Cyborgs are so last year. Become a technopath today!
That's why rather than clicking on any of the actual options I edited the URL to submit for choice=E, but as per the follow-up message it seems to have defaulted to the "resisting social pressure" option. Which... I guess I was doing by trying to choose an option that wasn't present.
The problem with trait selection is always in the second-order effects - for example, kind people are easy to exploit by the less kind, and happy people are not as driven to change things through their dissatisfaction. A population of kind and happy people are not going to tend towards climbing any social ladder, and will rapidly be ousted by less kind and less happy people. The blind idiot god doesn't just control genetic change, but societal change, and we're even worse at controlling or predicting the latter.
Given how fast human progress is going, it won’t be long before we have more efficient moth traps that can respond to adaptation, or before we find a reliable “one fell swoop” solution (like gene drives for mosquitoes, chemotherapy for cancer, or mass vaccination for smallpox).
We do, in fact, already have several foolproof methods of moth elimination, involving setting the ambient temperature to several hundred degrees, entirely evacuating the air from the space, or a small thermonuclear warhead. The reason that we don't use these methods, of course, is that there are things we're trying to optimise for that aren't merely Moth Death, such as "continuing to have a house afterwards". This is probably also an analogy for something.
Typically, a painting isn’t even the same color as a real thing
Then you can start getting into the weeds about "colour as qualia" or "colour as RGB spectral activation" or "colour as exact spectral recreation". But spectral activation in the eyes is also not consistent across a population - which we pathologise if their cones are "too close" as colourblindness, but in practice is slightly different for everyone anyway.And that's not even getting into this mess...
Ah, I think I follow - eliminating contextual data as much as possible can dispel the illusion - e.g. in the below image, if the context around squares A and B were removed, and all you had were those two squares on a plain background, the colour misattribution shouldn't happen. I guess then I'd say the efficacy of the illusion is dependent on how strongly the generalisation has proved true and useful in the environment, and therefore been reinforced. Most people have seen shadows before, so the one below should be pretty general; the arrow illusion is culturally variable as seen here, precisely because if your lifelong visual environment had few straight lines in it you're not likely to make generalisations about them. So, in the ML case, we'd need to... somehow eliminate the ability of the model to deduce context whatsoever, whereupon it's probably not useful. There's a definite sense where if the image below were of the real world, if you simply moved the cylinder, the colours of A and B would obviously be different. And so when an AI is asked "are squares A and B the same colour", the question it needs to answer implicitly is if you're asking them to world-model as an image (giving a yes) or world-model as a projection of a 3D space (giving a no). Ideally such a model would ask you to clarify which question you're asking. I think maybe the ambiguity is in the language around "what we want", and in many cases we can't define that explicitly (which is usually why we are training by example rather than using explicit classification rules in the first place).
There's also Pepper's ghost, where there's a sense in which the "world model altered to allow for the presence of a transparent ethereal entity" is, given the stimulus, probably the best guess that could be made without further information or interrogation. It's a reasonable conclusion, even if it's wrong - and it's those kinds of "reasonable but factually incorrect" errors which is really us-as-human changing the questions we're asking. It's like if we showed a single pixel to an AI, and asked it to classify it as a cat or a dog - it might eventually do slightly better than chance, but an identical stimulus could be given which could have come from either. And so that confusion I think is just around "have we given enough information to eliminate the ambiguity". (This is arguably a similar problem problem to the one discussed here, come to think of it.)
yes - a perfect in-situ example of babble's sibling, prune
Sure - let's say this is more like a poorly-labelled bottle of detergent that the model is ingesting under the impression that it's cordial. A Tide Pod Challenge of unintended behaviours. Was just calling it "poisoning" as shorthand since the end result is the same, it's kind of an accidental poisoning.
The analogous output would probably optical illusions - adversarial inputs to the eyeballs that mislead your brain into incorrect completions and conclusions. Or in the negative case, something that induces an epileptic seizure.
I did do a little research around that community before posting my comment; only later did I realise that I'd actually discovered a distinct failure mode to those in the original post: under some circumstances, ChatGPT interprets the usernames as numbers. In particular this could be due to the /r/counting subreddit being a place where people make many posts incrementing integers. So these username tokens, if encountered in a Reddit-derived dataset, might be being interpreted as numbers themselves, since they'd almost always be contextually surrounded by actual numbers.