September 30, 2022

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Chrome browser will get more native machine learning capabilities.

3 min read

Chrome browser will get more native machine learning capabilities.



Chrome browser will get more native machine learning capabilities.

Machine learning is a technology that Google is developing heavily today, and features machine learning-powered features in almost all of its products (such as computational photography used in Pixel phones, and automatic enhancement, classification, and recognition in Google Photos) .

Google has previously brought machine learning-driven features to Chrome, such as making web images accessible to the visually impaired, live captions for videos, and built-in phishing detection (not just matching URLs) .

In a recent blog post, Google revealed some of the ML features that have recently landed in Chrome and will be launched in the future.

With the blessing of ML technology, Chrome will become better to use.

As Google explains :

To further improve the browsing experience, we’re also constantly evolving the way people interact with web notifications.

On the one hand, page notifications help to provide updates on sites you care about; on the other hand, notification permission prompts can become a nuisance.

To help people browse the web with minimal interruptions, Chrome predicts when users won’t give sites permission to send page notifications and silences those prompts.

In the next version of the Chrome browser, we’ll be rolling out an ML model that makes these predictions entirely on-device.

Chrome browser will get more native machine learning capabilities.

In future versions, Google also plans to use the same technology to adjust the Chrome toolbar in real time, with different buttons, such as a share icon or voice search, appearing when and where you might be using it.

Chrome browser will get more native machine learning capabilities.

Looking back at the features introduced in the past, last summer Google announced performance improvements as part of the Chrome 92 update, reducing the time to compute phishing classification results from 1.8 seconds to 100 milliseconds.

Then in March it updated its machine learning model, which was able to detect 2.5 times more potential phishing attacks and malicious websites than the previous model.



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