From the course: AI Data Strategy: Data Procurement and Storage

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Building intelligent systems with data protection

Building intelligent systems with data protection

From the course: AI Data Strategy: Data Procurement and Storage

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Building intelligent systems with data protection

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- [Instructor] We've talked about bias in AI systems, but there's another fundamental challenge that's reshaping how we build AI products, privacy. Not just basic data protection, but actually building AI systems that can learn and improve while keeping sensitive information truly private. Let's look at this by way of an example. Imagine a major healthcare AI project. The team had built this incredibly sophisticated disease prediction model, trained on millions of patient records. The accuracy was impressive, over 90% in early tests. But here's the twist. They discovered that their model was accidentally memorizing specific patient details. In such a case, someone with the right technical knowledge could potentially extract sensitive medical information about individuals from the model itself. This example goes to show just why privacy-preserving AI is an essential non-negotiable. So, how are the leading AI teams doing this? Federated learning is a decentralized way to train AI…

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