[0:00] GPT, or Generative Pre-trained Transformer,
[0:03] is a large language model, or an LLM,
[0:08] that can generate human-like text.
[0:10] And I've been using GPT in its various forms for years.
[0:15] In this video we are going to number 1,
[0:18] ask "what is an LLM?"
[0:22] Number 2, we are going to describe how they work.
[0:26] And then number 3,
[0:28] we're going to ask, "what are the business applications of LLMs?"
[0:32] So let's start with number 1, "what is a large language model?"
[0:36] Well, a large language model
[0:40] is an instance of something else called a foundation model.
[0:49] Now foundation models are pre-trained on large amounts of unlabeled and self-supervised data,
[0:55] meaning the model learns from patterns in the data in a way that produces generalizable and adaptable output.
[1:01] And large language models are instances of foundation models applied specifically to text and text-like things.
[1:09] I'm talking about things like code.
[1:11] Now, large language models are trained on large datasets of text, such as books, articles and conversations.
[1:18] And look, when we say "large",
[1:21] these models can be tens of gigabytes in size
[1:24] and trained on enormous amounts of text data.
[1:27] We're talking potentially petabytes of data here.
[1:31] So to put that into perspective,
[1:33] a text file that is, let's say, one gigabyte in size,
[1:38] that can store about 178 million words.
[1:44] A lot of words just in one Gb.
[1:47] And how many gigabytes are in a petabyte?
[1:51] Well, it's about 1 million.
[1:57] Yeah, that's truly a lot of text.
[1:59] And LLMs are also among the biggest models when it comes to parameter count.
[2:04] A parameter is a value the model can change independently as it learns,
[2:08] and the more parameters a model has, the more complex it can be.
[2:11] GPT-3, for example, is pre-trained on a corpus of actually 45 terabytes of data,
[2:20] and it uses 175 billion ML parameters.
[2:25] All right, so how do they work?
[2:28] Well, we can think of it like this.
[2:30] LLM equals three things:
[2:34] data, architecture, and lastly, we can think of it as training.
[2:44] Those three things are really the components of an LLM.
[2:47] Now, we've already discussed the enormous amounts of text data that goes into these things.
[2:53] As for the architecture,
[2:55] this is a neural network and for GPT that is a transformer.
[3:03] And the transformer architecture enables the model to handle sequences of data
[3:07] like sentences or lines of code.
[3:09] And transformers are designed to understand the context of each word in a sentence
[3:13] by considering it in relation to every other word.
[3:17] This allows the model to build a comprehensive understanding of the sentence structure
[3:21] and the meaning of the words within it.
[3:23] And then this architecture is trained
[3:25] on all of this large amount of data.
[3:29] Now, during training, the model learns to predict the next word in a sentence.
[3:33] So, "the sky is..." it starts off with a with a random guess, "the sky is bug".
[3:41] But with each iteration, the model adjusts its internal parameters
[3:45] to reduce the difference between its predictions and the actual outcomes.
[3:50] And the model keeps doing this gradually improving its word predictions
[3:53] until it can reliably generate coherent sentences.
[3:57] Forget about "bug", it can figure out it's "blue".
[4:02] Now, the model can be fine tuned on a smaller, more specific dataset
[4:07] Here the model refines its understanding to be able to perform this specific task more accurately.
[4:13] Fine tuning is what allows a general language model
[4:16] to become an expert at a specific task.
[4:18] OK, so how does this all fit into number 3, business applications?
[4:23] Well, for customer service applications,
[4:27] businesses can use LLMs to create intelligent chatbots that can handle a variety of customer queries,
[4:33] freeing up human agents for more complex issues.
[4:37] Another good field, content creation.
[4:40] That can benefit from LLMs which can help generate articles,
[4:44] emails, social media posts, and even YouTube video scripts.
[4:49] Hmm, there's an idea.
[4:51] Now, LLMs can even contribute to software development.
[4:57] And they can do that by helping to generate and review code.
[5:00] And look, that's just scratching the surface.
[5:03] As large language models continue to evolve,
[5:05] we're bound to discover more innovative applications.
[5:09] And that's why I'm so enamored with large language models.
[5:14] If you have any questions, please drop us a line below.
[5:17] And if you want to see more videos like this in the future,
[5:20] please like and subscribe.
[5:22] Thanks for watching.