Machine learning is going to fundamentally alter our world for the better. We can confidently say that because machine learning is making great strides in improving sectors such as healthcare and manufacturing and assisting in the prediction of supply and demand levels across a plethora of industries.
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 the 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 analytics to discover hidden insights without being explicitly programmed where to look.
Applications of machine learning in healthcare are evident as it can process more information and spot more patterns when compared to humans, by several orders of magnitude. In customer-facing businesses, it is also enabling marketing personalisation. The more a company understands about its customers, the better it can serve them.
Perhaps the most obvious use of machine learning is its use in online search engines. The engine uses this technology to observe how an individual responds to search results. Over time, the search engine becomes more adept and ensures delivery of better results in the future.
One of the most exciting applications of machine learning can be seen in the various types of smart cars now being developed. A recent IBM survey of top auto executives saw some 74% of them stating that they expected smart cars to hit the roads by 2025.
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.
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 should not be limited to only ‘understanding’ the rules of driving, but they should also be able to monitor the movements and signals of other cars and infrastructure and make split-second decisions.
It should be obvious then that driverless cars will require an immense amount of data gathering and analysis. These cars will need to connect to cloud-based traffic and navigation services. Information sources can range from technology 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 now 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 Internet of Things (IoT) will become a crucial platform. It will be IoT that enables 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 answer is yes! 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), also the licensed Sigfox operator for SA.
SqwidNet 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.
There is no doubt that we are on the cusp of a technological revolution. In light of this, the launch of operators such as SqwidNet are welcomed enablers as the IoT and machine learning look set to fundamentally alter the way our world works.