Decoding ChatGPT: Unveiling the Technology Behind the Conversational AI

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0:43Publishers Weekly says, in Baldacci's long list of heroes, Walter Nash is among the most memorable. Experience the full cast audio production of Nash Falls, led by McLeod Andrews. Available from Hachette Audio wherever audiobooks are sold. Also available in hardcover and ebook. In the past, we've gone over very roughly how ChatGPT works. Today, I'm going to do a very technical dive into how ChatGPT works, some of the technicalities behind it. I'm going to go into as much technical detail as possible, and I'll try to still explain this in a way that the average person can understand. So I guess at its core, ChatGPT is based on a type of machine learning called deep learning.

1:24So it's a form of artificial intelligence that uses neural networks to learn patterns and relationships in large amounts of data. In the case of ChatGPT, the neural net has been trained on a vast amount of data, including books, articles, and other written materials. Essentially, a whole bunch of materials that were digitized by either Google, scraped by Microsoft's Bing, or gathered as a bunch of other companies from all over the Internet. So the neural network is used by ChatGPT. well the specific one that is used is called the transformer and this type of neural net is particularly effective at processing sequences of text data like sentences and paragraphs so the transformer which is something that was actually created or a paper was written essentially in 2017 by some researchers at google so you know it's kind of interesting shout out to google they were on top of this way back in 2017 but they I guess did not capitalize on it in the same way but the transformer is made up of multiple layers of nodes each which perform a specific operation on the input data so when a user inputs a question or request into chat chpt the text is first pre-processed by the model so this involves breaking the input down into a bunch of individual words and then converting each word into a numerical representation that can be understood by the neural network right very interesting it's not literally looking at the words and understanding them, it's breaking down the questions or the sentences, assigning different values to each word, which is going to help it to then predict what a response should be.

2:56So once the input has been pre-processed, it's fed into the transformer. So this transformer then uses its layers of nodes to essentially analyze the input and generate a response. The response is generated one word at a time, with each word being determined by the neural network's analysis of the previous words in the sequence. So the process of generating a response involves two key steps. Number one is encoding, and then number two is decoding. So encoding involves taking the input text, right, the question you ask it, and representing it in a way that can be understood by the neural network.

3:33Decoding involves taking the neural network's output and converting it into natural language text that can be understood by the user. So similar to how when you ask it a question, right, it takes your question and it gives every single word in your question a different value that it can understand, it will create a response that is in the similar kind of code, and it has to change that into natural language, which is a part of the, you know, incredible advancements that we're seeing here. So the encoding step is performed using a process called tokenization. So this essentially just involves breaking the input text down into individual words or tokens and converting each token into a numerical representation.

4:14So this numerical representation is then fed into the neural network's input layer. The neural network's layer then processes the input, generating a series of outputs that represents the probability of various words being the next word in the sequence. So what that's saying is essentially when it's coming up with a response to your question or your query that you give ChadCpt, it's coming up with a whole but like right it's like it's pretty much like an auto um it's like a it's like a next word predictor like on google when you're typing a text um or on gmail when you're typing an email and it's saying you know this is probably the next thing you're going to say it has like a handful of words that it thinks could be next and it's saying i'm giving this word a 95 chance to be next this one 80 right and and then whatever one has the highest one it's usually going to be going with that so the decoding step involves um oh and i would i should also say that these probabilities are then used to generate the next word in the sequence with the process repeating until the desired response has been generated, right?

5:13So it just keeps throwing up a handful of words, giving them a probability percentage of which one should come next, picking the highest probability, and then it just keeps spinning through. That's how it's essentially responding to you. And the reason why that's important is because this thing isn't sitting there thinking or computing necessarily as much as it is just predicting what word should come next. So when we look at the decoding step, this actually involves taking the neural network's output and converting it into natural language text, right? So it's created kind of these like tokens, this output, which obviously we wouldn't understand, and it now has to convert that into natural language text.

5:49So this is done by using a process called text generation. Text generation essentially just involves taking the numerical outputs from the neural network and converting it back into words. So the text generation process is guided by a technique called beam search and this involves generating multiple possible responses and selecting the one that is the most likely to be correct based on the neural network's output probabilities right that we were talking about earlier and the selected response is then returned to the user as the model's final output. So overall chat GPT is a very impressive technical achievement as you can see this thing is very sophisticated.

6:28It's helping to push the boundaries of natural language processing. I think we've all seen that to a very large degree. It's quite a sophisticated neural network, an advanced machine learning tool that has these techniques that make it possible to generate natural language text that's almost indistinguishable from text written by humans. And of course, we've talked about a lot of its different strengths and weaknesses in the past, But I think it's important to just note that, you know, some of the key strengths of ChatGPT is its ability to really understand context. And this is because it's been trained on just such a massive amount of data, which really allows it to recognize patterns and relationships in language that would be really difficult or impossible for sometimes even a human to identify.