Lately, artificial intelligence has been very much the hot topic in Silicon Valley and the broader tech scene. To those of us involved in that scene it feels like an incredible momentum is building around the topic, with all kinds of companies building A.I. into the core of their business. There has also been a rise in A.I.-related university courses which is seeing a wave of extremely bright new talent rolling into the employment market. But this is not a simple case of confirmation bias - interest in the topic has been on the rise since mid-2014, as the graph below of Google Search frequency for the terms "Artificial Intelligence" and "Machine Learning" (more on that shortly) show: The noise around the subject is only going to increase, and for the layman it is all very confusing. Depending on what you read, it's easy to believe that we're headed for an apocalyptic Skynet-style obliteration at the hands of cold, calculating supercomputers, or that we're all going to live forever as purely digital entities in some kind of cloud-based artificial world.

London, June 10 (IANS) Understanding the hierarchical structure of biological networks like human brain -- a network of neurons -- could be useful in creating more complex, intelligent computational brains in the fields of artificial intelligence and robotics, says a study. Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, and metabolic networks. This means they have separate units that can each be repeatedly divided into smaller and smaller subunits. To understand as to why biological networks evolve to be hierarchical, researchers from the University of Wyoming and the French Institute for Research in Computer Science and Automation (INRIA) simulated the evolution of computational brain models, known as artificial neural networks, both with and without a cost for network connections. They found that hierarchy evolves not because it produces more efficient networks, but instead because hierarchically wired networks have fewer connections.

Artificial intelligence is a rapidly evolving process on the verge of revising many industries, including healthcare. According to a report by Frost & Sullivan the AI market in healthcare will hit 6 billion by 2021, having been at 600 million, just two years ago. Cognitive solutions such as IBM's Watson system can assess enormous amounts of patient data, provide guidance and decision support, and improve clinical workflow. "The goal is to support the physician, not replace him or her" said Anil Jain, vice president of IBM's Watson Health and an internist and medical informatics specialist at the Cleveland Clinic. The IBM "Redefining Boundaries" study divulges that healthcare executives believe that this technology will require them to reassess most aspects of the business in the next few years.

"The future is here," or so stated Adrian Flux Insurance Services, an insurance broker out of Norfolk, England, that announced it will issue policies for self-driving cars. "Unlike every insurance policy that you've ever held in the past, driverless car insurance needs to cover you against a whole host of modern problems, not just your typical bumps and scrapes," the company stated in a news release. The U.K. allows the extensive testing of fully autonomous vehicles on public roads, as do the states of California, Florida, Michigan, Nevada, Texas and the District of Columbia in the U.S. New vehicles are increasingly coming with advanced driver assistance systems (ADAS), which can take control of the vehicle to ensure it stops before hitting an object or maintains speed and distance between other vehicles. Google's self-driving pod car has no steering wheel. California is considering regulations that would require a human driver behind every autonomous vehicle's steering wheel.

The robot, named Gaia, outside of a dorm on Harvard's campus. Would you let a stranger into your apartment building? Granting an unknown person access to a building was a humorous premise for a Seinfeld episode, but the decision to trust a stranger reveals insights into human psychology and touches on broader issues of trust in society. But what if, instead of a human, a robot knocked at your door? It's a question that Harvard University senior Serena Booth set out to answer with the help of a small, wheeled robot -- well, more like a roving nightstand -- that she stationed at the entrances to several dorms on campus.

Data Science at the Command Line is a new book written by Jeroen Janssens. This website contains information about the webcast from August 20th, instructions on how to install the Data Science Toolbox, and an overview of all the command-line tools discussed in the book. This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. Discover why the command line is an agile, scalable, and extensible technology.

When Apple launched Siri more than four years ago, it felt revolutionary. If you didn't see it then, go back and watch the video. Listen to the cheering, almost disbelieving reaction from the audience, and notice then-Apple exec Scott Forstall's giggling amazement that this voice control thing actually works. Apple's virtual assistant was the first of its kind. But have you used Siri recently?

Machine learning software is set to transform the way IT professionals manage their infrastructures in large Australian organisations, by seeking out potential problems before they affect any single user, Bede Hackney, ANZ managing director at Nimble Storage, says. Machine learning replaces traditional IT systems that require constant monitoring of each component, which means technicians don't have to waste time working out where the fault is and forming a solution. According to Hackney, the'app-data gap' is the challenge IT management faces when gaps between application and data stores become a problem because of the many differing IT infrastructure components. "A major app-data gap can often disrupt data delivery, degrade worker productivity, create customer dissatisfaction and damage a company's overall speed of business. However, it can be difficult to quickly find a solution because the factors leading to application slowdowns can come from a range of issues across the infrastructure stack", Hackney says.

Understanding the hierarchical structure of biological networks like human brain -- a network of neurons -- could be useful in creating more complex, intelligent computational brains in the fields of artificial intelligence and robotics, says a study. Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, and metabolic networks. This means they have separate units that can each be repeatedly divided into smaller and smaller subunits. To understand as to why biological networks evolve to be hierarchical, researchers from the University of Wyoming and the French Institute for Research in Computer Science and Automation (INRIA) simulated the evolution of computational brain models, known as artificial neural networks, both with and without a cost for network connections. They found that hierarchy evolves not because it produces more efficient networks, but instead because hierarchically wired networks have fewer connections.

This course will help you understand all of this and more. More than that, this course will enable you to create models for real-world predictive analytics problems like the ones you discussed above. At the end of the course, you will form teams and will compete against the best data scientists in the world in a Kaggle competition.