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> The thing is that it is good at classifying things. If by "classifying things" you mean doing what Wolfram Alpha's natural language front end does--figuring
by pdonis 4y ago
> The thing is that it is good at classifying things.
If by "classifying things" you mean doing what Wolfram Alpha's natural language front end does--figuring out which solution engine to pass the question to, and translating the question into what that solution engine needs--then yes. That's what your examples show. And that concedes my point: that something like GPT-3 might make a good natural language front end for something like Wolfram Alpha's solution engines; but it's not good at actually giving correct answers on its own. It's bad at that, for every type of question.
- shagie 4y agoYep - the things I've found it good at is stuff like "here is data, answer questions about it" (I've got dabblingings in writing a private Alexa skill that does a better weather report than the rules based one that is currently used) or zero shot classifications. For example with the zero shot: https://news.ycombinator.com/item?id=34156626 https://news.ycombinator.com/item?id=34156626 And this is a "not everything goes to Wolfram" - the weather report that it has ( https://www.wolframalpha.com/input?i=chicago+weather https://www.wolframalpha.com/input?i=chicago+weather ) doesn't provide quite the right data (though it is rather neat). The other thing I need to look at is the question are some of the other, simpler models acceptable for these classifications. The Curie model is 10x cheaper than the Davinci model. And Babbage and Ada are even cheaper. The system is interesting... but even with some dabbling one can see that this isn't something to trust for knowledge, but rather work with for translating one "language" into another and then using that in conjunction with another layer that can use that translated "language".