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Samsung thinks big with Note7

Earlier this week Samsung announced the Galaxy Note7, a 5.7-inch smartphone featuring a water resistant body and S Pen and a new iris scanning option for increased security.

In launching its new with the Galaxy Note7 phablet in New York this week, Samsung Electronics hopes to build on the company’s category-defining leadership “with innovative features that set a new standard for large-screen devices”.

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The new 5.7-inch smartphone features:

    • Top-of-the-line security that combines Samsung Knox with biometric authentication, including a new iris scanning feature;
    • A water resistant body and S Pen (IP68); and
    • Immersive entertainment features with HDR video streaming capabilities.

“The Galaxy Note7 combines productivity and entertainment, with strong security features,” says Craige Fleischer, Director of Integrated Mobility at Samsung Electronics South Africa. “The device strikes a balance between work and play, enabling people to achieve more than what they thought possible on a smartphone. Powering a robust ecosystem, it is the ideal device for those who want to achieve more in life.”

Samsung provided the following information on the new handset:

Advanced Security: Now in the Blink of an Eye

Samsung understands the importance of keeping content on users’ phones protected. Coupled with Samsung Knox, an industry-leading security platform, the Galaxy Note7 provides heightened security and privacy, with highly advanced biometric authentication, including fingerprint scanner and iris scanning technology. The Galaxy Note7 offers more authentication options that can be used interchangeably depending on how people use their phones, no matter where they are or what they are doing.

The Galaxy Note7 instils a new level of user confidence with Secure Folder, a separated folder that adds an extra layer of authentication to keep private and personal information safe.

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Galaxy Note7 S Pen: Quickly and Easily Bring Ideas to Life

The enhanced 7th generation S Pen is more than just a writing tool – it is the gateway to getting more done efficiently and accurately. Users can bring ideas to life the moment inspiration strikes without unlocking the screen by using Screen off memo and simply pinning the memo to the Always On Display. The S Pen gives users the ability to easily create and share. Access the best of the S Pen’s creative abilities with Samsung Notes, a new unified app allowing users to make handwritten notes, draw, or edit memos from one location.

The new S Pen writes with ease and precision. The smaller 0.7mm tip and improved pressure sensitivity provides a real pen-like feeling. With the peace of mind that IP68 water resistant provides, the Galaxy Note7 S Pen lets users write down their thoughts without interruption, even when the screen gets wet.

Best-in-Class Entertainment 

The Galaxy Note7 features a gorgeous, curved 5.7-inch QHD Super AMOLED screen delivering a bold and immersive picture on a smartphone. It is ready to stream HDR video, enabling a cinema-like environment including brighter colours and a deeper black, which ensure users get the full perspective that the filmmaker intended. In addition, the Galaxy Note7 features Game Pack, available through Google Play, including top game titles, an easier redemption process and new user benefits.

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Creating Connected Experiences

For people who live to create, tell stories and transport people to entirely new worlds, the Galaxy Note7 can be used with the new high-resolution (3840×1920) Gear 360 camera, to capture, edit and share 360-degree images and videos on the go. These memories can then be shared with friends via platforms including YouTube 360 and Facebook and can be relived with the ultra-immersive Gear VR. By pairing the Galaxy Note7 with the new Gear VR powered by Oculus, users can explore premium virtual reality almost anywhere. Galaxy Note7’s incredible Super AMOLED screen and powerful processor for ultra-immersive viewing.

“With the Galaxy Note7, Samsung continues to defy boundaries of engineering to perfect the user experience through innovative design and technology. We are extremely excited about bringing the Iris scanner security technology to the mass consumer market through the Note7. This is an example of Samsung pushing the frontier of what’s possible. The Galaxy Note7 is more than just a smartphone; it’s a connection point to Samsung’s ecosystem of devices, software and services,” says Fleischer.

The Galaxy Note7 will be available locally from September.

For additional product information, please visit www.samsungmobilepress.com, http://news.samsung.com/galaxy or www.samsung.com/galaxy

Samsung Galaxy Note7 Product Specifications

  Galaxy Note7
OS Android 6.0.1 (Marshmallow)
Network LTE Cat.12 / LTE Cat.10 / LTE Cat.9  *May differ by markets and mobile operators
Dimension 153.5 x 73.9 x 7.9mm, 169g
AP Octa core (2.3GHz Quad + 1.6GHz Quad), 64 bit, 14 nm process
Memory 4GB RAM (LPDDR4), 64GB (UFS 2.0)
Display 5.7” Quad HD Dual edge Super AMOLED

2560 x 1440 (518ppi)

Camera Rear: Dual Pixel 12MP OIS (F1.7), Front: 5MP (F1.7)
Battery 3,500 mAh, Fast Charging on wired and wireless

Wireless Charging compatible with WPC and PMA

Payment NFC, MST
Connectivity Wi-Fi 802.11 a/b/g/n/ac (2.4/5GHz), MIMO(2×2) 620Mbps,

Bluetooth® v 4.2 LE, ANT+, USB Type-C, NFC,

Location (GPS, Glonass, Beidou)

Sensors Barometer, Fingerprint Sensor, Gyro Sensor, Geomagnetic Sensor, Hall Sensor, HR Sensor, Iris Sensor, Proximity Sensor, RGB Light Sensor
Audio MP3, M4A, 3GA, AAC, OGG, OGA, WAV, WMA, AMR,

AWB, FLAC, MID, MIDI, XMF, MXMF, IMY, RTTTL, RTX, OTA

Video MP4, M4V, 3GP, 3G2, WMV, ASF, AVI , FLV, MKV, WEBM

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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, discusses the 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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