AI models train on enormous datasets, from billions of words in language models to millions of labeled images in computer vision systems. These training datasets can reach terabytes of raw data. Somehow, the models that result from this training are often just a few gigabytes in size. This size discrepancy can seem puzzling. If an AI needs all that data to learn, why isn’t the model itself as large as the dataset?
AI Models Don’t Memorize Data—They Learn Relationships
At first glance, you might assume that AI models store all the data they’ve been trained on. After all, the model needs access to this data to generate predictions or recognize patterns, right? Not exactly.
During training, AI models learn from the data rather than memorize it. The goal of training an AI is to teach it the relationships between different elements within the data—whether those are words, pixels in an image, or user behaviors in a dataset. Once the model learns those relationships, it no longer needs access to the raw data. It’s similar to how a person doesn’t need to remember every word of a book to understand its themes and apply the knowledge they’ve gained.
As an example, consider a language model. Trained on billions of sentences, the AI learns grammar rules, how words co-occur, and how meaning shifts in different contexts. But after the training process, the AI doesn’t need to retain every sentence it’s seen. Instead, it stores the patterns of how language works, and applies those patterns to generate or interpret new sentences.
Patterns as Parameters
So if AI models don’t store the raw data, what do they store? The answer is parameters. These parameters are numerical values that represent the relationships and patterns the AI has learned during training.
In a large language model like GPT-3, trained on hundreds of billions of words, the system adjusts parameters to reflect how words relate to each other. For example, the model learns how often the word “dog” precedes words like “barks” or “runs,” and stores that relationship. These probabilities, and the relationships they represent, are condensed into a set of parameters. Think of them as a summary of what the model has learned.
These parameters allow the AI to generalize and apply its knowledge to new data that it hasn’t seen before. Rather than looking up specific examples from its training data, the AI uses the parameters to generate new responses or recognize objects. For instance, a vision model trained to identify cats doesn’t store every cat image it has seen. Instead, it stores the essential features that make a cat—a combination of shapes, colors, and textures.
Not Compression—Abstraction
The size difference between AI models and their training datasets speaks to AI’s ability to generalize and abstract. Rather than storing data, AI models learn patterns and relationships from their training, allowing them to discard the raw data once they’ve captured the essential knowledge.
The ability to distill vast training data into a set of relationships allows AI models to stay small while retaining immense power. But what does it say about our world—and our human experience—that the immense data behind our conversations, books, and art can be reduced to such a compact form? A question for another day ….
Jayson Adams is a technology entrepreneur, artist, and the award-winning and best-selling author of two science fiction thrillers, Ares and Infernum. You can see more at www.jaysonadams.com.