This project is a RAND Venture. Funding for this venture was provided by philanthropic contributions from RAND supporters and income from operations. The research was conducted by the Center for Global Risk and Security (CGRS), part of International Programs at the RAND Corporation. This report is part of the RAND Corporation research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors.

Artificial intelligence is one of the most exciting scientific developments in human history. Only a decade ago, AI was a far-fetched fairy tale. Advancements in deep learning have propelled AI enthusiasts' wildest dreams into reality. In fact, artificial intelligence is already a part of our everyday lives, whether it be in the form of your email app that learns what messages you frequently trash or the digital assistant tracking your speech patterns. AI is more than a passing fad or unfortunate bubble.

NASHVILLE, Tenn.--(BUSINESS WIRE)--Digital Reasoning, a leader in cognitive computing, was announced as the company with the'Best Artificial Intelligence Technology' at the WatersTechnology.com Digital Reasoning created the Synthesys cognitive computing platform, which is one of the most widely-adopted AI systems within financial services and used by many of the world's leading investment banks in applications ranging across risk and compliance, financial crime, and customer insights. Synthesys understands and analyzes human communications. Most communications data is unstructured, making it virtually unreadable using conventional technology. By applying artificial intelligence, Synthesys makes sense of human language in text, audio and images, and resolves who is talking about what and with whom.

In 2017, expect artificial intelligence to become a reality in B2B marketing. Artificial intelligence involves the use of computer systems to perform all kinds of daily tasks that have traditionally required human intelligence. Some of its capabilities include decision-making, visual perception, speech recognition, and language translation. While artificial intelligence has been a staple in science-fiction movies, experts predict that artificial intelligence is about to come to life and have a major impact on the marketing efforts of B2B businesses over the next five years. Yet, too many B2B marketers admit that they are unwise to the ways of artificial intelligence and lack a strong understanding of the advanced technology.

Inception v3 is a deep convolutional neural network trained for single-label image classification on ImageNet data set. The TensorFlow team already prepared a tutorial on retraining it to tell apart a number of classes based on our own examples. We are going to modify the retraining script retrain.py If you just want to jump to the resulting code, it's here with all the necessary files and information required to make it work. From now on, I will assume that you have cloned the mentioned repository and refer to its files.

The last decade has seen enormous progress in both the theory and practical solutions to the problem of matrix completion, in which the goal is to estimate missing elements of a matrix given measurements at some subset of its locations. Originally viewed from a combinatorial perspective [1], it is now usually approached from a statistical perspective in which additional structural assumptions (e.g., low-rank, sparse factors etc) not only make the problem tractable but allow for provable error bounds from noisy measurements [2]-[8]. Tensors, which we will view as multi-way arrays, naturally arise in slew of practical applications in the areas of signal processing, computer vision, neuroscience, etc. [9], [10]. Often in practice tensor data is collected in a noisy environment and suffers from missing observations. Given the success of matrix completion methods, it is no surprise that recently there has been a lot of interest in extending the successes of matrix completion to tensor completion problem [11]-[13]. In this work we consider the general problem of tensor completion.

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles.

This paper describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble, produced by bagging multiple trees. The process of bagging and combining results from multiple trees, produces numerous diagnostics which, with interactive graphics, can provide a lot of insight into class structure in high dimensions. Various aspects are explored in this paper, to assess model complexity, individual model contributions, variable importance and dimension reduction, and uncertainty in prediction associated with individual observations. The ideas are applied to the random forest algorithm, and to the projection pursuit forest, but could be more broadly applied to other bagged ensembles. Interactive graphics are built in R, using the ggplot2, plotly, and shiny packages.

When faced with high frequency streams of data, clustering raises theoretical and algorithmic pitfalls. We introduce a new and adaptive online clustering algorithm relying on a quasi-Bayesian approach, with a dynamic (\emph{i.e.}, time-dependent) estimation of the (unknown and changing) number of clusters. We prove that our approach is supported by minimax regret bounds. We also provide an RJMCMC-flavored implementation (called PACBO) for which we give a convergence guarantee. Finally, numerical experiments illustrate the potential of our procedure.

We describe and analyze a new boosting algorithm for deep learning called SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct ensembles of classifiers, SelfieBoost boosts the accuracy of a single network. We prove a $\log(1/\epsilon)$ convergence rate for SelfieBoost under some "SGD success" assumption which seems to hold in practice.