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No, I'm not concerned. When an AI is trained on a largely raw, uncurated set of low-quality data (eg most of the public internet), it's going to miss subtle distinctions some humans might prefer that it make. I'm confident that pretty quickly the majority of the general public using such AIs will begin to intuitively understand this. Just as they have developed a practical, working understanding of other complex technology's limitations (such as auto-complete algorithms). No matter how good AI gets, there will always be some frontier boundary where it gets something wrong. My evidence is simply that even smart humans trying their best occasionally get such subtle distinctions wrong. However, this innate limitation doesn't mean that an AI can't still be useful. What I am concerned about is that AI providers will keep wasting time and resources trying to implement band-aid "patches" to address what is actually an innate limitation. For example, exception processing at the output stage fails in ways we've already seen, such as AI photos containing female popes or an AI lying to deny that HP Lovecraft had a childhood pet (due to said pet having a name that was crudely rude 100 years ago but racist today). The alternative of limiting the training data to include only curated content fails by yielding a much less useful AI. My, probably unpopular, opinion is that when AI inevitably screws up some edge case, we get more comfortable saying, basically, "Hey, sometimes stupid AI is gonna be stupid." The honest approach is to tell users upfront: when quality or correctness or fitness for any given purpose is important, you need to check every AI output because sometimes it's gonna fail. Just like auto-pilots, auto-correct and auto- everything else. As impressive as AI can sometimes be, personally, I think it's still lingering just below the threshold of "broadly useful" and, lately, the rate of fundamental improvement is slowing. We can't really afford to be squandering limited development resources or otherwise nerfing AI's capabilities to pursue ultimately unattainable standards. That's a losing game because there's a growing cottage industry of concern trolls figuring out how to get an AI to generate "problematic" output to garner those sweet "tsk tsk" clicks. As long as we keep reflexively reacting, those goalposts will never stop moving. Instead, we need to get off that treadmill and lower user expectations based on the reality of the current technology and data sets. |
We seem to have a culture of completely paranoid people now.
When the internet came along every conversation was not dominated by "but what about people knowing how to build bombs???" the way most AI conversation flips to these paranoid AI doomer scenarios.