Let's clarify, this isn't about whether the models are capable. They are very capable and impressive. This is more about whether we can use the same type of metric we use for humans to compare and conclude if they are "intelligent".
It's not just semantics, the metrics are supposed to tell us the potential of the model. If they can solve extremely hard PhD problems, it should be the case that we're already in the singularity, and they should be solving absolutely everything in whatever field they were trained in, because it's not just PhD level, it's a machine that has a ton of memory, compute and never sleeps. However, once you use these models extensively, it becomes apparent they are just synthesizing data, and not as much understanding it in a way that would allow them to extrapolate into anything else as humans do.
I think this point is a little hard to explain. I'll just emphasize, these are smart systems, and they can do a lot, but there is still a disconnect between, let's say, a PhD level model and a human with a PhD, in the "quality" of what we would call "intelligence" of both entities (human and machine).
Human metrics of intelligence have always felt like rubbish. We never did this well. I would describe intelligence as effective adaption leading to survival and growth or prospering. Memorization, comprehension, speed of response etc. those are magnifying factors that are valued, we view them as components of intelligence, but llms are proving this is not the whole, without effective application, they are not intelligence. Perhaps learning is the difference? How to measure that?
Someone describing string theory is the literary equivalent of fractal structures in snowflakes. Lovely, complex, possibly unique, but not proof of a level of intelligence- for the string theorist maybe it is intelligent, perhaps persuading someone to fund their grant, which enables them to eat, shelter etc. Might be a bit harsh on string theory. Saying it is proof of an amount of intelligence leads us to falsifiable statements.
It's not just semantics, the metrics are supposed to tell us the potential of the model. If they can solve extremely hard PhD problems, it should be the case that we're already in the singularity, and they should be solving absolutely everything in whatever field they were trained in, because it's not just PhD level, it's a machine that has a ton of memory, compute and never sleeps. However, once you use these models extensively, it becomes apparent they are just synthesizing data, and not as much understanding it in a way that would allow them to extrapolate into anything else as humans do.
I think this point is a little hard to explain. I'll just emphasize, these are smart systems, and they can do a lot, but there is still a disconnect between, let's say, a PhD level model and a human with a PhD, in the "quality" of what we would call "intelligence" of both entities (human and machine).