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Wait for Netflix’s big picture in SA

Netflix finally announced its arrival in South Africa last week, but it looked like a false start. However, writes ARTHUR GOLDSTUCK, there is a bigger picture.

The global leader in online video-on-demand movies and TV finally arrived in South Africa last week, but something looked lost in translation. Numerous titles and shows that make the American offering so attractive were missing. Pricing was in dollars. This country wasn’t even mentioned in the official announcement made at the CES 2016 tech expo in Las Vegas.

However, this is the reality of being one of 130 new Netflix territories announced at the same time. Far more lucrative markets, including Russia, India and South Korea, were part of the same switch-on. Why on earth would it give South Africa priority in its marketing presence?

The fact is that Netflix has merely switched on local availability, rather than physically launched locally. With even its pricing for South Africa based on the US structure, while offering nothing like the US range, existing video-on-demand services like Showmax, MTN’s VU and OnTapTV are not yet quaking in their boots. They probably still have a few months to assess Netflix’s local offering and ensure they are sufficiently differentiated.

Already, locally relevant content and pricing that takes into account local circumstances act as major differentiators. As a result, Netflix faces massive challenges in entering South Africa. That doesn’t mean its entry was premature, though.

Firstly, the market has exploded with competitors and options, meaning that many of the most likely users would already be grabbed by the end of 2016. Showmax has made tremendous strides in bolstering its offering, OnTapTV is moving in aggressively, MTN has relaunched its FrontRow service as VU and Times Media’s VIDI remains an option – although reports of its demise are rife.

Secondly, fibre to the home is accelerating much more rapidly than anticipated, making this a more viable market more quickly than had been expected.

Thirdly, the longer Netflix waits, the more time the competitors have to flesh out their offering to make it comparable to or better than that of Netflix. Similar dynamics may well be at work in some of the other new territories.

Clearly, the marketing power and global reputation of Netflix will be a major advantage, but the fact that it has arrived almost by stealth does not bode well for cleaning up the market. Showmax has a heavy marketing presence here and, along with the other local players, has a strong emphasis on acquiring and generating locally relevant content. That means it will own many niches before Netflix even realises these exist.

DStv is unlikely to be threatened in the short term, but it’s clear entertainment godfather Naspers started Showmax as an insurance policy against Netflix and other video-on-demand players. The thinking is that, should people migrate from DStv to video on demand, try to keep them within the same stable.

However, the real strength of DStv lies in its live sports coverage, and that’s an area where no video on demand service can compete at this stage. People who subscribe to DStv only for movies and series can be expected to migrate rapidly to VoD, because it will simply make more sense both economically and in terms of choice of content and viewing time.

Those live sports rights, in particular English premier League football, are the jewels in DStv’s crown. In Nigeria, for example, that alone has killed off the competition. Locally, a high proportion of DStv subscribers are locked in because of sports, and DStv won’t allow slicing-and-dicing of its bouquet to offer sports exclusively at a lower cost: that would be the equivalent of rearranging the proverbial deckchairs on a Titanic.

For those who are not interested in sports, Naspers created the most viable competitor to Netflix, namely Showmax. It has far more content than Netflix presently makes available in South Africa, thanks to snapping up exclusive rights to first broadcasts of a wide range of popular series, and has a strong local content catalogue that is non-existent on Netflix for now. This all translates into Naspers cannibalising itself before Netflix can.

That said, we have not yet seen massive take-up of existing services.

The main reason is that the connectivity environment has not been very conducive to streaming video, and almost every single service misread the market in terms of pricing. MTN even relaunched its service under a new name with new pricing so as not to be seen to be cutting prices. The rest have all dropped their prices. It is very possible that, when Netflix launches more formally in South Africa, it will provide a Rand-based price that is more in line with the R89-R99 monthly subscription from other providers.

For the South African market, streaming video-on-demand is still a long way from being a mass-market offering. Its requirements in appropriate devices, reasonable bandwidth and monthly subscription fee means that it is still geared towards the upper end of the market.

However, we should never underestimate the public’s appetite for entertainment. Considering that DStv has more than 5-million households subscribed, the potential for streaming video is massive. The reason so many services have launched in this country while the environment  is not yet conducive to streaming video is that they don’t want to be playing catch-up when they market is more ready. The early players will get the low-hanging fruit of ready and available customers who are installing fibre-to-the-home, and anyone delaying entry runs the risk of losing out on that lucrative market.

Ironically, the Netflix announcement is likely to do more in South Africa for Showmax than for Netflix itself.  It has already boosted Showmax as it draws attention to the sector, and demands comparisons between the two, with the local service inevitably looking like the better option.

Ultimately, however, it should be borne in mind that Netflix has merely activated a South African page, meaning its open to business from South Africans, but it has not yet formally launched a physical presence in South Africa. This is why it can be argued that it was a “soft launch”, and more of an “Oh hi, South Africa” greeting than an invasion of the country.

With the rest of the world coming on board at the same time, we couldn’t expect too much local love on day one. But Netflix has one very powerful arrow in its quiver: grand plans to unify its licensing structures across the globe.

On the day of launch the official Netflix Twitter account put out this deeply significant statement of intent: “Still prisoners of territorial licensing — moving quickly to have global availability of all content on Netflix.”

When that day comes, the skirmishes for local market share will become a full blown war. Expect a few more competitors to be gone with the wind a couple of years from now.

* 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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