# How Machine Learning improved the Chrome address bar on Windows, Mac and ChromeOS

DevFeed: [How Machine Learning improved the Chrome address bar on Windows, Mac and ChromeOS](<https://devfeed.tech/articles/how-machine-learning-improved-the-chrome-address-bar-on-windows-mac-and-chromeos-4182.md>)

Original publisher: [Read original article](<https://blog.chromium.org/2024/04/how-machine-learning-improved-chrome.html>)

Author: Chromium Blog (noreply@blogger.com)

Published: 2024-04-29T16:02:00Z

Content type: article

Language: en

Sources: [Chromium Blog](<https://devfeed.tech/sources/chromium-blog.md>)

Topics: [Google AI](<https://devfeed.tech/topics/google-ai.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>)

Tags: [chrome](<https://devfeed.tech/tags/chrome.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [none](<https://devfeed.tech/tags/none.md>), [search](<https://devfeed.tech/tags/search.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [windows](<https://devfeed.tech/tags/windows.md>)

## AI overview

Chrome M124 integrates machine-learning models into the desktop omnibox to make web-page suggestions more precise and relevant. The article describes replacing inflexible hand-tuned scoring formulas with an ML-trained scoring model for a feature used at very large scale.

## Source excerpt

Used billions of times each day, the Chrome address bar (which we call the "omnibox") is a powerful tool to make searching the web easier, whether you're trying to quickly find your tabs or bookmarks, return to a web page you previously visited, or find information. With the latest release of Chrome (M124), we're integrating machine learning models to power the Chrome omnibox on desktop, so that web page suggestions are more precise and relevant to you. In the future, these models will also help improve the relevance scoring of search suggestions. Here's a closer look at some of the important insights that help our team build this integration and where we hope the new model takes us. How we got here As the engineering lead for the team responsible for the omnibox, every launch feels special, but this one is truly near and dear to my heart. When I first started working on the Chrome omnibox, I asked around for ideas on how we could make it better for users. The number one answer I heard was, "improve the scoring system." The issue wasn't that the scoring was bad. In fact, the omnibox often feels magical in its ability to surface the URL or query you want! The issue was that it was inflexible. A set of hand-built and hand-tuned formulas did the job well, but were difficult to improve or to adapt to new scenarios. As a result, the scoring system went largely untouched for a long time. For most of that time, an ML-trained scoring model was the obvious path forward. But it took many false starts to finally get here. Our inability to tackle this challenge for so long was due to the difficulty of replacing the core mechanism of a feature used literally billions of times every day. Software engineering projects are sometimes described as "building the plane while flying it." This project felt more like "replacing all the seats in every plane in the world while they're all flying." The scale was enormous and the changes are felt directly by every user. This ambitious underta