OxygenOS is the foundation of the OnePlus 3T’s Great User Experience

OxygenOS has been OnePlus’s default and primary ROM for almost two years now, first released for the OnePlus One as a flashable zip for users to break away from the increasingly-unsupported but excellent CyanogenMod S ROMs their phone originally shipped with.

This software offering took center-stage with the OnePlus 2 back in 2015, a year full of devices we mostly remember for their flaws rather than their virtues. The OnePlus 2 itself almost encompassed all of the issues many people had with 2015 flagships — its Snapdragon 810 held it back, it removed up-and-coming features like NFC for no good reason, and then offered me-too (or me-first) hardware that wasn’t properly utilized, like the Type-C port devoid of USB 3.0 speeds or fast charging. OnePlus had a really good thing with their original “flagship killer”, but they blew it with their sequel, the punchline being the comical label of “2016 flagship killer”. The OnePlus 2 was an exercise in disappointing compromises, yet there was a short period of time through which I used that device exclusively despite having prettier, faster, and less-compromised smartphones within my reach. It was all thanks to OxygenOS, the one redeeming quality that allowed me to enjoy the OnePlus 2.

OxygenOS back then was a lot more bare-bones than it is now, with a slightly different design philosophy. Back at their originally unveiling ahead of the OnePlus 2’s launch, we learned that OnePlus wanted to create a slim and fast experience, not unlike the CyanogenMod 11S ROM their first phone shipped with. The AMAs with the software team further revealed that they were emphasizing staying close to stock, but adding tactful features that could only add to the experience. They somewhat nailed it with their first attempt, and OxygenOS was indeed extremely close to Stock Android, with relatively-fast performance on the OnePlus 2 and some features that preemptively emulated future features from the then-upcoming Marshmallow, while borrowing some custom ROM favorites as well.

The Good

In a way, custom ROMs were part of Oxygen’s DNA — by then, the OnePlus One already built a reputation among enthusiasts for being developer-friendly, and plenty of users loved the fact that they could get whatever feature or customization they wanted going on it. It was no surprise, then, that OxygenOS – a ROM that was also developed by some legacy custom ROM developers that OnePlus hired – would offer a similar feel; it really felt like a lightweight custom ROM in many ways, though with a higher degree of consumer-grade polish (granted, it still had a long way to go, as we’ll detail below).

It was with the OnePlus 3 that OxygenOS really got to shine. No longer was the lightweight ROM overburdened by faulty silicon — now it packed a faster, more-efficient Snapdragon 820 flanked by a copious 6GB of RAM and zippy UFS 2.0 storage. The OnePlus 3’s OxygenOS kept all of what made the original great, then further added to its feature set once more, in tactful and measured ways. I would go as far as saying that my initial impressions with OxygenOS on the OnePlus 3 beat those I’ve had with both any other OEM stock ROM, and even recent OxygenOS releases that haven’t matched the surprise I found while originally reviewing the device. It was a breath of fresh air, and while we see a relatively larger number of OEMs offering Stock Android ROMs today, I think OnePlus nailed it there and then. Some Nougat features, speedy responsiveness, a proper dark theme to make use of the device’s AMOLED panel, and an aesthetically-pleasing UI were all positives I loved from the get-go. Then OnePlus had to change some of that, momentarily.

The Ugly

The OnePlus 3’s Community Builds adopted a new user interface that deviated from the Stock approach of the original OxygenOS, changing colors and generally making it less attractive with worse proportions and animations in system UI elements like the notification panel, recents menu and settings. This wasn’t surprising when one considers that these changes were instrumental in the eventual merger of the OxygenOS and HydrogenOS frameworks.

The company figured out that a unified team working on a unified base with different application layers could help speed up updates, and address one of the more-criticized aspects of previous devices. The Community Builds, then, were growing pains that culminated in the (luckily short-lived) Marshmallow stock ROM for the OnePlus 3T. The concept of having community builds with various testing channels is brilliant and a great community engine, but at the time, said updates had disappointed me.

It felt like change for the sake of change, perhaps a petty compromise between Hydrogen and Oxygen

In my OnePlus 3T review, I noted I wasn’t a big fan of this new approach to software. While it didn’t deviate too much from the original OxygenOS, none of the changes felt clever or meaningful, and none improved upon the simplicity and effortlessness of the original package.

It felt like change for the sake of change, perhaps a petty compromise between HydrogenOS and OxygenOS, given both featured such disparate design philosophies. Enthusiast outcry and fan feedback ensued, and the company reversed the change with the Nougat update, which offers a Pixel-like blue for its default accent color and a more traditional user interface. It was at this point where OxygenOS not only redeemed itself, but also became my favorite flavor of Android– finally surpassing the Pixel XL’s, which I held in the highest regard.

The Bad

OxygenOS has also had (and overcame) many hurdles and criticism. Some of its more pointless features stubbornly remain in there, such as the Shelf feature which was permissible on the OnePlus 2, as it was a promise of things to come, yet is still a not worthy contender today. There have been numerous conscious decisions and unconscious mistakes, though, that temporarily opaqued the better parts of the ROM. For example, upon the release of the OnePlus 3, OxygenOS was massively underutilizing the hardware of the device. I was one of the few that called this out and proposed a solution, and OnePlus was quick to act and react to the criticism by modifying its settings and allowing for more apps to remain in the background. Still, it made us all ponder, why would they not allow their 6GB smartphone to actually offer the capabilities they proudly advertised?

Another short-lived complaint was the exclusion of an sRGB mode to balance out the saturated default calibration of OnePlus’ “Optic AMOLED” panel. The company set out to make its display stand out and went as far as giving it its own buzzword, yet it turned out to be extremely color inaccurate and saturated, and it even displayed some banding and contrast issues off the bat. AnandTech and independent reporters were quick to point this out, and OnePlus offered reviewers an OTA with a proposed fix which quickly trickled down to consumer builds. It was, once more, a quick fix, but it still begs the question: why would OnePlus advertise Optic AMOLED as a better viewing experience, when no metrics backed that up? Especially since the changes were mostly calibration, which wasn’t well-received at launch (luckily, sRGB mode ended up being surprisingly color-accurate).

And that’s not all. We also caught OnePlus cheating on benchmarks, through a code block that specifically targeted certain application packages by name, and then adjusted scaling behavior to minimize score variance. This was unacceptable and the company addressed it quickly enough, but at the same time, once more it makes us wonder: why would they? It’s not like it introduced critical gains, it’s not like the OnePlus 3 couldn’t sustain class-leading performance without artificial cheating mechanisms. We suspect that it might have been a result of the merger between the two different software teams, but that wouldn’t excuse the previously-mentioned decisions.

Nor would it excuse a plethora of decisions that raised our concerns over the phone’s security and software integrity, with some of the problems we reported like IMEI leaking being wholly within their knowledge and control. None of this can be dismissed, even if it doesn’t directly impact our forward-facing UX (the point of this article).

I do take solace in the fact that OnePlus has been addressing all of these issues, however, and listening to feedback from its forum-goers and enthusiasts from communities like XDA and reddit, as well as reviewers. We’ve actually influenced OxygenOS and prompted OnePlus to fix issues, remove non-sense or add or refine useful functionality. The company is clearly open to feedback, and while they have messed up their software support on previous devices, they seem to committed enough to the OnePlus 3 and OnePlus 3T. Which brings me to my next and final point.

One of OnePlus’ Crown Jewels

It is actually really impressive, from a customer’s perspective, to see OxygenOS evolve so much with such frequent updates and ongoing feature inclusions. It has seen near-constant improvement, though not always consistent. In this regard, OnePlus has redeemed itself from the atrocious support that its older devices received (and are still suffering). If ignoring the OnePlus X and OnePlus 2 was instrumental in achieving a better user experience on the OnePlus 3, then I am inclined to say it was a smart decision for the future of the company. Truth be told, OxygenOS has improved a lot and has adopted many, many new and useful features while cleaning up its mistakes and embracing a new personality through UI refinements. All of this wasn’t without growing pains, and it’s been a tumultuous ride at times, but when I look at the final package I can’t help but recognize the software is near-perfect for the demographic I belong to, the niche I am part of (and you probably are too), and what I personally expect from my device.

I am not the kind of person that believes Stock Android is an intrinsic good and a positive aspect of whatever phone decides to feature it, either. In fact, I find myself liking OxygenOS, and even TouchWiz for that matter, now that they are further from Stock — it just needs to be done right. The short-lived community builds pre-Nougat hadn’t done it well enough, just like Samsung’s awkward and early half-baked adoption of Material Design hadn’t done it right either. Now, both OxygenOS and TouchWiz have found their own identity and a matching UI that enables both to offer the features they need for the people they target. Not all OEM ROMs have been evolving equally in the past couple of years, and in my opinion, none have done it better than OxygenOS in the end, not even TouchWiz which I also believe has been getting a lot better.

It’s not that it’s close to Stock, but that it’s close-enough in all the right places, and that it changes what could use a better solution. It’s also not that it’s just fast, but that it’s as fast as I expect from this hardware. The features they added are ones I use frequently, such as scrolling screenshots and quick capture editing, and while they aren’t unique (Samsung, in particular, has introduced much of this way before), they are implemented rather seamlessly at no expense to the user. There are still bits and pieces to address (I, for one, would like to see more customization) but considering that this ROM was a quick patch to a rough legal problem, devised just over two years ago, OnePlus has done a very good job with OxygenOS and it makes for an exceptional daily driver.

What do you think of Oxygen OS? I want to hear your opinion as well, so sound off below!

Early reports indicate that Samsung’s new flagship devices are off to a pretty great start, with the lineup breaking the previous pre-orders records set by the Galaxy S7 and Galaxy S7 Edge. However, not all things are bright and beautiful: the Galaxy S8 and S8+ have attracted a quite a bit of criticism for its red-tinted display on some units.

The issue of reddish display first came to know when the Galaxy S8 and Galaxy S8+ went on sale in South Korea, with many owners posting images of red-tinted display of their Galaxy S8 units on social media sites. It was believed the culprit had to do with Samsung’s attempt to strengthen red color on their OLED panels. Samsung reportedly developed deep red OLED displays for Galaxy S8 devices in order to avoid the issue of too much green due to the uneven distribution of subpixels in a pentile matrix. Initially, Samsung denied such issue was due to a hardware fault and advised users to adjust the color balance on their devices from within the display settings. But as more consumers raised their voices on the issue, Samsung announced on Thursday that a new software update would be rolled out next week to readjust the color balance on affected units.

Now according to a new report from the US-based review publication, Consumer Reports, the problem of the reddish display on the Galaxy S8 is not something that one should worry about.

To check the veracity of the reddish tint issue, Consumer Reports tested eight Galaxy S8 devices and found that out of eight devices, four Galaxy S8 had slightly reddish tint. The publication also notes that the issue is not immediately noticeable unless the user is comparing the two devices side-by-side.

“Our display evaluators noted that displays of four of our test models appeared slightly more red than the other four. It’s unclear how much consumers might object to the red tint, especially if they weren’t looking at two phones side-by-side.”

Additionally, Consumer Reports also said Samsung would release the software update as early as next week to address the color balance problem.

So while the issue is still there in some units, the good news is that it’s just a result of a software calibration problem, or at least it can reportedly be addressed through software, and should be fixed with the upcoming software update.

Source: BusinessKorea

What do you think? Is Samsung underplaying the significance of the displays’ red tint, or is it really not a real issue? Sound off in the comments!

No antivirus software is perfect. That’s why it’s important to a lot of people to have a reliable firewall as well. GlassWire is a great way to monitor and control what apps are connecting to the internet. This firewall is normally priced at $99 but right now it’s 70% off for $29. This will cover up to three PCs for a lifetime subscription.

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• Firewall reveals all your network activity so you can easily see what your computer is doing in the background


• Notifies you when a new app or service accesses the web for the first time


• Reveals network activity that occurred while you were away or logged out from your computer


• Monitors remote servers where you host websites, apps, or games


• Keeps you under your bandwidth usage by alerting you to all possible internet overages


• Incognito mode hides all network activity or lets you clear it

Firewall reveals all your network activity so you can easily see what your computer is doing in the background

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Purchases made through XDA Depot benefit XDA. Our sponsors help us pay for the many costs associated with running XDA, including server costs, full time developers, news writers, and much more. While you might see sponsored content (which will always be labeled as such) alongside Portal content, the Portal team is in no way responsible for these posts. Sponsored content, advertising and XDA Depot are managed by a separate team entirely. XDA will never compromise its journalistic integrity by accepting money to write favorably about a company, or alter our opinions or views in any way. Our opinion cannot be bought.

A use of old technology in a new way is transforming the potential capabilities of IoT.

Myriad Group, a mobile software company that originated in Switzerland in the 1990’s, have created Pressto, a connected bundle built on Myriad’s IoT platform, ThingStream.io.

The device uses the Unstructured Supplementary Service Data (USSD) protocol to send 182 character messages to the Pressto server across cellular networks. Pressto bundles a connected button, GSM connectivity, and management platform in a single package which allows you to focus on building the application that the button press triggers. The press of a button transfers a small payload of information to your application which includes GPS coordinates along with time/date and simple button status information.

What is USSD?

USSD is a protocol used by Global system for Mobile Communications (GSM) cellular telephones to communicate with the service provider’s computers. It can be used to provide independent calling services such as a callback service (to reduce phone charges while roaming), enhance mobile marketing capabilities or interactive data services, people know it most commonly as a means to query a phone’s available credit.

I spoke to Neil Hamilton, VP Business of development at Myriad Group to find out more.

USSD is commonly used for mobile money transfers in developing markets such as Africa, India and Latin America where many people do not possess bank accounts. This application has allowed for payment of utility bills or money transfers. Hamilton explained that:

“If we start thinking about IoT use cases where a device needs to transmit small data payloads (not videos or big files, but kilobytes per day) then we could use the USSD network to do that…  We kind of enable a GSM equivalent of a LP-WAN because USSD doesn’t need as much processing power. It also uses far less battery power and therefore devices can be much cheaper when compared to if I’m trying to roll out on LTE where I need more expensive components model to communicate via LTE.”

Myriad Group have established global roaming network access with 600 plus carriers and through the use of supplied embedded sim cards their users can transmit data and submit signals from almost anywhere in the world.

“Then effectively we provide a small code library to whoever is making their devices. And that enables the translation of the data. If it’s a sensor with a motion control we make sure we convert that into a format that we can be transported over USSD. Don’t forget there’s no internet involved. So we kind of spoof an internet language over USSD, our gateway converts that back into internet language and it goes downstream to an application.”

What kind of use cases suit USSD?

The USSD as a conduit for transferring data works particularly well in small, fast moving, remote scenarios such as logistics and tracking, as Hamilton explains:

“Cargo companies work with different end-to-end carriers and they don’t know where cargo is going. if you want to get a heartbeat on a container from almost anywhere, it’s difficult to do. We purchase wholesale connectivity and enable GSM to compete with LPWAN services, business the carriers are missing out on.  We don’t want to say ubiquitous service at any time but it’s definitely one where things are remote or moving.”

Agricultural and environmental services companies can be faced the challenge of trying to monitor hectares of farming land or agtech solutions when they’re often near roads that get busy at certain times of day or cell base stations get really and they can’t always have ubiquitous connectivity.

See also: How to turn hardware into IoT by simplifying connectivity?

Hamilton notes that the more they discuss USSD as a data conduit for industrial applications, the more use cases emerge. Today, a number of sensor manufacturers are exploring sensing-as-a- service model. If you’re a high-value sensor manufacturer you typically sell to a distributor who sells to someone who makes something and so on.

“If you start to sell your device completely connected, then it will work anywhere. And then someone could log onto a corresponding app from the sensor manufacturer to then point the data to whatever application you want to deliver it to. It means there are potential opportunities opening up right out on the edge for people to change their markets.”

The Pressto button was originally designed for proof of concept purposes to demonstrate use cases, but it has attracted a surprising amount of interest according to Hamilton but they’ve got more in development:

“We’ve got a very interesting workflow platform coming for how to manage connected devices and that’s where we’re heading now, building up the platform side to offer some more value added and useful services for industrial companies that want to connect up their things.”

It’s already been a month since Google released the first Android O Developer Preview (time sure flies by fast!), and as with any new version of Android – there’s a lot to dig into. We’ve published plenty of articles about Android O already, but there’s one feature that I feel hasn’t really received the attention it deserves: the Autofill Framework.

Autofill in Android O

Password managers are a dime a dozen these days (though we’re partial to the open-source KeePass), but it’s only with Android O that Google truly officially supports password managers. With Android O, third-party applications can fill the roll of an autofill service, which communicate with apps through the new Autofill Framework. Apps that use standard View elements will work with the Autofill Framework out of the box, though there are additional steps that developers can take to optimize for autofill to ensure that any of the app’s custom Views can be autofilled.

When an autofillable View comes into focus, the Autofill Framework will invoke an autofill request. The autofill service responds by sending back certain Autofill Datasets (such as the username, password, address, credit card numbers, etc.) that the user can then select. The autofill service is specified by the user in Settings –> Apps & Notifications –> Default Apps –> Autofill app.

Autofill App in Android O. Credits: Lastpass.

The explanation of the new Autofill Framework above is just a brief summary of what is happening on both the requesting app’s and the autofill service’s end. What’s most important for your understanding here is not the exact details of how autofill works in Android O, but the fact that the password manager apps themselves no longer handle detecting when a View can be autofilled.

Recommended Reading: AgileBits shows off what Android O’s Autofill Framework will look like

Autofill before Android O

Compare that to how autofill worked before Android O. Before password managers had any sort of official method to detect when a View could be autofilled, each application had to implement an Accessibility Service to scan the current View in order to find autofillable fields.

The use of an Accessibility Service, however, can result in considerable lag under certain conditions. The lag associated with your typical password manager’s Accessibility Service, though, is so apparent that popular services such as LastPass even have support pages up regarding the issue. These support pages typically tell you that your only recourse for dealing with excessive lag caused by their Accessibility Service is to either disable the Accessibility Service or switch to using their own custom input method. Either way, you lose any sort of autofill ability.

But why exactly does LastPass’s Accessibility Service, or any other password manager’s Accessibility Service, seem to cause so much lag? The reason is because of how these password managers have to utilize Accessibility Services to detect input fields. An Accessibility Service’s attributes are defined in an XML resource file within the APK, so we can see how the Service works by decompiling the APK file.

Below is the resource file taken from decompiling the LastPass APK:

<?xml version="1.0" encoding="utf-8"?>
<accessibility-service android:description="@string/accessibility_service_description" 
android:accessibilityEventTypes="typeViewFocused|typeWindowContentChanged" 
android:accessibilityFeedbackType="feedbackGeneric" 
android:notificationTimeout="200" 
android:accessibilityFlags="flagReportViewIds" 
android:canRetrieveWindowContent="true" 
android:canRequestEnhancedWebAccessibility="true"
xmlns:android="http://schemas.android.com/apk/res/android" />

From this, we can glean the following information: LastPass’s Accessibility Service requests two Event types to monitor – TYPE_VIEW_FOCUSED and TYPE_WINDOW_CONTENT_CHANGED. It does this because it needs to know when an app/webpage’s content changes or comes into focus, and then it retrieves the current window content to look for any password input fields. But since the service constantly does this on two extremely frequently firing Accessibility Events, it results in lag. For a more in-depth discussion of how Accessibility Services can cause lag, I refer to you my previous article on the matter.

Recommended Reading: “Working as Intended” – An Exploration into Android’s Accessibility Lag

Android O Kills Two Birds with One Stone

Prior to Android O, there’s not really much developers of password managers could do to mitigate this lag. That’s because password managers had no way of knowing when an autofillable input field was on the screen without enabling an Accessibility Service to constantly monitor for them. But thanks to the new Autofill Framework in Android O, these password managers can now retire their Accessibility Services. Instead, the apps that need data entry themselves will request the Autofill Framework to call the autofill service that will then send the data. Thanks to this new framework, not only will password entry become much easier for users since they no longer have to rely on an additional input method, but the lag associated with enabling password managers’ Accessibility Services will be a thing of the past.

I know that for some of you, this fact may not be ground-breaking, but I thought that since the discussion around the Accessibility Service was so mute this topic might have been worth rekindling. Just some food for thought this weekend!

What do you think of Android O’s new Autofill Framework? Let us know in the comments below!

Cutting corners in a cut-throat market

Any press is good press, or so they say. That’s a phrase often used when a company finds itself in a precarious situation — but as Samsung learned first hand, in a hyperactive segment like this ever changing mobile space, the smallest misstep can turn out to have grave ramifications.

Huawei is learning this first hand with the negative press surrounding its recently-launched P10 and P10 Plus series of devices. They are no stranger to negative press surrounding the quality of their hardware. First came the Nexus 6P build quality debacle with shattering visors and bending devices, and recently the Honor 6X screen and build quality issues notched another one in their belt. One would think they would be walking a finer line when it comes to their smartphones, but the latest news surrounding them says otherwise.

As Aamir wrote about in an earlier article, Huawei is in the midst of a controversy due to how it handles its flagships’ components. They have already admitted to sourcing components from various sources, but further investigation is showing that these components feature vastly different technologies and capabilities from RAM speeds to the type of internal memory that is used. Utilizing components from various sources is a very common practice among OEM’s. Samsung does this with its cameras in its flagships known to alternate between its own ISOCELL sensor and a Sony one. Apple does this with its SOC’s alternating between Samsung and TSMC for manufacturing (which has brought its share of negative press) and it is widely known that HTC sources its displays from multiple partners too. 

Batteries, displays, cameras, internal storage, and RAM can all be sourced from various partners. This helps supply chain constraints and it can also help with quality assurance… generally, it is perfectly fine. What is not fine is when these components offer vastly differing experiences depending on your luck of the draw, which is what Huawei is offering right now. While many users may never notice a difference between LPDDR3 and LPDDR4 or UFS 2.1 and eMMC 5.1; there is a quantifiable difference, and when you are paying flagship prices every customer deserves the same or at the very least a similar experience. By giving users a random combination of RAM and storage types, their customers aren’t buying a flagship, but a lottery ticket for a chance to earn the full product Huawei advertised.

Unlike other cases of component variety with a smartphone line, a large part of Huawei’s advertising relied entirely on the flagship components a user might have. Huawei, like many other Chinese manufacturers, puts a clear and heavy emphasis on marketing their devices’ power and performance. It’s no surprise, then, that they’d pick the faster RAM and UFS2.1 storage as talking points for their ad campaigns and promotional materials. In fact UFS2.1 was advertised for all markets of the Mate 9 as recently as last week; however, today only a few market pages actually show that specification. Obviously, though, it becomes a problem when a significant portion of P10 customers may not get the UFS 2.1 storage, or the LPDDR4 RAM, or both. People paid for a flagship under the notion that they’d get a flagship, with flagship components throughout. When people pay for a Samsung device, ISOCELL and Sony sensors offer comparable performance with differences that, to my knowledge, haven’t shown significant and quantifiable deltas — there is a mostly-lateral difference between the two. There is a clear hierarchy between LPDDR4 and LPDDR3 RAM, however, and the same goes for UFS 2.1 and eMMC 5.1 storage.

A worrying aspect to much of this is what it means for what Huawei is as a company, and what it’s trying to do. Giving credit where it is due, Huawei devices have come a long way. The Mate 9 and Honor 8 Pro are fantastic phones offering a solid build, vastly-improved software, and an overall appealing package. Huawei is trying hard to break into the large and somewhat stagnant US market and while on the surface they have nearly all the components to succeed, the lack of good decision making from Huawei serves to undermine all that they have accomplished. It’s not just their phones that have this issue either.

The Huawei Watch was and still is one of the best Android Wear devices you can buy today; it has a stellar screen, fantastic look, excellent battery life, and more. But instead of capitalizing on its success, Huawei has made one of the most boring and uninteresting devices to date – the Huawei Watch 2.

Similarly, the Honor 5X was a solid piece of hardware crippled by its software, which can be easily remedied. But instead building on that solid foundation they release its successor – the Honor 6X – which ships with an out of date OS version, crippled build quality, and packs one of the least durable displays on the market. The P10, which we have been speaking about, also ships with no oleophobic coating which is not only a standout in the market (particularly its segment), but is also just dumb. The recent string of decisions from Huawei are troubling seeing as their success in the US market, which can greatly help them in the long run, depends largely on enthusiasts, positive media coverage and word of mouth at this time; and despite my largely positive experiences with the Honor 8 Pro and Mate 9 I am hesitant to recommend them friends and family. The North American market does not appreciate corner cutting, and Huawei is truly not breaking away from the stereotype that troubles Chinese manufacturers in the US so much.

While Huawei’s decision making in these past few months leaves a lot to be desired, there is still time for them to correct their course and get back to improving their offerings. They need to be cautious though, they do not have the clout of a company like Samsung which can fairly easily rebound from total meltdown. There were some who thought Samsung would never come out from what happened with the Note 7, but soon after the S8 announcement they are boasting some of the best preorder numbers yet. Huawei makes a compelling phone with software that is quickly catching up to the competition, they just need to make sure they don’t shoot themselves in the foot by poor decision making because in an ultra-competitive market, someone is always nipping at their heels.

What do you think about the P10’s hardware lottery? Let us know in the comments.

Despite the fact that Google’s Chrome browser is the market leader in Internet browsers, Google is always working to bring additional features to millions of users in order to stay competitive. Scroll anchoring is one such feature that, after nearly 10 months in testing, was finally released to end users. But even if it takes Google months to officially release its experimental features, users can go to chrome://flags to try them out right now. Just recently we showed you how to enable the new Custom Context Menu feature in Chrome Dev or Chrome Canary, for instance. This time, we wanted to talk about a potentially exciting feature that Google is working on called copyless paste.

Copyless Paste

This new feature can be found in Chrome versions 59+, meaning users on the Chrome Dev or Chrome Canary channels can access it. All you have to do is paste the following text into your address bar: chrome://flags#enable-copyless-paste.

Just based on the description of this flag, it seems like a really smart change to potentially integrate Google Chrome with the rest of the apps on our device. The feature promises to provide suggestions for text input based on recent web browsing context, and as an example it states that if you are looking at a restaurant’s website and switch to the Google Maps app then the keyboard would display that restaurant’s name as a search suggestion.

Unfortunately, it doesn’t appear that this flag actually works yet. I’ve tried going to numerous restaurants’ webpages and then opening the Maps app, but I never saw the text input suggestions in Gboard that the feature promised. I’ve also asked a friend running Android O to try to get this feature to work, but to no avail.

We’ve heard rumors of this feature being worked on before Android O was officially unveiled, and based on the description of the “Copy Less” feature that was exclusively provided to VentureBeat at the time, we have strong reason to suspect that this Chrome flag and the rumored feature are one and the same.

I am not exactly sure how this feature actually works under the hood (any experts in Chromium are free to chime in), but I do know that it is currently in active development and testing so it may take some time for this feature to actually start working.

We’ll be following the development of this feature to see how it pans out, and will update you if/when it starts working.

In less than 15 years, one quarter of all U.S. travel could be in shared, electric self-driving vehicles, according to a new study by the Boston Consulting Group.

The impact will be felt most in cities where over one million people live, as these will be the launch locations for driverless ride-sharing services. Ford CEO Mark Fields has already said the company will launch a ride-sharing platform in cities by 2021.

See Also: SoftBank wants autonomous shuttle on public roads by 2020

Initial public backlash to driverless cars will subside, according to BCG, once the economic argument becomes clear to U.S. commuters. In the study, the researchers said the everyday commuter in Chicago may be able to save $7,000 per year by moving to a self-driving, ride-sharing platform in the future.

“The automotive industry is on the brink of a major transformation, and it’ll be here faster than people realize,” Justin Rose, a BCG partner leading its digital efforts for industrial companies, said in a statement. “For millions of Americans living in large cities, the next vehicle they purchase may be the last car they ever own.”

BCG expects automakers and tech firms to revoke control and ownership of the vehicle in the future, moving instead to a more elongated profit model, ride-sharing. Instead of a one-time, large payment for the car, consumers would pay each time they use the vehicle.

Differing business models

There are different economic models for automakers to choose from. One could be a lease model where the consumer pays to use the car for a certain amount of time; an alternative could see consumers pay for every ride and change cars each time.

BCG has high expectations for the self-driving industry. It expects 4.7 million autonomous cars to replace five million conventional vehicles on the road today, and believes the new driverless vehicles to travel 1.5 trillion kilometers.

It is hard to judge the exact time self-driving cars will become the vehicle of choice for commuters. Infrastructure and regulations could hold automakers back a few years, although the U.S. government and telecommunication providers have both shown interest in expediting the deployment of autonomous vehicles. Most estimates are between 2025 – 2035.

Internet of Things (IoT) is changing the world around us. This may look like a bold statement to many, but that’s the truth. From elevators to planes, there will be 30 billion connected devices in the next three years. Clearly, IoT is moving fast, and tech giants are taking bold measures to constantly push the boundaries of what can be achieved with the IoT.

The recent Genius of Things Summit that was organized in Munich, Germany was a demonstration of how IBM’s unrivaled IoT ecosystem is changing the way we live, work, and play. Here are a few notable developments that caught my interest.

Digital twin

The traditional way of conceptualizing, designing, and developing a product is time- and resource-intensive. Digital twin is an initiative to improve the efficiency of the process by using cloud-based virtual image of an asset maintained throughout the lifecycle. The asset, in turn, remains accessible to every individual involved in the process, thereby allowing people to work collaboratively, reduce errors, and improve efficiency.

Airbus and Schaeffler are the two companies that are currently using digital twin engines and bearings. Airbus is using this technology to create a digital thread that allows different engineering divisions to collaborate. This way, in case a problem is discovered, the company can explore whether the product is due to inadequate maintenance, poor manufacturing, or a fault in the design.

Cognitive commerce

The ultimate objective of cognitive commerce is to offer a truly personalized service to customers based on their precise preferences. To achieve this, a wide spectrum of technologies is used, from speech recognition to machine learning.

Visa is working in collaboration with IBM to offer its customers the flexibility to make payments from any IoT connected device. This, in turn, will eliminate the need to carry sensitive financial information embossed on payment cards, thereby making the customer journey simpler, easier, and more secure.

Predictive maintenance

As the name implies, predictive technology analyzes the data collected using sensors to predict the maintenance needs of an asset. This, in turn, improves asset availability, reduces maintenance costs, and improved customer satisfaction.

SNCF, which is a leading freight and passenger transport service, has collaborated with IBM to connect its entire rail system to the IoT ecosystem. Using the data collected from sensors, SNCF will be able to predict repair and maintenance needs of its trains and tracks, improve the security and availability of its assets, and reduce the downtimes associated with unexpected downtimes.

While the Internet of Things (IoT) has the power to change our world, we are still at the beginning of the transformational journey that will revolutionize the way we live and work for the better. In the next few years, we can expect to see incredible advancements being made by tech giants, such as IBM and other companies.

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Artificial Intelligence. I can’t think of a reference to the intellect that would feel more unauthentic and fake. It’s no wonder people turn to The Terminator or The Matrix to fathom what it’s all about. Fortunately, after 60 years of AI rumors fueled by academia and movies, we’re finally starting to see signs that it means more than just robots taking over.

Working in the tech industry, it’s ironic that the AI lightbulb clicked not through an understanding of machine learning or engineering — but through the human challenges we face in the technology world.

See also: Look at all the amazing things AI can do for lawyers

While at Yahoo! and Apple, it was amazing to be part of technologies that not only helped enable the modern cloud today but continue to support its more than doubling in performance every year. But on the people side, opening a new data center meant hiring a team with over 100 man-years of various expertise and experience. As these skills became harder and harder to find, we realized that since we couldn’t find more people that had them, we needed to figure out how to get more out of people that didn’t.

We’re living in a world where the evolution of technology is exponentially outpacing that of people. AI is now empowering machines with the intelligence to form its own insights and thoughts. What were sensors are now evolving into senses, where machines can acutely comprehend things like sight, sound, and touch.

The Internet of Things (IoT) is enabling “self-driving” actions to be performed, based on those thoughts and senses. Right in front of our eyes, technology is making the leap from being the inanimate tools of yesterday to serving as our collaborative and conscious AI-powered co-workers of tomorrow. As people, our responsibility is to serve as the manager and mentor of this new form of co-worker.

Using comprehensive human-to-machine learning interfaces, all people will have the ability to effectively teach and mentor our new co-workers using one’s own natural human language. Yes, human knowledge will be the foundation of what makes artificial intelligence real. This is why AI will prove to be more useful as an extension of human intelligence, than a substitution for it. It is then that it will no longer be viewed as Artificial Intelligence, but understood as Augmented Intelligence.

Enter augmented intelligence

AI is going to augment natural human intelligence and enable people to gain the world’s collective expertise while requiring less time and study than what has been required to become an expert in any one thing today. Traditionally in humans, an expert’s mind possesses fewer possibilities for slower growth, while a beginners mind offers many possibilities for rapid growth.

Augmented Intelligence will empower us with the best of both. In doing so, there is no reason to think that our own personal capabilities for intellectual advancement cannot equal or surpass the doubling per year that we see in computers. Yes, we will see the day when the augmented human expert will be able to get 2x smarter, every year.

Being a leader of people, I learned long ago that my own success is more defined by the augmented intelligence and capabilities that my team provides than anything I could solely do as an individual. The same will apply to the new performance enhancing co-workers that will augment our intelligence and capabilities. Through today’s eye, this picture of our future may be seen as heresy or a superhuman threat. That said, the same fear occurred in the past when it was suggested that all people could be given the power of reading.

Evolution is life. Embrace the future.

Apple’s secretive self-driving project has received a permit to test driverless vehicles in California, which could be our first preview of the iPhone maker’s autonomous technology.

The self-driving tech has been in development, under the codename Project Titan, for a few years. During that time, the project has supposedly moved from a hardware, software, and services package to a more software oriented project.

See Also: Detroit passes Silicon Valley as center for self-driving research

Apple has registered three Lexus RX vehicles and six drivers, which does give some credence to the reports of it focusing primarily on software. The permit does not disclose what sensors the company plans to use.

All companies registered with the California’s DMV have to file disengagement and crash reports every month, this could be our first insight into the sophistication of Apple’s tech.

Apple technically isn’t even working on a car?

Apple has still not publicly acknowledged it is working on a self-driving vehicle and declined to comment on the permit.

It said in a letter to the National Highway Traffic Safety Administration (NHTSA) that it is “investing heavily in the study of machine learning and automation, and is excited about the potential of automated systems in many areas, including transportation,” which looks like the closest we’re going to get to a confirmation until near launch.

The company will join a growing list of tech firms testing autonomous vehicles in California, including Waymo (Google’s self-driving division), Tesla, General Motors (with Cruise Automation) and Uber.

Ever since Google first introduced the concept of software navigation keys to Android, users have been asking for a way to customize what keys are available to users. Although custom ROMs have offered this level of customization for years, it’s only in the first Android O Developer Preview do we find an official method from Google to modify the nav bar. However, like many features before it, this nav bar tuner did not appear out of nowhere, and was actually secretly in testing for Android Nougat. It was only recently, though, that we discovered that this hidden nav bar tuner in Android Nougat can actually be accessed without needing root access, a custom ROM, or System UI mods. Hence, a new avenue of rootless customization has opened up for many users, and today we’ll be guiding you through one popular request: how to add media playback controls to the nav bar when playing music (Android 7.0+, no root needed!)

As you can see in the screen capture above, my test device (an unrooted, bootloader locked Google Nexus 6 device on Android 7.0 Nougat) has the standard set of nav bar keys until music playback is initiated in Google Play Music. When music playback begins, two new keys are added to the nav bar: a button to play the previous track and a button to play the next track. These keys stay on the nav bar until I dismiss the Google Play Music notification – that way, I can still use my phone for other apps while retaining these playback control keys until I decide that I’m done listening to music.

Although my screen capture above shows this setup being used for Google Play Music, this can easily be modified to work with virtually every music, podcast, or radio app that’s out there – so long as that app displays a notification during playback and accepts media previous/next keys (both highly likely). This tutorial is slightly modified from my original tutorial aimed at Android O users, however, many, many more users will be able to take advantage of this tutorial as it is not limited to users running the Android O Developer Preview. That being said, let’s get started.

Add Media Playback Keys to the Nav Bar when Playing Music

Requirements

Setup: Custom Navigation Bar

The reason we need Custom Navigation Bar is obvious – this application is what will allow us to modify the nav bar to display these media playback keys. (Technically, we don’t actually need this app for these modifications as we can use shell commands or other Tasker plugins, but to make things easier for our users, we will show how to set this up using this wonderful app.) Notification Listener is needed to monitor what notifications get posted to the status bar, so we know when music playback has started and ended. Finally, Tasker is the automation app that bridges the gap between Notification Listener and Custom Navigation Bar – it uses Notification Listener to detect when music has started/ended and then trigger Custom Navigation Bar to change the nav bar accordingly.

The first thing we need to do is to make sure that it’s even possible to modify the nav bar on your device. If your device is one of the ones listed as compatible in the Custom Navigation Bar thread, then chances are it will be. We can verify by running through the brief tutorial that accompanies this app.

Install the app from the Google Play Store, then open up the app and proceed through the introductory screens. Custom Navigation Bar will ask you to grant it a certain permission called WRITE_SECURE_SETTINGS in order to proceed with using the app. There are two ways you can do this, as stated in the application.

1. If you have a rooted device, Custom Navigation Bar will request superuser access. Grant it, and the app will automatically grant itself this permission.


2. If your device is not rooted, then you will need to grant the permission through ADB. Open up a command prompt/terminal on your machine, and then enter the following command: adb shell pm grant xyz.paphonb.systemuituner android.permission.WRITE_SECURE_SETTINGS

Once you’ve granted the app this permission through either of the two methods above, then the app will proceed with a compatibility test. If your nav bar doesn’t change, then you’re unfortunately out of luck. If your nav bar changes to display a right arrow button, then congrats your device is supported! We can now move on to modifying our nav bar.

Setup: Notification Listener

In order for Notification Listener to intercept notifications, we have to grant it a special permission known as the “notification access” permission. This permission is not granted through a standard permission dialog, but needs to be granted through a special settings menu by the user. Luckily, this is very simple to do. Simply open up the Notification Listener app and the app will bug you to enable this permission. Just press the button and the app will take you to the screen where you can grant the app this permission. Enable notification access for the app.

Tutorial

Once you’ve confirmed that Custom Navigation Bar is compatible with your device and that notification access is enabled for Notification Listener, it’s time to set up this all up. The first thing we need to do is to create a new profile in Custom Navigation Bar that, when enabled, will add a previous/next key to our nav bar. Here are the step-by-step instructions:

1. Open up Custom Navigation Bar and tap on Profiles under the Automation section.


2. Tap on the + icon on the top right to add a new Profile.


3. Tap on the Profile that was just created.


4. Under the Profile section, tap on Name to name the profile. Name it Media Control.


5. Under the “Extra left button” section press Type. Select Keycode as the type.


6. Now under the “Extra left button” section you will see two additional options. Tap on Keycode.


7. Scroll down and find the Media Previous key.


8. Now tap on Icon under “Extra left button.” For the icon select skip previous.


9. Repeat steps 5-8 but for “Extra right button.” This time, however, the keycode will be Media Next and the icon should be skip next.


10. Test your Profile by scrolling back up and checking Enabled. If you see the previous/next nav bar keys at the bottom, then this profile works!

Now that we’ve got the Custom Navigation Bar profile set up, we will create our Tasker Profile that will enable/disable this profile when music is playing. First, we will create the Profile that will trigger when our music/podcast/radio app posted a notification. Here are the step-by-step instructions:

1. Open up Tasker and create a new Profile by tapping on the + icon on the bottom right.


2. Select the Event context.


3. Tap on Plugin.


4. Select the Notification Listener plugin.


5. Select the notification listener action that pops up.


6. Tap on the pencil icon to open up Notification Listener’s configuration.


7. Leave the notification event as posted but under apps select the app(s) that you want to monitor. For instance, I picked Google Play Music here. Tap the checkmark icon in the top right when done.


8. Back in Tasker, press the back arrow key in the top left to go back to Tasker’s main screen.


9. Tasker will ask you to attach a Task to this Profile we just made. Select to create a New Task. Don’t bother naming the Task.


10. Once you’re in Tasker’s Task editing screen, add a new Action by tapping on the + button in the bottom middle.


11. Select Plugin from the Action categories.


12. Choose the Custom Navigation Bar plugin.


13. Tap the pencil icon again which this time will bring us to Custom Navigation Bar’s configuration page.


14. For the action leave it as “Enable profile.” Under Select profile, choose Media Control. Hit the checkmark in the top right when done.


15. Press back, and then back once more until you’re at Tasker’s main screen.

The above Tasker Profile we created will activate the Media Control Custom Navigation Bar profile to add the media playback keys when media playback begins, but now we need to disable the Media Control profile when we dismiss the media app’s notification. Here are the instructions:

1. Create a new Profile and select the Event context.


2. Go to Plugin –> Notification Listener –> Notification Listener.


3. Under “Notification event” this time select Removed. Again select the same app(s) that you want to monitor. I chose Google Play Music here. Tap the checkmark when done.


4. Go back to Tasker’s main screen where it will ask you to add a Task to this new Profile. Add a Task but don’t bother naming it.


5. Once you’re in Tasker’s Task editing screen, add a new Action. Go to Plugin –> Custom Navigation Bar.


6. This time for “Action” select to Disable profile but again choose the Media Control profile. Tap the checkmark button up top when done.


7. Exit out of the Task back to Tasker’s main screen.

When you’ve made both Tasker Profiles, one for when the media app’s notification is posted and another for when those same notification(s) are removed, you’re done. Tasker will now display media playback keys in your nav bar whenever media playback has started, and clear the nav bar of these keys when media playback has ended!

Using Shell Commands

Given how easy it is to use XDA Senior Member paphonb‘s Custom Navigation Bar app, I don’t really see the need for providing detailed step-by-step instructions on how to do this with other Tasker plugins such as SecureTask or AutoTools (or the run shell function in Tasker). However, it is certainly possible, and at the very least I will provide a summary of the commands you need to replicate this setup without the use of paphonb’s app.

The first thing you need to do is install either SecureTask or AutoTools. You will need to grant the WRITE_SECURE_SETTINGS permission to whichever app you pick in order to control the nav bar tuner.

For SecureTask:

adb shell pm grant com.balda.securetask android.permission.WRITE_SECURE_SETTINGS

For AutoTools:

adb shell pm grant com.joaomgcd.autotools android.permission.WRITE_SECURE_SETTINGS

Next, you will need to download the icons that you will use for the previous/next keys. You’ll need the icons in the PNG format, and as for the size, you can determine the size of the icons you need by looking up your device’s display density metrics on Material.io and correlating that with an icon size reference chart. IconsDB.com is a good resource for free icons. Save the icons you will be using as previous.png and next.png in a folder called /NavIcons on the root directory of your storage.

Finally, you will be entering this command to show the media control buttons:

settings put secure sysui_nav_bar "key(88:file:///storage/emulated/0/NavIcons/previous.png),back;home;recent,key(87:file:///storage/emulated/0/NavIcons/next.png)"

where key #88 refers to KEYCODE_MEDIA_PREVIOUS and key #87 refers to KEYCODE_MEDIA_NEXT.

Then to revert your nav bar keys to the default layout (ie. when you swipe away the media playback notification), enter this command:

settings put secure sysui_nav_bar "space,back;home;recent,menu_ime"

In essence, the Tasker Profile setup will be the exact same as Notification Listener’s configuration above won’t change. But if you choose not to use the Custom Navigation Bar app to control the nav bar, then you can use the above two shell commands as an alternative. Just note that, unless you are rooted and using the “run shell” action in Tasker, the process to get these commands into SecureTask or AutoTools is all on you. It’s really not that hard to do, but many users find just using paphonb’s app easier to use so I won’t go into much more detail here.

Conclusion

That’s it for this tutorial. In future tutorials I will show off more potential practical uses of changing your nav bar, especially in a contextual manner using an automation app such as Tasker.

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All machines eventually break down. Self-driving vehicles are no exception.

Autonomous vehicles pose two problems for the future of vehicles. The removal of the driver means there is no person providing feedback on how the vehicle performs over time. You are removing the point-person who says “something feels wrong, this needs to be checked out.” An autonomous truck could easily arrive at its destination with one fewer wheels than it left with at its origin without recognizing there is a problem.

An autonomous truck could easily arrive at its destination with one fewer wheels than it left with at its origin without recognizing there is a problem.

See also: Automotive 2.0 – the new road ahead for autonomous vehicles

Autonomous vehicles also create a second maintenance problem – the sensor systems they use can fail. An autonomous vehicle operating with a faulty sensor is an on-the-road hazard. Imagine the wheel-speed sensor that reports the vehicle has stopped when it is actually traveling at highway speeds. A human would know to disregard that faulty input because it “feels” wrong. An autonomous vehicle could respond by continuously accelerating.

With the advent of autonomous vehicles, now, more than ever, systems need to be created for advanced diagnostics. AI should not just be used to make vehicles drive autonomously, but also to allow vehicles self-diagnose future and upcoming issues.

The new AI for maintenance

The concept of predicting breakdowns is nothing new. People have been using statistics for decades to calculate mean-time-to-failure – it is how the automotive industry came up with replacing parts based on number of miles driven. However, the ‘mean-time’ means that some parts repaired will have significant useful life yet and others will break before you get around to fixing them. AI allows something to be done that was unfeasible in the past – actively monitor every vehicle while it is in use.

Established companies have been working in this place for a while now. They operate on the notion that if you have been collecting data then they can put enough experts on the problem to create a solution. SAP, IBM, and Pivotal Labs are all making plays into the space. The problem is that they require that a company has been collecting data, knew what data to take, and knew to keep it.

As the industry matures and companies collect more data and gain large historical datasets, their solutions will be powerful. But nimble startups can use speed to their advantage in this situation by rapidly deploying a solution that will give them a permanent head start on collecting the sensor data needed to train the AI systems.

One such company is Uptake. They have had phenomenal growth, breaking a $1 billion dollar valuation within a year of incorporation and being named Forbes’ 2015 Hottest Startup. They did this by collecting a dataset from scratch, first with locomotives and then with other vehicles, through this they ended up with a partnership with Caterpillar. Their future looks to be diverging away from vehicles and towards bringing predictive maintenance to other industries.

Preteckt follows Uptakes footsteps in collecting its own dataset, but it targets the vehicles that are more commonly seen on the roads. Preteckt started with 18-wheelers and has already diversified into buses, and the hardware and software architecture that has been developed is portable to smaller vehicles. Preteckt’s technology has already been deployed in an autonomous truck and could be migrated to autonomous cars in the future.

The future is not “Star Trek”

Science fiction has people asking machines to run diagnostics to see if something is wrong, or what is wrong. The concept is flawed. The machines will know before you ask and will tell you what will go wrong with them next. They will tell you how to best take care of them to ensure that they do not fail on you. This is what you can look forward to with the autonomous vehicles of the future – a peace of mind in that your vehicle will not have any on-the-road surprises for you.

But why stop at eliminating surprises. Once vehicles can know their upcoming maintenance needs and drive themselves – why won’t they just take themselves to the mechanic when your schedule says you don’t need it. Maintenance will become an “out-of-sight, out-of-mind” concept making the ownership of a vehicle that much more enjoyable.

VB Profiles Connected Cars Landscape

This article is part of our connected cars series. You can download a high-resolution version of the landscape featuring 250 companies here.

The Samsung Galaxy S8 and Galaxy S8+ were near the end of March, and Samsung’s marketing and advertising machinery has been hard at work to ensure word about the devices and all of their new features and functionality reaches far and wide. Being Samsung’s current flagships, there are a lot of expectations from the devices, and early opinions suggest Samsung may be onto yet another successful flagship.

With the devices now reaching the hands of consumers, we’d like to pose this question to the people that matter: you!

What do you think of the new Samsung Galaxy S8 and S8+? Did you pre-order the device and have you received it yet? How has your early experience been so far with the device? Do you like Samsung’s newest design language? What do you not like about the device so far? Is the device living up to your expectations so far?

Let us know in the comments below!

We’ve all been inundated with the hype surrounding Internet of Things “smart stuff” and the impending arrival of our robot overlords, so we tend to minimize the mind-blowing wonder of the responsive and intelligent computing metamorphosis that is upon us.

For years, the IoT community has been saying that if we really want “things” to be of value, they cannot be dumb. The first wave was getting everything connected, and we have made headway there. The next step is to actually make “things” smarter. 

There are a variety of commercial solutions that do not really deliver on the promise of automating our way to more productive lives. And the concerns over properly securing our connected things still weigh heavily. But there really have been transformative leaps in computing capability and achievable functionality. The killer use case for IoT is on the horizon, but before defining what that is and describing how it is going to manifest, I think it’s important to broadly identify how we got here.

The “Trinity”

The impact of the open source movement in driving exponential leaps in technological advancement cannot be minimized. The algorithms and computing infrastructure that drive “smart” things — IoT, Artificial Intelligence, and machine learning capabilities — have been around for decades. Anyone at the NSA can tell you as much.

The difference now is in accessibility to the masses. These technologies were once jealously guarded, closed off from the wider world, and only available within formidable institutions possessing vast resources in both personnel and compute power. Open source changed all that. New things no longer have to be constructed from ground zero, thus supercharging the innovation cycle. The widespread access to knowledge bases and software allows anyone so inclined to build upon the shoulders of giants and leverage the wisdom of crowds.

The creative explosion fueled by open source helped give rise to the cloud, which is the second movement responsible for ushering in our new era of computing. Freed from the physical limitations and expense of individual server stacks and on-premise storage, the “app for everything” age dawned and the capacity for on-demand collection and consumption of big data was unleashed. Once we could scale compute power unconstrained by geography, our technology became mobile and the dream of smaller and increasingly powerful devices trafficking in colossal quantities of information became a reality.

Big data gives lifeblood to modern computing. But data does not do anything and, in itself, has no value. This brings us to the third movement in the “smart” revolution: analytics. The types of augmented computing that people encounter in everyday life now — voice recognition, image recognition, self-driving and driver-assisting cars — are founded in concepts that rose out of analytics and the pursuit of predictive analytics models, which was all the rage just a few short years ago.

The disheartening realization with predictive analytics was that, to train effective models, you need both massive amounts of data and scores of data scientists to continually build and maintain and improve data models. We were once again running up against the roadblocks of access and resource constraint.

And so we arrive at the present, where things are shifting in a new direction. The difference now is that we do not need to recruit an army of data scientists to build models; we have taught our programs to remove some of those roadblocks for themselves.

Inherent intelligence

Our AI-driven systems, especially Deep Learning systems, can now be fed millions upon millions of training sets, train in days/hours, and continuously re-train as more data becomes available. Open source tools and cloud computing are still important and evolving, and we still traffic in loads of data to perform lightning-fast analysis, but our programs now incorporate AI as the engine to make themselves “smarter.”

Expertise from vastly different computing realms has congealed to imbue programs with previously unimagined capabilities. The paradox is that as the cloud becomes ever more powerful and less expensive, the smart IoT strategy is to move much of the first line of entry processing away from the cloud and to the edge. This serves two purposes: to enable on-device decisions without needing cloud intervention and to deliver edge patterns and analytics to the cloud for fast second-stage analytics. Tiny AI engines can now perform analysis in near real time on edge devices and “things” no larger than a matchbook. And as these points of computational power grow increasingly commonplace in ordinary objects — intelligent routers and gateways, autonomous vehicles, real-time medical monitoring devices — their potential functionality expands exponentially.

John Crupi, Vice President and Engineering System Architect, Greenwave Systems

Artificial intelligence at the edge

In the early days of IoT (aka M2M), the focus was on getting data up to the cloud when possible. FTPing log files every night was the rage. When General Electric came on the scene with the “industrial internet,” everyone began talking about real-time live data connectivity. That was a big jump from FTP, but people treated edge devices as simply “things” that transferred data to the cloud for analytics. We are now in the midst of an exponential reverse fan out of that thinking. Real-time requirements are redefining the paradigm. The cloud is now shifting into the role of IoT support and second-tier analytics, and the processing is getting pushed out to the edge.

For example, we have been working with a company developing a next-generation medical monitoring device. Initially, we assumed with such a small device, we would send raw data from the device to the cloud for analysis. But that is not what was desired, nor is it what transpired. The company wanted the analytics on the monitor. They wanted the analytics and pattern detection to occur directly on the device, to take actions on the device, and for only “intelligent” (as opposed to raw) data be sent up to the cloud. The model differed dramatically from standard industrial M2M operations — where everything would be connected, and batches of data coming in from all sources would be collected and processed on some set timeline at some central repository.

The whole purpose of connecting now is to obtain instantaneous precision results at the point of entry for immediate answers. Even the low latency involved in “traditional” cloud-processing with hundreds of thousands if not millions and billions of devices is not as efficient for real-time edge analytics as using this new architecture. In some cases, you can achieve a data reduction of 1,000x by just sending the analytics and patterns vs. raw data to the cloud. 

We no longer deal in dumb collection devices; we need them to do more than just curate. They must be artificially (and naturally) intelligent — capable of doing pattern recognition and analytics in their tiny engines. They push those results up to the cloud for other uses. As this ideal proliferates, so, too, do the possible applications.

As is perfectly embodied in the example of an autonomous car, this dual edge/cloud analytics model produces precision, real-time results that can be continually and automatically refined against ever-growing troves of more data, thus producing valuable, useable information and powering productive action. Even a year ago, I would have called B.S. on this notion for widespread IoT and AI integration — but edge computing and AI have really broken out of the lab and into our world. It will yield outcomes we have never seen before.

The killer use cases for IoT are manifesting through truly intelligent edge devices — in solutions that are purpose-built for specific problems or tasks, then interconnected and subjected to patterns that move beyond their initial application. As more and more smarter, AI-enabled “things” are incorporated into our everyday lives and operate at the edges of our inter-communicating networks, we will see things moving beyond merely being connected and into actively embodying intelligence. Smart stuff indeed.

This article is produced in partnership with Greenwave Systems.

The author is Vice President and Engineering System Architect at Greenwave Systems, where he guides development on the edge-based visual analytics and real-time pattern discovery environment AXON Predict. He has over 25 years of experience executing enterprise systems and advanced visual analytics solutions.

A lot has been said about the Internet of Things (IoT) — a broad development in various technologies across industries that is fundamentally changing the innovation cycle everywhere —  but how much is real?

“One of the things that we should grasp about the IoT is that we are currently in that stage when technology gets incredibly hyped,” says Jason Collins, Vice President of IoT Marketing at Nokia.

Continuing further, he describes the hype by comparing the early days of the internet when static pages and hyperlinks did not ignite the full potential of the internet and the crazy boom and predictable bust over web-based businesses came and went.  Still, the world was left with the valuable piece of network and services infrastructure we now call the Internet.

“So the internet turned out to be kind of a big deal,” says Collins half-jokingly, before getting serious about how we can size the Internet of Things.

Keeping that outcome in mind, what is the potential size of the Internet of Things and how do we value it?

Valuing the Internet is tough.  Today we are going online with our computers and smartphones and connecting billions of nodes; however, the value will extend well beyond that. 

“Members of our Bell Labs team analyzed this and determined that it will be 36x the value of today’s Internet,“ he says. “That potential value of the IoT is dependent upon the number of devices connected and users’ perceived and experienced value of IoT devices and applications.”

If you think about that potential, we quickly recognize that we’re in the very early stages of how this connected technology can change the very fundamentals of digital transformation and business growth in the next decades.

How can enterprises leverage this growth opportunity?

Prior to the dawn of this new machine-type (M2M) connectivity, there were two main drivers of business — developing products and services and the sales of those products and services.

But this approach is now getting a major upgrade thanks to IoT technology. Key to this pivotal transformation is the data being produced in torrents by the connected devices that are expanding rapidly across businesses.

“But while this new connected world seems to be allowing enterprises and their customers alike to benefit from a huge pool of data, it’s not as simple as that,” says Marc Jadoul, Market Development Director in IoT at Nokia.

Perhaps it’s best to think of this in terms of “analog to digital.” Machines and networks that learn about their effective behavior through gathering data, and analyzing how to use them.   He explained further that we should think of the IoT beyond an environment of communicating things and instead as a “connective tissue” or a “global nervous system” that provides context, and – why not? – meaning.  This is the first step towards getting value out of the IoT.

Building upon that, the IoT then provides a “platform to solve problems” like the Internet once did via search and discovery. “Platforms like Google not only gave us access to the information but provided context,” says Jadoul. “In that same sense, Uber has provided a disruptive model for public transport and Airbnb a new platform for guest housing.  They use connectivity and data to transform business models today and, eventually, you will see the IoT becoming an innovation platform in many other areas, like connected cars, digital healthcare, or smart homes.”  The possibilities are endless because big data and new services will be driving the growth. 

Wireless sensor networks are evolving into analytics-enabled applications, making IoT into a “bigger and richer experience than the current M2M,” says Jadoul.

However, digital transformation must go beyond the platform, the data, and the (still too often) siloed applications. It requires a shift in the culture and mindset of organizations in order to generate significant benefit from this technology.

Who’s leading the growth within an enterprise?

New innovation found in M2M often came from internally focused and driven cost savings and process optimization efforts, a.k.a. command and control.  This is what we often call the Industrial IoT, or Industry 4.0.

While the early days of the IIoT were focused on these drivers, a new emerging Enterprise IoT approach will enable greater growth through product and service innovation, and yet-unseen business models.  With that in mind, it isn’t surprising that the early enthusiasts of this new technology are not only on the traditional IT side of the corporate “houses,” but also in their product management ranks, the people who face the customers and are looking for portfolio innovation, an enhanced customer experience, and of course new revenue opportunities.

“The sooner that companies start seeing IoT as a catalyst for growth rather than a way for the IT guys to trim costs, the faster IoT will get off the ground in enterprises,” says Jadoul.

Where is IoT headed?

As connected technology matures and a shift in mindset occurs, IoT will create new value for its stakeholders.

“Companies have to start looking at solving business problems and extend their thinking beyond vertical, point applications,” says Lee L’Esperance, Business Modeling Principal at Nokia.”If they remain strictly verticalized, its siloed and the value is limited.” But seeing the benefit across traditional business groups, products and services will unlock true value, he adds.

IoT can be very impactful to business but it needs to be architected for creating a connective tissue rather establishing than point-to-point links.  Motivating the ability to architect an IoT solution within a business context is about getting the business models right – and finding the sweet spots for creating value, growth, and RoI.  We will explore developing business models in the next article and how you can create new value opportunities for your stakeholders.

This article was produced in partnership wth Nokia.