In this post, we are going to introduce you to the Support Vector Machine (SVM) machine learning algorithm. We will follow a similar process to our recent post Naive Bayes for Dummies; A Simple Explanation by keeping it short and not overly-technical. The aim is to give those of you who are new to machine learning a basic understanding of the key concepts of this algorithm. A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems and as such, this is what we will focus on in this post.

Portland, Ore.: Addressing customer requests for a single tool for one-to-one marketing, Lytics launches Lytics Personalization, a web-based product that allows a non-technical marketer to quickly and easily tailor a website to individual visitors based on their preferences and interests. Lytics Personalization is powered by the Lytics Customer Data Platform, which is the industry's most sophisticated means for applying machine learning and data science to the act of building consumer profiles. With Lytics Personalization, a non-technical marketer can craft – in minutes – a message that greets website visitors in individualized ways. For example, a new visitor may receive a message offering a newsletter sign-up, while a returning visitor with affinity for the brand across channels may see suggested content (e.g., a blog post, white paper, or announcement) based on their preferences. And sometimes personalization is about suppressing marketing rather than increasing it: Imagine a visitor with an open customer-support ticket who is "about to churn" receiving an offer for assistance rather than a promotion.

The next BriefingsDirect Voice of the Customer big-data case study discussion focuses on the Tower of Babel problem for disparate data, and explores how Alation manages multiple data types by employing machine learning and crowdsourcing. We'll explore how Alation makes data more actionable via such innovative means as combining human experts and technology systems. To learn more about how enterprises and small companies alike can access more data for better analytics, please join Stephanie McReynolds, Vice-President of Marketing at Alation in Redwood City, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions. Gardner: I've heard of crowdsourcing for many things, and machine learning is more-and-more prominent with big-data activities, but I haven't necessarily seen them together.

Although the laws are fictional, they have become extremely influential among roboticists trying to program robots to act ethically in the human world. Now, Google has come along with its own set of, if not laws, then guidelines on how robots should act. In a new paper called "Concrete Problems in AI Safety," Google Brain--Google's deep learning AI division--lays out five problems that need to be solved if robots are going to be a day-to-day help to mankind, and gives suggestions on how to solve them. And it does so all through the lens of an imaginary cleaning robot. Let's say, in the course of his robotic duties, your cleaning robot is tasked with moving a box from one side of the room to another.

I've been thinking about "big ideas" recently and how powerful they can be. What strikes me is that so many of these ideas are so easily stated, such as "Cure Cancer" or "Put a man on the moon," but are really difficult to accomplish. At Google, the company began with a goal "to organize the world's information and make it universally accessible and useful." As simply as this goal can be stated and, in some sense, understood, it also is a driver for an endless array of initiatives, intermediate goals, blind alleys, successes, and failures. One can adhere to this goal while motivating the development of machine translation, indexing and rank ordering searches of the World Wide Web, evolving massive datacenters and high capacity fiber networks and collaborative applications that allow concurrent editing of documents, presentations, and spreadsheets, to mention just a few examples.

Humans have been afraid of the dangers posed by AI and hypothetical robots or androids since the terms first entered common parlance. Much early science fiction, including stories by Isaac Asimov and more than a few plots of classic Star Trek episodes dealt with the unanticipated consequences humans might encounter if they created sentient AI. It's a fear that's been played out in both the Terminator and Matrix franchises, and echoed by luminaries like Elon Musk. Now, Google has released its own early research into minimizing the potential danger of human/robot interaction, as well as calling for an initial set of guidelines designed to govern AI and make it less likely that a problem will occur in the first place.

Facebook today is publishing an academic paper and a blog post detailing Torchnet, a new piece of open-source software that's designed to streamline deep learning, a type of artificial intelligence. Deep learning is a trendy approach that involves training artificial neural networks on lots of data, like photos, and then getting the neural networks to make predictions about new data. Rather than build a completely new deep learning framework, of which there are many, Facebook chose to build on top of Torch, an open-source library to which Facebook has previously contributed. "It makes it really easy to, for instance, completely hide the costs for I/O [input/output], which is something that a lot of people need if you want to train a practical large-scale deep learning system," Laurens van der Maaten, a research scientist in Facebook's Artificial Intelligence Research (FAIR) lab, told VentureBeat in an interview. Torchnet, which is written in Lua and can run on standard x86 chips or graphics processing units (GPUs), also lets programmers reuse certain code, which means doing less work and lowering the chances of introducing bugs, said van der Maaten.

Musk's warnings should be heeded. OpenAI is his less dramatic way of confronting this threat. By making everything "open," humanity has a greater opportunity to control the growth and direction of AI. But NPQ readers might want to ask if anyone other than tech leaders should be making these decisions. Silicon Valley is at the center of innovation, but ethics and many other concerns are forced to the forefront with the advent of AI. Should there be government or United Nations oversight?

Customer behaviour and the development of new technology are inextricably linked, and no technology is poised to impact our daily lives more than artificial intelligence (AI). AI is not a single technology but a convergence of statistical models, algorithms and approaches that make software'smart', whereby it starts to mimic human thinking processes. AI is already present in our digital lives, powering everything from dynamic product pricing on Amazon to song recommendations on Spotify – but we are still only at the dawn of witnessing its power. Perhaps the most impactful uses of AI will be in eCommerce – an area of immense growth in its own right. We are starting to see applications of AI via cognitive technology, a simulation of human thought processes, to respond to natural human language.

It is a sophisticated instrument that intertwines ultra rapid computing with innovative material science and power electronics to optimize and balance electrical energy flow in real time in any electrical environment. It ensures that electricity is distributed with perfect stability and consumed with ideal efficiency. The SineSync is the Battery Management System that leverages this incredible technology to create a true digital microgrid in a box. Perfectly optimized electrical energy flow delivers higher quality and more stable power with the lowest possible power consumption. The entire residential electrical network is protected with renewable lightning protection.