Hopefully it stands for AC Generation Improvements. If it prioritizes income it will bleed the planet dry. It needs to solve how expensive our cost is on the planet first or its entire existence was a mistake.
The definition has already been stretched to not fit the previous models. There is no meaningful, static definition that significantly predates current capabilities.
There's a reason why ai xrisk doomers had to come up with the term ASI.
I would seriously suggest that everyone take a look at the wikipedia page for AGI from the month before ChatGPT was released, compare it to the current version, and not come to that conclusion.
The first sentence is “understand or learn any intellectual task that a human can.” Whatever you think of the benefits of LLMs, they don’t understand and they can only learn during the training period and with very minor adjustments in post training. So, no I don’t think any of these models are generally intelligent.
I have not seen any instance of this frequently-made assertion which is at all justified. It seems to rely on a definition of "understand" which is more about spirituality than actual observable evidence (they clearly can comprehend even complex tasks well enough to execute on them, and if you won't call that "understanding", you're playing word games rather than stating an objective fact).
Likewise, agents can literally come to a greater understanding of a problem through trial and error, and there are plenty of mechanisms to retain that knowledge. If you don't want to call that "learning", you're just making a choice to define it in a way more restrictive than how we use it for humans, and intentionally making communication more difficult.
It seems to rely on a definition of "understand" which is more about spirituality than actual observable evidence
"Understanding" has enough philosophical leeway in its use to allow at least the possibility of sentience as a prerequisite.
This is where the discussion about LLM capabilities becomes genuinely difficult, and dismissing that difficulty as "word games" or "spirituality vs evidence" is not helpful.
Agents are always combining the same underlying weights to their inputs, relying on the same maps of semi-semantic space and the relationships between those that it was leaning towards at training time. The fact that it’s successful in making lots of people have an Eliza effect doesn’t make it understand something. It’s simulating understanding based on an enormous corpus of text, much of which is people working through things or sharing an understanding of something. Unless you believe that all intellectual activity is about finding the space between words you shouldn’t believe LLMs have any chance at understanding anything.
Continual Learning? Why is this even a question? Isn’t it a well-known glaring issue with the current models? They cannot learn/adapt to new skills (in any permanent sense) once they are deployed.
I think you're speeding past the word "average" in the sentence. I'd argue that current frontier models already exceed the abilities of average humans across the majority of tasks you can do on a computer, although you might be able to argue that they tend to be a bit slower?
That latter part is debatable though - have you seen a non-technical person try to figure out something new on a computer?
" I'd argue that current frontier models already exceed the abilities of average humans " for things that fit in their context window sure but LLMs can't learn over time the way humans can. One example is LLMs are very good at writing a few thousands line of code but they absolutely cannot write coherent million line codebases. By average human I meant the average skill level for the job. AGI would need to be able to pass a interview and get hired and the perform well enough to not get fired.
Yeah it's not true that for every job, it is better than median worker of that job. But it is conceivable that for almost all jobs it is already better than the median human (not just workers of that job).
You have to understand that the median human is terrible at (almost) everything. Humans, the only examples of general intelligence we know, are economically valuable precisely because they can train themselves to specialise at a (relatively) narrow task over time. You don’t measure how good a coding model is by how well it programs relative to Doctors, or how well it can prove theorems relative to baristas, or how well it can write coherent novels relative to programmers. That would be a dumb metric.
> Humans, the only examples of general intelligence we know
Our intelligence only seems "general" to us, because we're viewing it through our own eyes. Our "intelligence" is specialized to our survival, and we're terrible at most tasks outside that scope.
It's decent at rote coding tasks, but I haven't seen these things be reliable enough outside of that specific task to make the claim that it can do the work of any information worker.
What is your definition of AGI that the current LLMs don't fit?
Autonomously Generating Income (which is why it will never be released to the general public)
Hopefully it stands for AC Generation Improvements. If it prioritizes income it will bleed the planet dry. It needs to solve how expensive our cost is on the planet first or its entire existence was a mistake.
As the old saying goes, I’ll know it when I see it. The current 5.x generation isn’t it.
Always one goalpost away from what we have.
You’d have to really stretch the definition of AGI to make the current models fit
The definition has already been stretched to not fit the previous models. There is no meaningful, static definition that significantly predates current capabilities.
There's a reason why ai xrisk doomers had to come up with the term ASI.
I would seriously suggest that everyone take a look at the wikipedia page for AGI from the month before ChatGPT was released, compare it to the current version, and not come to that conclusion.
https://en.wikipedia.org/w/index.php?title=Artificial_genera...
The first sentence is “understand or learn any intellectual task that a human can.” Whatever you think of the benefits of LLMs, they don’t understand and they can only learn during the training period and with very minor adjustments in post training. So, no I don’t think any of these models are generally intelligent.
> they don’t understand
I have not seen any instance of this frequently-made assertion which is at all justified. It seems to rely on a definition of "understand" which is more about spirituality than actual observable evidence (they clearly can comprehend even complex tasks well enough to execute on them, and if you won't call that "understanding", you're playing word games rather than stating an objective fact).
Likewise, agents can literally come to a greater understanding of a problem through trial and error, and there are plenty of mechanisms to retain that knowledge. If you don't want to call that "learning", you're just making a choice to define it in a way more restrictive than how we use it for humans, and intentionally making communication more difficult.
The "it's not X it's Y" where Y qnd X are the same indicates a lack of understanding.
It seems to rely on a definition of "understand" which is more about spirituality than actual observable evidence
"Understanding" has enough philosophical leeway in its use to allow at least the possibility of sentience as a prerequisite.
This is where the discussion about LLM capabilities becomes genuinely difficult, and dismissing that difficulty as "word games" or "spirituality vs evidence" is not helpful.
Agents are always combining the same underlying weights to their inputs, relying on the same maps of semi-semantic space and the relationships between those that it was leaning towards at training time. The fact that it’s successful in making lots of people have an Eliza effect doesn’t make it understand something. It’s simulating understanding based on an enormous corpus of text, much of which is people working through things or sharing an understanding of something. Unless you believe that all intellectual activity is about finding the space between words you shouldn’t believe LLMs have any chance at understanding anything.
From that same page:
Various criteria for intelligence have been proposed (most famously the Turing test) but to date, there is no definition that satisfies everyone
Continual Learning? Why is this even a question? Isn’t it a well-known glaring issue with the current models? They cannot learn/adapt to new skills (in any permanent sense) once they are deployed.
AGI should be able to do every job a human can do using a computer at least as well as the average human.
And what is it worse at than an average human today that can be done on a computer?
almost everything? AGI has to be able to completely replace a human in any information worker role indefinitely.
I think you're speeding past the word "average" in the sentence. I'd argue that current frontier models already exceed the abilities of average humans across the majority of tasks you can do on a computer, although you might be able to argue that they tend to be a bit slower?
That latter part is debatable though - have you seen a non-technical person try to figure out something new on a computer?
" I'd argue that current frontier models already exceed the abilities of average humans " for things that fit in their context window sure but LLMs can't learn over time the way humans can. One example is LLMs are very good at writing a few thousands line of code but they absolutely cannot write coherent million line codebases. By average human I meant the average skill level for the job. AGI would need to be able to pass a interview and get hired and the perform well enough to not get fired.
Yeah it's not true that for every job, it is better than median worker of that job. But it is conceivable that for almost all jobs it is already better than the median human (not just workers of that job).
You have to understand that the median human is terrible at (almost) everything. Humans, the only examples of general intelligence we know, are economically valuable precisely because they can train themselves to specialise at a (relatively) narrow task over time. You don’t measure how good a coding model is by how well it programs relative to Doctors, or how well it can prove theorems relative to baristas, or how well it can write coherent novels relative to programmers. That would be a dumb metric.
> Humans, the only examples of general intelligence we know
Our intelligence only seems "general" to us, because we're viewing it through our own eyes. Our "intelligence" is specialized to our survival, and we're terrible at most tasks outside that scope.
But in any case, I think more than 10% of information workers today can be replaced by current-generation models indefinitely.
It's decent at rote coding tasks, but I haven't seen these things be reliable enough outside of that specific task to make the claim that it can do the work of any information worker.
That's already been true for a while, you're overestimating the average human. They just have different failure modes.
When it understands why 6 7 is funny