Supercomputers used to be a market unto themselves. In their time, giants such as IBM, Control Data Corporation (CDC), Evans & Sutherland (E&S), Silicon Graphics and Cray Computing ruled the supercomputing market. But with Moore's Law restricted to only scaling transistor count, scale-out has taken over a conference series that had epitomized scale-up. Because the nature of supercomputing has changed so much in the past two decades, the market has expanded into a much broader high-performance computing (HPC) market. At this year's SC16 conference, SGI made news by being purchased by Hewlett Packard Enterprise (HPE) and the Cray booth looked a bit forlorn.

SAP chief executive Bill McDermott is preparing the German software giant for the next big trend in the world of technology: artificial intelligence. The company is working hard to become the market leader in machine learning for businesses, its CEO told Handelsblatt. CEO Bill McDermott wants SAP to focus heavily on developing intelligent applications for business software and experts say there is enormous potential in artificial intelligence and machine learning. Under the leadership of its American chief executive, the German software group has acquired companies for €20 billion ($21.3 billion) since 2010 so as not to miss out on cloud computing. It's been a successful move as the company's quarterly figures show.

Keras is a great library for hands-on on neural networks, and it has a ton of great examples that makes it very easy to create ANNs & DNNs. So easy in fact, that you could even build one without knowing what's going on. I used the CNN model from this Keras blog post to create a simple sentiment analysis model. But to fully understand what I had just done, I had to dig a little deeper. The basic model outlined in the post is using pre-trained word embeddings of the text to train a CNN for sentiment analysis. I have shown it below, with a few minor changes to padding sizes (border_mode'same'), so that the convolution output size stays the same as its input (for simplicity).

Companies like Google, IBM and SAP are making massive investments in artificial intelligence. German software maker SAP is investing heavily in artificial intelligence, or AI, especially for business software. AI is expected to fundamentally change our lives in the near future. Cars that navigate autonomously through traffic. Digital assistants that provide doctors with key information needed to make a diagnosis.

Artificial Intelligence is quickly becoming more science fact than science fiction. And despite concerns about thinking machines taking human jobs -- or one day outsmarting their makers -- the federal government's investment in the technology is growing, and could be applied to a wide range of functions, from health care to public safety to criminal justice.

The headline story here is that for the first time a system has been developed that exceeds human performance in one of the most difficult of all human speech recognition tasks: natural conversations held over the telephone. This is known as conversational telephone speech, or CTS. The reference datasets for this task are the Switchboard and Fisher data collections from the 1990s and early 2000s. The apocryphal story here is that human performance on the task is about 4% error rate. But no-one can quite pin down where that 4% number comes from.

In a way, AI is about understanding, and then mimicking how we think, learn and process information. The science and applications of AI have evolved since the early years: 1950's 1980's 2010's Generation 1 (From 1950's): Rule Based Systems (No Learning) In the early days, most applications of AI were rule-based computer programs (commonly known as Expert Systems) designed to solve problems that human brains performed easily. Such AI programs required experts to develop rules and combine with programs to solve problems. It required a programmer to write a program to capture the knowledge of a subject matter expert. The program then asked a series of questions to a user (usually not an expert in that subject) and then based on the answers/input provided, the computer would suggest a "solution" to the problem.

The key to getting good at applied machine learning is practicing on lots of different datasets. This is because each problem is different, requiring subtly different data preparation and modeling methods. In this post, you will discover 10 top standard machine learning datasets that you can use for practice. Each dataset is summarized in a consistent way. This makes them easy to compare and navigate for you to practice a specific data preparation technique or modeling method. Each dataset is small enough to fit into memory and review in a spreadsheet.

Now you can use Chatbots to get news and information in a personalized pattern. Famous media companies like CNN and Fox news have already launched their Chatbots on platforms like Facebook Messenger, Line and Kik as well as on voice-activated devices like Amazon's Alexa. Facebook has unveiled new capabilities for businesses and publishers on Messenger, enabling users to chat with CNN to get breaking news and personalized stories. People will now be able to chat with the companies and publishers like they would do with their friends. CNN is using chatterbots for Facebook Messenger to interact with users in a natural and human-like way.

The high growth rate of the hardware market for deep learning is attributed to the growing need for hardware platforms with a high computing power to run deep learning algorithms. There is increasing competition among established as well as startup players, leading to new product developments including both hardware development and software platforms to run deep learning algorithms and programs. For instance, Graphcore (a U.K.-based company) is developing the intelligent processing unit (IPU) for machine learning technology for use in applications from driverless cars to cloud computing. Some of the companies involved in the development of hardware for the deep learning technique are Google, Inc. (U.S.), Microsoft Corporation (U.S.), Intel Corporation (U.S.), Qualcomm, Inc. (U.S.), IBM Corporation (U.S.), and others.