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WHERE AI PREDICTIONS GO WRONG  
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Kelsey Piper
June 7, 2024
Vox
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_ How close are large language models to achieving general
intelligence? Observers offer diametrically opposed answers. But both
skeptics and boosters may be too sure of themselves. _

Artificial Intelligence & AI & Machine Learning, mikemacmarketing

 

What should we make of large language models (LLMs)? It’s quite
literally a billion-dollar question. 

It’s one addressed this week in an analysis
[[link removed]] by former OpenAI employee Leopold
Aschenbrenner, in which he makes the case that we may be only a few
years away from large language model-based general intelligence that
can be a “drop-in remote worker” that can do any task human remote
workers do. (He thinks that we need to push ahead and build it so that
China doesn’t get there first.)

His (very long but worth reading) analysis is a good encapsulation of
one strand of thinking about large language models like ChatGPT: that
they are a larval form of artificial general intelligence (AGI) and
that as we run larger and larger training runs and learn more about
how to fine-tune and prompt them, their notorious errors will largely
go away. 

It’s a view sometimes glossed as “scale is all you need
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and more computing power. GPT-2 was not very good, but then the bigger
GPT-3 was much better, the even bigger GPT-4 is better yet, and our
default expectation ought to be that this trend will continue. Have a
complaint that large language models simply aren’t good at
something? Just wait until we have a bigger one. (Disclosure: Vox
Media is one of several publishers that has signed partnership
agreements with OpenAI. Our reporting remains editorially
independent.)

Among the most prominent skeptics of this perspective are two AI
experts who otherwise rarely agree: Yann LeCun, Facebook’s head of
AI research, and Gary Marcus, an NYU professor and vocal LLM skeptic.
They argue that some of the flaws in LLMs — their difficulty with
logical reasoning tasks, their tendency toward “hallucinations
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vanishing with scale. They expect diminishing returns from scale in
the future and say we probably won’t get to
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artificial intelligence by just doubling down on our current methods
with billions more dollars.

Who’s right? Honestly, I think both sides are wildly
overconfident. 

Scale does make LLMs a lot better at a wide range of cognitive tasks,
and it seems premature and sometimes willfully ignorant to declare
that this trend will suddenly stop. I’ve been reporting on AI for
six years now, and I keep hearing skeptics declare that there’s some
straightforward task LLMs are unable to do and will never be able to
do because it requires “true intelligence.” Like clockwork, years
(or sometimes just months) later, someone figures out how to get LLMs
to do precisely that task. 

I used to hear from experts that programming was the kind of thing
that deep learning could never be used for
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it’s now one of the strongest aspects of LLMs
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When I see someone confidently asserting that LLMs can’t do some
complex reasoning task, I bookmark that claim. Reasonably often,
it immediately turns out
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its top-tier competitors can do it after all.

I tend to find the skeptics thoughtful and their criticisms
reasonable, but their decidedly mixed track record makes me think they
should be more skeptical about their skepticism.

We don’t know how far scale can take us

As for the people who think it’s quite likely we’ll have
artificial general intelligence inside a few years, my instinct is
that they, too, are overstating their case. Aschenbrenner’s argument
features the following illustrative graphic:

I don’t want to wholly malign the “straight lines on a graph”
approach to predicting the future; at minimum, “current trends
continue” is always a possibility worth considering. But I do want
to point out (and other critics have as well
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axis here is ... completely invented. 

GPT-2 is in no respects particularly equivalent to a human
preschooler. GPT-3 is much much better than elementary schoolers at
most academic tasks and, of course, much worse than them at, say,
learning a new skill from a few exposures. LLMs are sometimes
deceptively human-like in their conversations and engagements with us,
but they are fundamentally not very human; they have different
strengths and different weaknesses, and it’s very challenging to
capture their capabilities by straight comparisons to humans. 

Furthermore, we don’t really have any idea where on this graph
“automated AI researcher/engineer” belongs. Does it require as
many advances as going from GPT-3 to GPT-4? Twice as many? Does it
require advances of the sort that didn’t particularly happen when
you went from GPT-3 to GPT-4? Why place it six orders of magnitude
above GPT-4 instead of five, or seven, or 10?

“AGI by 2027 is plausible ... because we are too ignorant to rule it
out ... because we have no idea what the distance is to human-level
research on this graph’s y-axis,” AI safety researcher and
advocate Eliezer Yudkowsky responded to Aschenbrenner
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That’s a stance I’m far more sympathetic to. Because we have very
little understanding of which problems larger-scale LLMs will be
capable of solving, we can’t confidently declare strong limits on
what they’ll be able to do before we’ve even seen them. But that
means we also can’t confidently declare capabilities they’ll
have. 

Prediction is hard — especially about the future

Anticipating the capabilities of technologies that don’t yet exist
is extraordinarily difficult. Most people who have been doing it over
the last few years have gotten egg on their face. For that reason, the
researchers and thinkers I respect the most tend to emphasize a wide
range of possibilities.

Maybe the vast improvements in general reasoning we saw between GPT-3
and GPT-4 will hold up as we continue to scale models. Maybe they
won’t, but we’ll still see vast improvements in the effective
capabilities of AI models due to improvements in how we use them:
figuring out systems
[[link removed]] for
managing hallucinations, cross-checking model results, and better
tuning models to give us useful answers.

Maybe we’ll build generally intelligent systems that have LLMs as a
component. Or maybe OpenAI’s hotly anticipated GPT-5 will be a huge
disappointment, deflating the AI hype bubble and leaving researchers
to figure out what commercially valuable systems can be built without
vast improvements on the immediate horizon. 

Crucially, you don’t need to believe that AGI is likely coming in
2027 to believe that the possibility and surrounding policy
implications are worth taking seriously. I think that the broad
strokes of the scenario Aschenbrenner outlines — in which an AI
company develops an AI system it can use to aggressively further
automate internal AI research, leading to a world in which small
numbers of people wielding vast numbers of AI assistants and servants
can pursue world-altering projects at a speed that doesn’t permit
much oversight — is a real and scary possibility. Many people are
spending tens of billions of dollars
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bring that world about as fast as possible, and many of them think
it’s on the near horizon. 

That’s worth a substantive conversation and substantive policy
response, even if we think those leading the way on AI are too sure of
themselves. Marcus writes
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and I agree — that “if you read his manuscript, please read it for
his concerns about our underpreparedness, not for his sensationalist
timelines. The thing is, we should be worried, no matter how much time
we have.”

But the conversation will be better, and the policy response more
appropriately tailored to the situation, if we’re candid about how
little we know — and if we take that confusion as an impetus to get
better at measuring and predicting what we care about when it comes to
AI.

_A version of this story originally appeared in the Future Perfect
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