At the IFA 2016 expo in Berlin this week, Lenovo launched the Yoga Book, claimed to be the world’s thinnest and lightest 2-in-1 tablet, designed for productivity while on-the-go.
The Yoga Book is built for mobility and to solve the most common challenge among tablet users: how to achieve productivity and entertainment in one device.
“Until now, we’ve been using tablets in ways we weren’t meant to: for productivity, for example, which becomes painful when typing or applying a stylus onto a touch screen that you’re using on-the-go,” Lenovo declared in a statement it released on Thursday. “The Yoga Book removes that difficulty by taking the fundamental building blocks from the DNA of what makes a great tablet – namely portability, long battery life and a rich app ecosystem – and entwines it into a strand of creativity and productivity through a suite of powerful new hardware and software features.”
• Instant halo keyboard
• Dual-use stylus that writes on paper and screen
• Productivity-driven Book UI
“We set out to redefine the tablet category conundrum, namely that consumers no longer separate their activities into productivity and entertainment – it all blends together, and so should the device they use,” said Jeff Meredith, vice president and general manager, Android and Chrome Computing, Lenovo. “The Yoga Book introduces keyboard and handwriting input capability in an elegantly simple, unconventionally slender tablet design. We believe our unique design will offer tablet, 2-in-1 and traditional notebook buyers a first-of-its-kind option for evolving usage trends.”
Lenovo provided the following information:
With two panels that open up like an ultra-thin notebook, the Yoga Book is unconventionally slender and light years removed from the tablet that you’re accustomed to using on the go or while sitting in your home. As the world’s thinnest 2-in-1, the Yoga Book is 9.6mm closed, tapering to 4.05mm at its slimmest edge – a thickness of just under three pennies. And because it’s also the lightest 2-in-1 in the world at 690 grams (1.52 pounds), the Yoga Book is made to match the mobility of a smartphone, so you can easily hold and carry, just like a book. Users who take the Yoga Book with them on day trips have the option to work anywhere – on a busy commute, in a packed waiting room or on a crowded countertop – if and when they feel like it, thanks to the thin and light design, 15-hour battery life and a watchband hinge that folds 360 degrees. And if users don’t feel like working, they’ll have a top-of-the-line entertainment tablet to keep them company, with a 10.1-inch IPS FHD screen, high-quality sound enhanced with Dolby Atmos and 64GB of memory.
Instant Halo Keyboard
The Yoga Book’s first productivity feature is also what makes the thin and light design possible: the halo keyboard, a full touch screen backlit keyboard that weaves software and hardware into one fluid interface. The touch screen is made with glass that was meticulously chosen to give a rough, matte feel and finish, along with anti-glare coating to ensure the best possible touch-typing experience. The keyboard lacks any physical keys, showing up as a solid white outline on the Yoga Book’s second panel only when it’s needed. The halo keyboard constantly ‘learns about and adapts to’ the typing habits of its user, with built-in prediction and artificial learning software. This software also allows for continuous optimization. Along with built-in, sensitive haptic technology, which enables touch feedback to guide typing and reduce mistakes, the halo keyboard far surpasses the typing experience and speed of a normal tablet, and is comparable with that of a physical keyboard.
Real-Pen Accessory – Dual Use Stylus
The flush surface of the halo keyboard feature also allows for a few additional uses when paired with the Yoga Book’s standard real-pen accessory, a dual-use stylus. Inspired by the elegance and simplicity of real notebooks, Yoga Book is an acknowledgement that we all still love to write and draw on paper. Users can now write with the real-pen accessory that holds real ink tips onto a piece of paper or notepad covering the multi-use keyboard panel, or as a stylus when applied straight onto the panel. Everything they create, from doodles and drawings to notes, is instantly digitized and saved with the Lenovo note-saving app. Roughly the size of a conventional ink pen, the real-pen accessory is powered by Wacom feel IT technologies to work with the state-of-the-art electro-magnetic resonance (EMR) film housed within the multi-use keyboard, which enables this real-time digitization.
The multi-use keyboard and real-pen accessory recreate the natural feel of drawing flat on a paper surface instead of directly onto a computer screen, without having to block parts of the art work with the hand or stylus. Or you can draw directly on the screen as well, depending on preference. The real-pen accessory can draw with the precision of a pencil or paintbrush, with 2,048 pressure levels and 100-degree angle detection. In addition, you’ll never have to charge or replace it – the real-pen accessory doesn’t require batteries and its ink can be replaced with standard ink tips, just like that of a conventional pen.
Book UI and Hinge
As a 2-in-1 that weaves together both hardware and software, Yoga Book truly brings work and play into one tablet through the Book UI, the Yoga Book’s specially adapted Android 6.0 operating system that draws from the best UI features of laptops and tablets. The Book UI allows several apps to run at once through multiple windows that can be pinned, maximized or minimized, as well as a taskbar that keeps track of your apps and common Windows keyboard shortcuts and action keys. This additional new workload is easily handled by the Yoga Book’s powerful Intel Atom X5 processor and 4GB of memory. And Windows users also have the option to work on that platform, as the Yoga Book is available on Windows 10.
Constructed from a combination of magnesium and aluminium alloys, the Yoga Book is robust in build and guaranteed to turn heads. As with all Yoga products, it has the distinctive watchband-style hinge. This time, the hinge is engineered to be smaller and features a custom-made three-axis hinge, with 130 different mechanical pieces comprising five different materials. Lab tested more than 25,000 times, the Yoga Book form offers a smooth, seamless transition between the four modes – Browse, Watch, Create and Type. The Yoga Book with Android is available in Gold or Gunmetal, while the Yoga Book with Windows comes in Carbon Black.
Pricing for the Yoga Book will start at €499 for the Android version and €599 for the Windows version. Pricing and availability may vary from country to country. All will be globally available beginning in September. In the US, the Yoga Book will be sold online and at Walmart stores nationwide by the end of October.
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:
- 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.