Generative AI: insights into the technology

Overview

Teaching: 15 min
Exercises: 0 min

Questions

• What are the main components behind Large Language Models?

• How are they used?

Objectives

• Basic theoretical introduction into LLMs

• Understanding workflows through web interface and API

Generative AI: insights into the technology

During this session we will learn more about LLMs: the technology behind LLMs and how they are used. It will go into the basic functionalities of LLMs, which are needed to make optimal use of prompts and models.

PDF of the presentation belonging to this session.

Summary of the presentation

• Artificial Intelligence (AI): Technology aimed at mimicking human abilities like reasoning, learning, problem-solving, and perception.
• Machine Learning (ML): Uses algorithms and statistical models to execute tasks based on pattern recognition and inference, without explicit instructions.
• Statistics: The foundational science for analyzing and interpreting data, essential for AI development.
• Deep Learning (DL): A subset of ML that utilizes layered neural networks, excelling in complex tasks such as speech and image recognition.
• Generative AI: Focuses on creating new content (e.g., images, texts, sounds) from trained data, emphasizing the versatility in output types.

Process of Tokenizing and Embedding

• Tokenization: Breaking down text into smaller units called tokens (e.g., words).
• Embedding: Converts tokens into numerical vectors that represent both the literal and contextual meanings of the tokens. This process is critical for machine understanding and manipulation of language.

Applications and Developments in Generative AI

• Prompt Engineering: Often referred to as prompt hacking, it involves crafting queries that guide AI to produce desired outcomes. Used in various applications from grant proposals to blog posts and image generation.
• Data Analytics and Application Integration: LLMs can be integrated with APIs like OpenWeather for real-time data processing and activity suggestions based on weather conditions.
• Automated Document Processing: Tools like Langchain and ChatGPT can process and generate structured outputs from documents such as scientific papers.

Key Points

• Integration of AI Across Disciplines

• The Role of Data and Machine Learning

• Practical Applications and Automation