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.
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 dissertation explores the integration of learning and analogy-making through the development of a computer program, called Analogator, that learns to make analogies by example. By "seeing" many different analogy problems, along with possible solutions, Analogator gradually develops an ability to make new analogies. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing research on analogy-making, in which typically the a priori existence of analogical mechanisms within a model is assumed. The present research extends standard connectionist methodologies by developing a specialized associative training procedure for a recurrent network architecture. The network is trained to divide input scenes (or situations) into appropriate figure and ground components. Seeing one scene in terms of a particular figure and ground provides the context for seeing another in an analogous fashion. After training, the model is able to make new analogies between novel situations. Analogator has much in common with lower-level perceptual models of categorization and recognition; it thus serves as a unifying framework encompassing both high-level analogical learning and low-level perception. This approach is compared and contrasted with other computational models of analogy-making. The model's training and generalization performance is examined, and limitations are discussed.
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.
Web Summit 2019 was an absolute success on so many levels. We walked away with a feeling of real accomplishment, having attended so many informative panel discussions and seizing networking opportunities. At Polcode, we know the value of attending events like this – it's an opportunity to have the leaders of our industry let us know what they think, and speak on their respective expertise. We can use the knowledge we gain and translate it into providing our clients with the absolute best in the industry, and with the most up-to-date information on what is best for their project. We wanted to share some of our Web Summit 2019 highlights and takeaways from the different perspectives of our team members.
As a Principal Automation Architect at Magenic, Paul Grizzaffi is following his passion for providing technology solutions to testing, QE, and QA organizations, including automation assessments, implementations, and through activities benefiting the broader testing community. An accomplished keynote speaker and writer, Paul has spoken at local and national conferences and meetings. In addition to spending time with his twins, Paul enjoys sharing his experiences and learning from other testing professionals; his mostly cogent thoughts can be read on his blog. Jennifer Bonine is a well-known speaker, teacher, and trainer at both national and international conferences. She has keynoted numerous Testing, Agile, and Development conferences.