Machine Learning Pathways To Agi
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Machine Learning Pathways to AGI
"Artificial General Intelligence (AGI): The Quest For Human Level Intelligence" is a groundbreaking exploration of the cutting-edge field of AGI. This comprehensive guide delves into the latest research, theoretical foundations, and potential impacts of human-level artificial intelligence. From cognitive architectures to ethical considerations, the book offers invaluable insights for researchers, students, and curious minds alike. Embark on an intellectual journey that unveils the future of AI and its profound implications for society, economy, and human-AI coexistence. Artificial General Intelligence, AGI, human-level AI, machine learning, cognitive architectures, AI ethics, future of technology, artificial intelligence research, AGI development, AI societal impact
The Artificial Intelligence and Machine Learning Blueprint: Foundations, Frameworks, and Real-World Applications
Author: Priyambada Swain
language: en
Publisher: Deep Science Publishing
Release Date: 2025-08-06
In the current era of data-centric transformation, Artificial Intelligence (AI) and Machine Learning (ML) are influencing organizational strategies and operations. The AI and Machine Learning Blueprint serves as a guide connecting academic concepts with industry applications. It is intended for both students seeking basic knowledge and professionals interested in deploying scalable AI systems. The book covers core mathematical principles relevant to AI, including linear algebra, probability, statistics, and optimization, and provides an overview of classical machine learning algorithms, neural networks, and reinforcement learning. Concepts are illustrated with practical examples, Python code, and case studies from sectors such as healthcare, finance, cybersecurity, natural language processing, and computer vision. Operational considerations are also addressed, with chapters on MLOps, model deployment, explainable AI (XAI), and ethics. The text concludes with information on emerging topics including generative AI, federated learning, and artificial general intelligence (AGI). With a blend of theoretical depth and practical relevance, this book is an essential blueprint for mastering AI and ML in today’s intelligent systems landscape.
Artificial Intelligence for Cognitive Systems: Deep Learning, Neuro- symbolic Integration, and Human-Centric Intelligence
Author: Samit Shivadekar
language: en
Publisher: Deep Science Publishing
Release Date: 2025-06-30
Artificial intelligence quickly changed from a theory to a practical power - it spreads through every part of modern life. As people go from specific uses to more general kinds of intelligence, they must face a main change. This change involves what machines do and how people think about intelligence. The book, Cognitive AI - From Deep Learning to Artificial General Intelligence, looks at that change. This writing serves a wide, serious group of people - it is for graduate students and researchers in artificial intelligence and cognitive science. Educators along with industry workers also read this to get a better grasp of the path from current AI systems to future cognitive architectures. We do not just list technologies. We deal with the concepts, morals, technical issues as well as societal problems that sit at the core of creating machines that think. The chapters lay out this story bit by bit; they start with basic learning systems. They move to cognitive modeling and designs. The book finishes with important questions about governance, combining fields along with how people will work in the future. Throughout the text, the reader learns about current subjects. Some of these are large language models, explaining how systems work, reasoning with symbols plus networks, the safety of general artificial intelligence, and people working with machines. I appreciate the researchers, collaborators along with students who inspired this work. The growing group of thinkers also recognizes that making intelligent systems requires scientific exactness and philosophical thought. My hope is that this book guides plus starts talks for anyone who wants AI to develop responsibly and creatively.