It is critical that South African companies taking the first step to the Internet of Things fully explore the possibilities of the technology. This is because the decisions they make now will determine the cost and longevity of the solutions developed in the future, writes ECKART ZOLLNER, Head of Business Development the Jasco Group.
The Internet of Things (IoT) will, as it grows, automate and inform an increasing number of operations, applications and processes. With no dedicated infrastructure in place for IoT – i.e., to send, receive or transport IoT signals – it is critical that first movers in South Africa begin to more fully explore the possibilities. The decisions they make now may well impact the cost and longevity of the solutions they are developing, and help determine the robustness of the foundations the country puts in place for IoT.
In South Africa, IoT presents a huge opportunity in key sectors, but uptake and development of IoT-driven solutions is still low outside of security, vehicle and asset tracking, and point of sale applications. In addition, the capacity to implement change is lacking – simple solutions, such as tracking of dustbin collection and emptying, is slow to happen because organisations battle to put in place the skills and technology processes needed. Clearly, more knowledge and awareness is needed in sectors to keep pace with opportunity – along with a sense of urgency.
IoT is coming. Are you ready?
The slow pace of change in South Africa is likely to change quickly in the next few years as communication capabilities are built into more and more devices, the IoT cloud grows exponentially, and disruptive solutions with better value propositions begin to emerge to oust incumbents in various sectors. Quite simply, customers want better services and cost efficiencies, and IoT-driven applications and solutions offer exactly this.
First movers are already preparing for this future, but they need to focus on more than the development of the IoT solution and the integration of IoT to existing or new processes; they need to now take a careful look at the long-term implications of making use of IoT.
IoT signals vary from a field device sending tiny bits of information every few seconds or minutes, to devices that broadcast a signal every few hours or days. These are tiny bits of data, but for the IoT solution to work, the network that the data is sent along needs to be 100% reliable.
There are few dedicated IoT network solution providers in South Africa. The major telcos all offer their own solutions. But GSM is expensive for IoT, and with high congestion on most networks and limited remote coverage, it’s not nearly as reliable as it needs to be. In addition, GSM is power hungry, requiring more bandwidth to move data. IoT data is characterised by small bursts of a few bytes of data. Thus, using GSM networks, the battery technologies used in field devices, which ideally need to last two to five years or more, are quickly depleted. This will add to the cost of the solution.
Find dedicated IoT network providers
In Europe, the Unite States and elsewhere, dedicated networks with new topologies are being developed for IoT. These networks are geared to low power devices and low volumes of data, and feature a mesh of repeater stations the ensure 100% throughput.
There are some options in South Africa. The globally defined Industrial Scientific and Medical (ISM) band which is also available in South Africa is open for use upon registration but investment in developing such a network is not insignificant. As the IoT data payload is still low, this is not a very lucrative or attractive market yet and there are few players champing at the bit to offer these services.
At present, because network choices are limited, companies offering IoT solutions select their own channel partners and mandate use of these networks. Thus, when customers sign up for the service they may not have a choice of networks. Similarly, organisations developing proprietary solutions are currently making use of whatever network provider solutions they can find, without fully investigating their options or understanding the long term impacts that network choices may impose – in terms of costs and management of devices.
What South Africa needs is a set of reliable dedicated IoT network providers that guarantee data throughput and conform to global standards. With IoT standards developing in China, the US and Europe, it will be important for South Africa to make a choice in terms of standards, not default to the one most commonly used by operators.
Choosing an IoT network provider – top three considerations
Key requirements for companies making use of IoT network providers include the following:
• Be specific in terms of defining requirements and needs
o Is national or defined geographic coverage needed
o How often will data need to be sent and received
o What connect and control specifications are in play
• Ensure the network provider is flexible
o Can they adapt to your IoT application to, for example, easily connect more devices, send more data more or less frequently, improve reporting?
o Do meet and incorporate key IoT standards
• High service levels are critical
o Does the network service provider have a network reserved and dedicated to IoT that offers high stability?
In South Africa, commercial applications of IoT are limited, but the opportunity and advantage that IoT presents across sectors is seeing a number of proprietary solutions emerge. If you are gearing to make use of IoT, consider your options carefully. Be aware of the limitations and challenges and make use of solution providers that are flexible, established and experienced, and demonstrate their understanding of IoT technologies. IoT technology is an emerging field but it’s going to be one that plays a big role in our digital future.
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:
- machine learning requires large amounts of (quality) data and;
- 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.
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.