Chrome is not going to win any awards for its memory usage. On the contrary, the most popular internet browser out there has often taken flak for its high memory usage. While Google has made many improvements to that over the past year or so, it appears that the company will now leverage machine learning to further reduce its browser’s memory usage.

Google has been talking up machine learning and artificial intelligence significantly this week during its I/O 2018 developers’ conference. It has showcased how it’s using this advanced technologies to make its products and services better.

The folks at Chromestory have spotted some new code for Chromium which reveals that a machine learning model may be added to score tabs based on whether they will be reactivated after they have been paused. Future tests will use this model as part of the tab discarder’s selection algorithm.

Tab discarding, according to Google, is a process in which Chrome automatically pauses one of the tabs that’s not actively being used when system memory is running low. The tab remains visible on the Chrome tab strip and it reloads when clicked.

The new code suggests that Google wants to leverage machine learning to further improve Chrome’s selection ability to decide which tabs are of little use to the user currently and can be paused. Just how significant the gains will be once this model is implemented remains to be seen. It’s unclear at this point when Google is going to take this idea beyond the experimental stage.

Filed in Web. Read more about Chrome, Google and Machine Learning.

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