It absolutely blows my mind that people swallowed "AGI is just around the corner" hook, line and sinker. We don't understand human cognition despite literally thousands of years of thought and study, but some nerds with a thousand GPUs are going to make a machine think? Ridiculous. People were misled by graphs that they were told pointed to a singularity right around the corner, despite obvious errors in the extant systems.
Anyone paying attention _DIDN'T_ buy it. It's only the AI-hype-bros who seriously considered AGI to be a real possibility within this decade, let alone THIS YEAR like a ton of people said (people who all suspiciously had a lot of financial gain to be had from believing that to be true.)
We don't understand human cognition despite literally thousands of years of thought and study, but some nerds with a thousand GPUs are going to make a machine think? Ridiculous.
DNA doesn't understand intelligence.
"The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." - Edsger Dijkstra.
And what we have now, with LLMs, met the standard I had for AGI just five years ago.
Only thing that changed for me since then is noticing that not only does everyone have a different definition of what AGI even is, also none of the three initials are even boolean-valued: "how intelligent" can be a number, or even a vector that varies by domain as linguistic intelligence doesn't have to match spatial intelligence; "how general" could be what percentage of human cognitive tasks the AI can do; "how artificial" is answered entirely differently by those who care differently about learning from first principles vs. programmed algorithms vs. learning from reading the internet.
People were misled by graphs that they were told pointed to a singularity right around the corner, despite obvious errors in the extant systems.
Tautology.
If there weren't obvious errors in the extant systems… there wouldn't be anything left to do.
Agreed. Dogs & 4 year olds & LLM's all have general intelligence. Defining "AGI" to mean "superhuman intelligence" seems ridiculous to me. The Turing test was the standard benchmark for so long, but now that an LLM can pass the Turing test we've moved the goal posts.
The Turing test was never a standard benchmark. It was common in pop science coverage, but no AI researcher (including Turing himself!) ever proposed it as a useful test of intelligence. It was an illustration by Turing that we can meaningfully talk about a machine thinking in the first place. We've known for over half a century since ELIZA that the ability to carry on a plausible natural language conversation is much more narrow than it intuitively seems.
What study are you referring to here? The ones I've seen don't particularly resemble anything Turing described. The preprint from UCSD got heavy coverage in the popular press but its headline claim is an elementary statistical mistake, modifying the test so it's no longer a binary comparison but still treating 50% as a meaningful pass threshold. It hasn't yet passed peer review nine months later, and I'm cautiously optimistic that it never will.
I agree in my view the modern language models already processes general intelligence: they are better at some tasks and worse than others compared to human, but in principle they can tackle any problem (if given agency like tool access)
this is probably a religious question so people will not be convinced of intelligent machines even if they have an EQ and IQ of 200 because they don't work the same way human cells do I suppose.
We don't understand human cognition despite literally thousands of years of thought and study
Is this a requirement for achieving AGI? The history of progression of the ML field indicates that the answer is "no". We don't really understand how concepts are encoded in today's models, yet that doesn't stop them from being economically useful. So why would the special case of AGI be any different?
It seems to me that some nerds with a thousand GPUs have clearly made a machine think. State-of-the-art AI models are better than me at a significant number of intellectual tasks; what would it mean, precisely, to say that I'm thinking when I write a bad poem but GPT-4o is not thinking when it writes a better one?
The specific term "AGI" has always referred to a program that strictly matches to human intelligence, with no significant areas where it's worse than a typical human. I agree that we're not there and not obviously close to there. But the idea that it's all a parlor trick and LLMs have no cognition at all seems obviously false to me.
It was not that long ago that solving a maze was thought of as AI. Or a bot in a video game adapting to your play style was AI.
Today, LLMs being trained to output text, images and video equal to or better than any human is now what we consider to be AI.
There is a semantic game we play to move the goalposts at every new tick of these technologies so that humans are still on top.
Maybe the next big trick is to define AGI as having a system that was not trained on the entire corpus of human output available on the internet, and train it with nothing. Can an AI be “born” with no model like a baby and learn to speak, walk, function in society without being primed for success with a state of the art model? Could you drop this AI into any human society, regardless of culture, and have it seamlessly integrate?
What precisely we consider “cognition” feels like a philosophical debate, one where we have no single true answer. And doesn’t really matter?
"AGI" has always referred to something a lot closer to your "next big trick" than an LLM. An AGI is a program that can slot into anywhere a human fits and do as well as a human would; GPT-4 or Claude can't do that.
I do think it's true that anyone a decade ago would have predicted their capabilities are impossible without AGI, which suggests that the entire conceptual framework is less useful than we thought. The eventual debate about whether we've "reached AGI" will probably have a lot more to do with the contract between OpenAI and Microsoft than any real paradigm shift in capabilities.
I guess I'm not sure what your point is. If you understand "thinking" to be a highly advanced capability that millions or billions of people can't do, sure, perhaps it's true that GPT-4 is not "thinking" in that sense. Most people understand the term to be more broad than that. If a four year old tries really hard to compute 4 + 16, I'd definitely say they're "thinking" about it, even if they don't get the right answer or make some silly mistake I can easily point out.
I haven't tried 4.5 but it is being touted as having greater EQ - presumably meaning emotion quotient. That sounds like progress, or at least something that's worth releasing to find out what people can do with it.
Having said that, 4.5 is clearly a misstep, one that should realign the goals of the AI industry to focus more on utility and cost effectiveness.
Isn't that just fine-tuning? LLMs can understand emotions just fine, but ChatGPT was always neutered and fine-tuned to act as a "helpful assistant" and claim it was "just a language model" and not say anything controversial.
The laymen's definition for AGI is utterly irrelevant to investors. If training needs to happen for an AI employee so there's an employee that can be copy/pasted and scaled up/down without overhead, so then put in the work to train it. You have to train a human employee as well anyway.
Unless you know what cognition is, it's not inherently ridiculous that a bunch of nerds with a thousand GPUs are going to make a machine think. What's ridiculous is that, when we don't understand human cognition, a bunch of nerds are sure they're going to make a machine think, and how close they are to doing it.
What's even more laughable is that OpenAI dumbed down the definition of AGI so much it doesn't align at all with general intelligence anymore, and people just accepted it.
Not only can they not reach AGI, they cannot reach their own definition of AGI. But people will still gobble whatever next lie Altman will sell them for $2,000 a month.
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn __any__ intellectual task that a human being can.
LLMs are nowhere near this. Not even close.
But let's get even more restrictive by looking at the actual required characteristics to declare a system an AGI:
There is wide agreement among artificial intelligence researchers that intelligence is required to do the following:
- reason, use strategy, solve puzzles, and make judgments under uncertainty;
- represent knowledge, including common sense knowledge;
- plan;
- learn;
- communicate in natural language;
- and integrate all these skills towards common goals.
Other important capabilities include:
- input as the ability to sense (e.g. see, hear, etc.), and
- output as the ability to act (e.g. move and manipulate objects, change own location to explore, etc.)
Once again, LLMs are not even scratching the surface of AGI.
Of course if you look at the current version of the page, where the bar has been massively lowered, it might look like AGI is achieved, but that's because the definition has changed, not the technology.
This is not AGI.
> Let's take an old version of the Wikipedia page about AGI, from end of 2022
sure, its not AGI by old wikipedia definition, which imo is human-centric and more definition of superintelligence (requires ability to exceed all existing humans in all tasks). But it is AGI by current wikipedia definition.
That is literally my point... The definition of AGI has been twisted so much by the ones that claim to be on the verge of achieving it that it is not anymore in line with what AGI has always meant.
Reading wikipedia about history of the term, it sounds like term was popularized by this book: https://www.amazon.com/Artificial-General-Intelligence-Cogni... which says that general means "ability so solve variety of tasks in variety of domains", not "all tasks in all domains". So "always meant" is easily challengeable here.
You don't think LLMs are thinking? You don't think they are approaching human levels of intelligence? What part of human cognition don't we understand?
fdsjgfklsfd says>You don't think LLMs are thinking? You don't think they are approaching human levels of intelligence? What part of human cognition don't we understand?
Ans. No, no and the entirety of human cognition.
ChatGPT et al contain collections of words. When prompted they generate word sequences found in known word sources. That's all. They don't observe or reason about the world. They don't even reason about words. They merely append the most likely next word to a sequence of words.
In fairness, GPT-3.5 and GPT-4 were much closer to AGI than anything before. Many people also believed AGI was around the corner in the old LISP days, when there was much less capital.
I still believe GPT-4 has some general intelligence, just a very tiny amount. It can take what it was trained on and slightly modify it to answer a slightly novel question.
I mean, according to many of the same people about a decade ago, the world economy should be running on bitcoin by now. And we should all be living in the ‘metaverse’. Like, it’s largely the same people who fall for every fad falling for this.
Comments
It absolutely blows my mind that people swallowed "AGI is just around the corner" hook, line and sinker. We don't understand human cognition despite literally thousands of years of thought and study, but some nerds with a thousand GPUs are going to make a machine think? Ridiculous. People were misled by graphs that they were told pointed to a singularity right around the corner, despite obvious errors in the extant systems.
Concentrated capital is truly a wild thing.
Anyone paying attention _DIDN'T_ buy it. It's only the AI-hype-bros who seriously considered AGI to be a real possibility within this decade, let alone THIS YEAR like a ton of people said (people who all suspiciously had a lot of financial gain to be had from believing that to be true.)
DNA doesn't understand intelligence.
"The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." - Edsger Dijkstra.
And what we have now, with LLMs, met the standard I had for AGI just five years ago.
Only thing that changed for me since then is noticing that not only does everyone have a different definition of what AGI even is, also none of the three initials are even boolean-valued: "how intelligent" can be a number, or even a vector that varies by domain as linguistic intelligence doesn't have to match spatial intelligence; "how general" could be what percentage of human cognitive tasks the AI can do; "how artificial" is answered entirely differently by those who care differently about learning from first principles vs. programmed algorithms vs. learning from reading the internet.
Tautology.
If there weren't obvious errors in the extant systems… there wouldn't be anything left to do.
Agreed. Dogs & 4 year olds & LLM's all have general intelligence. Defining "AGI" to mean "superhuman intelligence" seems ridiculous to me. The Turing test was the standard benchmark for so long, but now that an LLM can pass the Turing test we've moved the goal posts.
The Turing test was never a standard benchmark. It was common in pop science coverage, but no AI researcher (including Turing himself!) ever proposed it as a useful test of intelligence. It was an illustration by Turing that we can meaningfully talk about a machine thinking in the first place. We've known for over half a century since ELIZA that the ability to carry on a plausible natural language conversation is much more narrow than it intuitively seems.
What study are you referring to here? The ones I've seen don't particularly resemble anything Turing described. The preprint from UCSD got heavy coverage in the popular press but its headline claim is an elementary statistical mistake, modifying the test so it's no longer a binary comparison but still treating 50% as a meaningful pass threshold. It hasn't yet passed peer review nine months later, and I'm cautiously optimistic that it never will.
https://news.ycombinator.com/item?id=40386571
I agree in my view the modern language models already processes general intelligence: they are better at some tasks and worse than others compared to human, but in principle they can tackle any problem (if given agency like tool access)
this is probably a religious question so people will not be convinced of intelligent machines even if they have an EQ and IQ of 200 because they don't work the same way human cells do I suppose.
Is this a requirement for achieving AGI? The history of progression of the ML field indicates that the answer is "no". We don't really understand how concepts are encoded in today's models, yet that doesn't stop them from being economically useful. So why would the special case of AGI be any different?
It's like saying "We don't understand human cognition, therefore humans are not intelligent."
It seems to me that some nerds with a thousand GPUs have clearly made a machine think. State-of-the-art AI models are better than me at a significant number of intellectual tasks; what would it mean, precisely, to say that I'm thinking when I write a bad poem but GPT-4o is not thinking when it writes a better one?
The specific term "AGI" has always referred to a program that strictly matches to human intelligence, with no significant areas where it's worse than a typical human. I agree that we're not there and not obviously close to there. But the idea that it's all a parlor trick and LLMs have no cognition at all seems obviously false to me.
It was not that long ago that solving a maze was thought of as AI. Or a bot in a video game adapting to your play style was AI.
Today, LLMs being trained to output text, images and video equal to or better than any human is now what we consider to be AI.
There is a semantic game we play to move the goalposts at every new tick of these technologies so that humans are still on top.
Maybe the next big trick is to define AGI as having a system that was not trained on the entire corpus of human output available on the internet, and train it with nothing. Can an AI be “born” with no model like a baby and learn to speak, walk, function in society without being primed for success with a state of the art model? Could you drop this AI into any human society, regardless of culture, and have it seamlessly integrate?
What precisely we consider “cognition” feels like a philosophical debate, one where we have no single true answer. And doesn’t really matter?
"AGI" has always referred to something a lot closer to your "next big trick" than an LLM. An AGI is a program that can slot into anywhere a human fits and do as well as a human would; GPT-4 or Claude can't do that.
I do think it's true that anyone a decade ago would have predicted their capabilities are impossible without AGI, which suggests that the entire conceptual framework is less useful than we thought. The eventual debate about whether we've "reached AGI" will probably have a lot more to do with the contract between OpenAI and Microsoft than any real paradigm shift in capabilities.
That is AI. AI ≠ AGI.
"nerds with a thousand GPUs" - Sounds like the title of a Cole Porter tune:
"Nerds with a thousand GPUs,
Nerds with a thousand streams,
with you only I experience,
the love, the chat of my dreams!"
"So prompt me, and list me,
index me, repeat me,
I'm yours till I elide,
so in love, so in love,
so in love with you, my GPT, am I. "
-With apologies to Cole Porter and anyone blinded by reading this post.
https://youtu.be/qdeM24FFpvM
Literally millions or billions of people are better than me at a significant number of intellectual tasks
I guess I'm not sure what your point is. If you understand "thinking" to be a highly advanced capability that millions or billions of people can't do, sure, perhaps it's true that GPT-4 is not "thinking" in that sense. Most people understand the term to be more broad than that. If a four year old tries really hard to compute 4 + 16, I'd definitely say they're "thinking" about it, even if they don't get the right answer or make some silly mistake I can easily point out.
I haven't tried 4.5 but it is being touted as having greater EQ - presumably meaning emotion quotient. That sounds like progress, or at least something that's worth releasing to find out what people can do with it.
Having said that, 4.5 is clearly a misstep, one that should realign the goals of the AI industry to focus more on utility and cost effectiveness.
Isn't that just fine-tuning? LLMs can understand emotions just fine, but ChatGPT was always neutered and fine-tuned to act as a "helpful assistant" and claim it was "just a language model" and not say anything controversial.
The laymen's definition for AGI is utterly irrelevant to investors. If training needs to happen for an AI employee so there's an employee that can be copy/pasted and scaled up/down without overhead, so then put in the work to train it. You have to train a human employee as well anyway.
I don't think it is about concentrated capital but that venture capital, and markets themselves sell great "visions".
Unless you know what cognition is, it's not inherently ridiculous that a bunch of nerds with a thousand GPUs are going to make a machine think. What's ridiculous is that, when we don't understand human cognition, a bunch of nerds are sure they're going to make a machine think, and how close they are to doing it.
What's even more laughable is that OpenAI dumbed down the definition of AGI so much it doesn't align at all with general intelligence anymore, and people just accepted it.
Not only can they not reach AGI, they cannot reach their own definition of AGI. But people will still gobble whatever next lie Altman will sell them for $2,000 a month.
Absolutely pathetic.
that definition likely not their's but came from Microsoft when they dumped 10B into OAI and was a condition for revenue sharing clause.
One can argue that AGI is already achieved, LLMs are more proficient and general in knowledge tasks than any specific individual.
This is not AGI.
Let's take an old version of the Wikipedia page about AGI, from end of 2022, before the ChatGPT craze: https://en.wikipedia.org/w/index.php?title=Artificial_genera...
LLMs are nowhere near this. Not even close.
But let's get even more restrictive by looking at the actual required characteristics to declare a system an AGI:
Once again, LLMs are not even scratching the surface of AGI.
Of course if you look at the current version of the page, where the bar has been massively lowered, it might look like AGI is achieved, but that's because the definition has changed, not the technology.
sure, its not AGI by old wikipedia definition, which imo is human-centric and more definition of superintelligence (requires ability to exceed all existing humans in all tasks). But it is AGI by current wikipedia definition.
That is literally my point... The definition of AGI has been twisted so much by the ones that claim to be on the verge of achieving it that it is not anymore in line with what AGI has always meant.
Reading wikipedia about history of the term, it sounds like term was popularized by this book: https://www.amazon.com/Artificial-General-Intelligence-Cogni... which says that general means "ability so solve variety of tasks in variety of domains", not "all tasks in all domains". So "always meant" is easily challengeable here.
You don't think LLMs are thinking? You don't think they are approaching human levels of intelligence? What part of human cognition don't we understand?
fdsjgfklsfd says>You don't think LLMs are thinking? You don't think they are approaching human levels of intelligence? What part of human cognition don't we understand?
Ans. No, no and the entirety of human cognition.
ChatGPT et al contain collections of words. When prompted they generate word sequences found in known word sources. That's all. They don't observe or reason about the world. They don't even reason about words. They merely append the most likely next word to a sequence of words.
In fairness, GPT-3.5 and GPT-4 were much closer to AGI than anything before. Many people also believed AGI was around the corner in the old LISP days, when there was much less capital.
I still believe GPT-4 has some general intelligence, just a very tiny amount. It can take what it was trained on and slightly modify it to answer a slightly novel question.
And me running is closer to the speed of light than me walking, yet neither are even remotely close to the speed of light.
I mean, according to many of the same people about a decade ago, the world economy should be running on bitcoin by now. And we should all be living in the ‘metaverse’. Like, it’s largely the same people who fall for every fad falling for this.