What if we simply provide a magnetic field detector, aka compass, as an input device to our AI?
If that seems insufficient, how far before simulating full physics of a bird’s body as-is would be sufficient? (It also seems that such simulation is completely outside of scope for AI, because it has nothing to do with intelligence per se).
Adding a compass is unlikely to also make the bird disoriented when exposed to a weak magnetic field which oscillates at the right frequency. Which means that the emulated bird will not behave like the real bird in this scenario.
You could add this phenomenon in by hand. Attach some detector to your compass and have it turn off the compass when these fields are measured.
More generally, adding in these features ad hoc will likely work for the things that you know about ahead of time, but is very unlikely to work like the bird outside of its training distribution. If you have a model of the bird that includes the relevant physics for this phenomenon, it is much more likely to work outside of its training distribution.
What if we simply provide a magnetic field detector, aka compass, as an input device to our AI?
If that seems insufficient, how far before simulating full physics of a bird’s body as-is would be sufficient? (It also seems that such simulation is completely outside of scope for AI, because it has nothing to do with intelligence per se).
Adding a compass is unlikely to also make the bird disoriented when exposed to a weak magnetic field which oscillates at the right frequency. Which means that the emulated bird will not behave like the real bird in this scenario.
You could add this phenomenon in by hand. Attach some detector to your compass and have it turn off the compass when these fields are measured.
More generally, adding in these features ad hoc will likely work for the things that you know about ahead of time, but is very unlikely to work like the bird outside of its training distribution. If you have a model of the bird that includes the relevant physics for this phenomenon, it is much more likely to work outside of its training distribution.