you don't trust the results of one promt, but you trust multiple piped one into another?
This is really not at all unusual. Take aircraft for instance. One system is not reliable, for a multitude of reasons. A faulty sensor could be misleading, a few bits could get flipped by cosmic rays causing ECC to fail, the system itself could be poorly calibrated, there are far too many unacceptable risks. But add TMR[0][1] and suddenly you are able to trust things a lot more. This isn't to say that TMR is bullet proof e.g. incidents like [2], but redundancy does make it possible to increase trust in a system, and assign blame to what part of a system is faulty (e.g. if 3 systems exist, and 1 appears to be disagreeing wildly with 2 and 3, you know to start investigating system 1 first).
Would it work here? I don't know! But it doesn't seem like an inherently terrible or flawed idea if we look at past applications. Ensembling different models is a pretty common technique to get better results in ML, and maybe this approach would make it easier to find weak links and assign blame.
This isn't to say that TMR is bullet proof e.g. incidents like [2], but redundancy does make it possible to increase trust in a system, and assign blame to what part of a system is faulty (e.g. if 3 systems exist, and 1 appears to be disagreeing wildly with 2 and 3, you know to start investigating system 1 first).
You can only gain trust in this system if you understand the error sources for all three systems. If there’s any common mode errors then you can see errors showing up in multiple systems simultaneously. For example, if your aircraft is using pitot tubes [1] to measure airspeed then you need to worry about multiple tubes icing up at the same time (which is likely since they’re in the same environment).
So it would not add very much trust to implement TMR with three different pitot tubes. It would be better to combine the pitot tubes with completely different systems, such as radar and GPS, to handle the (likely) scenario of two or more pitot tubes icing up and failing completely.
Comments
This is really not at all unusual. Take aircraft for instance. One system is not reliable, for a multitude of reasons. A faulty sensor could be misleading, a few bits could get flipped by cosmic rays causing ECC to fail, the system itself could be poorly calibrated, there are far too many unacceptable risks. But add TMR[0][1] and suddenly you are able to trust things a lot more. This isn't to say that TMR is bullet proof e.g. incidents like [2], but redundancy does make it possible to increase trust in a system, and assign blame to what part of a system is faulty (e.g. if 3 systems exist, and 1 appears to be disagreeing wildly with 2 and 3, you know to start investigating system 1 first).
Would it work here? I don't know! But it doesn't seem like an inherently terrible or flawed idea if we look at past applications. Ensembling different models is a pretty common technique to get better results in ML, and maybe this approach would make it easier to find weak links and assign blame.
[0]: https://en.wikipedia.org/wiki/Triple_modular_redundancy
[1]: https://en.wikipedia.org/wiki/Air_data_inertial_reference_un...
[2]: https://www.atsb.gov.au/media/news-items/2022/pitot-probe-co... causing total confusion among the TMR
This isn't to say that TMR is bullet proof e.g. incidents like [2], but redundancy does make it possible to increase trust in a system, and assign blame to what part of a system is faulty (e.g. if 3 systems exist, and 1 appears to be disagreeing wildly with 2 and 3, you know to start investigating system 1 first).
You can only gain trust in this system if you understand the error sources for all three systems. If there’s any common mode errors then you can see errors showing up in multiple systems simultaneously. For example, if your aircraft is using pitot tubes [1] to measure airspeed then you need to worry about multiple tubes icing up at the same time (which is likely since they’re in the same environment).
So it would not add very much trust to implement TMR with three different pitot tubes. It would be better to combine the pitot tubes with completely different systems, such as radar and GPS, to handle the (likely) scenario of two or more pitot tubes icing up and failing completely.
[1] https://en.wikipedia.org/wiki/Pitot_tube?wprov=sfti1