Isaac Asimov describes how AI will liberate humans and their creativity (1992)

LLMs are statistical models trained on human-generated text. They aren't the perfectly logical "machine brains" that Asimov and others imagined.

The upshot of this is that LLMs are quite good at the stuff that he thinks only humans will be able to do. What they aren't so good at (yet) is really rigorous reasoning, exactly the opposite of what 20th century people assumed.

> LLMs are statistical models trained on human-generated text.

I mean, not only human-generated text. Also, human brains are arguably statistical models trained on human-generated/collected data as well...

Maybe; at some level are dogs' brains also simple sensory-collecting statistical models? A human baby and a dog are born on the same day; that dog never leaves that baby's side, for 20 years. It sees everything it sees, it hears everything it hears, it is given the opportunity to interact with its environment in roughly the same way the human baby does, to the degree to which they are both physically capable. The intelligence differential after that time will still be extraordinary.

My point in bringing up that metaphor is to focus the analogy: When people say "we're just statistical models trained on sensory data", we tend to focus way too much on the "sensory data" part, which has led to for example AI manufacturers investing billions of dollars into slurping up as much human intellectual output as possible to train "smarter" models.

The focus on the sensory input inherently devalues our quality of being; that who we are is predominately explicable by the world around us.

However: We should be focusing on the "statistical model" part: that even if it is accurate to holistically describe the human brain as a statistical model trained on sensory data (which I have doubts about, but those are fine to leave to the side): its very clear that the fundamental statistical model itself is simply so far superior in human brains that comparing it to an LLM is like comparing us to a dog.

It should also be a focal point for AI manufacturers and researchers. If you are on the hunt for something along the spectrum of human level intelligence, and during this hunt you are providing it ten thousand lifetimes of sensory data, to produce something that, maybe, if you ask it right, it can behave similarity to a human who has trained in the domain in only years: You're barking up the wrong tree. What you're producing isn't even on the same spectrum; that doesn't mean it isn't useful, but its not human-like intelligence.

Well the dog brain and human brain are very different statistical models, and I don't think we have any objective way of comparing/quantifying LLMs (as an architecture) vs human brains at this point. I think it's likely LLMs are currently not as good as human brains for human tasks, but I also think we can't say with any confidence that LLMs/NNs can't be better than human brains.

For sure; we don't have a way of comparing the architectural substrate of human intelligence versus LLM intelligence. We don't even have a way of comparing the architectural substrate of one human brain with another.

Here's my broad concern: On the one hand, we have an AI thought leader (Sam Altman) who defines super-intelligence as surpassing human intelligence at all measurable tasks. I don't believe it is controversial to say that we've established that the goal of LLM intelligence is something along these lines: it exists on the spectrum of human intelligence, its trained on human intelligence, and we want it to surpass human intelligence, on that spectrum.

On the other hand: we don't know how the statistical model of human intelligence works, at any level at all which would enable reproduction or comparison, and there's really good reason to believe that the human intelligence statistical model is vastly superior to the LLM model. The argument for this lies in my previous comment: the vast majority of contribution of intelligence advances in LLM intelligence comes from increasing the volume of training data. Some intelligence likely comes from statistical modeling breakthroughs since the transformer, but by and large its from training data. On the other hand: Comparatively speaking, the most intelligent humans are not more intelligent because they've been alive for longer and thus had access to more sensory data. Some minor level of intelligence comes from the quality of your sensory data (studying, reading, education). But the vast majority of intelligence difference between humans is inexplicable; Einstein was just Born Smarter; God granted him a unique and better statistical model.

This points to the undeniable reality that, at the very least, the statistical model of the human brain and that of an LLM is very different, which should cause you to raise eyebrows at Sam Altman's statement that superintelligence will evolve along the spectrum of human intelligence. It might, but its like arguing that the app you're building is going to be the highest quality and fastest MacOS app ever built, and you're building it using WPF and compiling it for x86 to run on WINE and Rosetta. GPT isn't human intelligence; at best, it might be emulating, extremely poorly and inefficiently, some parts of human intelligence. But, they didn't get the statistical model right, and without that its like forcing a square peg into a round hole.

Attempting to summarize your argument (please let me know if I succeeded):

Because we can't compare human and LLM architectural substrates, LLMs will never surpass human-level performance on _all_ tasks that require applying intelligence?

If my summary is correct, then is there any hypothetical replacement for LLM (for example, LLM+robotics, LLMs with CoT, multi-modal LLMs, multi-modal generative AI systems, etc) which would cause you to then consider this argument invalid (i.e. for the replacement, it could, sometime replace humans for all tasks)?