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‘Let us build the Web we want’ – Berners-Lee

This open letter sent out by World Wide Web inventor Tim Berners-Lee on 12 March 2017 calls on Internet users to stand up to fake news, loss of privacy and unethical advertising.

Today marks 28 years since I submitted my original proposal for the world wide web. I imagined the web as an open platform that would allow everyone, everywhere to share information, access opportunities and collaborate across geographic and cultural boundaries. In many ways, the web has lived up to this vision, though it has been a recurring battle to keep it open. But over the past 12 months, I’ve become increasingly worried about three new trends, which I believe we must tackle in order for the web to fulfill its true potential as a tool which serves all of humanity.

1)   We’ve lost control of our personal data

The current business model for many websites offers free content in exchange for personal data. Many of us agree to this – albeit often by accepting long and confusing terms and conditions documents – but fundamentally we do not mind some information being collected in exchange for free services. But, we’re missing a trick. As our data is then held in proprietary silos, out of sight to us, we lose out on the benefits we could realise if we had direct control over this data, and chose when and with whom to share it What’s more, we often do not have any way of feeding back to companies what data we’d rather not share – especially with third parties – the T&Cs are all or nothing.

This widespread data collection by companies also has other impacts. Through collaboration with – or coercion of – companies, governmentsare also increasingly watching our every move online, and passing extreme laws that trample on our rights to privacy. In repressive regimes, it’s easy to see the harm that can be caused – bloggers came arrested or killed, and political opponents can be monitored. But even in countries where we believe governments have citizens’ best interests at heart, watching everyone, all the time is simply going too far. It creates a chilling effect on free speech and stops the web from being used as a space to explore important topics, like sensitive health issues, sexuality or religion.

2)   It’s too easy for misinformation to spread on the web

Today, most people find news and information on the web through just a handful of social media sites and search engines. These sites make more money when we click on the links they show us. And, they choose what to show us based on algorithms which learn from our personal data that they are constantly harvesting. The net result is that these sites show us content they think we’ll click on – meaning that misinformation, or ‘fake news’, which is surprising, shocking, or designed to appeal to our biases can spread like wildfire. And through the use of data science and armies of bots, those with bad intentions can game the system to spread misinformation for financial or political gain.

3)   Political advertising online needs transparency and understanding

Political advertising online has rapidly become a sophisticated industry. The fact that most people get their information from just a few platforms and the increasing sophistication of algorithms drawingupon rich pools of personal data, means that political campaigns are now building individual adverts targeted directly at users. One source suggests that in the 2016 US election, as many as 50,000 variations of
adverts were being served every single day on Facebook, a near-impossible situation to monitor. And there are suggestions that some political adverts – in the US and around the world – are being used in unethical ways – to point voters to fake news sites, for instance, or to keep others away from the polls. Targeted advertising allows a campaign to say completely different, possibly conflicting things to different groups. Is that democratic?

These are complex problems, and the solutions will not be simple. But a few broad paths to progress are already clear. We must work together with web companies to strike a balance that puts a fair level of data
control back in the hands of people, including the development of new technology like personal “data pods” if needed and exploring alternative revenue models like subscriptions and micropayments. We
must fight against government over-reach in surveillance laws, including through the courts if necessary. We must push back against misinformation by encouraging gatekeepers such as Google and Facebook
to continue their efforts to combat the problem, while avoiding the creation of any central bodies to decide what is “true” or not. We need more algorithmic transparency to understand how important decisions that affect our lives are being made, and perhaps a set of common principles to be followed. We urgently need to close the “internet blind spot” in the regulation of political campaigning.

Our team at the Web Foundation will be working on many of these issues as part of our new five year strategy – researching the problems in more detail, coming up with proactive policy solutions and bringing together coalitions to drive progress towards a web that gives equal power and opportunity to all. I urge you to support our work however you can – by spreading the word, keeping up pressure on companies an  governments or by making a donation. We’ve also compiled a directory  of other digital rights organisations around the world for you to explore and consider supporting too.

I may have invented the web, but all of you have helped to create what it is today. All the blogs, posts, tweets, photos, videos, applications, web pages and more represent the contributions of millions of you around the world building our online community. All kinds of people have helped, from politicians fighting to keep the web open, standards organisations like W3C enhancing the power, accessibility and security of the technology, and people who haveprotested in the streets. In the past year, we have seen Nigerians stand up to a social media bill that would have hampered free expression online, popular outcry and protests at regional internet shutdowns in Cameroon and great public support for net neutrality in both India and the European Union.

It has taken all of us to build the web we have, and now it is up to all of us to build the web we want – for everyone.  If you would like to be more involved, then do join our mailing list, do contribute to us, do join or donate to any of the organisations which are working on these issues around the world.

– Sir Tim Berners-Lee

* For more information, visit http://webfoundation.org/2017/03/web-turns-28-letter. Internet users are also asked by the World Wide Web Foundation to share this letter on Twitter using the hashtag #HappyBirthdayWWW

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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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