How Much Data Is Enough? Why the Riemann Zeta Function Predicts the Future of AI & Discovery

<p>In biomedical AI, we constantly ask: <em>Do we need more data, or do we need a smarter model?</em></p><p>In this episode, we break down <strong>The Zeta Law of Discoverability</strong>—a theoretical framework linking sample size complexity to the Riemann zeta function. We explore how signal-to-noise accumulates across spectral modes like a &quot;Tower of Hanoi&quot; puzzle, why certain diseases (like Alzheimer’s) can be detected with small datasets while others (like psychiatric conditions) need massive samples, and the counterintuitive <em>&quot;three-modality paradox&quot;</em>—how adding seemingly redundant data (like text) can dramatically boost sample efficiency by steepening spectral decay.</p><p></p>