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AIs can double check. Agent to agent confirmation and validation is a thing. In any case, the point is that we have learned techniques to compensate for human

AIs can double check. Agent to agent confirmation and validation is a thing. In any case, the point is that we have learned techniques to compensate for human fallibility. We will learn techniques to compensate for gen AI fallibility, as well. The objection that AIs can be wrong is far less a barrier to the rise of their utility than is often supposed.

bena 3y ago
You see how that's worse, right? The original argument put forth was that "an inherently unreliable tool cannot gauge its own reliability". Someone responded that humans fit that description as well. I said we don't. We can and do verify our own reliability. We can essentially test our assumptions against the real world and fix them. You then claimed we couldn't do that "self-sufficiently". I responded that while that is true for some tasks, for a lot of tasks, we can. That an AI can't check itself and won't even try. And now you're telling me that they can check against each other. But if you can't trust the originals, asking them if they trust each other is kind of pointless. You're not really doing anything more than adding another layer. For example: If I put my pants on backwards, I correct that myself. Without the need to check and/or balance against any other person. I am self-correcting to a large degree. The AI would not even know to check its pants until someone told it it was wrong. The objection isn't that "AIs can be wrong", the objection is that AIs can't really tell the difference between correct and incorrect. So everything has to be checked, often with as much effort as it would take to do the thing in the first place.
- ryanklee 3y ago
  This is a very narrow view of how LLMs can interact to improve inference accuracy. Different LLMs have different capabilities. They can be used in coordination to improve results. Your objections seem to rely on a restricted view that says "we can't do better" but with no evidence. Whereas we have plenty of evidence of massive, continual improvement in the very areas you are holding up as problematic.

bena 3y ago

You see how that's worse, right? The original argument put forth was that "an inherently unreliable tool cannot gauge its own reliability". Someone responded that humans fit that description as well. I said we don't. We can and do verify our own reliability. We can essentially test our assumptions against the real world and fix them. You then claimed we couldn't do that "self-sufficiently". I responded that while that is true for some tasks, for a lot of tasks, we can. That an AI can't check itself and won't even try. And now you're telling me that they can check against each other. But if you can't trust the originals, asking them if they trust each other is kind of pointless. You're not really doing anything more than adding another layer. For example: If I put my pants on backwards, I correct that myself. Without the need to check and/or balance against any other person. I am self-correcting to a large degree. The AI would not even know to check its pants until someone told it it was wrong. The objection isn't that "AIs can be wrong", the objection is that AIs can't really tell the difference between correct and incorrect. So everything has to be checked, often with as much effort as it would take to do the thing in the first place.

ryanklee 3y ago
This is a very narrow view of how LLMs can interact to improve inference accuracy. Different LLMs have different capabilities. They can be used in coordination to improve results. Your objections seem to rely on a restricted view that says "we can't do better" but with no evidence. Whereas we have plenty of evidence of massive, continual improvement in the very areas you are holding up as problematic.