Because in military situations, as well as for self-driving cars, information must be processed faster than humans can achieve, determination of context computationally, also known as situational assessment, is increasingly important. In this article, we introduce the topic of context, and we discuss what is known about the heretofore intractable research problem on the effects of interdependence, present in the best of human teams; we close by proposing that interdependence must be mastered mathematically to operate human-machine teams efficiently, to advance theory, and to make the machine actions directed by AI explainable to team members and society. The special topic articles in this issue and a subsequent issue of AI Magazine review ongoing mature research and operational programs that address context for human-machine teams. In 1983, William Lawless blew the whistle on Department of Energy (DOE) mismanagement of military radioactive wastes. After his PhD, he joined DOE's citizen advisory board at its Savannah River Site where he coauthored over 100 recommendations on its cleanup.
Hierarchical planning has attracted renewed interest in the last couple of years. As a consequence, the time was right to establish a workshop devoted entirely to hierarchical planning – an insight shared by many supporters. In this paper we report on the first ICAPS workshop on Hierarchical Planning held in Delft, The Netherlands, in 2018 as well as on the second workshop held in Berkeley, CA, USA, in 2019. Hierarchical planning approaches incorporate hierarchies in the domain model. In the most common form, the hierarchy is defined among tasks, leading to the distinction between primitive and abstract tasks.
Aside from staying alive and healthy, the biggest concern most people have during the pandemic is the future of their jobs. Unemployment in the U.S. has skyrocketed, from 5.8 million in February 2020 to 16.3 million in July 2020, according to the U.S. Bureau of Labor Statistics. But it's not only the lost jobs that are reshaping work in the wake of COVID-19; the nature of many of the remaining jobs has changed, as remote work becomes the norm. And in the midst of it all, automation has become potentially a threat to some workers and a salvation to others. In our upcoming special issue, titled "Automation and jobs in the new normal," we examine this tension and explore the good, bad, and unknown of how automation could affect jobs in the immediate and near future.
Biometrics such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition etc. as a means of identity management has become commonplace nowadays for various applications. Biometric systems follow a typical pipeline that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction and recognition based solely on biometric data. The objective of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in advanced deep learning-based biometric systems.
In this guest post, you will learn by example how to do two popular machine learning techniques called random forest and extremely random forests. In fact, this post is an excerpt (adapted to the blog format) from the forthcoming Artificial Intelligence with Python – Second Edition: Your Complete Guide to Building Intelligent Apps using Python 3.x and TensorFlow 2. Now, before you will learn how to carry out random forests in Python with scikit-learn, you will find some brief information about the book. The new edition of this book, which will guide you to artificial intelligence with Python, is now updated to Python 3.x and TensorFlow 2. Furthermore, it has new chapters that, besides random forests, cover recurrent neural networks, artificial intelligence and Big Data, fundamental use cases, chatbots, and more. Finally, artificial Intelligence with Python – Second Edition is written by two experts in the field of artificial intelligence; Alberto Artasanches and Pratek Joshi (more information about the authors can be found towards the end of the post). Now, in the next section of this post, you will learn what random forests and extremely random forests are.
A fundamental goal of artificial intelligence research and development is the creation of machines that demonstrate what humans consider to be intelligent behavior. Effective knowledge representation and reasoning (KR&R) methods are a foundational requirement for intelligent machines. The development of these methods remains a rich and active area of artificial intelligence research in which advances have been motivated by many factors, including interest in new challenge problems, interest in more complex domains, shortcomings of current methods, improved computational support, increases in requirements to interact effectively with humans, and ongoing funding from Defense Advanced Research Projects Agency and other agencies. The article by Richard Fikes and Tom Garvey, Knowledge Representation and Reasoning – A History of DARPA Leadership, highlights several decades of advances in KR&R, paying particular attention to research on planning and on the impact of DARPA's support. Fikes and Garvey are joined by David Israel, a principal scientist in the Artificial Intelligence Center at SRI International, who provides his own brief commentary on KR&R.
Various artificial intelligence (AI) technologies have pervaded daily life. For instance, speech recognition has enabled users to interact with a system using their voice, and recent advances in computer vision have made self-driving cars commercially available. However, if not carefully designed, people with different abilities (e.g., loss of vision, weak technical background) may not receive full benefits from these AI-based approaches. This Special Issue focuses on bridging or closing the information gap between people with disabilities and needs. Manuscripts should be submitted online at www.mdpi.com
The Coronavirus Disease 2019 (COVID-19) pandemic has caused extreme strains on health systems, public health infrastructure, and economies of many countries. As of April 2020, millions of people have been infected, and more than two billion globally are staying home to avoid coronavirus. This raging pandemic continues to disrupt our lives while the scientific community is rushing to find a cure for COVID-19. We can leverage artificial intelligence (AI) and big data to help combat the COVID-19 pandemic. For example, ongoing AI efforts aim to expedite the development of a safe and effective COVID-19 vaccine, use networks to repurpose drugs for COVID-19, predict antibacterial properties of new molecules, and design machine-readable datasets of scientific literature on the novel coronavirus.
Both AI and cybersecurity are nearly omnipresent in our daily lives, and the intersection of the two is of increasing importance as our world becomes more connected, more "intelligent," and more reliant on online or automated systems. AI technology can impact existing problems in cybersecurity, national security, physical safety, and even media consumption. The threats are sometimes more sophisticated than ever -- but often not. As attack and defense systems evolve, the need for human expertise becomes more imperative -- not less. And some of the seemingly most onerous threats, like deepfakes and the increasing presence of AI-powered cameras, have practical and political solutions.
VentureBeat's second special issue is nigh. Following our first special issue, Power in AI, this next one focuses on AI and security. Each special issue is a package of articles that explores a central topic from a variety of angles, from voices in industry, academia, and our newsroom. Whether we're aware of it or not, both AI and cybersecurity are nearly omnipresent in our daily lives at this point, and together they're of increasing importance as our world becomes more connected, more "intelligent," and more reliant on online or automated systems. Yet both can seem intractably technical, even for tech-savvy people.