We propose a general modeling and inference framework that composes probabilistic graphical models with deep learning methods and combines their respective strengths. Our model family augments graphical structure in latent variables with neural network observation models. For inference, we extend variational autoencoders to use graphical model approximating distributions with recognition networks that output conjugate potentials. All components of these models are learned simultaneously with a single objective, giving a scalable algorithm that leverages stochastic variational inference, natural gradients, graphical model message passing, and the reparameterization trick. We illustrate this framework with several example models and an application to mouse behavioral phenotyping.

Tasked with squeezing billions of transistors onto fingernail-sized slabs of silicon, chip designers are asking whether machine learning can help. In the view of electronic design automation firms, machine learning tools could chisel rough edges off complex chips, improving productivity, optimizing trade-offs like power consumption and timing, and testing that chips are ready for manufacturing. Though chip design is still a creative process, engineers need tools that abstract the massive number of variables in modern chips. Using statistics, the software generates models fitted to simulations that replicate how physical chips will work. The tools would seem to be prime candidates for machine learning, which can be trained to find hidden insights in data without explicit programming.

Chatbots, deep learning, concierge apps, big data -- these words may seem meaningless today, but they offer an incredible opportunity available for event professionals who are willing to embrace innovation. Artificial intelligence (AI) will soon revolutionize the events industry and play a pivotal role in growing your event. From customer support to event management and marketing, AI may change how we experience live events -- and give yours a competitive advantage. Join Event Manager Blog's Julius Solaris as he answers your most burning questions about AI. What is useful and what is not?

With AI or deep learning able to integrate with content marketing efforts, current data suggests that although 57.1% of US marketers remain unlikely to use AI or deep learning in their 2017 content marketing, a significant number felt differently. BrightEdge and Survey Monkey polled 1,019 marketers worldwide and found that a third (31.4%) of respondents said they would use AI to help flesh out their content marketing strategy this year. And an additional 8.7% said they were very likely to do so. Meanwhile, 2.8% said they're already using AI to develop their content efforts, eMarketer reports. Additionally, more marketers are likely investing in AI because they're confident there is a demand for it.

Editor's Note: This article first appeared in Spektrum der Wissenschaft, Scientific American's sister publication, as "Digitale Demokratie statt Datendiktatur." "Enlightenment is man's emergence from his self-imposed immaturity. Immaturity is the inability to use one's understanding without guidance from another." The digital revolution is in full swing. How will it change our world? The amount of data we produce doubles every year. In other words: in 2016 we produced as much data as in the entire history of humankind through 2015. Every minute we produce hundreds of thousands of Google searches and Facebook posts. These contain information that reveals how we think and feel. Soon, the things around us, possibly even our clothing, also will be connected with the Internet. It is estimated that in 10 years' time there will be 150 billion networked measuring sensors, 20 times more than people on Earth. Then, the amount of data will double every 12 hours. Many companies are already trying to turn this Big Data into Big Money. Everything will become intelligent; soon we will not only have smart phones, but also smart homes, smart factories and smart cities. Should we also expect these developments to result in smart nations and a smarter planet? The field of artificial intelligence is, indeed, making breathtaking advances. In particular, it is contributing to the automation of data analysis. Artificial intelligence is no longer programmed line by line, but is now capable of learning, thereby continuously developing itself. Recently, Google's DeepMind algorithm taught itself how to win 49 Atari games. Algorithms can now recognize handwritten language and patterns almost as well as humans and even complete some tasks better than them. They are able to describe the contents of photos and videos. Today 70% of all financial transactions are performed by algorithms. News content is, in part, automatically generated. This all has radical economic consequences: in the coming 10 to 20 years around half of today's jobs will be threatened by algorithms. It can be expected that supercomputers will soon surpass human capabilities in almost all areas--somewhere between 2020 and 2060. Experts are starting to ring alarm bells.

Deep neural networks, or deep learning, as the field is also called, have the potential to revolutionize scientific discovery. But as these networks are applied to more and more disciplines, many scientists, whose very enterprise is founded on explanation, have been left with a nagging question: Why, model, why? This interpretability problem is galvanizing a new generation of researchers in both industry and academia. Just as the microscope revealed the cell, these researchers are crafting tools that will allow insight into how neural networks make decisions. Some tools probe the artificial intelligence (AI) without penetrating it; some are alternative algorithms that can compete with neural nets, but with more transparency; and some use still more deep learning to get inside the black box. Taken together, they add up to a new discipline.

Big data has met its match. In field after field, the ability to collect data has exploded, overwhelming human insight and analysis. But the computing advances that helped deliver the data have also conjured powerful new tools for making sense of it all. In a revolution that extends across much of science, researchers are unleashing artificial intelligence (AI), often in the form of artificial neural networks, on these mountains of data. Unlike earlier attempts at AI, such "deep learning" systems don't need to be programmed with a human expert's knowledge.

Over the past few months, industry and government have pledged more than $500-million toward AI, a commitment that has led to the rise of powerful institutions such as the Montreal Institute for Learning Algorithms, the Vector Institute and the Alberta Machine Intelligence Institute. These structures are well positioned to keep churning out cutting-edge research, train the next generation of AI leaders, and advance the innovation and technology transfer of AI. Our three AI Institutes are set up to offer Canadian businesses similar training programs and there's good reason for them to use these resources: Canadian enterprises that consider investing in state-of-the-art machine-learning and data infrastructure can enjoy results such as increased efficiency in manufacturing, better management of underwriting risk, minimization of fraud and reduction of health-care costs. Among the most urgent are ensuring the market is well supplied by streamlining immigration, ensuring higher education and industrial research-funding programs are well capitalized and targeted, modifying tax policies to encourage entrepreneurship and streamlining research and development tax credits to support AI investments.

This book is really great book, Deep neural networks may include a few fundamental approaches. Each deep learning model can be successful in certain domains, but the comparison between models isn't always possible unless the models have been estimated on the same data collection. Deep neural networks are feed forward meaning that data is transferred from the input to output once, without looping back.

Google is betting big on artificial intelligence. Its DeepMind subsidiary has announced plans to expand its operations to Canada in order to accommodate the company's ever-growing research initiatives. In a blog post yesterday, CEO Demis Hassabis revealed DeepMind will be opening its first ever international research space in Edmonton. The undertaking will unfold in a close collaboration with the University of Alberta (UAlberta). "It was a big decision for us to open our first non-UK research lab," Hassabis said.