Yeah, sorry about that. I didn’t put much effort into my last comment.
Defining intelligence is tricky, but to paraphrase EY, it’s probably wise not to get too specific since we don’t fully understand Intelligence yet. In the past, people didn’t really know what fire was. Some would just point to it and say, “Hey, it’s that shiny thing that burns you.” Others would invent complex, intellectual-sounding theories about phlogiston, which were entirely off base. Similarly, I don’t think the discussion about AGI and doom scenarios gets much benefit from a super precise definition of intelligence. A broad definition that most people agree on should be enough, like “Intelligence is the capacity to create models of the world and use them to think.”
But I do think we should aim for a clearer definition of AGI (yes, I realize ‘Intelligence’ is part of the acronym). What I mean is, we could have a more vague definition of intelligence, but AGI should be better defined. I’ve noticed different uses of ‘AGI’ here on Less Wrong. One definition is a machine that can reason about a wide variety of problems (some of which may be new to it) and learn new things. Under this definition, GPT4 is pretty much an AGI. Another common definition on this forum is an AGI is a machine capable of wiping out all humans. I believe we need to separate these two definitions, as that’s really where the core of the crux lies.
I think a good definition for AGI is capability for open-ended development, the point where the human side of the research is done, and all it needs to reach superintelligence from that point on is some datacenter maintenance and time, so that eventually it can get arbitrarily capable in any domain it cares for, on its own. This is a threshold relevant for policy and timelines. GPT-4 is below that level (it won’t get better without further human research, no matter how much time you give it), and ability to wipe out humans (right away) is unnecessary for reaching this threshold.
I think we also care about how fast it gets arbitrarily capable. Consider a system which finds an approach which can measure approximate actions-in-the-world-Elo (where an entity with an advantage of 200 on their actions-in-the-world-Elo score will choose a better action 76% of the time), but it’s using a “mutate and test” method over an exponentially large space, such that the time taken to find the next 100 point gain takes 5x as long, and it starts out with an actions-in-the-world-Elo 1000 points lower than an average human with a 1 week time-to-next-improvement. That hypothetical system is technically a recursively self-improving intelligence that will eventually reach any point of capability, but it’s not really one we need to worry that much about unless it finds techniques to dramatically reduce the search space.
Like I suspect that GPT-4 is not actually very far from the ability to come up with a fine-tuning strategy for any task you care to give it, and to create a simple directory of fine-tuned models, and to create a prompt which describes to it how to use that directory of fine-tuned models. But fine-tuning seems to take an exponential increase in data for each linear increase in performance, so that’s still not a terribly threatening “AGI”.
Sure, natural selection would also technically be an AGI by my definition as stated, so there should be subtext of it taking no more than a few years to discover human-without-supercomputers-or-AI theoretical science from the year 3000.
Defining intelligence is tricky, but to paraphrase EY, it’s probably wise not to get too specific since we don’t fully understand Intelligence yet.
That’s probably true, but that would imply we would understand even less what ‘artificial intelligence’ or ‘artificial general intelligence’ are?
Spelling it out like that made me realize how odd talking about AI or AGI is. In no other situation, that I’ve heard of, would a large group of folks agree that there’s a vague concept with some confusion around it and then proceed to spend the bulk of their efforts to speculate on even vaguer derivatives of that concept.
Yeah, sorry about that. I didn’t put much effort into my last comment.
Defining intelligence is tricky, but to paraphrase EY, it’s probably wise not to get too specific since we don’t fully understand Intelligence yet. In the past, people didn’t really know what fire was. Some would just point to it and say, “Hey, it’s that shiny thing that burns you.” Others would invent complex, intellectual-sounding theories about phlogiston, which were entirely off base. Similarly, I don’t think the discussion about AGI and doom scenarios gets much benefit from a super precise definition of intelligence. A broad definition that most people agree on should be enough, like “Intelligence is the capacity to create models of the world and use them to think.”
But I do think we should aim for a clearer definition of AGI (yes, I realize ‘Intelligence’ is part of the acronym). What I mean is, we could have a more vague definition of intelligence, but AGI should be better defined. I’ve noticed different uses of ‘AGI’ here on Less Wrong. One definition is a machine that can reason about a wide variety of problems (some of which may be new to it) and learn new things. Under this definition, GPT4 is pretty much an AGI. Another common definition on this forum is an AGI is a machine capable of wiping out all humans. I believe we need to separate these two definitions, as that’s really where the core of the crux lies.
I think a good definition for AGI is capability for open-ended development, the point where the human side of the research is done, and all it needs to reach superintelligence from that point on is some datacenter maintenance and time, so that eventually it can get arbitrarily capable in any domain it cares for, on its own. This is a threshold relevant for policy and timelines. GPT-4 is below that level (it won’t get better without further human research, no matter how much time you give it), and ability to wipe out humans (right away) is unnecessary for reaching this threshold.
I think we also care about how fast it gets arbitrarily capable. Consider a system which finds an approach which can measure approximate actions-in-the-world-Elo (where an entity with an advantage of 200 on their actions-in-the-world-Elo score will choose a better action 76% of the time), but it’s using a “mutate and test” method over an exponentially large space, such that the time taken to find the next 100 point gain takes 5x as long, and it starts out with an actions-in-the-world-Elo 1000 points lower than an average human with a 1 week time-to-next-improvement. That hypothetical system is technically a recursively self-improving intelligence that will eventually reach any point of capability, but it’s not really one we need to worry that much about unless it finds techniques to dramatically reduce the search space.
Like I suspect that GPT-4 is not actually very far from the ability to come up with a fine-tuning strategy for any task you care to give it, and to create a simple directory of fine-tuned models, and to create a prompt which describes to it how to use that directory of fine-tuned models. But fine-tuning seems to take an exponential increase in data for each linear increase in performance, so that’s still not a terribly threatening “AGI”.
Sure, natural selection would also technically be an AGI by my definition as stated, so there should be subtext of it taking no more than a few years to discover human-without-supercomputers-or-AI theoretical science from the year 3000.
That’s probably true, but that would imply we would understand even less what ‘artificial intelligence’ or ‘artificial general intelligence’ are?
Spelling it out like that made me realize how odd talking about AI or AGI is. In no other situation, that I’ve heard of, would a large group of folks agree that there’s a vague concept with some confusion around it and then proceed to spend the bulk of their efforts to speculate on even vaguer derivatives of that concept.