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A few days in the past, I used to be desirous about what you might want to know to make use of ChatGPT (or Bing/Sydney, or any comparable service). It is simple to ask questions, however everyone knows that these huge language fashions usually generate false solutions. Which begs the query: if I ask ChatGPT one thing, how a lot do I must know to find out if the reply is right?

So I did a fast experiment. As a brief programming undertaking, a couple of years in the past I made an inventory of all prime numbers lower than 100 million. I used this record to create a 16-digit quantity that was the product of two 8-digit prime numbers (99999787 occasions 99999821 is 9999960800038127). I then requested ChatGPT if this quantity was prime and the way it decided if the quantity was prime.

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ChatGPT appropriately responded that this quantity was not prime. That is sort of shocking as a result of in the event you’ve learn quite a bit about ChatGPT, you realize that math is not considered one of its robust factors. (There’s in all probability an enormous record of primes someplace in your coaching set.) Nevertheless, his reasoning was incorrect, and that’s rather more attention-grabbing. ChatGPT offered me with a bunch of Python code that applied the Miller-Rabin primality take a look at and mentioned that my quantity was divisible by 29. The code as offered had a few fundamental syntax errors, however that wasn’t the one drawback. . First, 9999960800038127 just isn’t divisible by 29 (I am going to allow you to show it to your self). After correcting the plain errors, the Python code seemed like an accurate implementation of Miller-Rabin, however the quantity that Miller-Rabin generates just isn’t an element, it’s a “token” testifying to the truth that the quantity you might be testing for is just isn’t prime The quantity it generates can be not 29. So ChatGPT did not truly run this system; Not surprisingly, many commenters have observed that ChatGPT would not run the code you write. You additionally misunderstood what the algorithm does and what its output means, and that is a much bigger mistake.

I then requested him to rethink the rationale for his earlier reply, and acquired a really well mannered apology for being incorrect, together with a distinct python program. This program was proper from the beginning. It was a brute power primality take a look at that examined each integer (each odd and even!) smaller than the sq. root of the quantity underneath take a look at. Neither elegant nor performative, however right. However once more, as a result of ChatGPT would not truly run this system, it gave me a brand new record of “prime components”, none of which had been right. Curiously, you included your anticipated (and incorrect) output within the code:

n = 9999960800038127
components = factorize(n)
print(components) # prints [193, 518401, 3215031751]

I am not saying that ChatGPT is ineffective, removed from it. He’s good at suggesting methods to unravel an issue and might lead you to the proper resolution, whether or not he provides you an accurate reply or not. Miller-Rabin is attention-grabbing; I knew it existed, however I would not have bothered on the lookout for it if I hadn’t been requested. (That is a pleasant irony: ChatGPT requested me to.)

Going again to the unique query: ChatGPT is sweet at offering “solutions” to questions, but when you might want to know that a solution is right, you want to have the ability to clear up the issue your self or do the required analysis. clear up that drawback. It is in all probability a win, however you need to watch out. Do not put ChatGPT in conditions the place correctness is an issue except you might be prepared and capable of do the onerous work your self.

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Getting the Right Answer from ChatGPT – O’Reilly

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