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.

Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

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.

In this post, you will discover the top 10 books available right now on Artificial Intelligence. There are quite a few available online in which you may purchase. Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

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.

Call for Papers Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are specifically designed for include: behavior or speech recognition, cognition or learning. We have all heard about Artificial intelligence in Health as the use of complex algorithms and software by computers to estimate health characteristics in the analysis of complicated medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input. The difference between AI technology and traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user.

AI has the capacity to maximise the social, economic, and environmental benefits of big data sources for decision-making. Researchers, governments, companies, non-governmental organisations, and citizen groups are actively experimenting, innovating, and adapting AI tools to tackle policy relevant problems. Research and development are needed to properly design sound theoretical methods and applied tools for generating policy relevant information, implementing policy objectives, designing more effective policies, or measuring their impact while mitigating potential risks.

This Special Issue is devoted to the recent advances in prediction models. Novel methods, new applications, comparative analyses of models, case studies, and state-of-the-art review papers are particularly welcomed. Prediction models are essential to many scientific domains and are gaining widespread popularity. Health care, cybersecurity, education, credit card fraud detection, social media, cloud computing, software measurement, quality and defect simulation, cost and effort estimations, software reuse and evaluation, computational mechanics, theoretical physics, astrophysics, materials design innovation, disease diagnosis, hydrological modeling, earth systems, atmospheric sciences, weather and extreme events prediction, hazard mapping, natural disasters warning systems, policy-making, energy systems, time-series forecasting, and climate change modeling are among the popular applications of prediction models in the literature. The beneficial aspects and the generalizability of prediction models in various technological and scientific domains have highly increased the progression, competitiveness, and research impact of different fields.