What is an AI Model?
An AI model is a program that has been trained to recognize patterns and make predictions. Think of it as a mathematical recipe: data goes in, and predictions or outputs come out.
The "model" is essentially a file containing billions of numbers (parameters) that represent what the AI has learned.
Model vs. Application
ChatGPT is an application. GPT-4 is the model behind it. One model can power many applications.
Key Model Characteristics
Parameters
The numbers adjusted during training. More parameters generally means more capable (but not always). GPT-4 has over a trillion parameters; smaller models might have millions.
Architecture
The structure of the model—how information flows through it. Common architectures include Transformers, CNNs, and RNNs.
Training Data
What the model learned from. Models are shaped by their training data—its quality, quantity, and biases.
Types of AI Models
Language Models (LLMs)
Trained on text to understand and generate language. Examples: GPT-4, Claude, Llama, Gemini. Used for chatbots, writing, coding, and analysis.
Image Models
Trained to understand or generate images. Examples: DALL-E, Midjourney, Stable Diffusion (generation); ResNet, CLIP (understanding).
Multimodal Models
Can handle multiple types of data—text, images, audio. Examples: GPT-4V, Gemini. Can describe images, answer questions about them, etc.
Specialized Models
Trained for specific tasks: AlphaFold for protein structure, Codex for code, Whisper for speech recognition.
Open vs. Closed Models
Closed/Proprietary
- Owned by companies (OpenAI, Anthropic, Google)
- Access via API only—can't see or modify the model
- Often more powerful due to massive resources
Open Source/Weights
- Released for anyone to use (Meta's Llama, Mistral)
- Can run locally, modify, and fine-tune
- Community can improve and specialize them
Model Versions and Variants
- Base model — Trained on general data
- Fine-tuned — Further trained on specific tasks or domains
- Instruct/Chat — Optimized to follow instructions or have conversations
- Quantized — Compressed to run on smaller devices
Choosing a Model
Consider:
- Task — What do you need it to do?
- Quality — How accurate does it need to be?
- Speed — How fast do you need responses?
- Cost — What's your budget?
- Privacy — Can you send data to the cloud?
Summary
- • AI models are trained programs that make predictions
- • Key characteristics: parameters, architecture, training data
- • Models can be open source or proprietary
- • Choose based on task, quality, speed, cost, and privacy needs