Statistics over understanding. Hammer meets nail. That's it in a nutshell. Fans of Generative AI also hide behind its mystery and opacity as if to say, "We can't look inside so perhaps it is really doing more than just statistics", "Humans learn statistically. Our AIs are learning like humans", or "Perhaps the world is really just statistics all the way down".
If you are saying that statistics is a good starting point in a domain where we have very little understanding, then I would wholeheartedly agree. I also agree that statistics did turn out to be more powerful than expected. Where this paradigm falls short is in domains where we have a lot of knowledge but our AIs still can't approach human performance. We need to find a way to install what we know into our AIs and let them build meaningful world models.
I don't know if it requires a clean-slate approach but simply different approaches. More specifically, I would like to see some of the money now funding the LLM land-grab and use it to fund alternatives and I would like to see the hype surrounding the statistical approach cut in half.
I've never liked the "symbol grounding" term. It seems to imply that it just takes some sort of grounding module to suddenly give everything meaning. As I see it, it's the central issue. A symbol isn't really a symbol unless it is attached to its meaning which means a world model. Until some AI contains a very substantial world model and the machinery to use it and enhance it on the fly, there will be no AGI. As LLMs do not even attempt to build a world model, except for one based on word order, I doubt it will get anywhere close to AGI.
Disagree. It's all just patching. Until the AI can learn on its own, we won't get far with LLMs. Humans use language to communicate. Their cognition is not based on language. This is important. Any AI that is centered on language will always be at a severe disadvantage with respect to reproducing human cognition.
Dear Gary, that's a lovely way to put it, 'statistics over understanding'!
The statistics are derivative in nature, dependent on word order, pixel order, syllable/tone order... , which have no *inherent* meaning [foreign languages are foreign, when the symbols and utterances mean nothing to those who weren't taught their meaning; same w/ music, math, chemical formulae, nautical charts, circuit diagrams, floor plans...].
Symbols have no inherent meaning, they only have shared meaning. And we can impart such meaning to an AI that shares the world with us, like we do with humans and animals that share the world with us - physically. Everything else is just DATA, ie DOA.
I believe that meaning frequently depends on what we can do with items. Of course, the reference needs to be shared socially. However, to even assign meaning requires someone who cares about the thing. And why do we care? Because of the actionable quality of the thing the symbol refers to.
We often conceptualize the world in terms of our perceptions and cognitive representations; we should not forget that much of our survival depends on how we can change the world to match our goals.
'A responsible caregiver' is usually who teaches infants - moms, other fam members, nannies... then it's pre-school teachers and classmates, then it's society at-large... Without all of this, learning about the world is difficult if not impossible [animal behavior and instincts aside]. Even the sense of Self might be induced via others, possibly.
"Responsible caregiver", I love that. It reminds me of a theme from "The Little Prince".
"You are not special yet. No one has tamed you, and you have tamed no one. My fox was like you. He was like a hundred thousand other foxes. But I have made him my friend, and now he is unique."
Everything in the world can be unique, and given a name, but doing so is difficult without a purpose. Very high level, but personally, I believe that's also behind cognitive impairment in major depression. If nothing has a purpose, nothing has a meaning. Naming, taming, selecting one thing over another becomes impossible because there is no reason to do so. Perception, attention, memory retrieval: all atrophy because they need to be applied because of something, not just to something.
Nice! That quote from 'The Little Prince' (and the entire book in fact) is 'fire', lol.
You hit the nail on the head, about 'purpose', including how lack of it could stem from depression. The reverse might be true too - inability to lead a meaningful/purposeful life might lead to boredom, withdrawal, anger and a bunch of other feelings (eg in societies with high unemployment, lack of opportunities, corrupt governments that don't care about society's progress etc).
Scientific exploration is also driven by a 'need' to find meaning in nature, to understand it, benefit from its phenomena etc. The search for 'meaning' at a deeper level can occur even in the most dire circumstances, eg as documented in Viktor Frankl's amazing work [eg. described in https://www.pursuit-of-happiness.org/history-of-happiness/viktor-frankl/].
Now when we compare all these aspects about being 'human', with something like an LLM that does dot products, with its practitioners claiming parity... Lol.
It's also very interesting to talk about chess with ChatGPT: "Can you play chess" - ChatGPT says yes, let's go. It can explain all the rules, opening principles, movement of the pieces, tactics such as pins and fork.
It even spits out some correct moves, using correct notation. But sooner or later it will make moves that are illegal (such as jumping over the opponent's pieces with a rook), despite ChatGPT being able to explain eloquently that rooks can't jump over pieces - it lacks understanding that that's exactly what it did.
This is kind of funny, AI can do some things that humans can't do, but it also makes mistakes that a human would not do. Smarter than many, then dumber than all.
I so appreciate these illustrated posts about AI because the non-technically-minded can grasp the main point, that these fancy algorithmic agglomerations are still failing in very fundamental ways. Controversy over when AGI might arrive and why open source models are dangerous are important, but those more esoteric topics also seem like sci-fi to the general reader. This does not. Another article I'm happy to cross post for my readers!
That first defensive e/acc screenshot though, apparently the AI was "teached" better than him... and this is who is designing our future? Good game, humanity.
You are so clearly right on this front, I'm curious why you think people are trying to say otherwise. Is it because that's how they can (try to) raise $7T? Because they actually have no clue how to build AGI? Because they think LLMs can ultimately get the same result (from a practical standpoint) as AGI?
LLMs are a bit like my kids. They are unpredictable, don’t do what you tell them and are very hard to behave correctly. And they occasionally break things.
"When we're trying to sell it we call it AI and when we're trying to make it work we call it pattern recognition." -- my "elevator speech" to Honeywell management during the DARPA Grand Challenge.
"Your *other* left foot." -- My drill sergeant in USAF OTS.
I am almost at the point in giving up trying to have philosophical conversations with the engineering minded (and business-minded, and most "scientists"). Too demanding, and I am not getting paid... I get though: they want to build things and get things done, not question their assumptions (unless forced too, and even then it's tough).
However, there is no excuse for so-called "scientists" not being willing to be philosophical, since the best scientists in history were also philosophers (Einstein, Werner Heisenberg, etc.). Now they are more often like a kind of technician, or engineers, bureaucrats, businessmen, politically savvy, working in their extreme specialities, not (at least professional) questioning the most basic assumptions about reality, the nature of intelligence, self, understanding, consciousness, what exists, the goal of life, etc. "Who has time for that, except for the 'theory of the leisure class' " as one academic humorously put it.
And meanwhile academic philosophy has gotten lost (psychology too, with its assumptions hardened into dogma), into a rut, trying to be a handmaiden to science, or merely doing conceptual analysis in hyper-specialties no one can understand, irrelevant to living life, losing it's way from the original "love of wisdom" the Greeks knew...
The fact is, no one really knows what intelligence or understanding are. But you have to start somewhere – if doing engineering, then you start from the bad assumptions you have and see what happens – which is what we are seeing now (they just need to be more honest about it); but for science you need to question the assumption; and in philosophy, it goes even deeper, to the source, where we are in the realm of the Unknown – not a comfortable place for many (including the players mentioned about). I see that "somewhere" as empirical, from direct ("inner") experience, to really start to get anywhere regarding intelligence, understanding, and awareness... and few dare to venture there.
The post uses universals" they have never: and other indications that these posts are opinions with a axe to grind not a scientific expository.
The examples show problems that can be exposed. It would be more interesting to see the power and the limitations that GenAI as all techniques and technologies will have.
"always tried to use statistics as a proxy for deeper understanding"
What would we mean by deeper understanding... do i or you have deep understanding?
Yes I do but actually people only have "deep understanding" at the time they defend their thesis, and only about the topic they studied...It is hard to be up to date and comprehensive to get to be that much of an expert.
Maybe not for you, but aggregating human knowledge and communicating prompts in social terms has become a productive thing for hundreds of millions of people.
maybe it is only strict filtering mechanisms built into the AI by the developers to reduce bizzare outputs, thereby forcing the AI to rely more on statistically more prevalent images
Gary, intuitively I agree with all your arguments, but have been playing my own devil’s advocate.
Without referring to current empirical evidence, what are in your view the most succinct fundamental reasons why LLMs will never reach “understanding”, which I interpret as the ability to robustly reason and apply logic,
I.e. that we hit the limits of the current paradigm, and bigger may get better but never anything close to flawless?
If LLMs can roughly be understood to be statistical memory machines, that can adequately represent and reproduce the knowledge that is in the training data, would it be plausible that perfect data for a specific domain, containing all required knowledge and reasoning pathways for that domain (eg known medicine), lead to robust reasoning? Like training a simple regression on real world data from newtonian experiments will lead to a perfect ML model for that physics domain, even without having theoretical conceptual understanding? So in a sense, it gains some implied understanding?
If you just look responses from ChatGPT it can be confounding because it appears like it "understands" in a human-like way. In software one of the most important decisions software architects make is to design the architecture (or framework) to meet the current and future product requirements. The Transformer Architecture is specially designed for LLMs. It is not designed to understand, but to predict a response to a query in text, images, speech, etc
The English language supports around 1 million words. Every word is defined "by the company it keeps". So the word "work" in a massive weighted vector space is very remotely associated with the planet "Mars" But "Mars" is hugely weighted with "planet" . Then you have words that keep the same company, for example "apple" and "pear" since they are both fruits.
Same with images that are more naturally represented as vectors. With primary Red, Blue, Yellow intensity it mimics the way we see colors on our computer screen.
If you vaguely understand the word "illusion" that's exactly what it is.
Statistics over understanding. Hammer meets nail. That's it in a nutshell. Fans of Generative AI also hide behind its mystery and opacity as if to say, "We can't look inside so perhaps it is really doing more than just statistics", "Humans learn statistically. Our AIs are learning like humans", or "Perhaps the world is really just statistics all the way down".
If you are saying that statistics is a good starting point in a domain where we have very little understanding, then I would wholeheartedly agree. I also agree that statistics did turn out to be more powerful than expected. Where this paradigm falls short is in domains where we have a lot of knowledge but our AIs still can't approach human performance. We need to find a way to install what we know into our AIs and let them build meaningful world models.
I don't know if it requires a clean-slate approach but simply different approaches. More specifically, I would like to see some of the money now funding the LLM land-grab and use it to fund alternatives and I would like to see the hype surrounding the statistical approach cut in half.
I've never liked the "symbol grounding" term. It seems to imply that it just takes some sort of grounding module to suddenly give everything meaning. As I see it, it's the central issue. A symbol isn't really a symbol unless it is attached to its meaning which means a world model. Until some AI contains a very substantial world model and the machinery to use it and enhance it on the fly, there will be no AGI. As LLMs do not even attempt to build a world model, except for one based on word order, I doubt it will get anywhere close to AGI.
Disagree. It's all just patching. Until the AI can learn on its own, we won't get far with LLMs. Humans use language to communicate. Their cognition is not based on language. This is important. Any AI that is centered on language will always be at a severe disadvantage with respect to reproducing human cognition.
Dear Gary, that's a lovely way to put it, 'statistics over understanding'!
The statistics are derivative in nature, dependent on word order, pixel order, syllable/tone order... , which have no *inherent* meaning [foreign languages are foreign, when the symbols and utterances mean nothing to those who weren't taught their meaning; same w/ music, math, chemical formulae, nautical charts, circuit diagrams, floor plans...].
Symbols have no inherent meaning, they only have shared meaning. And we can impart such meaning to an AI that shares the world with us, like we do with humans and animals that share the world with us - physically. Everything else is just DATA, ie DOA.
I believe that meaning frequently depends on what we can do with items. Of course, the reference needs to be shared socially. However, to even assign meaning requires someone who cares about the thing. And why do we care? Because of the actionable quality of the thing the symbol refers to.
We often conceptualize the world in terms of our perceptions and cognitive representations; we should not forget that much of our survival depends on how we can change the world to match our goals.
100%.
'A responsible caregiver' is usually who teaches infants - moms, other fam members, nannies... then it's pre-school teachers and classmates, then it's society at-large... Without all of this, learning about the world is difficult if not impossible [animal behavior and instincts aside]. Even the sense of Self might be induced via others, possibly.
ALL of this requires a body.
"Responsible caregiver", I love that. It reminds me of a theme from "The Little Prince".
"You are not special yet. No one has tamed you, and you have tamed no one. My fox was like you. He was like a hundred thousand other foxes. But I have made him my friend, and now he is unique."
Everything in the world can be unique, and given a name, but doing so is difficult without a purpose. Very high level, but personally, I believe that's also behind cognitive impairment in major depression. If nothing has a purpose, nothing has a meaning. Naming, taming, selecting one thing over another becomes impossible because there is no reason to do so. Perception, attention, memory retrieval: all atrophy because they need to be applied because of something, not just to something.
Nice! That quote from 'The Little Prince' (and the entire book in fact) is 'fire', lol.
You hit the nail on the head, about 'purpose', including how lack of it could stem from depression. The reverse might be true too - inability to lead a meaningful/purposeful life might lead to boredom, withdrawal, anger and a bunch of other feelings (eg in societies with high unemployment, lack of opportunities, corrupt governments that don't care about society's progress etc).
Scientific exploration is also driven by a 'need' to find meaning in nature, to understand it, benefit from its phenomena etc. The search for 'meaning' at a deeper level can occur even in the most dire circumstances, eg as documented in Viktor Frankl's amazing work [eg. described in https://www.pursuit-of-happiness.org/history-of-happiness/viktor-frankl/].
Now when we compare all these aspects about being 'human', with something like an LLM that does dot products, with its practitioners claiming parity... Lol.
It's also very interesting to talk about chess with ChatGPT: "Can you play chess" - ChatGPT says yes, let's go. It can explain all the rules, opening principles, movement of the pieces, tactics such as pins and fork.
It even spits out some correct moves, using correct notation. But sooner or later it will make moves that are illegal (such as jumping over the opponent's pieces with a rook), despite ChatGPT being able to explain eloquently that rooks can't jump over pieces - it lacks understanding that that's exactly what it did.
Precisely
This is kind of funny, AI can do some things that humans can't do, but it also makes mistakes that a human would not do. Smarter than many, then dumber than all.
I so appreciate these illustrated posts about AI because the non-technically-minded can grasp the main point, that these fancy algorithmic agglomerations are still failing in very fundamental ways. Controversy over when AGI might arrive and why open source models are dangerous are important, but those more esoteric topics also seem like sci-fi to the general reader. This does not. Another article I'm happy to cross post for my readers!
If we give developers $7T, THEN will the program be able to generate images of people writing with their left hand?
Add anti-southpaw bias to the list of Silicon Valley sins. My persecution complex is fired up and ready to go.
🤣
That first defensive e/acc screenshot though, apparently the AI was "teached" better than him... and this is who is designing our future? Good game, humanity.
You are so clearly right on this front, I'm curious why you think people are trying to say otherwise. Is it because that's how they can (try to) raise $7T? Because they actually have no clue how to build AGI? Because they think LLMs can ultimately get the same result (from a practical standpoint) as AGI?
$
LLMs are a bit like my kids. They are unpredictable, don’t do what you tell them and are very hard to behave correctly. And they occasionally break things.
I can't wait to create the right handed writer/guitarist and wrong clock videos generated by OpenAI Sora.
Two quotes popped into my head when I read this:
"When we're trying to sell it we call it AI and when we're trying to make it work we call it pattern recognition." -- my "elevator speech" to Honeywell management during the DARPA Grand Challenge.
"Your *other* left foot." -- My drill sergeant in USAF OTS.
The other left :D
I am almost at the point in giving up trying to have philosophical conversations with the engineering minded (and business-minded, and most "scientists"). Too demanding, and I am not getting paid... I get though: they want to build things and get things done, not question their assumptions (unless forced too, and even then it's tough).
However, there is no excuse for so-called "scientists" not being willing to be philosophical, since the best scientists in history were also philosophers (Einstein, Werner Heisenberg, etc.). Now they are more often like a kind of technician, or engineers, bureaucrats, businessmen, politically savvy, working in their extreme specialities, not (at least professional) questioning the most basic assumptions about reality, the nature of intelligence, self, understanding, consciousness, what exists, the goal of life, etc. "Who has time for that, except for the 'theory of the leisure class' " as one academic humorously put it.
And meanwhile academic philosophy has gotten lost (psychology too, with its assumptions hardened into dogma), into a rut, trying to be a handmaiden to science, or merely doing conceptual analysis in hyper-specialties no one can understand, irrelevant to living life, losing it's way from the original "love of wisdom" the Greeks knew...
The fact is, no one really knows what intelligence or understanding are. But you have to start somewhere – if doing engineering, then you start from the bad assumptions you have and see what happens – which is what we are seeing now (they just need to be more honest about it); but for science you need to question the assumption; and in philosophy, it goes even deeper, to the source, where we are in the realm of the Unknown – not a comfortable place for many (including the players mentioned about). I see that "somewhere" as empirical, from direct ("inner") experience, to really start to get anywhere regarding intelligence, understanding, and awareness... and few dare to venture there.
But I've already said too much.
Well said. Clap, clap
The post uses universals" they have never: and other indications that these posts are opinions with a axe to grind not a scientific expository.
The examples show problems that can be exposed. It would be more interesting to see the power and the limitations that GenAI as all techniques and technologies will have.
"always tried to use statistics as a proxy for deeper understanding"
What would we mean by deeper understanding... do i or you have deep understanding?
Yes I do but actually people only have "deep understanding" at the time they defend their thesis, and only about the topic they studied...It is hard to be up to date and comprehensive to get to be that much of an expert.
Maybe not for you, but aggregating human knowledge and communicating prompts in social terms has become a productive thing for hundreds of millions of people.
maybe it is only strict filtering mechanisms built into the AI by the developers to reduce bizzare outputs, thereby forcing the AI to rely more on statistically more prevalent images
Gary, intuitively I agree with all your arguments, but have been playing my own devil’s advocate.
Without referring to current empirical evidence, what are in your view the most succinct fundamental reasons why LLMs will never reach “understanding”, which I interpret as the ability to robustly reason and apply logic,
I.e. that we hit the limits of the current paradigm, and bigger may get better but never anything close to flawless?
If LLMs can roughly be understood to be statistical memory machines, that can adequately represent and reproduce the knowledge that is in the training data, would it be plausible that perfect data for a specific domain, containing all required knowledge and reasoning pathways for that domain (eg known medicine), lead to robust reasoning? Like training a simple regression on real world data from newtonian experiments will lead to a perfect ML model for that physics domain, even without having theoretical conceptual understanding? So in a sense, it gains some implied understanding?
If you just look responses from ChatGPT it can be confounding because it appears like it "understands" in a human-like way. In software one of the most important decisions software architects make is to design the architecture (or framework) to meet the current and future product requirements. The Transformer Architecture is specially designed for LLMs. It is not designed to understand, but to predict a response to a query in text, images, speech, etc
The English language supports around 1 million words. Every word is defined "by the company it keeps". So the word "work" in a massive weighted vector space is very remotely associated with the planet "Mars" But "Mars" is hugely weighted with "planet" . Then you have words that keep the same company, for example "apple" and "pear" since they are both fruits.
Same with images that are more naturally represented as vectors. With primary Red, Blue, Yellow intensity it mimics the way we see colors on our computer screen.
If you vaguely understand the word "illusion" that's exactly what it is.
all prediction, no interrogable world model that can be reasoned over
Thanks. And what’s your view on JEPA in that respect, the approach LeCunn is taking?
https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/