My experience with Large Language Models
Artificial intelligence has changed the world as we know it. Many companies are dropping of their previous metaverse frenzy to get their hands on some of the potential profit that AI products offer. At this point, you might be familiar with the concept of what an AI model is, or at least, a rudimentary image of what the concept entails.
Well, we might not be there just yet, and it might seem that it's progressing at a rather alarming rate, but in reality, we are really far from the concept of a fully automated, accurate and efficient AI.
With this article, I want to talk a little bit about LLMs (or Large Language Models), My experience with them, as a Student and frontend developer, and their impact that they have on our world.
An AI model, at it's core, is a big neural network, trained using hundreds, thousands, heck even millions of tokens. From what I understand, tokens are basically words; Whenever you see a model with a number and a letter as it's suffix, it usually means the amount of "tokens" that they've been trained on. These models are trained using rewards, like we use treats to tell our dogs what's good or bad. What I mean by this is that, these models "try" to predict which response out of a given prompt gives out the most results (aka. the best result). This process is called re-enforcement training and it's the most widely used training method used in AI models.
Now that you probably have, at the very least, a better grasp of what I am talking about, and how it works, I'll talk about my overall experience with Gemini/Bard, Google's "magnum opus" on the current AI arms race.
