Not who you asked but you don’t want your AI to train itself based on the questions random users ask because it could introduce incorrect or offensive information. For this reason llms are usually trained and used in a separate step. If a user gave the llms private information you wouldn’t want it to learn that information and pass it on to other users so there are protections in place usually to stop it from learning new things while just processing requests.
These companies absolutely collect the prompt data and user session behavior. Who knows what kinda analytics they can use it for at any time in the future, even if it’s just assessing how happy the user was with the answers based on response. But having it detached from your person is good. Unless they can identify you based on metrics like time of day, speech patterns, etc
Prompt data is pointless and useless without a human to create a feedback loop for it, at which point it wouldn’t have context anyway. Also human effort to correct spelling dnd other user errors at the outset anyway. Hugely pointless and unreliable.
Not to mention, what good would it do for training? It wouldn’t help the model at all.
You can collect the data and figure out how to use it later. Just look at the Google leaks lately and what they collect, it’s literally everything down to the length of clicks and full walks through the site
Collecting data about user interests is in itself valuable, and it’s plausible to use various metrics to analyze it, something as simple as sentiment analysis, which has been broadly done. Sentiment analysis has predated modern ML by a long margin, but you can read the wiki page on that
But yeah just think about stuff like Google trends, tracking interest in topics, as an example of what such data could be used for. And deanonymizing the inputs is probably possible to some degree, aside from the obvious trust we place in DDG as a centralized failure point
You’re confusing analytics with direct input storage and reuse of prompt data to train somehow, as in your original comment.
Analytics has absolutely nothing to do with their model usage and training, and would pointless. Observing keywords and interests is standard analysis stuff. I don’t even think anyone even cares about it anymore.
Not how that works.
I’m curious, how does it work?
Not who you asked but you don’t want your AI to train itself based on the questions random users ask because it could introduce incorrect or offensive information. For this reason llms are usually trained and used in a separate step. If a user gave the llms private information you wouldn’t want it to learn that information and pass it on to other users so there are protections in place usually to stop it from learning new things while just processing requests.
These companies absolutely collect the prompt data and user session behavior. Who knows what kinda analytics they can use it for at any time in the future, even if it’s just assessing how happy the user was with the answers based on response. But having it detached from your person is good. Unless they can identify you based on metrics like time of day, speech patterns, etc
Prompt data is pointless and useless without a human to create a feedback loop for it, at which point it wouldn’t have context anyway. Also human effort to correct spelling dnd other user errors at the outset anyway. Hugely pointless and unreliable.
Not to mention, what good would it do for training? It wouldn’t help the model at all.
You can collect the data and figure out how to use it later. Just look at the Google leaks lately and what they collect, it’s literally everything down to the length of clicks and full walks through the site
Collecting data about user interests is in itself valuable, and it’s plausible to use various metrics to analyze it, something as simple as sentiment analysis, which has been broadly done. Sentiment analysis has predated modern ML by a long margin, but you can read the wiki page on that
But yeah just think about stuff like Google trends, tracking interest in topics, as an example of what such data could be used for. And deanonymizing the inputs is probably possible to some degree, aside from the obvious trust we place in DDG as a centralized failure point
You’re confusing analytics with direct input storage and reuse of prompt data to train somehow, as in your original comment.
Analytics has absolutely nothing to do with their model usage and training, and would pointless. Observing keywords and interests is standard analysis stuff. I don’t even think anyone even cares about it anymore.