The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Making Algorithms Trustworthy with David Spiegelhalter - TWiML Talk #212
Dec 20, 2018
David Spiegelhalter, Chair of the Winton Center at Cambridge and President of the Royal Statistical Society, dives into why trustworthiness is crucial for AI systems. He highlights the difference between being trusted and being trustworthy, stressing rigorous evaluations akin to drug development. Spiegelhalter discusses empowering patients through transparent AI in healthcare, enhancing decision-making with personalized explanations. Throughout, he advocates for integrating diverse expert insights to improve fairness and transparency in algorithmic practices.
23:25
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Quick takeaways
- The distinction between being trusted and being trustworthy is crucial for AI development, focusing on earning trust through reliable practices.
- Robust evaluation phases in algorithm testing, akin to drug testing, are essential for ensuring algorithms deliver trustworthy performance in real-world applications.
Deep dives
Challenges in AI Deployment
Many enterprises face difficulties in transitioning from proof of concept to real-world AI deployment. This struggle primarily revolves around addressing issues of security, trust, compliance, and costs associated with using large language models (LLMs). The discussion highlights the importance of robust frameworks like Cisco's Motific, which aims to expedite the deployment process while ensuring a foundation of trust and efficiency. Such innovations are crucial for enterprises looking to leverage generative AI technology effectively.
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