Pre-training
Initial training phase where models learn general patterns from large datasets. Pre-trained models can then be fine-tuned for specific tasks with much less data.
In-Depth Explanation
Pre-training is the first phase of modern AI model development, where models learn general representations from massive datasets before task-specific adaptation.
Pre-training approaches:
- Language models: Predict next tokens (GPT) or masked tokens (BERT)
- Vision: Contrastive learning, masked image modeling
- Multimodal: Align images with text descriptions (CLIP)
Pre-training characteristics:
- Massive datasets (billions of examples)
- Self-supervised (no manual labels needed)
- Computationally expensive (weeks on many GPUs)
- Done once, used many times
What pre-trained models learn:
- Language: Grammar, facts, reasoning patterns
- Vision: Edges, textures, objects, scenes
- General capabilities applicable to many tasks
The pre-training + fine-tuning paradigm:
- Pre-train on vast general data (expensive, done once)
- Fine-tune on specific task data (cheap, done many times)
- Or use zero/few-shot prompting (no additional training)
Business Context
Pre-training is why you don't need Google-scale resources for AI. Foundation models like GPT-4 and Claude pre-train once; you fine-tune or prompt for your needs.
How Clever Ops Uses This
We leverage pre-trained foundation models for Australian businesses, using fine-tuning or RAG when customisation is needed without pre-training costs.
Example Use Case
"GPT-4 pre-trained on trillions of tokens of internet text, learning language patterns that enable it to assist with almost any text task."