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Yes, in a limited, operational sense: AI systems have been shown to learn and use symbol-like representations for specific tasks. Whether those representations have meaning grounded in the system itself—or whether AI understands symbols as people do—remains unresolved. Generating content that contains symbols is not the same as creating representations a system can use, and neither alone proves human-like understanding.
What does it mean for a symbol to have meaning?
A symbol can be a word, mark, or internal representation that stands for something or helps a system perform a task. In AI research, authors do not always mean the same thing by “symbol,” so it helps to distinguish a representation that is useful for a task from one whose meaning is grounded in the system.
Stevan Harnad’s 1990 paper, “The symbol grounding problem,” asks how a formal symbol system could interpret its symbols intrinsically, rather than relying on meanings supplied by an outside interpreter. For example, a system may process a token effectively because its designers connected that token to a label or task. Its successful processing does not by itself show that the system has an independent understanding of what the token means.
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What did the SEA-net experiment demonstrate?
In a 2023 paper, Yang Chen, Liangxuan Guo, and Shan Yu proposed SEA-net, a neural-network framework for learning symbols and using them to configure a task-solving network. The system includes a context-dependent processing module. In the authors’ account, symbolic inputs are learned alongside the network’s parameters and used in solving a task, rather than serving only as words generated for an audience.
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A bounded image-category task
The researchers tested SEA-net on CIFAR-100 category identification. They report classification accuracy ranging from 0.69 to 0.92 across the 100 categories, compared with a 0.5 chance level specified in their paper for that experiment. Those figures describe the reported task and setup; they are not a general measure of AI understanding.
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 a result of their particular comparison, not evidence that the learned symbols are equivalent in meaning to words.
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What the result says—and what it does not
The experiment is evidence that a network can learn and use task-effective symbol-like representations in a defined setting. The paper also describes learning new categories through symbolic manipulation and communication between systems. But the demonstrated process was guided by supervised learning and human-defined categories. The result does not establish open-ended language competence, independent meaning, or human-like semantic understanding.
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Why “symbol emergence” can be a stronger claim
Learning a representation is not necessarily the same as explaining how a shared symbolic system emerges. A 2025 review of models of symbol emergence in communication warns that many such models already assume discrete symbols or signals at the outset. It also notes that models can treat communication mainly as a way to describe the world, or simplify meaning to a mapping between a signal and a referent.
Those assumptions matter because communication may develop through more than signal-to-object associations. Perception, sensorimotor activity, coordination, interaction, and the environment can all shape how signals are used. If a model starts with discrete signals already specified, it may explain how those signals are learned or applied without showing how a symbolic convention first arises.
Why grounding remains an open question
A 2025 perspective on symbol grounding for generative AI argues that meaning can depend on relations among objects, symbols, and human practices. That view directs attention to how people interpret and use AI outputs in context; it is not proof that a computer experiences the world as a person does.
Different kinds of evidence should therefore stay distinct. A representation might be tied to a task’s function, to perception or action, or to social practice. A system might classify, communicate, or generalize in a test. Each result can tell us something about how the representation works, but none automatically settles whether its meaning is intrinsic to the system.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHow to assess a claim that AI has created symbols
When evaluating a particular study, ask what the researchers mean by “symbol” and look for concrete evidence about its origin and use:
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- Origin: Were discrete symbols specified in advance, learned from data, or formed through interaction?
- Grounding: Are the representations connected to a task, perception, sensorimotor action, or social practice?
- Demonstrated use: Can the system use them to classify, act, communicate, or generalize—and on which tested cases?
- Human role: Did people define the categories, labels, rewards, or interpretations that shape what the representations mean?
For SEA-net, the evidence supports a focused claim: learned symbol-like representations helped a network perform a specified category-identification task. The broader claim—that general-purpose AI creates intrinsically meaningful symbols in the human sense—goes beyond what that experiment establishes.
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