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More in AI skepticism news: Turns out most AI benchmarks are bullshit!
https://rdi.berkeley.edu/blog/trustworthy-benchmarks-cont/
Specifically the following benchmarks are trivially exploitable: SWE-bench, WebArena, OSWorld, GAIA, Terminal-Bench, FieldWorkArena, and CAR-bench.
I don't have too much to add to this, but I'll try. Assuming this paper isn't bullshit itself, it makes you wonder why no one was looking more closely at the results submitted by various AI companies. In one of our other discussions about this recently, someone said:
When I asked if they had manually verified them, they said they hadn't. It seems a lot of the things people claim about AI and its capabilities are "too good to verify", similar to how salacious stories about the other tribe in culture war stories are "too good to verify". It seems to me that a lot of people want to believe that AGI, or the death of software development, or similar things, are right around the corner. As a result, they often believe whatever the claims of sociopaths like Sam Altman, or the weirdos who believe in AGI over at Anthropic, tell them. Including, potentially, the benchmark results we see published with every new release. On the other hand, to be fair, skeptics like me can certainly be quick to believe negative stories about AI. I mean, look at me rushing to post this negative story about it here.
Regardless, I am personally of the opinion that we are near a breaking point regarding AI. I think either the bubble is going to pop and a lot of the things people claimed AI was going to take over aren't going to materialize, or they are an we are in for some major economic disruption. I don't think "AGI" is around the corner in either case though. And certain professions like SEO slop writer, translator, and others are definitely disrupted forever regardless.
I become more and more skeptical that the current LLM approach to AI is going to get us close to AGI, let alone ASI, whatever that means. The models are just clearly not that smart, and if you don’t realize this it’s because (1) you are not that smart or (2) you’re using it in the domain that the architecture happens to be good at, which is language (which includes chatting but also programming and some formal math.)
People are super language-oriented and so are overly impressed/distracted when something can do human language so well. We’re just dazzled by language use, and that massively biases our perception.
The technical field most dazzled by LLMs is programming, which is also basically just a translation job. Society has been under the misapprehension that being a computer translator is a super hard and intellectual job, partly because it has paid so well in the past couple of decades. This is just because there aren’t any people who are natively bilingual in English and computer, in the same way there are lots of people who natively bilingual in English and Spanish. Like an English teacher in China, people were able to arbitrage this lack of supply, and people mistook the existence of this arbitrage as evidence that the field is super difficult.
If you are in a technical field outside of this it is very obvious, and has been for a long time, that the current architecture is bad and progress stalled. A bunch of programmers will lose their status, like so many loser English teachers in China, but beyond that the current path isn’t going to change much.
Programming is an extremely g-loaded activity. Technical interviews at silicon valley tech companies are not far from straight up IQ tests. When I taught programming, I encountered a lot of students who were very diligent and motivated but hit a brick wall because they just didn't have the cognitive equipment to think at the level of abstraction required to reason about non-trivial programs. I think that, prior to the age of LLMs, you would be hard pressed to find a working programmer with a 100 IQ. I doubt the same can be said of transistors.
For some subset of g, where g is pure logic. For other subsets of g, especially those related to mechanical reasoning and 2nd order effects, I have a large pile of former FAANG resumes that have failed the conversion to nuts, bolts, and actual atoms engineering that argues otherwise. Not to say they arent intelligent, but a "pure generalist" is not a hard requirement.
I think this is one of those "theory vs practice" things. In theory, programming is an extremely intellectually straining endevour, and the academic pipline in the West is set up with some pretty fine filters. In practice though with the way big companies PM the development, deployment, and maintenence of most software today you dont actually need an above-average IQ to excel (this is a feature, not a bug). I agree with badger that the market rate for programming salaries has probably overstated the relative intellectual demand compared to other professions of similar educational requirements.
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