Comment by turzmo
1 day ago
It is always a mistake to assume that because computers can do X, that they will be able do everything that a human that can do X is expected to be able to do.
Solving frontier math problems is mind boggling, but perhaps it says more about frontier math problems than it does about LLMs. Same with chess which was solved decades ago.
My experience is that LLMs can be useful but are underwhelming in terms of intelligence, namely the ability to quickly learn from experience and grow with you while you accomplish a task.
Thinking of them as beings is something we should move away from.
Case in point you'd expect any human who could produce a lean proof certificate to be able to write a human readable proof as well.
So far LLMs seem a bit lacking in that regard.
Why wouldn't they be able to grow, its just one data point more in their training, in your notes or in their instructions.
It doesn't matter if they are beings or not. They will replace most humans in the offices like machines replaced most humans in fields or industrial plants.
Because context, at least in my experience, doesn't seem to encapsulate the sort of dynamism that I'm thinking of. Conversations are flat and repetitive. It still feels like a very good semantic search more than talking to an intelligent human.
I don't know if they will replace most humans the way you're imagining. I think the industry has done a very good job of selling this point, but that they are still quite far from this.
Because we don't let them learn. Everyone runs the same static generalist models, instead of having every model instance learn from everything it does. New models come out occasionally, but they have not been trained on the private data you have not shared with the AI company.
You can work around that to some extent with explicit instructions, but it doesn't scale nearly as well as actual model training.
*but they have not been trained on the private data you have not shared with the AI company* Not shared vs Unavailable(offline)