🎓CURSO PROMPT ENGINEERING en Español - GRATIS - 🤖CLASE 01- Introducción a los modelos de texto

Introduction to Artificial Intelligence and Language Models

Welcome and Course Overview

• Joaquín Barberá introduces himself and the course on artificial intelligence, emphasizing the importance of engagement through likes and comments for better video visibility.

• The first class is primarily theoretical, focusing on a general introduction to artificial intelligence (AI) and text models that will be explored in subsequent lessons.

Defining Artificial Intelligence

• AI is defined as a field of computer science aimed at creating systems capable of performing tasks typically requiring human intervention, such as learning, decision-making, and pattern recognition.

• The concept revolves around programming computers to simulate human brain functions to execute tasks without direct human involvement.

Branches of Artificial Intelligence

• AI encompasses various branches including machine learning, natural language processing (NLP), robotics, etc., with a focus on NLP in this course.

Understanding Models in AI

• A model is described as a mathematical or computational representation used for making decisions or predictions based on input data.

• The process involves receiving input data, processing it through algorithms (logical/mathematical instructions), and producing an output for human use.

Types of AI Models

Text-Based Models

• The course will concentrate on text-based models within NLP that perform reading/writing tasks like summarization and translation.

Conversational Models

• Conversational models generate text suitable for dialogue; ChatGPT is highlighted as an example.

Image Generation Models

• These deep learning models can create new images from textual inputs; MidJourney is mentioned as a popular tool.

Video Generation Models

• Video generation models produce sequences based on input data; VGAN is noted as one such model.

Audio Generation Models

• These models generate new sounds/audio tracks from given inputs; WaveNet serves as a well-known example.

Introduction to Natural Language Processing (NLP)

What is NLP?

• NLP focuses on enabling computers to understand, interpret, and generate human language effectively.

Capabilities of NLP Systems

• Computers utilize algorithms and machine learning techniques to process large volumes of text, identifying patterns in human language for effective comprehension and generation.

Language Modeling Techniques

Predictive Text Modeling

• Language models are trained to predict the next word in a sequence based on prior context—this forms the basis of language modeling.

Understanding Large Language Models and Prompt Engineering

Overview of Large Language Models (LLMs)

• Large language models (LLMs) are trained on vast datasets using deep neural networks to learn language patterns. For instance, GPT models exemplify LLMs.

• The trend is shifting away from creating larger models due to resource demands; instead, research focuses on smaller models that can achieve similar performance levels.

Language Modeling Fundamentals

• Language modeling relies on the predictable relationships between words in a language rather than random usage. Context influences which words are likely to follow others.

• For example, in the phrase "el cubito de hielo está," the model predicts that "frío" or "helado" are more probable next words compared to unrelated options like "caliente."

Characteristics of GPT Models

• GPT models, such as GPT-3 and its successors, are conversational AI derived from text-based models. They utilize transformer networks for specific natural language processing tasks.

• These models generate coherent text and perform various NLP tasks including summarization, translation, and question answering.

Other Notable LLMs

• Besides GPT models, other significant LLMs include Google's Open Assistant and open-source alternatives like Bloom.

Introduction to Prompts and Prompt Engineering

• A prompt is an instruction given to a natural language processing model indicating the expected type of response. For example, “completa la frase el mar es” expects descriptive completions like “azul” or “precioso.”

• Prompt engineering involves creating and optimizing prompts for better model responses. An engineer tailors prompts based on desired outcomes to enhance relevance and quality.

Role of a Prompt Engineer

• A prompt engineer crafts suitable instructions for obtaining specific responses from the model while also optimizing these prompts for improved results.

Conclusion