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Time for infinity recycling

Infinity recycling – an idea produced by circular economy thinking may just become the norm, especially as our economy is run by a ‘take-make-dispose’ approach that generates a huge amount of waste, writes TIM PLENDERLEITH of Aurecon.

Who ever said ‘happily ever after’ was just the stuff of fairytales?

These days those words are written into the soles of Lionel Messi’s cleats. (Or at least, that’s the idea.) The “Sport Infinity” range by sports apparel company Adidas uses worn-out cleats and, by combining them with scrap materials from other industries, reimagines them into high quality new shoes. “The football boots of the future could contain everything from carbon used in aircraft manufacturing to fibres of the boots that scored during the World Cup,” Adidas said in a statement.

It’s called infinity recycling – one of the many good ideas wrought by circular economy thinking – and it may just be the Sunday game norm someday.

With three billion new middle-class consumers expected to enter global markets in the next 15 years, we can expect three billion more hungry appetites for the resources and infrastructure required to meet their lifestyle demands.

Currently, our economy is run by a ‘take-make-dispose’ linear approach that generates a breathtaking amount of waste. According to Richard Girling’s book Rubbish!, 90% of the raw materials used in manufacturing don’t even make it out the factory doors, while 80% of products made are thrown away within the first six months of their life cycle. The resource crunch is more like a suffocation, with our incriminating fingerprints all over the planet’s throat. The extractive industry’s approach is unsustainable – raw materials are being depleted quicker than they can regenerate.

The circular economy may be a highly practical solution to our planet’s burgeoning woes. The idea behind a circular economy is to rethink and redesign the way we make stuff. Rather than ditching your worn-out old jeans, send them into the factory for recycling and upgrade to a new pair. Done with your old iPhone 5? Reconsider buying the Puzzlephone, which can be easily disassembled, repaired and upgraded over a ten-year lifespan.

In the circular economy, products are not downgraded, as they are in recycling, but reimagined to infuse the same, if not more, value back into the system. Basically, there’s no such thing as waste in a circular system – all waste bears the raw materials to become something else. By finding fresh, creative ways to use the same resources, a one-way death march to unsustainable collapse is inadvertently avoided.

Could we halt the downward spiral by using waste to solve the waste crisis?

With McKinsey rolling out projections as high as $1 trillion to gain from a closed-loop economy, circularity seems to have our ‘thumbs up’ in principle. The truth is however, we are a far cry from adopting its practical reality in our design-distribution streams. So how will we get there? If the circular economy is indeed the way of the future, what needs to change now to usher it in? Could the circular economy define the end of the extractive industry as we know it?

We have to believe in a new buying power

The Kingfisher Group has much to say on the future shift in consumerism, and they’re using power tools to say it. Rather than buying that drill that is used on average six minutes in a year, why not rent it for the day? Surely it would be better value for money on that rare occasion when a hinge is loose?

Their company, along with others like Mud Jeans and Philips, are paving the way for new ideology and design around products and how we relate to them. Consumerism is moving to stewardship, with the emphasis on service over product acquisition. So, in other words, the ‘pay per use’ contractual agreements associated with smartphones could extend to washing machines, DIY equipment or even Levi jeans. Access, not ownership, to a product will be the new trading power. This will launch fantastic new intelligent systems to undergird the process.

But it will firstly require a good deal of unlearning and open-mindedness for us who have been immersed in linear thinking.

We have to up our game

Within the former linear structure, sales were the success markers. Manufacturing and design simply had to align just enough to make the product sparkle, shine and ultimately sell. They didn’t have to consider the total fossil fuel emission of production or its biodegradability in landfill. The product’s recyclability was not in question. It was only the swipe of the credit card.

A circular economy, however, is really complex. It accounts for a product’s entire life cycle in its design. Systems-level redesign and skills we haven’t yet imagined will be needed in order to recall, repair and reincarnate products into an upgraded former self. Rapid innovation will generate IoT platforms and seamless technologies into new services and product offerings.

The need for ongoing research and development will drive STEM (Science, Technology, Engineering, Mathematics) disciplines. We need to prepare for these complexities, so that the added layers of life cycles are anticipated in tomorrow’s briefs and an egg-on-face situation is narrowly averted.

We have to collaborate

Circular solutions will only realise sustainable, future-proofed ecosystems if everybody is on board. Perhaps even more important than the engineers and designers, governance and regulation are crucial in endorsing these processes. Redesigning supply chains and business models require robust round-table discussions between businesses, universities, social groups and policymakers.

Initiatives such as the Ellen MacArthur Foundation’s Circular Economy 100 embraces this idea that closed-loop ambitions can never be achieved by working in isolation. This group ties together supply chain leaders, industries and geographies. From designers to academics, CEOs to city mayors, people are locking heads and sharing their complementary expertise. The result of which is a more effective and holistic solution that generates wins for both the planet and our pockets.

Linear thinking can’t meet the needs of the emerging circular economy. However, all is not lost. Draw a straight line long enough and it would actually envelop the globe, paradoxically making a circle. What we need is linear thinkers to be open-minded to extrapolate their thinking out far enough in order to, ultimately, draw the same conclusion – that a circular approach is actually where all roads lead. Going forward, drawing circles around our consumer behaviour may be the best way to draw the line.

* Tim Plenderleith, Market Director for Manufacturing at Aurecon.

 

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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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