theres serious overlap. they are not mutually exclusive. the ‘text generator’ is utilizing the search prompt to identify the most likely “next word” which would translate to most likely the best result for the search.
theoretically, its just a better search engine being able to handle an obscene number of variables. theoretically.
It’s more like:
Traditional search pipes first page of results to the bot. The bot reads the pages from the results and tries to identify an answer or the best result from the set. Both the bot summary and the adjusted ranking for the results are returned. This gives a chance at a better experience for the user because they don’t have to read all the pages themselves to try find the answer they were looking for. However there is a huge margin for error since the bot is underpowered due to Google balancing the amount they pay for each search with the amount they earn for each search. So there end up being misinterpretations, hallucinations, biased content etc.
If they used a top end model like Claude Sonnet 3.7 and piped it enough contextual information, the AI summaries would be quite accurate and useful. They just can’t afford to do that and they want to use their own Gemini bs.
Perhaps true, but with the nature of the errors involved (generating anything instead of error messages for lacking info) and requisite reviewing, which itself demands research (which was what it was being used to shortcut to begin with in this context), isn’t it still something of an ill fit for this?
They know it’s bad. They want you locked in to their ecosystem. The goal is to be the first to get consumers locked in. So they’re rushing to market with incomplete products because if they don’t release NOW someone else might beat them to it.
theres serious overlap. they are not mutually exclusive. the ‘text generator’ is utilizing the search prompt to identify the most likely “next word” which would translate to most likely the best result for the search.
theoretically, its just a better search engine being able to handle an obscene number of variables. theoretically.
It’s more like: Traditional search pipes first page of results to the bot. The bot reads the pages from the results and tries to identify an answer or the best result from the set. Both the bot summary and the adjusted ranking for the results are returned. This gives a chance at a better experience for the user because they don’t have to read all the pages themselves to try find the answer they were looking for. However there is a huge margin for error since the bot is underpowered due to Google balancing the amount they pay for each search with the amount they earn for each search. So there end up being misinterpretations, hallucinations, biased content etc.
If they used a top end model like Claude Sonnet 3.7 and piped it enough contextual information, the AI summaries would be quite accurate and useful. They just can’t afford to do that and they want to use their own Gemini bs.
Perhaps true, but with the nature of the errors involved (generating anything instead of error messages for lacking info) and requisite reviewing, which itself demands research (which was what it was being used to shortcut to begin with in this context), isn’t it still something of an ill fit for this?
They know it’s bad. They want you locked in to their ecosystem. The goal is to be the first to get consumers locked in. So they’re rushing to market with incomplete products because if they don’t release NOW someone else might beat them to it.