With a single focus, Intel’s Vaunt has more potential than Google Glass

Back in October of 2013, I got my own pair of Google Glass in order to cover the technology. The site where I worked at the time paid the $ 1,500 cost, and I later spent my own $ 225 to add custom frames that could handle my eyeglass prescription. Given the fate of Glass, we clearly didn’t get a good return on those investments.

Still, there were some things to like about the experience. Glass brought contextual information “closer” to me a relatively non-intrusive way. And that’s exactly what Intel’s smart glasses prototype, known as Vaunt, can do.

When I first read about Vaunt over at The Verge earlier this week, I thought less about the hardware and more about that vision of context and personally important data. That’s because all of our technological advances in mobile computing have impacted this theme.

I look at it this way:

  • In the desktop age, the web brought us closer to data on other computers.
  • Connected laptops brought us closer to data when away from the desktop.
  • Phones put that data in our hand and pocket almost wherever we were.
  • Smartwatches let us wear that data, bringing it even closer
  • Smart glasses can beam that data — at least in the case of Vaunt — directly on our retinas.

Every step of that progression gets us physically closer to contextual information. I suppose the next, or maybe final, step is a Matrix-like jack that simply ports that data directly into our brains, but who knows? Regardless, this is an important theme as more devices around us create gobs of data. The fewer barriers there are between us and the information we want, the faster we can use or act upon it.

And that’s why I’m excited about Vaunt’s potential, perhaps more so than I was about that of Google Glass.

To contrast the two at a high level, Vaunt isn’t trying to take smartphone functions — such as taking photos and videos, a key reason Glass never had a chance of mainstream success — and move them to your eyes. Instead, the product is singularly focused on very specific information that you will want at a specific time and/or place.

That approach has benefits from a hardware perspective too. t’s why you essentially can’t tell the difference between Vaunt and a traditional pair of glasses. They appear to be standard eyeglass frames to both you and the people around you.

Without the need to include a camera sensor, microphone or speaker, the small chips and display components fit inside the frames. Eliminating the camera also allows for a smaller battery since powering an image sensor typically uses a lot of energy. Using a low-powered, single color laser for the retina projection helps with battery life too when compared to the color display used in Google Glass.

By distilling potential product features into essentially one — simple but very useful information — Vaunt actually solves a problem; something Glass sort of did but other extra features came along for the distracting ride. In fact, I don’t see much of a distraction factor with Vaunt because they don’t look like some technological device nor will people even realize that your retina is receiving information.

Clearly, this doesn’t mean Vaunt will be successful. In fact, Intel isn’t even sure of how Vaunt will be used. That’s why the company will be launching an early access program for developers at some point this year. Intel is just providing the technology while developers will provide the functions that they think people will want.

Think of Vaunt then as a new hardware platform with a very limited feature set. That feature is very powerful though: It takes us one step even closer to the information that personally matters most to us..

Stacey on IoT | Internet of Things news and analysis

IDC predicts market potential for IoT professional services

IDC recently made public its report on Internet of Things (IoT) professional services. In the study, IDC examines the market for Internet of Things (IoT) professional services, which is forecast to grow rapidly.

“Projected spending growth for IoT professional services is high but on a relatively small base of spending,” says Gard Little, research director, Digital Transformation Professional Services Research. “In 2017, IDC has launched its research on IoT professional services overall, and this study provides more detail on the very important professional services segment.”

As per the IDC report IoT hardware will be the biggest technology category in 2018, with $ 239 billion going largely toward modules and sensors, along with some spending on infrastructure and security. IoT-related services will be the second largest technology category, followed by software and connectivity.

In addition to a rise in investments in IoT products and services, the IDC research describes how software and services will play a major role in the success of IoT project.

 

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Research reveals potential dangers of ‘prejudiced’ AI

Research reveals potential dangers of 'prejudiced' AI

A new paper on so-called ‘black-box’ AI models reveals the dangers of our increasing dependence on opaque systems – and offers up a method for combating prejudiced AI.

Just last week, we reported on the UK Financial Stability Board’s warning that the use of AI and machine learning could compound any future financial crisis. Now, research published on academic journal database arXiv has highlighted another harmful aspect of our dependency on automated risk assessment. In short, it could be prejudiced, to harmful effect.

Black-box risk scoring AIs can be found throughout our financial and criminal justice systems. They are adept at processing vast quantities of data to determine whether an individual meets the desired risk criteria to qualify for a loan or be granted bail. Through machine learning, such systems evolve over time, identifying trends and making associations within the information they’re processing.

Read more: SAP: Banks must prepare for open banking age

Are we making AIs prejudiced?

However, these AIs are only as capable as their models (and the data they are fed) permit them to be. While it is usually illegal to consider factors such as race in these cases, black-box AIs are typically opaque in their methods. Algorithms can recognize that education levels or addresses correlate with other demographic information.

The institutions using them either don’t fully understand their AI’s methods or are using proprietary products, the workings of which suppliers refuse to divulge. There is a very real danger that the limited data sets and methods used by these systems is resulting in unethical bias.

This latest report, Detecting Bias in Black-Box Models Using Transparent Model Distillation, is led by Sarah Tan of Cornell University and provides the means to rid our AIs of prejudice.

Model distillation is a method of improving the performance of almost any machine learning algorithm by training numerous different models on the same data and then averaging their predictions. The output of these ‘teacher models’ is distilled into a faster, simpler ‘student model’, without significant loss of accuracy.

Read more: Concirrus on a Quest to help marine insurers manage risk

How can we better understand AI?

Tan’s method differs in that it uses two labels to train the AI – a risk score and the actual outcome the risk score was intended to predict. Her team have outlined how these labels relate to each other in a way that eliminates their bias. They achieve this by assessing whether contributions of protected features to the risk score are statistically different from contributions to the actual outcome.

In the past, more transparent models such as this have resulted in reduced prediction accuracy – creating tension between less transparent but more accurate models and clearer but less precise solutions. When the decision could determine whether an individual is granted bail or a loan, it’s a tricky choice with high-stakes implications.

This latest development allows black-box AI users to retrain them with the actual outcomes. “Here, we train a transparent student model to mimic a black-box risk score teacher. We intentionally include all features that may or may not be originally used in the creation of the black-box risk score, even protected features, specifically because we are interested in examining what the model learns from these variables,” describes the report. “Then, we train another transparent model to predict the actual outcome that the risk score was intended to predict.”

In other words, the black-box risk score (such as a credit score) is compared to the actual outcome (whether a loan defaulted). Any systematic differences between the risk scoring model and the actual outcome are then identified as bias – those variables from the initial data set that weren’t factors in the outcome.

Read more: Fintech app Glance Pay heralded as ‘the next PayPal’

Real-world examples

Tan and her colleagues trialed the method on loan risks and default rates from the peer-to-peer company LendingClub. It identified that the lender’s current model was probably also ignoring the purpose of the loans for which it was calculating risk – an important variable that has been proven to correlate with risk.

They also tested their model against COMPAS, a proprietary score that predicts recidivism risk in the area of crime (and the subject of scrutiny for racial bias). Its proponents argue that it is race-blind – that is, not prejudiced – as it doesn’t use race as an input.

However, ProPublica previously analyzed and released data on COMPAS scores and true recidivism outcomes of defendants in Broward County, Florida. They found that, “black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts (45 percent versus 23 percent).”

Tan’s model was able to back this up by demonstrating biases against certain age groups and races within COMPAS, while its own model, trained on the true outcomes, showed no evidence to support this.

With further testing and development, Cornell University’s solution could serve to please everyone – from the institutions that employ AI, to the individuals that must live by their conclusions.

Most importantly, it introduces transparency to critical AI models, while retaining accuracy. As we become ever more dependent on AI, across all walks of life, it’s vital that we understand how they reach conclusions – or we risk blind acceptance of prejudiced decisions.

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Nearly 50 Percent of Enterprises Have Only Achieved Half of Their “Intelligence” Potential

Nearly 50 Percent of Enterprises Have Only Achieved Half of Their “Intelligence” Potential

Nearly 50 Percent of Enterprises Have Only Achieved Half of Their “Intelligence” Potential

Zebra study: Only five percent of companies are considered truly “intelligent” enterprises, leveraging ties between physical and digital worlds for better visibility and actionable insights.

Zebra Technologies Corporation, today revealed the results of its inaugural “Intelligent Enterprise Index.” This global survey analyzes where companies are on the journey to becoming an Intelligent Enterprise; how they are connecting the physical and digital worlds to improve visibility, efficiencies and growth.

Forty-eight percent are on the path to becoming intelligent enterprises, scoring between 50-75 points on the overall index. Only five percent exceeded 75 points on the index.

The Intelligent Enterprise Index measures to what extent companies today are meeting the criteria that define today’s Intelligent Enterprise. Some of the criteria include Internet of Things (IoT) vision and adoption plan as well as business engagement in developing a return on investment for IoT. The criteria were identified by leading executives, industry experts and policymakers across different industries at the 2016 Strategic Innovation Symposium: The Intelligent Enterprise, which was hosted by Zebra in collaboration with the Technology and Entrepreneurship Center at Harvard (TECH) last year.

The framework of an Intelligent Enterprise is based on technology solutions that integrate cloud computing, mobility, and the Internet of Things (IoT) to automatically “sense” information from enterprise assets. Operational data from these assets, including status, location, utilization, or preferences, is then “analyzed” to provide actionable insights, which can then be mobilized to the right person at the right time so they can be “acted” upon to drive better, more-timely decisions by users anywhere, at any time.

Key survey findings:

  • IoT vision is strong and investment set to increase. Forty-two percent of companies spend more than $ 1 million toward IoT annually, with an average of $ 3.1 million per year, and 75 percent expect that number to increase in the next one to two years. In fact, 42 percent of companies expect their IoT investment to increase by 11-20 percent. Notably, 57 percent of companies have an IoT vision and are currently executing their IoT plans. Although only 36 percent currently have company-wide deployment, it is expected that 62 percent will have it deployed company-wide in the future.
  • Customer experience is driving IoT. Seventy percent of companies claim the largest driver of IoT investment is improving the customer experience. In the future, increasing revenue (53 percent) and expanding into new markets (51 percent) are expected to be the largest drivers.
  • Business engagement is top of mind, but culture should be given more consideration. Seventy-seven percent of companies have a method in place to measure ROI from their IoT plan, and 71 percent have IoT plans that address both the cultural and process changes necessary to implement it.
  • Many companies lack an adoption plan. More than 50 percent of companies expect resistance to adopt their IoT solution, yet don’t have a plan in place to address it. Only 21 percent who expect resistance, have a plan to address it.
  • Companies keep employees informed, but there is room for more. Approximately 70 percent of companies share information from their IoT solutions with their employees more than once a day, of which more than two-thirds share in real or near-real time. However, only 32 percent provide actionable information to all employees, and information is provided either via email (69 percent) or as raw data (62 percent).
Survey background and methodology:

  • The online survey was fielded from August 3-23, 2017 across a wide range of segments, including healthcare, manufacturing, retail and transportation and logistics.
  • In total, 908 IT decision makers from nine countries were interviewed, including the U.S., U.K./Great Britain, France, Germany, Mexico, Brazil, China, India, and Australia/New Zealand.
  • Eleven metrics were used to understand where companies are on the path to becoming an Intelligent Enterprise, including: IoT Vision, Business Engagement, Technology Solution Partner, Adoption Plan, Change Management Plan, Point of use Application, Security & Standards, Lifetime Plan, Architecture/Infrastructure, Data Plan and Intelligent Analysis.

Tom Bianculli, Chief Technology Officer, Zebra Technologies:

“An ‘Intelligent Enterprise’ is one that leverages ties between the physical and digital worlds to enhance visibility and mobilize actionable insights that create better customer experiences, drive operational efficiencies or enable new business models. This is a journey for enterprise organizations so we wanted to see where most companies are in the process. Clearly, many are still forming their IoT strategies, but we are seeing segments that have identified targeted use cases and are aggressively deploying solutions.”

Leaders from across industries developed a list of criteria that define today’s “Intelligent Enterprise.”
Zebra Technologies recently commissioned a survey to measure to what extent companies are meeting these criteria.

infographic: how intelligent are the enterprises

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IoT Brings Potential Opportunity to Africa

About 70 years ago, the world was introduced to the digital computers revolution which made the computation of millions of operations as fast and easy as 1+2. This simplified so many time-consuming activities and brought about new applications that amazed the world. Then, about 40 years ago the advent of networks and inter-networks (or the Internet) revolutionized the way we work and live by connecting the hundreds of millions of computing devices that have invaded our homes and offices.

Today, we are at the beginning of a new revolution, that of the Internet of Things (IoT), the extent of which might only be limited by our imagination. Internet of Things refers to the rapidly growing network of objects connected through the Internet. The objects can be sensors such as a thermostat or a speed meter, or actuators that open a valve or that turn on/off a light or a motor. These devices are embedded in our everyday home and workplace equipment (refrigerators, machines, cars, road infrastructure, etc.) or even the human body. These devices connected to powerful computers in the “cloud” might change our world in a way that few of us can imagine today. It is estimated that there are more than 4 billion IoT devices today and that by 2020, tens of billions of devices will be connected to the Internet.

Already, today, a considerable number of IoT applications are imagined and even realized.  Homes are being connected to their owners, wherever they are, in a way that no water leakage and no fire remains undetected. Vehicles and the road infrastructures are being connected so that cars are refusing to hit each other even if when the driver forgets to break, intelligent road lights adapt themselves to the traffic so that your stay at a traffic light is minimized. IoTs are also improving the health of many heart patients by monitoring their hearts’ conditions continuously in such a way that the patient’s doctor is aware of a pending heart attack even before he/she feels the pain. These are just a few applications of IoT. Many agree that no aspect of human life will escape to the new IoT revolution.

But IoT is not all rosy. There are also some risks that we are just learning about. IoT devices have proved to be major security concerns since recently some security breaches that exploited vulnerabilities of IoT devices have affected millions of computers throughout the world, creating major disturbances. There are also new dangers to our privacy for which we are not well prepared. The information gathered by these devices (that we even find in our living rooms!) can be used against us violating our privacy. Imagine, while you watch your TV, you TV is watching you and might be sending tons of information to people you don’t even know.

Moreover, IoT technology has not yet matured. IoT devices speak various languages and come with deferent behaviors making their integration a big challenge.

IoT might bring new opportunities to Africa in general and the developing world to solve its many problems but also to develop new applications that might contribute to the world at large. Unlike the previous technological revolutions where we in the developing world woke up too late to be leaders in the technology, today, thanks to the Internet, we can learn about the evolution of IoT at the same time as others in the developed world and aspire to be leaders and not just followers in the field. We can not only “enjoy” finished technologies as we used to do in the past but shape the new technology by participating in its development and its standardization in organizations such as the Internet Engineering Task Force.

It is with this vision that Internet Society, Addis Ababa University, and International Center for Theoretical Physics organized a pilot 5-day workshop in Addis Ababa from September 23 to 29, in order to create the understanding and interest on IoT in Ethiopia. If successful, similar workshops will be organized in other parts of Africa.

For more information about the workshop visit: http://wireless.ictp.it/Ethiopia/

Explore how IoT could shape the future of the Internet.

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