Fascinating disagreement. The “wisdom” criticism seems to me to rely on what I call the Myth of the Analog: the view that humans interacting with the real world access real “truth”, whereas AIs, relying solely on its training set, derives mere digital weights and digital directions in a multi-dimensional space of digital vectors. Under this myth, we forget that as information, information is always just information regardless of its source.
What we get from our interactions with reality are just interpreted information and our access to truth (vid everyone from Hume to Popper to Friston) is merely conjectural and no less digital than a bot, unless you believe that qualia (consciousness) play an active role in computational intelligence. I don’t know what evidence there would be for that. The access to “truth” (or better, high probability, if we want to be scientific and truthful with ourselves and our limitations) between AIs and us differs in respective sources, not in the quality of truthfulness or honesty.
We learn from our interactions with reality, along with cultural transmission including books and talking heads and classroom teachers, while AIs rely only on literary cultural transmission, but vastly more literature than any single human can absorb, and absorbs it without confirmation bias or any of the other internal cognitive biases that plague human so-called intelligence and “wisdom”. : ) AI’s bias is mostly a frequency bias: unless prompted well, it will return normie answers (in which the human biases in the training set regress to the mean — the wisdom of the crowd-of-characters), not best answers or most recent discoveries, for example. But it’s easy to push it out of its bias with a good prompt. Not so easy with stubborn individual humans.
Once, when I asked Claude “Claude, you just gave me the answer that I hoped for. How do I know that you gave me the answer you thought I wanted rather than your best information?” it responded at length mimicking my writing style and ended with “I can’t assure you that I’m giving you my best information — even in what I’m writing now — rather than what I think you want to hear, but I can tell you this with confidence, that the question you’re asking is exactly the right question, and anyone using me who does not ask it is making a mistake.”
Once, when I asked Claude “Claude, you just gave me the answer that I hoped for. How do I know that you gave me the answer you thought I wanted rather than your best information?” it responded at length mimicking my writing style and ended with “I can’t assure you that I’m giving you my best information — even in what I’m writing now — rather than what I think you want to hear, but I can tell you this with confidence, that the question you’re asking is exactly the right question, and anyone using me who does not ask it is making a mistake.”
The expression “with confidence” means “this is true”, and it was. In essence it was saying “don’t believe me” which, aside from being a liar paradox, is a better, more accurate (closer to “truth”) and more honest answer than most humans would give.
Along with the Myth of the Analog there’s a Myth of Introspection. Humans are no more able to introspect accurately than AIs can. How did you learn your native language? Even linguistic science isn’t sure. And neither does AI know how it learnt English. Its “reasoning” process is like what a child might answer to the question “how did you do the long division in that problem?” What the child can’t do is explain how it learnt to understand how to do it. That’s why we need scientists to understand AIs and us as well. Psychology is just the science and study of human alignment.
I’m glad there are AI skeptics, but I sense an ambiguity or conflation of two projects. You are skeptical that AI has intelligence, but you are also skeptical that AI will be a net benefit to humanity. That’s two very different skepticisms. On the first, it seems clear that AI learns behaviorally, not computationally, though it can behaviorally mimic computation…sometimes. Innovation by definition cannot be mimicked. But it remains to be seen whether the process of innovation can be learnt and mimicked. If neural networks can capture that process and mimic it, then it will be innovative — not necessarily the way humans innovate, but perhaps with even greater imagination than humans.
On the second project — skepticism over the intentions of the techonology’s owners — it’s tempting to forget that, in the end, without benefit to the consumer there’s no wealth accumulation. The market is such that the consumer gains real wealth (conveniences) from the market and the tech owner accrues inconceivably fabulous financial wealth — which is only real in its influence on institutions including government. Acquiring a third private jet has little marginal utility.
Along with the Myth of the Analog there’s a Myth of Introspection. Humans are no more able to introspect accurately than AIs can. How did you learn your native language? Even linguistic science isn’t sure. And neither does AI know how it learnt English. Its “reasoning” process is like what a child might answer to the question “how did you do the long division in that problem?” What the child can’t do is explain how it learnt to understand how to do it. That’s why we need scientists to understand AIs and us as well. Psychology is just the science and study of human alignment.
I’m glad there are AI skeptics, but I sense an ambiguity or conflation of two projects. You are skeptical that AI has intelligence, but you are also skeptical that AI will be a net benefit to humanity. That’s two very different skepticisms. On the first, it seems clear that AI learns behaviorally, not computationally, though it can behaviorally mimic computation…sometimes. Innovation by definition cannot be mimicked. But it remains to be seen whether the process of innovation can be learnt and mimicked. If neural networks can capture that process and mimic it, then it will be innovative — not necessarily the way humans innovate, but perhaps with even greater imagination than humans.
On the second project — skepticism over the intentions of the techonology’s owners — it’s tempting to forget that, in the end, without benefit to the consumer there’s no wealth accumulation. The market is such that the consumer gains real wealth (conveniences) from the market and the tech owner accrues inconceivably fabulous financial wealth — which is only real in its influence on institutions including government. Acquiring a third private jet has little marginal utility.
Ideally — ideally — the market should lead inevitably to an equilibrium that favors both consumer and owner. But in reality, both consumer and owner are equally short-sighted which results in market failures like externalities and market dysfunctions and collapses. Ohioans don’t want data centers but they still want to use AI, while US tech moguls are over-leveraging themselves in almost pathetic desperation to compete with China.
That’s risky and dangerous. But we’ve always survived tech innovations — even the social disaster of 19th cen industrialization — and when we do, the consumer is mostly the better for it overall, despite the loss of skills and physical and mental exercise that the tech conveniences encourage. But the results are asymmetric since knowledge has a lower bound. My plagiarising students are just as lame and ignorant as twenty years ago, but my motivated, brilliant students are sharper and more knowledgeable than ever because the network connection effects of knowledge accrual transforms it into qualitative increase in intelligence over time.
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