Category: Machine learning

Keeping up with the latest Android news

on September 7, 2017

Ars Technica recently published a deep-dive review of Android 8.0 (Oreo) that contains several interesting tidbits about what the author called “Android’s biggest re-architecture, ever.” After reading the details, it’s hard to argue with that assessment.

The article’s thorough analysis includes a list of the changes Oreo is bringing to the UI, notification settings, locations service settings, and more. In addition to the types of updates that we usually see, a few key points stand out.

Project Treble, a complete reworking of Android’s foundational structure intended to increase the speed and efficiency of update delivery
A serious commitment to eliminating silent background services, giving users more control over their phone’s resources, and potentially enabling significant gains in battery life
Increased machine learning/neural network integration for text selection and recognition
A potential neural network API that allows third-party plugins
Android Go, a scaled-down version of Android tuned for budget phones in developing markets

There’s too much information about each of the points to discuss here, but I encourage anyone interested in Android development to check out the article. Just be warned that when they say “thorough,” they mean it, so it’s not exactly a quick read.

Right now, Oreo is available on only the Google Pixel and Pixel XL phones, and will not likely be available to most users until sometime next year. Even though widespread adoption is a way off, the sheer scale of the expected changes requires us to adopt a long-term development perspective.

We’ll continue to track developments in the Android world and keep the community informed about any impact that those changes may have on MobileXPRT and BatteryXPRT. If you have any questions or suggestions for future XPRT/Android applications, let us know!

Justin

Posted in Android, Android O, Battery life, Google, Machine learning, Mobile devices, MobileXPRT, Open Source, Phones | Tagged Android, BatteryXPRT, MobileXPRT, Oreo |

Machine learning everywhere!

By Bill Catchings

on August 10, 2017

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?

Bill

Posted in Google, Machine learning, MobileXPRT | Tagged Google, machine learning, MLXPRT, MobileXPRT |

Evaluating machine learning performance

By Bill Catchings

on June 8, 2017

A few weeks ago, I discussed the rising importance of machine learning and our efforts to develop a tool to help in evaluating its performance. Here is an update on our thinking.

One thing we are sure of is that we can’t cover everything in machine learning. The field is evolving rapidly, so we think the best approach is to pick a good place to start and then build from there.

One of the key areas we need to hone in on is the algorithms that we will employ in MLXPRT. (We haven’t formally decided on a name, but are currently using MLXPRT internally when we talk about what we’ve been doing.)

Computer vision, or image detection, seems to be a good place to start. We see three specific sets of algorithms to possibly cover. Worth noting, there is plenty of muddying of lines amongst these sets.

The first set of computer vision algorithms performs image classification. These algorithms identify things like a cat or a dog in an image. Some of the most popular algorithms are Alexnet and GoogLeNet, as well as ones from VGG . The initial training and use for these was on the ImageNet database, containing over 10 million images.

The next set of algorithms in computer vision performs object detection and localization. The algorithms identify the contents and their spatial location in an image, and typically draw bounding boxes around them. A couple of the most popular algorithms are Faster R-CNN and Single Shot MultiBox Detector (SSD).

The final set of computer vision algorithms perform image segmentation. Rather than just drawing a box around an object, image segmentation attempts to classify each pixel in an image by the object it is a part of. The result looks like a contour/color map that shows the different objects in the image. These techniques can be especially useful in autonomous vehicles and medical diagnostic imaging. Currently, the leading algorithms in image segmentation are fully convolution networks (FCN), but the area is developing rapidly.

Even limiting the initial version of MLXPRT to computer vision may be too broad. For example, we may end up only doing image classification and object detection.

As always, we crave input from folks, like yourself, who are working in these areas. What would you most like to see in a machine learning performance tool?

Bill

Posted in Benchmarking, computer vision, Machine learning |

Learning about machine learning

By Bill Catchings

on April 27, 2017

Everywhere we look, machine learning is in the news. It’s driving cars and beating the world’s best Go players. Whether we are aware of it or not, it’s in our lives–understanding our voices and identifying our pictures.

Our goal of being able to measure the performance of hardware and software that does machine learning seems more relevant than ever. Our challenge is to scan the vast landscape that is machine learning, and identify which elements to measure first.

There is a natural temptation to see machine learning as being all about neural networks such as AlexNet and GoogLeNet. However, new innovations appear all the time and lots of important work with more classic machine learning techniques is also underway. (Classic machine learning being anything more than a few years old!) Recursive neural networks used for language translation, reinforcement learning used in robotics, and support vector machine (SVM) learning used in text recognition are just a few examples among the wide array of algorithms to consider.

Creating a benchmark or set of benchmarks to cover all those areas, however, is unlikely to be possible. Certainly, creating such an ambitious tool would take so long that it would be of limited usefulness.

Our current thinking is to begin with a small set of representative algorithms. The challenge, of course, is identifying them. That’s where you come in. What would you like to start with?

We anticipate that the benchmark will focus on the types of inference learning and light training that are likely to occur on edge devices. Extensive training with large datasets takes place in data centers or on systems with extraordinary computing capabilities. We’re interested in use cases that will stress the local processing power of everyday devices.

We are, of course, reaching out to folks in the machine learning field—including those in academia, those who create the underlying hardware and software, and those who make the products that rely on that hardware and software.

What do you think?

Bill

Posted in AI, AR, Augmented reality, Automated cars, Automation, computer vision, Future of performance evaluation, Machine learning, What makes a good benchmark? |

Thinking ahead to WebXPRT 2017

By Justin Greene

on April 13, 2017

A few months ago, Bill discussed our intention to update WebXPRT this year. Today, we want to share some initial ideas for WebXPRT 2017 and ask for your input.

Updates to the workloads provide an opportunity to increase the relevance and value of WebXPRT in the years to come. Here are a few of the ideas we’re considering:

For the Photo Enhancement workload, we can increase the data sizes of pictures. We can also experiment with additional types of photo enhancement such as background/foreground subtraction, collage creation, or panoramic/360-degree image viewing.
For the Organize Album workload, we can explore machine learning workloads by incorporating open source JavaScript libraries into web-based inferencing tests.
For the Local Notes workload, we’re investigating the possibility of leveraging natural-brain libraries for language processing functions.
For a new workload, we’re investigating the possibility of using online 3D modeling applications such as Tinkercad.

For the UI, we’re considering improvements to features like the in-test progress bars and individual subtest selection. We’re also planning to update the UI to make it visually distinct from older versions.

Throughout this process, we want to be careful to maintain the features that have made WebXPRT our most popular tool, with more than 141,000 runs to date. We’re committed to making sure that it runs quickly and simply in most browsers and produces results that are useful for comparing web browsing performance across a wide variety of devices.

Do you have feedback on these ideas or suggestions for browser technologies or test scenarios that we should consider for WebXPRT 2017? Are there existing features we should ditch? Are there elements of the UI that you find especially useful or would like to see improved? Please let us know. We want to hear from you and make sure that we’re crafting a performance tool that continues to meet your needs.

Justin

Posted in BenchmarkXPRT development community, Browser-based benchmarks, Collaborative benchmark development, Future of performance evaluation, Let us know your thoughts, Machine learning, Performance benchmarking, WebXPRT, WebXPRT 2017, What makes a good benchmark? |

Reflecting on 2016

By Justin Greene

on January 12, 2017

The beginning of a new year is a good time to look back on the previous 12 months and take stock of everything that happened. Here’s a quick recap of a very busy year:

In 2016, the XPRTs travelled quite a bit. Eric went to CES in Las Vegas, Mark attended MWC in Barcelona, and Bill flew out to IDF16 in Shenzhen.

We also sent a team to Seattle for the first XPRT Women Code-A-Thon, an event we’re very proud to have sponsored and co-hosted along with ChickTech, a nonprofit organization dedicated to increasing the number of women in tech-related fields. The Code-a-thon also served as inspiration for an eight-part video series entitled Women Coding for Change. The series explains the motivation behind the Code-a-thon and profiles several of the participants. If you haven’t watched the videos, check them out. They’re well worth the time.

Speaking of videos, we also published one about Nebula Wolf, the mini-game workload produced through our first collaboration with the North Carolina State Senior Design Center. That experience was promising enough for us to partner with another student team this past fall, which resulted in a virtual reality app that we hope to share with the community in the near future.

Of course, we also continued work on our suite of benchmark tools and related resources. We released TouchXPRT 2016 to the public, published the Exploring TouchXPRT 2016 white paper, and released the TouchXPRT 2016 source code to community members.

In 2016, we unveiled the XPRT Weekly Tech Spotlight, a new way for device vendors and manufacturers to share verified test results with buyers around the world. We put 46 devices in the spotlight throughout the year and published Back-to-School, Black Friday, and Holiday device showcases.

In the last quarter of 2016, we celebrated our most widely-used benchmark, WebXPRT, passing the 100,000-run milestone. WebXPRT is still going strong and is as useful and relevant as ever!

Finally, we ended the year with the exciting news that we’re moving forward with efforts to develop a machine-learning performance evaluation tool. We look forward to engaging with the community in the coming year as we tackle this challenge!

As always, we’re grateful for everyone who’s helped to make the BenchmarkXPRT Development Community a strong, vibrant, and relevant resource for people all around the world. Here’s to a great 2017!

Justin

Posted in CES, ChickTech Seattle, IDF, Machine learning, Mobile World Congress, Nebula Wolf, North Carolina State University, Source code, TouchXPRT 2016, Trade Shows, Video, Virtual reality, White papers, Women Coding for Change, Women in STEM, XPRT Weekly Tech Spotlight, XPRT Women Code-a-Thon |

Category: Machine learning

Keeping up with the latest Android news

Machine learning everywhere!

Evaluating machine learning performance

Learning about machine learning

Thinking ahead to WebXPRT 2017

Reflecting on 2016

Check out the other XPRTs: