Do you think there’s something to be said about an LLM feedback vortex? As in, teacher’s using ai’s to check student’s work who also submitted work created by AI. Or, judges in law using AI’s to filter through counsel’s arguments which were also written by AI?
I feel like your recommendations could be paired nicely with some in-house training videos, and external regulations that limit the degree / percentage involvement of AI’s. Some kind of threshold or ‘person limit’ like elevators have. How could we measure the ‘presence’ of LLM’s across the board in any given scenario?
So the issue you are describing is that LLM generated information can have errors and hallucinations, and it gets published various places, and this gets consumed by future models and updates to current models.
So now a hallucination has a source, sometimes a credible one, such as a publication to a journal, a wiki, or just some graduate student at a good school who’s homework is online.
The fix for this is ultimately to whitelist reliable information and somehow prevent learning or clean out false information.
Reliable information:
Pre December 22 publications by credible authors
Raw data collected by robots or real camera sensors from the physical world.
Raw data published by reliable sources
I am tempted to say “record the hashes to the blockchain” so you can trace when it was generated, and sign the hash with a private key that the camera sensor manufacturer or robot manufacturer attests is real. (But like every blockchain application a central database held by a third party might be better)
And yes ideally you ingest none of the trash, ultimately you shouldn’t trust anything but real empirical information or models validated against reality.
I am not sure this is a reasonable ask for government regulations? Perhaps NIST could determine that AI systems that didn’t use reliable data for their sources can’t advertise or present themselves to users as anything but unreliable toys? But why make it illegal? Why isn’t Wikipedia illegal, it’s unreliable.(yet in practice excellent).
Do you think there’s something to be said about an LLM feedback vortex? As in, teacher’s using ai’s to check student’s work who also submitted work created by AI. Or, judges in law using AI’s to filter through counsel’s arguments which were also written by AI?
I feel like your recommendations could be paired nicely with some in-house training videos, and external regulations that limit the degree / percentage involvement of AI’s. Some kind of threshold or ‘person limit’ like elevators have. How could we measure the ‘presence’ of LLM’s across the board in any given scenario?
So the issue you are describing is that LLM generated information can have errors and hallucinations, and it gets published various places, and this gets consumed by future models and updates to current models. So now a hallucination has a source, sometimes a credible one, such as a publication to a journal, a wiki, or just some graduate student at a good school who’s homework is online.
The fix for this is ultimately to whitelist reliable information and somehow prevent learning or clean out false information.
Reliable information:
Pre December 22 publications by credible authors
Raw data collected by robots or real camera sensors from the physical world.
Raw data published by reliable sources
I am tempted to say “record the hashes to the blockchain” so you can trace when it was generated, and sign the hash with a private key that the camera sensor manufacturer or robot manufacturer attests is real. (But like every blockchain application a central database held by a third party might be better)
And yes ideally you ingest none of the trash, ultimately you shouldn’t trust anything but real empirical information or models validated against reality.
I am not sure this is a reasonable ask for government regulations? Perhaps NIST could determine that AI systems that didn’t use reliable data for their sources can’t advertise or present themselves to users as anything but unreliable toys? But why make it illegal? Why isn’t Wikipedia illegal, it’s unreliable.(yet in practice excellent).