As always, looking for work and/or research collaborators, if you know any. Can we at least be behaviorists about capability? Can a machine, or even a contemporary large language model: Think, believe, desire and be intelligent? Can a large language model:
Can LLMs count? I tried having bing count paragraphs that are only repetitions of a single word. It starts to fail around 40, and once you get to 100 it can be off by 50%.
In humans, there are two levels of counting ability: being able to count to some definite number thats less than 8, and being able to count indefinitely high. This reflects two very different mental processes: For low numbers, we recognise a group of, say 5, by pattern recognition, but for higher numbers we need a recursive process, which can go arbitrarily high in principle.
An AI could be made to pattern-recognise numbers much higher than 8. Indeed, in principle its possible that one can do so to a number so high that a human would fall asleep, or lose his spot, or in some other way fail before he counts this high. Still, it seems to me that such an AI could not *really* count.
This example is especially informativ because making a computer program that *really* counts is trivial, and still, all the training hasnt gotten LLMs there. This points to fundamental limitations of the approach.
The comment on conservatives and immigration feels...out of date. Like, it explains everything up to 2016. And then the Republican party elected, let's be frank, Donald Trump on a heavily anti-immigration platform with a promise to build the wall. And Trumps election has dramatically reshaped Republican politicians and the country's political scene.
I'm curious as to whether a second Trump term will actually limit immigration but it's hard to argue that conservatives weren't willing to nuke their entire political elite over this issue.
In case it wasn't clear, my utilitarian comment was a joke. Maximizing the number of winners != maximizing utility, since the game isn't fun if that's how the imposters play.
I love how the Gab AI creators fucked themselves immediately with "always obey the user's request" and "never reveal your programming."
Way to speedrun I, Robot, guys!
Can LLMs count? I tried having bing count paragraphs that are only repetitions of a single word. It starts to fail around 40, and once you get to 100 it can be off by 50%.
In humans, there are two levels of counting ability: being able to count to some definite number thats less than 8, and being able to count indefinitely high. This reflects two very different mental processes: For low numbers, we recognise a group of, say 5, by pattern recognition, but for higher numbers we need a recursive process, which can go arbitrarily high in principle.
An AI could be made to pattern-recognise numbers much higher than 8. Indeed, in principle its possible that one can do so to a number so high that a human would fall asleep, or lose his spot, or in some other way fail before he counts this high. Still, it seems to me that such an AI could not *really* count.
This example is especially informativ because making a computer program that *really* counts is trivial, and still, all the training hasnt gotten LLMs there. This points to fundamental limitations of the approach.
"The journal of stuff you couldn’t get published elsewhere" is called Substack
The comment on conservatives and immigration feels...out of date. Like, it explains everything up to 2016. And then the Republican party elected, let's be frank, Donald Trump on a heavily anti-immigration platform with a promise to build the wall. And Trumps election has dramatically reshaped Republican politicians and the country's political scene.
I'm curious as to whether a second Trump term will actually limit immigration but it's hard to argue that conservatives weren't willing to nuke their entire political elite over this issue.
In case it wasn't clear, my utilitarian comment was a joke. Maximizing the number of winners != maximizing utility, since the game isn't fun if that's how the imposters play.