I don’t think the Chinese room is a good analogy for this. The Chinese room has a conscious person at the center. A better analogy might be a book with a phrase-to-number conversion table, a couple number-to-number conversion tables, and finally a number-to-word conversion table. That would probably capture transformer’s rigid and unthinking associations better.
No, you’re thinking of the first scene of the movie where a fly falls into the teletype machine and causes it to type ‘tuttle’ instead of ‘buttle’.
I’m pretty sure fused add multiply with store is part of the AVX instruction set.
I always find it very funny when someone suggests anarcho-something as a solution to all of capitalism’s problems. How exactly do you plan to enforce that? Do you think social pressure & shunning will do anything more than create a class of extremists with an oppositional philosophy?
This feels a lot like ‘Come to Brazil!’
Recursion makes it cheaper to run in the dev’s mind, but more expensive to run on the computer. Subroutines are always slower than a simple jump.
Hand written assembly is much more powerful than a turing-complete high level language because it lets you fuck up everything. Rust and python are way too wimpy to allow a user to destroy their computer.
What do you mean by offensive?
So you made a meme about how your opponent is completely irrational and you are a paragon of logic and reason, and then proceeded to declare yourself the winner?
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.
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.
If C++/C were real languages for real programming they’d enforce unreadability in the compiler.
No sane language designer would say “It is imperative that you write the most unreadable code possible” then write a compiler that says “oh your code doesn’t triple dereference pointers? lol lmao that rocks”
They have played you all for fools.
Rust is the WORST programming “language.”
iirc, this board isn’t nearly as interesting as it seems. You can’t actually do any multiprocessing, because only one CPU can be activated at a time. So you can’t actually leverage the two CPUs to do anything that each CPU couldn’t do on it’s own.
Recently, I’ve just given up trying to use cuda for machine learning. Instead, I’ve been using (relatively) cpu intensive activation functions & architecture to make up the difference. It hasn’t worked, but I can at least consistently inch forward.
I’m not sure I understand your argument. Are you saying that the emulated processor executes instructions while the SoC doesn’t? Every instruction that goes to the x86 is broken down into several SoC instructions, which the SoC executes in order to emulate what an x86 would do. Saying that the emulated x86 is booting/running Linux, but the SoC is not is like saying that computers can’t run java code, they can only run jvm.
You can’t choose where you grow up. :(