Building a more helpful browser with machine learning

At Google we use technologies like machine learning (ML) to build more useful products — from filtering out email spam, to keeping maps up to date, to offering more relevant search results. Chrome is no exception: We use ML to make web images more accessible to people who are blind or have low vision, and we also generate real-time captions for online videos, in service of people in noisy environments, and those who are hard of hearing.

This work in Chrome continues, so we wanted to share some recent and future ML improvements that offer a safer, more accessible and more personalized browsing experience. Importantly: these updates are powered by on-device ML models, which means your data stays private, and never leaves your device.

More peace of mind, less annoying prompts

Safe Browsing in Chrome helps protect billions of devices every day, by showing warnings when people try to navigate to dangerous sites or download dangerous files (see the big red example below). Starting in March of this year, we rolled out a new ML model that identifies 2.5 times more potentially malicious sites and phishing attacks as the previous model – resulting in a safer and more secure web.

To further improve the browsing experience, we’re also evolving how people interact with web notifications. On the one hand, page notifications help deliver updates from sites you care about; on the other hand, notification permission prompts can become a nuisance. To help people browse the web with minimal interruption, Chrome predicts when permission prompts are unlikely to be granted based on how the user previously interacted with similar permission prompts, and silences these undesired prompts. In the next release of Chrome, we’re launching an ML model that makes these predictions entirely on-device.

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