AI used to be split into two worlds. Images and “unstructured” data? Deep learning nailed that. Spreadsheets and databases? Traditional algorithms owned that space.
Then someone figured out how to make neural networks treat words like pixels. That breakthrough was embeddings. I’ll explain what that means in a second—just keep reading.
How Language Models Actually Work
The NLP breakthrough wasn’t grammar rules. It was a simple game: predict the next word.
That’s literally all ChatGPT does. No magic—it’s just really good at guessing what comes next.
When you train on Wikipedia guessing the next word, the model accidentally learns patterns. “The capital of France is…” probably means “Paris” comes next. It’s all statistics.
I used to think AI was some mysterious intelligence. Now I see it’s just math—really sophisticated pattern matching. Not Skynet, just calculus on steroids.
This is called a pretrained language model. Once it understands language patterns, you can fine-tune it for specific tasks with way less data. Fine-tuning is like taking a generalist and making them a specialist—same brain, focused knowledge.
Embeddings: Making Words Mathematical
How do you tell a computer that “Monday” and “Tuesday” are similar, but “Monday” and “Blueberry” aren’t? Computers only understand numbers.
The old way (one-hot encoding) made every category equally different. Monday vs Tuesday? Same distance as Monday vs Blueberry. Useless.
Embeddings fix this by turning words into numbers that actually capture meaning.
Embeddings map words into a multi-dimensional space where:
- Monday and Tuesday are close together
- King minus Man plus Woman equals Queen (seriously)
- Categories and numbers can work together in the same network
The space is huge—hundreds of dimensions capturing all sorts of relationships.
Deep Learning for Boring Data
Everyone thinks deep learning is for images and self-driving cars. But embeddings let neural networks crush “boring” spreadsheet data—sales forecasting, credit risk, all that stuff.
The trick: treat every category (store ID, zip code, product type) like a word. The network learns what these “words” mean and finds patterns humans miss. I’ve used this to spot data leakage that manual analysis missed.
The Bottom Line
The model architecture matters less than how you represent the data. Pixels, words, spreadsheet rows—turn them into good embeddings and the network handles the rest.
But there are traps. Next time I’ll write about realistic training and how to actually tell if your model works—or if it’s just cheating.