FLC: Pedagogy for the Age of AI

Examining recent advances in how artificial intelligence (AI) and robotics have elicited widespread debate over their benefits and drawbacks for humanity. The emergent technologies have for instance implications within medicine and health care, employment, transport, manufacturing, agriculture, and armed conflict. This volume covers multidisciplinary research, examines current research frontiers in AI/robotics and likely impacts on societal well-being, human - robot relationships, as well as the opportunities and risks for sustainable development and peace.

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately?

This open access book offers a strategic perspective on AI and the process of embedding it in society. The history of these technologies provides us with useful insights about what it takes to direct the introduction of AI in society. The WRR identifies five key tasks to structurally work on this process: demystification, contextualisation, engagement, regulation and positioning. By clarifying what AI is (demystification), creating a functional ecosystem (contextualisation), involving diverse stakeholders (engagement), developing directive frameworks (regulation) and engaging internationally (positioning), societies can meaningfully influence how AI settles. Collectively, these activities steer the process of co-development between technology and society, and each representing a different path to safeguard public values.

Bring a new degree of interconnectivity to your world by building your own intelligent robots Key Features Leverage fundamentals of AI and robotics Work through use cases to implement various machine learning algorithms Explore Natural Language Processing (NLP) concepts for efficient decision making in robots. As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence. Get started with robotics and artificial intelligence. Apply simulation techniques to give your robot an artificial personality. This book is also for enthusiasts who want to gain knowledge of AI and robotics.

Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. This book offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers.

Author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. This practical book teaches you the skills necessary to put AI and machine learning to work at your company. Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide.

Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. This guide is built on practical lessons that let you work directly with the code.

This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, back propagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems.

Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. Inside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning.

Use AI-assisted tools like GitHub Copilot to go from idea to application faster than you can say "ChatGPT". AI-assisted coding tools like GitHub Copilot and ChatGPT empower you to create useful Python applications without learning all the low-level details of a programming language.

This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. You'll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications.

Learn how AI-assisted coding using ChatGPT and GitHub Copilot can dramatically increase your productivity (and fun) in writing regular expressions and other programmes. Regular Expression Puzzles and AI Coding Assistants is the story of two competitors. On the one side is David Mertz, an expert programmer and the author of the Web's most popular Regex tutorial. On the other are the AI powerhouse coding assistants, GitHub Copilot and OpenAI ChatGPT. Here's how the contest works: David invents 24 Regex problems he calls puzzles and shows you how to tackle each one. When he's done he has Copilot and ChatGPT work the same puzzles. What they produce intrigues him. Which side is likelier to get it right? Which will write simple and elegant code? Which one makes the smartest use of lesser-known Regex library features? Read the book to find out. David also offers AI best practices, showing how smart prompts return better results.