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Much research in Artificial Intelligence deals with a single agent having complete control over the world. A variation of this is Distributed AI (DAI), which is concerned with the collaborative solution of global problems by a distributed group of entities. This book deals with Decentralized AI (DzAI), which is concerned with the activity of an autonomous agent in a multi-agent world. The word ``agent'' is used in a broad sense, to designate an intelligent entity acting rationally and intentionally with respect to its goals and the current state of its knowledge. A number of these agents coexist and may collaborate with other agents in a common world; each agent may accomplish its own tasks, or cooperate with other agents to perform a personal or global task. The agents have imperfect knowledge about each other and about their common world, which they can update either through perception of the world, or by communication with each other.The papers were originally presented at a workshop held at King's College, Cambridge, and have been revised for this book.
The purpose of this proceedings is to stimulate exchange and discussion of research in the field of multi-agent systems. A multi-agent system consists of at least two agents that are engaged in some task that may require coordination, cooperation and/or competition. An autonomous agent has its own goals, capabilities and knowledge. The actions of an agent occur in the context of other agents that may have structures and strategies different from the agent's own. Multi-agent problems arise when several autonomous agents share a common environment. These problems may result from limited resources, shared or competing goals, etc. This MAAMAW workshop proceedings emphasizes multi-agent systems of all sorts from very simple to very complex agents and agent organizations.
In the sector of modern finance, a new issue emerges – the fragility of traditional financial systems in the face of technological evolution. The march of time has brought forth formidable challenges, shaking the foundations of age-old norms. This evolving financial paradigm grapples with challenges such as trust issues, geographical limitations, and exclusivity. In response to these challenges, Decentralized Finance and Tokenization in FinTech offers profound insights and solutions to navigate the complexities of this era. This book delves into the disruptive forces of Decentralized Finance (DeFi) and the revolutionary nature of Tokenization, ultimately paving the way toward a decentralized future. This comprehensive resource seeks to contribute significantly to current research and understanding in the realms of DeFi and Tokenization. It serves as an educational cornerstone, providing in-depth insights into fundamental concepts, technologies, and applications for both newcomers and seasoned professionals. By demystifying technical complexities, addressing challenges, and analyzing comparative advantages, the book empowers readers to navigate the evolving landscape. From decentralized governance models to global perspectives on DeFi, it fosters thought leadership and inspires discussions on the societal, economic, and technological impacts of decentralized finance and tokenization.
This book covers the growing convergence between Blockchain and Artificial Intelligence for Big Data, Multi-Agent systems, the Internet of Things and 5G technologies. Using real case studies and project outcomes, it illustrates the intricate details of blockchain in these real-life scenarios. The contributions from this volume bring a state-of-the-art assessment of these rapidly evolving trends in a creative way and provide a key resource for all those involved in the study and practice of AI and Blockchain.
Explainable AI (XAI) is an upcoming research field in the domain of machine learning. This book aims to provide a detailed description of the topics related to XAI and Blockchain. These two technologies can benefit each other, and the research outcomes will benefit society in multiple ways. Existing AI systems make decisions in a black box manner. Explainable AI delineates how an AI system arrived at a particular decision. It inspects the steps and models that are responsible for making a particular decision. It is an upcoming trend that aims at providing explanations to the AI decisions. Blockchain is emerging as an effective technique for XAI. It enables accessibility to digital ledgers amongst the various AI agents. The AI agents collaborate using consensus and decisions are saved on Blocks. These blocks can be traced back but cannot be changed. Thus, the combination of AI with blockchain provides transparency and visibility to all AI decisions. BlockXAI is also being widely used for improving data security and intelligence. The decisions made are consensus based and decentralized leading to highly efficient AI systems. This book also covers topics that present the convergence of Blockchain with explainable AI and will provide researchers, academics, and industry experts with a complete guide to BlockXAI.
Blockchain technology allows value exchange without the need for a central authority and ensures trust powered by its decentralized architecture. As such, the growing use of the internet of things (IoT) and the rise of artificial intelligence (AI) are to be benefited immensely by this technology that can offer devices and applications data security, decentralization, accountability, and reliable authentication. Bringing together blockchain technology, AI, and IoT can allow these tools to complement the strengths and weaknesses of the others and make systems more efficient. Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications deliberates upon prospects of blockchain technology using AI and IoT devices in various application domains. This book contains a comprehensive collection of chapters on machine learning, IoT, and AI in areas that include security issues of IoT, farming, supply chain management, predictive analytics, and natural languages processing. While highlighting these areas, the book is ideally intended for IT industry professionals, students of computer science and software engineering, computer scientists, practitioners, stakeholders, researchers, and academicians interested in updated and advanced research surrounding the functions of blockchain technology in AI and IoT applications across diverse fields of research.
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems Generating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers Compressing AI models so that computational, memory, storage, and network requirements can be substantially reduced Addressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data Overcoming cyberattacks on mission-critical software systems by leveraging federated learning
In the evolving landscape of finance, traditional institutions grapple with challenges ranging from outdated processes to limited accessibility, hindering the industry's ability to meet the diverse needs of a modern, digital-first society. Moreover, as the world embraces Decentralized Finance (DeFi) and Artificial Intelligence (AI) technologies, there becomes a need to bridge the gap between innovation and traditional financial systems. This disconnect not only impedes progress but also limits the potential for financial inclusion and sustainable growth. AI-Driven Decentralized Finance and the Future of Finance addresses the complexities and challenges currently facing the financial industry. By exploring the transformative potential of AI in decentralized finance, this book offers a roadmap for navigating the convergence of technology and finance. From optimizing smart contracts to enhancing security and personalizing financial experiences, the book provides practical insights and real-world examples that empower professionals to leverage AI-driven strategies effectively.
Artificial Intelligence is transforming every industry, but if you want to win with AI, you have to put it first on your priority list. AI-First companies are the only trillion-dollar companies, and soon they will dominate even more industries, more definitively than ever before. These companies succeed by design--they collect valuable data from day one and use it to train predictive models that automate core functions. As a result, they learn faster and outpace the competition in the process. Thankfully, you don't need a Ph.D. to learn how to win with AI. In The AI-First Company, internationally-renowned startup investor Ash Fontana offers an executable guide for applying AI to business problems. It's a playbook made for real companies, with real budgets, that need strategies and tactics to effectively implement AI. Whether you're a new online retailer or a Fortune 500 company, Fontana will teach you how to: • Identify the most valuable data; • Build the teams that build AI; • Integrate AI with existing processes and keep it in check; • Measure and communicate its effectiveness; • Reinvest the profits from automation to compound competitive advantage. If the last fifty years were about getting AI to work in the lab, the next fifty years will be about getting AI to work for people, businesses, and society. It's not about building the right software -- it's about building the right AI. The AI-First Company is your guide to winning with artificial intelligence.
Anarchy is coming. Decentralization is accelerating, and technology is facilitating the trend. Nobody trusts traditional institutions or authority figures anymore. Bitcoin, open source, Uber, social media, and the Arab Spring are all examples of anarchy in action.