> don't have a shred of intelligence. ... They don't understand, only synthesize from the data they were trained on.
Couldn't you say that about 99% of humans too?
Couldn't you say that about 99% of humans too?
And of course, if you don't limit yourself to "advancing the state of the art at the far frontiers of human knowledge" but allow for ordinary people to make everyday contributions in their daily lives, you get even more. Sure, much of this knowledge may not be widespread (it may be locked up within private institutions) but its impact can still be felt throughout the economy.
Even this assumes that everyone has a specialization in which 1% of people contribute to the sum of human knowledge. I would probably challenge that. There are a lot of people in the world who do not do knowledge-oriented work at all.
Your math assumes each person has exactly one thing they do in life. The shoe factory worker could also be a gardener. He might not make any advancements in gardening, but his contribution means that if you add up all the fields of specialization the sum is greater than the population of humans. Take 1% of that sum and it’s greater than 1% of humans. 1% of people in a specialization is not the same as 1% of specialists. In fact, I would say it’s a much higher proportion of specialists making contributions (especially through collaboration).
Oh, and don’t get caught up on the 1% number. I used it as shorthand for whatever small number it is. Maybe it’s only 10 people in some hyper-specialized field. But that doesn’t matter. Some other field may have thousands of contributors. You don’t have to be a specialist in a field to make a contribution to that field, for example: glassmakers advanced the science of astronomy by making the telescope possible.
How? By also "synthesizing the data they were trained on" (their experience, education, memories, etc.).
if you don't limit yourself to "advancing the state of the art at the far frontiers of human knowledge" but allow for ordinary people to make everyday contributions in their daily lives, you get even more
This isn't a throwaway comment. I do this all the time myself, at work. Everywhere I've worked, I do this. I challenge the assumptions and try to make things better. It's not a rare thing at all, it's just not revolutionary.
Revolutions are rare. Perhaps only a handful of them have ever happened in any one particular field. But you simply will not ever go from Aristotelian physics to Newtonian physics to General Relativity by merely "synthesizing the data they were trained on", as the previous comment supposed.
Edit: I should also say something about experimentation. You can't do it from an armchair, which is all an LLM has access to (at present). Real people learn things all the time by conducting experiments in the world and observing the results, without necessarily working as formal scientists. Babies learn a lot by experimenting, for example. This is one particular avenue of new knowledge which is entirely separate from experience, education, memories, etc. because an experiment always has the potential to contradict all of that.
Of course it does, but only after the fact. You don't have any experience of the result of the experiment before you perform it.
Sure, they can't have apples fall on their heads like Newton had but they can totally observe the apple falling on someones head in a video
I have strong doubts that LLMs have any understanding whatsoever of what's happening in images (let alone videos). The claim (I've sometimes heard) that they possess a world model and are able to interpret an image according to that model is an extremely strong one, that's strongly contradicted by the fact that they: a) continue to hallucinate in pretty glaring ways, and b) continue to mis-identify doctored (adversarial) images that no human would mis-identify (because they don't drastically alter the subject).
For me the most glaring difference to how humans work is the lack of online learning. If that prevents them from being able to innovate, I'm not so sure.
The lack of online learning is a critical fault. Much of what humans learn (such as anything based on mathematics) has a dependency tree of stuff to learn. But even mundane stuff involves a lot of dependent learning. For example, ask an LLM to write a cookbook and it can synthesize from recipes that are already out there but good luck having it invent new cooking techniques that require experimentation or invention (new heat source, new cooking utensils, etc).
Btw, it looks like there is a growing body of research evaluating exactly this. I found this nice overview with even some benchmarks specifically for scientific innovation: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Dis...
Where's the proof we don't do exactly this? The mind as a prediction engine is one of the handful most accepted theories.
Real progress in science is made by the hard collection and cataloguing of data every single day, not by armchair philosophizing.