NTT DoCoMo has launched an artificial intelligence (AI) engine that analyzes shelf allocation in stores and warehouses using photos taken with smartphones and other common devices. The image recognition engine employs DoCoMo's AI technology and constitutes part of NTT Group's corevo AI technology. Object-detection technology detects individual items in an image with over 98% accuracy and object-recognition technology identifies specific products with over 95% accuracy by matching them with images stored in a database. Currently, shelf-analysis technology requires products to be placed in the front row and facing forward to ensure high-precision recognition. DoCoMo says its new engine can recognize products on shelves without special arrangement, even when they are packed tightly together.

Artificial intelligence could emulate human bias, including sexism, if there is no oversight on data used to create it, experts at the world's largest mobile phone fair in Barcelona warned Thursday. "We're all very aware the machines will learn the same bias as those who coded them," Emma McGuiguan, in charge of technology at consultants Accenture, said at the Mobile World Congress. AI is the science of programming machines or computers to reproduce human processes, like learning and decision making. Julie Woods-Moss, chief innovation officer at Indian mobile operator Tata Communications, said that in order to do this, a large amount of human-led data was needed. "We have to be very careful that we don't encourage AI to be biased," she said, calling on professionals in the sector to find ways to identify these biases.

SenseTime, a leading Chinese startup specialized in artificial intelligence (AI) research and development, has established an alliance with Massachusetts Institute of Technology (MIT) to promote the further application of the technology widely utilized in facial recognition. The Chinese AI researcher and developer, currently valued at around $3 billion, said the cooperation aims to explore new avenues across MIT in areas like computer vision, human-intelligence inspired algorithms, medical imaging, and robotics. Tang Xiao'ou, a founder of SenseTime who is also a PhD '96 MIT alumnus specialized in computer vision and deep learning, said he expects the cooperation between the world's best and brightest talents will further promote AI's development and benefit society. Founded in 2014, the Chinese startup is currently working with a number of well-known Chinese brands including China Mobile, UnionPay, Sina Weibo, as well as major smartphone companies in China to provide machine learning technology. SenseTime's advanced facial recognition expertise has also helped attract leading investments from Qualcomm and CDH Investments.

For this year's report, I took a stab at learning all the CSS/JS that I've always wanted to know, and then went after the task of making a fully responsive report. I've succeeded in doing so, and so this PDF version isn't as good as the real thing. In the next few days I will be sharing a link to the real digital experience. But for now -- enjoy this static version of the report which has a few parts that couldn't render to static form. Because ... this year's report is truly computationally designed and therefore needs to be expressed appropriately (smile). Expect a video version on my new YouTube channel "John Maeda is Learning." What can I do about it? As the marginal return on computing power (a la Moore's law) diminishes and technology is less of a differentiating factor, the value of design has entered the foreground. Five (20%) of the top cumulative-funded VC- backed ventures that have raised additional capital since 2013 are noted to have designer co-founders.

Imagine if the things around your house could respond to your voice even when you were shouting over a smoke alarm, keep track of each individual wandering through the house, unlock your front door just by identifying your voice, and even identify your emotions. Those are all capabilities that Microsoft is preparing to add to its Project Oxford, a set of cloud-based machine learning services introduced last May at Microsoft's Build conference. Ars took a deep dive on Project Oxford's first wave of machine learning-based services last year. Those services performed a number of image processing and recognition tasks, offered text-to-speech and speech recognition services, and even converted natural language into intent-based commands for applications. The services are the same technology used in Microsoft's Cortana personal assistant and the Skype Translator service, which translates voice calls in six languages (and text messages in 50 languages) in real-time.

That means future head-mounted displays (HMDs) based on Qualcomm's standalone headset kit will feature more efficient foveated rendering. Since they can tell where you're looking, they'll be able to dedicate most of their graphics power to make that part of the experience as sharp and clear as possible. They can even downgrade graphics on parts of the screen you're not looking at, which could lead to systems with lower specs and lower price tags. Eye tracking will also make experiences more personal and interactive. Avatars and in-game characters could look at you when you look at them, which could make interactions in VR social networks much more enjoyable.

Yeah, it's only March, but phone season has begun for 2018. Dozens of new handsets were unveiled at Mobile World Congress, the largest smartphone show on Earth, (here are the highlights) and Samsung's Galaxy S9 is already on its way to early birds. Also, Google is now circulating a developer preview of the next Android version, currently codenamed Android P. With all this action, we're beginning to get a picture of what smartphones in 2018 will look like. Here are some of the more interesting trends you may see on your next phone. It's been years since smartphones didn't all look mostly the same.

The British chip design firm ARM came up with the processors used in virtually all the world's smartphones. Now it plans to add the hardware that will let them run artificial-intelligence algorithms, too. ARM announced today that it has created its first dedicated machine-learning chips, which are meant for use in mobile and smart-home devices. The company says it's sharing the plans with its hardware partners, including smartphone chipmaker Qualcomm, and expects to see devices packing the hardware by early 2019. Currently, most small or portable devices that use machine learning lack the horsepower to run AI algorithms, so they enlist the help of big servers in the cloud.

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!! Customer churn refers to the situation when a customer ends their relationship with a company, and it's a costly problem. Customers are the fuel that powers a business. Further, it's much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn. The good news is that machine learning can help. For many businesses that offer subscription based services, it's critical to both predict customer churn and explain what features relate to customer churn.

Every time there is a big wireless, telecom or technology trade show, the big question I am always asked by the media as a telecom and wireless analyst, is simple. What was the key message or take away from the show? Last week, at the world's largest wireless trade show, Mobile World Congress 2018 in Barcelona, Spain, the answer was clear. First it is about 5G, with plenty of AI and IoT mixed in. Yes, our world is rapidly changing. So, what will 5G, AI and IoT do for us?