Artificial Intelligence & Machine Learning (45 risultati)

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Paperback. Condizione: new. Paperback. PrefaceIn the ever-evolving landscape of artificial intelligence and computing, neuromorphic computing stands as a beacon of innovation, promising a paradigm shift in how we design and build intelligent machines. For decades, the conventional Von Neumann architecture, while powerful, has grappled with the inherent limitations of separating processing and memory, leading to the infamous "memory wall." Meanwhile, the human brain, an organ of unparalleled complexity and efficiency, effortlessly processes vast amounts of information with remarkable speed and minimal energy consumption. Neuromorphic computing seeks to bridge this gap, drawing profound inspiration from the brain's intricate structure and function to create systems that are not just faster, but fundamentally smarter and more energy-efficient.Purpose of the BookThe primary purpose of "Neuromorphic Computing: Architectures That Think Like Us" is to demystify this exciting and rapidly advancing field. We aim to provide a comprehensive and accessible exploration of neuromorphic computing, from its biological underpinnings to its cutting-edge hardware implementations and diverse applications. This book is designed to serve as a foundational guide for understanding the core principles, the technological advancements, and the immense potential of brain-inspired systems. We will delve into how these architectures are designed to overcome the limitations of traditional computing, enabling new frontiers in AI, robotics, and cognitive modeling. Ultimately, our goal is to empower readers with the knowledge to grasp the significance of this revolution and to contribute to its future.Who Should Read This BookThis book is tailored for a diverse audience, including: Students and Researchers in computer science, electrical engineering, neuroscience, and artificial intelligence who are looking to understand the fundamentals and latest advancements in neuromorphic computing.Engineers and Developers interested in designing and programming brain-inspired hardware and software for real-world applications.AI Practitioners and Enthusiasts curious about the next generation of artificial intelligence and how biological principles are shaping its evolution.Anyone with a keen interest in the intersection of biology, technology, and the future of intelligent systems. Whether you are a seasoned expert or new to the field, this book offers valuable insights and practical knowledge.How to Use This BookThis book is structured to guide you through the complexities of neuromorphic computing in a logical and progressive manner: Part I: Foundations of Neuromorphic Computing lays the groundwork by introducing the core concepts, historical context, and the biological inspirations behind neuromorphic architectures. If you're new to the subject, start here to build a strong conceptual understanding.Part II: Hardware Architectures and Technologies dives deep into the silicon, exploring the leading neuromorphic chips and the emerging technologies that are making these brain-inspired systems a reality. This section is crucial for understanding the physical manifestation of neuromorphic principles.Part III: Algorithms and Learning Mechanisms focuses on the software side, detailing the learning rules and programming frameworks that enable neuromorphic systems to learn and adapt. For those looking to work with or develop for these systems, this part offers essential practical insights.Part IV: Applications and Case Studies showcases the diverse and impactful uses of neuromorphic computing across various domains, from computer vision to robotics and cognitive modeling. These chapters provide real- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Condizione: new. Paperback. This in-depth exploration delves into the core concepts, architectures, applications, and future trajectory of "Multimodal AI: The Future of Intelligent Systems." Structured into four comprehensive parts, this book serves as a vital guide for researchers, practitioners, and anyone seeking to understand the transformative power of AI systems that can perceive and process information from multiple modalities. By seamlessly integrating insights from text, image, audio, sensor data, and more, multimodal AI promises to unlock a new era of intelligent systems capable of richer understanding, more nuanced interaction, and ultimately, more impactful real-world applications.PART I: Fundamentals of Multimodal AI lays the groundwork by introducing the fundamental principles and challenges associated with building intelligent systems that transcend the limitations of single-modality processing.Chapter 1: Introduction to Multimodal AI sets the stage by defining the core concept of multimodal AI. It elucidates how these systems aim to mirror human cognition by integrating and interpreting information from diverse sources. The chapter meticulously dissects the understanding of multimodal systems, highlighting the inherent complexity and the potential for synergistic information gain when different modalities are combined. For instance, understanding a news report becomes significantly richer when textual information is coupled with relevant images or videos. Similarly, a spoken command gains clarity when accompanied by visual cues or gestures.The chapter further addresses the key challenges and opportunities within this burgeoning field. Challenges include the heterogeneity of data formats, the difficulty in aligning and fusing information from disparate sources, the computational demands of processing high-dimensional multimodal data, and the semantic gap between low-level sensory inputs and high-level conceptual understanding. However, these challenges are counterbalanced by immense opportunities. Multimodal AI promises enhanced robustness, improved accuracy, and the ability to tackle tasks that are inherently multimodal in nature, such as understanding human emotions through facial expressions and tone of voice, or navigating complex environments using visual and sensor data.Finally, Chapter 1 showcases compelling real-world applications and use cases that underscore the transformative potential of multimodal AI. Examples span various domains, including: Human-Computer Interaction: More natural and intuitive interfaces that understand speech, gestures, and gaze.Robotics: Robots capable of navigating complex environments, manipulating objects based on visual and tactile feedback, and interacting seamlessly with humans.Healthcare: Enhanced medical diagnosis through the integration of imaging data, patient history, and genomic information.Autonomous Driving: Safer and more reliable self-driving cars that fuse data from cameras, lidar, radar, and other sensors.Content Understanding: More accurate and context-aware analysis of multimedia content, including image and video captioning, and cross-modal retrieval.Chapter 2: Modalities and Data Types delves deeper into the specific types of data that multimodal AI systems work with. It provides a comprehensive overview of text, image, and audio processing, outlining the fundamental techniques used to extract meaningful features from each modality. For text, this involves natural language processing (NLP) techniques such as tokenization, part-of-speech tagging, sentiment analysis, and semantic embedding. For images, computer vision techniques like convolutional neural networks (CNNs) are crucial for feature extracti Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condizione: new. Paperback. Agentic AI: The Rise of Autonomous, Goal-Driven IntelligenceArtificial Intelligence (AI) has evolved rapidly over the past few decades, moving from simple rule-based systems to complex, deep learning architectures that can process and generate human-like outputs. However, a significant leap in this journey is the emergence of Agentic AI - a class of AI systems designed to operate with a level of autonomy, intelligence, and goal-directed behavior that sets them apart from conventional AI systems.What is Agentic AI?Agentic AI refers to autonomous, goal-driven AI systems capable of making independent decisions, adapting to their environment, and collaborating with other agents to achieve complex objectives. Unlike narrow AI, which excels at specific tasks, and even general AI, which aims to mimic broader human intelligence, Agentic AI focuses on creating entities that can perceive, reason, and act in a manner similar to intelligent agents in the natural world.Agentic AI systems are characterized by their ability to: Perceive and interpret their surroundings through advanced sensory inputs.Reason about their goals, constraints, and available actions.Learn from past experiences and adapt to new situations.Interact and cooperate with other agents in complex environments.Make decisions under uncertainty and dynamically adjust their strategies.Why Study Agentic AI?Studying Agentic AI provides numerous benefits for students, researchers, and industry professionals. Understanding the principles and applications of Agentic AI can: Prepare for the Future of Autonomous Systems: As industries increasingly rely on autonomous technologies, professionals with a deep understanding of Agentic AI will be in high demand.Improve Decision-Making and Problem-Solving Skills: Developing and working with intelligent agents enhances critical thinking and strategic planning abilities, essential for fields like robotics, finance, healthcare, and defense.Gain Insights into Complex Systems: Agentic AI provides a framework for understanding how complex systems work, from multi-agent robotics to financial markets and autonomous transport networks.Contribute to Cutting-Edge Research: Agentic AI is a frontier field with numerous unsolved challenges, offering exciting opportunities for academic research and innovation.Develop Ethical and Responsible AI Systems: Understanding the ethical implications of autonomous agents helps ensure that future AI technologies are designed with safety, fairness, and accountability in mind.Structure of the BookThis book is organized into six comprehensive parts, covering foundational concepts, core components, practical applications, and future challenges in Agentic AI: Part I: Foundations of Agentic AIExplores the historical evolution of AI, the fundamental principles of agentic systems, and the distinctions between narrow, general, and agentic AI.Part II: Core Components of Agentic AIFocuses on the critical building blocks of agentic systems, including perception, reasoning, learning, and goal-driven behavior.Part III: Building and Training Agentic SystemsProvides practical insights into designing, training, and optimizing autonomous agents for real-world applications.Part IV: Real-World Applications of Agentic AICovers the diverse uses of agentic systems in robotics, finance, healthcare, and other industries where autonomous decision-making is transforming traditional workflows.Part V: Ethical and Societal ImplicationsDiscusses the ethical challenges and responsibilities associated with developing and deplo Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.