Information Computation Gap

The "information computation gap" refers to the discrepancy between the minimal amount of data needed to solve a problem (information-theoretic lower bound) and the amount required by computationally efficient algorithms. Current research focuses on identifying and characterizing this gap in various machine learning contexts, including language models, quantum machine learning, and reinforcement learning, often employing techniques like contrastive learning and statistical query lower bounds to analyze algorithmic efficiency. Understanding this gap is crucial for developing more efficient algorithms and for bridging the divide between the capabilities of artificial and biological systems, ultimately impacting the design and performance of AI systems across diverse applications.