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Career disruption is real

Technology is reinventing how we live our lives, and while it me seem like another trend, MARTIN PIENAAR, COO at Mindworx Consulting, cautions every employer and employee to take note that this is a real thing and it is likely to eliminate 60% of the jobs we know today.

Everyone is talking about disruption and disruptors and how technology is reinventing how we live our lives at home and work. And while it may seem like just another trend or catchy business phrase, I caution every employer and employee to take note: this is a real thing! Exponential technologies are likely to eliminate 60% of the jobs we know today and if you don’t get to grips with what this means for your company and how you work, you’re not guaranteed of work in the future – which is closer than you think.

The next industrial revolution is here

Not for the first time are we experiencing a revolution that is threatening jobs and disrupting industries. Just think agricultural revolution, industrial revolution and even more recently in the technology age, how word processors obliterated the typing pool.

The next industrial revolution is here. People connected in real time by mobile phones and billions of connected sensors, are resulting in a revolution driving efficiency and productivity. Devices are getting cheaper, more powerful and more efficient which is pushing the internet into the industrial world. In this world, capital expenditure is giving way to monthly operating costs, where for example, the low cost of cloud computing allows for the growth of greenfields organisations which means more entrepreneurship and resultant innovation.

Companies need to gallup with technology

In this tech-era, companies should measure themselves on their responsiveness, not just the traditional assets and regulatory frameworks that have secured their success in the past.

Competitors of the future will likely not be the same as the past, and they will be faster, cheaper and do it better than you can. There is not an industry unaffected.

Employees need to reinvent themselves too

It’s highly unlikely that businesses of the future will insource all functions. The business model is likely to be a mix of own and outsourced pieces and “employees” will need skills in managing outsourced relationships.

“On-demand” skills must be mixed with full time teams in order to allow companies to rapidly scale up and down based on innovation cycles, but also to ensure they’re constantly resourced with current and best-of-breed skills. In order to stay competitive, companies will need to ensure that their permanent employees stay current too.

Over 53 million Americans are already participating in the part time, “gig” or “on-demand” economy. We expect this to grow over time.

Websites like Freelancer and Upwork (which is not yet active in South Africa) have allowed employers to find skills more easily. These trends will continue. In fact over the decade ending in 2015, the only net growth in staffing in the US market was in the “gig” economy, primarily Uber drivers.

Reskilling for emerging technologies like artificial intelligence/machine learning, big data, virtual and augmented reality, blockchain, robotics and the internet of things will soon be essential. Many of these technologies are coming out of a deceptive phase and becoming disruptive in the unlikeliest of industries. Robots are advising financial services clients, virtual reality is being used to solve pain issues in the medical realm and driverless cars have completed many millions of kilometers in California and Texas.

21st century skills are not about reading, writing and arithmetic

Companies and individuals who want to stay relevant will need to be up to date and competent in many of these technologies. If we carry on providing “broadcast” education rather than training for the attributes required in the 21st century, we are doing our youth, and ourselves, a disservice as they will be incompetent to cope in the workplace.

The qualities of curiosity, initiative, persistence, adaptability, leadership, social and cultural awareness are the basic foundational requirements for success in the new world of work.

And cross-team collaboration, creative thinking and prototyping are going to be the key attributes in a high-speed world.

And when you think that people are also starting to live longer – the current mean lifespan of 67 could well start to reach 100 over the next 2 decades – workers may be forced to work for longer and have to stay up to date with technology changes too.

The good news is that significant opportunities exist to grow skills outside of schools and universities, with massive online open courses (MOOCs) being offered by organisations like MIT, Coursera and iTunesU.

Real proof of a real change

Just in case you’re still not convinced that the disruption trend is here to stay, and will have a significant impact on the world of work, consider the following…

Business messaging service Slack is working on bots that will replace managers’ roles to get updates, follow up on tasks and send information to others. This type of technology will start to erode the roles of middle managers. Expect big improvements in productivity.

Airbnb has bought a blockchain company. The reason is to build a digital reputation system, which makes ratings immutable and could be used on the site to access premium properties, or elsewhere as a form of digital ID (not unlike a credit rating). It’s early days yet, but one gets a sense of how this technology will be used in future.

Many new industries will use people initially, but automate tasks as technology matures. An example is Uber and Lyft investing in self driving cars, Airbnb looking to unlock doors to rented homes using a mobile app (as against a person playing the key giver role), and online concierge services using artificial intelligence to replace humans.

We are living in very exciting times, but they are scary times for those who are not investing in their skills. Short term shedding of jobs is inevitable so standing still it just not an option when it comes to upskilling. But there are lots of new opportunities being created also. Think about how Airbnb and Uber have absorbed excess capacity; imagine when excess human capacity can be economically harnessed, it will create exciting new markets. I hope you’ll be ready.

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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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