Artificial Intelligence has become a topic of intense interest throughout the cloud developer and enterprise IT communities. Accordingly, attendees at the upcoming Cloud Expo @ThingsExpo at the Javits Center in New York, June 6-8, will find fresh new content in a new track called Cognitive Computing Artificial Intelligence, Machine Learning, Deep Learning. We are now in the third, and most successful, generation of groundbreaking Artificial Intelligence (AI) research and deployments. With it comes new capabilities for Machine Learning (ML) and Deep Learning (DL), as organizations of all sizes work to create and use new insights from their existing operations while also seeking to develop new ways of making their systems and their organizations much smarter. Cloud Expo is still accepting submissions for this new track, so please visit www.cloudcomputingexpo.com for the latest information.

Although artificial intelligence (AI) has experienced a number of "springs" and "winters" in its roughly 60-year history, it is safe to expect the current AI spring to be both lasting and fertile. Applications that seemed like science fiction a decade ago are becoming science fact at a pace that has surprised even many experts. The stage for the current AI revival was set in 2011 with the televised triumph of the IBM Watson computer system over former Jeopardy! This watershed moment has been followed rapid-fire by a sequence of striking breakthroughs, many involving the machine learning technique known as deep learning. Computer algorithms now beat humans at games of skill, master video games with no prior instruction, 3D-print original paintings in the style of Rembrandt, grade student papers, cook meals, vacuum floors, and drive cars.1 All of this has created considerable uncertainty about our future relationship with machines, the prospect of technological unemployment, and even the very fate of humanity. Regarding the latter topic, Elon Musk has described AI "our biggest existential threat." Stephen Hawking warned that "The development of full artificial intelligence could spell the end of the human race." In his widely discussed book Superintelligence, the philosopher Nick Bostrom discusses the possibility of a kind of technological "singularity" at which point the general cognitive abilities of computers exceed those of humans.2 Discussions of these issues are often muddied by the tacit assumption that, because computers outperform humans at various circumscribed tasks, they will soon be able to "outthink" us more generally. Continual rapid growth in computing power and AI breakthroughs notwithstanding, this premise is far from obvious.

Elon Musk's artificial intelligence platform OpenAI introduced a new program to train robots entirely in simulation. Now they've added a new algorithm, named one-shot imitation learning, which will only require humans to demonstrate a task once in VR for a robot to learn it. The system is powered by two neural networks. The first takes a camera image and determines objects' spatial position in relation to the robot -- but it was trained only with a host of simulated images, meaning it was taught how to interact with the real world before it ever actually saw the real world. The second imitates tasks shown by the demonstrator by scanning through recorded action and paying attention to frames that tell it what to do next.

I helped work on a thing last weekend that I can't write about, yet, and then last week I found my way to San Jose for Nvidia's GPU Technology Conference, and fine, all right, OK, I'm convinced: Now that the smartphone boom is plateauing, AI/deep learning is the new coal face of technology -- and, at least for now, Nvidia bestrides it like many parallel colossi. I use the metaphor "coal face" advisedly. It's the place where advances are being made, where the most value is being created … but it's also a messy business, often with little visibility, with many ways to go terribly wrong. Neural networks are still more applied science than they are engineering, although it's beginning to move along that spectrum. The Nvidia GPU conference featured a sizable zone of scientific posters exploring the cutting edge of GPU usage, something you don't see at a lot of tech conferences.

"There is a real demand for developers who not only understand artificial intelligence, but know how to apply it in commercial applications," said Christian Plagemann, vice president of Content at Udacity, who will be working with the institute on self-driving car content. "NVIDIA is a leader in the application of deep learning technologies and we're excited to work closely with their experts to train the next generation of artificial intelligence practitioners." The Deep Learning Institute is also expanding to include new deep learning training labs, new coursework for educators, and new DLI certified training partners. The company will work with Microsoft Azure, IBM Power and IBM Cloud teams to bring the institute's content to cloud solutions.

Linking human whole-body motion and natural language is of great interest for the generation of semantic representations of observed human behaviors as well as for the generation of robot behaviors based on natural language input. While there has been a large body of research in this area, most approaches that exist today require a symbolic representation of motions (e.g. in the form of motion primitives), which have to be defined a-priori or require complex segmentation algorithms. In contrast, recent advances in the field of neural networks and especially deep learning have demonstrated that sub-symbolic representations that can be learned end-to-end usually outperform more traditional approaches, for applications such as machine translation. In this paper we propose a generative model that learns a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (RNNs) and sequence-to-sequence learning. Our approach does not require any segmentation or manual feature engineering and learns a distributed representation, which is shared for all motions and descriptions. We evaluate our approach on 2,846 human whole-body motions and 6,187 natural language descriptions thereof from the KIT Motion-Language Dataset. Our results clearly demonstrate the effectiveness of the proposed model: We show that our model generates a wide variety of realistic motions only from descriptions thereof in form of a single sentence. Conversely, our model is also capable of generating correct and detailed natural language descriptions from human motions.

Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.

Fingerprinting based WLAN indoor positioning system (FWIPS) provides a promising indoor positioning solution to meet the growing interests for indoor location-based services (e.g., indoor way finding or geo-fencing). FWIPS is preferred because it requires no additional infrastructure for deploying an FWIPS and achieving the position estimation by reusing the available WLAN and mobile devices, and capable of providing absolute position estimation. For fingerprinting based positioning (FbP), a model is created to provide reference values of observable features (e.g., signal strength from access point (AP)) as a function of location during offline stage. One widely applied method to build a complete and an accurate reference database (i.e. radio map (RM)) for FWIPS is carrying out a site survey throughout the region of interest (RoI). Along the site survey, the readings of received signal strength (RSS) from all visible APs at each reference point (RP) are collected. This site survey, however, is time-consuming and labor-intensive, especially in the case that the RoI is large (e.g., an airport or a big mall). This bottleneck hinders the wide commercial applications of FWIPS (e.g., proximity promotions in a shopping center). To diminish the cost of site survey, we propose a probabilistic model, which combines fingerprinting based positioning (FbP) and RM generation based on stochastic variational Bayesian inference (SVBI). This SVBI based position and RSS estimation has three properties: i) being able to predict the distribution of the estimated position and RSS, ii) treating each observation of RSS at each RP as an example to learn for FbP and RM generation instead of using the whole RM as an example, and iii) requiring only one time training of the SVBI model for both localization and RSS estimation. These benefits make it outperforms the previous proposed approaches.

One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background, but want to rapidly acquire one and begin using deep learning in their product or platform. Basically, if you are interested in reading this book and haven't been turned off by the content of this post, the book is likely for you. The book starts off covering the required background for understanding later material, along with historical context and elementary explanations of the technical concepts. In fact, the entire first part of the book is dedicated to building the technical foundation required to study deep learning.

Backed by purpose-built HPC systems designed for maximum performance, HPE announces a comprehensive GPU-based solutions portfolio tailored to a new frontier of AI and deep learning capabilities. Explosive data growth and a rising demand for real-time analytics are making high performance computing (HPC) technologies increasingly vital to success. Organizations across all industries are seeking the next generation of IT solutions to facilitate scientific research, enhance national security, ensure economic stability, and empower innovation to face the challenges of today and tomorrow. HPC solutions are key to quickly answering some of the world's most daunting questions. From Tesla's self-driving car to quantum computing, artificial intelligence (AI) is enabling unparalleled compute capabilities and outmatching humans at many cognitive tasks.