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The robots are coming!

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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Cybercrooks eye smart buildings

In countries like the United States, the growth of smart buildings is estimated to reach 16.6% by 2020 compared to 2014, although this expansion is not limited to the US but rather is taking place on a global scale.  This growth is largely due to the fact we live in a world increasingly permeated by technology, in which process automation and the search for energy efficiency contribute not only to sustainability, but also to cost reduction – a goal pursued in all industries, public and private alike. Naturally, the construction industry is no exception, says Carey van Vlaanderen, CEO at ESET South Africa.

Smart buildings use technology to control a wide range of variables within their respective environments with the aim of providing more comfort and contributing to the health and productivity of the people inside them. To do so, they use so-called Building Automation Systems (BAS).  With the arrival of the Internet of Things (IoT), smart buildings have redefined themselves. With the information they obtain from smart sensors, their technological equipment is used to analyse, predict, diagnose and maintain the various environments within them, as well as to automate processes and monitor numerous operational variables in real time. Ambient temperature, lighting, security cameras, elevators, parking and water management are just some of the automatable services currently supported by the technology.

To put the possibilities of this smart infrastructure into perspective, is the example of a smart building in Las Vegas where, two years ago, they decided to install a sophisticated automation system to control the use of the air conditioning (keeping in mind Las Vegas has a hot desert climate and very little rain), so it is turned on only when there are people present. This decision led to a saving of US$2 million during the first year after the smart system was installed, due to the reduction in energy consumption achieved by automating the process. Marriott Hotels implemented a similar system across the entire chain that is expected to generate an estimated US$9.9 million in energy savings.

Another example of automation through smart devices is that of a supermarket in the United Kingdom. The store installed a smart system in its parking lot that generates a kinetic energy from the movement of cars passing through it, and then uses that energy to power the checkouts.

At first glance, we may not see any security risk in these smart buildings.  It is likely, however, that at some point the entire smart network is connected to a single database, and that is where the risk is. Particularly if we consider that many IoT devices are manufactured by different suppliers, who may not have paid due attention to security considerations during their design and manufacturing process.

Possibility of a smart building being attacked

The risk of a security incident taking place in an intelligent building is linked to the motivations of cybercriminals, who mainly seek to achieve economic gain through their actions, as well as to impact and spread fear.

There are already some tools such as Shodan that allow anybody to discover vulnerable and/or unsecured IoT devices connected publicly to the internet. If you run a search using the tool, you can find thousands of building automation systems in its lists, complete with information that could be used by an attacker to compromise a device. In February 2019, around 35,000 building automation systems worldwide appeared in Shodan within public reach via the internet.

This means that someone could take control of a BAS after finding it through a search.  If, for example, a criminal used Shodan for building automation systems to attack, they will find IP addresses. If they copy those IP addresses into the address bar of a web browser, in many cases this will bring up an interface for gaining access, where they need to enter a username and password. If the password is a default password of if it can be cracked easily through a brute force attack, the attacker will gain access to the system monitoring panel, which contains information similar to the companies located in the smart building.

Once the attackers have access to this public information and can monitor, for example, how the air conditioning works, they could make a phone call pretending to be from the maintenance company and say they are going to send a technician. At the same time, the attackers could request remote access, which would give them access to the server and allow them to control the building. Once they have control, they could alter the building’s heating or air conditioning or adjust the way any of the other automated systems operate and then demand payment of a ransom in using a system that allow them to remain anonymous, such as cryptocurrency, in exchange for not shutting the building down.

Siegeware: a very real threat

Cybercriminals are already carrying out such attacks when they have the opportunity. This kind of attack is siegeware, or “the code-enabled ability to make a credible extortion demand based on digitally impaired building functionality”

In conclusion, the low cost of IoT devices for buildings and the advances of technology for building automation systems is leading to changes with an impact on security. This drive toward automation and the use of smart devices to gather data – in order to give a building’s users more comfort and to make more efficient use of resources such as energy – is also leading to increased security risks. As a result, the possibility of a cybercriminal launching a ransomware attack on asmart building is already a reality.

Considerations to keep in mind

There are a number of security considerations and requirements to keep in mind:

  • Review the devices’ security specifications and work on the basis of the ‘security by design’ concept
  • Set a suitable budget for security
  • Choose partners that have knowledge of security issues
  • Install software for managing vulnerabilities
  • Ensure cooperation between the different areas and/or departments

For operational issues:

  • Update the devices regularly
  • Implement a replacement plan for when devices’ support life cycles end
  • Exercise a precaution in respect of connected devices
  • Monitor connected devices

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How we can break out of the productivity/technology trap

The tyre industry is a microcosm of the dilemma in which South African manufacturers find themselves, writes JACQUES RIKHOTSO, MD at Bridgestone

Many of South Africa’s industries have been built on the back of abundant cheap labour. Mining is the obvious example, but the manufacturing sector has also been shaped by thefact of cheap labour. For many years, cheap labour was arguably a huge advantage, enabling us to become a world-leading mining country and also to create significant agricultural and manufacturing capabilities. But, in the end, it has had the unintended consequence stifling investment in equipment and masking a skills deficit that will be very hard to overcome.

To understand the dynamics, it’s as well to begin by reminding ourselves that productivity is, at the crudest level, the relationship between output and input. Humans are still the most important input contributors, and so labour costs are a significant factor in the productivityequation.

In South Africa and other developing economies, labour costs are low whereas in thedeveloped world, they are high. South African manufacturers (and miners and farmers) have thus typically used more people to produce the same amount of units than a European or American manufacturer would do, while still managing to compete on price and often on quality. However, the much more expensive labour costs in the developed world, while causing short-term pain, have always meant that the business case for investing in the latest technology to make those expensive humans even more productive has always been strong.

By contrast, the business case for investing in up-to-date equipment has been weak in South Africa. If more output was required, more people was typically a cheaper answer than better equipment. We have therefore remained a fairly labour-intensive market, which is good given our unemployment issues, but raises two specific and daunting challenges:

We need to make major investments in equipment. In my industry, I would venture to say we are 15-20 years behind developed countries when it comes to the deployment ofequipment. This was not too much of a problem for a long while because the old equipment was still cost-effective and could turn out the products needed at the right quality and price. However, tyre technology has now moved on to such an extent that the old machines simply are not capable of producing the new generation of products. Radiallised Agricultural/Underground Mining Sector Tyres and light weighted tyres for electric cars, for example, represent significant advances in tyre design. Current machinery cannot be adapted to produce either them; a substantial investment in new equipment will be necessary.

Another factor is that the industry dynamics have changed over the past few years. Theadvent of cheap, mass-produced tyres from the Far East means that in many instances, fleet owners are not retreading existing tyres but rather purchasing these cheap ones new. To compete, local tyre manufacturers need to move upwards on the Technology Cost Curve by investing in technology is less electricity-intensive, deploys minimum labour and requires maintenance in order to compete with high-volume producers.

The other consequence of competing with lower cost producers is the need to write down older retreading capacity and invest in more modern equipment.

Because our investment in equipment has been so low for so long, we are not talking about incremental investment but something much more significant in many areas at once.

This massive new wave of investment will not be restricted to manufacturing equipment. High-tech data-driven modern equipment associated with the Fourth Industrial Revolution will also require factory layouts to be revamped in order to accommodate new IT infrastructure and robotic capacity, as is already being used in the developed world.

This is essential if we are able to compete in the longer term.

We need to make major investments in skills, both at the corporate and national levels. Investments in new technology will create a need for a new generation of skilled operators. The new machines require totally different skills—hard-won dexterity with gears and levers is making way for skills on touchscreens, the ability to type and, crucially, to read and action screen-based instructions quickly. Sadly, many of the cadre of experienced operators will not be able to reskill and companies will need to give serious thought to their future.

However, in Bridgestone’s experience, the younger generation of operators often has thepotential for reskilling on modern machines, and we are already busy with that process.

Being part of a global group is a massive advantage, because our regions are all at different stages of industrial development, and some have undertaken a similar journey into the modern era. Our Japanese factories, in particular are industry leaders in tyre manufacture. We can therefore rely on previous experience and, most important of all, cansend key employees to acquire the necessary training and experience at one of our sister facilities. Such a person can then be used as a champion within the company, to train colleagues and promote new ways of working. In our experience, such an approach does work, but it takes time and effort.

South Africa’s status as a manufacturing country has been in the balance for some years thanks to our lack of investment in new technology, but there is no doubt that a strong manufacturing sector is critical in rebuilding in the economy. To re-ignite our manufacturing, we have to escape the technology/ production trap.

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