Probabilistic Graphical Modeling and Variational Inference play an important role in recent advances in Deep Reinforcement Learning. Aiming at a self-consistent tutorial survey, this article illustrates basic concepts of reinforcement learning with Probabilistic Graphical Models, as well as derivation of some basic formula as a recap. Reviews and comparisons on recent advances in deep reinforcement learning with different research directions are made from various aspects. We offer Probabilistic Graphical Models, detailed explanation and derivation to several use cases of Variational Inference, which serve as a complementary material on top of the original contributions.

The average person spends around 3 hours a day on social media. Now, the impact of social media can be seen both on the personal life and work processes. The HBR study found that over the past two decades, the time spent by employees in collaborative activities has raised at least by 50%. Inner-communication is now becoming a highly important thing in enterprise companies. How much time do you think an average enterprise worker spends on emails, calls, and meetings?

For machine learning perception problems, human-level classification performance is used as an estimate of top algorithm performance. Thus, it is important to understand as precisely as possible the factors that impact human-level performance. Knowing this 1) provides a benchmark for model performance, 2) tells a project manager what type of data to obtain for human labelers in order to get accurate labels, and 3) enables ground-truth analysis--largely conducted by humans--to be carried out smoothly. In this empirical study, we explored the relationship between resolution and human classification performance using the MNIST data set down-sampled to various resolutions. The quantitative heuristic we derived could prove useful for predicting machine model performance, predicting data storage requirements, and saving valuable resources in the deployment of machine learning projects. It also has the potential to be used in a wide variety of fields such as remote sensing, medical imaging, scientific imaging, and astronomy.

Nevertheless, fairness in machine learning remains a problem. Machine learning algorithms have the risk of amplifying societal stereotypes by over associating protected attributes, e.g., race and gender, with the main prediction task [33]. Eventually they are capable of exhibiting discriminatory behaviors against certain subgroups. For example, a recruiting tool believes that men are more qualified and shows bias against women [26], facial recognition performs extremely poorly for darker skin females [5], recognition accuracy is very low for subgroup of people in pedestrian detection of self-driving cars [33]. The fairness problem might cause adverse impacts on individuals and society. It not only limits a person's opportunities which he/she is qualified, but also might further exacerbate social inequity. Among different machine learning models, the fairness problem of deep learning models has especially attracted attention from academia and industry recently. First, deep learning models have outperformed conventional machinePermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

The majority of experts and opinion leaders believe that artificial intelligence (AI) is going to revolutionise many industries, including healthcare [1]. In the short term, the power and potential of AI appear most suitable for complementing human expertise. In other words, machines will help humans do a better job. Consequently, it is anticipated that AI will help with repetitive tasks, in-depth quantification and classification of findings, improved patient and disease phenotyping and, ultimately, with better outcomes for patients, physicians, hospital administrators, insurance companies and governments [2]. This focus issue of the Netherlands Heart Journal aims to help general cardiologists explore the state of the art of AI in cardiology.

We live in times of high-tech euphoria marked by instances of geopolitical doom-and-gloom. There seems to be no middle ground between the hype surrounding cutting-edge technologies, such as Artificial Intelligence (AI) and their impact on security and defence, and anxieties over their potential destructive consequences. AI, arguably one of the most important and divisive inventions in human history, is now being glorified as the strategic enabler of the 21st century and next domain of military disruption and geopolitical competition. The race in technological innovation, justified by significant economic and security benefits, is widely recognised as likely to make early adopters the next global leaders. Technological innovation and defence technologies have always occupied central positions in national defence strategies.

As a subfield of machine learning, \emph{reinforcement learning} (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey will discuss the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

In several reinforcement learning (RL) scenarios such as security settings, there may be adversaries trying to interfere with the reward generating process for their own benefit. We introduce Threatened Markov Decision Processes (TMDPs) as a framework to support an agent against potential opponents in a RL context. We also propose a level-k thinking scheme resulting in a novel learning approach to deal with TMDPs. After introducing our framework and deriving theoretical results, relevant empirical evidence is given via extensive experiments, showing the benefits of accounting for adversaries in RL while the agent learns

Statistical inference and machine learning algorithms have traditionally been developed for data available at a single location. Unlike this centralized setting, modern datasets are increasingly being distributed across multiple physical entities (sensors, devices, machines, data centers, etc.) for a multitude of reasons that range from storage, memory, and computational constraints to privacy concerns and engineering needs. This has necessitated the development of inference and learning algorithms capable of operating on non-collocated data. Such algorithms can be divided into two broad categories, namely, distributed algorithms and decentralized algorithms . Distributed algorithms correspond to the setup in which the data-bearing entities (henceforth referred to as "nodes") only communicate with a single entity (referred to as master node, central server, parameter server, fusion center, etc.), which is tasked with generating the final result. Such distributed setups arise in the context of parallel computing, where the focus is computational speedups and/or overcoming memory/storage bottlenecks, and federated systems, where "raw" data collected by individual nodes cannot be shared with the master node due to either communication constraints (e.g., sensor networks) or privacy concerns (e.g., smartphone data). Decentralized algorithms, on the other hand, correspond to the setup that lacks a central server; instead, individual nodes in this setup communicate among themselves over a network (often ad hoc) to reach a common solution (i.e., achieve consensus) at all nodes. Such decentralized setups arise either out of the need to eliminate single points of failure in distributed setups or due to practical constraints, as in the internet of things and autonomous systems. We refer the reader to Figure 1 for examples of distributed and decentralized setups.Is it distributed or is it decentralized? Inference and learning from non-collocated data have been studied for decades in computer science, control, signal processing, and statistics. Both among and within these disciplines, however, there is no consensus on use of the terms "distributed" and "decentralized." Though many works share the definitions provided in here, there are numerous authors who use these two terms interchangeably, while there are some other authors who reverse these definitions. Inference and machine learning algorithms involving non-collocated data are broadly divisible into the categories of ( i) distributed algorithms and ( ii) decentralized algorithms.

This is essential reading for those interested in incorporating robotics into their organization! Jonathan Padgett, VP, UiPath, talks to ReadITQuik about the many fascinating aspects of Robotic Process Automation (RPA) and how RPA can improve processes and drive efficiencies. Learn about how to select the best RPA provider and RPA best practices in this geektastic interview on one of the latest IT trends. Robotic Process Automation (RPA) technology integrates with the workforce to not only improve execution but also to tackle routine business processes. Think of a UiPath bot as a teleworker or virtual employee to which your employees can delegate repetitive (although necessary) tasks to free up your most valuable resources (your people) to focus on more strategic, creative and interpersonal work.