Black Friday and Cyber Monday are almost here, and you may be feeling overwhelmed by the sea of tech gifts to choose from. The XPRTs are here to help. We’ve gathered the product specs and performance facts for some of the hottest tech devices in one convenient place—the XPRT Spotlight Black Friday Showcase. The Showcase is a free shopping tool that provides side-by-side comparisons of some of the season’s most popular smartphones, laptops, Chromebooks, tablets, and PCs. It helps you make informed buying decisions so you can shop with confidence this holiday season.
Want to know how the Google Pixel 3 stacks up against the Apple iPhone XS or Samsung Galaxy Note9 in web browsing performance or screen size? Simply select any two devices in the Showcase and click Compare. You can also search by device type if you’re interested in a specific form factor such as consoles or tablets.
The Showcase doesn’t go away after Black Friday. We’ll rename it the XPRT Holiday Buying Showcase and continue to add devices throughout the shopping season. So be sure to check back in and see how your tech gifts measure up.
If this is the first you’ve heard about the XPRT Weekly Tech Spotlight, here’s a little background. Our hands-on testing process equips consumers with accurate information about how devices function in the real world. We test devices using our industry-standard BenchmarkXPRT tools: WebXPRT, MobileXPRT, TouchXPRT, CrXPRT, BatteryXPRT, and HDXPRT. In addition to benchmark results, we include photographs, specs, and prices for all products. New devices come online weekly, and you can browse the full list of almost 150 that we’ve featured to date on the Spotlight page.
If you represent a device vendor and want us to feature your product in the XPRT Weekly Tech Spotlight, please visit the website for more details.
Do you have suggestions for the Spotlight page or device recommendations? Let us know!
As we mentioned a few weeks ago, we’re in the early planning stages for the next version of MobileXPRT—MobileXPRT 3. We’re always looking for ways to make XPRT benchmark workloads more relevant to everyday users, and a new version of MobileXPRT provides a great opportunity to incorporate emerging tech such as AI into our apps. AI is everywhere and is beginning to play a huge role in our everyday lives through smarter-than-ever phones, virtual assistants, and smart homes. The challenge for us is to identify representative mobile AI workloads that have the necessary characteristics to work well in a benchmark setting. For MobileXPRT, we’re researching AI workloads that have the following characteristics:
- They work offline, not in the cloud.
- They don’t require additional training prior to use.
- They support common use cases such as image processing, optical character recognition (OCR), etc.
We’re researching the possibility of using Google’s Mobile Vision library, but there may be other options or concerns that we’re not aware of. If you have tips for places we should look, or ideas for workloads or APIs we haven’t mentioned, please let us know. We’ll keep the community informed as we narrow down our options.
I usually think of machine learning as an emerging technology that will have a big impact on our lives in the not too distant future through applications like autonomous driving. Everywhere I look, however, I see areas where machine learning will affect our lives much sooner in a myriad of smaller ways.
A recent article in Wired described one such example. It told about the work some MIT and Google researchers have done using machine learning to retouch photos. I would do this by using a photo editing program to do something like adjust the color saturation of a whole photo. Instead, their algorithm applies different filters to different parts of a photo. So, faces in the foreground might get different treatment than the sunset in the background.
The researchers train the neural network using professionally retouched photos. I love the idea of a program that automatically improves the look of my less-than-professional personal photos.
What I found more exciting, however, is that the researchers could make their software efficient enough to run on a smartphone in a fraction of a second. That makes it significantly more useful.
This technology is not yet available, but it seems like something that could show up in existing photo or camera apps before long. I hope to see it soon on a smartphone in my hand!
All of that made me think about how we might incorporate such an algorithm in the XPRTs. When I started reading the article, I was thinking it might fit well in our upcoming machine-learning XPRT. By the time I finished it, however, I realized it might belong in a future version of one of the other XPRTs, like MobileXPRT. What do you think?