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Machine learning in 2018

We are almost to the end of 2017 and, as you have probably guessed, we will not have a more detailed proposal of our machine learning benchmark ready by the end of the year.

The key aspects of the benchmark proposal we wrote about a few months ago haven’t changed, but we are running behind schedule. We are still hoping to have the proposal ready in Q1 2018 and the tool based on that proposal later in the year. We will keep you posted.

In the meantime, we hope you enjoy as much as we did the recent CGP Grey tech video explanation of machine learning. There are actually two videos—the first one gives a general overview and then the second one does a better job of looking at the current state of machine learning. It talks mainly about the training aspects of machine learning rather than the inference aspects that we are looking into with AIXPRT/MLXPRT.

From all of us in the BenchmarkXPRT Development Community, we hope you and yours have a wonderful holiday and a great start to 2018!


Machine learning everywhere!

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?


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