What does the phenomena of “moving the goalposts” for what counts as AI tell us about AI?
It’s often said that people repeatedly revising their definition of AI, often in response to previous AI tests being passed, is evidence that people are denying/afraid of reality, and want to put their head in the sand or whatever. There’s some truth to that, but that’s a comment about humans and I think it’s overstated.
Closer to what I want to talk about is the idea AI is continuously redefined to mean “whatever humans can do that hasn’t been automated yet”, often taken to be evidence that AI is not a “natural” kind out there in the world, but rather just a category relative to current tech. There’s also truth to this, but not exactly what I’m interested in.
To me, it is startling that (I claim) we have systems today that would likely pass the Turing test if administered by Alan Turing, but that have negligible impact on a global scale. More specifically, consider fine-tuning GPT-4 to mimic a typical human who lacks encyclopedic knowledge of the contents of the internet. Suppose that it’s mimicking a human with average intelligence whose occupation has no overlap with Alan Turing’s expertise. Now if you put Turing in communication with ChatAverageJoe and ask him to determine whether it’s a machine or human without any info about modern AI progress, examples of past behavior, or previous attempts by others testers, I think it’s quite likely he’d not be able to distinguish it from a human even after a fairly lengthy conversation.
Without pretending to anywhere near as smart as Turing, I would guess he would go about probing the AI armed only with a small subset of the tests/tricks that the field of AI researchers have developed over decades of trial and refinement. Turing would have to independently invent them without the benefit of this rich history.
A natural objection is that even laymen can start to notice ChatGPT’s deficiencies fairly quickly, but I suspect this is largely a combination of (a) they don’t begin from a position of real uncertainty, (b) ChatGPT attempts to be way more broadly knowledgable than a human, and (c) most modern people have an intuition for distinguishing computer-like behavior from human-like behavior that they get from everyday tasks like interacting with an automated phone system. (I think there’s probably a curse-of-knowledge thing here where you can’t even remember what it’s like to not have a gut instinct for some of this stuff.)
If this is true, it’s striking that current AI systems, inclusive of ChatGPT, are definitely not “powerful” in the sense of Transformative AI, i.e., inducing global economic growth significantly above historical rates.b
Now, even if I haven’t convinced you that ChatAverageJoe would pass the Turing-the-man test, I hope this at least gestures at the idea that coming up with an operational threshold years in advance for when AI will be powerful is super hard. I don’t think Alan Turing could do it, so what chance do us mortals have? The refusal to commit oneself to the best threshold one can think of at the time is not necessarily head-in-the-sand behavior.
Of course, relative to Turing, we do have the serious advantage of having access to a half century of technological progress and analysis, but this just goes to my point: identifying what automated systems are likely to be powerful is still an iterative “I’ll know it when I see it” kind of process that resists clean tests.
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- A draft of this originally appeared on Twitter.↵
- I’m not making a claim here about whether we are temporally close to TAI, nor am I saying that the economy would be growing just as fast without all our current automated systems; I’m just saying that economic growth with current systems is well within historical trends, and would remain so if tech development were frozen.↵