Artificial intelligence (AI) is not new, but it is revolutionizing the world. Paired with emerging technologies, the applications for AI currently appear to be endless. From accelerating the pace of life saving drugs to streamline operations for cost-savings and revenue amplification, AI platforms are omnipresent, and their impact is inescapable. IBM terms it the "innovation equation," and explains that AI became the world's fastest-growing tech tool for one reason: necessity. The digital age ushered in previously unthinkable quantities of data.

Artificial intelligence (AI) is not new, but it is revolutionizing the world. Paired with emerging technologies, the applications for AI currently appear to be endless. From accelerating the pace of life saving drugs to streamline operations for cost-savings and revenue amplification, AI platforms are omnipresent, and their impact is inescapable. IBM terms it the "innovation equation," and explains that AI became the world's fastest-growing tech tool for one reason: necessity. The digital age ushered in previously unthinkable quantities of data.

Here's an attention-grabbing idea: Deploying cellular-enabled Industry 4.0 solutions can generate a 10-20x operational cost-savings ROI (return on investment) over the course of five years. This is according to a joint research study from ABI Research and Ericsson. The research also suggests Industry 4.0 solutions can generate up to 8.5% in operational cost savings, which, for a factory or industrial site, can equate to an operational cost savings of up to $600 per square meter per year. Industry 4.0, also known as the fourth industrial revolution, is the idea that connectivity, automation technologies, and digitization are creating the fourth major revolution in the business of manufacturing. Thanks to trends like leveraging the IoT (Internet of Things), including wireless networking and sensors to collect machine data and enable predictive maintenance, as well as 3D printing, robots and cobots on the factory floor, machine learning and AI (artificial intelligence), 5G, and digital twins, among other trends, the Industry 4.0 market is projected by MarketsandMarkets to reach almost $157 billion by 2024. A big part of Industry 4.0 is the use of AI technologies to enable smarter machines that can take on tasks like self-monitoring and diagnosis autonomously.

At the core of today's state-of-the-art Artificial Intelligence (AI) algorithms is the ability to learn patterns from a sample of data. In the manufacturing context, an example of such a pattern might be the ways in which a set of parameters contained in that data, which are related to a process in a factory, vary together. When considering using AI, it is important to understand what the data requirements are. The general answer as to what constitutes the "right" data for AI-enabled process optimisation, is the set of data that is sufficient to describe how changes to a process's parameters affect quality. The bulk of process data can generally be represented as a table, or a collection of tables, comprising columns (parameters) and rows (production examples, representing, say, one production batch per row).

When the machine learning renaissance started in 2013 – thanks to the development of new graphic processing units accelerating data processing by over 100 times – Frans Cronje and Daniel Schwartzkopff started positioning themselves to fill the gap in the South African market. "Daniel and I met while doing our undergrad studies at the University of Cape Town and collectively came up with the idea to start an artificial intelligence (AI) company," says Frans Cronje, the managing director of DataProphet. "At the time there was hardly any competition in the field and skills were scarce, as you could not even study machine learning here." They initially offered machine learning consultancy services to retail, manufacturing and finance companies and from there catapulted into the products market when they started DataProphet in 2015. "We were relatively new to the game. Working as consultants helped us gain valuable experience and insights into the common challenges faced by these industries, which in turn allowed us to create service solutions for these challenges," says Cronje.

Digital All Stars is a series of articles which aims to celebrate the best of South African digital. The articles, which will appear on Memeburn and Ventureburn, recognise and celebrate South Africa's best digital entrepreneurs, business people, advertisers, and media professionals among others. In this piece we take a look at some interesting African startups involved in developing artificial intelligence (AI) solutions. South African startup DataProphet last year received a significant investment of an undisclosed amount from Yellowwoods Capital Holdings to expand its international offering. As part of the deal, DataProphet will act as the advanced analytics partner for the group.

There are few highlights for most tech startups as sought after as cracking the international market and for most of these startups none quite as tantalising as breaking into Silicon Valley. The home of some of the world's biggest tech companies including Apple Inc., Facebook and Google, Silicon Valley is the leading hub and startup ecosystem for high-tech innovation and development. According to a January 2016 report from the Martin Prosperity Institute titled Rise of the Global Startup City, the San Francisco Bay Area, which spans Silicon Valley and San Francisco, remains the world's leading centre for venture capital investment attracting nearly 11 billion, more than a quarter of all global venture investment. A growing number of South African startups have also sought to grab a piece of the Silicon Valley slice, however, this does take some work. If you have your sights set on entering the international playing field, the most difficult thing is getting your foot in the door, says Daniel Schwartzkopff, commercial director and co-founder of Cape Town-based startup and machine learning specialists, DataProphet, who also spends part of his time in the US working with DataProphet's Silicon Valley-based clientele.

Artificial intelligence (AI) is an emotionally loaded term that strikes fascination into some and fear into others. But if we strip it of fantasy and ignore cyborgs and apocalypse, there is a near-term, practical side of AI that is already unfolding. Most humans can recognise a chair because they have learnt what a chair is – they can identify thousands of examples of chairs even if they have never seen that chair before. Instead of memorising every image of what a chair could be, humans learn what a chair is and then apply that to new images and examples of chairs. But how does a computer learn what a chair is?

Artificial intelligence (AI) is an emotionally loaded term that strikes fascination into some and fear into others. But if we strip it of fantasy and ignore cyborgs and apocalypse, there is a near-term, practical side of AI that is already unfolding. Most humans can recognise a chair because they have learnt what a chair is – they can identify thousands of examples of chairs even if they have never seen that chair before. Instead of memorising every image of what a chair could be, humans learn what a chair is and then apply that to new images and examples of chairs. But how does a computer learn what a chair is?