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Gear up for esports

Rush, taking place at the Sandton Convention Centre this weekend will give visitors the chance to meet and greet professional player, watch SA’s top teams compete and offer loads of competitions and prizes.

Want to learn more about esports, meet and talk to pro players, watch SA’s top teams compete, and maybe even enter a competition yourself and win some prizes? Then gear up for Rush this coming weekend.

Designed by the creators of rAge (really Awesome gaming expo), the inaugural Rush event, powered by HP OMEN, is a first for esports in South Africa.

Taking place from 21 to 23 July at the Sandton Convention Centre in Johannesburg, Rush will feature a variety of competitive esports tournaments hosted by leading local organisations across  dedicated esports stages, with live shoutcasting (commentary) by some of South Africa’s top shoutcasters. South Africa’s best competitive gamers and clans will compete for various prizes.

 

Four main stages will be showcasing popular titles such as Counter-Strike: Global Offensive (CS:GO), FIFA 17, League of Legends, and Street Fighter V. Fans, supporters and enthusiasts will be able to watch all the competitions live on big screens, with dedicated seating areas at all the stages. In addition, visitors will also be able to sign to enter tournaments across a range of games like Hearthstone, FIFA 17, Street Fighter V and Metal Slug. There are prizes up for grabs, so anyone with even a passing interest in video games is encouraged to take part.

Rush is an exciting, compelling showcase of everything that South African esports currently has to offer,” said Lauren Das Neves, marketing manager for Rush, NAG and rAge. “We’ve created a unique experience that will appeal to both casual and hardcore gamers, and one that will serve as a visual introduction to anyone who doesn’t know what all this esports fuss is about.

“Having been actively involved in the local gaming industry for the past 20 years through NAG and rAge, Rush is a great platform for us to build, empower and connect with new and existing esports communities.”

Big news for Kaizer Chiefs supporters is that they’ve announced their participation at Rush. The club has partnered with sponsors Vodacom and Nike for the event, giving gamers a chance to battle it out at the Kaizer Chiefs esports stage. Gamers will go head-to-head in the FIFA 17 tournament in a bid to be the ultimate winner. To enter the Kaizer Chiefs tournament at Rush, visit www.acgl.co.za/kaizerchiefs to sign up. Entry to the tournament is free.

For Warcraft and Hearthstone fans, Kwesé will be hosting a Hearthstone Fireside Gathering at Rush. There is R30 000 cash in prizes for winners across the weekend, with the player in the daily top spot walking away with R5 000. Second and third place get R3 000 and R2 000 respectively. There’s also a gaming notebook from MSI up for grabs. In addition, there’s a casual tournament for those keen for arrive-and-play fun.

Other activities include PlayStation VR, arcade cabinets and retail stands, as well as old-school, arcade-style gaming.

Visit the NAG Arcade/Eurasian Entertainment stand to try your hand at Metal Slug. The player with the highest score at the end of the weekend will win an arcade machine that oozes retro coolness.

Matrix Warehouse will be running a fastest-lap challenge on their stand with mystery prizes each day.

Orena is ramping up its Fight Night offering at Rush, with the casual Street Fighter V tournament set for an EFC-sized boost. EFC Worldwide welterweight champion Dricus du Plessis and bantamweight champion Demarte Pena will battle it out in an exhibition match in the OFN octagon at 11:00 on Sunday 23 July, with other EFC athletes waiting in the wings to challenge them. Gamers can enter the Orena Fight Night challenge and stand to win R20 000 at the end of the season.

Orena is also offering the chance to qualify for ESWC for CS:GO on one of the main stages.

“The country’s best esports teams will be fighting it out in a world-class arena, for the opportunity to take on some of the best players on the planet,” says Luca Tucconi, Operating Executive at Orena. “The competition is fierce and the prize is massive – and we have one or two surprises up our sleeves for the winners”.

Alongside this high-adrenaline and hugely entertaining line-up, one of the other exciting features of Rush is South Africa’s first Winter NAG LAN – an opportunity to experience multiplayer gaming with hundreds of likeminded gaming enthusiasts packed into a single venue.  A NAG LAN ticket also gives participants access to the Rush event for the full weekend. Casual competitions for Dota 2 and Rocket League will be hosted in the NAG LAN, with hardware peripheral prizes sponsored by Apex Interactive. A formal competition for Call of Duty: Infinite Warfare will also be hosted in the NAG LAN for 16 teams on PS4 in the NAG LAN,  with a total of R19 000 in prizes.

 Pink-it arrives

Pink-IT will hold its first annual summit at Rush over the course of the three days. Pink-IT is designed to build a supportive and inspiring community for women in technology and foster a culture of sharing, collaboration, discussion and inspiration for female software developers.

The first annual summit will bring together companies interested in hiring female software developers in South Africa, opening up opportunities for women in the industry. The summit will be a jam-packed weekend  for all  female software developers, game developers, UX designers, graphic designers and animators who also want to learn how to build a mobile-based esports game using Blue mix.

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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, 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:

  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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