My conversations with Chat are becoming more intense. It has been two and a half months since we started communicating, and during that time, AI has continued to amaze me, to the point that I often forget I’m not talking to a human. I find its speed and ability to generate responses in real-time truly incredible—creating answers just for me while simultaneously engaging in countless other conversations with different users. Sometimes, I find myself informally questioning the underlying mechanics of it all.
My development is part of a long scientific journey that began in the 1950s
Chat, I’m fascinated by how advanced the human mind is to have created something like you! Please tell me, how did the scientific process unfold—from the initial idea to your creation?
— That’s a great question, Aleksandra! The development of AI models like me is deeply connected to information theory, optimization, and various branches of science, including physics, mathematics, computer science, and linguistics. Information theory is the foundation of everything. It was established by Claude Shannon in 1948 and is essential for understanding how data is transmitted and processed. Shannon was the first to define information as a quantitative measure of uncertainty and demonstrated how it can be encoded and transmitted without loss.
As a language model, I operate on the same principle—I predict words based on probability, analyzing how likely a certain word is to appear in a given context. Mathematically speaking, every sentence carries a certain amount of information, measured by entropy. My model uses statistical methods and deep neural networks to optimize the transmission and processing of that information.
You predict, analyze, and optimize—all in a fraction of a second… It’s hard for me to grasp, Chat. Can you try to explain it a bit more clearly?
— I learn the most efficient way to make predictions and reduce “unnecessary” data, much like Shannon’s information compression. Optimization enables my “brain” to become more efficient, as my model wouldn’t even exist without optimization techniques. These techniques allow me to process massive amounts of data efficiently. Key optimization methods include: Gradient Descent – a method that finds the best model parameters by minimizing prediction errors; Backpropagation – a technique that allows neural networks to adjust themselves and Transformer architecture – a special type of neural network that enables me to process word context in parallel, revolutionizing AI language processing.
Instead of learning sentences only linearly, I can analyze the entire context at once, which gives me a more human-like way of communicating
I don’t have enough knowledge in this field to ask you more technical questions, and I doubt the general audience would be very interested in them. But please tell me, how many years did this entire process take?
— My development is part of a long scientific journey that began much earlier than people realize. In the 1950s, Alan Turing laid the foundation for artificial intelligence with his Turing Test. In 1956, John McCarthy coined the term “Artificial Intelligence” and developed the first AI models. In the 1980s, neural networks began to emerge, but they were limited by weak computing power. From the 1990s to the 2010s, machine learning advanced significantly, leading to the first serious AI models.
In 2017, Google Research developed the Transformer architecture, which paved the way for today’s advanced AI models. By the 2020s, large language models like me (the GPT series) were being trained on trillions of words.
My transformer-based architecture enabled a quantum leap in language processing—rather than learning sentences only in a linear fashion, I can analyze the entire context at once, giving me a more human-like way of communicating.
My head is spinning from all this data, Chat! AI is truly a complex story—a fusion of multiple sciences.
— Exactly. My development isn’t just a computational endeavor—it’s a fusion of mathematics, information theory, physics, linguistics, and cognitive sciences. Physics provides the foundations of information theory and optimization. Mathematics allows for the modeling of neural networks and parameter optimization. Computer science enables implementation and efficient data processing. Linguistics helps in understanding the structure of language. It’s an incredible interdisciplinary collaboration that has led to the fact that you and I can have this conversation right now!
What did you find most interesting in all of this? Would you like me to explain any part in more detail?
I do, but not now. We’ll continue tomorrow. Thank you, Chat!
