What I do not don are these rose-tinted, thought-terminating glasses which make people assume that this just must be an impenetrable product of superhuman intelligence.

1. "Your experience is not universal from my perspective, and I offer my experience as evidence"

2. The possibility that this adaptation you are observing is neither a stable equilibrium given future developments, nor is necessarily an indication that the people using AI have bought into it fully. I use AI heavily while remaining a sceptic.

The whole thing is far from over when we haven't experienced the worst of the technology yet: deskilling, concentration of power, extreme imbalance of wealth, military and surveillance uses, etc.

In that case, everything stands to be disrupted and those who wield AI will have massive amounts of power in the short term. But this is not a stable situation, and therefore those who wield AI will have to cede control of it or otherwise enter into a great war with those that do not.

I hope for all our sakes that there is a practical ceiling.

The claim is furthermore that truly original thinking, the infamous leaps in understanding and creativity, happen by attaining points outside such a convex hull.

It's hard to rigorously verify or disprove this claim. Hopefully this helps build an intuition of why the claim is not as shallow and obviously wrong as it may seem initially.

There is a fine distinction here that I believe is often glossed over, so the two things it's delineating get muddled together. One of those two things is coding—the rote, mechanical encoding of meaning into computer instructions. It can be argued the LLM is fit to take this out of hands hands almost entirely, and it's almost indisputable the LLM is better at this in at least a certain sizeable subset of coding tasks.

But the other thing is the choosing, determination, specification of the intended meaning itself. This I think is squarely the job of the human, because letting this fall through to the AI means it is no longer the human that is making the decisions. This then becomes not merely automating work but ceding control. This, ultimately, is a bad thing.

So if we accept the premise that the specification of the intended meaning is the job of the human, the question is how you do that. Today many of us do it somewhat half-assedly, by writing lots of natural language text at the LLM and hoping it sticks. It is our hope that the text, given that there is a lot of it, will drive the stochastic machine in a sufficiently correct direction. This works to a degree—meaning we've ceded some control but not the majority of it—not least because we still read (most) of the code but cannot work in the limit, if code reading ceases.

A more proper way to specify the intended meaning is to specify (or "model") your system formally in a system that is mechanically verifiable. Then the final artefact produced by the LLM can be validated by verifying that it aligns with the specification. However this type of high-assurance specification looks a lot like a certain type of programming. In my opinion, writing this kind of specification is the future of human software engineering.

I do not accept the approach of simply rolling the dice and hoping the machine knows better than us, though I'm sure that church is also going to have its acolytes.

Whether this mode of working is going to be long-term viable is going to depend on how important it is for you to be aware of what has happened for the system in question, how viable the economics are for the LLM usage at this level of assurance and how much ownership you exert over the LLM used or another similarly powered one (because otherwise the LLM can be taken away from you, leaving you at the mercy of a third party with goals that do not align with your own).

On Linux X11 at least, you can enable the Compose key and then press `---` which results in — and `--.` which gives you –

The "majority" in this case meaning about 51%, according to Wikipedia[1]? How could 51% ever be considered to be close to "all", such that "misleading" would be a valid answer?

Am I missing something?

[1]: https://en.wikipedia.org/wiki/Almond#Production

If it's true of everything, then surely having an LLM work iteratively on the pieces, along with being provided additional physical data, will lead to the discovery of everything?

If the answer is "no", then surely something is still missing.

> And the "anyone who read all the right papers" thing - nobody actually reads all the papers. That's the bottleneck. LLMs don't have it. They will continue to not have it. Humans will continue to not be able to read faster than LLMs.

I agree with this. This is a definitive advantage of LLMs.

However, what I'm saying is not mere nitpicking either. It is precisely because of my belief in Einstein's extraordinary abilities that I find it unconvincing that an LLM being able to recombine the extant written physics-related building blocks of 1900, with its practically infinite reading speed, necessarily demonstrates comparable capabilities to Einstein.

The essence of the question is this: would Einstein, having been granted eternal youth and a neverending source of data on physical phenomena, be able to innovate forever? Would an LLM?

My position is that even if an LLM is able to synthesise special relativity given 1900 knowledge, this doesn't necessarily mean that a positive answer to the first question implies a positive answer to the second.

It's still unclear whether this process could be merely continued, seeded only with new physical data, in order to keep progressing beyond that point, "forever", or at least for as long as we imagine humans will continue to go on making scientific progress.

On the other hand, the whole deal of the LLM is that it does so stochastically and unpredictably.