This is the fourth part in'A Brief History of Neural Nets and Deep Learning'. In this part, we will get to the end of our story and see how deep learning emerged from the slump neural nets found themselves in by the late 90s, and the amazing state of the art results it has achieved since. "Ask anyone in machine learning what kept neural network research alive and they will probably mention one or all of these three names: Geoffrey Hinton, fellow Canadian Yoshua Bengio and Yann LeCun, of Facebook and New York University."1 When you want a revolution, start with a conspiracy. With the ascent of Support Vector Machines and the failure of backpropagation, the early 2000s were a dark time for neural net research. LeCun and Hinton variously mention how in this period their papers or the papers of their students were routinely rejected from being published due to their subject being Neural Nets.

At Twitter Inc., Hugo Larochelle's job is to develop an understanding of how users of the social network are connected to each other and what interests them in order to categorize and promote content that includes tweets, images and videos. To help accomplish that, he and his fellow data analysts use an emerging technology: deep learning tools. As Larochelle, a research scientist at Twitter, explained during a presentation at the Deep Learning Summit in Boston this month, deep learning is a category of machine learning that seeks to understand complex problems, such as interpreting images or text-based natural language. He and other proponents say deep learning techniques -- which lean heavily on the use of neural networks -- are more useful than traditional machine learning when data analytics applications involve unstructured data or require subjective interpretations. And deep learning is quickly becoming a hot field in the realm of advanced data analytics.

Losing jobs to technology is nothing new. Since the industrial revolution, roles that were once exclusively performed by humans have been slowly but steadily replaced by some form of automated machinery. Even in cases where the human worker is not completely replaced by a machine, humans have learnt to rely on a battery of machinery to be more efficient and accurate. A report from the Oxford Martin School's Programme on the Impacts of Future Technology said that 47% of all jobs in the US are likely to be replaced by automated systems. Among the jobs soon to be replaced by machines are real estate brokers, animal breeders, tax advisers, data entry workers, receptionists, and various personal assistants. But you won't need to pack up your desk and hand over to a computer just yet, and in fact jobs that require a certain level of social intelligence and creativity such as in education, healthcare, the arts and media are likely to remain in demand from humans, because such tasks remain difficult to be computerised.

The past few years have seen machine learning emerge as one of the trendiest topics within the technology sector as it allows computers to find hidden insights from the volumes of data being collected without being explicitly programmed where to look. The resources available to process it have increased dramatically. One of the exciting aspects about machine learning is that its applications are virtually endless and in fact it has been transforming a wide variety of industries in interesting ways. In this post, I would like to share my experience using machine learning algorithms, in particular how we, at Capgemini, have developed the capabilities to successfully integrate machine learning-based technology into a framework to help improve service delivery in the public sector. Many machine learning applications are all around us: Amazon and Netflix online recommendation systems, Spotify and Pandora's personalised playlists, Facebook's automatic face recognition and friends recommendations, Google's personalised searches and adds, Uber's prediction of customer demand and pre-location of cars, to mention just a few.

When AlphaGo, Google's artificial intelligence program, defeated champion Go player Lee Sedol earlier this year, everyone praised its advanced software brain. But the program, developed by Google's DeepMind research team, also had some serious hardware brawn standing behind it. The program was running on custom accelerators that Google's hardware engineers had spent years building in secret, the company said. With the new accelerators plugged into the AlphaGo servers, the program could recognize patterns in its vast library of game data faster than it could with standard processors. The increased speed helped AlphaGo make the kind of quick, intuitive judgments that define how humans approach the game.

Watch this webinar, presented by Kris Hammond, Chief Scientist of Narrative Science, to learn about the different subfields of technologies that fall under the umbrella of AI such as machine learning, advanced analytics, and advanced natural language generation. Viewers will finish the webinar understanding how the different AI technologies emulate human reasoning and how they may be able to apply these technologies to their own business.

There are reasons to believe that true AI is right around the corner but I don't see it coming from the mainstream AI community. Right now, they are all having a feeding frenzy over a soon to be obsolete technology. There is no question that deep learning is a powerful and useful machine learning technique but it works in a narrow domain: the classification of labeled data. Someone has to go through the data and carefully label each sample according to a category or class. This is kind of lame because this is not the way humans and animals learn.

"At BakerHostetler, we believe that emerging technologies like cognitive computing and other forms of machine learning can help enhance the services we deliver to our clients," said Bob Craig, Chief Information Officer. "We are proud to team up with innovators like ROSS and we will continue to explore these cutting-edge technologies as they develop." "BakerHostetler's commitment to the future of the legal practice and ensuring they continue to deliver the highest level of value to their clients completely aligns with our vision at ROSS Intelligence," said Andrew Arruda, CEO/Cofounder. "BakerHostetler has been using ROSS since the first days of its deployment and we are proud to partner with a true leader in the industry as we continue to develop additional AI legal assistants." About ROSS Intelligence ROSS Intelligence began out of research at the University of Toronto in 2014 with the goal of building an AI legal research assistant to allow lawyers to enhance and scale their abilities.

The University of Washington School of Law is delighted to announce a public workshop on the law and policy of artificial intelligence, co-hosted by the White House and UW's Tech Policy Lab. The event places leading artificial intelligence experts from academia and industry in conversation with government officials interested in developing a wise and effective policy framework for this increasingly important technology. The event is free and open to the public but requires registration. Jack M. Balkin is Knight Professor of Constitutional Law and the First Amendment at Yale Law School. He is the founder and director of Yale's Information Society Project, an interdisciplinary center that studies law and new information technologies.

Have you ever wanted to order a bucket of fried chicken without having to speak to a single a human being? Now you can! KFC, in partnership with Chinese search engine giant Baidu, has just opened the world's first human-free fast food restaurant in Shanghai, reports SoHu. The intelligent robot concept store, Original (pronounced, "Original Plus"), looks unlike any KFC you've ever seen. The interior is designed in a traditional Chinese garden style with bamboo, flowers, and jade accents. Customers enter through a big circular doorway.