Fundamentos de Inteligencia Artificial: Qué Es y Cómo Funciona

Artificial intelligence (AI) has revolutionized the way we interact with technology, allowing us to communicate with machines using natural language. Understanding how these models work is key to take advantage of them in our day-to-day personal, work or business life. Here we will review fundamental aspects: what is artificial intelligence, how language models (LLM) work, tokenization, embeddings and the training of these models.

What is artificial intelligence and how is it structured?

Artificial intelligence is a science aimed at replicating in machines human behavior such as reasoning, information processing, problem solving and decision making. Within AI there are:

• Machine learning: Analyzes large volumes of data to identify patterns.
• Deep learning: Uses neural networks to process complex information such as images, videos and audios.
• Generative artificial intelligence (GNI): Generates new information from already available data.
• Language models (LLM): They are able to understand multiple languages and dialects, generating coherent and useful texts.

How do artificial intelligence models understand our language?

Three key processes are required for machines to understand our language:

What is tokenization?

Tokenization converts text into numbers, allowing computers to process them. It takes words and characters and assigns them specific numerical values, including punctuation marks that indicate features of the language used.

What are embeddings in artificial intelligence?

Embeddings assign multidimensional contexts to words, facilitating the understanding of semantic relationships between them:

• They represent words in multi-dimensional spaces.
• They identify meanings according to the context and association between terms.
• Advanced models can have billions of dimensions to accurately define words.

What is the role of training in AI models?

Training makes it possible to generate text coherently by predicting what word comes next. This process involves:

• Pre-training: The model learns patterns by processing large amounts of written information.
• Advanced training: Refinement with human input or secondary models to increase accuracy.
• Technological tools such as GPUs play a key role in this training.

What is attention, query, key and values in artificial intelligence?

Attention allows the model to identify which words or phrases are the most important in a text to generate relevant content. It uses:

• Queries: Questions that help determine inherent references in the text.
• Keys: Key words identified.
• Values: Semantic meanings resulting from the union of words.

This structure allows replicating human-like understanding in machines, the basis for today's generative AI.

How can you learn and apply artificial intelligence now?

Taking advantage of this technology involves understanding its foundations first. You can start with specialized courses in software engineering, LLM fundamentals, Prompt Engineering or practical manuals that explain these processes clearly.

In addition, experimenting directly with models such as ChatGPT allows you to discover practical applications according to specific needs, professional or personal requests.

Now that you know the basics, what applications would you personally give to this technology in your daily life or work? Share it here in the comments!