The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Silicon Valley, or the Greater Bay Area, is the 18th largest economy in the world, more than half the size of Canada's economy and bigger than Switzerland, Saudi Arabia or Turkey. This is because the region has become the world leader in research and development of emerging technologies such as artificial intelligence, robotics, software and virtual reality. "Software is eating the world," said Silicon Valley investor Marc Andreessen famously in 2011. It was controversial but prescient. Five years later, software-driven machines and drones perform surgery, write news stories, compose music, translate, analyze, wage war, guard, listen, speak and entertain.

A consequence of this decision is that Keras has its own graph datastructure for handling computational graphs, rather than relying on the native graph datastructure from TensorFlow or Theano. As a result, Keras can do offline shape inference in Theano (a much needed yet missing feature in Theano), and can do easy model sharing or model copying. For instance, when you call a Keras model on a new input ( y model(x)), Keras is reapplying all operations contained in the graph underlying your model, which is made possible by the fact that Keras manages that graph independently of TensorFlow/Theano. In fact, it's even possible to: 1) define a Keras model with the Theano backend, 2) switch to the TensorFlow backend, and 3) re-apply your (Theano-built) Keras model on a TensorFlow input, this creating a TF version of what was initially a Theano model (note that in practice we don't allow to switch backends in the middle of a session, since that would be quite unsafe --the user may mix up TF and Theano tensors-- but it's possible to do it manually if you are familiar with Keras internals).

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

BOSTON, Aug. 3, 2016 – AnDevCon – Hewlett Packard Enterprise (NYSE: HPE) today announced the availability of HPE Mobile Center 2.0, an integrated software development testing suite designed to help organizations rapidly build, test, and deliver high-quality mobile applications. HPE Mobile Center leverages HPE Haven OnDemand, HPE Fortify on Demand, and open source testing tools including Selenium and Appium, allowing developers to test any app operation and cover any user scenario. To significantly scale the number of devices that are tested, HPE Mobile Center is now integrated with Amazon Web Services Device Farm (AWS Device Farm) – a service that helps mobile app developers quickly and securely test their apps on smartphones, tablets, and other devices to improve the quality of their Android and iOS apps. HPE is expanding Mobile Center capabilities to provide users with a predictable and repeatable mechanism for testing, monitoring, and improving any mobile app. The integration with HPE Haven OnDemand and HPE Fortify on Demand provides development teams with powerful capabilities that leverage machine learning from testing data and user feedback to help guide the application development direction, with security built in from the beginning.

What comes first: the chicken or the egg? People have asked that question for ages, but nobody seems to have the answer. Which entity created the second one? How does one help the other to evolve? How do they work separately, or together? And what happens if they finally combine into one platform?

From July 20th to July 28th 2016, I had the opportunity of attending the 6th Lisbon Machine Learning School. The Lisbon Machine Learning School (LxMLS) is an annual event that brings together researchers and graduate students in the fields of NLP and Computational Linguistics, computer scientists with an interest in statistics and ML, and industry practitioners with a desire for a more in-depth understanding. Participants had a chance to join workshops and labs, where they got hands-on experience with building and exploring state-of-the-art deep learning models, as well as to attend talks and speeches by prominent deep learning and NLP researchers from a variety of academic and industrial organisations. You can find the entire programme here. In this blog post, I am going to share some of the highlights, key insights and takeaways of the summer school.

I want to create a deep neural network who's first layer maps words to vector embeddings. Essentially: Y Wx b where Y is the corresponding word embedding and x is the one-hot encoded vector for a word. However, I found feeding such a large x as a placeholder for each word full of zero makes the model extremely slow, so I was wondering if there are any alternatives aside from running word2vec in C and then just feeding the already embedded word vectors in.

Apple's Siri, Microsoft Cortana, Amazon's Echo, Google's speech recognition tech is high on trends these days. Ever wondered how these software get to know what we are saying? Well Thanks to technology of Natural Language Processing due to which language miscommunication gap has been decayed. Humans are being at the topmost position on the food-chain because of their intelligence. Everything humanity has developed in the past 10,000 years is the result of a discovery.

Check out AI Trends new report on Intelligent Bots and AI World's Intelligent Assistant Workshop, Nov 7, San Fran Starting in the 1980s, technology companies like Apple, Microsoft, and many others presented computer users with the graphical user interface as a means to make technology more user-friendly. The average consumer wasn't going to learn binary code to use a computer, so the great minds at these leading technology companies slapped a screen on technology and offered an interface that provided icons, buttons, toolbars, and other graphical elements so that the computer could be easily consumed by a mass market. Today it's hard to even imagine technological devices without a screen and a graphical presentation -- until now.