.webp) |
When it comes to artificial intelligence, many people imagine that an AI model stores information in a database, just like a traditional application would. The idea is simple: if the AI is able to answer questions and generate texts, it must have access to a data archive where it stores everything it has learned. But the reality is very different.
AI does not have a database like a traditional app
AI models, like ChatGPT, don’t work by storing text data in a table or archive to be pulled from when needed. Unlike a search engine or management software that relies on a relational database, an AI model generates answers on the fly based on what it has learned during the training phase.
How does an AI model store information?
An AI model learns from information during the training process, which is done through a technique called machine learning, and specifically, deep learning. Here’s how it works:
Training on large datasets: The model is exposed to huge amounts of text, from books, articles, websites, and other sources. This data is not stored verbatim, but is used to update the weights of the neural connections of the neural network.
Distributed representation of information: Instead of storing data directly, the model learns patterns and relationships between words, creating a statistical representation of the language. Each word, phrase or concept is transformed into a mathematical structure based on numerical vectors.
Answer generation: When you ask an AI a question, the model does not retrieve a ready-made answer from a database, but generates text in real time based on statistical probabilities between words and what it has learned during training.
Where are the weights of an AI model stored?
The weights of the neural network are actually stored, but not in a traditional database. Instead, they are saved in specific files that represent the state learned by the model during the training process. These files can be stored on:
Hard drives or SSDs in servers: Companies that develop AI models (such as OpenAI, Google, Meta) save weight on powerful servers, often in dedicated data centers.
Cloud storage: Many models are stored on cloud infrastructures, such as AWS, Google Cloud, or Microsoft Azure, for easy access and deployment.
Specific binary files: Weights are saved in files such as .pt (PyTorch), .h5 (TensorFlow/Keras), or other proprietary formats. These files contain millions (or billions) of numerical parameters that define the behavior of the model.
What do the weights contain?
Weights do not store sentences or textual information directly, but represent mathematical connections between neurons in the network. In practice, they encode how the model attributes meaning to words and generates text based on probabilities.
Why is this important?
This difference has some fundamental implications:
There is no direct memory: an AI model cannot remember specific information like a database would. It cannot, for example, remember details of past conversations unless they are retained in the current session.
Data is not retained after generation: once the conversation ends, the AI no longer has access to what was previously said.
Knowledge is limited to training: the model only knows what was available in the data it was trained on, up to a certain point in time. It cannot update itself autonomously as a database would with new data. However, there are techniques such as Retrieval-Augmented Generation (RAG), which combine artificial intelligence with information retrieval systems, allowing the model to consult external sources and generate answers based on more up-to-date data.
To keep in mind
The idea that an AI has an internal database from which it draws information is a common misconception. In reality, AI models operate through neural networks and mathematical representations of language, generating text based on statistical probabilities thanks to the parameters of the model (such as weights) rather than drawing from a data archive. Understanding this mechanism helps us to better use artificial intelligence and understand its limits and potential.
Follow me #techelopment
Official site: www.techelopment.it
facebook: Techelopment
instagram: @techelopment
X: techelopment
Bluesky: @techelopment
telegram: @techelopment_channel
whatsapp: Techelopment
youtube: @techelopment
