Why Distributed Edge Data is the Future of AI

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Presented by

Rita Wouhaybi, Intel; Heiko Ludwig, IBM; Erin Farr, IBM

About this talk

AI is disrupting so many domains and industries and by doing so, AI models and algorithms are becoming increasingly large and complex. This complexity is driven by the proliferation in size and diversity of localized data everywhere, which creates the need for a unified data fabric and/or federated learning. It could be argued that whoever wins the data race will win the AI race, which is inherently built on two premises: 1) Data is available in a central location for AI to have full access to it, 2) Compute is centralized and abundant. Edge AI though, defies these assumptions. If centralized (or in the cloud) AI is a superpower and super expert, edge AI is a community of many smart wizards. As humans, we can appreciate the power of cumulative knowledge over a central superpower. In this webinar, we will touch on: • The value and use cases of distributed edge AI • How data fabric on the edge differs from the cloud and its impact on AI • Edge device data privacy trade-offs and distributed agency trends • Privacy mechanisms for federated learning, inference, and analytics • How interoperability between cloud and edge AI can happen
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