can we encourage neural networks to develop semantic understanding of our surroundings? And how can we leverage the emerging structured knowledge to improve in downstream tasks such as question answering or image generation? These are the questions that will be explored in the talk, in which I will present models for multi-step synthesis of and reasoning over multi-object scenes, describe their key design principles and underlying mechanisms, and illustrate the benefits they offer in terms of enhanced controllability, increased data-efficiency, and improved interpretability of their internal representations and reasoning process.
Drew A. HudsonPhD Student in Computer Science, Stanford University
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