The Three Types of AI Rigor
IAmTimNguyen · x · 2026-07-15
The author counters the common critique that "deep learning is like alchemy," arguing that this captures only part of the picture: modern AI is not purely undisciplined experimentation. It possesses significant rigor, yet still lacks answers to fundamental questions. The author proposes three categories of AI rigor: - **Conceptual rigor**: Clarity of terms and paradigms, such as what "intelligence," "AGI," or "alignment" truly mean. - **Epistemological rigor**: Whether empirical results elevate into scientific understanding that can be reproduced, predicted, and explained. - **Operational rigor**: System stability and reliability across benchmarks, evaluations, and deployments. The article emphasizes that many debates seemingly about the same attribute actually involve people evaluating entirely different dimensions. For instance, when assessing if a model is "intelligent," some look at breadth, others at planning, sample efficiency, or the presence of a world model. Conceptual clarity directly influences how we measure, optimize, and build. Epistemologically, while AI experiments are easy to reproduce in principle, conclusions are frequently skewed by random seeds, hyperparameters, implementation details, benchmark choices, and compute budgets. Reproducing a number doesn't mean reproducing the original conclusion. We still lack robust theories regarding generalization, out-of-distribution failures, and adversarial robustness. Regarding interpretability, although neural networks are mathematically definable, the learned features remain difficult for humans to understand directly. A specific behavior might stem from the data, the optimization process, internal representations, and their interactions rather than a single factor. The author concludes that modern AI excels in operational rigor but still falls short in conceptual and epistemological rigor.
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