Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.

Although there has been a rapid and greatly publicized growth of data analytics and machine learning methodologies across many applications, and in virtually every industry, these developments seem to have almost completely been missed in the semiconductor integrated circuit (IC) space. With the 14nm process technology node currently in production, and both 10 and 7nm nodes at different stages of development, the IC'ecosystem' is being restructured and consolidated across its four traditional components (i.e., fabless design companies, electronic design automation and intellectual property suppliers, process and metrology tools suppliers, and silicon foundries). There are, however, intrinsic technology factors (e.g., the continual deceleration of geometric scaling and the delayed introduction of key patterning technologies) that are primary sources of disruption to this restructuring. There are also critical hidden gaps and bottlenecks in the design-to-manufacturing data information pipeline. The deployment of carefully selected data analytics techniques (with/without machine learning algorithms) therefore represents a strategic opportunity to enable a 2 year/node ('more-Moore') cycle at 10nm and below in the semiconductor industry.

PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.

SYSPRO is unveiling the new capabilities to 24 leading enterprise software analysts from 12 different global firms during a two week roadshow. It has labeled its tour, "Going for the Gold in Best of Class ERP" and is showcasing sophisticated leading-edge technology advances, such as bots, predictive search, and product integrations to the Internet of Things (IoT), plus a host of powerful new, role-based data access for users. Industry analysts have been predicting for the last 12 months or more that such technologies will erase some of the key competitive barriers that might otherwise impede newer or mid-sized manufacturing firms from competing as effectively as they'd like. "To compete effectively today in a global market, manufacturers and distributors of all sizes need to leverage state-of-the-art technologies that can optimize profit margins while delivering a better customer experience," said Predrag Jakovlevic, Principal Analyst, Enterprise Applications, for Technology Evaluation Centers. "While giant ERP companies often dominate the news, companies like SYSPRO are now announcing delivery of some of the hottest new IT capabilities embedded within their software, like bots and predictive search, and have made commitments to bringing machine learning, artificial intelligence and IoT to market in 2017. The point is, very affordable ERP solutions like SYSPRO can give even small and mid-sized manufacturers the ability to be very successful against much larger market competitors."

Lenovo Group Ltd, the world s largest personal computer maker, will launch its augmented-reality-enabled smartphone in October in China, as part of its broad efforts to boost phone sales with innovative products. Yang Yuanqing, CEO and Chairman of the Beijing-based company, said on Saturday the Phab2 Pro, the world s first smartphone to host AR applications without the need of other accessories, will help create new business models by bringing new interactive experience to consumers. The new device, first unveiled in June, is based on Google Inc s Tango project and allows users to play virtual dominoes on a physical table and shoot digital robots that inhabit users living rooms. "Technology innovation and business model innovation are part of the new path for growth," Yang said, adding the company aims to leverage cutting-edge technologies to change the way people live and work. He made the comments at the B20 summit which was held in Hangzhou, capital of Zhejiang province on Saturday.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.

The technology press is abuzz these days with stories about Machine Learning (ML) and Artificial Intelligence (AI) -- every other week it seems we're hearing about a new AI surpassing human ability at some task or other, and just as often we hear about exciting new start-ups revolutionizing traditional problem spaces using machine learning. We also see the odd notable AI failure every now and then. It can be hard to conceptualize what people are talking about when it comes to AI; and of course there's also the question of the so-called Singularity (an artificial "superintelligence" arising and causing runaway technological growth): just how near is it? This post is the first in a series on machine learning, and aims to bring some clarity to the subject, explaining how the concepts of machine learning and artificial intelligence relate to each other. It also describes at a high-level how basic ML works today to solve problems.

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.