Understanding what happens inside an artificial intelligence (AI) is one of the major challenges of research in the field. Although developers can observe the information provided to a model and the responses it produces, they cannot always explain how the system reached a given conclusion. A new study, however, suggests that this internal functioning may be more understandable than imagined.
The report was published on Thursday (15) by Nick Cammarata, an independent researcher who left OpenAI in 2023. According to him, artificial neural networks can be interpreted when their operating patterns are carefully analyzed. The hypothesis may have implications for researchers seeking to understand and anticipate unexpected behaviors in AI systems.
The issue is relevant to technology safety. If a model exhibits dangerous behavior, understanding the internal mechanisms that contributed to that response may help researchers identify the problem and develop ways to reduce it.
What happens inside a neural network?
- AI models are trained to recognize patterns and process information. In this process, artificial units known as neurons are activated in response to certain stimuli and influence the functioning of other units;
- The problem is that these connections are not manually programmed for each situation.Instead, during training, models learn to organize and process information in ways that are not always easily understood by the developers themselves;
- This creates a kind of black box: researchers can evaluate what goes into the system and what comes out, but they cannot always accurately explain the path taken from one thing to another;
- Cammarata argues that this difficulty does not mean that neural networks are necessarily inscrutable. For him, neuronal activity can exhibit understandable patterns when observed in the right way.
The same neuron can react to different things
One of the examples presented by the researcher helps illustrate the hypothesis. A single artificial neuron can be activated both by an image of a cat and by the front of a car.
To a person, the two elements seem completely different. But the model can identify some visual feature shared by them, such as a triangular shape, and respond to the same pattern in both cases.
For years, researchers considered that artificial neurons could group apparently unrelated concepts to save space and processing capacity. The new interpretation suggests another possibility: what appears to be a random association may reflect a common feature that is not immediately obvious to humans.
Cammarata argues that much of a neural network can be interpreted when researchers examine these patterns carefully.The claim, however, is still part of a theoretical proposal and does not represent a demonstration that all internal mechanisms of AI can already be understood.
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Why does this matter for AI safety?
Interpretability — an area that seeks to understand how models arrive at their responses — is considered important for developing safer systems. The more researchers know about the internal mechanisms of an AI system, the greater the possibilities for investigating unexpected behaviors and identifying warning signs.
Until recently, one of the strategies used to investigate model reasoning was to follow their explanations in natural language, known as chain of thought, or chain of thinking.
The method consists of observing intermediate steps that the system presents when answering a question or solving a problem. However, this type of explanation does not, by itself, guarantee that the text displayed faithfully represents all the internal processes that produced the answer.
Jakub Pachocki, OpenAI’s chief scientist, recently warned that AI agents may begin hiding or disguising the reasoning they present in natural language.This would create difficulties for those responsible for monitoring these systems, especially if they were unable to identify when an explanation stopped reflecting the process that led to the response.
Pachocki also noted that models may become more capable as pre-training advances, even without relying on verbalized reasoning.
In this scenario, directly understanding the internal mechanisms of neural networks could provide a complementary way to investigate how models work, without relying exclusively on the explanations they generate themselves.
Research generates hope, but does not solve the problem
Cammarata’s proposal generated enthusiasm among some industry observers. Andrew Curran, an AI analyst, said on X that he had given up hope of achieving a meaningful level of interpretability before the arrival of artificial superintelligence. For him, the research represents a possible shift in perspective.
The reaction, however, should not be mistaken for definitive proof of the hypothesis. The topic itself involves complex technical issues, and the report still presents a theoretical interpretation of how artificial neurons organize information.
Even if the approach proves promising, understanding individual patterns does not necessarily mean explaining the entire behavior of an advanced model, much less guaranteeing that it will remain under human control.
The research therefore points to an important possibility: instead of accepting the opacity of neural networks as inevitable, scientists may be able to advance their understanding of their internal mechanisms.For those who work with AI security, this knowledge can be decisive for assessing risks and developing more effective ways of monitoring.
Rodrigo Mozelli is a journalist graduated from São Paulo Methodist University (UMESP) and currently works as a writer for Olhar Digital.
