An AI glossary

Navigating the world of Artificial Intelligence is daunting. That’s why you need to understand the fundamental concepts and terminology. This AI glossary is designed to provide clear, concise explanations for essential AI terms, helping you stay informed and make the most of these advanced technologies. Whether you’re an expert or just starting out, this blog will equip you with the knowledge you need.

Continued Pre-Training

Continued pre-training means taking an already trained AI and teaching it more with new information. It’s like sending someone who already knows how to cook to a specialized cooking class to learn new recipes.

Continued Pre-Training of Language Models (Focus on general concepts, as videos specifically on continued pre-training can be rare.)

Embeddings and Vectors

Embeddings are a way to turn complex information (like words or pictures) into numbers so that computers can easily compare and understand them. It’s like turning a picture into a code that a computer can read and use.

Embeddings in Machine Learning – Simply Explained

Fine-Tuning

Fine-tuning is the process of taking a pre-trained AI model and training it further on a specific dataset to make it perform better on a particular task. It’s like taking someone who already knows how to play the piano and teaching them to play a specific song perfectly.

What is Fine-Tuning in Machine Learning?

Grounding

Grounding makes sure that generated content is based on accurate and verifiable information. It involves linking the output of a language model to real-world data or knowledge sources. It’s like using a factual reference or source to support your statements in an essay.

Grounding AI Explained: How to stop AI hallucinations

Knowledge Graphs

Knowledge graphs are like giant maps of information where different facts and ideas are connected. They help computers understand relationships between different pieces of information, like a big web of knowledge.

What is a Knowledge Graph?

Prompt Chaining

Prompt chaining involves linking several questions or prompts together. This helps the AI follow along better, like having a step-by-step conversation where each question builds on the last one.

Prompt Chaining and In-context Learning

Prompt Engineering

Creating prompts means carefully crafting the questions or statements we give to an AI so that it responds in the way we want. It’s like asking someone questions in a way that makes them give you the best answers.

Introduction to Prompt Engineering: How to Effectively Use ChatGPT & Other AI Language Models

Reinforcement Learning (RL)

Reinforcement learning is about training an AI by letting it try different actions and learn from the results, like a child learning to ride a bike by getting better each time they try.

Reinforcement Learning (RL) – An Introduction

Reinforced Learning From Human Feedback (RLHF)

RLHF is when people help train an AI by giving it feedback on how well it’s doing. It’s comparable to a coach giving tips to a player to help them improve their game.

Reinforcement Learning from Human Feedback

Retrieval-Augmented Generation (RAG)

RAG combines the strengths of information retrieval and text generation. It retrieves relevant information from a large dataset to help generate more accurate and informed responses. Think of it It’s like asking a librarian for specific information to write a detailed report.

Introducing RAG: Retrieval-Augmented Generation

Supervised Learning

Supervised learning is teaching AI by showing it lots of examples where we already know the right answers. It’s like training a dog by showing it a trick and giving it a treat when it does it right.

Supervised Learning: Crash Course AI #10

Better AI understanding = better results

Understanding these essential AI terms equips you to navigate the complex world of Artificial Intelligence with confidence. This AI glossary serves as a quick reference to help you make the most of AI technologies. Keep this guide handy as you explore and engage with AI, making sure you stay informed and effective in your AI endeavors. For deeper insights, consider exploring additional resources on each topic. Happy learning!

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