| These examples are terrific, but the framing is ridiculous. - GPT-3 answers can be incorrect, and don't carry enough context with them for the reader to engage critically. - Text is often an inefficient presentation of an answer and Google's knowledge card results can do more and more (while adopting the risk above). - LLM's are a ways from being scalable at this quality to a fraction of the throughput of Google queries. - Search increasingly benefits from user-specific context, which is even harder to integrate at a reasonable expense into queries at massive throughput. - Google is also regularly putting forward LLM breakthroughs, which will of course impact productized search. As an NLP practitioner who depends on LLMs, I'm excited as anyone about this progress. But I think some folks are jumping to a conclusion that generative AIs will be the standalone products, when I think they'll be much more powerful as integrated into structured product flows. |
It is also capable of far more than relaying information, as such it is also serving the purpose of Q/A sites like Stack Overflow. You can put wrong code into it and ask for bug fixes and it will return often exactly the correct fix.
Framed as a search engine it obviously fails on some measure, framed as a research assistant it exceeds Google by leaps and bounds (which suffers greatly from adversarial SEO gumming up its results).