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alt="The dataset contains movements and physiological responses of badminton players and can be used to build AI-driven coaching assistants. This development could improve the quality of forehand clear and backhand drive strokes across all skill levels, from beginners to experts (Credit: SeungJun Kim at GIST)."
CSAIL article

In sports training, practice is the key, but being able to emulate the techniques of professional athletes can take a player’s performance to the next level. AI-based personalized sports coaching assistants assist with this by utilizing published datasets. With cameras and sensors strategically placed on the athlete's body, these systems can track everything, including joint movement patterns, muscle activation levels, and gaze movements.

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alt="MIT researchers’ "consensus game" is a game-theoretic approach for language model decoding. The equilibrium-ranking algorithm harmonizes generative and discriminative querying to enhance prediction accuracy across various tasks, outperforming larger models and demonstrating the potential of game theory in improving language model consistency and truthfulness (Credits: Alex Shipps/MIT CSAIL)."
CSAIL article

Imagine you and a friend are playing a game where your goal is to communicate secret messages to each other using only cryptic sentences. Your friend's job is to guess the secret message behind your sentences. Sometimes, you give clues directly, and other times, your friend has to guess the message by asking yes-or-no questions about the clues you've given. The challenge is that both of you want to make sure you're understanding each other correctly and agreeing on the secret message.

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Three new frameworks from MIT CSAIL reveal how natural language can provide important context for language models that perform coding, AI planning, and robotics tasks (Credit: Alex Shipps/MIT CSAIL, with components from the researchers and Pixabay).
CSAIL article

Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of complex concepts that skip less-important details — and thus sputter when asked to do more sophisticated tasks.

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Researchers from MIT and elsewhere designed a communication framework that enables academics to ask for research help on social media using meronymous communication, in which the asker only reveals certain verified aspects of their identity. They found that meronymous communication encouraged people to ask questions they otherwise might not have for fear of judgment from more senior scientists (Credits: MIT News; iStock).
CSAIL article

Have you ever felt reluctant to share ideas during a meeting because you feared judgment from senior colleagues? You’re not alone. Research has shown this pervasive issue can lead to a lack of diversity in public discourse, especially when junior members of a community don’t speak up because they feel intimidated.