Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

IT giant Fujitsu has been developing a series of in-house technologies aimed at the burgeoning market of artificial intelligence and machine learning. Although the company has made less fanfare of its ambitions in this regard than companies like IBM, Google and Microsoft, the Japanese multinational seems intent on expanding its datacenter business into this new high-value segment. The step-up in AI focus has been especially noticeable over the past several months, where hardly week went by without an announcement of a new technology or use case. In fact, Fujitsu has issued no less than 15 press releases on AI or machine learning since the beginning of 2016. Most are the result of technologies developed at Fujitsu Laboratories.

Last week, Apple launched its latest MacBook Pro laptops. They are deluxe, powerful machines with innovations including the Touch Bar, where the top row of function keys is replaced by a touch-sensitive screen where different functions appear, changing as you change programs. The new models also have a fingerprint sensor built into the power button, to buy stuff online faster than you can say "impulse purchase". In this exclusive interview, Philip Schiller, Senior Vice President of Worldwide Marketing, told The Independent all about them. Schiller is a familiar face at Apple keynotes, mixing a dry sense of humour with bullish, on-message statements.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

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." One of the central foundations of deep learning is linear regression; using probability theory to gain deeper insight into the "line of best fit." This is the first step to building machines that, in effect, act like neurons in a neural network as they learn while they're fed more information. In this course, you'll start with the basics of building a linear regression module in Python, and progress into practical machine learning issues that will provide the foundations for an exploration of Deep Learning. Access 20 lectures & 2 hours of content 24/7 Use a 1-D linear regression to prove Moore's Law Learn how to create a machine learning model that can learn from multiple inputs Apply multi-dimensional linear regression to predict a patient's systolic blood pressure given their age & weight Discuss generalization, overfitting, train-test splits, & other issues that may arise while performing data analysis The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer.

Under President Obama's leadership, America continues to be the world's most innovative country, with the greatest potential to develop the industries of the future and harness science and technology to help address important challenges. Over the past 8 years, President Obama has relentlessly focused on building U.S. capacity in science and technology. This Thursday, President Obama will host the White House Frontiers Conference in Pittsburgh to imagine the Nation and the world in 50 years and beyond, and to explore America's potential to advance towards the frontiers that will make the world healthier, more prosperous, more equitable, and more secure. Today, to ready the United States for a future in which Artificial Intelligence (AI) plays a growing role, the White House is releasing a report on future directions and considerations for AI called Preparing for the Future of Artificial Intelligence. This report surveys the current state of AI, its existing and potential applications, and the questions that progress in AI raise for society and public policy.

The Spain-headquartered startup, which today is announcing $6 million in Series A funding, is using deep learning to help startups and larger companies recruit developers. Specifically, its AI tech is analysing the code of millions of developers via their open source contributions in order to match them to appropriate job openings. "We use this analysis to understand how good they are at any given language and framework and match them with companies looking for developers," is how Source{d} co-founder and COO Jorge Schnura explains it. He also says it isn't just about identifying code quality or a developer's ability, but also coding style and other nuances that differentiates one developer from another. "We can [find] people who are similar to your team," adds Schnura. "This is all unsupervised learning since we don't tell our algorithms which features to look for, it defines them itself".

Greetings and welcome to the PROS Holdings Inc Third Quarter 2016 Earnings Call. At this time, all participants are in a listen-only mode. A brief question-and-answer session will follow the formal presentation. It is now my please to introduce your host Stefan Schulz, Chief Financial Officer. Good afternoon, everyone and thank you for joining us. With me on today's call is Andres Reiner, President and Chief Executive Officer. Before we begin, we must caution you that some of today's remarks, including our guidance, our strategy, our competitive position, future business prospects, revenue, bookings, market opportunities, as well as statements made during the question-and-answer session, contain forward-looking statements. These statements are based on present information and are subject to numerous and important factors, risks and uncertainties, which could cause actual results to differ materially from the results implied by these or other forward-looking statements. PROS does not assume any obligation to update the forward-looking statements provided to reflect events that occur, or circumstances that exist, after the date on which they are made. Additional information concerning risks and other factors that may cause actual results to differ can be found in the Company's filings with the SEC. Also, please note that a replay of today's webcast will be available in the Investor Relations section of our website at pros.com. We encourage everyone to review this additional information. Finally, I would like to point out that in addition to reporting financial results in accordance with Generally Accepted Accounting Principles, or GAAP, PROS reports certain financial results, as well as forward-looking guidance, on a non-GAAP basis. A reconciliation of each non-GAAP measure to the most directly comparable GAAP measure, to the extent available without unreasonable efforts is available on the press release distributed earlier today, and in the Investor Relations section of our website. Good afternoon, everyone and thank you for joining us on today's call.

The World Intellectual Property Organization has developed a ground-breaking new "artificial intelligence"-based translation tool for patent documents, handing innovators around the world the highest-quality service yet available for accessing information on new technologies. WIPO Translate now incorporates cutting-edge neural machine translation technology to render highly technical patent documents into a second language in a style and syntax that more closely mirrors common usage, out-performing other translation tools built on previous technologies. WIPO has initially "trained" the new technology to translate Chinese, Japanese and Korean patent documents into English. Patent applications in those languages accounted for some 55% of worldwide filings in 20141. Users can already try out the Chinese-English translation facility on the public beta test platform.

Arthur C. Clarke famously stated that "any sufficiently advanced technology is indistinguishable from magic." No current technology embodies this statement more than neural networks and deep learning. And like any good magic it not only dazzles and inspires but also puts fear into people's hearts. One known property of artificial neural networks (ANNs) is that they are universal function approximators. This means that any mathematical function can be represented by a neural network.