Automation and Validation (www.johndcook.com)

Recent discussions in the AI/ML community highlight the critical nature of validating AI outputs, especially given that even a model with a 90% accuracy rate can still produce significant errors. Verification methods, including consistency checks and formal proofs, are essential in ensuring the reliability of AI systems. For instance, simple checks like ensuring transaction inputs match outputs can provide initial validation, while more complex approaches like using certificates for optimization problems can ascertain correctness at a lower computational cost than finding solutions themselves. The significance of this validation process is particularly pronounced in high-stakes applications, such as collision avoidance systems, where errors can lead to catastrophic outcomes. While formal methods, utilizing tools like Lean or Rocq, offer rigorous correctness proofs, they come at a steep cost in time and resources. However, the risk remains that AI-generated proofs could also be flawed. Despite this, the extensive research and development invested in theorem provers reduce the likelihood of errors. Ultimately, the discourse raises pivotal questions about reliability in AI, emphasizing the importance of not only producing AI outputs but ensuring their correctness through rigorous validation methods.