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naraburns  nihil supernum  1yr ago (text post)   16083 thread views

Culture War Roundup for the week of December 19, 2022

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What fraction of human beings alive today do you think could generate something that plausibly looks like SPICE code? What fraction of those could "with some handholding (or, rather, explicit statements of how to fix the circuits) ... get closer to something functional?" What fraction could give "some justification for how it connected the nodes of each individual device?"

That's a bizarre question. It's well known that computers can do some things better than humans. Exactly what kind of conclusion do you draw from "computers can create garbage SPICE code and most humans can't", that you can't draw from "computers can add two fifty digit numbers and most humans can't" or "computers can spellcheck a 200 page document and most humans can't"?

Speak plainly.

I think I'm speaking pretty plainly. I'm asking OP to consider how ChatGPT performs in relation to an average human. This is a pretty common question people consider when talking about AI performance. After all, the Turing Test is one of the oldest and best-known tests of computer intelligence.

I am asking OP to consider these questions as a way of pushing back against statements like the following:

Seeing it underperform so much in my field is giving me a sort of Gellmann Amnesia effect for people touting how it can write code on its own.

"Underperform" is an interesting choice of words here, because it seems that the bar for performance is being set at "subject matter expert." Obviously ChatGPT is not at that level. To paraphrase Arnold Kling on the most recent EconTalk episode, "it's about at the level of an undergrad BS artist who didn't study for the test." But consider how much training and skill it takes a human to reach the level of "undergrad BS artist" and how few humans are able to attain even that level of performance. I think OP should be more impressed with how far we've come. We don't need to go a whole lot further to close the gap between "undergrad BS artist" and "skilled electrical engineer." The former often becomes the latter with just a few years of additional education.

I'm asking OP to consider how ChatGPT performs in relation to an average human.

But, assuming that the answer is that ChatGPT performs better than an average human, what conclusion do you mean to draw from that? You haven't stated anything. And computers have been able to perform particular tasks better than an average human for a long time.

That's like saying "computers can add numbers better than humans, so why doesn't the computer know that I want to add some numbers with my broken code?" There is no inherent strength in computers such that any program ran on a computer gains the ability to add numbers well. In other words, yes, computers can add numbers - but ChatGPT is not a computer, ChatGPT is a giant system of matrix multiplications and nonlinear transforms that happens to run on a computer. It would have exactly the same capabilities if a team of trillions of clerks evaluated it on paper. The ability of computers to add large numbers is not anywhere exposed to GPT as a reasoning system so that it could make use of it.

I was kind of thinking the same thing.

I'm trained as an EE, too. I could sketch a CMOS inverter, but not be very confident in my answer without checking. SPICE would be right out--my years of simulation experience in other languages don't change the fact that I never touched it.

That changes with reference materials a la Sedra-Smith. The AI technically had access to such materials, but that's not really how it operates; they're just part of the training data that pushed its weights into the delivered configuration. The finer points of electronics were lost in compression; you could say the same for my memories of electronics lab, but I kept ahold of an algorithm to look it up and refresh that knowledge.

OP's experiment is an interesting test for the automation of knowledge jobs. Clearly, we aren't there yet.

With zero prep time? I'd guess less than 0.01%. Apparently there are ~100,000 electrical engineers in the US, which means 0.028% of people are EE; microelectronics is a sub-field and SPICE code is frequently generated by netlisting a schematic, not writing by hand. I guess that is pretty low, so could be fair that the AI isn't great at it.

With coaching? If you're just drawing schematics it would probably be a substantial amount, inverters are basically the "hello, world!" of microelectronics. SPICE code could be trickier, but I only used that because the AI can't draw things (as far as I know). That said the AI does give explanations of the circuit well above something a beginner could learn, just misplaced/incorrect kind of like if someone were to plagiarize their explanation by copy-pasting things off of google and replacing keywords where they think it would make it relevant to the question they're working on.

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