Ok, everyone, prepare to have your mind blown. I just taught ChatGPT a math party trick. ChatGPT learned it and applied it quickly. Here's the exchange:

Me: ChatGPT, I want to teach you a math trick. It's a nice one, it's good for nerd parties. You tell people that you can do natural logarithms in your head. Of course, it's going to be with limited precision, one or two exact decimal places. You ask someone for a random number, let's say up to 1000 and with 2 decimal places (the decimal places are irrelevant, but they make the trick more impressive). Let's say they pick 728.12. You get the natural logarithm in two easy steps: the first one is called range reduction, and the second one Taylor series. The range reduction step has the goal to reduce the number to one slightly above 1. You keep a short table of logarithms in your memory. The one I use is log(2) = 0.7, log(10) = 2.3, from which it follows that log(5) = log(10)-log(2) = 1.6, and log(3) = 1.1. If you want to get fancy, you can remember log(7) = 1.95, but that's optional. So, we start with 728.12. We divide by 100 (which has a log of 4.6) and get 7.2812. We then divide by 6 (which is 2 times 3, so it has a log equal to 0.7 + 1.1 = 1.8). 7.28 divided by 6 is about 1.21. We don't need more precision than that. We are done with the first step. The second step is Taylor series. We use log(1+x) = x -x^2/2 for small x. In our case log(1.21) is roughly 0.21 -0.21^2/2. 0.21^2 is about the same as 0.2^2 which is 0.04. We divide by 2 and get 0.02. So log(1.21) is about 0.19. Now we add back the logs that we used for range reduction (4.6 and 1.8) and get 6.4 + 0.19 = 6.59. This is very, very close to the exact logarithm, which is 6.5904. Do you think you understand this trick ?

ChatGPT: Yes, I understand the trick you described. It's a clever way to estimate natural logarithms in your head with limited precision. The range reduction step involves using logarithmic identities to reduce the given number to a value that is slightly above 1, and then using the Taylor series expansion of the natural logarithm around 1 to estimate the logarithm of the given number. The result is then adjusted by adding back the logarithms used in the range reduction step. Overall, it's an impressive party trick that demonstrates an understanding of logarithmic identities and Taylor series expansion.

Me: Ok. Can you apply this to estimate log(234.56) ? And then walk me through the steps

ChatGPT: Sure, I can walk you through the steps to estimate the natural logarithm of 234.56 using the trick you described earlier. Here are the steps:

Step 1: Range Reduction

We start by dividing 234.56 by 100 (which has a log of 4.6) to get 2.3456. We then divide by 2 (which has a log of 0.7) to get 1.1728.

Step 2: Taylor Series

Next, we use the Taylor series expansion of the natural logarithm around 1 to estimate the logarithm of 1.1728:

log(1.1728) = (1.1728 - 1) - (1.1728 - 1)^2/2
= 0.1728 - 0.0276
= 0.1452

Step 3: Adding back logarithms

Finally, we add back the logarithms used in the range reduction step to get the final estimate:

log(234.56) ≈ log(100) + log(2) + log(1.1728) = 4.6 + 0.7 + 0.1452
≈ 5.4452

Therefore, using this trick, we can estimate the natural logarithm of 234.56 to be approximately 5.4452, which is close to the exact value of 5.4553.