> defining AGI as matching the cognitive versatility and proficiency of a well-educated adult
Seems most of the people one would encounter out in the world might not posses AGI, how are we supposed to be able to train our electrified rocks to have AGI if this is the case?
If no one has created a online quiz called "Are you smarter than AGI?" yet based on the proposed "ten core cognitive domains", I'd be disappointed.
I was going to make a mildly snide remark about how once it can consistently make better decision than average person, it is automatically qualifies, but the paper itself is surprisingly thoughtful in describing both: where we are and where it would need to be.
Don't get me wrong, I am super excited about what AI is doing for technology. But this endless conversation about "what is AGI" is so boring.
It makes me think of every single public discussion that's ever been had about quantum, where you can't start the conversation unless you go through a quick 101 on what a qubit is.
As with any technology, there's not really a destination. There is only the process of improvement. The only real definitive point is when a technology becomes obsolete, though it is still kept alive through a celebration of its nostalgia.
AI will continue to improve. More workflows will become automated. And from our perception, no matter what the rapidness of advancement is, we're still frogs in water.
We have SAGI: Stupid Artificial General Intelligence. It's actually quite general, but works differently. In some areas it can be better or faster than a human, and in others it's more stupid.
Just like an airplane doesn't work exactly like a bird, but both can fly.
Is it about jobs/tasks, or cognitive capabilities? The majority of the AI-valley seems to focus on the former, TFA focuses on the latter.
Can it do tasks, or jobs? Jobs are bundles of tasks. AI might be able to do 90% of tasks for a given job, but not the whole job.
If tasks, what counts as a task: Is it only specific things with clear success criteria? That's easier.
Is scaffolding allowed: Does it need to be able to do the tasks/jobs without scaffolding and human-written few-shot prompts?
Today's tasks/jobs only, or does it include future ones too? As tasks and jobs get automated, jobs evolve and get re-defined. So, being able to do the future jobs too is much harder.
Remote only, or in-person too: In-person too is a much higher bar.
What threshold of tasks/jobs: "most" is apparently typically understood to mean 80-95% (Mira Ariel). Automating 80% of tasks is different to 90% and 95% and 99%. diminishing returns. And how are the tasks counted - by frequency, by dollar-weighted, by unique count of tasks?
Only economically valuable tasks/jobs, or does it include anything a human can do?
A high-order bit on many people's AGI timelines is which definition of AGI they're using, so clarifying the definition is nice.
Interesting read. I agree completely with their Introduction, that the definition of AGI is constantly shifting, and this leads to endless (and useless) debates.
What I find cool about the paper is that they have gathered folks from lots of places (berkley, stanford, mit, etc). And no big4 labs. That's good imo.
tl;dr; Their definition: "AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult."
Cool. It's a definition. I doubt it will be agreed on by everyone, and I can see endless debates about just about every word in that definition. That's not gonna change. At least it's a starting point.
What I find interesting is that they specifically say it's not a benchmark, or a test set. It's a framework where they detail what should be tested, and how (with examples). They do have a "catchy" table with gpt4 vs gpt5, that I bet will be covered by every mainstream/blog/forum/etc out there -> gpt5 is at ~50% AGI. Big title. You won't believe where it was one year ago. Number 7 will shock you. And all that jazz.
Anyway, I don't think people will stop debating about AGI. And I doubt this methodology will be agreed on by everyone. At the end of the day both extremes are more ideological in nature than pragmatic. Both end want/need their view to be correct.
I enjoyed reading it. Don't think it will settle anything. And, as someone posted below, when the first model will hit 100% on their framework, we'll find new frameworks to debate about, just like we did with the turing test :)
There’s already a vague definition that AGI is an AI with all the cognitive capabilities of a human. Yes, it’s vague - people differ.
This paper promises to fix "the lack of a concrete definition for Artificial General Intelligence", yet it still relies on the vague notion of a "well-educated adult". That’s especially peculiar, since in many fields AI is already beyond the level of an adult.
You might say this is about "jaggedness", because AI clearly lacks quite a few skills:
> Application of this framework reveals a highly “jagged” cognitive profile in contemporary models.
But all intelligence, of any sort, is "jagged" when measured against a different set of problems or environments.
So, if that’s the case, this isn’t really a framework for AGI; it’s a framework for measuring AI along a particular set of dimensions. A more honest title might be: "A Framework for Measuring the Jaggedness of AI Against the Cattell–Horn–Carroll Theory". It wouldn't be nearly as sexy, though.
To define AGI, we'd first have to define GI. Humans are very different. As park rangers like to say, there is an overlap between the smartest bears and the dumbest humans, which is why sometimes people can't open bear-proof trash cans.
It's a similar debate with self driving cars. They already drive better than most people in most situations (some humans crash and can't drive in the snow either for example).
Ultimately, defining AGI seems like a fools errand. At some point the AI will be good enough to do the tasks that some humans do (it already is!). That's all that really matters here.
> To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition
Cattell-Horn-Carroll theory, like a lot of psychometric research, is based on collecting a lot of data and running factor analysis (or similar) to look for axes that seem orthogonal.
It's not clear that the axes are necessary or sufficient to define intelligence, especially if the goal is to define intelligence that applies to non-humans.
For example reading and writing ability and visual processing imply the organism has light sensors, which it may not. Do all intelligent beings have vision? I don't see an obvious reason why they would.
Whatever definition you use for AGI probably shouldn't depend heavily on having analyzed human-specific data for the same reason that your definition of what counts as music shouldn't depend entirely on inferences from a single genre.
The problem, I guess, with these methods is, they consider human intelligence as something detached from human biology. I think this is incorrect. Everything that goes in the human mind is firmly rooted in the biological state of that human, and the biological cycles that evolved through millennia.
Things like chess-playing skill of a machine could be bench-marked against that of a human, but the abstract feelings that drive reasoning and correlations inside a human mind are more biological than logical.
How about AFI - artificial fast idiot. Dumber than a baby, but faster than an adult. Or AHI - artificial human imitator.
This is bad definition, because human baby is already AGI when it's born and it's brain is empty. AGI is the blank slate and ability to learn anything.
86 comments
[ 4.5 ms ] story [ 63.1 ms ] threadSeems most of the people one would encounter out in the world might not posses AGI, how are we supposed to be able to train our electrified rocks to have AGI if this is the case?
If no one has created a online quiz called "Are you smarter than AGI?" yet based on the proposed "ten core cognitive domains", I'd be disappointed.
It makes me think of every single public discussion that's ever been had about quantum, where you can't start the conversation unless you go through a quick 101 on what a qubit is.
As with any technology, there's not really a destination. There is only the process of improvement. The only real definitive point is when a technology becomes obsolete, though it is still kept alive through a celebration of its nostalgia.
AI will continue to improve. More workflows will become automated. And from our perception, no matter what the rapidness of advancement is, we're still frogs in water.
Just like an airplane doesn't work exactly like a bird, but both can fly.
Is it about jobs/tasks, or cognitive capabilities? The majority of the AI-valley seems to focus on the former, TFA focuses on the latter.
Can it do tasks, or jobs? Jobs are bundles of tasks. AI might be able to do 90% of tasks for a given job, but not the whole job.
If tasks, what counts as a task: Is it only specific things with clear success criteria? That's easier.
Is scaffolding allowed: Does it need to be able to do the tasks/jobs without scaffolding and human-written few-shot prompts?
Today's tasks/jobs only, or does it include future ones too? As tasks and jobs get automated, jobs evolve and get re-defined. So, being able to do the future jobs too is much harder.
Remote only, or in-person too: In-person too is a much higher bar.
What threshold of tasks/jobs: "most" is apparently typically understood to mean 80-95% (Mira Ariel). Automating 80% of tasks is different to 90% and 95% and 99%. diminishing returns. And how are the tasks counted - by frequency, by dollar-weighted, by unique count of tasks?
Only economically valuable tasks/jobs, or does it include anything a human can do?
A high-order bit on many people's AGI timelines is which definition of AGI they're using, so clarifying the definition is nice.
https://x.com/m2saxon/status/1979349387391439198
AGI was already here the day ChatGPT released: That's Peter Norvig's take too: https://www.noemamag.com/artificial-general-intelligence-is-...
What I find cool about the paper is that they have gathered folks from lots of places (berkley, stanford, mit, etc). And no big4 labs. That's good imo.
tl;dr; Their definition: "AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult."
Cool. It's a definition. I doubt it will be agreed on by everyone, and I can see endless debates about just about every word in that definition. That's not gonna change. At least it's a starting point.
What I find interesting is that they specifically say it's not a benchmark, or a test set. It's a framework where they detail what should be tested, and how (with examples). They do have a "catchy" table with gpt4 vs gpt5, that I bet will be covered by every mainstream/blog/forum/etc out there -> gpt5 is at ~50% AGI. Big title. You won't believe where it was one year ago. Number 7 will shock you. And all that jazz.
Anyway, I don't think people will stop debating about AGI. And I doubt this methodology will be agreed on by everyone. At the end of the day both extremes are more ideological in nature than pragmatic. Both end want/need their view to be correct.
I enjoyed reading it. Don't think it will settle anything. And, as someone posted below, when the first model will hit 100% on their framework, we'll find new frameworks to debate about, just like we did with the turing test :)
Also, weird to see Gary Marcus and Yoshua Bengio on the same paper. Who really wrote this? Author lists are so performative now.
This paper promises to fix "the lack of a concrete definition for Artificial General Intelligence", yet it still relies on the vague notion of a "well-educated adult". That’s especially peculiar, since in many fields AI is already beyond the level of an adult.
You might say this is about "jaggedness", because AI clearly lacks quite a few skills:
> Application of this framework reveals a highly “jagged” cognitive profile in contemporary models.
But all intelligence, of any sort, is "jagged" when measured against a different set of problems or environments.
So, if that’s the case, this isn’t really a framework for AGI; it’s a framework for measuring AI along a particular set of dimensions. A more honest title might be: "A Framework for Measuring the Jaggedness of AI Against the Cattell–Horn–Carroll Theory". It wouldn't be nearly as sexy, though.
It's a similar debate with self driving cars. They already drive better than most people in most situations (some humans crash and can't drive in the snow either for example).
Ultimately, defining AGI seems like a fools errand. At some point the AI will be good enough to do the tasks that some humans do (it already is!). That's all that really matters here.
[1]: The capability to continually learn new information (associative, meaningful, and verbatim). (from the publication)
Cattell-Horn-Carroll theory, like a lot of psychometric research, is based on collecting a lot of data and running factor analysis (or similar) to look for axes that seem orthogonal.
It's not clear that the axes are necessary or sufficient to define intelligence, especially if the goal is to define intelligence that applies to non-humans.
For example reading and writing ability and visual processing imply the organism has light sensors, which it may not. Do all intelligent beings have vision? I don't see an obvious reason why they would.
Whatever definition you use for AGI probably shouldn't depend heavily on having analyzed human-specific data for the same reason that your definition of what counts as music shouldn't depend entirely on inferences from a single genre.
Things like chess-playing skill of a machine could be bench-marked against that of a human, but the abstract feelings that drive reasoning and correlations inside a human mind are more biological than logical.
This is bad definition, because human baby is already AGI when it's born and it's brain is empty. AGI is the blank slate and ability to learn anything.