Thanks to its deep learning toolkit, Microsoft is making huge strides in computer-based speech recognition. Just this September, a Microsoft research team achieved an error rate of 6.3 percent on the Switchboard speech recognition benchmark, meaning the software interpreted just 6.3 percent of all words it "heard" incorrectly. The researchers used a recurrent neural network architecture, called long short term memory. Less than a month later, training on a 30,000-word library, they were able to get that down to the 5.9 percent -- about the same percentage of incorrect words that professional transcribers made on the same phone call recordings. It was the very first time that a computer has been able to recognize the words in a conversation as well as people can.

Jürgen Schmidhuber is painting an image of the future of our Universe. And it's plain to see, we are neither a real part of it, nor is it our Universe at all. "In 2050 there will be trillions of self-replicating robot factories on the asteroid belt," he tells the audience at WIRED2016. Humans are not going to play a big role there, but that's ok. We should be proud of being part of a grand process that transcends humankind more than the industrial revolution.

Technology predictions for next year are already filtering in, with artificial intelligence and full-blown analytics and other platforms increasingly seen as among the keys to opening the big data floodgates in 2017. Market watcher Forrester forecasts that AI spending will triple next year as companies "convert customer data into personalized experiences." Application drivers include advanced analytics, machine learning and cognitive computing platforms. The AI spending prediction is bolstered by recent investments in emerging machine and deep learning technology developers. For example, U.K. AI chip developer Graphcore emerged from stealth mode this week with a $30 million funding round aimed at advancing its "intelligent processing unit" (IPU) strategy that combines advanced parallel processing with software tools and libraries. The startup's IPU card plugs into PCI buses to boost the performance of x86 servers.

This year, the Association for Computing Machinery (ACM) celebrates 50 years of the ACM Turing Award, the most prestigious technical award in the computing industry. The Turing Award, generally regarded as the'Nobel Prize of computing', is an annual prize awarded to "an individual selected for contributions of a technical nature made to the computing community". In celebration of the 50 year milestone, renowned computer scientist Melanie Mitchell spoke to CBR's Ellie Burns about artificial intelligence (AI) – the biggest breakthroughs, hurdles and myths surrounding the technology. EB: What are the most important examples of Artificial Intelligence in mainstream society today? MM: There are many important examples of AI in the mainstream; some very visible, others blended in so well with other methods that the AI part is nearly invisible.

Many have written about how deep learning is taking over the world and why that is important; I cannot echo them enough. Playing with deep learning is the closest I've ever felt to being a magician, and it's become clear to me that every (great) piece of software will be powered by deep learning within the next 3 years. However, deep learning isn't mainstream yet, so I thought I'd share work by some very talented contributors, in the hopes to bring it just that little bit closer. Here's ten reasons why I think deep learning is living up to the hype…

Scientists have created a "Nightmare Machine" - an artificial intelligence system that can understand what makes certain images frightening, and tranform harmless-looking images into stuff of nightmares. The primary reason for building Nightmare Machine was to explore the common fear inspired by intelligent computers, said researchers including Pinar Yanardag from Massachusetts Institute of Technology (MIT) Media Lab. They wanted to confront the anxiety inspired by AI, and simultaneously test if a computer is capable of understanding and visualising what makes people afraid. The designers used "deep learning" - a system that mimics the neural connections in a human brain - to teach a computer what makes for a frightening visual, according to Manuel Cebrian, a principal research scientist at CSIRO in Australia. "Deep-learning algorithms perform remarkably well in several tasks considered difficult or impossible," Cebrian said.

Jason Brownlee's Machine Learning Mastery materials are very good. He sells a bundle of material on topic such as Machine Learning, R, Python, Weka, and so on, which are cheaper if you buy the whole bundle. If he publishes something that is not part of the bundle, you get it a discount. At least that is what happened with me. Based on all the materials I have looked at over the last year, his materials are probably the best place to start along with DSC, of course.Thank you.

In UK Channel 4 documentaries series "Faking it", Paul O'Hare, a painter and decorator from Liverpool, was given just four weeks to transform himself into a fine artist and attempt to fool the critics at a London art gallery. We are going to show how to do it in less than half an hour and with a little help of Docker, AWS and Deep Learning, including the time you need to read this entry. In order to speed up your transformation, we are going to rely on an artificial intelligence system. This AI system is based on a Deep Neural Network that creates artistic images indistinguishable (we think) from the works of an artist. By combining the content of one image -- a portrait or a landscape photography -- with the style of another image -- typically, the works of a recognized artist -- .

Episode Summary: In one of our most recent consensus, we took a close look at future trends in artificial intelligence consumer applications, but it's also interesting to see what's happening now in businesses. Chris Nicholson is the CEO of Skymind.io, which offers deep learning applications that integrate with Hadoop and Spark. In this episode, Nicholson sheds light on current trends that he sees across industries and best practices for implementing AI solutions to gain consistent return on investment. Brief Recognition: Chris Nicholson leads Skymind, the commercial support arm of the open-source framework Deeplearning4j. Skymind helps companies in telecommunications, finance, retail and tech build enterprise deep learning applications, notably fraud detection, using data such as text, time series, sound and images.

Artificial Intelligence (AI) has always been the subject of dreams and visions about the distant future of humankind. Even though we are nowhere near a conscious robotic system, nowadays, AI systems are ubiquitous and showing tremendous successes in various fields of our everyday life. We are using these on a daily basis, often without even noticing. Whether it is the Virtual Personal Assistants on our mobile phones (such as Siri, Google Now, and Cortana), self-driving cars, the ranking of the web pages given your search query, or the classical textbook examples such as spam filtering and recommendation systems of online media providers and marketplaces like Amazon. Various fields of AI have made a major leap forward in the recent years. As most AI systems are too complex to be defined manually, we have to resort to automatically learning rules and patterns from data using sophisticated Machine Learning (ML) techniques.