The Appearance of Wisdom

Late one night, after negative findings in my representation-learning research, I remembered Yann LeCun discussing Plato’s Phaedrus and the distinction between the appearance of wisdom and understanding. The thought stayed with me because I had just watched a plausible idea fail a test I had hoped it would pass.

In Phaedrus, 275a–b, Socrates recounts Thamus’s warning that access to written material can make people seem knowledgeable without bringing them understanding. I read this as a question about what we do with information. Having an account of something within reach does not mean we have understood it.

I think perfection is a self-aware delusion: we continue to pursue it while recognizing that we may never reach it. There is something of that tension in my fascination with neural networks. Comparatively simple mathematical operations, composed and trained at scale, produce systems with remarkable capabilities. Their usefulness can tempt me to outsource the effort of understanding along with the work itself.

Reading an explanation, remembering it, and producing a similar explanation are different from comprehending why it holds. I notice this in my own learning. Familiar language can make an unfamiliar mechanism feel understood until I try to implement it or explain a failure. A confidently incorrect answer becomes especially difficult to judge when accurate and inaccurate details are woven together. Recognizing some of the facts may make me trust the rest, even when I lack the knowledge needed to separate them.

That concern extends to the accumulation of generated material. What happens if we produce information faster than people or models can evaluate it? If unchecked material enters later training, evaluation, or self-improvement processes, could errors survive while the systems become more convincing? I have been calling this possibility recursive self-destruction, as a speculative counterpoint to recursive self-improvement. It is a provisional phrase for a concern I want to examine, not an established scientific concept or a claim that I have discovered a new failure mode.

The question also turns back on evaluation. Could a benchmark reward increasingly persuasive answers without adequately testing comprehension or reliability? A higher score can be meaningful, but I still want to know what the test distinguishes and what it allows to pass unnoticed. I do not have empirical evidence here that benchmarks are converging on mere appearance. I am asking how we would recognize that possibility, including in evaluations I design myself.

My interest in V-JEPA and related representation-learning approaches brings the question closer to perception. V-JEPA 2 learns predictive representations from video rather than reconstructing every pixel. What aspects of physical reality does such a representation retain, and how do they differ from the world as a person perceives it? Predicting a representation successfully does not, by itself, answer what kind of understanding that representation supports.

I keep thinking about a visual illusion involving a rotating line and a briefly flashed line. They can be physically aligned at the instant of the flash yet appear displaced. This resembles the flash-lag effect. It offers a reminder that the stimulus and the experience of it need not coincide, but it does not settle why. Eagleman and Sejnowski’s experiments motivated a postdictive account; differential-latency accounts and predictive explanations are part of the debate. I should not treat one illustration as proof that perception is simply prediction.

Still, it makes me wonder how much of what I perceive depends on expectations, temporal integration, or information arriving around the event. Is hallucination a useful metaphor for this constructive aspect of perception, or does it blur distinctions that matter? A human visual illusion and an unsupported model answer are different phenomena. I am interested in whether comparing their representations can sharpen a question, without assuming they share a mechanism.

Does growing access to information expand comprehension at the same pace? When an answer becomes easier to obtain, how do I tell whether I have become better able to understand it? And as learning systems become more capable, what would let us distinguish a better account of the world from a more convincing appearance of one?