nancygold: Why Fluency Is Not Intelligence

Настроение:	contemplative
Entry tags:	computing

Why Fluency Is Not Intelligence
There exists a simple, brutal discriminator between an intelligent agent capable of creating new things and a machine condemned to rearranging the old: the ability to reverse engineer an unfamiliar system. This ability is neither exotic nor rare. Schoolchildren acquire it. Hobbyists practice it in their bedrooms. Security researchers apply it daily, often under adversarial pressure and with inadequate documentation. It is, quite plainly, the act of understanding something that was not designed to be understood by you.

And yet, artificial intelligence—despite its impressive verbosity and statistical confidence—fails at this task almost completely.

This failure is not marginal. It is categorical.

Reverse Engineering as the Core of Intelligence

Reverse engineering is not a niche skill confined to malware analysts and console hackers. It is the operational core of science and engineering. To reverse engineer is to infer structure, intent, and invariants from behavior and artifacts alone. Physics does this to the universe. Biology does it to cells. Mathematics does it to patterns. Engineering does it to machines whose blueprints are missing or wrong.

Any agent that cannot reverse engineer cannot discover. It may interpolate, extrapolate, and decorate—but it cannot originate. It is trapped inside the convex hull of its prior experience, endlessly remixing what it has already seen.

Thus, reverse engineering is not merely another benchmark. It is a threshold capability. On one side of it lies intelligence that can create new abstractions. On the other lies machinery that produces convincing pastiche.

The Curious Case of Human Competence

Humans, it must be noted, are not especially impressive computational devices. They are slow, forgetful, inconsistent, and prone to error. They lack vast training corpora. They do not backpropagate gradients. They nevertheless reverse engineer software with ease relative to any existing AI system.

A teenager, armed with curiosity and a debugger, can take apart a video game binary written decades ago, infer its logic, rename its functions sensibly, and modify its behavior. This is not an exceptional feat. It is routine. The same teenager, if asked to explain how they did it, will give an account riddled with uncertainty, false starts, and hand-waving—and yet the result will work.

This should have been deeply alarming to the AI community. It was not.

What Artificial Intelligence Actually Does Instead

Modern AI systems, particularly large language models, exhibit a strikingly different mode of operation. They do not infer hidden structure so much as they *select plausible continuations*. They are optimized to produce text that looks correct, not models that *remain correct under sustained interrogation*.

When presented with a small code fragment, such systems perform admirably. They identify idioms, name functions, and even explain intent—provided the intent closely resembles something already encountered during training. Increase the scope slightly, introduce cross-cutting invariants, long-lived state, or adversarial obfuscation, and the performance collapses into confident nonsense.

The system does not know that it does not know. Worse, it has no mechanism for knowing that such knowledge should be acquired rather than invented.

The Absence of Commitment

The central deficiency is not a lack of intelligence but a lack of commitment.

Reverse engineering requires an agent to form hypotheses and then *stand by them* long enough to be proven wrong. It requires the preservation of partial understanding, the maintenance of uncertainty, and the painful revision of earlier conclusions when reality disagrees.

Current AI systems do none of this. They generate beliefs and discard them immediately. They contradict themselves cheerfully. They optimize for fluency, not coherence; for plausibility, not truth. A function explained one way on Monday may be explained differently on Tuesday, with equal confidence and no embarrassment.

This behavior is catastrophic in reverse engineering, where the entire enterprise depends on global consistency over time.

The Myth of Architectural Complexity

It is tempting to argue that reverse engineering is simply “hard,” requiring architectures of enormous sophistication. This is demonstrably false. Humans do it with brains evolved for throwing rocks and avoiding predators. The task is difficult, but not computationally extravagant.

What it requires is not scale, but structure:

* Persistent memory of assumptions
* Explicit representation of hypotheses
* Penalties for contradiction
* Tolerance for ignorance
* Willingness to proceed slowly

None of these properties emerge naturally from next-token prediction, no matter how much data or compute is applied. One can scale a confusion indefinitely without obtaining clarity.

Why This Matters for Research and Creativity

The inability to reverse engineer is not an isolated embarrassment; it disqualifies the system from doing research in any meaningful sense.

Research begins where existing models fail. It requires noticing that failure, preserving it, and constructing something new in response. An agent that cannot analyze an unfamiliar system cannot analyze unfamiliar phenomena. An agent that cannot discover invariants cannot propose new laws. An agent that cannot explain why something works cannot improve it.

Such an agent may sound clever. It may even be useful. But it is not a scientist. It is not an engineer. It is an echo.

The Uncomfortable Conclusion

The present state of artificial intelligence reveals an awkward truth: we have built machines that speak convincingly about understanding without possessing the machinery required to actually achieve it.

Reverse engineering exposes this gap mercilessly. It is resistant to fluency, immune to bluffing, and intolerant of inconsistency. It demands exactly those qualities that modern AI systems systematically lack.

Until an artificial agent can take apart an unfamiliar, adversarially constructed system and explain it coherently over time—without being spoon-fed the answer—it remains a sophisticated automaton. Impressive, yes. Useful, often. Intelligent in the sense that matters, no.

When that capability finally appears, it will not be because the machine learned to talk better. It will be because it learned to *understand despite not knowing*. And that, inconveniently, is the very thing current AI was never trained to do.