Row 64416

Row ID: 64416 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 64416 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Part 1. *Disclaimer: I do not work at OpenAI the information on specifics of GPT-4 is unverified and may be inaccurate since GPT-4 is very strictly closed source, and all information regarding its exact design is little better than highly educated speculation*. You are not wrong, but you are also VERY wrong.

GPT-4 is a neural network composed of about 8 other neural networks with about 220,000,000,000 parameters each. Saying that modern conversational LLMs are "just putting word after word" is like saying human brains are "just sending signals through the nervous system", Yes, in some sense that might be true, but it's a very misleading thing to say.

And saying that "the math is not even that complicated" is hilarious, since you could argue that neural networks are one of the most complicated mathematical creations humans have ever produced.

See, a neural network in its simplest form is just a very complicated algorithmically randomized calculation. The basic premise is that we create a machine where depending on X(weights biases and all internal parameters that are algorithmically controlled) the input can in theory produce the output we want. A super simple example of this is we could say we want the Input 0.2 to result in the output 0.9 so we say `Input * X = output` then we use an algorithm to semi-randomly change the value of X until `Input * X = 0.9` and therefore X = 4.5 Now, my example was a single number input and a single number output, and X was also just a single number that we could simply multiply the input by. In a real LLM, the input is going to need to be capable of taking in every possible combination of every possible character you can type into the input field, including emojis and characters from every language including languages like Chinese which contains 8,105 characters, not to mention being able to take image inputs where every single pixel is processed.

The output, similarly to the input, has to be capable of outputting any possible combination of every possible character. Now X in this case, is going to need to be sufficiently complex to be able to make any input result in any output. This is the really hard part... the problem is that it is easy enough to say if the input is a = 1.8 b = 0.1 then the output should = 1.5 however, it is a lot harder to say if the input is a = 1.8 b = 0.1 or a = 0.6 b = 0.1 or a = 0.1 b = 1.7 etc, the output should still always = 1.5 but as soon as the input is a = 1.7 b = 0.1 instead of a = 1.8 b = 0.1 then the answer should = 0.5 this is a minuscule problem compared to what even very simple neural networks are built to solve.

FieldValue
text Part 1. *Disclaimer: I do not work at OpenAI the information on specifics of GPT-4 is unverified and may be inaccurate since GPT-4 is very strictly closed source, and all information regarding its exact design is little better than highly educated speculation*. You are not wrong, but you are also VERY wrong. GPT-4 is a neural network composed of about 8 other neural networks with about 220,000,000,000 parameters each. Saying that modern conversational LLMs are "just putting word afte…
label r/chatgpt
dataType comment
communityName r/ChatGPT
datetime 2024-05-23
username_encoded Z0FBQUFBQm5Lak1iU0liTDBMc2F5Mjd0WG1pWUs5bVF5cmxCUWl1cG1BYjhsaHVOUjRHT3BZa3JEenR4WTJISXU2VUpOUTBramNxN004cG5iV25jQU1CMXVSc196TnhGTkE9PQ==
url_encoded Z0FBQUFBQm5Lak9ycUs5M2RBME15al85eFY2Vmg3RU1waGt5NUQzenFCWC1HZGVPR1JqU0Z0ajdNUno5aHpNcTVHWERseld1dVlsZk9BUTdTZmxVb0FTN2ZiOUNpcEtPZndCT2RBRF92cGl3b2ZlVG9tNEpwblkwRHVVUmxmVFlGQ2JNWXdRSFVZdnpVallhbGFKRENJc29OajNiYllQN3Vpdy1sMWZ5WW1VNk1qa1o1QzltSnNaRU9iQng4UUwtbEFfX2pudDNuRmNy

Raw Record

{
  "text": "Part 1.  \n  \n*Disclaimer: I do not work at OpenAI the information on specifics of GPT-4 is unverified and may be inaccurate since GPT-4 is very strictly closed source, and all information regarding its exact design is little better than highly educated speculation*.  \n  \nYou are not wrong, but you are also VERY wrong. \n\nGPT-4 is a neural network composed of about 8 other neural networks with about 220,000,000,000 parameters each. Saying that modern conversational LLMs are \"just putting word after word\" is like saying human brains are \"just sending signals through the nervous system\", Yes, in some sense that might be true, but it's a very misleading thing to say.\n\nAnd saying that \"the math is not even that complicated\" is hilarious, since you could argue that neural networks are one of the most complicated mathematical creations humans have ever produced. \n\nSee, a neural network in its simplest form is just a very complicated algorithmically randomized calculation.  \nThe basic premise is that we create a machine where depending on X(weights biases and all internal parameters that are algorithmically controlled)  \nthe input can in theory produce the output we want.  \n  \nA super simple example of this is we could say we want the Input 0.2 to result in the output 0.9  \nso we say `Input * X = output` then we use an algorithm to semi-randomly change the value of X until `Input * X = 0.9` and therefore X = 4.5   \nNow, my example was a single number input and a single number output, and X was also just a single number that we could simply multiply the input by.  \n  \nIn a real LLM, the input is going to need to be capable of taking in every possible combination of every possible character you can type into the input field, including emojis and characters from every language including languages like Chinese which contains 8,105 characters, not to mention being able to take image inputs where every single pixel is processed.\n\nThe output, similarly to the input, has to be capable of outputting any possible combination of every possible character.  \n  \nNow X in this case, is going to need to be sufficiently complex to be able to make any input result in any output. This is the really hard part...   \nthe problem is that it is easy enough to say if the input is  \na = 1.8 b = 0.1   \nthen the output should = 1.5  \nhowever, it is a lot harder to say if the input is  \na = 1.8 b = 0.1  \nor   \na = 0.6 b = 0.1  \nor   \na = 0.1 b = 1.7  \netc, the output should still always = 1.5 but as soon as the input is   \na = 1.7 b = 0.1  \ninstead of   \na = 1.8 b = 0.1  \nthen the answer should = 0.5  \nthis is a minuscule problem compared to what even very simple neural networks are built to solve.",
  "label": "r/chatgpt",
  "dataType": "comment",
  "communityName": "r/ChatGPT",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1iU0liTDBMc2F5Mjd0WG1pWUs5bVF5cmxCUWl1cG1BYjhsaHVOUjRHT3BZa3JEenR4WTJISXU2VUpOUTBramNxN004cG5iV25jQU1CMXVSc196TnhGTkE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9ycUs5M2RBME15al85eFY2Vmg3RU1waGt5NUQzenFCWC1HZGVPR1JqU0Z0ajdNUno5aHpNcTVHWERseld1dVlsZk9BUTdTZmxVb0FTN2ZiOUNpcEtPZndCT2RBRF92cGl3b2ZlVG9tNEpwblkwRHVVUmxmVFlGQ2JNWXdRSFVZdnpVallhbGFKRENJc29OajNiYllQN3Vpdy1sMWZ5WW1VNk1qa1o1QzltSnNaRU9iQng4UUwtbEFfX2pudDNuRmNy"
}
Explore Dataset Explore Row

Entry Information