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CMU research shows compression alone may unlock AI puzzle-solving abilities
New research challenges prevailing idea that AI needs massive datasets to solve problems.
CompressARC avoids this trial-and-error approach, relying solely on gradient descent—a mathematical technique that incrementally adjusts the network's parameters to reduce errors, similar to how you might find the bottom of a valley by always walking downhill. While it successfully solves puzzles involving color assignments, infilling, cropping, and identifying adjacent pixels, it struggles with tasks requiring counting, long-range pattern recognition, rotations, reflections, or simulating agent behavior. Critics might argue that CompressARC could be exploiting specific structural patterns in the ARC puzzles that might not generalize to other domains, challenging whether compression alone can serve as a foundation for broader intelligence rather than just being one component among many required for robust reasoning capabilities.
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