I think you’re right that there’s not a g factor for AI systems (besides raw amount of computation, which I think is a huge caveat); I nevertheless think that arguments that route thru “intelligence is a thing” aren’t Platonic (b/c of the difference between ‘thing’ and ‘form’). While I normally give ‘intelligence explosion’ arguments in a scalar fashion, I think they go thru when considering intelligence as a vector or bundle of cognitive capacities instead. The Twitter OP that Scott is responding to calls the argument with cognitive capacities ‘much weaker’, but I think it’s more like “slightly weaker” instead.
A large reason for the existence of the g factor in humans is that humans all have approximately the same architecture. The large majority of humans (although not all) have a working visual system, a functioning long term memory, and a working grasp of language. Furthermore, most humans have mastered basic motor control, and we have decent intuitions about physical objects. Most humans are able to set and pursue at least simple goals and act to try to maintain their personal comfort.
I don’t think this is looking at humans at the right level of abstraction; I think that it matters a lot that any individual brain is using basically the same neuron design for all bits of the brain (but that neuron design varies between humans). Like, g correlates with reaction speed and firearm accuracy. If brains were using different neural designs for different regions, then even if everyone had the same broad architecture I would be much more surprised at the strength of g.
This makes me suspect that things like ‘raw amount of computation’ and ‘efficiency of PyTorch’ and so on will be, in some respect, the equivalent of g for AI systems. Like, yes, we are getting improvements to the basic AI designs, but I think most of the progress of AI systems in the last twenty years has been driven by underlying increases in data and computation, i.e. the Bitter Lesson. [This is making the same basic point as Scott’s section 3.]
When it comes to AI models, it’s necessary to break up the concept of intelligence because AI models are composed of multiple multiple distinct functions. It’s very easy to get an AI that passes some sorts of tests but fails others.
I think this was a sensible view five years ago, but is becoming less and less sensible in a foundation model world, where the impressive advances in capabilities have not come from adding in additional domains of experience so much as by increasing the size of model and training data. Like, I think the Voyager results you mention before are pretty impressive given the lack of multimodality (did you think next-token-prediction, with a bit of scaffolding, would do that well on Minecraft tasks?).
If the next generations of transformers are multi-modal, I think it really won’t take much effort to have them code football-playing robots and do pretty well on your FQ.
I think you’re right that there’s not a g factor for AI systems (besides raw amount of computation, which I think is a huge caveat); I nevertheless think that arguments that route thru “intelligence is a thing” aren’t Platonic (b/c of the difference between ‘thing’ and ‘form’). While I normally give ‘intelligence explosion’ arguments in a scalar fashion, I think they go thru when considering intelligence as a vector or bundle of cognitive capacities instead. The Twitter OP that Scott is responding to calls the argument with cognitive capacities ‘much weaker’, but I think it’s more like “slightly weaker” instead.
I don’t think this is looking at humans at the right level of abstraction; I think that it matters a lot that any individual brain is using basically the same neuron design for all bits of the brain (but that neuron design varies between humans). Like, g correlates with reaction speed and firearm accuracy. If brains were using different neural designs for different regions, then even if everyone had the same broad architecture I would be much more surprised at the strength of g.
This makes me suspect that things like ‘raw amount of computation’ and ‘efficiency of PyTorch’ and so on will be, in some respect, the equivalent of g for AI systems. Like, yes, we are getting improvements to the basic AI designs, but I think most of the progress of AI systems in the last twenty years has been driven by underlying increases in data and computation, i.e. the Bitter Lesson. [This is making the same basic point as Scott’s section 3.]
I think this was a sensible view five years ago, but is becoming less and less sensible in a foundation model world, where the impressive advances in capabilities have not come from adding in additional domains of experience so much as by increasing the size of model and training data. Like, I think the Voyager results you mention before are pretty impressive given the lack of multimodality (did you think next-token-prediction, with a bit of scaffolding, would do that well on Minecraft tasks?).
If the next generations of transformers are multi-modal, I think it really won’t take much effort to have them code football-playing robots and do pretty well on your FQ.