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MWC: TCL kicks off BlackBerry deal with KEYone

TCL Communication has unveiled the BlackBerry KEYone at this year’s Mobile World Congress. This launch represents the first BlackBerry smartphone released from TCL Communication under a new brand licensing agreement.

TCL provided the following information:

The KEYone pairs the best of BlackBerry Limited’s software and security with TCL Communication’s commitment to delivering high-quality, reliable smartphones to customers around the world. The BlackBerry KEYone will be available globallyi beginning in April and will be priced at or under €599 EUR/£499 GBP/$549 USD.

“Impressively designed to be distinctly different, the BlackBerry KEYone reimagines how we communicate by offering unmatched productivity and the world’s most secure Android smartphone experience,” said Nicolas Zibell, CEO for TCL Communication. “We’re humbled to play such an important role in the future of BlackBerry smartphones, which have been so iconic in our industry, and we’re eager to prove to the BlackBerry community that their excitement around this new BlackBerry smartphone is something they can be proud of as well.”

“We want to congratulate TCL Communications on the launch of KEYone,” said Alex Thurber, Senior Vice President and General Manager of Mobility Solutions for BlackBerry. “We have worked closely with TCL to build security and the BlackBerry experience into every layer of KEYone, so the BlackBerry DNA remains very much in place. We couldn’t be more excited to help bring it to market and introduce it to BlackBerry fans.”

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DISTINCTLY DIFFERENT

Tucked into an anodized aluminum frame and soft touch textured back, this new BlackBerry smartphone is built to offer the best in durability while still remaining easy on the eyes. Featuring a 4.5-inch display

(1620×1080 resolution / 434 PPI 3:2 aspect ratio) with Corning  Gorilla Glass 4 that offers greater impact and scratch resistance, the KEYone combines a touch display with a physical keyboard to give users more useable space for typing than a typical 5.5-inch all-touch smartphone.

With BlackBerry KEYone, you can forget what you knew about keyboards. The device’s Smart Keyboard responds to touch gestures mimicking the heritage of the BlackBerry trackpad making web browsing, reading emails and writing messages with flick typing a much smoother and intuitive experience. This Smart Keyboard can also be easily programmed to launch up to 52 customizable shortcuts, such as pressing “I” for your inbox or “M” to access maps; providing even greater ease of use. In addition, KEYone is the first smartphone to provide the security of a fingerprint sensor built directly into the keyboard spacebar, for added functionality and security.

DISTINCTLY BLACKBERRY

Beyond the iconic BlackBerry design that’s been curated  for the modern user, the BlackBerry KEYone comes with a number of features and security enhancements making this smartphone distinctly BlackBerry. Out of the box, the device runs Android 7.1 – giving users access to the entire Google Play store and apps – and will receive Google security patch updates. It comes loaded with BlackBerry Hub®, bringing all your messages into one consolidated place; including emails, texts and messages from any social media account. Another benefit of BlackBerry Hub, is the ability to manage multiple email accounts without switching between apps, with support for Gmail, Yahoo Mail, Outlook, Microsoft Exchange accounts, and many other IMAP and POP3 email providers.

What really sets a BlackBerry smartphone apart from any other Android device are the enhanced security features built into every device right from the start. From a hardened operating system to BlackBerry Limited’s proprietary technique for establishing a hardware root of trust adding security keys to the processor, the BlackBerry KEYone is intentionally designed to offer the most secure Android smartphone experience possible. This device comes pre-loaded with DTEK™ by BlackBerry, offering constant security monitoring and protection of your operating system and apps by letting you know when your privacy could be at risk and how you can take action to improve it. A quick glimpse lets you see the overall security rating for your device  and provides simple access allowing you to easily improve your security status. This BlackBerry security application monitors your other apps, alerting you if one is accessing your camera to take a picture or video, turning your microphone on, sending a text message, or accessing your contacts or location.

THE BLACKBERRY KEYONE EXPERIENCE

Beyond the design and security features making the BlackBerry KEYone so distinct, these additional features truly reinvent mobile communications for the business and enterprise user. At the core of this new BlackBerry smartphone is the Qualcomm Snapdragon 625 mobile platform with the Qualcomm Adreno 506 GPU.

This means BlackBerry KEYone  users will enjoy long battery life thanks to more efficient battery usage and fast LTE speeds for super-fast file sharing. It includes Qualcomm® Quick Charge™ 3.0 technology, making it possible for the 3505 mAh battery on the KEYone – the largest ever in a BlackBerry device, to receive up to 50 percent charge in roughly 36 minutes. And for when you only have a few moments to grab a fast charge, BlackBerry’s Boost can turbo charge your battery to get you the most charge with the limited time you have.

“We are proud to be working with TCL on the BlackBerry KEYone,” said Enrico Salvatori, senior vice president and president, Qualcomm EMEA. “The Snapdragon 625 mobile platform with X9 LTE and the Adreno 506 GPU is purpose-built for users who demand superior performance and connectivity coupled with outstanding battery-life.”

 

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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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LG unveils NanoCell TV range

At the recent LG Electronics annual Innofest innovation celebration in Seoul, Korea, the company unveiled its new NanoCell range: 14 TVs featuring ThinQ AI technology. It also showcased a new range of OLED units.

The new TV models deliver upgraded AI picture and sound quality, underpinned by the company’s second-generation α (Alpha) 9 Gen 2 intelligent processor and deep learning algorithm. As a result, the TVs promise optimised picture and sound by analysing source content and recognising ambient conditions.

LG’s premium range for the MEA market is headlined by the flagship OLED TV line-up, which offers a variety of screen sizes: W9 (model 77/65W9), E9 (model 65E9), C9 (model 77/65/55C9) and B9 (model 65/55B9).

NanoCell is LG’s new premier LED brand, the name intended to highlight outstanding picture quality enabled by NanoCell technology. Ensuring a wider colour gamut and enhanced contrast, says LG, “NanoColor employs a Full Array Local Dimming (FALD) backlight unit. NanoAccuracy guarantees precise colours and contrast over a wide viewing angle while NanoBezel helps to create the ultimate immersive experiences via ultra-thin bezels and the sleek, minimalist design of the TV.”

The NanoCell series comprises fourteen AI-enabled models, available in sizes varying from 49 to 77 inches (model 65SM95, 7565/55SM90, 65/55/49SM86 and 65/55/49SM81).

The LG C9 OLED TV and the company’s 86-inch 4K NanoCell TV model (model 86SM90) were recently honoured with CES 2019 Innovation Awards. The 65-inch E9 and C9 OLED TVs also picked up accolades from Dealerscope, Reviewed.com, and Engadget.

The α9 Gen 2 intelligent processor used in LG’s W9, E9 and C9 series OLED TVs elevates picture and sound quality via a deep learning algorithm (which leverages an extensive database of visual information), recognising content source quality and optimising visual output.

The α9 Gen 2 intelligent processor is able to understand how the human eye perceives images in different lighting and finely adjusts the tone mapping curve in accordance with ambient conditions to achieve the optimal level of screen brightness. The processor uses the TV’s ambient light sensor to measure external light, automatically changing brightness to compensate as required. With its advanced AI, the α9 Gen 2 intelligent processor can refine High Dynamic Range (HDR) content through altering brightness levels. In brightly lit settings, it can transform dark, shadow-filled scenes into easily discernible images, without sacrificing depth or making colours seem unnatural or oversaturated. LG’s 2019 TVs also leverage Dolby’s latest innovation, which intelligently adjusts Dolby Vision content to ensure an outstanding HDR experience, even in brightly lit conditions.

LG’s audio algorithm can up-mix two-channel stereo to replicate 5.1 surround sound. The α9 Gen 2 intelligent processor fine-tunes output according to content type, making voices easier to hear in movies and TV shows, and delivering crisp, clear vocals in songs. LG TVs intelligently set levels based on their positioning within a room, while users can also adjust sound settings manually if they choose. LG’s flagship TVs offer the realistic sound of Dolby Atmos for an immersive entertainment experience.

LG’s 2019 premium TV range comes with a new conversational voice recognition feature that makes it easier to take control and ask a range of questions. The TVs can understand context, which allows for more complex requests, meaning users won’t have to make a series of repetitive commands to get the desired results. Conversational voice recognition will be available on LG TVs with ThinQ AI in over a hundred countries.

LG’s 2019 AI TVs support HDMI 2.1 specifications, allowing the new 4K OLED and NanoCell TV models to display 4K content at a remarkable 120 frames per second. Select 2019 models offer 4K high frame rate (4K HFR), automatic low latency mode (ALLM), variable refresh rate (VRR) and enhanced audio return channel (eARC).

To find out more about LG’s latest TVs and home entertainment systems, visit https://www.lg.com/ae.

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