The Physics of Artificial Intelligence: Understanding Intelligence Through the Laws of Nature baixar o livro de graça

What if the next breakthrough in Artificial Intelligence is not found in computer science alone—but in physics? Artificial intelligence is usually presented through algorithms, neural networks, data, and computation. But beneath every AI system lies something more fundamental: energy, information, probability, thermodynamics, quantum mechanics, and physical limits. The Physics of Artificial Intelligence explores the surprising connections between modern AI and the physical laws that govern our universe. From Landauer’s Principle and the thermodynamic cost of computation to quantum information, neural networks, complexity theory, neuromorphic computing, symmetry, diffusion models, and emergent intelligence, this book develops a physics-informed framework for understanding AI.
Inside the Book, You’ll Explore:
The Physics of Computation
Discover how information has a physical cost and why energy, entropy, and computation are fundamentally connected.

Thermodynamics & Statistical Physics
Explore entropy, free energy, Boltzmann distributions, cross-entropy, and statistical mechanics as lenses for understanding learning systems.

Geometry & Optimization
Examine information geometry, energy landscapes, gradient descent, variational methods, and the mathematical structures underlying AI optimization.

Quantum AI
Explore quantum information, quantum computing, quantum machine learning, and quantum neural networks—and where genuine quantum advantage remains an open research question.

The Physical Limits of Intelligence
Investigate complexity theory, the Bekenstein bound, thermodynamic constraints, and fundamental limits on computation and intelligence.

Emergence & Complex Systems
Examine phase-transition-like behavior, scaling, emergence, chaos, and the distinction between useful physical analogies and established mechanisms.

Neuromorphic Intelligence
Discover how principles from biological brains can inspire energy-efficient artificial intelligence.

Symmetry, Diffusion & Generative AI
Explore symmetry, equivariant neural networks, stochastic processes, diffusion models, and their connections to modern generative systems.

The Future of Physics-Guided AI
Explore research directions connecting physics, machine learning, quantum computing, neuromorphic systems, and intelligence.

A Different Way to Think About AI

The book distinguishes between mathematical correspondence, physical frameworks, and structural analogy—helping readers understand which connections are rigorous and which remain active areas of research.

Who Is This Book For?

• AI and machine-learning practitioners
• Physicists interested in artificial intelligence
• Computer science and engineering students
• Researchers exploring physics-inspired machine learning
• Quantum computing and AI enthusiasts
• Readers interested in information theory, complexity, emergence, and intelligence

You do not need to be an expert in both physics and AI. The book builds connections progressively for technically minded readers familiar with calculus, linear algebra, and basic probability.

If you want to understand AI not merely as software, but as a physical process constrained by energy, matter, information, and computation—this book offers a new lens through which to see artificial intelligence.

The future of AI may be computational.
But its ultimate limits are physical.

Procurando The Physics of Artificial Intelligence: Understanding Intelligence Through the Laws of Nature? Aqui você encontra tudo sobre este livro de Eslam Wahba. Nesta página estão a descrição da obra, os detalhes da edição (299 páginas) e os formatos disponíveis para baixar: pdf, mp3, áudio-livro, kindle. Se você gosta de Livros Internacionais, Engenharia e Transporte, Engenharia, Elétrica e Eletrônica, Teoria Eletromagnética, explore também outros títulos da mesma categoria no LivrosQualidade. Veja ainda as outras obras de Eslam Wahba em nosso catálogo.