Core Viewpoint - The current programming languages used in the AI field are fundamentally flawed, and a new unified language called Tensor Logic is proposed to bridge the gap between logic reasoning and neural computation [1][10][18]. Group 1: Critique of Current AI Programming Languages - Pedro Domingos criticizes existing AI programming languages, particularly Python, stating it was "never designed for AI" and lacks support for automated reasoning and knowledge acquisition [11][12]. - Other languages like LISP and Prolog, while enabling symbolic AI, suffer from scalability issues and lack learning support [15]. - The attempt to combine deep learning with symbolic AI in neural-symbolic AI is deemed a poor integration of both approaches [16][17]. Group 2: Introduction of Tensor Logic - Tensor Logic aims to provide a unified framework for expressing neural networks and symbolic reasoning, allowing learning, reasoning, and knowledge representation to unfold within the same mathematical framework [18][19]. - The equivalence between logical rules and tensor operations suggests that traditional symbolic reasoning can be transformed into tensor computations, eliminating the need for specialized logic engines [21]. Group 3: Implementation of Tensor Logic - Tensor Logic utilizes tensor equations to represent various AI methods, including neural networks, symbolic AI, kernel methods, and probabilistic graphical models [33][40]. - Each statement in Tensor Logic is a tensor equation, facilitating automatic differentiation and eliminating the distinction between program structure and model structure [28][25]. - The language allows for a continuous transition from precise reasoning to fuzzy analogy by adjusting the temperature parameter of activation functions, balancing logical reliability and neural network generalization [31].