However most humans can do at least some of the things given they spend the required effort.
Some problems presented needs a very large context and some are not much solvable (e.g. trading) since market responds to traders' actions, as well, making it effectively an oracle problem (of computation).
On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them. However, brains in nature never stops. Wonder, daydream, sleep, self-evolve, clean up and eliminate memories and views and much more.
But this is assuming the model is the entire story. The original comment you were replying to pointed out that the harness is just as important.
The hardware of human intelligence is not a singular thing that is uniform throughout. You cannot take the prefrontal cortex white matter out of someone's head and say you are holding a person. Much of the parts of our brains that enable much of our intelligence, is made of different specialized stuff. The visual cortex and sensorimotor regions aren't only there for input and output, they are used by the more thinky parts of the brain to do visualization and spatial reasoning. The cerebellum contains billions of neurons making little oscillator circuits and PID-like self-regulation machines that help make muscles do what they're supposed to, but also provide attention and time perception.
Heck, our brains contain language models, that train themselves up based on a glut of data over a span of about 10 years, and then they become more or less set in stone for the rest of our lives. Of course we can learn languages, but the "Critical Period" is a very real thing that produces a permanent architecture for some grammatical structures, or things like the ability to partition a lexicon by gender for faster lexical access which cannot be learned as an adult if your native language did not have gender.
I'm not trying to make a direct analogy, the point is that the language model doesn't need to be fully "generally intelligent" all on its own for there to exist a general intelligence, because the language model can be part of a generally intelligent system, which can do things like form, recall, and manage memories which are by now a standard feature in basically every chatbot.
That's true, but my general gripe is the static nature of the whole system. Even if humans' neuroplasticity reduces with age, it never goes to zero. I started to learn English at a very early age, yet my ability to communicate with it soared around 20, because I started to use it more and more.
Same with instruments. I started to play instruments at an early age, but started to play guitar around that age. Well, I'm not a virtuoso, but can play and more importantly can improve.
These AI systems we built are static things. We generally try to make them more intelligent by augmenting the context they can see, but the model doesn't evolve in every turn, for example.
Intelligence is a multi-faceted and multi-input construct, that's true, and GPT-6 may do amazing things w.r.t. other models, I didn't try it yet. OTOH, my main call is to remember that these are still static algorithms fed with enormous amount of data. They are more rooted on statistics rather than fixed inputs. In short, they are still fitting to the frame of "advanced search".
In general, I'm not against the tech, but the hype. I have other gripes about how AI is being built, but that's not subject of this comment.
> On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them
The parent commenter noted:
"if a harness alone can help the LLM saturate the benchmark with a near perfect score then the combination of the two must still be effectively AGI"
Harnesses absolutely can enable models to continue thinking about things. And LLMs do wonder and explore weird ideas like daydreams when you allow them to do this.
However, this doesn't change the fact that you are pumping more and more tokens to a static model's context window, even if you do compaction, the model is not more intelligent than previous turn.
I'm guessing their defn of AGI is something like the sum total of all humans' abilities? Still though some of those tasks (e.g. beat an index fund) may very well be impossible, and worse yet a lot of those tasks are not coherently defined.
The point is replacing humans, and for that it only has to equal them at lower cost (including factors like not needing sleep and being easily clonable). The outperformance lies in the cost savings, not necessarily in the intelligence.
However most humans can do at least some of the things given they spend the required effort.
Some problems presented needs a very large context and some are not much solvable (e.g. trading) since market responds to traders' actions, as well, making it effectively an oracle problem (of computation).
On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them. However, brains in nature never stops. Wonder, daydream, sleep, self-evolve, clean up and eliminate memories and views and much more.
But this is assuming the model is the entire story. The original comment you were replying to pointed out that the harness is just as important.
The hardware of human intelligence is not a singular thing that is uniform throughout. You cannot take the prefrontal cortex white matter out of someone's head and say you are holding a person. Much of the parts of our brains that enable much of our intelligence, is made of different specialized stuff. The visual cortex and sensorimotor regions aren't only there for input and output, they are used by the more thinky parts of the brain to do visualization and spatial reasoning. The cerebellum contains billions of neurons making little oscillator circuits and PID-like self-regulation machines that help make muscles do what they're supposed to, but also provide attention and time perception.
Heck, our brains contain language models, that train themselves up based on a glut of data over a span of about 10 years, and then they become more or less set in stone for the rest of our lives. Of course we can learn languages, but the "Critical Period" is a very real thing that produces a permanent architecture for some grammatical structures, or things like the ability to partition a lexicon by gender for faster lexical access which cannot be learned as an adult if your native language did not have gender.
I'm not trying to make a direct analogy, the point is that the language model doesn't need to be fully "generally intelligent" all on its own for there to exist a general intelligence, because the language model can be part of a generally intelligent system, which can do things like form, recall, and manage memories which are by now a standard feature in basically every chatbot.
That's true, but my general gripe is the static nature of the whole system. Even if humans' neuroplasticity reduces with age, it never goes to zero. I started to learn English at a very early age, yet my ability to communicate with it soared around 20, because I started to use it more and more.
Same with instruments. I started to play instruments at an early age, but started to play guitar around that age. Well, I'm not a virtuoso, but can play and more importantly can improve.
These AI systems we built are static things. We generally try to make them more intelligent by augmenting the context they can see, but the model doesn't evolve in every turn, for example.
Intelligence is a multi-faceted and multi-input construct, that's true, and GPT-6 may do amazing things w.r.t. other models, I didn't try it yet. OTOH, my main call is to remember that these are still static algorithms fed with enormous amount of data. They are more rooted on statistics rather than fixed inputs. In short, they are still fitting to the frame of "advanced search".
In general, I'm not against the tech, but the hype. I have other gripes about how AI is being built, but that's not subject of this comment.
> On the other hand, we must be aware that these models are static, and they indeed stop when nobody asks something or requests an action from them
The parent commenter noted:
"if a harness alone can help the LLM saturate the benchmark with a near perfect score then the combination of the two must still be effectively AGI"
Harnesses absolutely can enable models to continue thinking about things. And LLMs do wonder and explore weird ideas like daydreams when you allow them to do this.
However, this doesn't change the fact that you are pumping more and more tokens to a static model's context window, even if you do compaction, the model is not more intelligent than previous turn.
Nature doesn't work that way.
1 reply →
Humans are AGI simply because if they want to, they can achieve it (it just takes some effort).
Humans are general intelligence, not artificial general intelligence. :)
I'm guessing their defn of AGI is something like the sum total of all humans' abilities? Still though some of those tasks (e.g. beat an index fund) may very well be impossible, and worse yet a lot of those tasks are not coherently defined.
They can't, by definition, have the A part btw.
I haven't met a person who doesn't wonder about things.
AGI has always been expected to outperform humans or else what is the point of it?
The point is replacing humans, and for that it only has to equal them at lower cost (including factors like not needing sleep and being easily clonable). The outperformance lies in the cost savings, not necessarily in the intelligence.