4 ms·

Here’s the fundamental problem. By any reasonable standard, a “clinical pharmacist for 10 years” should understand that there are circumstances where there is

Here’s the fundamental problem. By any reasonable standard, a “clinical pharmacist for 10 years” should understand that there are circumstances where there is a correct answer, and incorrect answers are potentially catastrophic. You, clearly, are either unable to recognize that or are unable to recognize that LLM are dangerously useless in such contexts. Either you already know the correct answer, making this useless, or you are not competent to utilize its answers because you can’t tell whether it’s about to kill someone with plausible nonsense. And if a “clinical pharmacist for 10 years” can lose sight of this really fundamental issue, this whole thing needs to be halted. Governmentally if necessary. And it won’t be. It’s bad enough that “bad actors” will leverage this for their purposes, but the presumably competent simply turning off their brains over this is the most perplexing glitch ever to be on public display.

aiappreciator 4y ago
"Either you already know the correct answer, making this useless, or you are not competent to utilize its answers because you can’t tell whether it’s about to kill someone with plausible nonsense" Evaluating if an answer is correct or not is easier than coming up with a correct answer from scratch. NP != P. if you don't understand this basic fact, then you are not competent enough to comment on AI.
- underanalyzer 4y ago
  A few of the comments in this thread seem to be misusing mathematics in order to lend more credence to themselves. At the risk of responding to low quality flamebait here are some problems with your statements. 1. P = NP refers to a two very specific sets of problems (which might actually be the same set) not any general question. There are problems that we know don't fall into P or NP, (for example the Halting Problem). Also whether or not P=NP is an open question almost the opposite of a fact. 2. You claim: "Evaluating if an answer is correct or not is easier than coming up with a correct answer from scratch." This is the right idea but not quite correct. The correct statement is as follows: "Evaluating if an answer is correct is not harder than the difficulty of coming up with a correct answer from scratch." This is because evaluating some answer can still be just as hard as the original problem. In fact sometimes it's uncomputable (if the original problem is also uncomputable). To use an example from above consider the question: "Does a program x halt?" If I tell you "no" it could be impossible to verify my answer unless you have solved the halting problem. To bring this back to reality, again if GPT-4 is wrong about some complex medical question it doesn't mean it's mathematically easier to figure that out than solving the problem from scratch.

aiappreciator 4y ago

"Either you already know the correct answer, making this useless, or you are not competent to utilize its answers because you can’t tell whether it’s about to kill someone with plausible nonsense" Evaluating if an answer is correct or not is easier than coming up with a correct answer from scratch. NP != P. if you don't understand this basic fact, then you are not competent enough to comment on AI.

underanalyzer 4y ago
A few of the comments in this thread seem to be misusing mathematics in order to lend more credence to themselves. At the risk of responding to low quality flamebait here are some problems with your statements. 1. P = NP refers to a two very specific sets of problems (which might actually be the same set) not any general question. There are problems that we know don't fall into P or NP, (for example the Halting Problem). Also whether or not P=NP is an open question almost the opposite of a fact. 2. You claim: "Evaluating if an answer is correct or not is easier than coming up with a correct answer from scratch." This is the right idea but not quite correct. The correct statement is as follows: "Evaluating if an answer is correct is not harder than the difficulty of coming up with a correct answer from scratch." This is because evaluating some answer can still be just as hard as the original problem. In fact sometimes it's uncomputable (if the original problem is also uncomputable). To use an example from above consider the question: "Does a program x halt?" If I tell you "no" it could be impossible to verify my answer unless you have solved the halting problem. To bring this back to reality, again if GPT-4 is wrong about some complex medical question it doesn't mean it's mathematically easier to figure that out than solving the problem from scratch.