(I get the feeling we’re close to this bubble’s end.)
Anyways, welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned soo many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
(Credit and/or blame to David Gerard for starting this.)
A number of responses (like, 2 out of the 3 that it shows) refer to ‘the scaling laws’ without elaboration. Is this some jargon special to AI bros?
yeah essentially
the scaling laws are basically that AGI/ASI can be created by just scaling up the current LLMs
But they really aren’t! The actually interesting scaling laws from about 5-7 years ago that opened up machine learning to more applications all show that everything about machine learning is power-law diminishing returns with linear improvements in any metric requiring exponential increases in numbers of parameters / amounts of data / number of backprop cycles applied.
The scaling laws maxed out around mid 2023 with the release of GPT-4 - that was around the time that everyone got excited about “test time inference” which is where LLMs spit out reasoning streams to convince you they are “thinking”. If you look at where LLMs have improved since then it’s mainly in tasks where they can be strapped into a harness and given concrete feedback like “your program didn’t compile” which are also coincidentally use cases where data from gpt and Claude users can easily be harvested and new training data can be squeezed out of underpaid third world mturkers.
In other words they’ve improved their training data in the specific domains they are operating in like we’ve been doing in machine learning since the mid 90s…
The big labs have just been doing an incredible job of marketing (and particularly in Anthropic’s case, brain damaging themselves into believing their own marketing)