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AI for Beginners: Understanding AI Terminology

Updated: Nov 20

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AI for Beginners

We live in a world surrounded by Artificial Intelligence at every step. For many years, little has changed in this field, but the moment ChatGPT was revealed to the world, there was an incredible technological development around AI.

The world has been flooded with a wave of new terms and concepts, and the average person has trouble keeping up with it all!

When I asked ChatGPT to show me the AI concepts on a diagram so I could understand it, it generated me the image below - not particularly useful.

An AI generated image showing AI relationships
Hey ChatGPT, can you create a diagram describing basic AI concepts and relations between them?

So, I decided to explain a few of the most important concepts and the relationships between them using simple language.

Artificial Intelligence (AI)

Think of AI as smart robots or computer programs that can think and learn like humans. It's like teaching a computer to be smart enough to solve problems on its own.

Machine Learning

This is a special way of teaching computers to learn from data. It's like learning to recognize cats by looking at many pictures of cats and not-cats. Machine Learning enables computers to learn from lots of examples (data) to make decisions or predictions.

Deep Learning

A part of Machine Learning but more complex, deep learning is like having an extremely detailed brain model in a computer, allowing it to learn and recognize patterns from data even better.

Natural Language Processing (NLP)

This involves teaching computers to understand and respond to human language. So, when you talk to Siri, Alexa, or more recently, ChatGPT or Bard, they use NLP to understand and reply to you.

LLM (Large Language Model)

IImagine a giant, super-smart robot that has read almost every book, website, and article out there. Now, because it's read so much, it's really good at understanding and using language — that's what a Large Language Model (LLM) is. It's a type of AI that can write essays, solve problems, or even make jokes, using what it has learned from all that reading.

Large Language Models (LLMs) can be categorized based on their primary functions and applications in natural language processing (NLP). The two main categories - text generation and embeddings - represent significant areas of focus for these models. Here’s a breakdown:

Text Generation Models

These models are designed to generate coherent and contextually relevant text. They can be used for a variety of applications including writing assistance, chatbots, content creation, and more.

  • Examples: GPT-4 (OpenAI), Llama 2 (Meta), Claude 2 (Anthropic)

  • Applications: Chatbots, creative writing, automated content generation, and language translation.

  • Characteristics: They are adept at producing human-like text and can continue a given piece of text in a coherent manner, maintaining style, tone, and context.

Embedding Models

Embedding models are focused on converting text into numerical representations (embeddings) that capture the semantic meaning of words, phrases, or even entire sentences. These embeddings are then used in various downstream NLP tasks.

  • Examples: BERT (Google), RoBERTa (Facebook), DistilBERT, Electra

  • Applications: Sentiment analysis, text classification, information extraction, question answering, and search applications.

  • Characteristics: They excel in understanding the context and nuances of language, making them ideal for tasks requiring deep language comprehension.

RL (Reinforcement Learning)

Think about training a pet. When your pet does something good, you give it a treat, and if it does something bad, it gets no treat. Over time, your pet learns to do more of the good stuff to get more treats. Reinforcement Learning for computers is similar. A computer program learns to make decisions by trying different things and getting rewards or no rewards. It's like a game where the computer learns the best moves to win the most points.

Generative AI

Generative AI is like a creative artist inside a computer. Just like an artist can create new paintings or compose music, Generative AI creates new things after learning from many examples. So if you show it thousands of pictures of dogs, it can draw a completely new picture of a dog that doesn't exist. Or if you show it many songs, it can compose a brand-new song all by itself. It's like having a robot friend that's excellent at creating cool, new stuff!

An image showing the relationship between AI concepts
Basic AI concepts and the relation between them

Computer Vision

This is about teaching computers to see and understand images and videos. It's like giving computers eyes and the ability to recognize what's in a picture.

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