The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems and also limits its full potential. In many other areas of machine learning, AutoML has shown that it is possible to automate such design choices, and AutoML has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games, such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey, we seek to unify the field of AutoRL, provide a common taxonomy, discuss each area in detail and pose open problems of interest to researchers going forward.

Artificial intelligence (AI) is a cutting-edge technology that is being adopted by forward-thinking businesses. The concept of artificial intelligence, on the other hand, has been around for decades. In 1955, "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" was published, which coined the term "artificial intelligence." Dartmouth University sponsored the first AI research project in 1956, which is widely regarded as the start of artificial intelligence. So, why is AI gaining popularity now, more than sixty years later?

The last decade has seen the increasingly important, even dominant, application of deep learning (DL) in the field of various applications. Conventional machine learning methods have been the focus of intense investigations for years; however, they have limited capabilities, are biased to dataset selection, and are faced with an overwhelming challenge to integrate large, heterogeneous data sources. On the other hand, recent advancements in deep learning architectures, coupled with high-performance computing, have demonstrated significant breakthroughs in dealing with complexities by radically changing research methodologies toward a data-oriented approach. This Special Issue encourages authors, from academia and industry, to submit new research results about positioning and navigation models based on machine learning for complex systems. Manuscripts should be submitted online at www.mdpi.com

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.

In a galaxy [not at all] far, far away… was a commonly accepted idea that the bigger banks could hire the best talent, implement state of the art technologies, and ultimately enhance bottom line revenues via improved risk modeling, novel product offerings, etc. This concept is the proverbial'black box' of large banking institutions and how their success has been perceived for many years, which in broad strokes, is fairly accurate. But what has changed in the recent years and why are smaller institutions so keen on getting their hands on this sexy new tech? For starters, those huge banks have made extraordinarily large investments over the past decade in order to continue validation of their claim to the top rungs of industry and to service the largest clients available. However, thanks to their valiant efforts of progressivism, and some well-placed IPO funding rounds of promising AI unicorns, they managed to provide an industry fresh off its second'AI winter' with the funding necessary to inspire entirely new solutions applicable to the broader public. The other important aspect of this boom in a large bank's successful employment of data science applications is attributed to the vast quantities of data amassed at a scale that's exponentially larger than that of smaller banks.

Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair8 and interpretable16 machine learning. While important, much of this work has been limited in scope to the "last mile" of data analysis and has disregarded both the system's design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). In this article, we argue two points. First, the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build. Second, our responsibility for the operation of these systems does not stop when they are deployed. To make our discussion concrete, consider the use of predictive analytics in hiring. Automated hiring systems are seeing ever broader use and are as varied as the hiring practices themselves, ranging from resume screeners that claim to identify promising applicantsa to video and voice analysis tools that facilitate the interview processb and game-based assessments that promise to surface personality traits indicative of future success.c Bogen and Rieke5 describe the hiring process from the employer's point of view as a series of decisions that forms a funnel, with stages corresponding to sourcing, screening, interviewing, and selection. The hiring funnel is an example of an automated decision system--a data-driven, algorithm-assisted process that culminates in job offers to some candidates and rejections to others. The popularity of automated hiring systems is due in no small part to our collective quest for efficiency.

For the third year running, AI is the top priority for CEOs, according to a survey of CEOs and senior executives released by Gartner on Wednesday. The findings also revealed that the metaverse, which has received a lot of hype in the last year, especially since the rebranding of Facebook to Meta, is not as relevant to business leaders – 63% say that they do not see the metaverse as a key technology for their organization. It's not a big surprise that AI continues to be on the mind of top business leaders. As TechRepublic reported in June 2021, 97% of senior executives planned to invest heavily in AI. Jobs in AI, which are often high-pay, are also in demand, according to the jobs board Indeed.com.

As we randomly search terms on the internet, we often encounter "machine learning" and "deep learning" and how they are revolutionizing the way in which we live our lives. At present, machine learning is almost used everywhere from self-driving cars, email spam detection, recommender systems that we see in Netflix and Amazon, credit card fraud detection used by banks and so on. The list goes on and on with potential new applications being created. Therefore, it is very important to stay updated with the latest trends and understand what machine learning actually is and get a good broader understanding of some of the types of machine learning. In this article, I would explain machine learning and the different categories of machine learning.

With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress on palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein image algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by discussing the challenges, insights, and future perspectives in generating synthetic palm vein images for further works.

Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable methods or develop novel techniques for given tasks. In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy. To get the basic idea why we need image augmentation, we introduce the challenges in computer vision tasks and vicinity distribution.