Building AI Systems You Can Trust

In this episode of AI + a16z, Distributional cofounder and CEO Scott Clark, and a16z partner Matt Bornstein, explore why building trust in AI systems matters more than just optimizing performance metrics. From understanding the hidden complexities of generative AI behavior to addressing the challenges of reliability and consistency, they discuss how to confidently deploy AI in production. Why is trust becoming a critical factor in enterprise AI adoption? How do traditional performance metrics fail to capture crucial behavioral nuances in generative AI systems? Scott and Matt dive into these questions, examining non-deterministic outcomes, shifting model behaviors, and the growing importance of robust testing frameworks. Among other topics, they cover: The limitations of conventional AI evaluation methods and the need for behavioral testing. How centralized AI platforms help enterprises manage complexity and ensure responsible AI use. The rise of "shadow AI" and its implications for security and compliance. Practical strategies for scaling AI confidently from prototypes to real-world applications.Follow everyone:Scott ClarkDistributionalMatt BornsteinDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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