Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic fluctuations. We connect these physics-inspired AI algorithms by unifying them under a single mathematical framework that we call Thermodynamic AI. Seemingly disparate algorithmic classes can be described by this framework, for example, (1) Generative diffusion models, (2) Bayesian neural networks, (3) Monte Carlo sampling and (4) Simulated annealing. Such Thermodynamic AI algorithms are currently run on digital hardware, ultimately limiting their scalability and overall potential. Stochastic fluctuations naturally occur in physical thermodynamic systems, and such fluctuations can be viewed as a computational resource. Hence, we propose a novel computing paradigm, where software and hardware become inseparable. Our algorithmic unification allows us to identify a single full-stack paradigm, involving Thermodynamic AI hardware, that could accelerate such algorithms. We contrast Thermodynamic AI hardware with quantum computing where noise is a roadblock rather than a resource. Thermodynamic AI hardware can be viewed as a novel form of computing, since it uses a novel fundamental building block. We identify stochastic bits (s-bits) and stochastic modes (s-modes) as the respective building blocks for discrete and continuous Thermodynamic AI hardware. In addition to these stochastic units, Thermodynamic AI hardware employs a Maxwell's demon device that guides the system to produce non-trivial states. We provide a few simple physical architectures for building these devices and we develop a formalism for programming the hardware via gate sequences. We hope to stimulate discussion around this new computing paradigm. Beyond acceleration, we believe it will impact the design of both hardware and algorithms, while also deepening our understanding of the connection between physics and intelligence.

翻译：许多人工智能（AI）算法受物理学启发并利用随机涨落。我们通过一个统一的数学框架——称之为热力学人工智能——将这些受物理学启发的AI算法联系起来。看似不同的算法类别均可由该框架描述，例如：(1) 生成扩散模型、(2) 贝叶斯神经网络、(3) 蒙特卡洛采样以及(4) 模拟退火。此类热力学AI算法目前运行于数字硬件上，这从根本上限制了其可扩展性与整体潜力。物理热力学系统中自然存在随机涨落，此类涨落可被视为一种计算资源。因此，我们提出了一种新型计算范式，其中软件与硬件变得不可分割。我们的算法统一性使我们能够识别出一个涉及热力学AI硬件的全栈范式，该范式可加速此类算法。我们将热力学AI硬件与量子计算进行对比：后者中噪声是障碍而非资源。热力学AI硬件可被视为一种新型计算形式，因为它采用了一种新颖的基本构建单元。我们分别将随机比特（s-bit）和随机模式（s-mode）识别为离散型与连续型热力学AI硬件的构建单元。除这些随机单元外，热力学AI硬件还采用了一种麦克斯韦妖装置，用以引导系统产生非平凡状态。我们提供了几种构建此类装置的简单物理架构，并发展了一种通过门序列对硬件进行编程的形式化方法。我们期望能激发围绕这一新型计算范式的讨论。除加速计算外，我们相信它将影响硬件与算法的设计，同时加深我们对物理学与智能之间联系的理解。