As a software developer who took an elective in neural networks - when people call LLMs stochastic parrots, that's not criticism of their results.
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@lmorchard @leeloo @wolf480pl good grief now I have to sound like Sam friggin Altman, and there is clearly something very wrong with that man.
But your description ignores that humans need a solid 6 months of "training data" to get object permanence, never mind the concept of categories or species of animals, never mind understanding the category differences between cats and foxes well enough to reliably tell one from the other.
@lmorchard @leeloo @wolf480pl I guess part of it is maybe that I don't think intelligence is some exclusively human thing. LLMs clearly aren't human-like intelligent. I'm personally confident they're not as intelligent as any primate.
But are they as intelligent as a shrimp? I think they've got to be more intelligent than a mosquito.
I wouldn't turn to a shrimp for advice but they're not *without* intelligence.
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@lmorchard @leeloo @wolf480pl I guess part of it is maybe that I don't think intelligence is some exclusively human thing. LLMs clearly aren't human-like intelligent. I'm personally confident they're not as intelligent as any primate.
But are they as intelligent as a shrimp? I think they've got to be more intelligent than a mosquito.
I wouldn't turn to a shrimp for advice but they're not *without* intelligence.
@dragonfrog @lmorchard @leeloo @wolf480pl
Are the images reflected in a distorted mirror the product of intelligence (of the mirror)?
They are coherent, a literal transform of the input images, reflected and produce a recognizable, if distorted and changed version.
A traditional function output. Let's add some noise to make it non-deterministic, a wind blowing through that minutely distorts the surface.
Intelligible output following from the input, but the mirror itself isn't intelligent.
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@dragonfrog @lmorchard @leeloo @wolf480pl
Are the images reflected in a distorted mirror the product of intelligence (of the mirror)?
They are coherent, a literal transform of the input images, reflected and produce a recognizable, if distorted and changed version.
A traditional function output. Let's add some noise to make it non-deterministic, a wind blowing through that minutely distorts the surface.
Intelligible output following from the input, but the mirror itself isn't intelligent.
@dragonfrog @lmorchard @leeloo @wolf480pl
The intelligence apparently making the meaning is pre-encoded in the input. Likewise, the vector math is extracting and exposing structure, encoded in language, put there originally by the intelligent humans.
There is no world model or understanding. That's why counting the "r" in strawberry or simply counting to 200 is so challenging.
The behavior can reasonably be called intelligent, but it's due to borrowed, reformulated, extracted intelligence
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As a software developer who took an elective in neural networks - when people call LLMs stochastic parrots, that's not criticism of their results.
It's literally a description of how they work.
The so-called training data is used to build a huge database of words and the probability of them fitting together.
Stochastic because the whole thing is statistics.
Parrot because the answer is just repeating the most probable word combinations from its training dataset.Calling an LLM a stochastic parrot is lile calling a car a motorised vehicle with wheels. It doesn't say anything about cars being good or bad. It does, however, take away the magic. So if you feel a need to defend AI when you hear the term stochastic parrot, consider that you may have elevated them to a god-like status, and that's why you go on the defense when the magic is dispelled.
@leeloo As a side note, I sometimes worry about how much parroting happens in academia among humans even before/without LLMs, where people repeat things without understanding what they’re talking about. I guess at least for students, it sometimes is about learning to talk the talk, and then gradually developing more understanding and genuine thinking around topics. At least we humans are capable of developing that understanding if we bother to try.
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@leeloo The thing is, how can we sure that human intelligence does not essentially work in the same way? My Christian believe tells me we have a soul and LLM's do not, that may be the difference. But from an agnostic perspective, we might reach the point where one cannot tell the difference.
@tobifant
A LLM is not able to reason. It can fool you into believing it is intelligent and self aware, where in fact it just parrots the patterns it has stored. These patterns are however very human-like as they are the result of training on texts written by actual humans.The fun part starts now where the entire internet got flooded by #ai generated content. All of this will be the training set for the next generation of LLM's. What could possibly go wrong?
@leeloo -
@leeloo I myself like calling LLMs "glorified autocomplete". Or "Т9 на максималках" in Russian.
It's surprising just how defensive some people get when I say that even when they agree with my definition. They keep believing that just give this thing more parameters and something magical, something more than sum of its parts will emerge, any moment now, just one more model generation, just one more order of magnitude, I promise.
@grishka
The fun part is that the next generation will have the current state of the internet as its training set. An internet that is flooded by #ai generated content.The biggest issue those ai companies face at the moment is how to only ingest human generated content and filter out as much as possible of all of the ai generated crap that is out there.
Good luck with that.
@leeloo -
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R relay@relay.an.exchange shared this topic
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As a software developer who took an elective in neural networks - when people call LLMs stochastic parrots, that's not criticism of their results.
It's literally a description of how they work.
The so-called training data is used to build a huge database of words and the probability of them fitting together.
Stochastic because the whole thing is statistics.
Parrot because the answer is just repeating the most probable word combinations from its training dataset.Calling an LLM a stochastic parrot is lile calling a car a motorised vehicle with wheels. It doesn't say anything about cars being good or bad. It does, however, take away the magic. So if you feel a need to defend AI when you hear the term stochastic parrot, consider that you may have elevated them to a god-like status, and that's why you go on the defense when the magic is dispelled.
@leeloo If I want to disparage, I say "LLMs are just a word list with a randomizer". It's slightly less accurate, because it's a very specific kind of word list and a likewise specific kind of randomizer, but it gets the cultists all riled up. So that's cool.
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As a software developer who took an elective in neural networks - when people call LLMs stochastic parrots, that's not criticism of their results.
It's literally a description of how they work.
The so-called training data is used to build a huge database of words and the probability of them fitting together.
Stochastic because the whole thing is statistics.
Parrot because the answer is just repeating the most probable word combinations from its training dataset.Calling an LLM a stochastic parrot is lile calling a car a motorised vehicle with wheels. It doesn't say anything about cars being good or bad. It does, however, take away the magic. So if you feel a need to defend AI when you hear the term stochastic parrot, consider that you may have elevated them to a god-like status, and that's why you go on the defense when the magic is dispelled.
nope. What you describe as "stocastical parrot" is Markov, Hidden Markov Model (HMM) , not a VLLM.
You can find an HMM in your mobile phone, AKA T9, AKA "keyboard suggestions".
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R relay@relay.mycrowd.ca shared this topic
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nope. What you describe as "stocastical parrot" is Markov, Hidden Markov Model (HMM) , not a VLLM.
You can find an HMM in your mobile phone, AKA T9, AKA "keyboard suggestions".
@uriel
What part exactly are you saying nope to.Dispelling the magic and god-like status or some specific detail?
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@uriel
What part exactly are you saying nope to.Dispelling the magic and god-like status or some specific detail?
nope to the bunch of bullshit you wrote under the assumption a VLLM is a Hidden Markov Model , aka "stochastic parrot".
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@calcifer
> But the hype is unreal and legitimately dangerous.I blame Sam Altman for that 100%
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nope to the bunch of bullshit you wrote under the assumption a VLLM is a Hidden Markov Model , aka "stochastic parrot".
@uriel
Ah, so you are saying that you decided that I said something I never did, and then saying nope to that, so that you can pretend that you have a real argument.Like when creationists try arguing against evolution using pseudo-scientific arguments to hide that they are defending the bible.
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As a software developer who took an elective in neural networks - when people call LLMs stochastic parrots, that's not criticism of their results.
It's literally a description of how they work.
The so-called training data is used to build a huge database of words and the probability of them fitting together.
Stochastic because the whole thing is statistics.
Parrot because the answer is just repeating the most probable word combinations from its training dataset.Calling an LLM a stochastic parrot is lile calling a car a motorised vehicle with wheels. It doesn't say anything about cars being good or bad. It does, however, take away the magic. So if you feel a need to defend AI when you hear the term stochastic parrot, consider that you may have elevated them to a god-like status, and that's why you go on the defense when the magic is dispelled.
Oh, the good old “I was misunderstood.” I genuinely hope your communication skills improve someday, so you can finally express your ideas clearly
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nope to the bunch of bullshit you wrote under the assumption a VLLM is a Hidden Markov Model , aka "stochastic parrot".
@uriel
What I'm saying is that you are beating a strawman of your own making and putting words in my mouth. -
@robotistry
@leeloo
so it's a parrot not because it's a matrix of probabilities, but because its hasn't experienced the real-world consequences of its words/actions and updated the probabilities based on those consequences?@wolf480pl @leeloo No. Maybe this will help.
0: one action, no choice (clockwork automaton, wind-up toy)
1: different actions, no choices (RC car)
2: choice, no plan (reactive robot)
3a: plan, no on-line or off-line learning (adaptive robot)
3b: plan, no on-line learning (same number for 3a and 3b because these are effectively the same when operating)
4: on-line learning - but only what and how it has been told
5a: ability to spontaneously generate new categories of output without being explicitly asked or told to do so (WBEAT)
5b: ability to spontaneously identify new categories of the same kinds of input WBEAT
6: ability to spontaneously identify new kinds of things to learn WBEAT
7: ability to spontaneously identify new ways to learn WBEAT
8: ability to choose new things to learn WBEATLLMs that you're not training are category 3b. They are static machines, responding to your input like an elevator responding to a button push.
LLMs that learn are category 4.
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@wolf480pl @leeloo No. Maybe this will help.
0: one action, no choice (clockwork automaton, wind-up toy)
1: different actions, no choices (RC car)
2: choice, no plan (reactive robot)
3a: plan, no on-line or off-line learning (adaptive robot)
3b: plan, no on-line learning (same number for 3a and 3b because these are effectively the same when operating)
4: on-line learning - but only what and how it has been told
5a: ability to spontaneously generate new categories of output without being explicitly asked or told to do so (WBEAT)
5b: ability to spontaneously identify new categories of the same kinds of input WBEAT
6: ability to spontaneously identify new kinds of things to learn WBEAT
7: ability to spontaneously identify new ways to learn WBEAT
8: ability to choose new things to learn WBEATLLMs that you're not training are category 3b. They are static machines, responding to your input like an elevator responding to a button push.
LLMs that learn are category 4.
1/2
@wolf480pl @leeloo Examples:
Category 5a: a text-based LLM that spontaneously, without being asked, learns to output musical notation.
Category 5b: a text-based LLM that spontaneously, unprompted, without being asked, learns that filenames can be used as input.
Category 6: a text-based LLM that spontaneously, without being asked (directly or indirectly) learns that it can output ascii images or generate sounds instead of sentences.
Category 7: a text-based LLM spontaneously changes its underlying code so that it can learn how to write novels by memorizing and imitating performances instead of via a matrix of probabilities (fundamental change to its internal capabilities)
Category 8: a text-based LLM chooses when to interact with the world.
(The original categories I developed years ago were based on what the system can modify: its weights, how many weights, what kinds of weights, etc. I think this might be clearer?)
I don't think even Moltbook is showing anything above 4.
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As a software developer who took an elective in neural networks - when people call LLMs stochastic parrots, that's not criticism of their results.
It's literally a description of how they work.
The so-called training data is used to build a huge database of words and the probability of them fitting together.
Stochastic because the whole thing is statistics.
Parrot because the answer is just repeating the most probable word combinations from its training dataset.Calling an LLM a stochastic parrot is lile calling a car a motorised vehicle with wheels. It doesn't say anything about cars being good or bad. It does, however, take away the magic. So if you feel a need to defend AI when you hear the term stochastic parrot, consider that you may have elevated them to a god-like status, and that's why you go on the defense when the magic is dispelled.
@leeloo A much better answer is "So are humans".
(according to everything we've so far been able to document regarding our own processes)
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R relay@relay.infosec.exchange shared this topic
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@leeloo A much better answer is "So are humans".
(according to everything we've so far been able to document regarding our own processes)
@troed
The part that we understand about how our brain works is so simple that we can understand it.The rest, we have no clue about.
Replicating the simple parts and pretending that will get us anywhere close to intelligence is the kind of magic I'm talking about.
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@troed
The part that we understand about how our brain works is so simple that we can understand it.The rest, we have no clue about.
Replicating the simple parts and pretending that will get us anywhere close to intelligence is the kind of magic I'm talking about.
@leeloo We don't know that. It's equally likely that we have a belief in that there must be some kind of "magic" in our brains that there simply isn't.
From a physics standpoint there can be no magic - the brain is just a large neural network with various inputs (wind blowing on arm hair etc) that results in outputs (mouth moving).
