Recently popularized by IBM's highly intelligent Watson supercomputer, which competed on the hit game show Jeopardy, cognitive computing refers to machines that are capable of learning concepts and patterns through advanced language processing algorithms. A system that involves incredibly advanced artificial intelligence, cognitive computing is one facet of computer science that isn't for the faint of heart. Although much of the hype is centered on big business and big data processing, there are a number of consumer applications. Whereas business leaders might use the technology to increase their bottom line, streamline daily operations and achieve greater profitability, consumers can take advantage of computing to ease some of the burdens of everyday life. In fact, many consumers are using some form of it without realizing it.

The scenario has played out through innumerable iterations in popular culture, the most popular being The Terminator series. Steven Spielberg, riffing on the film Stanley Kubrick was going to direct before his death, presented the counterpoint, espousing a benevolent vision of AI in A.I. Then there are more nuanced, ambiguous iterations, like the recent Ex Machina. New advances in algorithmic artificial intelligence, deep learning software, automation, and nanotechnology have made it abundantly clear that Ray Kurzweil's vision of the Singularity may also be not an if, but when. In fact, responding to Kurzweil's prediction of a cloud-based neocortex in the 2030s, entrepreneur Bryan Johnson of Braintree said, "Oh, I think it will happen before that." Johnson's more recent aspirations involve merging artificial intelligence with humans, a pursuit many would argue is already occurring on a vast scale when it comes to our use of smartphone technology and search engines.

Applying machine learning in marketing automation is not rocket science. It has one and only one purpose: make marketers' life easier. It helps you focus only the most important data, performs the must-have, but repetitive todos. Therefore your scope of duties will move to a more strategic position as a marketer. I'm about to I show you 4 examples when machine learning in marketing automation could truly help you.

What is the Loebner Prize? The Loebner Prize for artificial intelligence ( AI) is the first formal instantiation of a Turing Test. The test is named after Alan Turing the brilliant British mathematician. Among his many accomplishments was basic research in computing science. In 1950, in the article Computing Machinery and Intelligence which appeared in the philosophy journal Mind, Alan Turing asked the question "Can a Machine Think?"

Over the last few months, I've been focused on product design for chatbots--both text and voice. As a UX designer with a background in graphic design, it's been refreshing to shift focus towards non-visual user experiences. It's reasonable to assume the same people who consume news on social media are the same people interested in chatting with your bot on Facebook. If you are still deciding between a social media chatbot or your own, and you want to target millennials, this could help you make the decision. The content that you push and the tone and language of your bot may be very different that what you have thought initially.

IBM Watson may be able to win "Jeopardy!" The IBM Watson for Cybersecurity beta program launched this week with 40 partners around the world in an effort to help security analysts make better, faster decisions from vast amounts of data, but experts say this is the same promise offered by many other products. IBM said Watson for Cybersecurity will feature natural language processing that can help it to "understand the unique language of security." "The truth is a lot of security vendors today are attaching '[artificial intelligence]' or'cognitive' to a number of products that are really just advanced analytics or machine learning, which are also important elements that can help in the fight against cybercrime," Diana Kelley, executive security advisor for IBM Security, told SearchSecurity. "What Watson will bring to the equation that is unique is the ability to digest vast amounts of both structured data as well as all of the intelligence that exists in natural language, like blogs, white papers and research reports. For example, there are around 10,000 security research papers published each year, and 60,000 security blog posts published every month."

Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks ranging from computer vision and speech recognition to natural language processing. However, deep learning algorithms are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, deep compression significantly reduces the computation and storage required by neural networks. For example, for a convolutional neural network with fully connected layers, such as Alexnet and VGGnet, it can reduce the model size by 35x-49x. Even for fully convolutional neural networks such as GoogleNet and SqueezeNet, deep compression can still reduce the model size by 10x.

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So, you want to learn how to build machine learning algorithms? But where do you start? Becoming a data scientist is a really smart career move – it's possibly one of the most valuable jobs out there. That's just one of the reasons it was hailed by the Harvard Business Review as the'sexiest job of the twentieth century' back in 2012. But learning the skills you need to become a truly great data scientist, capable of building powerful machine learning systems with languages like Python and R, isn't easy.

Moore's law, which says computing power doubles every two years, is getting harder to achieve on a single chip. Tech industry heavyweights including IBM, NVIDIA, Google and Mellanox are now working together through the OpenPOWER Foundation to enhance speed at the system level by engineering chips, interconnects, accelerators, memory and other components that work together seamlessly. This will help ensure fast and powerful AI systems that support breakthroughs on numerous key fronts. AI's evolution will continue to drive innovation that makes for smarter cities, improved healthcare, and other advances that will continue to improve our lives.