What is artificial intelligence and how is it being used? What benefits is it likely to bring for society and for government? What is artificial intelligence and how is it being used? What benefits is it likely to bring for society and for government?

This story is part of a series inspired by the subjects and speakers appearing at BBC Future's World-Changing Ideas Summit in Sydney on 15 November. Find out more about the inspiring people coming to the meeting, including: Researcher Alex Gillespie on what artificial intelligence means for us Researcher Helen Christensen on how tech can spot and treat mental health issues Alan Finkel, Australia's chief scientist, on the future of energy BBC TV presenter Michael Mosley on the science of food and health Uber's Kevin Corti on the hidden patterns of city transport Researcher and TV presenter Emma Johnston on the impact of cities on oceans Experimental architect Rachel Armstrong on interstellar travel If you believe some AI-watchers, we are racing towards the Singularity – a point at which artificial intelligence outstrips our own and machines go on to improve themselves at an exponential rate. If that happens – and it's a big if – what will become of us? In the last few years, several high-profile voices, from Stephen Hawking to Elon Musk and Bill Gates have warned that we should be more concerned about possible dangerous outcomes of supersmart AI. And they've put their money where their mouth is: Musk is among several billionaire backers of OpenAI, an orgnisation dedicated to developing AI that will benefit humanity.

Last week I attended IBM (IBM) World of Watson as both a speaker and an attendee, and today as I sit in my neighborhood Starbucks (SBUX) thinking about everything, all I can say is WOW! This was one of the most interesting, inspiring and amazing events I have ever attended. And we are still in the very early stages of Watson, Cognitive and AI. I invite you to follow me as I learn more and write more about the wonderful world of Watson, all the companies that work with it and how it will change our industries, our businesses and our lives. As a wireless analyst and columnist, I come at this world of Watson from the wireless, telecom, internet and television angle.

It will come as no surprise to anyone familiar with the technology world that the rate of cyber attacks, the development of malware, and the exploitation of zero-day flaws makes is very difficult for IT teams and security specialists to keep up with let alone get ahead of cyber threats. Research from Symantec noted that nearly one million new malware threats emerge daily, and while there are many tools to make detecting rogue code an easier process, dealing with such an enormous amount of new threats appears to be an almost insurmountable task even for the best security teams and anti-virus systems. The answer to this, and the potential future of cyber security, looks to be the use of machine learning and artificial intelligence (AI) to apply clever computers and smart software to a problem that leaves humans on the back foot in the fight against hackers. Rather than sift through data harvested from across IT networks, machine learning algorithms can be trained to detect certain malware and threat signatures and proactively sniff out threats, bypassing the need for cyber security experts to disappear into a warren of file paths and scripts to find tell-tale signs of malware. Webroot is one such cyber security company applying machine learning techniques to power its threat intelligence service without requiring resource sapping and time-consuming manual processes.

Humans have been storing, retrieving, manipulating, and communicating information since the Sumerians in Mesopotamia developed writing in 3000 BCE. Since then, we have continuously developed more and more sophisticated means to communicate and push information. Whether unconsciously or consciously, we seem to always need more data, faster than ever. And with every technological breakthrough that comes along, we also have a set of new concepts that reshape our world. We can think back, for example, to Gutenberg's printing press.

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Click here to register and download your free 30-day trial of Intel Parallel Studio XE. We live in a world where humans rely more and more on computers to solve a variety of engineering problems―ranging from weather prediction to the discovery of lifesaving drugs. We are on the verge of another dramatic change where machines are capable of reaching and even exceeding humans in their ability to make decisions and solve complex problems. Computers have already beaten the best human players in Jeopardy* and Go*, and autonomous cars drive on the roads of California. This is all possible due to petaflop levels of compute power (thanks to Moore's Law) and the vast amounts of data available for training machine learning algorithms. At Intel, we work in close collaboration with our leading academic and industry fellow travelers to solve the hardware and software architectural challenges for Intel's upcoming multicore/manycore compute platforms. To help innovators tackle the complexities of machine learning, we are making performance optimizations available to developers through familiar Intel software tools, specifically through the Intel Data Analytics Acceleration Library (Intel DAAL) and enhancements to the Intel Math Kernel Library (Intel MKL).

Spending on cognitive systems and artificial intelligence (AI) across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. In its Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide IDC said the market for cognitive/AI solutions will experience a compound annual growth rate (CAGR) of 55.1 per cent over the 2016-2020 forecast period. According to David Schubmehl, research director, Cognitive Systems and Content Analytics at IDC, "Software developers and end user organizations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process" "Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for organizations to be planning and undertaking strategies that incorporate these wide-ranging technologies," he said. Schubmehl said identifying, understanding, and acting on the use cases, technologies, and growth opportunities for cognitive/AI systems will be a differentiating factor for most enterprises and the digital disruption caused by these technologies will be significant. The ability to recognize and respond to data flows using algorithms and rule-based logic enables cognitive/AI systems to automate a broad range of functions across many industries.

Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver the main talk of the Spark & Machine Learning Meetup in Brussels, "Creating an end-to-end Recommender System with Apache Spark and Elasticsearch."

The recent explosion of interest in data science, data mining, and related disciplines has been mirrored by an explosion in book titles on these same topics. One of the best ways to decide which books could be useful for your career is to look at which books others are reading. This post details the 10 most popular titles in Amazon's Data Mining Books category as of Nov 10, 2016, skipping over repeated titles as well as titles which have been obviously miscategorized and are of no use to our readers. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.