AI Training

What is AI Training?

Training is the process of teaching an AI model to perform a task by showing it examples. Think of it like teaching a child to recognize animals by showing them many pictures and telling them "this is a cat," "this is a dog."

During training, the AI adjusts millions of internal parameters (weights) to get better at the task. Once trained, the model can apply what it learned to new, unseen data.

The Training Process

1. Collect data — Gather thousands or millions of examples
2. Prepare data — Clean, label, and organize the data
3. Split data — Divide into training, validation, and test sets
4. Train — Feed training data through the model repeatedly
5. Validate — Check performance on validation data
6. Tune — Adjust settings to improve performance
7. Test — Final evaluation on held-out test data

Key Concepts

Epochs

One epoch = the model sees every training example once. Training typically involves many epochs—showing the same data repeatedly helps the model learn better.

Batch Size

Instead of updating weights after every single example, models process data in batches (groups of 32, 64, 128 examples). This makes training more efficient and stable.

Loss Function

A mathematical measure of how wrong the model's predictions are. Training aims to minimize this loss. Different tasks use different loss functions.

Learning Rate

How much to adjust weights after each batch. Too high and training becomes unstable; too low and it takes forever. Finding the right learning rate is crucial.

Common Training Challenges

Overfitting

When the model memorizes training data instead of learning general patterns. It performs great on training data but poorly on new data. Like a student who memorizes answers instead of understanding concepts.

Underfitting

When the model is too simple to capture the patterns in the data. It performs poorly even on training data.

Data Quality

Garbage in, garbage out. If training data is biased, mislabeled, or unrepresentative, the model will learn those problems.

Types of Training

• Supervised learning — Data comes with correct answers (labels)
• Unsupervised learning — Model finds patterns without labels
• Self-supervised learning — Model creates its own labels from data (how ChatGPT learned)
• Reinforcement learning — Model learns through trial and error with rewards

Training Modern AI Models

Training large language models like GPT-4 requires:

• Billions of text examples from the internet
• Thousands of specialized GPUs running for weeks
• Millions of dollars in computing costs
• Teams of engineers monitoring and adjusting