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Cloud gets critical in SA

Developments in the local ICT sector are beginning to converge, signifying a major change for cloud computing, says KABELO MAKWANE, Managing Director of Accenture Cloud First business in South Africa.

A number of recent developments in the broader information and communication technology (ICT) sector have converged, signifying a massive step change for cloud computing in the country.

Of great importance, has been the aggressive expansion of terrestrial fibre networks across South Africa’s major metros by numerous infrastructure providers, which has given businesses of all sizes, including those in decentralised locations, access to high-speed, uncapped broadband.

The continued roll out of high-speed wireless broadband in the form of 4G, and now 5G in certain areas, has also contributed to an increase in the adoption of basic cloud-based services such as web applications or smaller production systems.

In addition, and somewhat interrelated, is the impact and influence that workforce mobility has had, predominantly the ’bring your own device’ (BYOD) workplace paradigm. This trend is being driven by employees who want to use personal smartphones and tablets to access company networks, and the desire by businesses to improve workforce productivity and efficiency by ensuring on-demand access to applications and mission-critical systems and information.

Finally, in what is potentially the most significant recent development, and undoubtedly marks the tipping point for mainstream cloud adoption locally, Microsoft announced that Microsoft Cloud — including Microsoft Azure, Office 365 and Dynamics 365 — will, from 2018, be delivered from local data centres in Johannesburg and Cape Town.

The arrival of this in-country public cloud infrastructure, coupled with reliable, high-speed, end-to-end fibre access into the Microsoft data centres, means local businesses will soon lead in the New IT with access to a comprehensive cloud solution that answers many of the regulatory issues previously faced with off-shore hosting, such as data sovereignty and security.

Accordingly, Microsoft customers, which predominate business and public sectors, will now be more inclined to consider cloud computing, given the alignment of these factors, and the fact that vendor software licensing models have also evolved to preference cloud-based usage.

And the value proposition of Microsoft’s Azure cloud offering – a flexible, integrated platform that can operate as a full public or hybrid cloud solution, with enormous scalability – is even more enticing for customers that currently run on-premise SAP ERP solutions. Many organisations running legacy ERP systems are in need of an upgrade. They should therefore already be considering the next evolution of their core business systems, which form the heart of their everyday business operations.

If cloud computing in not already part of their next upgrade path, be it a full public solution or a hybrid model, then these companies are failing to grasp the magnitude of recent developments.

Firstly, the on-premise model often does not make business sense, both from a cost and an operational perspective. Cloud computing, on the other hand, enables businesses to leverage the investments, innovations and developments that cloud providers have already made. This negates the need to commit capex, and the considerable time and energy needed to develop standalone, on-premises solutions.

Secondly, many organisations using SAP as their core ERP system are, more than likely, already running specific elements in the cloud, with software-as-a-service solutions such as SAP SuccessFactors for human resources, or SAP Ariba for procurement. And SAP S/4HANA’s capabilities and suitability for cloud platforms as a ‘next generation’ ERP solution are accelerating the need to migrate to the cloud, to gain the flexibility and agility that required by successful businesses in the digital age.

Similarly, new companies or rapidly growing start-ups that are considering large-scale investments into core business systems for the first time are ideally placed to deploy out-of-the-box solutions into the cloud, fully bypassing the onerous on-premise approach, which today makes the most financial and operational sense. This also offers scalability, to facilitate any rate or degree of future growth due to the economies of scale that can be achieved in data centres.

There are also companies that may be in the process of a major ERP implementation, and are busy fine-tuning customisation. During this process they will need to consider how to achieve this in the most cost effective and efficient manner. The application development, testing and commissioning phases require significant computing real estate to succeed, with the public cloud environment is the best placed to facilitate this process in the most cost effective and efficient manner.

Accordingly, whatever position a company may find itself in, the cloud computing discussion currently taking place around boardroom tables needs to shift from a hypothetical debate, to one that seeks to define a strategy for the adoption of cloud computing and the migration of core business systems into the cloud.

Faced with the facts and the cumulative effect of recent develops, there can be little doubt left that for businesses in South Africa, all roads now lead to the cloud. And when it comes to provisioning cloud-based SAP solutions, Accenture believes that Microsoft Azure is a compelling value proposition. It offers cost benefits, and Azure is an open Microsoft platform, enabling greater interoperability with other systems and native integration with a variety of developer tools, including open source variants.

The time has therefore come for businesses to commit to a cloud strategy by first assessing their current core system lifecycle and subsequently developing a roadmap, ideally with a strategic cloud partner that understands every facet of the journey to cloud adoption, from the application assessment and cloud value realisation strategy, cloud transformation and migration, to ultimately cloud management and optimisation.

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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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