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experts and novices underestimate and overestimate their skills with the same frequency. It’s just that experts do that over a narrower range.

This thesis comes from a study that correctly attributes Dunning-Kruger‘s metacognitive gap to “statistical noise,” which is why the author claims that the effect is an illusion.

He’s not even wrong, and far from invalidating the very intuitive, very obviously correct DK effect, the study just explains it.

Greater familiarity with your own abilities is part of skill acquisition, so when you guess your “score,” you do so with more precision. More importantly, however, because you’re in the upper bell curve, random guesses have much more room when you underestimate your performance than when you overestimate it, even if you’re doing so with equal frequency.

Someone who sucks, on the other hand, has way more room to overestimate their performance than to underestimate it.

For instance, if I claimed that my LSAT score would be perfect, I’d still be accurate to within 5 points, so my overconfidence hardly seems like hubris.

Compare that to a business major I knew who guessed he would score in the 75th percentile only to end up in the 5th percentile. The gap between his mediocre expectations and the brutal reality of being illiterate was substantial. And yet, it almost seems unreasonable to expect him to guess accurately! What is he supposed to say, “I’m gonna get a zero”?

The point is, the DK effect is a statistical artifact, and that changes absolutely nothing.

On average. Since the average person is a basically illiterate.

Turing’s machines are notdigital by their construction

This isn’t even wrong.

Go take a theoretical computer science course. Lectures from MIT and Carnegie Mellon are available on YouTube.

Stop watching podcasts with pseudo-intellectual media grifters and read the actual research literature by real philosophers and mathematicians on these otherwise arcane topics.

Here is what we know for sure:

There can be no enumerable list of axioms for the true statements of mathematics. No computational procedure could exist to determine whether propositions are valid, provable, or even equivalent. And no matter how you formulate the number-theoretic axioms, a mathematician would always have insights (for instance, about whether a Diophantine equation has a solution) that are both clearly “true” and obviously unprovable. This holds true for all digital systems.

Here is what we don’t know for sure:

The metaphysical implications.

Your distinction between science and philosophy is incorrect. Science is inductive and abductive. It can’t “prove” things deductively. That’s mathematics and philosophy.

Philosophy also determines the formal systems we use as a basis of reasoning, for instance, in science.

You will find what I said in any philosophy of mathematics textbook dealing with the subject. In fact, I am paraphrasing the Oxford logician Joel David Hamkins.

You’re welcome to also read Shapiro’s famous paper. These results have been well understood for half a century, although because the implications are ultimately metaphysical and not mathematical, we can’t be sure of the wider consequences, if any.

You’re misunderstanding the implications of both the halting problem and Gödel’s first incompleteness theorem.

What Turing and Gödel independently proved is that a human observer can (theoretically) always have insights about mathematics and programming that are incomputable. That is, you cannot program or axiomatize or formalize or digitize everything that a mind can do. Period.

Analog computers are sufficiently different from digital systems to potentially emulate brain activity. But digital (discrete) methods are probably too constrained.

The situation is the following.

1. Brains are analog computers, which are digitally irreducible.
2. There are stringent limitations on Turing machines (digital computers),
3. We can’t extract semantics from syntax, and so…

We’ll probably need analog computation, currently in its infancy, to get artificial (inorganic) consciousness.

I study metaethics and philosophy of mathematics. These problems are real, and I am being honest with you.

The simplest way to understand this problem is as follows.

1. Analog computation is not digitally reducible. (Brains are analog computers.)

2. Turing’s infamous Halting Problem.

I can write more about this and point you to more technical discussions if you want.

It is not presumptuous at all. Inference to the best explanation is how you know (almost) anything.

1. This table isn’t conscious.

This is my justified belief. No inferential claim is guaranteed and all objective claims are inferential (which is why science isn’t absolute).

That said, I have strong reasons to think that tables aren’t conscious. They might be, but I’m epistemically compelled to believe otherwise.

2. ChatGPT isn’t conscious.

Ditto. It would be irrational for me to believe otherwise given the strong evidence.

That you “don’t know for sure” is an implied disclaimer for every scientific claim.

If the evidence is ambiguous, we say so. Regarding ChatGPT, the evidence is unambiguous.

3. I am conscious.

This is a non-inferential claim that I know through direct contact with reality. It is a priori.

This is called the problem of other minds. Of course I can’t be certain about the consciousness of others. I can only be certain about my own.

We do have a way of measuring the correlates of consciousness. But we have no clue how to detect the presence of subjective experience using quantitative methods.

Philosophy departments (which is where any discovery on this front will originate) are heavily defunded. If you’re waiting for physicists or biologists to figure this out you’ll be waiting even longer.

That doesn’t change the fact that I am conscious.

Also, I never said computers can’t be conscious. I said that digital computers (Turing machines) probably can’t. Quantum and analog computers have no such theoretical constraints and they’re far, far more prevalent given that they’re found in every living creature.

Brains aren’t impressive because of their compute (which is both immense and absurdly efficient) or their ability to predict the future (technically the main function of evolved minds). They’re impressive because they’re conscious. The fact that organic brains can also engage in hierarchical abstraction, which no digital computer (or Turing machine) can do by definition, is icing on the cake.

(The halting problem and Godel’s incompleteness and Traski’s undefinability theorems all seem to suggest that analog, not digital computation is more likely to be involved in consciousness, if at all.)