Here are upcoming meetings and conferences, for July 2016 and beyond. Save 10% with the KDNUGGETS registration code. Aug 29 - Sep 1, Image Processing, Computer Vision and Machine Learning based on Optimization and PDE. Use code CDOINSUR to save 10% on registration. Sep 23, MLconf Atlanta Machine Learning Conference - mention "KDNuggets" and save 18%.

Apps that help you translate different languages can be extraordinary helpful, but sometimes you want to understand a foreign language immediately. In a decade, that could very well be a reality thanks to advancements in artificial intelligence, Greg Corrado, the co-founder of Google's deep learning project dubbed Google Brain, said in a roundtable discussion with journalists Thursday. In 10 years, it will be possible to have a meeting where everyone speaks a different language, but can understand one another by wearing special earbuds, he said. "That is totally science fiction today, but it's the kind of thing I'd take the 10-year bet on," he said. To make that technology a reality, there will need to be advancements in a type of artificial intelligence called machine learning, or training computers to learn on their own.

In 1973, the statistician Francis Anscombe devised a fascinating demonstration showing why data should always be plotted before it is analyzed. The demonstration consisted of four data sets that had almost identical statistical properties. By this measure they are essentially the same. But when plotted, the data sets look entirely different. Anscombe's quartet, as it has become known, shows how good graphics allow people to analyze data in a different way, to think and talk about it on another level. Most scientists recognize the importance of good graphics for communicating complex ideas.

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.