AI vs ML
2025. g. janv. · Publifye AS
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Par šo e-grāmatu
'AI vs ML' provides a comprehensive exploration of the often-misunderstood distinction between artificial intelligence and machine learning, offering clarity through historical context, practical applications, and technical frameworks.
The book traces the parallel evolution of these technologies from the 1950s Dartmouth Conference to their current state, demonstrating how ML developed as a specialized subset of AI focused on data-driven learning systems, while AI encompasses broader attempts to simulate human intelligence.
Through three well-structured sections, the book examines fundamental concepts like neural networks and deep learning, before diving into real-world applications across industries such as healthcare and finance. What sets this work apart is its practical framework for decision-makers, helping them evaluate whether their specific technical challenges require AI, ML, or a combination of both.
The book supports its analysis with academic research and industry case studies, providing quantitative metrics on performance and economic impact. The content progresses logically from theoretical foundations to practical implementation strategies, incorporating decision trees and evaluation matrices for technology selection.
Written in an accessible yet technical style, it serves both as an educational resource and a decision-making guide for technology managers and business strategists. The book maintains objectivity throughout, addressing key debates about AI safety and ML bias while avoiding speculative predictions about future capabilities, making it particularly valuable for professionals involved in digital transformation initiatives.
The book traces the parallel evolution of these technologies from the 1950s Dartmouth Conference to their current state, demonstrating how ML developed as a specialized subset of AI focused on data-driven learning systems, while AI encompasses broader attempts to simulate human intelligence.
Through three well-structured sections, the book examines fundamental concepts like neural networks and deep learning, before diving into real-world applications across industries such as healthcare and finance. What sets this work apart is its practical framework for decision-makers, helping them evaluate whether their specific technical challenges require AI, ML, or a combination of both.
The book supports its analysis with academic research and industry case studies, providing quantitative metrics on performance and economic impact. The content progresses logically from theoretical foundations to practical implementation strategies, incorporating decision trees and evaluation matrices for technology selection.
Written in an accessible yet technical style, it serves both as an educational resource and a decision-making guide for technology managers and business strategists. The book maintains objectivity throughout, addressing key debates about AI safety and ML bias while avoiding speculative predictions about future capabilities, making it particularly valuable for professionals involved in digital transformation initiatives.
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