AI 2.0: Personalization That Respects Your Privacy

Author(s): Mukundan Sankar

Originally published on Towards AI.

The Hidden Revolution of AI: Personalization Without Intrusion

This member-only story is on us. Upgrade to access all of Medium.

Picture this: As you browse your favorite streaming platform like your Netflix, Amazon Prime or what have you- an amazing recommendation appears just when you need it most. Or you are shopping on the world’s biggest shopping platform and it gives you the perfect item you were seeking in your recommendations! It’s not just another suggestion; it feels tailor-made to match your unique taste and mood. Now picture this — the AI behind that recommendation didn’t invade your privacy, didn’t sift through your data, and didn’t creep into your life.

It sounds impossible, but it’s not. This is the quiet revolution unfolding in artificial intelligence.

The catalyst? Two groundbreaking techniques rewriting how AI personalizes without compromise: FedSelect: Personalized Federated Learning via Customized Selection of Parameters for Fine-Tuning and Personalized Federated Learning via Sequential Layer Expansion. These aren’t just academic papers. They’re blueprints for the AI of the future — a future where personalization meets privacy and efficiency meets scalability.

Let’s examine this revolution in detail and understand how it will impact you, me, and billions of others.

Artificial intelligence has a hidden issue: the personalized experiences we… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI