We provide a summary over architectural approaches that can be used to construct dependable learning-enabled autonomous systems, with a focus on automated driving. We consider three technology pillars for architecting dependable autonomy, namely diverse redundancy, information fusion, and runtime monitoring. For learning-enabled components, we additionally summarize recent architectural approaches to increase the dependability beyond standard convolutional neural networks. We conclude the study with a list of promising research directions addressing the challenges of existing approaches.

Summary: Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford's Human-Centered AI Institute. This new data gives us a much better idea of which global regions and which industries are adopting which AI/ML techniques. We know we've entered the era of exploitation of AI/ML but the $64 Billion question is how far along the curve are we and who exactly has implemented and will implement? By the way, $64 Billion is a reasonable estimate of global market spend in roughly four or five years, about 6 times where we are today. And that investment should yield about $4 Trillion in business value in that same time frame according to Gartner.

Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This chapter explores the characteristics, capabilities and intelli-gence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). The IOBT will experience unique challenges that are not yet well addressed by the current generation of AI and machine learning.

This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR), and summarises the status quo of applying artificial intelligence (AI) techniques. We first analyse the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu and the curriculum. Then, we transform the relationship model into the perception-planning-action model for exploring what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting human-robot teaching-learning process and AI-assisted educational techniques, illustrating the design guidelines and future research perspectives in intelligent tutoring robots.

If you want your company to improve its recruitment strategy this year, then it's well worth considering how these trends can help you do that. Things like social media and mobile platforms can help to promote a job advertisement or engage potential candidates. Or maybe you can evaluate your hiring processes to see which areas can benefit from AI technology, and examine what your company has done or can do to improve diversity in the workplace. In today's gadget-friendly economy, many companies are optimising their websites for mobile viewing. However, employers are losing a lot of opportunities by forgetting to create a mobile-friendly experience for jobseekers. What do jobseekers do on career pages?

Editor's Note: Download this Free eBook: Getting Started with Apache Spark 2.x – from Inception to Production In this blog post, we will give an introduction to machine learning and deep learning, and we will go over the main Spark machine learning algorithms and techniques with some real-world use cases. The goal is to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and data scientists work with domain experts, architects, developers, and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value. This overview is meant to provoke a review of your own data to identify new opportunities.

High dimensional data can contain a large amount of noise and importantly for visualization, the human brain can only comprehend three dimensions. Thus, there is a need to reduce data into an interpretable format by converting high dimensional data into two or three dimensions, which can subsequently be visualized using a two or three dimensional scatterplot. To meet the need for dimensionality reduction methods, a plethora of algorithms and associated fitting methods have been developed. A researcher wishing to perform dimensionality reduction for visualization will be presented with a choice of hundreds of algorithms. Which algorithm should be used? This paper describes a visualization framework called QVisVis and associated software tools implemented in R to help choose dimensionality reduction methods, tune these methods, and visually evaluate the quality of dimensionality reduction solutions. The major contributions of these paper are to review and synthesize previous work on evaluating and "visualizing" performance metrics, create an overall visualization framework for "visualizing" visualization quality, and implement the framework in an R toolkit.

This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.

The term artificial intelligence is now applied to such a wide range of applications these days, it's difficult to know what AI truly is. We hear that Google is using AI in search, Facebook uses it for facial recognition and Netflix is using AI to'conquer the world'. These examples are all very interesting, but they do leave many wondering what exactly AI is and how can they apply it, now, to their everyday marketing tasks? To help marketers understand AI and how it applies to our craft, Econsultancy recently held a Digital Outlook event in Singapore and invited marketing AI expert Deborah Kay, Founder of Digital Discovery, to give an overview of the state of the art. Helpfully, Ms. Kay provided a summary of the four most exciting areas of AI for marketing as well as many examples of how AI is being used in the real world.