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HTC unveils One A9

HTC has unveiled the latest addition to the HTC One family – the A9 – featuring the HTC Sense skin on top of the Android 6.0 Marshmallow operating system. 

The company announced: “Built for people who want a better choice, the HTC One A9 is a smartphone that combines striking design on the outside with the newest features of Android 6.0 Marshmallow with HTC Sense on the inside to create an unrivaled smartphone experience.”

HTC provided the following information:

An evolution of the iconic HTC One family design, the HTC One A9 draws on the natural beauty around us, by using rich, striking colors and unique finishes – an approach called “metalmorphics.” New curves, refined edges, and precision-cut ridges on the power button flawlessly combine with the super-thin metal frame giving the phone a natural and lightweight feel. The elegant dual finish, available in a Carbon Gray, Opal Silver, Topaz Gold and Deep Garnet, is painstakingly brushed with hairline detail and bead-blasted before the sides are polished, giving additional depth and richness that is unique to the smartphone market. Finally, in a stunning fusion of metal and glass, a 5-inch, edge-to-edge, 2.5D Corning Gorilla Glass 4, Full HD AMOLED screen cascades into the metal frame, giving you brighter and more vivid colors for brilliant graphics and gaming, even in direct sunlight.

Your best camera is the one you use whenever the moment strikes, so it should be able to capture stunning shots in any situation. The HTC One A9’s front and rear cameras, coupled with powerful yet simple editing tools, mean epic photos and videos are only a tap away. Its main 13MP rear camera features Optical Image Stabilization (OIS), which automatically minimises hand shake and corrects vibrations to give you a crystal-clear picture every time. Meanwhile, the front UltraPixel camera delivers the best self-portraits in any lighting condition, using HTC’s UltraPixel sensor to capture 300% more light than conventional smartphone cameras.

With the HTC One A9, no detail is too small. Offering an optional Pro mode to capture the perfect photo without being a photography expert, you can also save photos using RAW capture – a tool used by professional photographers – for an unmatched level of detail and post-shot editing flexibility. Or you can keep things simple with Photo Editor’s one-button enhancement feature, which automatically brightens colors and sharpens edges for unbelievably clear and crisp photos.

Sometimes a still photo just isn’t enough. With the HTC One A9’s Hyperlapse editing tool you can speed up your videos to 12 times as fast for a dramatic time-lapse effect, creating an immersive video that perfectly captures the moments that matter. Speed through your video or use capture mode to edit in slow motion, highlighting those unforgettable memories. Or use HTC Zoe to easily and automatically mix your best shots and videos into one professional-looking highlight reel that can be shared with friends and family to tell the story of any moment from every angle.

Game changing audio has always been core to the HTC One family – the HTC One A9 delivers amazing sound quality, with HTC BoomSound integrated into the headset combined with Dolby Audio surround technology, delivering immersive, vibrant sound that matches a live experience in your headphones by taking high-resolution audio to the next level. With a built-in DAC that delivers audio at 24-bit, 192KHz quality – better than CD quality – it provides a richness and depth like you’ve never before heard from your music collection. A powerful high-output headphone amplifier, with double the output of other handsets, provides more power and dynamic range so you can hear every nuance of your favorite music, videos and games. Optional high-resolution audio certified HTC Pro Studio Earphones deliver both noise isolation and truly immersive sound.

Cutting-edge software: Android 6.0 Marshmallow

The first non-Nexus smartphone powered by Android 6.0 Marshmallow, the HTC One A9 brings Google’s latest innovations and the simplicity of HTC Sense right out of the box. Combined with a multi-directional fingerprint scanner, Android Pay makes purchasing items as secure and as easy as “tap, pay, done.” Google’s Now on Tap feature also intuitively provides contextual information about whatever is on your screen – look up reviews for a movie from a text, hear a song mentioned in a blog post, or book seats at a restaurant from an email, all at the press of the home button.

With so many new features, the HTC One A9 has also been designed with all new power-saving capabilities to maximize battery life. Doze automatically turns off power hungry applications when your phone is left idle, such as when charging overnight, while App Standby knows when an app open in the background isn’t being used and shuts it down until you need it again. In addition, the phone’s new processor, AMOLED screen and support for the optional HTC Rapid Charger 2.0, for up to 75% faster charging are all optimized to maximize battery life throughout the day. In real terms this means, for example, that you can play 12 hours of HD video on one charge.

Featuring the newest Qualcomm Snapdragon 617 processor with integrated X8 LTE and 64-bit octa-core CPUs, the HTC One A9 is designed to deliver a perfect balance of powerful performance, fast connectivity and better battery life. With support for epic LTE wireless speeds through next-generation carrier aggregation technology, it delivers more high-definition streaming videos, more pulse-pounding high-resolution audio and more storage in the cloud to hold everything your heart desires. The phone is available in both 16 and 32GB configurations and features expandable memory, supporting up to 2TB in SD card storage, perfect for storing all your apps, games, photos, music and videos.

To protect your smartphone, pair it with the HTC Dot View II case, available in Obsidian, Deep Garnet, Sea Coral and Turquoise Blue. Its retro-inspired dot matrix cover reveals instant notifications, weather updates, caller ID and more, even while closed, and simple gesture controls bring music, flashlight, radio and voice recording tools closer than ever.

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‘Energy scavenging’ gets funding

As the drive towards a 5G future gathers momentum, the University of Surrey’s research into technology that could power countless internet enabled devices – including those needed for autonomous cars – has won over £1M from the Engineering and Physical Sciences Research Council (EPSRC) and industry partners.

Surrey’s Advanced Technology Institute (ATI) has been working on triboelectric nanogenerators (TENG), an energy harvesting technology capable of ‘scavenging’ energy from movements such as human motion, machine vibration, wind and vehicle movements to power small electronic components. 

TENG energy harvesting is based on a combination of electrostatic charging and electrostatic induction, providing high output, peak efficiency and low-cost solutions for small scale electronic devices. It’s thought such devices will be vital for the smart sensors needed to enable driverless cars to work safely, wearable electronics, health sensors in ‘smart hospitals’ and robotics in ‘smart factories.’ 

The ATI will be partnered on this development project with the Georgia Institute of Technology, QinetiQ, MAS Holdings, National Physical Laboratory, Soochow University and Jaguar Land Rover. 

Professor Ravi Silva, Director of the ATI and the principal investigator of the TENG project, said: “TENG technology is ideal to power the next generation of electronic devices due to its small footprint and capacity to integrate into systems we use every day. Here at the ATI, we are constantly looking to develop such advanced technologies leading towards our quest to realise worldwide “free energy”.

“TENGs are an ideal candidate to power the autonomous electronic systems for Internet of Things applications and wearable electronic devices. We believe this research grant will allow us to further the design of optimized energy harvesters.”

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What’s left after the machines take over?

KIERAN FROST, research manager for software in sub-Saharan Africa for International Data Corporation, assesses AI’s impact on the workforce.

One of the questions that we at the International Data Corporation are asked is what impact technologies like Artificial Intelligence (AI) will have on jobs. Where are there likely to be job opportunities in the future? Which jobs (or job functions) are most ripe for automation? What sectors are likely to be impacted first? The problem with these questions is that they misunderstand the size of the barriers in the way of system-wide automation: the question isn’t only about what’s technically feasible. It’s just as much a question of what’s legally, ethically, financially and politically possible.

That said, there are some guidelines that can be put in place. An obvious career path exists in being on the ‘other side of the code’, as it were – being the one who writes the code, who trains the machine, who cleans the data. But no serious commentator can leave the discussion there – too many people are simply not able to or have the desire to code. Put another way: where do the legal, financial, ethical, political and technical constraints on AI leave the most opportunity?

Firstly, AI (driven by machine learning techniques) is getting better at accomplishing a whole range of things – from recognising (and even creating) images, to processing and communicating natural language, completing forms and automating processes, fighting parking tickets, being better than the best Dota 2 players in the world and aiding in diagnosing diseases. Machines are exceptionally good at completing tasks in a repeatable manner, given enough data and/or enough training. Adding more tasks to the process, or attempting system-wide automation, requires more data and more training. This creates two constraints on the ability of machines to perform work:

  1. machine learning requires large amounts of (quality) data and;
  2. training machines requires a lot of time and effort (and therefore cost).

Let’s look at each of these in turn – and we’ll discuss how other considerations come into play along the way.

Speaking in the broadest possible terms, machines require large amounts of data to be trained to a level to meet or exceed human performance in a given task. This data enables the bot to learn how best to perform that task. Essentially, the data pool determines the output.

However, there are certain job categories which require knowledge of, and then subversion of, the data set – jobs where producing the same ‘best’ outcome would not be optimal. Particularly, these are jobs that are typically referred to as creative pursuits – design, brand, look and feel. To use a simple example: if pre-Apple, we trained a machine to design a computer, we would not have arrived at the iMac, and the look and feel of iOS would not become the predominant mobile interface. 

This is not to say that machines cannot create things. We’ve recently seen several ML-trained machines on the internet that produce pictures of people (that don’t exist) – that is undoubtedly creation (of a particularly unnerving variety). The same is true of the AI that can produce music. But those models are trained to produce more of what we recognise as good. Because art is no science, a machine would likely have no better chance of producing a masterpiece than a human. And true innovation, in many instances, requires subverting the data set, not conforming to it.

Secondly, and perhaps more importantly, training AI requires time and money. Some actions are simply too expensive to automate. These tasks are either incredibly specialised, and therefore do not have enough data to support the development of a model, or very broad, which would require so much data that it will render the training of the machine economically unviable. There are also other challenges which may arise. At the IDC, we refer to the Scope of AI-Based Automation. In this scope:

  • A task is the smallest possible unit of work performed on behalf of an activity.
  • An activity is a collection of related tasks to be completed to achieve the objective.
  • A process is a series of related activities that produce a specific output.
  • A system (or an ecosystem) is a set of connected processes.

As we move up the stack from task to system, we find different obstacles. Let’s use the medical industry as an example to show how these constraints interact. Medical image interpretation bots, powered by neural networks, exhibit exceptionally high levels of accuracy in interpreting medical images. This is used to inform decisions which are ultimately made by a human – an outcome that is dictated by regulation. Here, even if we removed the regulation, those machines cannot automate the entire process of treating the patient. Activity reminders (such as when a patient should return for a check-up, or reminders to follow a drug schedule) can in part be automated, with ML applications checking patient past adherence patterns, but with ultimate decision-making by a doctor. Diagnosis and treatment are a process that is ultimately still the purview of humans. Doctors are expected to synthesize information from a variety of sources – from image interpretation machines to the patient’s adherence to the drug schedule – in order to deliver a diagnosis. This relationship is not only a result of a technicality – there are ethical, legal and trust reasons that dictate this outcome.

There is also an economic reason that dictates this outcome. The investment required to train a bot to synthesize all the required data for proper diagnosis and treatment is considerable. On the other end of the spectrum, when a patient’s circumstance requires a largely new, highly specialised or experimental surgery, a bot will unlikely have the data required to be sufficiently trained to perform the operation and even then, it would certainly require human oversight.

The economic point is a particularly important one. To automate the activity in a mine, for example, would require massive investment into what would conceivably be an army of robots. While this may be technically feasible, the costs of such automation likely outweigh the benefits, with replacement costs of robots running into the billions. As such, these jobs are unlikely to disappear in the medium term. 
Thus, based on technical feasibility alone our medium-term jobs market seems to hold opportunity in the following areas: the hyper-specialised (for whom not enough data exists to automate), the jack-of-all-trades (for whom the data set is too large to economically automate), the true creative (who exists to subvert the data set) and finally, those whose job it is to use the data. However, it is not only technical feasibility that we should consider. Too often, the rhetoric would have you believe that the only thing stopping large scale automation is the sophistication of the models we have at our disposal, when in fact financial, regulatory, ethical, legal and political barriers are of equal if not greater importance. Understanding the interplay of each of these for a role in a company is the only way to divine the future of that role.

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