An identity theft epidemic looms in South Africa and passwords will not be enough to protect you. But there is a solution, writes ARTHUR GOLDSTUCK.
South Africans have to brace themselves for an identity theft epidemic, after a website exposed 60-million South African identity numbers, along with extensive personal details (see http://bit.ly/SAbreach).
Suddenly, it is not enough to choose complicated, hard-to-guess passwords for online services like Internet banking, email, backup sites and cellphone services. In many cases, one merely has to confirm a range of personal details – exactly like those exposed in the breach – to change a password and gain access to a website containing financially sensitive information.
It is for this very reason that information security experts have for many years recommended something called two-factor authentication (2FA). It means that, to access a site or service, one needs a physical form of authentication as well as digital verification like user names and passwords.
The typical solution is to use one’s smartphone, usually via a one-time password e-mailed or sent by SMS. While this meets the technical definition of two-factor authentication, it is useless if identity theft has been used to have a new SIM card issued with your number.
Enter U2F, or Universal Second Factor. Jointly developed in 2012 by Google and a company called Yubico, it was adopted a year later by an industry body, the FIDO (“Fast IDentity Online”) Alliance, as a standard for two-factor authentication.
According to Yubico, it “enables Internet users to securely access any number of online services, with one single device, instantly and with no drivers, or client software needed”. You still need separate passwords for each site, but a separate device validates them.
The main problem with the solution in South Africa has been the absence of suitable U2F devices. That, in turn, has largely been a factor of service providers like banks not embracing the standard.
But now, the game has changed, First, a growing number of major international organisations have built it into their security options, with Google, Facebook and Dropbox, among other, all having it as an option.
Secondly, and most important, a South African company has built the first home-grown U2F-compliant solution.
It’s called SOLID wekKey, and it looks like a small USB flash drive. It secures several hundred passwords with a single overarching password. A small, downloadable password manager application allows the user to transform all these passwords into strong passwords that are almost impossible to guess or crack.
It was developed by Ansys, a South African company based in Centurion. Ansys has made a name for itself manufacturing custom security products for clients, ranging from small businesses to large enterprises, across the defence, aerospace, industrial and telecommunications sectors. With webKey, it is venturing into designing and marketing its own products for the consumer market.
“The general public struggles with basic account security,” says Ansys CEO Teddy Daka. “Year after year, we see that easy to crack passwords such as ‘123456’ or ‘password’ are still in common use, and individuals rely on just one or two memorable passwords or passphrases to protect all their online accounts.”
He reminds the public that, while security experts recommend the use of long passwords made up of uncommon phrases, and that every account must be protected with a unique password, people tend to use the same simple credentials all the time. As this writer has pointed out many times, when a user name and password is stolen from one site, it can often be used across multiple services.
The real issue is that people tend to compromise security for the sake of simplicity. The more secure a solution, usually, the more complex, and therefore the less popular. However, we have entered an era when hackers are going after the big fish and the small alike. When it is as easy to break into a million small accounts as one big one, no one remains safe. That means the simple solutions are no longer secure enough.
“People use easy to remember passwords because they choose convenience over security,” says Daka. “This shouldn’t come as a surprise. We shouldn’t expect people to remember passwords that are made up of 25 random characters for an account they need to access every day.”
However, products like SOLID webKey do the remembering for the user. Yes, you can build complex pass phrases into a password locker on your smartphone, but the locker is as vulnerable as the phone itself. Keep the password on a separate device, and one extra barrier has been placed between the hacker and your peace of mind.
How does it work?
SOLID webKey uses a combination of physical password vault, contained on a USB device, and a small industry-standard software application called KeePass.
The full name of the application, KeePass Password Safe, sums up its role perfectly: it is the equivalent of placing your valuables in an industrial-strength safe. Of course, as Hollywood teaches us, no safe is completely foolproof, but this kind of solution gives the user a chance against both random hackers and the professionals looking for easy targets.
Typically, hackers would use malware, or infected software, delivered via cunning “phishing” email and other attacks, to steal passwords. The SOLID webKey guards against this by requiring a physical tap of the USB device before passwords can be accessed. Because the password is never typed in, but delivered via a hardware “token”, it can’t easily be intercepted.
This is the basis of both two-factor authentication (2FA) and the Universal Two-Factor (U2F) standard promoted by the FIDO Alliance.
The main obstacle to the wider uptake of U2F is the fact that it remains a mystery to most consumers, and even services like Gmail and Facebook – which come under regular, sustained attack – do not make a special effort to highlight the option. However, as the cyber war intensifies, U2F is expected to move to the front and centre of such sites’ efforts to protect their users.
“Two-factor authentication is rapidly becoming the norm, and is a proven way to secure accounts,” says Daka. “Through SOLID webKey, we hope to make it easier to use and therefore more popular with South Africans who want the best in online security.”
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.
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