At the inaugural O'Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities. There is no better place to imbibe the most up-to-date tech zeitgeist than at an O'Reilly Media event as has been proven again and again ever since the company put together the first Web-related meeting (WWW Wizards Workshop in July 1993). The conference was organized by Ben Lorica and Roger Chen, with Peter Norvig and Tim O'Reilly acting as honorary program chairs. Here's a summary of what I heard there, embellished with a few references to recent AI news and commentary: In contrast to traditional software, explained Peter Norvig, Director of Research at Google, "what is produced [by machine learning] is not code but more or less a black box--you can peak in a little bit, we have some idea of what's going on, but not a complete idea." Tim O'Reilly recently wrote in "The great question of the 21st century: Whose black box do you trust?": Because many of the algorithms that shape our society are black boxes… because they are, in the world of deep learning, inscrutable even to their creators – [the] question of trust is key. Understanding how to evaluate algorithms without knowing the exact rules they follow is a key discipline in today's world.

For millennia, we surpassed the other intelligent species with which we share our planet--dolphins, porpoises, orangutans, and the like--in almost all skills, bar swimming and tree-climbing. In recent years, though, our species has created new forms of intelligence, able to outperform us in other ways. One of the most famous of these artificial intelligences (AIs) is AlphaGo, developed by Deepmind. In just a few years, it has learned to play the 4,000-year-old strategy game, Go, beating two of the world's strongest players. Other software developed by Deepmind has learned to play classic eight-bit video games, notably Breakout, in which players must use a bat to hit a ball at a wall, knocking bricks out of it.

Computer-aided detection (CADe) systems are intended to identify a variety of cancers such as breast cancer, prostate cancer, and lung lesions. They are most commonly used to detect microcalcifications and masses on screening mammograms. Despite concerns regarding the benefits of CADe and the high rate of false positives and false negatives, the market has grown steadily over the last two decades, most notably in the US where more than 90% of mammograms are interpreted using CADe. This has largely been driven by the availability of reimbursement for the use of CADe in breast screening. It is far less commonly used for detecting other cancers, where reimbursement for using CADe is currently not available.

We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The truncated variables are assumed latent and integrated out to induce a marginal model. We show that the variables in the marginal model are non-Gaussian distributed and their expected relations are nonlinear. We use expectation-maximization to break the inference of the nonlinear model into a sequence of TGGM inference problems, each of which is efficiently solved by using the properties and numerical methods of multivariate Gaussian distributions. We use the TGGM to design models for nonlinear regression and classification, with the performances of these models demonstrated on extensive benchmark datasets and compared to state-of-the-art competing results.

Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.

Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations. However, they are also known for their limited modeling abilities, due to the Gaussian assumption. In this paper, we introduce a novel variant of GGMs, which relaxes the Gaussian restriction and yet admits efficient inference. Specifically, we impose a bipartite structure on the GGM and govern the hidden variables by truncated normal distributions. The nonlinearity of the model is revealed by its connection to rectified linear unit (ReLU) neural networks. Meanwhile, thanks to the bipartite structure and appealing properties of truncated normals, we are able to train the models efficiently using contrastive divergence. We consider three output constructs, accounting for real-valued, binary and count data. We further extend the model to deep constructions and show that deep models can be used for unsupervised pre-training of rectifier neural networks. Extensive experimental results are provided to validate the proposed models and demonstrate their superiority over competing models.

Data Science for Internet of Things provides a concise introduction to the application of Predictive learning algorithms to Internet of Things. This mini book is based on my teaching at Oxford University, UPM(University of Madrid) and also working with consulting clients.We first outline the key issues involved and then explores three key areas: Stream processing, Deep Learning and Sensor fusion for IoT. The book is also a recommended material for the Stanford University course: Building a Successful Business for the Internet of Things and Mobile (BUS20) $11.99

Artificial intelligence (AI) is one of those hyped topics that often comes and goes with little understanding of what's involved or how it can be monetized. It's not unique in that respect; robotics and a host of other topics come to mind. They typically peak as research turned into products that ride the wave and then disappear, or at least it seems that way. In actuality, though, they rarely become products. Also, at this juncture, the practical applications hide from the spotlight, but continue to grow in acceptance.

If you're still getting your arms around deep learning, start with the very basics. This video series by Deep Learning TV provides an introduction that assumes no knowledge of math, programming, or statistics. The series starts with neural networks and deep learning concepts and later gets into techniques such as convolutional nets, restricted Boltzmann machines, deep belief nets, recurrent nets, autoencoders, and recursive neural tensor nets.

Prologue: Today Deep learning is a buzz word like how data science and machine learning was yesterday. And it is no surprise that you would blow up with too information with too complicated terminologies and glossaries when you try to understand Deep learning with the materials available on online. This blog is written with an of aspect helping the layman to understand what is Deep learning without bringing complicated math and terminologies in first place. Some parts of this writing include fictional contents and it is for obvious reasons to teach Deep learning in much simpler way, however facts about the deep learning remains the same. Deep learning is not relatively new a field, it has been around in history for a long time.