The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, "insideHPC / insideBIGDATA AI/Deep Learning Survey 2016," to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

Yahoo, model Apache Spark citizen and developer of CaffeOnSpark, which made it easier for developers building deep learning models in Caffe to scale with parallel processing, is open sourcing a new project called TensorFlowOnSpark. The pairing of Spark and TensorFlow should make the deep learning framework more attractive to developers who are creating models that need to run on large computing clusters. For those that zoned out during the big-data boom, Apache Spark is an open source framework designed to increase the efficiency of parallel computing. Following in the steps of tools like Hadoop, Spark made it possible for companies like Netflix to process huge amounts of user data to offer up recommendations at scale. Machine learning frameworks like Google's TensorFlow and Caffe help people create deep learning models without the rigorous skill-set of a machine learning specialist.

Nest is improving both its apps and its camera smarts. An update to both iOS and Android apps (if your phones and tablets are on the latest versions) focuses on notifications, with Nest Aware subscribers getting the bulk of the benefits. Over the next few weeks, Aware customers will see automatic door detection appear on both their indoor and outdoor Nest Cam feeds. The cameras will attempt to recognize motion patterns over time, feeding the data into deep learning algorithms to make it all automated, automatically creating "activity zones" around doors it picks up. The cameras can then send you notifications when there's movement in that area.

Artificial intelligence is not one thing, but many, spanning several schools of thought. In his book The Master Algorithm, Pedro Domingos calls them the tribes of AI. As the University of Washington computer scientist explains, each tribe fashions what would seem to be very different technology. Evolutionists, for example, believe they can build AI by recreating natural selection in the digital realm. Symbolists spend their time coding specific knowledge into machines, one rule at a time.

"Enormous levels of investment are pouring into this technology. The achievements we have seen so far will surely pale against what the coming decades will bring." Artificial intelligence is getting a lot of attention right now. It was one of the key technological themes at the 2016 World Economic Forum and in June of last year, Andrew Ng, chief scientist at the Chinese web services company Baidu called "AI the new Electricity." As recently as Dec 5, 2016, Google and Elon Musk opened their AI platforms to the public, Uber launched Uber AI Lab and Apple announced that for the first time it will publish their AI research.

Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary. In this post, we've compiled great e-resources for you digital natives looking to explore the exciting world of Machine Learning and Neural Networks. But before you dive into the deep end, you need to make sure you've got the fundamentals down pat. It doesn't matter what catches your fancy, machine learning, artificial intelligence, or deep learning; you need to know the basics of math and stats--linear algebra, calculus, optimization, probability--to get ahead.

Over this course you will build multiple practical systems using natural language processing (NLP), the branch of machine learning and data science that deals with text and speech. You'll start with a background on NLP before diving in, building a spam detector and a model for sentiment analysis in Python. Learning how to build these practical tools will give you an excellent window into the mechanisms that drive machine learning. Build a spam detector & sentiment analysis model that may be used to predict the stock market Learn practical tools & techniques like the natural language toolkit library & latent semantic analysis Create an article spinner from scratch that can be used as an SEO tool Think this is cool? Check out the other bundles in this series, The Deep Learning and Artificial Intelligence Introductory Bundle, and The Advanced Guide to Deep Learning and Artificial Intelligence.

Effortless customer engagement is top of mind for Quinn Banks, senior product marketing manager at Farmers Insurance -- and he's spearheading the implementation of machine learning to get the company there. "We are working with machine learning to make our app more efficient when customers come in, or even to anticipate what a customer will need when they come into the application, based on their habits, their environmental changes, even Read More ... Tags: Corporate Enterprises Computer systems Customers Artificial intelligence Machine intelligence Machine learning Google crams machine learning into smartwatches in A.I. push Google is bringing artificial intelligence to a whole new set of devices, including Android Wear 2.0 smartwatches and the Raspberry Pi board, later this year. These devices don't require a set of powerful CPUs and GPUs to carry out machine-learning tasks. Google researchers are instead trying to lighten the hardware load to carry out basic A.I. tasks, as exhibited by last week's release of the Android Wear 2.0 operating system Read More ... Tags: Smart Devices Computer systems Smart Watches Artificial intelligence Machine intelligence Machine learning Wearable devices In this video from the 2017 HPC Advisory Council Stanford Conference, DK Panda presents: Best Practices: Designing HPC & Deep Learning Middleware for Exascale Systems. "This talk will focus on challenges in designing runtime environments for exascale systems with millions of processors and accelerators to support various programming models.

The dynamic Boltzmann machine (DyBM) has been proposed as a stochastic generative model of multi-dimensional time series, with an exact, learning rule that maximizes the log-likelihood of a given time series. The DyBM, however, is defined only for binary valued data, without any nonlinear hidden units. Here, in our first contribution, we extend the DyBM to deal with real valued data. We present a formulation called Gaussian DyBM, that can be seen as an extension of a vector autoregressive (VAR) model. This uses, in addition to standard (explanatory) variables, components that captures long term dependencies in the time series. In our second contribution, we extend the Gaussian DyBM model with a recurrent neural network (RNN) that controls the bias input to the DyBM units. We derive a stochastic gradient update rule such that, the output weights from the RNN can also be trained online along with other DyBM parameters. Furthermore, this acts as nonlinear hidden layer extending the capacity of DyBM and allows it to model nonlinear components in a given time-series. Numerical experiments with synthetic datasets show that the RNN-Gaussian DyBM improves predictive accuracy upon standard VAR by up to 35%. On real multi-dimensional time-series prediction, consisting of high nonlinearity and non-stationarity, we demonstrate that this nonlinear DyBM model achieves significant improvement upon state of the art baseline methods like VAR and long short-term memory (LSTM) networks at a reduced computational cost.