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Understanding Physics Related To AI
Unlock the Power of Physics in Artificial Intelligence
At first glance, physics and artificial intelligence seem worlds apart. Physics delves into the fundamental laws of matter and energy, while AI focuses on creating intelligent systems. Yet, a powerful and vital connection is emerging between these two fields. Understanding Physics Related To AI bridges this gap, offering an intuitive exploration for computer scientists, data scientists, engineers, and anyone intrigued by the physics concepts increasingly appearing in AI research.
Written by Jason Rudolph, this book demystifies the surprising kinship between physics and computation. Discover why concepts like "energy," "entropy," and "quantum" are becoming central to AI development and how physics provides powerful frameworks to tackle modern AI's biggest challenges – from energy efficiency to model interpretability.
Inside, you'll
The Language of Physics for Grasp core concepts like force, energy, and conservation laws and their analogies in computational optimization. Optimization Through a Physics Visualize AI training as navigating complex "energy landscapes," understanding gradient descent as "hill-rolling," and learning how techniques like simulated annealing help escape optimization traps. Statistical Ideas and the Boltzmann Dive into statistical mechanics, understanding macrostates, microstates, entropy, and the fundamental Boltzmann distribution that inspired models like Hopfield Networks and Boltzmann Machines. Dynamics, Information, and See how dynamical systems theory illuminates the behavior of Recurrent Neural Networks (RNNs) and how information itself acts as a physical quantity with thermodynamic consequences. Frontiers Where Physics Shapes Learn about cutting-edge areas like Physics-Informed Neural Networks (PINNs) that embed physical laws into code, Neuromorphic Computing creating brain-inspired hardware, and Quantum Machine Learning (QML) harnessing quantum phenomena. Interpretability and the Understand how physics principles contribute to making complex "black box" AI models more understandable and trustworthy. Whether you're looking to deepen your understanding of current AI techniques, explore new research frontiers, or gain intuition about the underlying principles shaping the future of intelligent systems, Understanding Physics Related To AI provides the essential conceptual toolkit. Embark on this journey to see how the laws governing the universe are illuminating the path toward more powerful, efficient, and interpretable artificial intelligence.
At first glance, physics and artificial intelligence seem worlds apart. Physics delves into the fundamental laws of matter and energy, while AI focuses on creating intelligent systems. Yet, a powerful and vital connection is emerging between these two fields. Understanding Physics Related To AI bridges this gap, offering an intuitive exploration for computer scientists, data scientists, engineers, and anyone intrigued by the physics concepts increasingly appearing in AI research.
Written by Jason Rudolph, this book demystifies the surprising kinship between physics and computation. Discover why concepts like "energy," "entropy," and "quantum" are becoming central to AI development and how physics provides powerful frameworks to tackle modern AI's biggest challenges – from energy efficiency to model interpretability.
Inside, you'll
The Language of Physics for Grasp core concepts like force, energy, and conservation laws and their analogies in computational optimization. Optimization Through a Physics Visualize AI training as navigating complex "energy landscapes," understanding gradient descent as "hill-rolling," and learning how techniques like simulated annealing help escape optimization traps. Statistical Ideas and the Boltzmann Dive into statistical mechanics, understanding macrostates, microstates, entropy, and the fundamental Boltzmann distribution that inspired models like Hopfield Networks and Boltzmann Machines. Dynamics, Information, and See how dynamical systems theory illuminates the behavior of Recurrent Neural Networks (RNNs) and how information itself acts as a physical quantity with thermodynamic consequences. Frontiers Where Physics Shapes Learn about cutting-edge areas like Physics-Informed Neural Networks (PINNs) that embed physical laws into code, Neuromorphic Computing creating brain-inspired hardware, and Quantum Machine Learning (QML) harnessing quantum phenomena. Interpretability and the Understand how physics principles contribute to making complex "black box" AI models more understandable and trustworthy. Whether you're looking to deepen your understanding of current AI techniques, explore new research frontiers, or gain intuition about the underlying principles shaping the future of intelligent systems, Understanding Physics Related To AI provides the essential conceptual toolkit. Embark on this journey to see how the laws governing the universe are illuminating the path toward more powerful, efficient, and interpretable artificial intelligence.
86 pages, Kindle Edition
Published April 15, 2025
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