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Herbert Roitblat's avatar

Hofstadter is right of course, but in trying to be polite, I think that he gives too much credence to the claim. Asking whether LLMs are good enough to be convincing is entirely the wrong question because it does not distinguish between the alternative causes of their success, or lack.

We know how transformers are built. We know what they are trained on. We know how they work. They are token guessers. Any claims that attribute other cognitive processes to them should have the burden of presenting extraordinary evidence. But in being polite, Hofstadter grants the logic of the claim and then notes that he disagrees with it.

The claim is rotten to the core because it is based on the logical fallacy of affirming the consequent. The claimant observes some behavior and then claims that the observed behavior proves a cause. The model produces text that a sentient entity might produce, but as Hofstadter observes, that does not mean that the model is sentient. The same text could be produced (as he notes) by a system that had read some science fiction books. You cannot conclude the nature of the cause from an observation of the effect.

This logical fallacy is extremely widespread in discussions of artificial intelligence. It is an example of confirmation bias. We look for data that confirm our hypotheses, rather than data that test our hypotheses.

Compare that with another claim by Hofstadter, himself. In 1979, he predicted that in order for a computer to play championship chess, it would have to be generally intelligence. Soon after that, championship-level chess programs were created that chose their moves based on tree traversal methods. To follow today's confirmation logic, Hofstadter could have argued that tree traversal methods ARE general intelligence, as proved by their ability to play championship-level chess. He did not make that claim, of course, but instead he recognized that chess playing did not require general intelligence. Knowing how the chess programs were written led him to change his prediction, not the other way around. We should all, everyone working in AI, take a page from Hofstadter (or should I say, take yet another page).

Intelligence is not just an engineering question, it is a scientific question. A program can behave as if it were intelligent by mimicking (with some stochastic variability) text that it has read or it can be intelligent by engaging in specific cognitive actions. An actor can recite the words of a mathematical genius without being a mathematical genius. If we want to make claims about HOW a model is producing some behavior, we have to structure experiments that can distinguish between alternative hypotheses. When those experiments are done, they seem to overwhelmingly support the hypothesis that language models are token guessers, nothing more.

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Dakara's avatar

"alert you to the power of the Eliza effect on intelligent humans"

It is disturbingly impressive. We are going to make many poor decisions because of it.

I continue to write as many varied examples as possible to demonstrate these machines are not any kind of thinking entity, but many remain unconvinced.

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