Most humans absolutely can write this with a suitable vector graphics tool such as inkscape or illustrator.

Surely, you're not suggesting that a fair comparison would be using a text editor?

If so, would you suggest an equivalent raster based task would only be fair, if the human would manually assigning RGB values to each pixel?

> Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.

Rule 2 follows rule 1.

Rule 3 & 4 is a variation of Keep it Simple, Stupid (KISS) from the 1960s.

... and... now I feel stupid, because I read the last part, which is summarizing it in the same way.

From my own observations, the types of people I previously observed to be sloppy in their thought processes and otherwise work, correlates almost perfectly with those that seem most eager to praise LLMs.

It's almost as if the ability to identify bullshit, makes you critical of the ultimate bullshit generator.

The remaining 99% had become a significant challenge to the greatest human achievement in distribution of knowledge.

If people used LLMs, knowing that all output is statistical garbage made to seem plausible (i.e. "hallusinations"), and that it just sometimes overlaps with reality, it would be a lot less dangerous.

There is not a single case of using LLMs that has lead to a news story, that isn't handily explained by conflating a BS-generator with Fact-machine.

Does this sound like I'm saying LLMs are bad? Well, in every single case where you need factual information, it's not only bad, it's dangerous and likely irresponsible.

But there are a lot of great uses when you don't need facts, or by simply knowing it isn't producing facts, makes it useful. In most of these cases, you know the facts yourself, and the LLM is making the draft, the mundane statistically inferable glue/structure. So, what are these cases?

- Directing attention in chaos: Suggest where focus needs attention from a human expert. (useful in a lot of areas, medicine, software development). - Media content: music, audio (fx, speech), 3d/2d art and assets and operations. - Text processing: drafting, contextual transformation, etc

Don't trust AI if the mushroom you picked is safe to eat. But use its 100% confident sounding answer for which mushroom it is, as a starting point to look up the information. Just make sure that the book about mushrooms was written before LLMs took off....

Answer is useful as is, and needS to be factually correct: Bad

Between [3] and [4] I added an assumption, without basis, of implied agency by [1].

War-mongering, and otherwise zero-sum mentality shouldn't make all sense if they have the technology to actually reach us. [3-body spoiler warning] Kinda like in the Three Body Problem. It was kinda silly how advanced the Trisolarian were, while still bothering traveling to earth, rather than approach the problem in any number of more obvious ways

But now that we're talking about these differences, I'd say that Apple users are notoriously complacent and defend Apple and their practices. So, perhaps in some part it is in an attempt to compensate for that? I'm still surprised how we've now accepted that Apple receives information pretty much every time we run a process (or rather, if it ran more than 12 hours ago, or has been changed).

Your question as to who these people are, is still valid. So my objection is only to the extent that it being unknown doesn't mean it isn't likely.

But, if we accept a less restrictive/strict outcome, then unguided exploration is very likely a net positive. I think many well known "unorthodox" musicians did this, and also attributed it to exactly that. Though I cannot recall specific examples. So take it with a grain of salt. I'll look up who I suspect.

Edit: Searching for examples of largely self taught musicians, there are lots of examples. What this implies, probably varies wildly. So feel free to correct the examples I encountered: Chopin, Hendrix, Schubert, Uematsu, Bowie.

Would I be correct in assuming that you're tired of people not doing proper data engineering, and in general having a irresponsible disregard for it?

At least, "soul crushing to constantly be fixing problems that shouldn't have happened in the first place" resonates a lot. But I mostly see it as a result of the aforementioned.