Microsoft just released two new Windows 10 builds to Insiders. The firm today started testing the next Windows 10 update, codenamed 19H1, with a select group of Insiders, and it’s introducing a major new improvement for the OS.
With Windows 10 Redstone 5, Microsoft is making changes to the update system. The company is implementing a new cloud-based logic for the Windows Update system, in order to avoid unexpected updates when you really need to get work done on your computer. The update utilizes a predictive model that will improve over time in order to better understand when you are going to use your device. This way, Windows can make sure it’s not disrupting your work and install the update when you are actually expecting it to. It will consider contextual things like if you were currently using your device before restarting or try to predict when you move from your device to grab a coffee, etc.
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Microsoft says the company found the new model to produce “promising results” internally. The reliability of the system is still up in the air, of course — but since it’s a deep learning system, it is going to improve over time as Microsoft trains the model with more data. This way, the system will not only be more reliable for everyone over time, but it should also be more personalized to your own habits and device usage. Still, Windows will have to install updates eventually, so it may still seem “unexpected” to some. Either way, the feature should ship with Redstone 5, which is expected to arrive for the public later this year, sometime around October.
Windows updates have long been annoying to regular consumers, mainly because they tend to unexpectedly disrupt their workflow. Microsoft has implemented features like Active Hours to help avoid this kind of behaviour in recent years, though most consumers aren’t really aware of the feature’s existence. An intelligent system that’s more proactive and designed into the core of the system sounds more effective, so I’m quite pleased to see Microsoft investing some good work here.
skane2600
<p>First of all, there's no guarantee that a deep learning system will improve over time. The other problem is that there's insufficient data. What an individual user considers a priority activity isn't going to be captured. </p><p><br></p><p>This may be the first time an 'AI' system is used to avoid a more powerful human-based system – simply letting the user decide if or when to update.</p>