In January 2016, Fan Hui, three-time champion of the east Asian board game Go, lost to a computer program developed by Google's subsidiary DeepMind. The game is far more difficult to master than chess, so it was a historic victory for artificial intelligence (AI) which has seen huge advances in recent years. But could AI one day perform more creative tasks? A small tech firm in the US is proving that even journalists aren't safe from the robot revolution with a product that automates the writing process. Wordsmith – an artificial writer developed by the North Carolina-based company Automated Insights – cherrypicks elements from a dataset and uses them to structure a "human sounding" article. As well as being able to use more emotive language, it varies diction and syntax to make its work more readable.

"All of this is woven into a multi-layered story based on the human struggle for power, which can only be experienced through the interactive medium of video games," developer Bulkhead Interactive's Howard Philpott writes. If you can't wait until the end of next month to play but will be in Cologne, Germany for Gamescom, good news, because the game will be on the show floor.

Japanese video game developer Square Enix announced Thursday that it's working on a role-playing game exclusively for the Apple Watch. The report comes after the video game publisher, famous for its iconic franchises "Final Fantasy" and "Dragon Quest," pulled installments of its "Chaos Rings" series from the App Store. Square Enix also reportedly filed a trademark for a game called "Cosmos Rings" in Europe. The company has now announced that the new game will be called "Cosmos Rings." The game is scheduled for a summer release and will be among one of the first role-playing games specifically designed for the Apple Watch.

This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. The first part in this series provided an overview over the field of deep learning, covering fundamental and core concepts. The third part of the series covers sequence learning topics such as recurrent neural networks and LSTM. I wrote this series in a glossary style so it can also be used as a reference for deep learning concepts. The earliest deep-learning-like algorithms that had multiple layers of non-linear features can be traced back to Ivakhnenko and Lapa in 1965 (Figure 1), who used thin but deep models with polynomial activation functions which they analyzed with statistical methods. In each layer, they selected the best features through statistical methods and forwarded them to the next layer. They did not use backpropagation to train their network end-to-end but used layer-by-layer least squares fitting where previous layers were independently fitted from later layers.

Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.

Thanks to Michal Malohlava, Amy Wang, and Avni Wadhwa of H20.ai for providing the following guest post about building ML apps using Sparkling Water and Apache Spark on CDH. The Sparkling Water project is nearing its one-year anniversary, which means Michal Malohlava, our main contributor, has been very busy for the better part of this past year. The Sparkling Water project combines H2O machine-learning algorithms with the execution power of Apache Spark. This means that the project is heavily dependent on two of the fastest growing machine-learning open source projects out there. With every major release of Spark or H2O there are API changes and, less frequently, major data structure changes that affect Sparkling Water.

What if your phone or computer became aware of itself? Would it be helpful, or would it want to dispense with you? Our obsession with artificial intelligence is becoming apparent in popular film. Here are the best 15 movies that explore the idea of smart systems becoming conscious. The concept appears as early as 1968 with the HAL computer system in 2001: A Space Odyssey and moves though last year s Ex Machina, with both movie revealing a scary view of a machine that wakes up.

BENGALURU: Mumbai-based Arya.ai offers its deep learning algorithms for developers to build intelligent AI systems that can adapt and do multiple things with minimal inputs from humans. From creating a diagnostic assistant for radiologists to a mathematical assistant for science academicians and on to drone image processing abilities, the uses appear to be really diverse. "We have already launched the advanced AI software tools in a closed group beta phase with developers internationally and researchers in select software companies, these developers are using these softwares for building robots that can assist professionals from different fields in their task," said Vinay Sankarapu, cofounder of Arya.ai (in picture). API for developers can be used for four specific categories. From creating custom APIs to use cases within computer vision, this could range from classifying or searching for products on e-commerce platforms by using visual inputs to security based face matching techniques, as well as language and reasoning, where event prediction can take place.

In December 1964, over a single evening session in Englewood Cliffs, New Jersey, John Coltrane and his quartet recorded the entirety of A Love Supreme. This jazz album is considered Coltrane's masterpiece – the culmination of his spiritual awakening – and sold a million copies. What it represents is all too human: a climb out of addiction, a devotional quest, a paean to God. Five decades later and 50 miles downstate, over 12 hours this April and fuelled by Monster energy drinks in a spare bedroom in Princeton, New Jersey, Ji-Sung Kim wrote an algorithm to teach a computer to teach itself to play jazz. Kim, a 20-year-old Princeton sophomore, was in a rush – he had a quiz the next morning.

Talking toys have come a long way since the original Furby. Now they're connected to the Internet, use speech recognition, and are raising a host of new questions about the online privacy and security of children. Hackers have already targeted toys. Late last year, Hong Kong-based digital toy maker Vtech admitted that cybercriminals accessed the personal information of 6.4 million children. Researchers have also shown how hackers can gain control of connected dolls.