Just a few short years ago, artificial intelligence was seen as nothing more than a villain in sci-fi movies. This revolutionary technology was used to insist that innovating too fast can lead to a future of robot overlords and murderous machines. From Hal 9000 in 2001: A Space Odyssey to Skynet in Terminator, artificial intelligence was in need of a good public relations person to change it's image. Fortunately, the tech community has been happy to oblige and AI technology has taken off like a self-landing rocket. According to data from CB Insights, investments in artificial intelligence have been through the roof in recent years. While investments hovered around 700 million in 2013, they have reached astronomical levels at nearly 2.4 billion in just a few short years.

Distributed learning is the problem of inferring a function in the case where training data is distributed among multiple geographically separated sources. Particularly, the focus is on designing learning strategies with low computational requirements, in which communication is restricted only to neighboring agents, with no reliance on a centralized authority. In this thesis, we analyze multiple distributed protocols for a large number of neural network architectures. The first part of the thesis is devoted to a definition of the problem, followed by an extensive overview of the state-of-the-art. Next, we introduce different strategies for a relatively simple class of single layer neural networks, where a linear output layer is preceded by a nonlinear layer, whose weights are stochastically assigned in the beginning of the learning process. We consider both batch and sequential learning, with horizontally and vertically partitioned data. In the third part, we consider instead the more complex problem of semi-supervised distributed learning, where each agent is provided with an additional set of unlabeled training samples. We propose two different algorithms based on diffusion processes for linear support vector machines and kernel ridge regression. Subsequently, the fourth part extends the discussion to learning with time-varying data (e.g. time-series) using recurrent neural networks. We consider two different families of networks, namely echo state networks (extending the algorithms introduced in the second part), and spline adaptive filters. Overall, the algorithms presented throughout the thesis cover a wide range of possible practical applications, and lead the way to numerous future extensions, which are briefly summarized in the conclusive chapter.

The art and science of training neural networks from large data sets in order to make predictions or classifications has experienced a major transition over the past several years. Through popular and growing interest from scientists and engineers, this field of data analysis has come to be called deep learning. Put succinctly, deep learning is the ability of machine learning algorithms to acquire feature hierarchies from data and then persist those features within multiple non-linear layers which comprise the machine's learning center, or neural network. Two years ago, questions were mainly about what deep learning is, and how it might be applied to problems in science, engineering, and finance. Over the past year, however, the climate of interest has changed from a curiosity about what deep learning is, and into a focus on acquiring hardware and software in order to apply deep learning frameworks to specific problems across a wide range of disciplines.

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

Session 1: Introduction to Tensorflow We'll cover the importance of data with machine and deep learning algorithms, the basics of creating a dataset, how to preprocess datasets, then jump into Tensorflow, a library for creating computational graphs built by Google Research. We'll learn the basic components of Tensorflow and see how to use it to filter images. Session 2: Training A Network W/ Tensorflow We'll see how neural networks work, how they are "trained", and see the basic components of training a neural network. We'll then build our first neural network and use it for a fun application of teaching a neural network how to paint an image. Session 3: Unsupervised And Supervised Learning This session goes deep.

Machine learning is the science of discovering statistical dependencies in data, and the use of those dependencies to perform predictions. During the last decade, machine learning has made spectacular progress, surpassing human performance in complex tasks such as object recognition, car driving, and computer gaming. However, the central role of prediction in machine learning avoids progress towards general-purpose artificial intelligence. As one way forward, we argue that causal inference is a fundamental component of human intelligence, yet ignored by learning algorithms. Causal inference is the problem of uncovering the cause-effect relationships between the variables of a data generating system. Causal structures provide understanding about how these systems behave under changing, unseen environments. In turn, knowledge about these causal dynamics allows to answer "what if" questions, describing the potential responses of the system under hypothetical manipulations and interventions. Thus, understanding cause and effect is one step from machine learning towards machine reasoning and machine intelligence. But, currently available causal inference algorithms operate in specific regimes, and rely on assumptions that are difficult to verify in practice. This thesis advances the art of causal inference in three different ways. First, we develop a framework for the study of statistical dependence based on copulas and random features. Second, we build on this framework to interpret the problem of causal inference as the task of distribution classification, yielding a family of novel causal inference algorithms. Third, we discover causal structures in convolutional neural network features using our algorithms. The algorithms presented in this thesis are scalable, exhibit strong theoretical guarantees, and achieve state-of-the-art performance in a variety of real-world benchmarks.

WIRE)--International Data Corporation (IDC) has published a 2016 IDC Innovators report recognizing pioneering players in the machine learning-based text analytics market. IDC Innovators are companies with under 50M in revenue that offer an inventive technology and/or groundbreaking new business model. Kira Systems, Loop AI Labs, NetBase, and Taste Analytics were all named as IDC Innovators in the machine learning-based text analytics market for 2016. "Organizations are continually looking to improve their handling of data, especially unstructured data, given the explosion of information that is available via the Internet today," said David Schubmehl, Research Director, IDC's Content Analytics, Discovery and Cognitive Systems research. "Understanding and utilizing this human-generated data is a significant challenge for most organizations and the use of machine learning based text analytics is rapidly becoming the best approach to dealing with this type of data."

SUMMARY: Artificial intelligence (AI) technologies offer great promise for creating new and innovative products, growing the economy, and advancing national priorities in areas such as education, mental and physical health, addressing climate change, and more. Like any transformative technology, however, AI carries risks and presents complex policy challenges along a number of different fronts. The Office of Science and Technology Policy (OSTP) is interested in developing a view of AI across all sectors for the purpose of recommending directions for research and determining challenges and opportunities in this field. The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields. The purpose of this RFI is to solicit feedback on overarching questions in AI, including AI research and the tools, technologies, and training that are needed to answer these questions.

The notion of applying deep learning techniques to medical imaging data sets is a fascinating and fast-moving area. In fact, in a recent issue of IEEE's Transactions on Medical Imaging journal, there's a fantastic guest editorial on deep learning in medical imaging, that provides an overview of current approaches, where the field is headed, and what sort of opportunities exist. As such, we pulled out some of our favorite nuggets from this article and summarize/extend upon them in Q&A form, so they're more easily digestible. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. One way is via transfer learning, which has been used to overcome the lack of large labeled data sets in medical imaging.

This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.