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Category: Machine learning

Doing things a little differently

I enjoyed watching the Apple Event live yesterday. There were some very impressive announcements. (And a few which were not so impressive – the Breathe app would get on my nerves really fast!)

One thing that I was very impressed by was the ability of the iPhone 7 Plus camera to create depth-of-field effects. Some of the photos demonstrated how the phone used machine learning to identify people in the shot and keep them in focus while blurring the background, creating a shallow depth of field. This causes the subjects in a photo to really stand out. The way we take photos is not the only thing that’s changing. There was a mention of machine learning being part of Apple’s QuickType keyboard, to help with “contextual prediction.”

This is only one product announcement, but it’s a reminder that we need to be constantly examining every part of the XPRTs. Recently, we talked a bit about how people will be using their devices in new ways in the coming months, and we need to be developing tests for these new applications. However, we must also stay focused on keeping existing tests fresh.  People will keep taking photos, but today’s photo editing tests may not be relevant a year or two from now.

Were there any announcements yesterday that got you excited? Let us know!

Eric

The things we do now

We mentioned a couple of weeks ago that the Microsoft Store added an option to indicate holographic support, which we selected for TouchXPRT. So, it was no surprise to see Microsoft announce that next year, they will release an update to Windows 10 that enables mainstream PCs to run the Windows Holographic shell. They also announced that they‘re working with Intel to develop a reference architecture for mixed-reality-ready PCs. Mixed-reality applications, which combine the real world with a virtual reality, demand sophisticated computer vision, and applications that can learn about the world around them.

As we’ve said before, we are constantly watching how people use their devices. One of the most basic principles of the XPRT benchmarks is to test devices using the same kinds of work that people do in the real world. As people find new ways to use their devices, the workloads in the benchmarks should evolve as well. Virtual reality, computer vision, and machine learning are among the technologies we are looking at.

What sorts of things are you doing today that you weren’t a year ago? (Other than Pokémon GO – we know about that one.) Would you like to see those sorts of workloads in the XPRTs? Let us know!

Eric

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