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MWC: Tech innovators prepare for next generation

In the past, Mobile World Congress was associated with new smartphones and tablets. But this year, the dominant themes were technologies providing the basis for the next generation of mobile devices, writes NADIA GONZALEZ, Africa VP Mobile Solutions & IoT at Gemalto.

Barcelona was the scene of another festival of technology, as the world’s most influential companies, journalists and engineers travelled to Mobile World Congress (MWC). MWC tends to be associated with new handset launches, but this year’s show was about more than just devices. There were indeed notable mobile handset stories, with some iconic brands making stunning comebacks, defying beliefs they had been consigned to history. The dominant themes, however, were technologies providing the basis for the next generation of mobile devices, namely 5G and the Internet of Things (IoT).

The year of the comeback

If you’d told someone a year ago that Nokia and BlackBerry handsets would be making a comeback, they might not have believed you. The former, once the world’s largest phone manufacturer, gave up trying to compete with Apple, Google and Samsung in 2014 to focus on networking, while the latter has rebranded itself as a cybersecurity company. However, at MWC this year HMD Global, which now owns the rights to make Nokia branded phones, released an updated version of the legendary Nokia 3310. BlackBerry, meanwhile, displayed its new Android smartphone.

With Samsung choosing not to reveal a new phone handset, and Apple and Google not making big announcements, the comeback kids stole the headlines. The question is, will the handsets sell?

Connected cars to hit the mainstream

We’ve got bad news for budding Formula One drivers; MWC17 demonstrated how drivers are going to be far less important to the functioning of a car, as the IoT and Artificial Intelligence (AI) takes over. One of our favourite innovations was the world’s first driverless supercar, capable of reaching speeds of up to 200mph. Manufactured by Robocar, the vehicle works through a combination of sensors and powerful cameras. The car’s operation is guided by algorithms, which means computer experts may decide the races of the future rather than the likes of Lewis Hamilton.

As the technology and new business models behind connected cars evolve, the Automotive Industry is transforming into what is called New Mobility. One of the big themes here is linking connected cars with the digital life of the driver or passenger, making them fully personalized. One notable development is the Virtual Car Key (VCK), a first example where the key, as part of a digital Car ID, will need to be securely stored on the end user mobile device. Opening a car and starting the engine is a crucial element of any comprehensive mobility app, and we’re likely to see many more developments in this area over the year ahead.

Artificial Intelligence

AI at MWC wasn’t limited to cars, with the technology appearing in many other areas. Future handsets from many manufacturers promise more advanced versions of virtual assistants like Siri and Cortana, which will learn from their user’s habits. Elsewhere O2 announced it would be turning to AI to manage customer service, speeding up processes and cutting costs. Similarly, Samsung announced it would be using an AI bot to train retail staff in managing customer queries. Clearly, organisations are recognising AI’s ability to not only improve the user experience, but also streamline operations and enhance the customer journey.

The potential of 5G and smart cities

5G represents the next generation of telecoms standards, ushering in a new era of connectivity and smart infrastructures. While we’re still some way off the technology being widely available, at MWC we saw many announcements about IoT-optimized machine-type communication and LTE Cat NB-IoT networks, technologies which are paving the way to 5G.

One area in which 5G will play an ever-important role is Smart City technology, which can be used to improve efficiency and benefit the environment. With billions of embedded sensors, governments and companies will be able to better monitor carbon emissions and track pollution levels. At the show, AT&T and GE announced a partnership to deliver environmentally-friendly IoT technology to cities across North and Central America. Intelligent sensor nodes will power a new generation of street lighting which will be fully integrated within light poles, allowing city governments to use existing poles and equip them with energy-efficient LED lighting.

We took this a step further with a demonstration of a smart city lighting and Electric Vehicle charging solution which uses smart sensors to transform street lights into intelligent platforms. Lights can be dimmed on demand depending on need, saving 50-80% in energy consumption, but they can also be used to alert and direct drivers and pedestrians to free parking spaces or charging stations. 5G technology based on the same principles can be applied to traffic, parking, and waste, enhancing city governance, and making it more environmentally-friendly.

Of course, for the smart city to work, the underlying infrastructure needs to be intelligent and secure. To enable a functioning street lighting system, for instance, there needs to be a secure connection between the lamps and a central control system. It is a complex process, with the potential for an undetected weakness in one part potentially compromising security for the entire system. Those building critical infrastructure and solutions for smart cities need to think very carefully and holistically about the networks and systems they are connecting, whether it’s car park, traffic or waste management projects they’re looking after.

To conclude, it’s clear MWC17 wasn’t just about mobile handsets. While product announcements from Nokia and BlackBerry were the key focus for some, this year’s conference should be interpreted as one dominated by the IoT connectivity solutions of the future; 5G, smart cities and connected transport. With the show over for another year, governments and other key stakeholders will need to keep collaborating on the best ways to connect, secure and monetize their IoT strategies to find success in the years ahead.

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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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