In the summer of 2013, as Matthew Zeiler was close to finishing a Ph.D. in artificial intelligence at New York University, he seemed to have every tech giant in the palm of his hand. Zeiler had left an internship with a Google AI group a few weeks earlier when he got a call from an unknown number while he was running along the Hudson River. It was Alan Eustace, then a senior vice president of engineering at Google, who had heard about Zeiler's AI chops. Eustace wanted Zeiler to join permanently. To entice him, Eustace told him he would make an offer that was among the highest Google had ever made to a new graduate, Zeiler recalls.

You may have heard the buzz about neural networks and deep learning, and want to learn more. But when you learn about the technology from a textbook, many people find themselves overwhelmed by mathematical models and formulas.

Artificial intelligence (A.I.) could become a game-changer for multiple industries. Powerful algorithms may soon be able to quickly sift through reams of data and information, delivering quantifiable insights for tasks such as enhancing guidance systems for self-driving cars, assisting physicians in diagnosing patients, or helping farmers implement plans that simplify the management and protection of their crops. Technology giants in the U.S. like IBM and Microsoft are exploring business opportunities where A.I. could have the most impact, but an ecosystem for this type of R&D is already thriving in Canada. Our neighbor to the north has produced several pioneers in A.I. Prominent computer scientists like Geoffrey Hinton, Ph.D., and Yoshua Bengio, Ph.D., started their careers in Toronto laying the groundwork for various A.I. oriented fields. Hinton, an engineering fellow at Google and professor emeritus of computer science at the University of Toronto, is considered a pioneer in training neural networks with multiple layers, a computing technique that provides A.I. with greater recognition capabilities.

Watson started as a follow-on project to IBM DeepBlue, the computer and AI program that defeated world chess champion Gary Kasparov. DeepBlue demonstrated that a computer could defeat a human in chess, a game with well-defined rules and limited, fully visible solutions. The real world, however, is much more complicated: information often is unstructured, problems ill defined, and solutions probabilistic at best. To equip AI to deal with the real world, IBM challenged its computer and data scientists to create a program that could defeat human contestants at Jeopardy!, a quiz show requiring answers to natural language questions over broad domains of knowledge otherwise known as unstructured data. As a quick refresher, artificial intelligence can be divided into three categories, as shown above.1The

Microsoft Corp. is setting up a new research lab focused on artificial intelligence with the goal of creating more general-purpose learning systems. The new lab, called Microsoft Research AI, will be based at the company's headquarters in Redmond, Washington, and involve more than 100 scientists from across various sub-fields of artificial intelligence research, including perception, learning, reasoning and natural language processing. The goal, said Eric Horvitz, the director of Microsoft Research Labs, is to combine these disciplines to work toward more general artificial intelligence, meaning a single system that can tackle a wide-range of tasks and problems. Such a system, for instance, might be able to both plan the best route to drive through a city and also figure out how to minimize your income tax bill, while also understanding difficult human concepts like sarcasm or gestures. This differs from so-called narrow AIs, which are just designed to perform a single task well -- for instance, recognize faces in digital photographs.

Sci-fi level Artificial Intelligence (AI) like HAL 9000 it was promised since 1960s, but PCs and robots kept dumb until recently. Now, tech giants and startups are announcing the AI revolution: self-driving cars, robo doctors, robo investors, etc. PwC just said that AI will contribute $15.7 trillion to the world economy by 2030. "AI" it's the 2017 buzzword, like "dot com" it was in 1999, everyone claims to be into AI. Don't be confused by the AI hype. Is this a bubble or real? AI is not easy or fast to apply. The most exciting AI examples come from universities or the tech giants. Self-appointed AI experts who promise to update any company to the latest AI in short time are doing AI misinformation. No "deep learning" will be soon implemented by the wide and general businesses. Most have too few digital data or still use pen and paper, and AI needs million data samples to learn something.

Deep learning has allowed technologists to employ AI in numerous business and consumer applications, but researchers are working feverishly to master other techniques that will broaden AI's reach.

In the latest of a series of investments in deep learning startups, NVIDIA is investing in Deep Instinct, an Israeli-based startup that uses deep learning to thwart cyberattacks. Deep Instinct uses a GPU-based neural network and CUDA to achieve 99 percent detection rates, compared with about 80 percent detection from conventional cyber security software. Its software can automatically detect and defeat the most advanced cyberattacks. NVIDIA vice president of business development Jeff Herbst said, "Deep Instinct is an emerging leader in applying GPU-powered AI through deep learning to address cybersecurity, a field ripe for disruption as enterprise customers migrate away from traditional solutions. We're excited to work together with Deep Instinct to advance this important field."

Artificial intelligence to uncover human biases IMAGE: Artificial intelligence detects discrimination and diversity in the large corporations. Early experiments such as Beauty.AI beauty contest developed by Youth Laboratories and Aging.AI predictor of chronological age developed by Insilico Medicine uncovered the various biases with the AI systems as well as the many opportunities for using AI to detect and report human biases. Advances in artificial intelligence and specifically in the fields of deep learning and reinforcement learning present the many threats and opportunities. "Bias, be it race, sex, age or any other type, is a hugely contributing factor that shapes science and society. This paper is important, not only because it demonstrates the apparent and permeating prejudice that exist in executive boards around the globe, but also because it shows how AI and deep learning can visualize bias in complex systems.", said Morten Scheibye-Knudsen, MD, PhD, Center for Healthy Aging, University of Copenhagen.

Get free deep learning resources: https://goo.gl/Z6vLDU Walk through several examples, and learn how to decide which method to use. The video outlines the specific workflow for solving a machine learning problem. The video also outlines the differing requirements for machine learning and deep learning. You'll learn about the key questions to ask before deciding between machine learning and deep learning.