Categories: FAANG

Combining Machine Learning and Homomorphic Encryption in the Apple Ecosystem

At Apple, we believe privacy is a fundamental human right. Our work to protect user privacy is informed by a set of privacy principles, and one of those principles is to prioritize using on-device processing. By performing computations locally on a user’s device, we help minimize the amount of data that is shared with Apple or other entities. Of course, a user may request on-device experiences powered by machine learning (ML) that can be enriched by looking up global knowledge hosted on servers. To uphold our commitment to privacy while delivering these experiences, we have implemented a…
AI Generated Robotic Content

Recent Posts

Having Fun with Ai

submitted by /u/Artefact_Design [link] [comments]

12 hours ago

Datasets for Training a Language Model

A good language model should learn correct language usage, free of biases and errors.

12 hours ago

Everyone can now fly their own drone.

TL;DR Using Google’s new Veo 3.1 video model, we created a breathtaking 1 minute 40…

12 hours ago

CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching

Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs.…

12 hours ago

Announcing BigQuery-managed AI functions for better SQL

For decades, SQL has been the universal language for data analysis, offering access to analytics…

12 hours ago