Swinburne University of Technology and the University of Melbourne are partnering with CA Technologies for a three year project to advance service virtualisation. Backed too by an Australian Research Council grant, the researchers will seek to find a method of using machine learning to automatically derive a service virtualisation model. "By using machine learning, development teams can write software without needing all the other systems within their environment. This will ultimately increase software development speed and reliability," CA explained in a statement. They will also look at ways of modelling a whole network of services, which takes into account their interdependencies.

"This challenge will pose a representative question to be answered by respondents using a completely automated system to sift through text reports and generate a finished intelligence product," the release stated on Tuesday. The purse consists of a top prize of $100,000 and multiple prizes of $30,000 for submissions from teams of high school students, the release explained. Entrants with the highest ranked submissions will be required to submit their source codes, the release noted.

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

This is the second part in a series where we analyze thousands of articles from tech news sites in order to get insights and trends about startups. So, if a sample mentions an IoT pacemaker startup, it should get the IoT tag in addition to the Health tag. Tagging the data was a similar process to the previous classifier, except that this time we took special care in tagging every sample with all the relevant categories. At this point, we are ready to repeat the same experiment we did in the previous post: classifying 100 articles and seeing what happens.

Backed by purpose-built HPC systems designed for maximum performance, HPE announces a comprehensive GPU-based solutions portfolio tailored to a new frontier of AI and deep learning capabilities. Explosive data growth and a rising demand for real-time analytics are making high performance computing (HPC) technologies increasingly vital to success. Organizations across all industries are seeking the next generation of IT solutions to facilitate scientific research, enhance national security, ensure economic stability, and empower innovation to face the challenges of today and tomorrow. HPC solutions are key to quickly answering some of the world's most daunting questions. From Tesla's self-driving car to quantum computing, artificial intelligence (AI) is enabling unparalleled compute capabilities and outmatching humans at many cognitive tasks.

To whet your appetite further, we suggest you check out what a classical machine learning problem looks like in TensorFlow. In the land of neural networks the most "classic" classical problem is the MNIST handwritten digit classification. We offer two introductions here, one for machine learning newbies, and one for pros. If you've already trained dozens of MNIST models in other software packages, please take the red pill. If you've never even heard of MNIST, definitely take the blue pill.

In a thrilling science talk, Kenneth Cukier looks at what's next for machine learning -- and human knowledge. Kenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper's technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages. Kenneth Cukier is the Data Editor of The Economist.

The man who saved the world from the hack that took down the NHS didn't pass his IT GCSE. Marcus Hutchins, who accidentally discovered a kill switch that helped shut down the WannaCry virus as it spread around the world, doesn't have the most basic IT qualification. And it's all because his teachers thought he was a hacker. The accidental hero's problems began when he was hauled into the head teacher's office at school and told to explain why the network was down. He couldn't and so was blamed for having hacked into the network – something that despite his claims not to have done anything, led to him being suspended.

About this course: Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity.

Right now, Machine Learning and Data Science are two hot topics, the subject of many courses being offered at universities today. Above, you can watch a playlist of 18 lectures from a course called Learning From Data: A Machine Learning Course, taught by Caltech's Feynman Prize-winning professor Yaser Abu-Mostafa. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. Learning From Data will be permanently added to our list of Free Online Computer Science Courses, part of our ever-growing collection, 1200 Free Online Courses from Top Universities.