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The robots are coming!

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Machine learning is going to alter our world, improving healthcare, the manufacturing industry and assisting in the prediction of supply and demand levels across various industries, writes RESHAAD SHA, Chief Strategy Officer and Executive Director at DFA.

For those who are into science fiction, the term ‘machine learning’ immediately conjures up images of computers taking over the world, either to send murderous terminators from the future to our present or to place us all inside the Matrix as living power batteries.

Fortunately, the truth about machine learning is not only far more prosaic, but also much more promising for the future of the human race. Basically, machine learning uses algorithms that iteratively learn from data, meaning that it enables computers to find hidden insights without being explicitly programmed where to look. The iterative aspect is especially important, as it means that as the computer is exposed to new data, it is able to independently adapt.

The process of machine learning is similar to that of data mining, in that both systems search through data to look for patterns. However, where data mining extracts information for human comprehension, machine learning uses it to detect patterns in data and to adjust its program actions accordingly. Incredibly, it’s a science that is not new; it is one that was, in fact, predicted nearly 70 years ago by Alan Turing, widely considered the father of theoretical computer science and artificial intelligence. He suggested that by 2000, computers would be able to ‘think’.

Turing was clearly not far off as the world is already seeing machine learning being put into practice across a range of sectors, and all of these vertical markets are benefiting enormously from the application of this technology. Some of the benefits are outlined below, but these are merely scratching the surface of where we might eventually go with this ground-breaking technology.

Innovative applications

For starters, it is applicable to healthcare, as machine learning algorithms can process more information and spot more patterns than humans can, by several orders of magnitude. This means they are more likely to pick up individual health issues, and do so more rapidly and effectively than a human diagnosis could. Machine learning can also be used to understand risk factors for disease in large populations.

In customer-facing businesses, it is also enabling marketing personalisation. The more that a company understands about its customers, the better it can serve them, and machine learning algorithms are providing the kind of intimate picture of a customer that enables such personalisation to take place.

Perhaps the most obvious use of machine learning is its use in online search engines, where the engine uses this technology and watches how you respond to search results, learning from these and ensuring it delivers better results in the future.

Of all the uses for machine learning, one of the most exciting ones – particularly for those of us old enough to remember ‘Knight Rider’ and his self-driving Trans Am – is in the various types of smart cars now being developed. A recent IBM survey of top auto executives saw some 74% of these stating they expected there would be smart cars on the roads by 2025.

These vehicles will not only integrate into the Internet of Things (IoT), but also learn about their owners and their environment. A smart car might adjust the internal settings automatically, based on the driver, report and even fix problems itself, will certainly be able to drive itself, and will offer real time advice about traffic and road conditions.In extreme cases the vehicle may even take evasive action to avoid a potential collision.

A good example of such smart cars is the Tesla models fitted with the company’s version 7.0 Autopilot system. Tesla’s Autopilot system makes use of machine-learning techniques that are continuously learning from human actions. Over the past year or so, this system has quietly been monitoring drivers as they drive various routes. The more often the car drives on a particular route, the more the machine learns how the human approaches, for example, a particular corner.

The idea, according to Tesla, is for the vehicles featuring Autopilot to be self-driving capable from the moment legislation catches up to the technology. And because it requires only simple software updates to stay relevant, users who purchased a vehicle a year ago will still be capable of utilising this feature when it becomes legal.

Let the machine drive

Naturally, machine learning lies at the very core of this long-awaited self-driving car revolution, which is clearly one of its most advanced and complex applications. Self-driving vehicles, after all, need to not only be able to ‘understand’ the rules of driving and how to actually drive, but must also be able to monitor the movements and signals of other cars and infrastructure, as well as being capable of learning to negotiate exceptions and make split-second decisions.

It should be obvious then that driverless cars will require an immense amount of data gathering and analysis; they will also need to connect to cloud-based traffic and navigation services, and will draw on leading technologies in sensors, displays, on-board and off-board computing, in-vehicle operating systems, wireless and in-vehicle data communication, analytics, speech recognition and content management. All of this leads to considerable benefits and opportunities: reduced accident rates, increased productivity, improved traffic flow, lowered emissions and much more.

The question is, how are cars expected to access all this data? After all, we are talking about information transmitted not only from other vehicles, but potentially from traffic lights, nearby buildings and rail crossings, not to mention GPS signals and even pedestrians’ phones, just to name a few.

It is here that the IoT will become a crucial platform, as it will be IoT-enabled sensors that are used to transmit most of this data to and from the automated vehicle. This, in turn, means that the network that these objects and sensors connect to will have to be cost efficient, ubiquitous and reliable.

But is South Africa – a country renowned for high data costs and ongoing struggles with connectivity – going to be in a position any time soon to have the kind of network necessary to facilitate self-driving cars?

The good news is yes. In fact, in all likelihood, SA will have an effective IoT network long before the first local cars start driving themselves, thanks to SqwidNet, a wholly-owned subsidiary of Dark Fibre Africa (DFA), which is also the licensed Sigfox operator for SA. Sigfox has a global network that spans 29 countries and is specifically designed to deliver IoT connectivity.

The company also has access to a wide range of IoT-based solutions, many of which have already been deployed in cities around the world. This means that not only will we have the network to enable future smart everything, but also a range of other solutions that will already have been tried and tested in other environments. In other words, by the time of their deployment, the new technology kinks will already have been worked out.

There is no doubt that we are on the cusp of another technological revolution, one which is going to make everyone’s lives easier and more connected. The IoT and machine learning look set to fundamentally alter the way our world works – in a manner that is exactly the opposite of a killer robot from the future.

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Prepare your cam to capture the Blood Moon

On 27 July 2018, South Africans can witness a total lunar eclipse, as the earth’s shadow completely covers the moon.

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Also known as a blood or red moon, a total lunar eclipse is the most dramatic of all lunar eclipses and presents an exciting photographic opportunity for any aspiring photographer or would-be astronomers.

“A lunar eclipse is a rare cosmic sight. For centuries these events have inspired wonder, interest and sometimes fear amongst observers. Of course, if you are lucky to be around when one occurs, you would want to capture it all on camera,” says Dana Eitzen, Corporate and Marketing Communications Executive at Canon South Africa.

Canon ambassador and acclaimed landscape photographer David Noton has provided his top tips to keep in mind when photographing this occasion.   In South Africa, the eclipse will be visible from about 19h14 on Friday, 27 July until 01h28 on the Saturday morning. The lunar eclipse will see the light from the sun blocked by the earth as it passes in front of the moon. The moon will turn red because of an effect known as Rayleigh Scattering, where bands of green and violet light become filtered through the atmosphere.

A partial eclipse will begin at 20h24 when the moon will start to turn red. The total eclipse begins at about 21h30 when the moon is completely red. The eclipse reaches its maximum at 22h21 when the moon is closest to the centre of the shadow.

David Noton advises:

  1. Download the right apps to be in-the-know

The sun’s position in the sky at any given time of day varies massively with latitude and season. That is not the case with the moon as its passage through the heavens is governed by its complex elliptical orbit of the earth. That orbit results in monthly, rather than seasonal variations, as the moon moves through its lunar cycle. The result is big differences in the timing of its appearance and its trajectory through the sky. Luckily, we no longer need to rely on weight tables to consult the behaviour of the moon, we can simply download an app on to our phone. The Photographer’s Ephemeris is useful for giving moonrise and moonset times, bearings and phases; while the Photopills app gives comprehensive information on the position of the moon in our sky.  Armed with these two apps, I’m planning to shoot the Blood Moon rising in Dorset, England. I’m aiming to capture the moon within the first fifteen minutes of moonrise so I can catch it low in the sky and juxtapose it against an object on the horizon line for scale – this could be as simple as a tree on a hill.

 

  1. Invest in a lens with optimal zoom  

On the 27th July, one of the key challenges we’ll face is shooting the moon large in the frame so we can see every crater on the asteroid pockmarked surface. It’s a task normally reserved for astronomers with super powerful telescopes, but if you’ve got a long telephoto lens on a full frame DSLR with around 600 mm of focal length, it can be done, depending on the composition. I will be using the Canon EOS 5D Mark IV with an EF 200-400mm f/4L IS USM Ext. 1.4 x lens.

  1. Use a tripod to capture the intimate details

As you frame up your shot, one thing will become immediately apparent; lunar tracking is incredibly challenging as the moon moves through the sky surprisingly quickly. As you’ll be using a long lens for this shoot, it’s important to invest in a sturdy tripod to help capture the best possible image. Although it will be tempting to take the shot by hand, it’s important to remember that your subject is over 384,000km away from you and even with a high shutter speed, the slightest of movements will become exaggerated.

  1. Integrate the moon into your landscape

Whilst images of the moon large in the frame can be beautifully detailed, they are essentially astronomical in their appeal. Personally, I’m far more drawn to using the lunar allure as an element in my landscapes, or using the moonlight as a light source. The latter is difficult, as the amount of light the moon reflects is tiny, whilst the lunar surface is so bright by comparison. Up to now, night photography meant long, long exposures but with cameras such as the Canon EOS-1D X Mark II and the Canon EOS 5D Mark IV now capable of astonishing low light performance, a whole new nocturnal world of opportunities has been opened to photographers.

  1. Master the shutter speed for your subject 

The most evocative and genuine use of the moon in landscape portraits results from situations when the light on the moon balances with the twilight in the surrounding sky. Such images have a subtle appeal, mood and believability.  By definition, any scene incorporating a medium or wide-angle view is going to render the moon as a tiny pin prick of light, but its presence will still be felt. Our eyes naturally gravitate to it, however insignificant it may seem. Of course, the issue of shutter speed is always there; too slow an exposure and all we’ll see is an unsightly lunar streak, even with a wide-angle lens.

 

On a clear night, mastering the shutter speed of your camera is integral to capturing the moon – exposing at 1/250 sec @ f8 ISO 100 (depending on focal length) is what you’ll need to stop the motion from blurring and if you are to get the technique right, with the high quality of cameras such as the Canon EOS 5DS R, you might even be able to see the twelve cameras that were left up there by NASA in the 60’s!

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How Africa can embrace AI

Currently, no African country is among the top 10 countries expected to benefit most from AI and automation. But, the continent has the potential to catch up with the rest of world if we act fast, says ZOAIB HOOSEN, Microsoft Managing Director.

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To play catch up, we must take advantage of our best and most powerful resource – our human capital. According to a report by the World Economic Forum (WEF), more than 60 percent of the population in sub-Saharan Africa is under the age of 25.

These are the people who are poised to create a future where humans and AI can work together for the good of society. In fact, the most recent WEF Global Shapers survey found that almost 80 percent of youth believe technology like AI is creating jobs rather than destroying them.

Staying ahead of the trends to stay employed

AI developments are expected to impact existing jobs, as AI can replicate certain activities at greater speed and scale. In some areas, AI could learn faster than humans, if not yet as deeply.

According to Gartner, while AI will improve the productivity of many jobs and create millions more new positions, it could impact many others. The simpler and less creative the job, the earlier, a bot for example, could replace it.

It’s important to stay ahead of the trends and find opportunities to expand our knowledge and skills while learning how to work more closely and symbiotically with technology.

Another global study by Accenture, found that the adoption of AI will create several new job categories requiring important and yet surprising skills. These include trainers, who are tasked with teaching AI systems how to perform; explainers, who bridge the gap between technologist and business leader; and sustainers, who ensure that AI systems are operating as designed.

It’s clear that successfully integrating human intelligence with AI, so they co-exist in a two-way learning relationship, will become more critical than ever.

Combining STEM with the arts

Young people have a leg up on those already in the working world because they can easily develop the necessary skills for these new roles. It’s therefore essential that our education system constantly evolves to equip youth with the right skills and way of thinking to be successful in jobs that may not even exist yet.

As the division of tasks between man and machine changes, we must re-evaluate the type of knowledge and skills imparted to future generations.

For example, technical skills will be required to design and implement AI systems, but interpersonal skills, creativity and emotional intelligence will also become crucial in giving humans an advantage over machines.

“At one level, AI will require that even more people specialise in digital skills and data science. But skilling-up for an AI-powered world involves more than science, technology, engineering and math. As computers behave more like humans, the social sciences and humanities will become even more important. Languages, art, history, economics, ethics, philosophy, psychology and human development courses can teach critical, philosophical and ethics-based skills that will be instrumental in the development and management of AI solutions.” This is according to Microsoft president, Brad Smith, and EVP of AI and research, Harry Shum, who recently authored the book “The Future Computed”, which primarily deals with AI and its role in society.

Interestingly, institutions like Stanford University are already implementing this forward-thinking approach. The university offers a programme called CS+X, which integrates its computer science degree with humanities degrees, resulting in a Bachelor of Arts and Science qualification.

Revisiting laws and regulation

For this type of evolution to happen, the onus is on policy makers to revisit current laws and even bring in new regulations. Policy makers need to identify the groups most at risk of losing their jobs and create strategies to reintegrate them into the economy.

Simultaneously, though AI could be hugely beneficial in areas such as curbing poor access to healthcare and improving diagnoses for example, physicians may avoid using this technology for fear of malpractice. To avoid this, we need regulation that closes the gap between the pace of technological change and that of regulatory response. It will also become essential to develop a code of ethics for this new ecosystem.

Preparing for the future

With the recent convergence of a transformative set of technologies, economies are entering a period in which AI has the potential overcome physical limitations and open up new sources of value and growth.

To avoid missing out on this opportunity, policy makers and business leaders must prepare for, and work toward, a future with AI. We must do so not with the idea that AI is simply another productivity enhancer. Rather, we must see AI as the tool that can transform our thinking about how growth is created.

It comes down to a choice of our people and economies being part of the technological disruption, or being left behind.

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