When Tesla Motors CEO Elon Musk proclaims that artificial intelligence is "our biggest existential threat," it makes headlines worldwide. But what goes unreported is that the very search engines people used to find Musk's comments are themselves an example of how AI has subtly but forcefully become a part of everyday, real-world life. When it comes to a discussion of AI, it helps to have a sense of history--as well as a sense of humor. Thanks to premonitory proclamations by Musk, Microsoft's Bill Gates, Cambridge's Stephen Hawking and other prominent technologists, AI has become a popular topic again, after a 20-year cooling-off period. It's tempting to assume that the "dire warnings" about AI being a threat to mankind were mostly tongue-in-cheek, but the end result is that just as it happened in the 1980s and '90s, the hype over AI is again outpacing the reality (virtual and otherwise). The first question that needs to be answered though is: Whatever happened to AI and why did it go underground for so many years?

"By 2025, AI systems could be involved in everything from population health management to digital avatars capable of answering specific patient queries," says Harpreet Singh Buttar, an analyst at Frost & Sullivan. Around 15 percent of the total artificial intelligence deals in the first quarter of 2016 went to digital health startups focusing on AI applications. Notable among them was the 12.3 million investment that patient-monitoring AiCure raised in a Series A funding round. Another key deal was the 25 million investment that Babylon Health raised for its remote healthcare service app. Babylon's Series A funding round was led by Investment AB Kinnevik, with participation from DeepMind Technologies, Hoxton Ventures and other players.

Bottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production. Machine learning's core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

The result of the UK's recent referendum to leave the EU has raised question marks over the fate of the European Union. Many people are wondering whether the Brexit decision will trigger another recession, pave the way for Scottish independence, or begin the demise of the EU as a whole. With so much uncertainty surrounding the possible outcomes, Federico Pascual from MonkeyLearn published a machine learning analysis of the Brexit result. The analysis is based on what people are saying about Brexit in more than 450,000 tweets using the hashtag #Brexit on Twitter. They filtered out the non-English tweets, leaving around 250,000, then ran a MonkeyLearn analysis using ready-to-use machine learning models and sentiment analysis to identify whether the tone was positive, negative or neutral.

In this series, we're discussing a topic that will transform the world we live in over the course of the next 25 years. We're going to see lots of drones, self driving cars, VR, and AR devices changing how we get around, how we transport things, and how we see and interact with the world, and it will all be powered by AI and neural nets. In part 1, we talked a little bit about what neurons are and how they work, and wrapped it up by showing a trivial example of how to sum synapse inputs and determine whether or not the neuron should fire, and finished off the article by suggesting a question: What about time? From here on out I'll be recording these adventures in a library called neurolib. If you're at all familiar with traditional neural nets, you're probably wondering when I'm going to start talking about gradient descent or Hidden Markov Models (HMM).

The'cognitive era' has arrived says IBM CEO Ginni Rometty, who was in Sydney yesterday to demonstrate Big Blue's cognitive computing technology Watson. The era marked the convergence of'man and machine' according to Rometty, who predicted that within five years every business decision would be aided by cognitive systems. IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. Ten years in the making, it's now being used by a number of Australian businesses. KPMG Australia yesterday announced it would be introducing Watson to its audit and assurance services to "accelerate teams' ability to analyse and act" on the "immense volumes of structured and unstructured data related to a company's financial and non-financial information".

As Machine Learning tools become mainstream, and ever-growing choice of these is available to data scientists and analysts, the need to assess those best suited becomes challenging. In this study, 20 Machine Learning models were benchmarked for their accuracy and speed performance on a multi-core hardware, when applied to 2 multinomial datasets differing broadly in size and complexity. It was observed that BAG-CART, RF and BOOST-C50 top the list at more than 99% accuracy while NNET, PART, GBM, SVM and C45 exceeded 95% accuracy on the small Car Evaluation dataset. On the larger and more complex Nursery dataset, we observed BAG-CART, BOOST-C50, PART, SVM and RF exceeded 99% accuracy, while JRIP, NNET, H2O, C45, and KNN exceeded 95% accuracy. However, overwhelming dependencies on Speed (determined on an average of 5-runs) were observed on a multicore hardware, with only CART, MDA and GBM as contenders for the Car Evaluation dataset.

Josh Lewis is the VP of Industry Applications at Alpine Data. Josh has ten years of experience across academia and industry in machine learning, data analysis, cognitive science and user experience. Prior to joining Alpine, Josh lead the frontend engineering team at Ayasdi where he built apps and APIs for the healthcare, pharmaceutical and finance verticals, as well as Ayasdi's domain-general data analysis and visualization software. Before joining Ayasdi, Josh was a PhD student and postdoc at the UC San Diego Cognitive Science Department where he investigated the role of human perception and insight in the data analysis process. He also developed novel software for applying unsupervised machine learning algorithms called Divvy, a project that was supported by a multi-year NSF grant.

Miniaturization is one of the most world-shaking trends of the last several decades. Computer chips now have features measured in billionths of a meter. Sensors that once weighed kilograms fit inside your smartphone. Researchers are aiming to take sensors smaller--much smaller. In a new University of Stuttgart paper published in Nature Photonics, scientists describe tiny 3D printed lenses and show how they can take super sharp images.

Consumer interests have changed, and they will keep changing. Social media switched the balance of power away from company-controlled communication and into the consumer camp -- not just in-the-moment, but precisely at their moment, on their devices, on the channels they choose. But with the advent of messaging and the recent injection of A.I. into messaging platforms in the form of chatbots, it's more than just communication that is shifting -- it's the entire way people and businesses interact. While messaging platforms are the future of consumer-brand engagement, A.I. technology is in its infancy as it relates to truly engaging with humans. There is a long way to go before people are having open-ended conversations with chatbots that don't end in disappointment. Before I explain my point, here's some background on messaging apps.