Artificial Intelligence: A Systems Approach from Architecture Principles to Deployment (MIT Lincoln Laboratory Series) Kindle Edition (1版)

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【簡介】 The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities. Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. Key features: In-depth look at modern computing technologies Systems engineering description and means to successfully undertake an AI product or service development through deployment Existing methods for applying machine learning operations (MLOps) AI system architecture including a description of each of the AI pipeline building blocks Challenges and approaches to attend to responsible AI in practice Tools to develop a strategic roadmap and techniques to foster an innovative team environment Multiple use cases that stem from the authors’ MIT classes, as well as from AI practitioners, AI project managers, early-career AI team leaders, technical executives, and entrepreneurs Exercises and Jupyter notebook examples 【目錄】

Artificial Intelligence: A Guide to Intelligent Systems (4版)

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【簡介】 What are the principles behind intelligent systems? How are they built? What are intelligent systems useful for? How do we choose the right tool for the job? These questions are answered by Michael Negnevitsky’s Artificial Intelligence: A Guide to Intelligent Systems. Unlike many books on computer intelligence, which use complex computer science terminology and are crowded with complex matrix algebra and differential equations, this text demonstrates that the ideas behind intelligent systems are simple and straightforward. This text assumes little or no programming experience as it tackles topics like expert systems, fuzzy systems, artificial neural networks, evolutionary computation, knowledge engineering, and data mining. 【目錄】 Introduction to Intelligent Systems 1.1 Intelligent Machines, or What Machines Can Do 1.2 The History of Artificial Intelligence, or From the ‘Dark Ages’ to Knowledge-based Systems 1.3 Generative AI 1.4 Summary Questions for Review References Expert Systems 2.1 Introduction, or Knowledge Representation Using Rules 2.2 The Main Players in the Expert System Development Team 2.3 Structure of a Rule-based Expert System 2.4 Fundamental characteristics of an expert system 2.5 Forward Chaining and Backward Chaining Inference Techniques 2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System 2.7 Conflict Resolution 2.8 Uncertainty Management in Rule-based Expert Systems 2.9 Advantages and Disadvantages of Rule-based Expert systems 2.10 Summary Questions for Review References Fuzzy Systems 3.1 Introduction, or What Is Fuzzy Thinking? 3.2 Fuzzy Sets 3.3 Linguistic Variables and Hedges 3.4 Operations of Fuzzy Sets 3.6 Fuzzy Inference 3.7 Building a Fuzzy Expert System 3.8 Summary Questions for Review References Frame-based Systems and Semantic Networks 4.1 Introduction, or What Is a Frame? 4.2 Frames as a Knowledge Representation Technique 4.3 Inheritance in Frame-based Systems 4.4 Methods and Demons 4.5 Interaction of Frames and Rules 4.6 Buy Smart: A Frame-based Expert System 4.7 The Web of Data 4.8 RDF – Resource Description Framework and RDF Triples 4.9 Turtle, RDF Schema and OWL 4.10 Querying the Semantic Web with SPARQL 4.11 Summary Questions for Review References Artificial Neural Networks 5.1 Introduction, or How the Brain Works 5.2 The Neuron as a Simple Computing Element 5.3 The Perceptron 5.4 Multilayer Neural Networks 5.5 Accelerated Learning in Multilayer Neural Networks 5.6 The Hopfield Network 5.7 Bidirectional Associative Memory 5.8 Self-organising Neural Networks 5.9 Reinforcement Learning 5.10 Summary Questions for Review References Deep Learning and Convolutional Neural Networks 6.1 Introduction, or How “Deep” Is a Deep Neural Network? 6.2 Image Recognition or How Machines See the World 6.3 Convolution in Machine Learning 6.4 Activation Functions in Deep Neural Networks 6.5 Convolutional Neural Networks 6.6 Back-propagation Learning in Convolutional Networks 6.7 Batch Normalisation 6.8 Summary Questions for Review References Evolutionary Computation 7.1 Introduction, or Can Evolution Be Intelligent? 7.2 Simulation of Natural Evolution 7.3 Genetic Algorithms 7.4 Why Genetic Algorithms Work 7.5 Maintenance Scheduling with Genetic Algorithms 7.6 Genetic Programming 7.7 Evolution Strategies 7.8 Ant Colony Optimisation 7.9 Particle Swarm Optimisation 7.10 Summary Questions for Review References Hybrid Intelligent Systems 8.1 Introduction, or How to Combine German Mechanics with Italian Love 8.2 Neural Expert Systems 8.3 Neuro-Fuzzy Systems 8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System 8.5 Evolutionary Neural Networks 8.6 Fuzzy Evolutionary Systems 8.7 Summary Questions for Review References Knowledge Engineering 9.1 Introduction, or What Is Knowledge Engineering? 9.2 Will an Expert System Work for My Problem? 9.3 Will a Fuzzy Expert System Work for My Problem? 9.4 Will a Neural Network Work for My Problem? 9.5 Will a Deep Neural Network Work for My Problem? 9.6 Will Genetic Algorithms Work for My Problem? 9.7 Will Particle Swarm Optimisation Work for My Problem? 9.8 Will a Hybrid Intelligent System Work for My Problem? 9.9 Summary Questions for Review References Data Mining and Knowledge Discovery 10.1 Introduction, or What Is Data Mining? 10.2 Statistical Methods and Data Visualisation 10.3 Principal Components Analysis 10.4 Relational Databases and Database Queries 10.5 The Data Warehouse and Multidimensional Data Analysis 10.6 Decision Trees 10.7 Association Rules and Market Basket Analysis 10.8 Summary Questions for Review References Glossary Index

Intelligent Systems for Rehabilitation Engineering

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INTELLIGENT SYSTEMS FOR REHABILITATION ENGINEERING Encapsulates different case studies where technology can be used as assistive technology for the physically challenged, visually and hearing impaired. Rehabilitation engineering includes the development of technological solutions and devices to assist individuals with disabilities, while also supporting the recovery of the disabled who have lost their physical and cognitive functions. These systems can be designed and built to meet a wide range of needs that can help individuals with mobility, communication, vision, hearing, and cognition. The growing technological developments in machine learning, deep learning, robotics, virtual intelligence, etc., play an important role in rehabilitation engineering. Intelligent Systems for Rehabilitation Engineering focuses on trending research of intelligent systems in rehabilitation engineering which involves the design and development of innovative technologies and techniques including rehabilitation robotics, visual rehabilitation, physical prosthetics, brain computer interfaces, sensory rehabilitation, motion rehabilitation, etc. This groundbreaking book Provides a comprehensive reference covering different computer assistive techniques for the physically disabled, visually and hearing impaired. Focuses on trending research of intelligent systems in rehabilitation engineering which involves the design and development of innovative technologies and techniques. Provides insights into the role of intelligent systems in rehabilitation engineering. Audience Engineers and device manufacturers working in rehabilitation engineering as well as researchers in computer science, artificial intelligence, electronic engineering, who are working on intelligent systems.