One of the most common myths of pairing smartphone and computer choices is the idea of needing to buy into an ecosystem. ARTHUR GOLDSTUCK explains the fallacy.
You hear it most commonly from iPhone users: “All my gadgets are from Apple, because they are all compatible and work together so seamlessly.”
Usually, they are referring to the combination of iPhone, iPad and MacBook. Usually, they are delighted with their choice of ecosystem, as there are few brands that produce as consistently excellent products as Apple across all categories. And, because of this delight, they usually also fall completely for the marketing hype about the ecosystem.
The reality is that there is almost no difference in the ecosystem experience of an iPhone user or Android phone user who also uses a MacBook, whether an Air, Pro or plain vanilla version of the iconic notebook.
Full disclosure: I’ve been an enthusiastic MacBook Air user for at least the past six years. It is the ultimate machine for long trips, ultra-portability and instant access: it is so thin and light, has amazing battery life on the other, and goes instantly from sleep to work mode merely by opening the lid.
The significance of the battery life is that I have never been on an international flight where I have run out of power. Even on the longest single-leg flights from South Africa, which would be up to about 16 hours, I would be sleeping or have the device packed away during meal times more than half the time, meaning that I can work on the machine for the entire rest of the flight.
This is a massive benefit in countering the loss of productivity that results from international travel.
Even taking aircraft out of the equation, one often finds that local events like conferences are not planned with notebook computers in mind, and one can often go a full day without access to a power point. The MacBook Air is the only device that has allowed me to remain connected and fully productive throughout such events.
But the magic of the device does not extend to the ecosystem within which it functions. Its operating system, the Mac OS, is so ancient, it is still resting on the laurels of the 2001 launch of Mac OS X. What was described back then as a “radical departure” is now an old revolutionary pulling the wool over the eyes of acolytes with its fading activist credentials
The acolytes are caught up in a reality distortion field similar to the trance into which Steve Jobs was able to place anyone trying to argue with him about Apple products – or almost any other issue. Reality, for them, is less important than their perception of reality.
That perception is fuelled by the fact that the iPhone and iPad are indeed deeply integrated, completely symbiotic and compatible to the extent that the very same app version can sometimes be used on both. Working on one device allows seamless transition, in the same app, to the other. The experience of the iOS operating system is almost identical on the two.
Like that seamless transition, perception also makes a seamless transition across to the MacBook, which is believed by many to integrate equally tightly with the iPhone. The reality is that it’s an entirely different operating system, one that is a decade overdue for an overhaul, and one that requires work-arounds for true compatibility. Just as it does with Android devices.
Yes, Apple’s iCloud for backup and syncing is seamlessly accessible on all three categories of product. But then so is Microsoft’s OneDrive and Google Drive. Apple’s horrible Mail client can sync across all three, but then so does Google’s more evolved Gmail.
Here’s the real dirty secret of device ecosystems: the mortal enemies, Microsoft and Google, have better software ecosystems than Apple and, aside from their operating systems, are almost totally device independent.
This means that the Microsoft Office suite as well as its OneDrive cloud service, can be experienced with almost full functionality on any Apple, Android or Windows machine. Apple’s productivity suite can only be used on Apple devices – or via a Web browser, meaning it is a limited experience.
The last shot from Apple fans is usually the fact that the FaceTime and Messages video, voice and chat apps are also compatible across all three categories of device. This is one area where Android and Windows cannot compete, as the apps are not available on other platforms.
But that is equally a negative: it means that users of those apps are locked out of the rest of the device universe. Users of WhatsApp, Skype and other non-denominational chat apps, on the other hand, can find kindred souls on any mobile device.
It’s not that it’s a mistake to stick to the Apple family of products. Mostly, the experience will only be good. But the bottom line is that you don’t have to be locked into Apple to have a satisfying device family life.
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:
- machine learning requires large amounts of (quality) data and;
- 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.
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.