Artificial intelligence (AI) is intelligence created by machines or software. The artificial intelligence and robotics market was worth US 10.7 billion in 2014 and is expected to be worth US 153 billion by 2020, and to have a disruptive impact of between US 14 to US 33 trillion. The component for artificial intelligence alone is worth US 70 billion. There are perils with artificial intelligence. Don't let anyone tell you there aren't.

In the opening installment, Dr. Kaku explains how artificial intelligence will revolutionize homes, workplaces and lifestyles, and how virtual worlds will become so realistic that they will rival the physical world. Robots with human-level intelligence may finally become a reality, and in the ultimate stage of mastery, we'll even be able to merge our minds with machine intelligence. For the first time on television, see how a severely depressed patient can be turned into a happy person at the push of a button - all thanks to the cross-pollination of neuroscience and artificial intelligence. I supply this video with no intention of gaining monetary compensation. Everything you see is strictly for the purpose of teaching and/or enlightenment.

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before. Deep Learning is becoming the method of choice for its exceptional accuracy and capturing capacity for unstructured data.

Nobel economics laureate and psychologist Daniel Kahneman -- considered the father of behavioral economics – retired from his teaching position at Princeton a few years ago to co-found a consulting firm in New York. In a talk at the recent Wharton People Analytics Conference, he said of his consulting experience that he had "expected to be awed" by the quality of the decision-making in organizations "that need to make profits to survive in a competitive world." "You look at large organizations that are supposed to be optimal, rational. And the amount of folly in the way these places are run, the stupid procedures that they have, the really, really poor thinking you see all around you, is actually fairly troubling," he said, noting that there is much that could be improved. Figuring out how to make the act of decision-making "commensurate with the complexity and importance of the stakes" is a huge problem, in Kahneman's view, to which the business world does not devote much thought.

The co-founder of AI startup Vicarious predicts that the tech industry is on the cusp of a major breakthrough in AI, CIO reports. D. Scott Phoenix said the availability of huge amounts of data and cheap storage, memory and computing power will enable AI to make a major leap as soon as this summer. A Bank of America report citing IDC research recently predicted that the AI industry will grow to 70 billion by 2020 from just 8.2 billion in 2013. "We saw something similar in the early days of software -- and the early days of the Internet," Phoenix told CIO. "We're entering that era of rapid improvement." Vicarious hasn't revealed much about what it plans to unveil next, but the company has been working hard to make AI more "general-purpose."

Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems. Which library should you use and which techniques should you focus on? In this post you will discover a 14-part crash course into deep learning in Python with the easy to use and powerful Keras library. This mini-course is intended for python machine learning practitioners that are already comfortable with scikit-learn on the SciPy ecosystem for machine learning. Applied Deep Learning in Python Mini-Course Photo by darkday, some rights reserved. Before we get started, let's make sure you are in the right place.

You've seen AI achieve incredible things like defeat world champions at Go, describe photos for the visually impaired and operate an elevator. But now, a startup in London has finally figured a genuinely useful application: brewing quality beers. IntelligentX offers four basic beers, including a classic British golden ale, a British bitter kissed with grapefruit, a hoppy American pale ale and a smokey Marmite brew. Once you've tasted them, you can chat with the company's Messenger bot to share your feedback, which its AI (built using IntelligentX's own machine learning algorithm) uses to improve on its recipes. That means that each batch of beer will have a unique flavor.

Gamification is all the rage these days. In order to distill this down to something simple was the task at hand. Here are the briefly highlights after reviewing more than 100 documents, articles and reports. Since people and management hate training, there is a new way to accomplish training, customer acquisition, corrective behavior and other staff behavior and customer participation issues. The game designer sets the goals, rewards (hard dollars and soft benefits such as recognition, etc.) management, legal and outcome.

This has been one of the most intriguing questions in science fiction and philosophy since the advent of machines. With modern technology, such questions are no longer bound to creative conjecture. Machine learning is all around us. From deciding which movie you might want to watch next on Netflix to predicting stock market trends, machine learning has a profound impact on how data is understood in the modern era. This tutorial aims to give you an accessible introduction on how to use machine learning techniques for your projects and data sets. In just 20 minutes, you will learn how to use Python to apply different machine learning techniques -- from decision trees to deep neural networks -- to a sample data set.

Top Machine Learning & Data Mining Books - in this post, we have scraped various signals (e.g. We have combined all signals to compute the Quality Score for each book and publish the list of top Machine Learning and Data Mining books. The readers will love the list because it is data-driven & objective. This book is very well rated on Amazon website and is written by three professors from USC, Stanford and University of Washington. The book's authors: Gareth James, Daniela Witten, Trevor Hastie, & Rob Tibshirani all have backgrounds in statistics.