This article was originally published on Grit Daily and is reproduced with permission.

One of the more commonly used AI developments is computer vision. Whether it is Google Lens or Pinterest Lens, it is easy to get users to adopt. After all, 1,074 photos are uploaded on Instagram every second – our smartphone cameras are now an extension of our eyes. So with computer vision, all you ask users to do is something that already comes naturally, and the magic happens.

That level of simplicity, and the fact that the tech giants have already adopted it broadly, means it is also harder for startups to create something unique. With Lens baked into most Android devices, creating a new app to identify objects, plants, books, and landmarks is a relatively pointless affair.

So when someone comes along with something that is both easy to use and different, it garners immediate attention.

mce Systems, a mobile device lifecycle management provider, has announced that its latest feature is now available for Android and iOS users on the UK Vodafone network. It targets the $52 billion mobile-device secondary market. While this initial white-labeled solution is being offered in one territory and on one provider, the company has plans to expand.

“The solution is built with a light IT footprint to simplify deployment across any operator’s customer lifecycle and reverse processing partner eco-system,” Liran Weiss, COO and cofounder at mce Systems, told me. “mce’s core markets are in the UK and North America. We are currently in discussion with customers and other major international providers on implementation of this competence.”

So what does it do, and how does it work?

One of the most significant issues in the secondary mobile market is grading the phone. And, in particular, the most critical element – the screen. Smartphone screens are crucial to secondary market buyers, and can even cause anxiety, so having a showroom-quality screen can significantly increase the value of the device. So how do we determine if it is scratched, or has dead pixels?

With mce’s solution, computer vision delivers the answer in seconds. With only the device’s camera and a mirror, users receive an AI-based assessment that is, according to the company, 99-percent accurate, determining the actual value of the device. That takes away the guesswork involved in the process and standardizes the result. Used smartphone prices fluctuate wildly between buyers, as each person is using a different method and not a data-backed approach.

In addition to the screen quality measurement, mce’s solution includes device verification, which checks the smartphone’s authenticity via an authentication code and International Mobile Equipment Identity (IMEI) number, ensuring the user’s device is not blacklisted. And it also has a pixel test. Users select the marked spots on the screen to assess how many of the device’s pixels are dead and determine screen reaction time to the user’s touch.

Importantly, when checking the screen quality, the technology captures the screen and identifies its perimeter while blanking its content to protect user privacy. The app even reminds users to switch off features that might compromise their security – and that of the eventual buyer – such as finding the phone using Android and iOS tracking features.

Each year mobile device manufacturers design new devices, drawing the attention of consumers around the world. Through their mobile operators, smartphone users often trade in their old devices at the operator’s retail locations and receive an upgraded device and a new plan. In the U.S. alone, mobile device users were returned a total value of $2 billion for their trade-ins in 2020, according to a Hyla Mobile report.

By extension, the worldwide consumers’ secondary market for used smartphones is growing and is expected to reach a global value of $65 billion by 2024.

With mce’s new system, users won’t have to go to a brick-and-mortar store to get a valuation, which many welcome, given the current global pandemic. Customers can assess their devices remotely, without the need to visit a mobile operator’s outlet.

In addition to smartphones, mce extends the technology to other devices, such as tablets and laptops.

“The mce platform is built to service any electronic device which retains a high residual second market resale value,” Weiss said.

Your phone is already smart, so what’s next? Artificial intelligence and machine learning is coming to the smartphone in ways much bigger than Siri. Earlier this year, Google announced they’ll be working with semiconductor company Movidius By licensing some very special Visual Processing Unit chips, Google plans to move away from data centers and into users’ real lives—and phones.

Current smartphones already use a certain amount of AI. Siri, Cortana, Alexa, Viv, and other artificial assistants are data-based machine-learning wonders. As are several apps, including Google’s own photo-sorting app, which mines your photos’ data and sorts them in appropriate categories. Machine learning and AI have been kept relatively far away from smartphones not only because the technology is still developing, but because it devours energy. The amount of data required to perform even the smallest task is huge and quickly sending and receiving information back from data centers is simply impractical. That’s why Google’s pact with Movidius is such a big deal.

The Movidius chips will bring low-power, machine-learning processors directly to your smartphone, meaning data can be examined right there, instead of going back and forth and around the world. More importantly, this new technology is hardly a “niche.” MIT researchers recently created the similar Eyeriss, and Apple recently purchased Perceptio, a startup developing AI possibilities for phones. The integration of a chip into the system of a phone is also a big step up from a single app that performs one function. Rather than just a new bit of software, the smartphone is seeing upgraded and specialized hardware coming out for the express purpose of growing AI. By creating better devices and machine learning atmospheres, the rate at which new opportunities will appear is exponential.

Common uses for the new technology seem obvious. AI is perfect for face and object recognition as well as object tracking. Apps are already being made to process language, spoken and written, and help users take better photos, but those are the obvious uses. Machine learning will help smartphones understand their users in unprecedented ways and eventually create relationships that are much more complex than “input question, output answer.”

For example, phones may be able to understand a users’ current emotions and moods. By tracking a user’s usual patterns, a program can begin to understand exactly how much they tend to use apps, how hard they press the screen or toss the phone around. Sensors that users tend to forget even exist can actively monitor a person’s state. For advertisers, this is the ultimate opportunity. If consumers’ phones know who is tired or who is hungry, they can create even more tailored offers and promotions. For users, this could also mean a world of new apps.

A deep learning system could pull data from interactions with smartphones, and from social media data and algorithms, to create new opportunities in every part of life, specifically in the area of health tracking and life gamification. For those who use HabitRPG, Super Better, or the other ultra popular goal setting and life tracking programs, AI will boost this to a new level—one where the user can’t cheat or make mistakes. Machine learning algorithms can also be used to generate better health and life predictions for users. In a time where no one leaves the house without their phone, smartphones seem to be the ideal place for AI to take shape.

New Phones Means New Branding

As AI gets consumerized, buyers are going to forced to adapt to a new method of understanding phones. The market is largely divided by Apple lovers and the androids, like Samsung, HTC and LG. Buyers often base their decisions on which phone sports the newest and slickest UI, leaving the phones without any huge underlying differences—especially among android phones. Once phone companies begin to integrate machine learning and building AI into the phone, itself, brands are going to begin to look very different. The difference between top-notch and bottom-shelf AI is huge. It’s no longer just about speed, but capability. Namely, those top-notch systems will be a world away from the cheap knock-offs. The pervasive autocorrect function is a great example, where some programs really shine and make users lives better, and some completely fail. More importantly, different users might have differing opinions on the same program.

Even among those top brands, the actual feel and usage of their AI will differ. Many companies are already looking to remove the idea of “searching” or manually entering data, entirely. Instead, they expect AI assistants to 100% replace the idea of a computer “device” in the next decade. This means, phones will no longer be a machine, but a real first iteration of daily interactive AI.

New surveys, including a popular one from Ericsson, reinforce the fact that more and more users are becoming comfortable with the integration of AI into their daily lives. More importantly, they’re expecting it. Over 100,000 consumers spanning 40 countries were surveyed, and one in two smartphone users admitted to preferring an AI interface over devices with screens. The generation of streaming natives, especially, is looking forward to the end of the screen age. They expect homes to be embedded with sensors in the upcoming years, and those sensors better be hooked up to future smartphones.

It’s difficult to say whether the expectations of those surveyed can really be met in the next decade, but the desire to involve AI in consumers’ lives is clear. The introduction of new hardware means smartphones can begin processing more data and expand the possibilities of machine learning. Companies will have to start picking teams and choose which startups and styles to bet on. In the end, advertisers may be happy to get more opportunities to understand consumers, but it’s the users who will really benefit from the new technology. AI will be making lives easier, and plucky new startups will be showing us all the apps, tech and tools we never knew we wanted.

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Silent Circle, the secure communications product manufacturer that brought forth the Blackphone has now revealed that a high-end, secure, Android tablet is in the pipeline.

Jon Callas, the co-founder and CTO of Silent Circle told CNBC, “We are in fact working on a tablet. We are not ready to disclose the exact features, but we want it to be a high-end tablet.”

“We want it to have the features people who use it every day will demand from it,” he added.

Blackphone, released in January this year, is a product of partnership between Silent Circle and Spanish smartphone manufacturer, Geeksphone and is made secure with its PrivatOS secure Android operating system.

With a focus on “privacy, security and control,” the PrivatOS utilizes encryption tools for sending pictures and browsing the Web, and Callas says that the same security measures will feature on the tablet.

Unlike startups coming up with products and apps to make existing smartphones more secure, like Vysk, Blackphone intends to remove all need for an add-on. However, as CNBC points out and analysts reiterate, building a customer base will require a fair amount of brand marketing and might be hard with the expected high-end price tag. Also the lack of an existing app store doesn’t make things easier.

Regarding the matter of Blackphone having no access to Google Play, Callas believes that there is a niche market that would want to stay away from the data collecting giant and an app store of the secure phone is on its way.

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(Image Credit: Maurizio Pesce)