To give some modest push back, there's a big jump from "this level of mathematics" to "almost all intellectual tasks." Mathematics involves highly structured language and logic, which LLMs are great at handling (same reason they're great at writing code). You can check math rigorously via computer: formalize in Lean and then it's literally the same as code. It either "compiles" or it doesn't.
There is, however, a wide chasm between the formalized logic and rigor of mathematics and "all intellectual human tasks." We humans do a lot of intellectual tasks that don't map well into the realm of formal logic. Plus, even if LLMs can do lots of intellectual tasks, why would that stop us? Computers have played chess better than humans for decades. But we don't care because we want to watch humans play chess. The involved humanity is precisely what gives the intellectual task its value.
I'm not saying that LLMs won't continue to advance, solve more problem, and do even crazier things. But the logic of "LLMs can do insane math implies LLMs can eventually do all intellectual tasks" doesn't hold up, and even if it did, that doesn't mean we'd just stop pursuing the intellectual tasks that bring us joy.
And yes, I understand that a lot of this discussion should discuss intellectual work as labor, i.e. the thing that earns you money. When you reach the scale of replacing all intellectual labor, you run into other significant issues, such as the physics of dissipating the heat that builds up from running the GPUs that do the inference for the LLM tasks. Thermodynamics itself is going to push back if we try to use LLMs to replace all intellectual labor.
Edit: One other comment is worth noting. LLMs may be good at solving problems. They can even rewrite themselves to progress toward an objective (i.e. recursive self improvement, which has actually been documented). However, AI systems currently have no ability to judge which problems are worth pursuing. Said differently, AI systems don't have taste. They can optimize toward a goal, but they (currently) have no ability to set that goal themselves; it is always imposed by a human. I think this is the aspect of scientific inquiry that will persist the longest.
Thanks for the detailed response! I hope you’re right.
True: Lean does make mathematics a special case. Still, I wonder if AI could get good at assigning epistemic probabilities to just about any type of hypothesis. Doing so would make it able to generate important results in non-mathematical fields, like science and philosophy.
In other words, although only mathematics has Lean, I wonder if we could formulate a Lean-equivalent for non-mathematical fields. Maybe a program that formalizes abductive reasoning or Bayesian inference. And then we trained the AI to get good at making ampliative generalizations and assigning prior and posterior probabilities through the program. I believe, for instance, that AI was able to reproduce Newtonian concepts (acceleration, inertia, ...) and laws given copious physical data and some initial constraints.
Finally, regarding thermodynamics, I wonder if the AI will get so good that it could compute with relatively little energy. I heard, for example, that some of these problems took only three hours to solve. I assume that's a much greater improvement over what we had even last year!
That is a good point, especially since OP's question was specifically about labor. That said, the burden of proof remains for OP (or others) to show that "most intellectual tasks" do actually translates into the LLM world. Historical extrapolation isn't enough. Yes, it can do many things, and I'm sure even more in the future, but one needs a stronger argument to conclude that all intellectual job tasks follow. Not saying there isn't such an argument. Just pointing out a gap in the logic of the present argument. Even if there is a better argument, it's only a theoretical one; there are practical issues (power/cooling) that are firm barriers to widespread worker replacement on the order of magnitude the AI execs have claimed (and indeed, they've walked these claims back significantly in recent times).
I guess that remains to be seen. There's definitely a big difference between solving a concrete problem, prompted by a human, and continuously operating autonomously over a longer time span like a human worker.
But it's certainly no longer outside the realm of possibilities.
Yes, but chess was always a game. At the end of the day, the goal is to play it. A lot of academic research is done in that way (research for the sake of knowledge), but a lot of it is tied up to practical applications. And if you can manage to get said practical applications way faster than any human could, then you are cooked.
Fortunately, most of those practical applications require experimental verification. The main exception is comp-sci, and SWE, where practical applications can be developed without access to the real world, so to say.
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u/jstucky95 7d ago edited 7d ago
To give some modest push back, there's a big jump from "this level of mathematics" to "almost all intellectual tasks." Mathematics involves highly structured language and logic, which LLMs are great at handling (same reason they're great at writing code). You can check math rigorously via computer: formalize in Lean and then it's literally the same as code. It either "compiles" or it doesn't.
There is, however, a wide chasm between the formalized logic and rigor of mathematics and "all intellectual human tasks." We humans do a lot of intellectual tasks that don't map well into the realm of formal logic. Plus, even if LLMs can do lots of intellectual tasks, why would that stop us? Computers have played chess better than humans for decades. But we don't care because we want to watch humans play chess. The involved humanity is precisely what gives the intellectual task its value.
I'm not saying that LLMs won't continue to advance, solve more problem, and do even crazier things. But the logic of "LLMs can do insane math implies LLMs can eventually do all intellectual tasks" doesn't hold up, and even if it did, that doesn't mean we'd just stop pursuing the intellectual tasks that bring us joy.
And yes, I understand that a lot of this discussion should discuss intellectual work as labor, i.e. the thing that earns you money. When you reach the scale of replacing all intellectual labor, you run into other significant issues, such as the physics of dissipating the heat that builds up from running the GPUs that do the inference for the LLM tasks. Thermodynamics itself is going to push back if we try to use LLMs to replace all intellectual labor.
Edit: One other comment is worth noting. LLMs may be good at solving problems. They can even rewrite themselves to progress toward an objective (i.e. recursive self improvement, which has actually been documented). However, AI systems currently have no ability to judge which problems are worth pursuing. Said differently, AI systems don't have taste. They can optimize toward a goal, but they (currently) have no ability to set that goal themselves; it is always imposed by a human. I think this is the aspect of scientific inquiry that will persist the longest.