Does ChatGPT Actually Learn?
It might surprise you to learn that artificial intelligence (AI) systems like ChatGPT don’t actually learn in the same way humans do. This is a common misconception, as many AI systems themselves may even claim they are learning. Articles and academic papers often perpetuate this idea. However, a more precise understanding of how and when AI systems learn – and when they don’t – will help you become a more productive and responsible user of AI.
AI Does Not Learn – At Least Not Like Humans
Misunderstandings around AI often arise from using familiar words, like “learning,” in contexts where they have different meanings. We know how humans learn through experience: We try something, fail, encounter new information, and adapt our understanding. AI systems, however, don’t learn this way. There are two fundamental differences.
First, AI systems don’t learn from specific experiences. They don’t understand the world in the way humans do. Instead, they “learn” by encoding patterns from vast amounts of data using mathematical processes. This happens during their training phase.
Consider large language models like GPT-4, used by ChatGPT. These models learn by encoding mathematical relationships between words (or tokens) to predict how words relate to each other. These connections are extracted from immense datasets and encoded through computationally intensive training. This is fundamentally different from human learning. This approach can lead to AI struggling with simple, everyday knowledge that humans naturally acquire. However, it is also incredibly powerful because language models have “seen” and processed text at a scale far beyond human capability. This is why they excel at language-based tasks such as writing, summarizing, coding, and conversing.
The fact that these systems don’t learn like us, but operate at a massive scale, makes them all-rounders in the kinds of tasks they perform.
Once Trained, the Learning Stops
Most AI systems that people commonly use, like ChatGPT, don’t learn after they’re built. You could even say AI systems don’t learn at all, that training is just how they’re built and not how they work. The “P” in GPT literally stands for “pre-trained.” In technical terms, AI systems like ChatGPT only engage in “training-time learning,” as part of their development, not in “run-time learning.” Systems that learn as they go, like your Netflix algorithm, exist but they are confined to a single task.
Being “pre-trained” means that large language models are essentially frozen in time. Any updates to their training data require costly retraining, or at least so-called fine-tuning for smaller adjustments. This implies that ChatGPT does not learn from your ongoing prompts. A large language model doesn’t remember anything out of the box. It only retains information within a single chat session. Close the window, or start a new session, and you start with a clean slate.
Ways exist to work around this, such as storing information about the user. However, these are implemented at the application level; the AI model itself doesn’t change until it’s retrained.
What This Means for Users
Be aware of what you receive from your AI assistant. Systems like ChatGPT use language models, not knowledge models, because they learn from text data. While the knowledge encoded through mathematical training is impressive, these models are not always reliable when handling questions that require factual recall. Their real strength is with language.
Do not be surprised if responses contain outdated information, since the model is frozen in time, or that ChatGPT does not remember any facts you tell it.
The good news is AI developers have created some clever workarounds. For example, some versions of ChatGPT are now connected to the internet. They can then perform a web search and insert the result into your prompt before generating the response. Another approach is that AI systems can remember things about you to personalize their responses. However, this is achieved with a trick. The large language model itself does not update in real time. Information about you gets stored in a separate database and inserted into the prompt. This mechanism is invisible, which means you cannot correct the model when it gets something wrong or teach it a fact that it would then remember to correct its answers for other users. The model can be personalized to a degree, but it still does not learn on the fly.
Users who understand how AI learns – or doesn’t – will be better at developing effective prompting strategies and treat the AI as an assistant that always requires checking. Let the AI assist. But make sure you do the learning, one prompt at a time.
