The Physics of Artificial Intelligence and Computation

AI and computation rely on physical systems—transistors, circuits, and memory devices—to process information.
Energy efficiency in computation is constrained by fundamental physical limits, such as Landauer’s principle.
Quantum computing offers the potential to accelerate AI algorithms by exploiting superposition and entanglement.
Neuromorphic computing mimics the brain’s physical structure to create energy-efficient AI hardware.
Thermodynamics and heat dissipation are key challenges in scaling AI computations.
Information theory bridges physics and computation, defining limits on data storage and transmission.
Optical and photonic computing explore using light to perform computations faster and with less energy.
The physical substrate of AI affects speed, efficiency, and scalability of machine learning systems.
Advanced AI may leverage exotic physics, like quantum annealing, for solving complex optimization problems.
Understanding the physics behind computation provides insight into both the limits and possibilities of artificial intelligence.