arXiv:2607.03634v1 Announce Type: new Abstract: Artificial intelligence (AI) has achieved extraordinary capabilities despite lacking many of the conceptual and scientific foundations associated with mature disciplines. Unlike traditional sciences, where reliable technology typically emerges from theoretical understanding, modern AI has progressed largely through performance-driven iteration and "alchemical" experimentation. This tension motivates a systematic analysis of AI through the lens of rigor. We introduce a three-part framework consisting of conceptual rigor (clarifying foundational co
The accelerating capabilities of AI in the absence of robust theoretical frameworks are prompting a critical re-evaluation of its foundational principles, as performance-driven iteration faces increasing limitations.
A systematic analysis of rigor in AI highlights the need for stronger conceptual and scientific foundations, which could significantly influence future research directions, development methodologies, and the eventual reliability and safety of AI systems.
This perspective suggests a potential pivot from purely empirical, performance-driven AI development to a more theoretically grounded approach, emphasizing conceptual clarity and scientific rigor.
- · AI foundational researchers
- · Academic institutions
- · High-assurance AI industries
- · Purely 'move fast and break things' AI developers
- · AI projects lacking theoretical grounding
Increased emphasis on formal methods and theoretical computer science within AI curricula and research.
A potential slowing of certain AI development paths as more stringent foundational requirements are adopted, but an increase in long-term robustness.
The emergence of new AI paradigms that blend empirical success with rigorous theoretical guarantees, leading to more trustworthy and explainable AI.
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Read at arXiv cs.AI