Technology

The headline is stupid but the article is interesting. Greg is saying that since last month for some unknown reason, AI bug reports have gotten good and useful, and something current Linux maintainers can handle. 

It's an affectation of The Register they like reporting real news with a sometimes quirky voice. It's also British so some of the language and humour doesn't quite work as well in other parts of the world.

That's The Register's style. Their a little weird with their copy, but their reporting has been solid, in my experience.

We Brits use Czar as a colloquialism for "person in charge of...".

So the head of the water regulator might be referred to as the water Czar (and they deserve a similar fate).

Your loss. The Register has been rock solid tech news (if a bit cheeky) for decades.

What I think you are also seeing is AI sucking at some things and doing better than humans in others.

AI is pretty great at adding unit tests to code, for example, where humans do a just-OK job. Or in writing code for a very direct well scoped small problem.

AI is just OK at understanding product nuance and choices during larger implementations, or getting end to end coding right for any complex use cases.

I tried one for the first time yesterday. It was mediocre at best. Certainly not production code. It would take just as much effort to refine it as it would to just write it in the first place.

If you read AI critics, you will see people presenting solid financial evidence of the failure of AI companies to do what they promised. Remember Sam Altman promised AGI in 2025? I certainly do, and now so do you.

Do you have any concrete evidence that this financial flop will turn around before it runs out of money?

They should all be destroyed

Traditional software was developed by humans as an artifact that, and to the degree that humans improved the software for some task, got better, but it was not guaranteed. Windows 11 is proof of that, and there are a laundry list of regressions and bugs introduced into software developed by humans. I acknowledge you say usually and especially for open source — I lukewarm agree with that statement but disagree that large LLMs or other generative models will follow this trend, and merely want to point out that software usually introduces bugs as it’s developed, which are hopefully fixed by people who can reason over the code.

Which brings us to AI models, and really they should just be called transformer models; they are statistical tensor product machines. They are not software in a traditional sense. They are trained to match their training input in a statistical sense. If the input data is corrupted, the model will actually get worse over time, not better. If the data is biased, it will get worse over time, not better. With the amount of slop generated on the web, it is extraordinarily hard to denoise and decide what’s good data and what’s bad data that shouldn’t be used for training. Which means the scaling we’ve seen with increased data will not necessarily hold. And there’s not a clear indication that scaling the model size, which is largely already impractical, is having some synergistic or emergent effect as hoped and hyped.

Also, we’re really not in the infancy of AI. Maybe the infancy of widespread hype for it, but the idea of using tensor products for statistical learning algorithms goes back at least as far as Smolensky, maybe before, and that was what, 1990?

We are in the infancy of I’d say quantum style compute, so we really don’t have much to draw on beyond theoretical models.

Generative LLM models have largely plateaued in my opinion.