Metamorph

Self-Creating Artificial Intelligence

De: Ajit Singh

Narrado por: Virtual Voice

"Metamorph: Self-Creating Artificial Intelligence" is a comprehensive and practical guide to designing and building AI systems that can modify their own architecture and code to adapt and improve over time. It serves as a bridge between foundational AI principles and the cutting-edge field of programmatic self-improvement, making it an essential resource for the next generation of AI developers and computer scientists.


Philosophy

The core philosophy of this book is "learning by doing." I believe that the most profound understanding of complex systems comes from building them. While theoretical knowledge is essential, it is inert without practical application. This book demystifies the advanced concept of self-creating AI by breaking it down into manageable, implementable steps. It posits that the future of AI lies not in creating static, highly-optimized models, but in developing meta-systems that can autonomously generate, test, and refine their own solutions, learning to solve problems in ways their creators may not have anticipated.


Key Features

1. Step-by-Step Implementation: Guides readers from basic building blocks to a fully functional capstone project.

2. Hands-On Practicals: Each chapter includes practical exercises and case studies that reinforce learning and develop real-world skills.

3. Simplified Algorithms: Focuses on intuitive and easy-to-understand algorithms for complex tasks like genetic programming and neural architecture search.

4. Complete Capstone Project: The final chapter is a comprehensive, do-it-yourself project that integrates all the concepts learned throughout the book into a live, working application of a self-creating AI.

5. Global Syllabus Compatibility: The content is carefully curated to align with the AI and Machine Learning syllabi of B.Tech/M.Tech programs at leading Indian and international universities.

6. Real-World Applications: Emphasis is placed on how these advanced techniques can be applied to solve practical problems in areas like automated machine learning (AutoML), robotics, and optimization.


Key Takeaways

Upon completing this book, the reader will be able to:

1. Understand the fundamental differences between static AI models and self-creating AI.

2. Design and implement the core components of a self-modifying system, including representation, evaluation, and modification mechanisms.

3. Apply genetic algorithms and evolutionary strategies to optimize AI architectures.

4. Build a basic Neural Architecture Search (NAS) framework from scratch.

5. Develop a complete, end-to-end capstone project demonstrating a self-creating AI in action.

6. Critically evaluate the ethical and safety challenges associated with autonomous, self-evolving systems.


Disclaimer: Earnest request from the Author.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

Thank you for your kind consideration!