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How AI can change your business right now

Data has become the new business currency and companies have access to more data than ever before. But the business differentiator now is what they do with the information. This is where AI comes in says LIKUN ZHAO, GM, Huawei Consumer Business Group SA

A recent global study by MIT Sloan Management Review and the Boston Consulting Group (BCG) found that more than 80% of organisations see artificial intelligence (AI) as a strategic opportunity for their business. The study surveyed companies from 21 industries in 112 countries, highlighting the fact that AI can serve multiple sectors, and is not just limited to a handful of business segments.

What is AI?

The term ‘artificial intelligence’ was actually coined way back in the 1950s, and since then its definition has broadened widely as technology becomes smarter, data becomes more prolific, and analytics become more nuanced. At its most basic level, AI refers to a system that is capable of learning from data. As more data is inputted, the system gets smarter and is able to take more action on its own, accomplishing what only humans could do previously, but in a much shorter timeframe and with increasing accuracy.

Data has become the new business currency, and, thanks to new technologies, companies have access to more data than ever before. But the business differentiator is not who has the most data but what they do with the information. And this is where AI comes in. AI essentially unlocks the power of data. Through the use of AI, systems can process massive amounts of data and transform this data into knowledge – knowledge about everything from customers and staff to procedures and time management.

Getting to know your customers

The better you know your consumers, the better you are able to give them more personalised brand experiences that will turn them into loyal customers. AI is an extremely useful tool to learn more about your clients’ preferences and buying habits. It is already being used extensively in ecommerce, where customers’ ‘Wish Lists’ and previous purchases can provide insight into their interests, likes and spending patterns. One of AI’s key benefits is that this data is available in real time, so online retailers can act on this information immediately. The insurance industry is also using AI to get to know its clients’ driving behaviour, which not only results in lower premiums for safe drivers, but also gives insurers more information about their clients that could be used for future company innovations that will address a glaring need they would otherwise not be aware of.

Optimising employee performance

Of course, your customers are not the only people important to your business. The advantages of AI are not solely externally focused but can also turn inward to put the spotlight on the individuals who make your business happen. AI can be used effectively in many HR processes, such as the hiring of new staff and performance reviews. In addition, it can assist in detecting potential staff issues, such as regular absenteeism, before they become problems, and even point to possible causes. AI is also an incredibly useful tool for compiling staff schedules and allocating resources on certain projects, in order to optimise productivity, augment outcomes and reduce costs.

Enhancing business operations

Whatever your business, AI can enhance how you run things. In manufacturing, for example, AI can be used to quickly resolve issues on the floor by accessing data on how similar problems were addressed in the past. In sales, cold calling can become a thing of the past as AI can help to generate more accurate leads. Delivery companies can use AI to manage and monitor their fleets, and more accurately assign tasks and achieve quicker deliveries. Across industries, AI can assist in driving down costs, optimising time management, compiling accurate reports and better serving customers. Real-time processing of data ensures businesses keep up to date with better information, better recommendations and more insightful predictive power, to optimise business decisions.

AI on your phone: the businessperson’s virtual assistant

Cellphones have been vital business tools for almost two decades now, and their importance has only accelerated as the idea of the flexible workforce starts to take shape. Smartphones are also becoming increasingly intelligent as AI is being integrated more and more into their systems and applications. However, AI has also been known to slow down phone performance considerably, drain battery life, as well as open users up to privacy problems – none of which are good for business.

As the first and only phone with on-device AI, the new Huawei Mate 10 Pro is able to address all these issues. Because the artificial intelligence is on the phone, all your information stays on the device, and you do not need a network to use traditionally data-draining apps. In addition, the Huawei Mate 10 is built to avoid battery waste, manage abnormal power consumption and maximise battery usage. This makes the phone an ideal business companion, as you will never be let down by a dying battery, even in the case of excessive usage.

Because it is an AI-enabled device, the Huawei Mate 10 Pro is also able to learn your cellphone habits and preferences, so over time it can become more intuitive and give you a more personalised experience. This kind of intelligence goes well beyond what Siri and other voice-activated assistants can do.

The Huawei Mate 10 Pro is giving people a taste of what artificial intelligence can do for business, especially in terms of having a more customised consumer experience. AI is undeniably the future of business, and companies that use it wisely will have a definite competitive advantage, or risk getting left far behind.

 

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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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