Siamak Ravanbakhsh, Barnab as P oczos, Jeff Schneider 1 and Dale Schuurmans, Russell Greiner 2 1 Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213 2 University of Alberta, Edmonton, AB T6G 2E8, Canada Abstract We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application of this stochastic approximation in training a family of Restricted Boltzmann Machines (RBM) that are closely linked to Bregman divergences. This family, that we call exponential family RBM (Exp-RBM), is a subset of the exponential family Harmoniums that expresses family members through a choice of smooth monotonic non-linearity for each neuron. Using contrastive divergence along with our Gaussian approximation, we show that Exp-RBM can learn useful representations using novel stochastic units. 1 Introduction Deep neural networks (LeCun et al., 2015; Bengio, 2009) have produced some of the best results in complex pattern recognition tasks where the training data is abundant. Here, we are interested in deep learning for generative modeling. Recent years has witnessed a surge of interest in directed generative models that are trained using (stochastic) back-propagation ( e.g., Kingma and Welling, 2013; Rezende et al., 2014; Goodfellow et al., 2014). These models are distinct from deep energy-based models - including deep Boltzmann machine (Hinton et al., 2006) and (convolutional) deep belief networkAppearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) 2016, Cadiz, Spain. Although, due to their use of Gaussian noise, the stochastic units that we introduce in this paper can be potentially used with stochastic back-propagation, this paper is limited to applications in RBM.

The following interview is one of many included in the report. Oriol Vinyals is a research scientist at Google working on the DeepMind team by way of previous work with the Google Brain team. He holds a Ph.D. in EECS from University of California, Berkeley, and a master's degree from University of California, San Diego. Oriol Vinyals: I'm originally from Barcelona, Spain, where I completed my undergraduate studies in both mathematics and telecommunication engineering. Early on, I knew I wanted to study AI in the U.S. I spent nine months at Carnegie Mellon, where I finished my undergraduate thesis.

Google forked out over 600 million ( 400 million) for a little-known London startup called DeepMind in 2014 without specifying how the company's artificial-intelligence technology would be used to increase Google's revenues, which already run into tens of billions of dollars every year. That all changed Wednesday when DeepMind announced that Google had found a use for DeepMind's technology in its enormous data centres. Since being acquired by Google, DeepMind's AI has been used to beat humans at board games and create free apps with the National Health Service. Neither application has helped Google make, or save, any money. But now Google is using a DeepMind-built AI system to control the huge air-conditioning units in its power-hungry data centres, where servers consume enough energy to power entire cities and get very hot in the process. The AI does this by predicting how much air conditioning will be needed to deal with an anticipated change in data-centre temperature, which fluctuates as demand for services like YouTube, Google Maps, and Gmail rises and falls.

Hello my AI enthusiasts, I heard a lot about Deep learning, machine learning,... in the news. I already have some programming skills (mostly Java, Python tho) and I guess I am not a complete newb to tech and the interwebs. Anyway, you all know that there is one problem with all the information on the Internet: There is too much of them. And that's why I ask you. Is there a guide / tutorial to machine learning?

Look no further than Google's Tensor Processing Unit (TPU), SoftBank's acquisition of ARM (SoftBank hopes to be a big player in AI), and now a venture-backed startup rolling out a family of "Deep Learning" computers. That startup is Wave Computing, based in Campbell, Calif. The six-year-old company came out of stealth mode Thursday (July 21), revealing its design of a massively parallel dataflow processing architecture called the Wave Dataflow Processing Unit (DPU) for deep learning. Derek Meyer, Wave Computing CEO, told EE Times, "In order to accelerate deep learning, the world needs a new computing architecture." Traditional computer architectures are designed for control flow-oriented applications.

As the pioneer in artificial intelligence field, Baidu chose the Inspur NF5568M4 heterogeneous supercomputing server in its unmanned auto road condition model training. Artificial intelligence has advanced through the years and voice recognition, intelligent hardware, and driverless cars are all technologies that influence our lives. Behind artificial intelligence technology is a neural network that is built from deep learning -- mimicking mechanisms of the human brain when interpreting data. In order to meet all the latest deep learning requirements, a high-performance CPU GPU co-processing acceleration server is growing to become the essential foundation for artificial intelligence hardware.

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities still classified as AI include advanced Chess and Go systems and self-driving cars. AI research is divided into subfields[4] that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[5] General intelligence is among the field's long-term goals.[6] Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it."[7] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.[8] Attempts to create artificial intelligence has experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the collapse of the Lisp machine market in 1987. In the twenty-first century AI techniques became an essential part of the technology industry, helping to solve many challenging problems in computer science.[9]

Patterns are what machine-learning algorithms exist to sniff out. But detecting those patterns is almost never the endgame. Typically, we use machine learning (a category in which I also include deep learning) to drill down to the patterns most relevant to some decision-support scenario, such as identifying fine-grained nuances of customer sentiment for use in target marketing or pinpointing the signs of imminent equipment failure through continuous sifting of scattered event-log databases. Once discovered, the statistical patterns can take on a programmatic life of their own that goes far beyond decision support in potential applications. As crystallized in machine-learning models, the patterns can become key assets in the development of other algorithmic applications that have little or no relevance to decision support.

DeepMind may be a master at one of the most complex games on Earth, but can it handle the day-to-day energy concerns of a Google data center? Yes, as it turns out, and with a vengeance. The power needs of a data center depend on lots of factors, from demand to the weather, and adjusting to or predicting these variables in order to achieve maximal power efficiency can be difficult indeed. Google has been applying machine learning to the problem, building a neural model with which its AI can keep all these factors in mind, so to speak. The researchers finally let DeepMind loose on a live data center -- and the results were immediately validating.

Microsoft has a brand-new conference, exclusively for data scientists, big data engineers, and machine learning practitioners. The Microsoft Data Science Summit, to be held in Atlanta GA, September 26-27, will feature talks and lab sessions from Microsoft engineers and thought leaders on using data science techniques and Microsoft technology, applied to real-world problems. Other topics of interest include building with bot frameworks, deep learning, Internet of Things applications, and in-depth Data Science topics. To register for the conference, follow the link below. Discounted day passes to Microsoft Ignite on September 28-29 are also available to Microsoft Data Summit registrants.