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.

@jasongorman
100%
I've been telling this for a while now.
Our tendency for pareidolia goes way beyond just seeing faces. We're desesperately trying to connect other intelligent beings...
@gdupont @jasongorman I didn’t know there’s a word for that 🤨

@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

Pareidolia - Wikipedia

@jasongorman A thought that slipped trough my mind also.
@jasongorman No, seriously - that's exactly what it is & it's a mechanism exploited by charlatans since always: https://softwarecrisis.dev/letters/llmentalist/
The LLMentalist Effect: how chat-based Large Language Models rep…

The new era of tech seems to be built on superstitious behaviour

Out of the Software Crisis
@jwcph @jasongorman I’ve always felt like the way chatbots speak was a little too general and assertive for me to believe/trust them, and I’m glad to learn *why* it is like that. The psychic comparison is very effective.
@jwcph @jasongorman I've never had a piece of toast with a face on it write hundreds of lines of code for me that actually works because I talked at it tho
@feld @jwcph Going by the hard evidence, nobody's had an LLM do that, either. Not without them fixing it.
@jasongorman @jwcph what do you mean? I tell it that it must pass the test case, pass the formatter, linter, and static analysis... and then it emitted code and executed what I asked and produced a working result that passed the test case

it automatically reads the results of the formatter, linter, compiler, static analysis, and tests cases and corrects any errors it encounters

if you don't give it thorough instructions it will produce garbage. so just give it the same guidance you would a jr dev along with examples of best practices
@feld @jasongorman Nobody believes you, dude. Let it go.
@jwcph @jasongorman why would I lie? I have literally nothing to gain by convincing someone that you *can* coerce these tools into doing something useful
@feld @jasongorman - and yet you're still trying...
@jwcph @jasongorman do you think spreading these lies will really make these tools disappear or something? what a strange obsession

they'll still be around 5 years from now. they're not going anywhere.
@jasongorman jokes aside, this is a very good metaphor
@tymwol I'm not even sure it's a metaphor 🙂

@jasongorman @tymwol

It's called the ELIZA effect, and we've known about it since 1966: https://en.wikipedia.org/wiki/ELIZA_effect

ELIZA effect - Wikipedia

@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.

@pete @Infrapink @jasongorman @tymwol It should also be noted that the people most in love with the bullshit-generating machine make a living out of generating bullshit themselves.

@Infrapink @jasongorman @tymwol I think both things apply. One reason that Eliza was so convincing is that it was "mimicking" a psychological therapy session. Where having your ideas repeated back to you is common and a good way of making connection.

It fulfilled peoples expectation of a session. Like faces on toast fulfill peoples expectations. All of the so called "intelligence" is actually in ourselves.

Chat tools can help us work out the answers ourselves sometimes - a well know developers trait. But mostly, it gives us what we expect, and that is good enough for us.

@jasongorman 💯 !
We should stare more often into the sky watching clouds.

*Azure breaks through wall* did somebody say the cloud?

@NatureMC @jasongorman

@jasongorman Digital Jesus toast.
@krypt3ia @jasongorman My toast just gets Calvary not Jesus. 😂

@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.

@StaticR @jasongorman do you have an example of a question answered by the RNG list?
@duco since they were all in twitch live chats no. There's probably a couple clips of particularly funny responses but digging them up might be a bit challenging.

@jasongorman

Pareidolia is fun and cool unless you are fooling yourself that the faces you see are real and they are talking to you.

@jasongorman if it looks like a duck and it quacks like a duck, it can't possibly be an artificial duck
@jasongorman Yes! This plus what I think of as the Stone Soup Effect. A claim of magic gets people to try it, but they don't notice that they're the ones doing most of the value-creating work.
@williampietri @jasongorman
"Finding books in babbling brooks"; that's either Shakespeare or Benford.
@jasongorman just as the “I want a lollipop” quote from the last #securitynow podcast*, this is also a nice piece of wishdom for Leo la Portes ( @leo ) quotes collection. I think 😇.

* = see page 15 of: https://www.grc.com/sn/SN-1027-Notes.pdf
@jasongorman @purplepadma Right on. As I’ve said so many times, I hate the term “hallucination” because it implies there is something in there that perceives and that therefore can hallucinate. In fact, the only hallucination is happening on our side of the keyboard.
@jasongorman oooh that’s good.
@jasongorman unfortunately, I feel like our tendency to see faces on toast is also what makes human society work
@jasongorman And staggering amounts of theft, of course.
@ndw @jasongorman @infryq
Sensible chuckle at this pair of replies

@Wifiwits

Faces on toast. Faces. That’s what it actually says. Faces on toast. Faces.
Darn my aging eyes!

@jasongorman nice. Brilliant phrasing vs. we *are* pattern-seeking machines.
@jasongorman
Well, computers are doing something that we interpret as mathematics…

@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.

@jasongorman

Also basic ignorance about how technology works.

Never to be underestimated, except by people who don't understand technology.

@jasongorman
LLM's are doing an automated Mentalism act.
@jasongorman I mean if we're really getting down to it, one might argue rather that LLMs just don't work, but our tendency to see faces on toast is what makes people able to sell them anyway.

@jasongorman

"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.)