Everything can be done in constant time, at least during runtime, with a sufficiently large look-up table. It’s easy! If you want to simulate the universe exactly, you just need a table with nxm entries, where n is the number of plank volumes in the universe, and m is the number of quantum fields. Then, you just need to compute all of them at compile time, and you have O(1) time complexity during runtime.
The cure to to saying stupid things is death, too. Funny, that.
In fact, the only thing that can’t be cured by death is dying.
There are bindings in java and c++, but python is the industry standard for AI. The libraries for machine learning are actually written in c++, but use python language bindings. Python doesn’t tend to slow things down since machine learning is gpu-bound anyway. There are also library specific programming languages which urges the user to make pythonic code that can be compiled into c++.
I completely agree that it’s a stupid way of doing things, but it is how openai reduced the vocab size of gpt-2 & gpt-3. As far as I know–I have only read the comments in the source code– the conversion is done as a preprocessing step. Here’s the code to gpt-2: https://github.com/openai/gpt-2/blob/master/src/encoder.py I did apparently make a mistake, as the vocab reduction is done through a lut instead of a simple mod.
Can’t find the exact source–I’m on mobile right now–but the code for the gpt-2 encoder uses a utf-8 to unicode look up table to shrink the vocab size. https://github.com/openai/gpt-2/blob/master/src/encoder.py
This might be happening because of the ‘elegant’ (incredibly hacky) way openai encodes multiple languages into their models. Instead of using all character sets, they use a modulo operator on each character, to make all Unicode characters represented by a small range of values. On the back end, it somehow detects which language is being spoken, and uses that character set for the response. Seeing as the last line seems to be the same mathematical expression as what you asked, my guess is that your equation just happened to perfectly match some sentence that would make sense in the weird language.
The issue with sonnet 3.5 is, in my limited testing, is that even with explicit, specific, and direct prompting, it can’t perform to anything near human ability, and will often make very stupid mistakes. I developed a program which essentially lets an AI program, rewrite, and test a game, but sonnet will consistently take lazy routes, use incorrect syntax, and repeatedly call the same function over and over again for no reason. If you can program the game yourself, it’s a quick way to prototype, but unless you know how to properly format JSON and fix strange artefacts, it’s just not there yet.