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Veterans prepare for high-tech to move into high-gear

If we thought the technology revolution was slowing down, fasten those seat-belts. You ain’t seen nothing yet, industry veterans tell ARTHUR GOLDSTUCK.

The dizzying speed of technology advance over the past 30 years, driven first by the advent of the personal computer, followed by the Internet and then by smartphones, was merely the curtain-raiser for the coming decade.

This view was expressed by one industry executive after another in interviews at last week’s Mobile World Congress (MWC) in Barcelona. And these were no start-up upstarts. These were industry veterans who had been instrumental in some of the landmark products and services that built the information technology industry we know today.

Pat Gelsinger, CEO of cloud computing giants VMware, was the first chief technology officer at Intel and architect of the original Intel 486 processor. As one-time head of Intel Labs, he led many of the research projects in the 1980s and 1980s that would help speed up the pace of high-tech change.

“We are at the dawn of a re-acceleration of the technology industry overall,” he said in an exclusive interview at MWC. “The next decade will see more change and new technology than in the last 20 or 30 years.

“An accelerating crescendo of technologies is coming together: cloud, mobile, big data, robotics, analytics, 3D printing, and more. It will bring together a reinforcing set of innovative activities.

“In the next decade, 75 per cent of the world’s population will have a persistent connection to the Internet with some smart device. Today it’s already 40 per cent. Soon, you’ll be able to touch half the world’s population.”

These devices, he said, will come into their own once intelligence is added.

“I can put intelligence into everything for almost zero cost, so while there are more people than machines connected today, in the next few years there will be twice as many machine-connected intelligent devices as human-connected intelligent devices. It will transform supply chains and our quality of life.”

Emerging markets, including South Africa, may well have “some of the greatest opportunities we have collectively over next decade,” he says. “Would someone in Ethiopia or Zambia be able to buy a $700 iPhone and $100 service? Of course not. But in markets where the price of phone is $20 and a service less than $10, we see rapid innovation around affordable access to core technologies, basic financial services, crop information, trading information.”

Gelsinger offered a fascinating vision of a future that is already possible.

“Tomorrow morning your smart device will wake you, and tell you: ‘last night you had a heart irregularity, so I’m waking you early and uploading your biometrics to the medical cloud, I’m running comparisons of your pattern with everyone in your DNA group. I’ve made a doctor’s appointment and loaded the directions into your self-driving car. I’ve moved your regular coffee order to a different Starbucks on your revised route, and made it decaffeinated because you’re seeing the heart doctor.’

“None of that is unreasonable to implement, but the results are life-changing.”

These sentiments were echoed by Frank Kern, chief executive officer of Aricent, a global technology services company with more than 12 000 staff focused on software and hardware innovation. He spent 30 years with IBM, including heading up its core consulting division, Global Business Services. He came out of retirement to take up the challenge of the future.

“This is the most exciting time yet,” he says. “Before, I was just in the boring old computer industry.

“I was around when IBM did a lot of interesting stuff. We created a services business, I ran the consulting business, and in 2009 I created an analytics practise with 9 000 people, worth $25-billion.

“But today is the most exciting time of all. It’s a time when you have a combination of an explosion of sensors, accelerating of communications, combined with the software capabilities of AI, and now we are designing the user interface of the future, the customer experience of the future.”

Aricent owns a renowned strategy and design company, frog, which was responsible for the design of several Apple computers, along with hardware for numerous global organisations. The parent company has also been in research and development of software for 25 years, with a strong focus on telecommunications, and taking a leading position in 5G, AI and autonomous vehicle software.

“We are able to see and participate in multiple trends going on, and all are accelerating at same time. It’s not only one thing right now; it’s all these things that, together, are creating this exciteme.”

Gelsinger puts it neatly into perspective.

“All of this gives me an almost child-like enthusiasm. I’ve been in the technology industry for 37 years. If you ever used a microprocessor or a USB drive, I helped do all of them. But, in many cases, the next decade is as exciting as the last three decades. Because so many of these things will become life-changing and business-changing.”

  • Arthur Goldstuck is founder of World Wide Worx and editor-in-chief of Gadget.co.za. Follow him on Twitter and Instagram on @art2gee

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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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