Recently, machine learning has prevailed in many academia and industrial applications. At the same time, quantum computing, once seen as not realizable, has been brought to markets by several tech giants. However, these machines are not fault-tolerant and can not execute very deep circuits. Therefore, it is urgent to design suitable algorithms and applications implementable on these machines. In this work, we demonstrate a novel approach which applies variational quantum circuits to deep reinforcement learning. With the proposed method, we can implement famous deep reinforcement learning algorithms such as experience replay and target network with variational quantum circuits. In this framework, with appropriate information encoding scheme, the possible quantum advantage is the number of circuit parameters with $poly(\log{} N)$ compared to $poly(N)$ in conventional neural network where $N$ is the dimension of input vectors. Such an approach can be deployed on near-term noisy intermediate-scale quantum machines.

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

Hyperparameter tuning is an omnipresent problem in machine learning as it is an integral aspect of obtaining the state-of-the-art performance for any model. Most often, hyperparameters are optimized just by training a model on a grid of possible hyperparameter values and taking the one that performs best on a validation sample (grid search). More recently, methods have been introduced that build a so-called surrogate model that predicts the validation loss for a specific hyperparameter setting, model and dataset and then sequentially select the next hyperparameter to test, based on a heuristic function of the expected value and the uncertainty of the surrogate model called acquisition function (sequential model-based Bayesian optimization, SMBO). In this paper we model the hyperparameter optimization problem as a sequential decision problem, which hyperparameter to test next, and address it with reinforcement learning. This way our model does not have to rely on a heuristic acquisition function like SMBO, but can learn which hyperparameters to test next based on the subsequent reduction in validation loss they will eventually lead to, either because they yield good models themselves or because they allow the hyperparameter selection policy to build a better surrogate model that is able to choose better hyperparameters later on. Experiments on a large battery of 50 data sets demonstrate that our method outperforms the state-of-the-art approaches for hyperparameter learning.

Project Management Institute released its 2019 Pulse of the Profession In-Depth Report: AI Innovators: Cracking the Code on Project Performance. The report provides an in-depth look at how artificial intelligence (AI) is impacting organizations and the project management profession. Findings reveal that AI disruption is happening and at a large scale: 81 percent of respondents report their organization is being impacted by AI technologies; 37 percent say adopting AI technologies is a high priority for their organization; and project professionals say they expect the proportion of the projects they manage using AI will jump from 23 to 37 percent over the next three years. These insights from a survey of 551 project management practitioners globally show that the presence of AI technologies will continue to grow, requiring shifts in how projects are managed and how organizations implement strategy. The report identifies six AI technologies that are impacting organizations globally.

Machine and reinforcement learning (RL) are being applied to plan and control the behavior of autonomous systems interacting with the physical world -- examples include self-driving vehicles, distributed sensor networks, and agile robots. However, if machine learning is to be applied in these new settings, the resulting algorithms must come with the reliability, robustness, and safety guarantees that are hallmarks of the control theory literature, as failures could be catastrophic. Thus, as RL algorithms are increasingly and more aggressively deployed in safety critical settings, it is imperative that control theorists be part of the conversation. The goal of this tutorial paper is to provide a jumping off point for control theorists wishing to work on RL related problems by covering recent advances in bridging learning and control theory, and by placing these results within the appropriate historical context of the system identification and adaptive control literatures.

If our models are used in new or unexpected cases, do we know if they will make fair predictions? Previously, researchers developed ways to debias a model for a single problem domain. However, this is often not how models are trained and used in practice. For example, labels and demographics (sensitive attributes) are often hard to observe, resulting in auxiliary or synthetic data to be used for training, and proxies of the sensitive attribute to be used for evaluation of fairness. A model trained for one setting may be picked up and used in many others, particularly as is common with pre-training and cloud APIs. Despite the pervasiveness of these complexities, remarkably little work in the fairness literature has theoretically examined these issues. We frame all of these settings as domain adaptation problems: how can we use what we have learned in a source domain to debias in a new target domain, without directly debiasing on the target domain as if it is a completely new problem? We offer new theoretical guarantees of improving fairness across domains, and offer a modeling approach to transfer to data-sparse target domains. We give empirical results validating the theory and showing that these modeling approaches can improve fairness metrics with less data.

As the capabilities and pervasiveness of machine learning (ML) and artificial intelligence (AI) increasingly affect society, there is increasing concern about the safety of such systems, i.e. the potential of accidental harm from implementation errors and unintended consequences in ML algorithms. As a result, there has been increasing interest in the nascent field of AI safety [1, 2, 3, 4, 5, 6], which seeks to understand and solve the technical challenges in developing and deploying AI that does what it is intended to do. The purpose of this chapter is to explore how the study of AI safety intersects with that of evolutionary computation (EC), to both highlight an exciting and important set of safety problems within EC, and to suggest that evolution and EC have important insights that could benefit the general study of AI safety. To frame the problem of AI safety, we adopt the framework of Amodei et al. [1], which defines AI safety as concerned with accidents in ML systems, and defines five problems within three broad categories of issues: (1) specifying the wrong objective function, (2) making safe and efficient use of a true but expensive objective (e.g.

Deep learning based data-driven approaches have been successfully applied in various image understanding applications ranging from object recognition, semantic segmentation to visual question answering. However, the lack of knowledge integration as well as higher-level reasoning capabilities with the methods still pose a hindrance. In this work, we present a brief survey of a few representative reasoning mechanisms, knowledge integration methods and their corresponding image understanding Figure 1: The diagram shows the information hierarchy for applications developed by various groups images and the knowledge associated with each level of information. of researchers, approaching the problem from a variety of angles. Furthermore, we discuss upon key efforts on integrating external knowledge with neural paper is to present a survey of recent works (including a few networks. Taking cues from these efforts, we of our works) in image understanding where knowledge and conclude by discussing potential pathways to improve reasoning plays an important role.

Automatic program correction is an active topic of research, which holds the potential of dramatically improving productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning and NLP have rekindled the hope to eventually fully automate the process of repairing programs. A key challenges is ambiguity, as multiple codes -- or fixes -- can implement the same functionality. In addition, dataset by nature fail to capture the variance introduced by such ambiguities. Therefore, we propose a deep generative model to automatically correct programming errors by learning a distribution of potential fixes. Our model is formulated as a deep conditional variational autoencoder that samples diverse fixes for the given erroneous programs. In order to account for ambiguity and inherent lack of representative datasets, we propose a novel regularizer to encourage the model to generate diverse fixes. Our evaluations on common programming errors show for the first time the generation of diverse fixes and strong improvements over the state-of-the-art approaches by fixing up to $61\%$ of the mistakes.

This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures. We analyzed and compared the most representative symptoms with its neural model counterpart, detailing the alteration introduced in the network that generates each of the symptoms, and identifying their strengths and weaknesses. For completeness we additionally cross-compared Bayesian and free-energy approaches. Models of schizophrenia mainly focused on hallucinations and delusional thoughts using neural disconnections or inhibitory imbalance as the predominating alteration. Models of autism rather focused on perceptual difficulties, mainly excessive attention to environment details, implemented as excessive inhibitory connections or increased sensory precision. We found an excessive tight view of the psychopathologies around one specific and simplified effect, usually constrained to the technical idiosyncrasy of the network used. Recent theories and evidence on sensorimotor integration and body perception combined with modern neural network architectures offer a broader and novel spectrum to approach these psychopathologies, outlining the future research on neural networks computational psychiatry, a powerful asset for understanding the inner processes of the human brain.