Before we can properly understand Large Language Models (LLMs), it's helpful to take a step back and look at the bigger picture: Artificial Intelligence (AI). You've likely heard the term AI used in many contexts, from science fiction movies to news articles about new technology. But what does it actually mean, especially in the context of the models we'll be studying?

At its core, Artificial Intelligence refers to the field of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. These tasks include things like learning, reasoning, problem-solving, perception, decision-making, and, importantly for this course, understanding and generating human language.

Think of it this way: humans learn from experience, recognize patterns, make judgments, and communicate. AI aims to build machines that can simulate these capabilities.

Core Concepts of AI

Simulating Intelligence: The fundamental goal is to create non-biological systems that exhibit intelligent behavior. This doesn't necessarily mean creating a conscious machine like in the movies; often, it means building systems that are very good at specific tasks previously done only by humans.
Learning from Data: A significant part of modern AI involves machine learning (ML). Instead of programming explicit rules for every possible situation, ML algorithms allow computers to learn patterns and make predictions from data. LLMs heavily rely on this principle, learning from vast amounts of text data.
Problem Solving: AI systems are often designed to solve specific problems, whether it's navigating a route, identifying objects in an image, or translating languages.
Interaction: Many AI systems are designed to interact with humans or the environment. This is where language understanding and generation become essential, forming the basis of Natural Language Processing (NLP), which we'll discuss next.

Broad Field, Specific Applications

AI is a very broad field, encompassing many sub-disciplines and techniques. Machine Learning is one such sub-discipline, and Natural Language Processing is another area, often overlapping significantly with ML. Large Language Models sit at the intersection of these fields.

Relationship between AI, Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLMs).

It's important to recognize that AI isn't a single technology but rather an umbrella term. Early AI focused more on rule-based systems (e.g., expert systems), while contemporary AI, particularly the kind relevant to LLMs, is heavily driven by machine learning applied to large datasets.

Understanding AI as this broader endeavor to simulate intelligent behaviors helps frame why LLMs, which deal specifically with the intelligent behavior of language use, are considered a significant advancement within the field. In the next section, we'll narrow our focus to Natural Language Processing, the specific area of AI concerned with language.