Research from Carnegie Mellon University indicates that a system can perform complex reasoning tasks using only information compression, without needing extensive datasets. The study, led by Isaac Liao and Professor Albert Gu, introduces 'CompressARC,' which demonstrates the ability to solve abstract pattern-matching puzzles from the Abstraction and Reasoning Corpus (ARC-AGI). This groundbreaking approach calls into question the necessity of pre-training AI systems on vast amounts of data, suggesting that even minimal examples can lead to effective problem-solving capabilities in AI.
"Can lossless information compression by itself produce intelligent behavior?" ask Isaac Liao, a first-year PhD student, and his advisor Professor Albert Gu from CMU's Machine Learning Department. Their work suggests the answer might be yes.
They created CompressARC, showcasing that complex reasoning tasks could be approached without the conventional dependency on massive datasets, challenging existing machine learning paradigms.
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