Posted by pranitha_m 7 hours ago
After a while it also ends up being the same, the job market is shaken and people now have to work just as much as they can but in different, newer jobs to make different newer things.
No post-scarcity commie utopia is ever going to be possible because even in that society, the people that work their ass off will be so ever slightly ahead of the others, so now everyone hast to do it to not be "left behind".
I have to constantly make sure it’s building things in a way that will fit neatly together, in a way doesn’t over (or under) -generalize. In a way that doesn’t waste time and energy on idiotic tests that aren’t really testing anything at all.
LLMs are powerful autocomplete with vast and broad memory, they do not think. And thinking is just a part of the human condition.
How does that not lead you to same conclusion that everyone else is having? That this is only going to improve.
It's so shortsighted to make an assessment only on what exists today.
This is really only true insofar as they are designed such that they output language tokens and their intermediate "chain of thought" is also done in language tokens, but now that's really only done for auditability. There are much more abstract layers before the model output layer of a single token prediciton and they could be designed to to "think" using these more abstract layers as intermediates instead like how many people think in concepts rather than words. There are even some research models on this (see Next Latent Models)[1]. Humans need to communicate in language because we don't have telepathic abilities but these models need not have such a constraint.
It does not matter whether LLMs do any thinking, or "thinking." The only thing that matters is whether they produce similar results to human thinking, even if it's through a different process (or no process at all). When a company decides to deploy an AI-powered customer service representative, it is not because the AI bot goes through the same thinking process as a human when answering a customer's request.
Except those don’t really fit, here, because the author is making a far more critical error in ascribing far too much to current human cognition. On the daily I see so-called “knowledge workers” failing to read basic signs, refusing to learn basic skills, and all too eager to cast off the slightest bit of critical thinking to a black box they can subscribe to - be it replacing curating a content library with streaming services and algorithms, or swapping a life of hard-won expertise for a swarm of chatbots and an API bucket. As someone with the uniquely privileged POV to be able to ingest vast sums of technical data but struggle with social data, I look at my fellow Americans and genuinely fret for what the next few decades hold for a cohort that seemingly cannot effectively communicate which direction they’re going but is all too content to hitch their accountability to an LLM they pay for.
In that sense, thinking has already become a hobby to the masses, in that it’s entirely optional to exist in current society at large. Autopay apps manage your bills, finance apps keep you on budget, fitness apps tell you to keep running, social media apps tell you who your friends (and enemies) are, video apps tell you what to watch, music apps tell you what to listen to, and cars increasingly tell you not just where to go but also to let them get there for you. I would argue that in that context, thinking is a critically endangered hobby we sorely need to sow new seeds for, lest we risk losing it entirely; AI does not seem keen to do that.
Now to be fair, so much of this is hyperspecialization knowledge overflowing into daily life and making existence a sort of cognitohazard in and of itself. We skip through hundreds of pages of legal boilerplate because we’re not lawyers, and then complain about all the rights we willingly signed away because of the volume of said agreements. Non-technical people engaging with modern computing are either addicts or miserable, unaware that this was both entirely optional and also inevitable in a populace that didn’t think for itself. Finance has become so hyperspecialized that it’s impossible for the layman to tell of something is good or bad, or even why the number went up or down on their portfolio in a given time period. LLM boosters say their tools will solve this complexity nightmare, yet the tools themselves grow increasingly complex just to eke out single-point gains on hyperspecific problems.
Which brings me to the OP again, their highly uncritical claims that AI will bring about a new revolution of thought itself, and that somehow this tool will both restore thought to an elevated pedestal while also making it nearly impossible to meter (at least on American currency; let’s not think too hard about how developing countries might be able to afford it). I don’t see any of that, but for the same reasons the OP feels it’s somehow inevitable. The only difference I can readily figure is our opinions of our fellow humans: I view us as in dire straits and able to do far better than we give ourselves credit for, while they view us as replaceable with servile digital labor for the better.
But a the conclusion is a nice one for people that will be able to pull it off.
"I hate this damn machine"
"I wish they would just sell"
"It never does what I want"
"Only what I tell it."