My AI skeptic friends are all nuts

This op ed suggests that it’s easier to audit a huge amount of code before merging it in than is to write the code from scratch. I don’t know about anyone else, but I generally find it easier to write exactly what I want than to mentally model what a huge volume of code I’ve never seen before will do?

(Especially if that code was spit out by an alien copypasta that is really good at sounding plausible with zero actual intelligence or intent?)

Like, if all I care about is: does it have enough unit tests and do they pass, then yeah I can audit that.

But if I was trying to solve truly novel problems like modeling proteins, optimizing travel routes, or new computer rendering techniques, I wouldn’t even know where to begin, it would take tons of arduous study to understand how the new project full of novel algorithms is going behave?

> So where will the experts come from?

They won't, save for a relative minority of those who enjoy doing things the hard way or those who see an emerging market they can capitalize on (slop scrubbers).

I wrote this post [1] last month to share my concerns about this exact problem. It's not that using AI is bad necessarily (I do every day), but it disincentivizes real learning and competency. And once using AI is normalized to the point where true learning (not just outcome seeking) becomes optional, all hell will break loose.

> Perhaps there is another way to develop the skills

Like sticking a fork in a light socket, the only way to truly learn is to try it and see what happens.

[1] https://ryanglover.net/blog/chauffeur-knowledge-and-the-impe...

Definitely not. I took this same basic idea of feeding videos into Whisper to get SRT subtitles and took it a step further to make automatic Anki flashcards for listening practice in foreign languages [1]. I literally feel like I'm living in the future every time I run across one of those cards from whatever silly Finnish video I found on YouTube pops up in my queue.

These models have made it possible to robustly practice all 4 quadrants of language learning for most common languages using nothing but a computer, not just passive reading. Whisper is directly responsible for 2 of those quadrants, listening and speaking. LLMs are responsible for writing [2]. We absolutely live in the future.

[1]: https://github.com/hiandrewquinn/audio2anki

[2]: https://hiandrewquinn.github.io/til-site/posts/llm-tutored-w...

What valid AI criticisms? Most criticisms of AI are not very deep nor founded in complexity theoretic arguments, whereas Yann LeCun himself gave an excellent 1 slide explanation of the limits of LLMs. Most AI criticisms are low quality arguments.

I’d love to see the authors of effusive praise of generative AI like this provide the proof of the unlimited powers of their tools in code. If GAI (or agents, or whatever comes next …) is so effective it should be quite simple to prove that by creating an AI only company and in short order producing huge amounts of serviceable code to do useful things. So far I’ve seen no sign of this, and the best use case seems to be generating text or artwork which fools humans into thinking it has coherent meaning as our minds love to fill gaps and spot patterns even where there are none. It’s also pretty good at reproducing things it has seen with variations - that can be useful.

So far in my experience watching small to medium sized companies try to use it for real work, it has been occasionally useful for exploring apis, odd bits of knowledge etc, but overall wasted more time than it has saved. I see very few signs of progress.

The time has come for llm users to put up or shut up - if it’s so great, stop telling us and show and use the code it generated on its own.