Now if these are not dominant steps toward a world of AI I don't know what is. Front this technology with a robot body and it becomes an intelligent being. They can park themselves, drive themselves, break automatically and navigate based on coordinates. Once they are able to learn and map out routes as well as incorporate traffic reports and weather disturbances to get you quickly and safely from A to B without risk…bingo. Google Assistant, Amazon Echo, Apple Siri and the other personal virtual assistants are bringing all daily rituals, habits and requests together via one central point.

Finnish technology firm Nokia has rolled out major updates in its "Motive Customer eXperience Solutions" (CXS) software portfolio with advanced machine-learning capabilities to reduce costs and improve customer experiences. Developed by Nokia Bell labs, Nokia Motive Service Management Platform (SMP) 7.0 and Motive Care Analytics (CAL) 2.0 use machine-learning algorithms -- advanced capabilities that give computers the ability to learn without being explicitly programmed, the company said in a statement. "By providing the earliest possible detection of network issues and streamlining help desk and self-care interactions, these new Nokia solutions reduce IT and care costs and result in more loyal customers," said Bhaskar Gorti, President of Applications an Analytics at Nokia. It enables service providers to quickly find the optimal remediation to issues when subscribers contact help desk agents or use self-care. "Nokia Motive CAL 2.0 automatically correlates customer help desk calls and self-care actions with network, service and third-party application topologies to identify call anomalies," the company said in a statement.

TechCrunch Disrupt in London is on December 5-6. As well as speakers and panels, we'll be featuring some demos by some amazing tech companies. The first will be by Boston Dynamics. Yes, folks, delegates to Disrupt London will get to see one of those amazing BD robots up close and personal, almost literally in the flesh (if they had any flesh, that is). Marc Raibert, CTO and Founder of Boston Dynamics will be demonstrating one of the amazing robots his team has created, but you'll have to come to find out which one… Raibert was Professor of Electrical Engineering and Computer Science at MIT and a member of the Artificial Intelligence Laboratory from 1986 through 1995.

Academic scientists devote their lives to research, often toiling away on problems that few people outside their discipline fully understand. Perhaps some are driven by pure curiosity or competition, while others have a personal interest in the topic at hand. For Shirley Pepke, a genomics researcher based in Los Angeles, the urgency to find answers comes from her own instinct for survival. Since 2014, she has been working on a tool capable of tailoring ovarian cancer treatment to each patient using genomics data and a machine learning algorithm. The first subject in this DIY precision medicine project was Pepke herself, who was diagnosed with stage IIIC ovarian cancer in September 2013.

This is a long article written by Sebastian Raschka. Sebastian Raschka is the author of the bestselling book "Python Machine Learning." As a Ph.D. candidate at Michigan State University, he is developing new computational methods in the field of computational biology. Sebastian has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning.

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.