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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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Earth 2050: memory chips for kids, telepathy for adults

An astonishing set of predictions for the next 30 years includes a major challenge to the privacy of our thoughts.

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By 2050, most kids may be fitted with the latest memory boosting implants, and adults will have replaced mobile devices with direct connectivity through brain implants, powered by thought.

These are some of the more dramatic forecasts in Earth 2050, an award-winning, interactive multimedia project that accumulates predictions about social and technological developments for the upcoming 30 years. The aim is to identify global challenges for humanity and possible ways of solving these challenges. The website was launched in 2017 to mark Kaspersky Lab’s 20th birthday. It comprises a rich variety of predictions and future scenarios, covering a wide range of topics.

Recently a number of new contributions have been added to the site. Among them Lord Martin Rees, the UK’s Astronomer Royal, Professor at Cambridge University and former President of the Royal Society; investor and entrepreneur Steven Hoffman, Peter Tatchell, human rights campaigner, along withDmitry Galov, security researcher and Alexey Malanov, malware analyst at Kaspersky Lab.

The new visions for 2050 consider, among other things:

  • The replacement of mobile devices with direct connectivity through brain implants, powered by thought – able to upload skills and knowledge in return – and the impact of this on individual consciousness and privacy of thought.
  • The ability to transform all life at the genetic level through gene editing.
  • The potential impact of mistakes made by advanced machine-learning systems/AI.
  • The demise of current political systems and the rise of ‘citizen governments’, where ordinary people are co-opted to approve legislation.
  • The end of the techno-industrial age as the world runs out of fossil fuels, leading to economic and environmental devastation.
  • The end of industrial-scale meat production, as most people become vegan and meat is cultured from biopsies taken from living, outdoor reared livestock.

The hypothetical prediction for 2050 from Dmitry Galov, security researcher at Kaspersky Lab is as follows: “By 2050, our knowledge of how the brain works, and our ability to enhance or repair it is so advanced that being able to remember everything and learn new things at an outrageous speed has become commonplace. Most kids are fitted with the latest memory boosting implants to support their learning and this makes education easier than it has ever been. 

“Brain damage as a result of head injury is easily repaired; memory loss is no longer a medical condition, and people suffering from mental illnesses, such as depression, are quickly cured.  The technologies that underpin this have existed in some form since the late 2010s. Memory implants are in fact a natural progression from the connected deep brain stimulation implants of 2018.

“But every technology has another side – a dark side. In 2050, the medical, social and economic impact of memory boosting implants are significant, but they are also vulnerable to exploitation and cyber-abuse. New threats that have appeared in the last decade include the mass manipulation of groups through implanted or erased memories of political events or conflicts, and even the creation of ‘human botnets’. 

“These botnets connect people’s brains into a network of agents controlled and operated by cybercriminals, without the knowledge of the victims themselves.  Repurposed cyberthreats from previous decades are targeting the memories of world leaders for cyber-espionage, as well as those of celebrities, ordinary people and businesses with the aim of memory theft, deletion of or ‘locking’ of memories (for example, in return for a ransom).  

“This landscape is only possible because, in the late 2010s when the technologies began to evolve, the potential future security vulnerabilities were not considered a priority, and the various players: healthcare, security, policy makers and more, didn’t come together to understand and address future risks.”

For more information and the full suite of inspirational and thought-provoking predictions, visit Earth 2050.

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How load-shedding is killing our cellphone signals

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Extensive load-shedding, combined with the theft of cell tower backup batteries and copper wire, is placing a massive strain on mobile network providers.

MTN says the majority of MTN’S sites have been equipped with battery backup systems to ensure there is enough power on site to run the system for several hours when local power goes out and the mains go down. 

“With power outages on the rise, these back-up systems become imperative to keeping South Africa connected and MTN has invested heavily in generators and backup batteries to maintain communication for customers, despite the lack of electrical power,” the operator said in a statement today.

However, according to Jacqui O’Sullivan, Executive: Corporate Affairs, at MTN SA, “The high frequency of the cycles of load shedding have meant batteries were unable to fully recharge. They generally have a capacity of six to 12 hours, depending on the site category, and require 12 to 18 hours to recharge.”

An additional challenge is that criminals and criminal syndicates are placing networks across the country at risk. Batteries, which can cost R28 000 per battery and upwards, are sought after on black markets – especially in neighbouring countries. 

“Although MTN has improved security and is making strides in limiting instances of theft and vandalism with the assistance of the police, the increase in power outages has made this issue even more pressing,” says O’Sullivan.

Ernest Paul, General Manager: Network Operations at SA’s leading network provider MTN, says the brazen theft of batteries is an industry-wide problem and will require a broader initiative driven by communities, the private sector, police and prosecutors to bring it to a halt.

“Apart from the cost of replacing the stolen batteries and upgrading the broken infrastructure, communities suffer as the network degrades without the back-up power. This is due to the fact that any coverage gaps need to be filled. The situation is even more dire with the rolling power cuts expected due to Eskom load shedding.”

Loss of services and network quality can range from a 2-5km radius to 15km on some sites and affect 5,000 to 20,000 people. On hub sites, network coverage to entire suburbs and regions can be lost.

Click here to read more about efforts to combat copper theft.

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