Another issue is the sheer
Posted: Sun Dec 22, 2024 10:19 am
I’m pushing the engine pretty hard to prove a point here, and modern machine-generated text is much less prone to hallucination than previous iterations. That said, any time you combine multiple sources without regard to their veracity or completeness, there’s a real risk that the end result will be plausible-sounding nonsense. Scale and the real-time internet This one’s pretty straightforward: What works at beta scale may not work at Google scale. As the late Bill Slawski would point out, just because Google has an idea — or even patents an idea — doesn’t mean that they implement that idea in search (for many reasons).
speed of the internet. ChatGPT is trained on mobile phone number database a static corpus — a moment in time. Google crawls and indexes the internet very quickly and can return information that is recent, localized, and even personalized. It’s worth noting that Google has invested massive amounts of money into machine learning. Google’s LaMDA (Language Model for Dialogue Applications) is capable of generating complex, human-like text. Google is well aware of the limitations and costs of these models.
If they’ve moved slowly in deploying them across search, there are probably good reasons. While the topic of bias is far beyond the scope of this article, scale also contributes to bias issues. Once you move on from a static, controlled corpus and open up machine learning models to the entire world of real-time content, human bias creeps in quickly (including racism, sexism, homophobia, and other destructive biases). At Google scale, reducing bias is a problem that requires a lot of human intervention and resources.
speed of the internet. ChatGPT is trained on mobile phone number database a static corpus — a moment in time. Google crawls and indexes the internet very quickly and can return information that is recent, localized, and even personalized. It’s worth noting that Google has invested massive amounts of money into machine learning. Google’s LaMDA (Language Model for Dialogue Applications) is capable of generating complex, human-like text. Google is well aware of the limitations and costs of these models.
If they’ve moved slowly in deploying them across search, there are probably good reasons. While the topic of bias is far beyond the scope of this article, scale also contributes to bias issues. Once you move on from a static, controlled corpus and open up machine learning models to the entire world of real-time content, human bias creeps in quickly (including racism, sexism, homophobia, and other destructive biases). At Google scale, reducing bias is a problem that requires a lot of human intervention and resources.