Catchy title. Empirical models, while useful, can't explain the how or why of a system. Having machine-learning (err, AI) models is great, but there's no idea of what will happen with edge cases or novel situations. Such models can provide data which augments experimental data.<p>Theories, ideally, can be used to propose new experiments. Data-only models can only interpolate.