The purpose of this web site is to help you understand, run, and interpret logistic regression analyses as quickly and easily as possible. Many visitors find this web site because they realize that their data does not fit the assumptions of regular linear regression (least-squares regression). Instead they realize they need to use a method specifically designed for data where the Y-variable is binary (all explained below). Other visitors are users of logistic regression and are seeking answers to a specific question. But in both cases, this web site is here to help you.

This video is an introduction to Semusi's work and context aware systems. Semusi's work is at the intersection of big data, sensors and machine learning. We are building context aware mobile systems that will be aware of the context in which they operate and their behaviour will be based on the context derived from the soft and hard sensor inputs. For e.g. if we know the current context of a user is in-car we can give her location based ads that are in the radius of a mile or if we know the context is walking, we can give her location based ads that are in the range of a few hundred feet. In the smartphone era, Context is King!

As organizations increasingly adopt new technologies such as Cloud, Software Defined Infrastructures (SDI), Mobile and the Internet of Things (IoT) it creates enormous challenges for IT Operations. Legacy monitoring tools and services simply cannot scale to address the complex and dynamic outputs of these modern IT architectures. IT infrastructure, when enabled with self-serving, self-healing and preventive capabilities ensures that employees are freed from repetitive operational tasks. Not only can these capabilities reduce year-over-year IT costs, but also eradicate human errors.

In 2013, years before DeepMind would unveil an AI that would defeat of one of the world's best Go players, the company published an influential paper showing how "deep reinforcement learning" could be used to teach computers how to play Atari 2600 video games. While GPUs were originally designed as specialized processors optimized to render millions of pixels required for simulating 3D environments, repurposing GPUs to train artificial intelligence algorithms has been commonplace for a while. But it wasn't until I read Andrej Karpathy's recent post on reinforcement learning, however, that something clicked about how interesting this is: Graphics cards, originally designed for human vision of video games, are now being used for computer "vision" of video games. When I was growing up, getting a graphics card was kind of a Big Deal. The first one I got for Christmas in 1997 was a Pure3D Canopus Voodoo card based on the 3dfx chipset. It let me run Quake smoothly on my Pentium Compaq, which was a top priority of my life at the time.

When creating bots you have to converse with the users and maintain context of the conversation so you can understand the intent of the users, do the necessary actions and provide users with the information they need. Doing it all by yourself is a large undertaking as it involves doing Natural Language Process (NLP) and providing yourself with some easy mechanism to improve your conversation models. To solve this problem various services exist e.g. In this tutorial we will look into how to use Wit.ai to create conversation models and use them in Elixir. This can be used in your messaging bot, IoT device or any other project where you want to take some actions based on conversing with user.

FUTURE OF WORK: Basic Income: A Sellout of the American Dream David H. Freedman MIT Technology Review "What's not to like about free money? Then there's the question of whether such a program might disconnect large swaths of our population from the positive aspects of working for a living--a potentially toxic side effect. And finally, there's little convincing evidence that large-scale technological unemployment is actually happening or will happen in the immediate future." ARTIFICIAL INTELLIGENCE: AI Is Learning to Meme Daniel Oberhaus Motherboard "Since memes and emoji are increasingly a part of what we consider human language (an emoji was the Oxford Dictionary's word of the year in 2015, after all), it was only a matter of time before someone applied neural networks to emoji to teach machines how to use them...Dango isn't just scanning your message looking for the word'horse' and then recommending the horse emoji. Rather, it is also taking into account the meaning of your message and other relevant factors, such as the fact that the eggplant emoji has, well, other meanings besides'eggplant.'"

"Wand Labs' technology and talent will strengthen our position in the emerging era of conversational intelligence, where we bring together the power of human language with advanced machine intelligence -- connecting people to knowledge, information, services and other people in more relevant and natural ways," David Ku, corporate vice president of the Information Platform Group at Microsoft, said in statement. "It builds on and extends the power of the Bing, Microsoft Azure, Office 365 and Windows platforms to empower developers everywhere." "The acquisition of Wand Labs will support Microsoft's chatbots strategy, which the company refers to as "conversations as a platform," Raul Castanon-Martinez, senior Analyst, enterprise mobility, 451 Research told us today. "This is CEO Satya Nadella's first big initiative." Castanon-Martinez said although there are redundancies in the technology developed by Wand Labs and Microsoft's own assets, the acquisition will also provide Microsoft with a talented group of individuals with expertise in conversational interfaces. This will help Microsoft significantly expand the team of experts that are currently working on its conversation-as-a-platform strategy, he said. "This will be critical moving forward; Microsoft has ambitious goals to become the leader in this space [and] the industry is moving at a fast pace.

Machine learning is no longer just a tool for data scientists. By taking advantage of recent advances in this technology, UI and UX designers can find ways to better engage with and understand their users. This O'Reilly report not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs. Using tangible, real-world examples, author Patrick Hebron explains how machine-learning applications can affect the way you design websites, mobile applications, and other software. You'll learn how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction.

Scientists, including one of Indian-origin, have developed a new artificial intelligence software that can turn any smartphone into an eye-tracking device. Eye-tracking technology - which can determine where in a visual scene people are directing their gaze - has been widely used in psychological experiments and marketing research, but the required pricey hardware has kept it from finding consumer applications. In addition to making existing applications of eye-tracking technology more accessible, the system developed by researchers at Massachusetts Institute of Technology (MIT) and University of Georgia may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness. "Since few people have the external devices, there is no big incentive to develop applications for them," said Aditya Khosla, an MIT graduate student. "Since there are no applications, there's no incentive for people to buy the devices. We thought we should break this circle and try to make an eye tracker that works on a single mobile device, using just your front-facing camera," he said.