At Udacity, we believe applications of artificial intelligence will bring transformative change to all industries, and not in some distant science-fiction future--we are seeing rapidly growing demand for AI-related skills right now, and new artificial intelligence jobs are emerging every day. This is exactly why we created our recently announced Artificial Intelligence Nanodegree program. Many of these jobs are still very new however, and we've learned from our program applicants--who already number in the thousands!--that So we took it upon ourselves to answer this question. To begin, we needed concrete data.

From technology bigwigs joining hands to assistants getting more "human," we have seen plenty of news and reports around AI. It's time to catch up! Wccftech Deals is bringing a massive discount on "The Deep Learning & Artificial Intelligence Introductory Bundle," which will help you learn the basics of AI. Artificial neural networks are the architecture that make Apple's Siri recognize your voice, Tesla's self-driving cars know where to turn, Google Translate learn new languages, and so many more technological features you quite possibly take for granted. Sign up for this introductory bundle and build your very first neural network – going beyond basic models to build networks that automatically learn features. Find out some details below, or head over to Wccftech Deals for more details. Deep Learning is a set of powerful algorithms that are the force behind self-driving cars, image searching, voice recognition, and many, many more applications we consider decidedly "futuristic."

I promised to write it long ago: here we go! Click on this link to see the most current version. I will update this article regularly. An old version can be found here and has many interesting links. All the material presented here is not in the old version.

Product Managers have enthusiastically adopted the data-driven approach to building products and have learnt not to rely solely on experience. For some features it is a continuous process that helps the Build-Measure-Learn iteration. Intuition backed by data is a product manager's most powerful weapon. If we have already made the shift towards data then why do we need Machine Learning, you ask? In this post, I am going to share why I believe every Product Manager should understand Machine Learning and where to start.

Many alarms have sounded on the potential for artificial intelligence (AI) technologies to upend the workforce, especially for easy-to-automate jobs. But managers at all levels will have to adapt to the world of smart machines. The fact is, artificial intelligence will soon be able to do the administrative tasks that consume much of managers' time faster, better, and at a lower cost. How can managers -- from the front lines to the C-suite -- thrive in the age of AI? To find out, we surveyed 1,770 managers from 14 countries and interviewed 37 executives in charge of digital transformation at their organizations.

Useful quantum computers are closer to becoming a reality as some of the world's biggest corporations try to bring the technology from the lab into the practical world. A quantum computer utilizes subatomic particles called qubits to speed up the solving of complex computations. Near-term expectations for quantum computers range from solving optimization problems to quantum-encrypted communications, and more. With the help of CB Insights' investment, acquisition, and partnership data, we identified 18 corporate groups involved in the development of commercialized quantum computing hardware and software. They are a diverse group of players, ranging from tech industry behemoths to defense contractors to national telecommunications companies.

In its quest to advance artificial intelligence research, Google's DeepMind has created systems capable of mastering a range of Atari computer games, and besting humans at the ancient Chinese board game Go. Now, the company is seeking to overcome an even bigger challenge -- creating an AI system that can play, and perhaps master, "StarCraft II." The company announced Thursday that it was teaming up with Blizzard Entertainment -- the maker of the real-time strategy game -- to open up "StarCraft II" to AI and machine learning researchers around the world. This means that anyone with the inclination and skills to do so can test and train their AI systems using StarCraft's complex gaming environment. "DeepMind is on a scientific mission to push the boundaries of AI, developing programs that can learn to solve any complex problem without needing to be told how. Games are the perfect environment in which to do this, allowing us to develop and test smarter, more flexible AI algorithms quickly and efficiently, and also providing instant feedback on how we're doing through scores," the company said in a statement.

More than 250 leading researchers from over 50 countries will gather at the Future Technologies Conference (FTC) in San Francisco next month. Running from 6 – 7 December, this event will focus on the future trends in technology and various experts will explore a plethora of topics such as: artificial intelligence, machine learning, data science, security, IoT, robotics. She will speak about cognitive computing and how recent changes in the availability of data have driven rapid advances in analytics, enabling smarter applications, and bringing us to this new era. "We'll see how Watson, the Jeopardy!-playing Ruzena Bajcsy, Professor at the University of California, Berkeley will explore about the development of personalized models of kinematic and dynamics of an individual during physical activities and its applications in improving healthcare. Ella Atkins Professor of Aerospace Engineering at the University of Michigan will describe long-term research in improving safety and robustness through autonomous contingency management with application to manned and unmanned aircraft. Dr. Atkins will also summarize her research on the use of nontraditional databases and real-time data to reduce risk to people and property. Other speakers include James Loudermilk - Senior Level Technologist at the Federal Bureau of Investigation (FBI) Science and Technology Branch, Bin He - Director of Institute for Engineering in Medicine, University of Minnesota and Arjuna Chala - Sr. Director of Innovation and Emerging Technology at HPCC Systems. In two days, the conference will host a total of six keynotes, five project demonstrations and 190 paper presentations organized into thirty sessions. The conference program also offers various networking opportunities including an evening reception which will give attendees the chance to mingle with other researchers after a day full of speakers, meetings, and thought-provoking topics. FTC 2016 is organized by The Science and Information (SAI) Organization whose mission is to connect global research community through journals, conferences and technical activities. Sponsors and partners for FTC 2016 include HPCC Systems, IEEE, IBM Research, University of Michigan, IBM Watson AI XPrize and Brown University. "We've invited experts from across the globe to attend and offer insights on the future of technology and research.

We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.

We present a Communication-efficient Surrogate Likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation and Bayesian inference. For low-dimensional estimation, CSL provably improves upon naive averaging schemes and facilitates the construction of confidence intervals. For high-dimensional regularized estimation, CSL leads to a minimax-optimal estimator with controlled communication cost. For Bayesian inference, CSL can be used to form a communication-efficient quasi-posterior distribution that converges to the true posterior. This quasi-posterior procedure significantly improves the computational efficiency of MCMC algorithms even in a non-distributed setting. We present both theoretical analysis and experiments to explore the properties of the CSL approximation.