These use cases–and so many more–all owe a debt to machine learning. By automatically discovering patterns that lead to insights and creating predictive models that drive actions, the technology has proven its value many times, and to many industries. More recently, machine learning has begun to make a name for itself in the field of cybersecurity. There are multiple reasons that manual cybersecurity practices on their own are no longer sufficient to protect businesses, governments and other organizations. All of these issues are further aggravated by the fact that threats are growing in scale and complexity.

Pattern analytics, deep learning, and machine learning have fueled a rapid rise in interest in GPU computing, in addition to GPU computing applications in high performance computing (HPC) and cloud-based data analytics. As a high profile example, Facebook recently contributed its "Big Sur" design to the Open Compute Project (OCP), for use specifically in training neural networks and implementing artificial intelligence (AI) at scale. Facebook's announcement of Big Sur says "Big Sur was built with the Nvidia Tesla M40 in mind but is qualified to support a wide range of PCI-e cards," pointing out how pervasive Nvidia's Tesla platform has become for AI research. Big Sur is a 4U high chassis housing a two-processor (2P) board connected to a daughter card featuring eight full-height double-width PCIe Gen3 x16 300W accelerator card slots intended to house GPU or other PCIe-based compute accelerators. The processor board and daughter card are linked via one PCIe Gen3 x16 slot in the initial implementation.

R is a purpose-built language meant for statistical computing, and is a clear winner for large-scale data-mining, visualization and reporting. You have easy access to a huge collection of packages (through the CRAN repository) that enable you to apply almost all kinds of Machine Learning algorithms, statistical tests and analysis procedures. The language itself has an elegant--albeit esoteric--syntax for expressing relationships, transforming data and performing parallelized operations.

'In an interview with the Big Issue published last week, the notorious David Icke took a break from claiming shape-shifting lizards ruled the world to make an astonishing warning about brain implants. "What this has all been heading towards is'implantables', where the devices are inserted under your skin and start to affect human consciousness from a central grid," he suggested. "The people in charge have always been massively outnumbered and their greatest fear is the human race waking up, but once they're able to alter our thought processes from a central computer it's game over. Read more: Rich Americans seek black market brain implants in bid to plug into artificial intelligence'matrix'

So I have a dashboard with reports I can interact with to understand why something happens, and that typically reduces the numbers of manual steps before I can make a decision and take some actions. Whether it's IoT, big data or analytics, companies have a lot more data to base their decisions on, and data-driven decision making sounds obvious. More positively, Avanade's new study of smart technologies says business leaders globally expect to be using digital assistants and automated intelligence for problem solving, analysing data, collaborating and making decisions – and they also expect them to increase revenues by more than a third. Certainly, early adopters that Accenture has spoken to who are using machine learning to improve the way they manage customer service, financial resources and risk and compliance, in sales and marketing and in developing new areas of business found "significant, even exponential, business gains" in costs, revenue and customer performance, by using a mix of what Oberoi calls "perceptual intelligence" using natural language and voice biometrics, advanced analytics and business decision support .

Business intelligence has gone from static reports that tell you what happened, to interactive dashboards where you can drill into information to try and understand why it happened. New big data sources, including Internet of Things (IoT) devices, are pushing businesses from those reactive analytics – whether you look back once a month to spot trends or once a day to check for problems – to proactive analytics that give you alerts and real-time dashboards. That makes better use of operational data, which is more useful while it's still current, before conditions change. "There's a demand for real-time dashboards," says Herain Oberoi from Microsoft's Cortana Analytics team. "A lot of businesses want to get the pulse of their business. But dashboards show things that have already happened."

Cancer is responsible for one-in-three deaths in Canada, according to the Canadian Cancer Society. To patients who are diagnosed, early detection can mean the difference between life and death. A microscope using AI is being touted as a powerful new instrument in the diagnostic toolkit--one that manages to snap an astounding 36 million images per second to catch cancer cells and identify their characteristics. The microscope was designed by a team at UCLA's California NanoSystems Institute, who say it's a way to identify cancer cells in patients' blood samples faster and more accurately than current methods. In a new study published in the journal Nature Scientific Reports, they describe how, using a patented microscope outfitted with a camera, they're able to photograph cells without destroying them.

There is considerable overlap among these, but some distinctions can be made. Of necessity, I will have to over-simplify some things or give short-shrift to others, but I will do my best to give some sense of these areas. Firstly, Artificial Intelligence is fairly distinct from the rest. AI is the study of how to create intelligent agents. In practice, it is how to program a computer to behave and perform a task as an intelligent agent (say, a person) would.

Artificial Intelligence (AI) is making progress in great strides, or at least it appears so! Almost no week passes by without some major announcements of new challenges solved by AI technology or new products powered by AI. Indeed many quantifiable factors attest an unprecedented level of activity: capital investments, number of academic papers, number of products involving AI technology, they all are on a steep rise in the past five years. Computers are already very capable at some specialized tasks that require reasoning and other abilities that we typically associate with intelligence. For example, computers can play a decent game of chess or can help us order our holiday photos. Despite this genuine progress, we are still a long way from human level intelligence because our best artificial intelligence systems are not general purpose. They cannot quickly adapt to novel tasks the way most humans can do.

Only three main occupations were available to intrepid job seekers 10,000 years ago: hunting, gathering and procreation. Since then, the job market has advanced dramatically, developing into something not only more diverse, but also more abstract. This progress was the result of human evolution, but also human innovation -- as the human race evolved, the scope of its needs changed (and so did the methods by which these needs were satisfied). Throughout history, we've always found ways to make our basic survival require less of our human focus, and we've witnessed subsequent booms in new professions -- specifically, vocations that didn't relate directly to merely subsisting, but thriving. In reality, the invention of flight, the moon landing and the digital revolution we saw at the end of the 20th century would not have happened if so much of our workforce hadn't been free to explore the frontiers of human understanding.