What makes LLMs work isn't deep neural networks or attention mechanisms or vector databases or anything like that.
What makes LLMs work is our tendency to see faces on toast.
What makes LLMs work isn't deep neural networks or attention mechanisms or vector databases or anything like that.
What makes LLMs work is our tendency to see faces on toast.
@circfruit
@jasongorman
There is a large research corpus in human cognition which studies how people think and how our thinking can be (easily) fooled.
It is usually completely ignored by AGI fanboys (so it's easier to make grand claim about artificial cognition)
@circfruit @gdupont @jasongorman
And a nice page for it as well.
https://en.wikipedia.org/wiki/Pareidolia
Pareidolia + theory of mind: mentalist effect
LLM: stochastic parrot
Mix and serve
A bit long, but good reading.
It's called the ELIZA effect, and we've known about it since 1966: https://en.wikipedia.org/wiki/ELIZA_effect
@Infrapink @jasongorman @tymwol
I think it's also related to the #PeterPrinciple... that people get promoted to a level of incompetence.
The chat part of #ChatGPT is really important because it allows us to correct the LLM's output and give it another chance to completely fool us. When we're satisfied, the output is above our present ability to detect that it is BS.
By "present ability" I mean we might not feel bothered to check the output, or we genuinely might think it's correct.
@jasongorman A streamer I watch has a bot that's literally just a random number generator picking from a set list of predefined phrases that runs on a 10 minute timer or when prompted by chat and you wouldn't believe how shockingly often its totally on point "reacting" to what's happening on screen or responding to chat. So much so that we've been joking it is actually sentient.
Actual chatgpt which that streamer used before that had less hits than the literal RNG list on a timer.
Pareidolia is fun and cool unless you are fooling yourself that the faces you see are real and they are talking to you.
@ArnimRanthoron @jasongorman
We have good theories explaining how syntactic rules can implement semantic rules. In fact, nearly 200 years of formal logic.
We also have good theories explaining why a next token generator does not implement linguistic understanding. But sadly these are not well understood outside linguistics and philosophy of language.
@jasongorman are you implying that Toastface isn't real?
Don't listen to him, Toastface.
"The fault, dear Brutus, lies not in our stars but in us."
We are the ones hallucinating rationality. (In our defense, LLMs pretty much optimize for plausibility.)
This piece "Language Is a Poor Heuristic for Intelligence": https://ninelives.karawynnlong.com/language-is-a-poor-heuristic-for-intelligence/
Really stuck with me, that we have "fluency heuristic" and LLMs are really good at fooling that.
Which is similar effect but explains more.
It really makes you realize how naive the "Turing Test" really was.
Seeing things in a random assortment of shapes. Like "That cloud looks like a sheep", "These stars look like a bear.", "This toast looks like the face of Mother Mary if you squint".
@jasongorman just returned to my browser which still had that image search tab open from when I’d done it early this morning.
I was…taken aback! 😵💫
They don’t have to think, reason, or know anything at all to work, no more than any other algorithm. On the rare occasion I use them, they more often than not work quite well. Here’s an example from a couple of days ago that worked perfectly on the first try with 0 pareidolia.