Mapping The Mind Of A Large Language Model
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Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reliable Machine Learning Systems
Author: Aruna Chakkirala
language: en
Publisher: Orange Education Pvt Limited
Release Date: 2025-04-15
Demystify AI Decisions and Master Interpretability and Explainability Today Key Features● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs● Implement XAI techniques using Python for model transparency● Learn global and local interpretability with real-world examples Book DescriptionInterpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape. What you will learn● Dissect key factors influencing model interpretability and its different types.● Apply post-hoc and inherent techniques to enhance AI transparency.● Build explainable AI (XAI) solutions using Python frameworks for different models.● Implement explainability methods for deep learning at global and local levels.● Explore cutting-edge research on transparency in transformers and LLMs.● Learn the role of XAI in Responsible AI, including key tools and methods.
Inevitable
Author: T. Dylan Daniel
language: en
Publisher: Whitney Morgan Media
Release Date: 2024-12-12
Modern science has done an extremely good job of getting to the bottom of a number of tough problems in information theory and the study of cognition. It is therefore surprising to see that the concept of artificial intelligence has derailed into speculation about intelligent machines. This book presents a contrasting vision of the future of intelligence. Instead of assuming that artificial general intelligence is on the way, the time has come to reconsider the way we think about AI in light of the facts that emerge from a careful study of the intelligence found in living minds. AI is a mere computer program, but it is special because it can quickly navigate the digital records human beings have been making as we share our culture, our ideas, and our thoughts with one another online and via literature & speech. Minds are the origin of the material that large language models are made out of, but too many researchers have reached the wrong conclusions about the differences between biological intelligence and artificial intelligence. INEVITABLE: Distributed Cognition & Network Superintelligence is written to explore the ways in which people are already using network technologies to organize and share a staggering amount of information with each other. Enter the vision of a future in which people have learned to proactively organize around our shared bodies of knowledge, of collective human intelligence. Language brings us the ability to share our mental processes with other people around the world as we build our social networks and move forward into an ever-more-connected way of life. Providing individuals with ever-increasing access to information has been a developing goal of modern technology for some time now. As distributed systems technologies improve, it will become increasingly important to maintain the shared collective human intelligence.
The AI Chronicles
This book results from the author’s nearly two decades of industry experience in software engineering, data science, and Generative AI. Specifically, I could say nine years in software engineering, ten years in data science, and one year in building LLMpowered applications. However, by doing that, I would ignore the overlap among the three areas and not do justice to the role of emergent properties that a mind exhibits. The book suits laymen, technologists, business analysts, and business teams.