If you sell your expertise for a living, you will discover significant benefits in transferring your knowledge to the world of Artificial Intelligence (AI). It is now possible to create an online chatbot with near human characteristics to deliver your intellectual property to the world. From your website your clients will be able to interact with the AI system to receive a personalised experience of the way you deliver your specialist skills. The Chatbot will be able to track the client's progress and adapt the learning experience to suit their individual mood, personality and abilities. Your unique talents will be instantly made available to a global audience 24 / 7. Up until August 2016 the software to build a chatbot has been in prototype with major corporations but now it's going mainstream.

To assist a child we must provide him with an environment which will enable him to develop freely. There's a kindergarten I walk past on the way to work, and I can't help but peek inside everyday. The classroom -- packed with toys and puzzles, music and books, flower planters and even an occasional cat -- was obviously crafted to be a rich and bustling world for kids to interact and play in. Contrary to its meaning, child's play is far from simple. Playing in a diverse, exciting universe is how we nurture a child's budding intelligence.

Now a days, most of the data is in textual form and we need some effective tools to process this unstructured and semi-structured data. Many search engines and advertisers are using machine learning algorithms for predicting customer behavior and content recommendations. There is a need to learn basics of large-data processing using predictive models with right tools. This course is designed to give you knowledge of machine learning tools and their applications in predictive analysis. You will learn to train, evaluate, and validate basic predictive models.

This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn't require a PhD, and you don't need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic. In this guide our goal will be to write a program that uses machine learning to predict, with a high degree of certainty, whether the images in data/untrained-samples are of dolphins or seahorses using only the images themselves, and without having seen them before. Here are two example images we'll use: To do that we're going to train and use a Convolutional Neural Network (CNN). We're going to approach this from the point of view of a practitioner vs. from first principles. There is so much excitement about AI right now, but much of what's being written feels like being taught to do tricks on your bike by a physics professor at a chalkboard instead of your friends in the park.

This is the second installment in a three-part review of 2016 in machine learning and deep learning. Part One, here, covered general trends. In Part Two, we review the year in open source machine learning and deep learning projects. Part Three will cover commercial machine learning and deep learning software and services. There are thousands of open source projects on the market today, and we cannot cover them all. We've selected the most relevant projects based on usage reported in surveys of data scientists, as well as development activity recorded in OpenHub. In this post, we limit the scope to projects with a non-profit governance structure, and those offered by commercial ventures that do not also provide licensed software. Part Three will include software vendors who offer open source "community" editions together with commercially licensed software.

Cray Inc. (Nasdaq: CRAY) announced the results of a deep learning collaboration between Cray, Microsoft, and the Swiss National Supercomputing Centre (CSCS) that expands the horizons of running deep learning algorithms at scale using the power of Cray supercomputers. Running larger deep learning models is a path to new scientific possibilities, but conventional systems and architectures limit the problems that can be addressed, as models take too long to train. Cray worked with Microsoft and CSCS, a world-class scientific computing center, to leverage their decades of high performance computing expertise to profoundly scale the Microsoft Cognitive Toolkit (formerly CNTK) on a Cray XC50 supercomputer at CSCS nicknamed "Piz Daint". By accelerating the training process, instead of waiting weeks or months for results, data scientists can obtain results within hours or even minutes. With the introduction of supercomputing architectures and technologies to deep learning frameworks, customers now have the ability to solve a whole new class of problems, such as moving from image recognition to video recognition, and from simple speech recognition to natural language processing with context.

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.

Glancing around school classrooms in 2016, it's easy to miss just how far technology has transformed learning over the last decade. The desks, whiteboards and rows of chairs are the same, but so much else has changed that can't be seen. A third of Britain's schools are asking students to bring their own tablets and laptops into the classroom now, coding has been on the national curriculum for three years, and more and more education is happening outside school through apps and digital services. But these changes are just the start. Artificial intelligence (AI) is the next giant leap in learning and, according to those working in the field of education and technology, we haven't seen anything yet.

We consider the problem of learning from noisy data in practical settings where the size of data is too large to store on a single machine. More challenging, the data coming from the wild may contain malicious outliers. To address the scalability and robustness issues, we present an online robust learning (ORL) approach. ORL is simple to implement and has provable robustness guarantee -- in stark contrast to existing online learning approaches that are generally fragile to outliers. We specialize the ORL approach for two concrete cases: online robust principal component analysis and online linear regression. We demonstrate the efficiency and robustness advantages of ORL through comprehensive simulations and predicting image tags on a large-scale data set. We also discuss extension of the ORL to distributed learning and provide experimental evaluations.

Next time you stop for gas at a self-serve pump, say hello to the robot in front of you. Its life story can tell you a lot about the robot economy roaring toward us like an EF5 tornado on the prairie. Yeah, your automated gas pump killed a lot of jobs over the years, but its biography might give you hope that the coming wave of automation driven by artificial intelligence (AI) will turn out better for almost all of us than a lot of people seem to think. The first crude version of an automated gas-delivering robot appeared in 1964 at a station in Westminster, Colorado. Short Stop convenience store owner John Roscoe bought an electric box that let a clerk inside activate any of the pumps outside. Self-serve pumps didn't catch on until the 1970s, when pump-makers added automation that let customers pay at the pump, and over the next 30 years, stations across the nation installed these task-specific robots and fired attendants. By the 2000s, the gas attendant job had all but disappeared.