Artificial intelligence has replaced big data this year as the most talked about new set of technologies. As with big data five years ago--behind the hype, the confusion generated by an ill-defined term, and the record funding by VC--we are starting to see emerging investments and practical applications where it matters most--in enterprises. A new report from Narrative Science, based on a survey of 235 business executives conducted by the National Business Research Institute (NBRI), sheds light on the state-of-AI in enterprises today and in the future: 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. Keep in mind that "AI technologies" is a broad term that includes machine and deep learning, recommendation engines, predictive and prescriptive analytics, automated written reporting and communications, and voice recognition and response. Big data has spawned the current interest and increased investment in artificial intelligence.

If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models.

These clumsy noisy human-built flying machines put human eyes into previously avian domains, scaring bears and provoking all sorts of natural animal rage. PowerVision Robot's new drone isn't just an attempt to usurp the sky from its feathered forebearers. No, their elaborate flying camera has the audacity to be egg-shaped, an affront to the entire lifecycle of the beasts of the sky. They believe their PowerEgg will be light enough to fit in a backpack, complete with a "360 Panoramic, 4K HD Camera, 3-axis Gimbal," according to their press release. It is also, notably, egg-shaped.

TV series "Humans" shows robots taking over simple human tasks Photo: Channel 4 Over the last decade, the types and definitions of Artificial Intelligence (AI) have ranged across a wide spectrum. A future of smart homes and smart cars, driven by AI, is no longer a distant reality. This future poses questions to businesses; principal among them is how businesses can adapt to AI. The practical impact on companies, and their workforces, is an increasingly pressing issue around the boardroom. The International Federation of Robots (yes, a real thing, not a Star Wars coalition) reported last year that robot sales in 2014 increased by 29% – the biggest increase ever recorded within a year, and this trend is expected to continue.

In this course, intended to expand upon your knowledge of neural networks and deep learning, you'll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you'll discover its wide range of applications, from generating image effects to modeling artificial organs. Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs) Build convolutional filters that can be applied to audio or imaging Extend deep neural networks w/ just a few functions Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course. The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning.

The world's best gamers may one day compete against the smartest computers Google cut power usage in its data centres by several percentage points earlier this year by trusting artificially intelligent software originally designed to beat 1980s-era Atari video games. And in the years to come, the Internet giant not only could save much more electricity but also solve far larger problems by taking on a much more complex video game. Research scientists at Google's DeepMind unit announced Friday they are developing a computer program that reads data about Blizzard Entertainment's "StarCraft II" games and learns how to play on its own. The software would have to figure out how to split its attention between micromanagement and long-term strategic decisions. It's that manoeuvring that could deliver big breakthroughs.

Las Vegas: Marvin is just a few inches tall, but he has a big mouth. "What do you think of that, human?" he sneers as he ties the scores 1-1. "Is that the best you can do?" he jeers as he takes a 2-1 lead. "Oh no, IBM will fire me if I lose," he says, as the score is tied 2-2. The decisive move comes up, and Marvin is beaten.

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 an accessible introduction to how to use machine learning techniques for your own projects and datasets. 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 dataset.

Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledgebase. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et al. 2015) for learning skills. Skill distillation enables the H-DRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et al. 2015) in sub-domains of Minecraft.