Dan Hendrycks: belief

Transcript context

…Yeah. Has this adapted over time, though? I mean, have you found that you've you've had to adopt this measured approach just to scale your efforts? Or is it because it's almost become normalized in your mind because you're thinking about it all the time, and it's become more analytical rather than emotional over time? Certainly, people would if if you'd be constantly reacting like how you first react to things, you might have more of a go look or don't look up type of situation once you, know, goes on the news. So I think it might be I I think that it'd be a a a combination of those. I think just, like, here here's the probabilities, you know, roughly for people conditioned on, like, thinking that you're getting AGI by 02/1930. Here's what people who, you know, think that that's plausible would think that the sort of risks are. Here are the sort of here's your exposure to these tail risks. Here are the most efficient ways of reducing this sort of tail risks, etcetera, etcetera. If if you're, you know, at 11, you know, emotionally throughout the whole thing, you know, people shut down and get defensive. So I don't think that's prudent or effective. As well as, you know, allowing your own emotions to get hijacked by it. I mean, you haven't to deal with a lot of variables here. There are a lot of really tricky trade offs. So if it's just sort of how just a constant gut reaction and you're fully involved constantly, I don't think you can make the trade offs well. Because they directly trade off. Like, versus China competition is a direct trade off to various other safety things. Things that make AIs more controllable now can trade off for can give rise to capabilities. Measuring, the capabilities of AI systems or tracking those can also help speed them up in some ways. So there's there's, it's it's pretty yeah. It's it's pretty tricky business. And so if there's a sort of black if there's black white emotions brought to, the subject matter, as opposed to it being continuity, I I I don't think 1 can reason through this. AI alignment is famously difficult. It's 1 of the most intractable challenges perhaps of of a generation. And, some some things that people think of as as alignment like RLHF, for example, I'm I'm sure you would agree with the statement that it it's something that makes models behave as if they are aligned, but perhaps it's it's not really aligning them in in the way that we would want to. And, you know, just emphatically, in the next year, I mean, if you could solve a single problem in alignment, what would it be, and what impact would it have?…

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