Many important problems can be represented and studied using graphs -- social networks, interacting bacterias, brain network modules, hierarchical image clustering and many more. If we accept graphs as a basic means of structuring and analyzing data about the world, we shouldn't be surprised to see them being widely used in Machine Learning as a powerful tool that can enable intuitive properties and power a lot of useful features. Graph-based machine learning is destined to become a resilient piece of logic, transcending a lot of other techniques. This post explores the tendencies of nodes in a graph to spontaneously form clusters of internally dense linkage (hereby termed "community"); a remarkable and almost universal property of biological networks. This is particularly interesting knowing that a lot of information can be extrapolated from a node's neighbor (e.g. So how can we extract this kind of information?

Ritika Gunnar is vice president of offering management, data and analytics at IBM. She has also served as a software engineer and as vice president for information integration and governance in IBM's platform analytics group. In this exclusive interview with SearchCloudApplications, she discusses the evolution of the data science industry and the skills that developers must possess to flourish in a data-driven world. Bringing development and IT ops together can help you address many app deployment challenges. Our expert guide highlights the benefits of a DevOps approach. Explore how you can successfully integrate your teams to improve collaboration, streamline testing, and more.

Peter Drucker was prescient about most things, but the computer wasn't one of them. "The computer ... is a moron," the management guru asserted in a McKinsey Quarterly article in 1967, calling the devices that now power our economy and our daily lives "the dumbest tool we have ever had." Drucker was hardly alone in underestimating the unfathomable pace of change in digital technologies and artificial intelligence (AI). AI builds on the computational power of vast neural networks sifting through massive digital data sets or "big data" to achieve outcomes analogous, often superior, to those produced by human learning and decision-making. Careers as varied as advertising, financial services, medicine, journalism, agriculture, national defense, environmental sciences, and the creative arts are being transformed by AI.

In recent weeks we've seen incredible action in the intelligent assistant market. Google announcing the Google Assistant and associated devices to take on Amazon's Alexa, Microsoft at Ignite 2016 touting a new and improved Cortana, Salesforce launching Einstein, and Viv -- a start-up by the developers of Siri -- bought by Samsung. These AI-driven enhancements are becoming ubiquitous -- from customer service to marketing, from the home to the car, and from the factory to the community. They all have one thing in common -- they use predictions to deliver results which help you. Predictions are the result of predictive analysis, which, like data science, is red hot in the minds of executives and CMO's.

All right, don't panic, but computers have created their own secret language and are probably talking about us right now. Well, that's kind of an oversimplification, and the last part is just plain untrue. But there is a fascinating and existentially challenging development that Google's AI researchers recently happened across. You may remember that back in September, Google announced that its Neural Machine Translation system had gone live. It uses deep learning to produce better, more natural translations between languages. Following on this success, GNMT's creators were curious about something.

Machine learning isn't confined to science fiction movie plots anymore; it's fueled the proliferation of technologies that touch our everyday lives, including voice recognition with Siri or Alexa, Facebook auto-tagging photos and recommendations from Amazon and Spotify. And many enterprises are eager to leverage machine learning algorithms to increase the efficiency of their network. In fact, some are already using it to enhance their threat detection and optimize wide area networks. As with any technology, machine learning could wreak havoc on a network if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat to avoid setting back their operations and turning the c-suite away from implementing this technology.

Usually aimed at the layman; your kids can understand them. A few ones are listed in the picture below. To view and access all the results, click on the Infographics link. Usually aimed at the layman; your kids can understand them. A few ones are listed in the picture below.

The world of today runs on oil – heating and cooling our homes, and powering almost all forms of transportation. Try and drive a car today without oil-based products, and you won't get far. Without oil, a car engine overheats, pistons and rings fuse to the cylinder walls, the engine block cracks and, of course, there is no gas. You could say oil is the key technology that allowed the automotive world we know today. But that's all about to change.

Let's punch through the noise around machine learning. The tech ecosystem is well acquainted with buzzwords. From "Web 2.0" to "cloud computing" to "mobile first" to "on-demand," it seems as though each passing year heralds the advent and popularization of new catchphrases to which fledgling companies attach themselves. But while the trends these phrases represent are real, and category-defining companies will inevitably give weight to newly coined buzzwords, so too will derivative startups seek to take advantage of concepts that remain ill-defined by experts and little-understood by everyone else. "It's clear that 9 of 10 investors have very little idea what AI is so if you're a founder raising money, you should sprinkle some AI into your pitch deck. Use of'artificial intelligence,' 'AI,' 'chatbot,' or'bot' are winners right now and might get you a little valuation bump or get the process to move quicker. If you want to drive home that you're all about that AI, use terms like machine learning, neural networks, image recognition, deep learning, and NLP. Then sit back and watch the funding roll in."

Artificial intelligence is the yin and blockchains are the yang of digital business. Artificial intelligence is the yin and blockchains are the yang of digital business. While AI helps us assess, understand, recognize and decide, blockchains can help us verify, execute and record. While the machine learning methods that are a part of AI help us find opportunity and improve decision making, smart contracts and blockchains can automate verification of the transactional parts of the process. AI and blockchains in that sense are complementary and synergistic.