AI can form and use new, task-useful symbol-like representations in constrained research settings. That is different from generating text or images that contain symbols—and it does not prove that an AI system gives those representations meaning in the way people do. The distinction turns on the symbol-grounding problem: where a symbol’s meaning comes from.
What counts as a symbol—and what does it mean to ground one?
In this debate, a symbol is a representational form a system uses for a task or communication. The word is not used identically by every researcher: some studies begin with discrete signals already defined, while others investigate how such representations might develop through learning and interaction.
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Generating content is not, by itself, evidence of symbol creation. A language model can produce a word, a label, or an explanation, but the output alone does not show whether the system has formed a representation that it can use, or whether that representation has meaning for the system rather than only for an outside interpreter.
Stevan Harnad framed this as the symbol-grounding problem in 1990: how can the interpretation of symbols be intrinsic to a formal symbol system instead of being supplied by people who interpret its symbols? A system may manipulate representations effectively while the categories, labels, and meanings attributed to them still come from people or from the task design.
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What did the SEA-net experiment demonstrate?
A concrete proposal comes from Yang Chen, Liangxuan Guo, and Shan Yu’s 2023 paper, “Emergence of Symbols in Neural Networks for Semantic Understanding and Communication.” Their SEA-net framework combines a task-solving network with a context-dependent processing module. Learned symbolic inputs help configure the network for its task; they are not simply words generated as an answer.
Results on CIFAR-100
In the paper’s CIFAR-100 category-identification experiments, the authors report accuracy ranging from 0.69 to 0.92 across 100 categories. They state a chance level of 0.5 for that experiment. These figures describe SEA-net on the reported task and setup; they are not a general score for AI understanding.
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The authors also report a 0.341 correlation between the semantic structure of SEA-net’s symbols and the compared natural-language word-vector structure. That is the result of their particular comparison, not a general measure of semantic equivalence between machine symbols and language.
What the symbols were used for
The paper describes learning new categories through symbolic manipulation and communication between systems. This is evidence that learned representations can play an operational role: they can help a model perform a task and support a form of communication in the study’s setup. The demonstrated process was guided by supervised learning with human-defined categories, so the experiment does not show that the system independently established what those categories mean.
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Why task success does not settle whether AI understands symbols
Classification performance can show that a representation is useful without answering whether its meaning is grounded intrinsically. To assess a claim about symbol creation, it helps to ask how the representation originated, what it is tied to, and what the system can do with it.
- Origin: Were discrete symbols specified in advance, learned from data, or formed through interaction?
- Grounding channel: Is the representation tied to a task, perception, sensorimotor activity, or social practice? These are distinct kinds of evidence.
- Use: Can the system use the representation to classify, act, communicate, or generalize to a new case? The tested task and its limits matter.
- Human involvement: Who defined the categories, labels, rewards, or interpretations that give the task its structure?
SEA-net offers evidence of task-effective internal symbols under specified conditions. Its CIFAR-100 results do not establish broad, human-like understanding or open-ended language competence.
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When do symbols really emerge?
A 2025 review of models of symbol emergence warns that the label “emergence” can overstate what a model explains. Many communication models begin with discrete symbols or signals already available, focus on describing or mapping the world, and simplify meaning to a signal–referent relationship. If the symbols are present from the start, a model may explain how they are used or coordinated without explaining how a usable symbolic system arose.
The review also emphasizes that communication can depend on more than a mapping between a signal and an object. Perception, sensorimotor activity, coordination, and social interaction can all shape how a communication system develops. A system that succeeds at mapping labels to categories therefore demonstrates something narrower than a complete account of how meaning emerges.
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Why context and human practice matter
A 2025 perspective on symbol grounding for generative AI argues that meaning can be negotiated among objects, symbols, and human practices. This shifts attention from whether a model produces a plausible label to how that label is interpreted and used in context.
That perspective offers a way to evaluate AI systems in situated settings; it is not evidence that a computer experiences the world as a person does. Contextual use, task performance, and subjective human-like understanding are separate claims, and evidence for one should not be treated as proof of the others.
So, can artificial intelligence create symbols?
Yes, in an operational research sense: systems can learn and use new symbol-like representations that help them perform tasks or communicate in a constrained setup. Whether those representations have meaning grounded intrinsically in the system—and whether that would amount to human-like semantic understanding—remains an open question. Content generation, useful internal representation, and grounded meaning are related, but they are not interchangeable.
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