In a general discussion of ethics your replies are very sensible. When discussing AI safety, and, in particular P(doom), they are not. Your analogy does not work. It is effectively saying trying to prevent AI from killing us all by blocking its access to the internet with a password is better than not using a password, but an AI that is a threat to us will not be stopped by a password and neither will it be stopped by an imperfect heuristic. If we don't have 100% certainty, we should not build it.
You are arguing that it is tractable to have predictable positive long term effects using something that is known to be imperfect (heuristic ethics). For that to make sense you would have to justify why small imperfections cannot possibly grow into large problems. It's like saying that because you believe that you only have a small flaw in your computer security nobody could ever break in and steal all of your data. This wouldn't be true even if you knew what the flaw was and, with heuristic ethics, you don't even know that.
This is totally misguided. If heuristics worked 100% of the time they wouldn't be rules of thumb, they'd be rules of nature. We only have to be wrong once for AI to kill us.
I invest in US assets myself but not because of any faith in the US, in fact the opposite - Firstly it's like a fund manager investing into a known bubble - You know it's going to burst but, if it doesn't burst in the next year or so you cannot afford the short/medium term loss relative to your competitors and, secondly, If the US crashes it takes down the rest of the world with it and is probably the first to recover so you might as well stick with it. None of this translates to faith in US, AI, governance. Your mention of positive-sum deals is particularly strange since, if the world has learned one thing about Trump, it is that he sees the world, almost exclusively, in zero sum terms.
Stating the obvious here but Trump has ensured that the USG cannot credibly guarantee anything at all and hence this is a non-starter for foreign governments.
I think it does. Certainly the way that I would do it would be to create a world map from memory, then overlay the coordinate grid, then just answer by looking it up. You answers will be as good as your map is. I believe that the LLMs most likely work from wikipedia articles - There are a lot of location pages with coordinates in wikipedia
Humans would draw a map of the world from memory, overlay the grid and look up the reference. I doubt that the LLMs do this. It would be interesting to see whether they can actually relate the images to the coordinates - I suspect not i.e. I expect that they could draw a good map, with gridlines from training data but would be unable to relate the visual to the question. I expect that they are working from coordinates in wikipedia articles and the CIA website. Another suggestion would be to ask the LLM to draw a map of the world with non-standard grid lines e.g. every 7 degrees
This is interesting but, in some ways, it should have been obvious - Everything we say, says something about who we are and what we say is influenced by what we know in ways that we are not conscious of. Magicians use subconscious forcing all the time along the lines of "Think of a number between 1 and 10"
I saw an, apparently relevant, video about AI generated music that claimed to be able to detect it by splitting it into its constituent tracks - It turns out that the tools for doing this (which use AI) work well with human music that was actually created from mixing individual tracks but badly for AI generated music (when you listen to the individual tracks they are obviously "wrong"). This is clearly because the AI does not (currently) create music by building it up from individual tracks (although clearly it could be made to do this). Instead it somehow synthesises the whole thing at once - It appears that AI images are similar in that they are not built up from individual components, like fingers. This does suggest that a way to better identify AI images is to have s/w identify the location of the skeletal joints in an image and check whether they can be mapped onto a model of an actual skeleton without distortion.