The focus of this report is on artificial intelligence (AI) and human-computer interface (HCI) technology. Observations, conclusions, and recommendations regarding AI and HCI are presented in terms of six grand challenge areas which serve to identify key scientific and engineering issues and opportunities. Chapter 1 presents the panel's definitions of these and related terms. Chapter 2 presents the panel's general observations and recommendations regarding AI and HCI. Finally, Chapter 3 discusses computer science, AI, and HCI in terms of the six selected "grand challenge" areas and three time horizons, that is, short term (within the next 2 years), midterm (2 to 6 years), and long term (more than 6 years from now) and presents additional recommendations in these areas.
Advances in artificial intelligence (AI) have the potential to transform the nature of scientific inquiry and lead to significant innovations in engineering. To date, AI has primarily been used alongside existing design and discovery practices to help researchers analyze or interpret data, e.g., predict the structure of proteins, track insect biodiversity, etc. However, AI will play a much bigger role in design and discovery in the near future ― developing novel scientific hypotheses and experiments and creating new engineering design processes ― all with minimal human involvement. While AI has the potential to spur innovation and further scientific understanding beyond the limits of the human mind and abilities, it could also exacerbate inequities, perpetuate human biases, and even create new ones. Maximizing the benefits of AI and avoiding its pitfalls, will require addressing real and imminent challenges. Leaps and Boundaries explores the opportunities, challenges, and implications of deploying AI technologies to enable scientific and engineering research design and discovery in Canada.
What You Need to Know about Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future
What You Need to Know about Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future
Are you confused about what all the rage behind artificial intelligence is and would like to learn more? This book covers everything from machine learning to robotics and the internet of things. You can use it as a nifty guidebook whenever you come across news headlines that talk about some new advancement in AI by Google or Facebook. By the time you finish reading, you will be aware of what artificial neural networks are, how gradient descent and back propagation work, and what deep learning is. You will also learn a comprehensive history of AI, from the first invention of automations in antiquity to the driver-less cars of today. Here's just a tiny fraction of what you'll discover: Understand how machines can "think" and how they learn Learn the five reasons why experts are warning us about AI research Find the answers to the top six myths of artificial intelligence Learn what neural networks are and how they work, the "brains" of machine learning Understand reinforcement learning and how it is used to teach machine learning systems through experience Become up-to-date with the current state-of-the-art artificial intelligence methods that use deep learning Learn the basics of recommender systems Expand your current view of machines and what is possible with modern robotics Enter the vast world of the internet of things technologies Find out why AI is the new business degree And much, much more! If you want to learn more about artificial intelligence, then scroll up and click "add to cart"!
This book presents selected tutorial lectures given at the summer school on Multi-Agent Systems and Their Applications held in Prague, Czech Republic, in July 2001 under the sponsorship of ECCAI and Agent Link. The 20 lectures by leading researchers in the field presented in the book give a competent state-of-the-art account of research and development in the field of multi-agent systems and advanced applications. The book offers parts on foundations of MAS; social behaviour, meta-reasoning, and learning; and applications.
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