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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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Win a Poster Heater with Gadget and Takealot.com

This winter Gadget and Takealot.com are giving away three Poster Heaters, which look like posters but become heaters when you plug them in.

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Three Gadget readers will each win a unit, valued at R550 each. To enter, follow @GadgetZA and @Takealot on Twitter and tell us on the @GadgetZA account how many Watts the heater consumes.

What’s the big deal about these heaters? Many of us are struggling to keep the balance between soaring electricity costs and the need to keep warm this winter.

However, the recently launched Poster Heater by EasyHeat and distributed in South Africa by Takealot.com is not only one of the most cost effective electric heaters currently on the market, it is also easy to setup and use.

As the name indicates, it is a poster similar to one you would hang on a wall. But, plug it in and it turns into a 300 Watt heater. The Poster Heater isn’t designed to heat hallways or large rooms, but rather smaller ones like a bedroom or a baby’s nursery or a dressing room.

It uses radiant heating, which means that it heats up in a couple of minutes and the heat is directed at the objects or people around it, quickly taking the chill out of the air and providing a comfortable ambient temperature.

The other advantage of radiant heating is that it doesn’t dry out the air like infrared or gas heaters. Users also don’t have to worry about their children or pets getting too close to it because, even though it gets hot, it can be touched.

To enter the competition follow the steps below:

Competition entry details:

1. Follow @GadgetZA and @Takealot on Twitter. (We will ONLY be accepting entires via Twitter, so please don’t enter through the comments section of this article.)

2. Tell us on Twitter, via @GadgetZA, mentioning @Takealot in your posting, how many Watts the Poster Heater consumes.

cleardot.gif3. The competition closes on 31 July 2018.

4. Winners will be notified via Twitter on 1 August and Takealot.com will be in touch to organise delivery.

5. The competition is only open to South African residents.

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Arts and Entertainment

Deezer to host Hotstix’s Mandela tribute playlist

Deezer is celebrating Nelson Mandela on the centenary of his birthday by hosting a tribute playlist created by music legend Sipho “Hotstix” Mabuse.  

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Mabuse, a legendary figure in African music, first rose to prominence in the 1970s with his band Harari and later developed a name for himself as a solo artist. One of his best known songs was the global hit BurnOut in the 1980s.

The playlist takes the listener on a captivating musical journey through the life of Nelson Mandela.  It was compiled by Mabuse, who consulted with Mandela’s family and friends to ensure that the music would be relevant and accurate. The playlist also features commentary by Mabuse, which was recorded in his Soweto home.  

“I have tried to tell the story of the music that Madiba loved,” says Mabuse. “The Playlist excludes the time in prison obviously, as Madiba would not have had exposure to music in that time.  We have focused on the music we know he loved before and after that period. This recording was really an emotional journey for me, but an incredible opportunity to document these memories.”

The playlist features the music the young Mandela loved, such as The Manhattan Brothers, Solomon Linda, Brenda Fassie and Miriam Makeba.  It includes struggle songs from Chicco, Johnny Clegg, Hugh Masekela and Yvonne Chaka Chaka.  The playlist also includes Mandela by Zahara, one of the younger artists who caught Madiba’s ear.

Mabuse also offers stories of his own songs, such as Shikisha, a song greatly beloved by the former President.

“I was delighted to share my thoughts and hope the listeners enjoyed the musical journey,” says Mabuse. “Madiba did enjoy music immensely and we all have a purpose wherever we are in the world to celebrate culture and to learn from different cultures and music forms and styles.”

This playlist was inspired by the Nelson Mandela 100 campaign, calling on corporates and individuals to act as sources of inspiration and engage in conversation and action.

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