Gary Marcus：神经符号混合已成主流，纯神经网络路线被边缘化

他以数学领域为例：Lean 定理证明器会检查甚至拒绝 LLM 的输出，Claude Code 也大量依赖规则、代码解释器、函数、条件与循环等源自经典符号 AI 的构件。数学系统使用的自动定理证明器直接承袭 Newell 和 Simon 在 1950 年代的符号主义工作。

Marcus 指出，Geoffrey Hinton 曾将符号比作“以太”、主张纯神经网络路线，这一立场被证明是错误的。他引用 François Chollet 的观点称，扩展（scaling）确有帮助但不充分，符号工具、代码解释器和 harness 已成为多数实用系统的核心组件，神经符号混合路线已全面胜出。

The 🗝️ fact: You can’t understand contemporary AI unless you can appreciate the difference between a model (that is part of a system) and a system as a whole. If a system as a whole includes both neural networks and symbols, it is by definition neurosymbolic. To think otherwise is like thinking that an engine IS a car, rather than a component of a car. You can’t make a car out of engines alone. and you can’t (in most cases) make robust, reliable AI systems out of neural networks alone. Instead, in many domains you get better results by *integrating* both neural networks and symbols. (Math, where e.g., Lean checks and sometimes rejects the work of LLMs is a good example of this; so is Claude Code). That was literally the point of my 2001 book The Algebraic Mind and has been a central point throughout my career. For a long time, people like @GeoffreyHinton argued otherwise, suggesting that all we needed were neural networks by themselves, and that symbols were an archaic waste of time. Hinton likened symbols to “aether”, as if they were complete scientific mistake. He was wrong. Systems like Claude Code depend heavily on combining neural networks with things like rules, code interpreters, functions, conditionals and loops - all symbolic constructs drawn from classical AI. Math systems use automated theorem provers, which flow directly from Newell and Simon’s symbolic work in the 1950s. Symbols weren’t a mistake; they were vital, just as neural networks are. Scaling helped, but by itself it wasn’t enough; symbolic tools and code interpreters and harnesses are now a central part of most working systems. Neurosymbolic AI has won, overwhelmingly; the pure neural networks that the dogmatists like Hinton used to argue for have been almost entirely displaced by neurosymbolic hybrids. As @fchollet has also noted, to pretend otherwise is just silly.📊 2 ⚡

This report is categorized under AITOP's "Industry" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to US-China foundation model benchmarks and commercialization comparison and AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Gary Marcus, GeoffreyHinton, Claude Code, neurosymbolic AI.

Source attribution: curated from Gary Marcus (@GaryMarcus).