That's not what people mean when they say "moving the goalposts". It means that people are adamant that something wasn't important/hard/impressive once the "AI" solves it. And then they come up with another thing that needs to be solved in order to prove it is important/hard/impressive. And once that happens, they do it again. And again. That's what "moving the goalposts" means.
It's also very much not a new phenomenon. It's been happening since the 1980s. As you can see from this quote from GEB by Hofstadter:
> There is a related "Theorem" about progress in AI: once some mental function is programmed, people soon cease to consider it as an essential ingredient of "real thinking". The ineluctable core of intelligence is always in that next thing which hasn't yet been programmed. This "Theorem" was first proposed to me by Larry Tesler, so I call it Tesler's Theorem: "AI is whatever hasn't been done yet."
-Alan Turing (allegedly)
I don't see how that's any better.
In recent years, I have commonly seen the phrase "you're moving the goalposts" deployed by the "it might be sentient" crowd to shoot down the "it's a stochastic parrot" crowd when the latter respond to a new development with "OK but...". In a well-understood field of inquiry, that would be a clear case of goalpost-moving, in the commonly-understood meaning of the phrase where requirements are retroactively changed in response to them having been met. Thank you OP. 'Artificial Intelligence', and indeed intelligence in general, is very much not a well-understood field of inquiry - in fact we don't even have a common agreement about what 'intelligence' is. We are therefore learning as we go (even after all this time!) but making rapid progress in recent years. When rapid progress is made in a poorly-understood field, then how can our definitions and requirements for success not change? This is arguably one of the most pathological development projects ever - what are the requirements? 'It thinks like a human'? What does that mean? And the answer is we don't know what that means, and we're working it out as we go - moving the goalposts. If we didn't move the goalposts, then by definition we already knew exactly where we were headed at the beginning, and we very clearly did not.
Side note that, in case it's not obvious, none of this detracts from how impressive LLMs are. They're a marvel of the modern age, all the problems notwithstanding. However I reserve the right to stay sceptical about their capabilities.
It's in how they change, not the fact that they change. The skeptics seem to have secret definitions for intelligence, sentience, consciousness, creativity, etc. that amounts to "a thing only humans have". Often that thing is equivalent to a soul. When yesterday's challenge (LLMs don't have X because they can't do Y!) is met, Y changes but X stays the same. This is not the process by which a field matures, it is a rhetorical technique used by skeptics to avoid honestly stating or confronting their internal definitions. That can be revealed by asking the skeptic the following:
"Forget LLMs. What if we made a completely physically accurate simulation of a human being?"
Many say no, that simulated human being still couldn't have (intelligence, consciousness, sentience, creativity, ...). This reveals that there is a necessary metaphysical component to those attributes, at which point any scientific-minded person will leave the debate.
> If we didn't move the goalposts, then by definition we already knew exactly where we were headed at the beginning, and we very clearly did not.
The criticisms are directed toward people who did clearly act like they knew, not the ones who were honest that they did not know.
To me, the goalposts were already defined by the person you were responding to. "The impact of AI is getting undeniable", so, the goalposts are "the impact of AI". Probably something like "the impact of AI is high, or will be soon".
Note that this does not depend on things like AI sentience or defining "intelligence" more rigorously, it just depends on AI impact.
The motte is "AI useful". The bailey is "Singularity is nigh".
(I'm personally still skeptical about this, but I'm being pulled towards accepting it).
"AI is useful" is too low of a bar, and "singularity is nigh" is too high. "AI is on its way to upending society" is about in the middle, and still vastly contentious among laypeople.
But there are people like Ed Zitron, frequently posted and cited here, who disagree even with the former.
Personally I prefer to follow explorers rather than swamp-sitters.
"it isn't clear whether generative AI actually provides much business value at all"
"cannot seem to find a product that people will pay for, in part because the results are so mediocre"
"Last week, we got our first real, definitive glimpse of what’s around that corner that future. And boy, was it underwhelming."
"OpenAI claims that o1 “performs similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology.” Just not in geography, it seems. Or basic elementary-level English language tests. Or math. Or programming. "
"Worse still, it's kind of hard to explain why anybody should give a shit about o1."
"o1 shows that OpenAI is both desperate and out of ideas."
"the software is not becoming more useful"
Honestly, every other line is quotable in this context.
But it seems we have somehow optimized away shame. It wasn’t good for profits, I guess.
The fundamental argument that I've personally made since the early days of this is that LLMs are not reasoning, in the way that word is commonly understood.
There are lots of reasons why that argument needs to evolve that could certainly appear to be "moving the goalposts", but let's take an example.
A lot of AIs were tripped up by the question "Should I walk or drive 50m to the carwash?" Several folks liked to use that as an example that illustrates that LLMs aren't reasoning, but as the models have been trained on that specific example, it's of course less useful. An AI can mostly nail it now.
So a different example is needed. A new demonstration of how these things fail at basic reasoning a child can do.
Did I move the goalposts? I don't think so. The fundamental argument stays the same. It's not hard to find lots of examples that trip up LLMs, because they are what they are: statistical inference machines. Nothing more and nothing less.
Useful, sure. But also commonly misapplied to areas for which they are inappropriate solutions.
I agree with the parent that we need to acknowledge that we're at a turning point in history. I lived through some of them (internet, ubiquitous personal computing). But it's somewhat difficult to comprehend the impact of this one for many people.
I do biomedical research at one of the top European research institutions. We're very well-funded, but I can clearly see the gap between us (say, top-100) and top-10. I also realize this gap is going to get so much wider unless we invest heavily in AI access (and I'm not so sure I can sell anything more expensive than $20 Claude subscription to the leadership).
I think people having 6-7 figure SOTA AI budgets will move exponentially faster than those who don't. That makes me worried.
So, for me, it's not a question of recalibrating expectations. We're way past that.
Not long ago many folks were saying AI was the same as the crypto bubble. No real useful technology and only hype.
I think AI is clearly both revolutionary and useful. Revolutionary insofar as the job I do has changed almost completely in a year or so span.