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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).

How AI is improving simulations with smarter sampling techniques

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).

AI pareidolia: Can machines spot faces in inanimate objects?

“Co-LLM” uses a general-purpose large language model to start replying to a prompt, with a “switch variable” intervening at certain words to call upon a more accurate answer from the expert model (Credits: Alex Shipps/MIT CSAIL).

Enhancing LLM collaboration for smarter, more efficient solutions

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How AI is improving simulations with smarter sampling techniques

AI pareidolia: Can machines spot faces in inanimate objects?

Enhancing LLM collaboration for smarter, more efficient solutions