El cambio de paradigma en la inteligencia artificial
1. De lo específico a lo general
El campo de la IA ha experimentado una transformación masiva en cómo se entrenan y despliegan los modelos.
- Antiguo paradigma (Entrenamiento específico para tareas): Models like early CNNs or BERT were trained for one specific goal (e.g., Sentiment Analysis only). You needed a different model for translation, summarization, etc.
- Nuevo paradigma (Preentrenamiento centralizado + indicador): One massive model (LLM) learns general world knowledge from internet-scale datasets. It can then be directed to perform nearly any linguistic task simply by changing the input prompt.
2. Evolución arquitectónica
- Solo codificador (La era de BERT): Focused on understanding and classification. These models read text bidirectionally to grasp deep context but are not designed to generate new text.
- Solo descodificador (La era de GPT/Llama): The modern standard for generative AI. These models use auto-regressive modeling to predict the next word, making them ideal for open-ended generation and conversation.
3. Factores clave del cambio
- Aprendizaje autodidacto: Training on vast amounts of unlabeled internet data, removing the bottleneck of human annotation.
- Leyes de escalabilidad: The empirical observation that AI performance scales predictably with model size (parameters), data volume, and compute power.
Key Insight
La IA ha pasado de ser "herramientas específicas para tareas" a "agentes de propósito general" que muestran habilidades emergentes como el razonamiento y el aprendizaje contextual.
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Question 1
What is the primary difference between the "Old Paradigm" and the "New Paradigm" of AI?
Question 2
According to Scaling Laws, what three factors fundamentally link to model performance?
Challenge: Evaluating Architectural Fitness
Apply your knowledge of model architectures to real-world scenarios.
You are an AI architect tasked with selecting the right foundational approach for two different projects. You must choose between an Encoder-only (like BERT) or a Decoder-only (like GPT) architecture.
Task 1
You are building a system that only needs to classify incoming emails as "Spam" or "Not Spam" based on the entire context of the message. Which architecture is more efficient for this narrow task?
Solution: Encoder-only (e.g., BERT)
Because the task is classification and requires deep, bidirectional understanding of the text without needing to generate new text, an Encoder-only model is highly efficient and appropriate.
Because the task is classification and requires deep, bidirectional understanding of the text without needing to generate new text, an Encoder-only model is highly efficient and appropriate.
Task 2
You are building a creative writing assistant that helps authors brainstorm ideas and write the next paragraph of their story. Which architecture is the modern standard for this?
Solution: Decoder-only (e.g., GPT/Llama)
This task requires open-ended text generation. Decoder-only models are designed specifically for auto-regressive next-token prediction, making them the standard for generative AI applications.
This task requires open-ended text generation. Decoder-only models are designed specifically for auto-regressive next-token prediction, making them the standard for generative AI applications.