I encourage you to ponder these images in detail. Try and think for yourself the most plausible method for a superintelligence to deduce general relativity from one apple image and one grass image.
Stopping here to add some thoughts, since this is actually a topic that has come up before here, in actually a pretty similar form of “here is a large blob of binary data that is not in any standard format, can you tell me what it represents”?
A lot of this stuff is not actually original thought on my part, but instead corresponds to the process I used to figure out what was happening with that binary data blob the last time this came up. Spoiler warning if you want to try your own hand at the challenge from that post: the following links contain spoilers about the challenge.
Link 1 : note that the image labeled “option 2”, and the associated description, should have been the very last thing, but Imgur is doing some dumb reordering thing. Aside from that everything is in the correct order.
Link 2: likewise is rendering in a slightly janky order.
The most plausible method to me would involve noticing that the RGB channels seem to be measuring three different “types” of “the same thing” in some sense—I would be very unsurprised if “there are three channels, the first one seems to have a peak activation at 1.35x the [something] of the third, and the second one seems to have a peak activation at 1.14x the [something] of the third” is a natural conclusion from looking at that picture.
From there I actually don’t have super strong intuitions about whether “build a 3-d model with a fairly accurate estimation of the shape of the frequency x intensity curve that results in the sensor readings at each pixel” is viable. If it is not viable, I think it mostly just ends there.
If it is viable, I think the next step depends on the nature of the light source.
If the light source is a black-body source (e.g. an incandescent bulb or the sun), I think the black-body spectrum is simple enough that it becomes an obvious hypothesis. In that case, the next question is whether the camera is good enough to detect things like the absorption spectrum of Hydrogen or Tungsten (for the sun or an incandescent bulb respectively), and, if so, whether the intelligence comes up with that hypothesis.
If the light source is a fluorescent light, there’s probably some physics stuff you can figure out from that but I don’t actually know enough physics to have any good hypotheses about what that physics stuff is.
The water droplets on the apple also make me expect that there may be some interesting diffraction or refraction or other interesting optical phenomena going on. But the water droplets aren’t actually that many pixels, so there may just flat out not be enough information there.
The “blades of grass” image might tell you interesting stuff about how light works but I expect the “apple with drops of water” image to be a lot more useful probably.
Anyway, end braindump, time to read the rest of the post.
Remember those pictures from earlier? Well I confess, I pulled a little trick on the reader here. I know for a fact that it is impossible to derive general relativity from those two pictures, because neither of them are real. The apple is from this CGI video, the grass is from this blender project. In neither case are Newtonian gravity or general relativity included in the relevant codes.
And if you are in a simulation, Ockham’s razor becomes significantly stronger. If your simulation is on the scale of a falling apple, programming general relativity into it is a ridiculous waste of time and energy. The simulators will only input the simplest laws of physics necessary for their purposes.
I do agree that the AI would probably not deduce GR from these images, since I don’t think there will be anything like absorption spectra or diffraction patterns accidentally encoded into this image, as there might be in a real image.
I don’t think “That Alien Message” meant to say anything like “an alien superintelligence could deduce information that was not encoded in any form in the message it received”. I think it mainly just meant to say “an entity with a bunch more available compute than a human, the ability to use that compute efficiently could extract a lot more information out of a message if it spent a lot of time on analysis than a human glancing at that image would extract”.
I do agree that the AI would probably not deduce GR from these images, since I don’t think there will be anything like absorption spectra or diffraction patterns accidentally encoded into this image, as there might be in a real image.
Real images are taken by real cameras, and usually it takes a whole lot more than a commercial smartphone or even a good DSLR to capture those things. They’re not optimized for it, you won’t just notice any spectral lines or diffraction patterns over the noise (diffraction patterns I guess you could if you set up a specific situation for them. Not accidentally, though).
Stopping here to add some thoughts, since this is actually a topic that has come up before here, in actually a pretty similar form of “here is a large blob of binary data that is not in any standard format, can you tell me what it represents”?
A lot of this stuff is not actually original thought on my part, but instead corresponds to the process I used to figure out what was happening with that binary data blob the last time this came up. Spoiler warning if you want to try your own hand at the challenge from that post: the following links contain spoilers about the challenge.
Link 1 : note that the image labeled “option 2”, and the associated description, should have been the very last thing, but Imgur is doing some dumb reordering thing. Aside from that everything is in the correct order.
Link 2: likewise is rendering in a slightly janky order.
The most plausible method to me would involve noticing that the RGB channels seem to be measuring three different “types” of “the same thing” in some sense—I would be very unsurprised if “there are three channels, the first one seems to have a peak activation at 1.35x the [something] of the third, and the second one seems to have a peak activation at 1.14x the [something] of the third” is a natural conclusion from looking at that picture.
From there I actually don’t have super strong intuitions about whether “build a 3-d model with a fairly accurate estimation of the shape of the frequency x intensity curve that results in the sensor readings at each pixel” is viable. If it is not viable, I think it mostly just ends there.
If it is viable, I think the next step depends on the nature of the light source.
If the light source is a black-body source (e.g. an incandescent bulb or the sun), I think the black-body spectrum is simple enough that it becomes an obvious hypothesis. In that case, the next question is whether the camera is good enough to detect things like the absorption spectrum of Hydrogen or Tungsten (for the sun or an incandescent bulb respectively), and, if so, whether the intelligence comes up with that hypothesis.
If the light source is a fluorescent light, there’s probably some physics stuff you can figure out from that but I don’t actually know enough physics to have any good hypotheses about what that physics stuff is.
The water droplets on the apple also make me expect that there may be some interesting diffraction or refraction or other interesting optical phenomena going on. But the water droplets aren’t actually that many pixels, so there may just flat out not be enough information there.
The “blades of grass” image might tell you interesting stuff about how light works but I expect the “apple with drops of water” image to be a lot more useful probably.
Anyway, end braindump, time to read the rest of the post.
Response on reading the rest of the post: “lol”.
I do agree that the AI would probably not deduce GR from these images, since I don’t think there will be anything like absorption spectra or diffraction patterns accidentally encoded into this image, as there might be in a real image.
I don’t think “That Alien Message” meant to say anything like “an alien superintelligence could deduce information that was not encoded in any form in the message it received”. I think it mainly just meant to say “an entity with a bunch more available compute than a human, the ability to use that compute efficiently could extract a lot more information out of a message if it spent a lot of time on analysis than a human glancing at that image would extract”.
Real images are taken by real cameras, and usually it takes a whole lot more than a commercial smartphone or even a good DSLR to capture those things. They’re not optimized for it, you won’t just notice any spectral lines or diffraction patterns over the noise (diffraction patterns I guess you could if you set up a specific situation for them. Not accidentally, though).