Explainable Ai Recipes
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Explainable Ai Recipes
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Author : Pradeepta Mishra
language : en
Publisher:
Release Date : 2023
Explainable Ai Recipes written by Pradeepta Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. You will: Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models.
Towards Ethical And Socially Responsible Explainable Ai
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Author : Mohammad Amir Khusru Akhtar
language : en
Publisher: Springer Nature
Release Date : 2024-08-30
Towards Ethical And Socially Responsible Explainable Ai written by Mohammad Amir Khusru Akhtar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Technology & Engineering categories.
"Dive deep into the evolving landscape of AI with 'Towards Ethical and Socially Responsible Explainable AI'. This transformative book explores the profound impact of AI on society, emphasizing transparency, accountability, and fairness in decision-making processes. It offers invaluable insights into creating AI systems that not only perform effectively but also uphold ethical standards and foster trust. Essential reading for technologists, policymakers, and all stakeholders invested in shaping a responsible AI future."
Explainable And Transparent Ai And Multi Agent Systems
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Author : Davide Calvaresi
language : en
Publisher: Springer Nature
Release Date : 2024-09-24
Explainable And Transparent Ai And Multi Agent Systems written by Davide Calvaresi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-24 with Computers categories.
This volume constitutes the papers of several workshops which were held in conjunction with the 6th International Workshop on Explainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2024, in Auckland, New Zealand, during May 6–10, 2024. The 13 full papers presented in this book were carefully reviewed and selected from 25 submissions. The papers are organized in the following topical sections: User-centric XAI; XAI and Reinforcement Learning; Neuro-symbolic AI and Explainable Machine Learning; and XAI & Ethics.
Explainable Ai Xai Making Machine Learning Models Interpretable And Trustworthy Cloud Computing
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Author : Amit Vyas
language : en
Publisher: Xoffencer international book publication house
Release Date : 2024-05-30
Explainable Ai Xai Making Machine Learning Models Interpretable And Trustworthy Cloud Computing written by Amit Vyas and has been published by Xoffencer international book publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-30 with Computers categories.
Both explainable artificial intelligence (XAI) and cloud computing are vital components because they both play a significant part in the creation of the landscape of artificial intelligence (AI) and computing infrastructure. XAI and cloud computing are two of the most important pillars in the world of current technology. The purpose of this introduction is to provide an overview of the fundamental concepts behind both Explainable AI and cloud computing. In this section, we will study the relevance of these notions, as well as their applications and the synergies that they offer. A solution that satisfies the critical requirement for interpretability and transparency in artificial intelligence systems is referred to as explainable artificial intelligence, or XAI for short. Understanding the method by which artificial intelligence algorithms arrive at conclusions is of the highest significance, particularly in sensitive industries such as healthcare, finance, and law. This is because the algorithms are growing more intricate and prevalent, and it is becoming increasingly important to understand how they arrive at their results. XAI techniques are intended to give insights into the inner workings and reasoning processes of artificial intelligence models, with the purpose of demystifying the "black box" nature of these models. XAI approaches are aimed to deliver these insights. In addition to allowing stakeholders to detect biases or mistakes and ensure compliance with regulations, increasing the interpretability of artificial intelligence systems enables stakeholders to have a greater degree of trust in these systems. The provisioning, administration, and distribution of computer resources are all fundamentally transformed by cloud computing, which is regarded to be a breakthrough technology. Cloud computing is also known as utility computing. The term "cloud computing" refers to the practice of storing, managing, and processing data through the utilization of a network of distant servers that are located on the Internet. This is in contrast to the conventional method of computing, which is dependent on the infrastructure and servers located locally. This technology offers organizations unrivaled scalability, flexibility, and cost-efficiency, making it possible for them to use computer resources on demand without the trouble of managing physical infrastructure.
Sustainable Farming Through Machine Learning
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Author : Suneeta Satpathy
language : en
Publisher: CRC Press
Release Date : 2024-11-25
Sustainable Farming Through Machine Learning written by Suneeta Satpathy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-25 with Technology & Engineering categories.
This book explores the transformative potential of machine learning (ML) technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how artificial intelligence/machine learning (AI/ML) can optimize resource management and improve overall productivity in farming practices. Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. The authors also cover applications in livestock management, including feed formulation and disease detection; they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore the ethical and social implications of using such technologies. This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.
Explainable Ai With Python
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Author : Leonida Gianfagna
language : en
Publisher: Springer Nature
Release Date : 2021-04-28
Explainable Ai With Python written by Leonida Gianfagna and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Computers categories.
This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.
Role Of Explainable Artificial Intelligence In E Commerce
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Author : Loveleen Gaur
language : en
Publisher: Springer Nature
Release Date : 2024-04-25
Role Of Explainable Artificial Intelligence In E Commerce written by Loveleen Gaur and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-25 with Business & Economics categories.
The technological boom has provided consumers with endless choices, removing the hindrance of time and place. Understanding the dynamic and competitive business environment, marketers know they need to reinforce indestructible customer experience with the support of algorithmic configurations to minimize human intrusion. World Wide Web (WWW) and online marketing have changed the way of conducting business; with artificial intelligence (AI), business houses can furnish a customized experience to fulfil the perceived expectation of the customer. Artificial intelligence bridges the gap between business and prospective clients, provides enormous amounts of information, prompts grievance redressal system, and further complements the client’s preference. The opportunities online marketing offers with the blend of artificial intelligence tools like chatbots, recommenders, virtual assistance, and interactive voice recognition create improved brand awareness, better customer relationshipmarketing, and personalized product modification. Explainable AI provides the subsequent arena of human–machine collaboration, which will complement and support marketers and people so that they can make better, faster, and more accurate decisions. According to PwC’s report on Explainable AI(XAI), AI will have $15.7 trillion of opportunity by 2030. However, as AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. But the rise of AI in business for actionable insights also poses the following questions: How can marketers know and trust the reasoning behind why an AI system is making recommendations for action? What are the root causes and steering factors? Thus, transparency, trust, and a good understanding of expected business outcomes are increasingly demanded.
Fifth Congress On Intelligent Systems
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Author : Sandeep Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-07-12
Fifth Congress On Intelligent Systems written by Sandeep Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-12 with Computers categories.
This book is a collection of selected papers presented at the Fifth Congress on Intelligent Systems (CIS 2024), organized by CHRIST (Deemed to be University), Bangalore, India, under the technical sponsorship of the Soft Computing Research Society, India, during September 4–5, 2024. The book covers high-quality research articles in the fields of soft computing, machine vision, robotics, computational intelligence, artificial intelligence, signal and image processing, data science techniques, and their real-world applications which are some of the recent advancements in the real-world technologies.
The Hype Driven Ai Mobile Cookbook Recipes For Success
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Author : M.B. Chatfield
language : en
Publisher: M.B. Chatfield
Release Date :
The Hype Driven Ai Mobile Cookbook Recipes For Success written by M.B. Chatfield and has been published by M.B. Chatfield this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
In this cookbook, M.B. Chatfield provides a comprehensive guide to choosing, using, and getting the most out of a hype-driven AI mobile. Chatfield begins by discussing the pros and cons of hype-driven AI mobiles. He then provides a detailed overview of the different types of AI features available in these phones. Chatfield also discusses the potential risks of hype-driven AI mobiles, such as bias, privacy concerns, and job displacement. The Hype-driven AI Mobile Cookbook is an essential resource for anyone who is considering buying a hype-driven AI mobile. It provides the information you need to make an informed decision and get the most out of your phone. #mobileAI #AI #phone #mobile #virtualassistants #artificialintelligence #mobiletechnology #AIonmobile #AIassistant #mobilelife #VPA #artificialintelligence #tech #productivity
Artificial Intelligence
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Author : David R. Martinez
language : en
Publisher: MIT Press
Release Date : 2024-06-11
Artificial Intelligence written by David R. Martinez and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-11 with Computers categories.
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