The Walk in the Park: Understanding How ChatGPT Works with a Real-Life Analogy
If you can't explain it to a six-year-old, you don't understand it yourself.
A Stroll through the Park
Imagine stepping into a vast park filled with a multitude of spots and crisscrossed by a myriad of pathways. This isn't any ordinary park; it's designed to be a living embodiment of data patterns and predictive behavior. Each spot in the park signifies a specific point of interest—a coffee shop, a picturesque lake, a bustling luna park, a serene picnic spot, or even a necessary restroom. The paths between these spots represent the routes people take as they explore the park, creating a network of connections that mirrors human behaviors and choices.
As visitors traverse the park, certain paths become well-trodden and clear—these are popular routes that most people take. For example, many visitors may start at the park entrance, make their way to the ticket station, and then head straight to the lunar park. This well-walked path would then become clear and wide, reflecting its popularity.
However, the routes people take don't just depend on their current location; past decisions also influence their future direction. Say, for instance, a visitor starts at the coffee shop and then goes to the luna park. Given this combination, the visitor is more likely to head towards the restroom next. On the other hand, if the visitor went from the coffee shop to the picnic spot, they might prefer heading towards the serene lake next. The specific sequence of spots visited paints a broader picture of the visitor's preferences and influences their likely next move.
Yet, the park, much like life, thrives in its possibilities. There are less frequented paths leading from each spot, representing the less common but equally valid choices people might make. Just because a path is less traveled doesn't mean it'll never be chosen. A visitor who started at the coffee shop and then went to the luna park might decide to head towards the lake next, taking the scenic route. This unpredictability adds a realistic touch to the visitor's journey, mirroring the diversity and freedom of choice we exercise in our daily lives.
ChatGPT - The AI Analogy
Now, imagine this park as a representation of a complex artificial intelligence model, like OpenAI's GPT-4, which powers ChatGPT. Each spot in the park signifies a specific word, and the paths between these spots represent connections or parameters, shaping the flow of conversation in the AI model.
The training process of the AI model is akin to how the paths in our park become prominent. The more a path is walked upon, the stronger the connection it represents—much like the word sequences that the model identifies as common and likely. For instance, if the model sees that "am" often follows "I" in the sentences it is trained on, it will form a strong connection between these two words, similar to a well-trodden path.
Just as in the park, the model's prediction of the next word depends not only on the last word but also on the sequence of previous words. If the sentence so far is "I have a...", the model doesn't merely consider "a" but the entire sequence—"I", "have", and "a"—when predicting the next word. The specific sequence of words influences the predictive patterns of the AI model, much like the overall context affects our sentence formations in everyday language.
However, language, like the park, is rich with possibilities. Just as there are less frequented paths in the park, there are less common but plausible word sequences in language. The model is probabilistic and can choose less frequent but contextually valid words, allowing it to generate creative and varied responses.
Despite this apparent understanding of context, the AI model doesn't understand the "scenery" or meaning of the words. It's merely recognizing patterns in the paths, much like identifying frequently traveled routes without knowing why these routes are popular.
It's vital to note that because ChatGPT operates on pattern recognition rather than deterministic calculations, there are instances when it can generate inaccurate or unexpected outputs. Drawing from our park analogy, predicting a visitor's path based on previous visitors' patterns doesn't guarantee perfect foresight. We don't read the visitor's mind; we merely guess their direction. Similarly, ChatGPT predicts the next word based on the pattern it discerns from the training data. However, the unpredictability and diversity of language can sometimes lead to it offering a different route, one that might not align with the intended meaning or context, underscoring that even in its impressive capability, it is still a guessing game based on patterns.
We also need to know that language models like ChatGPT are trained with tokens, rather than with words. A token is the smallest unit of text that the model can understand and generate. A token can be as short as one character or as long as one word. For example, in English, the sentence "ChatGPT is great!" would be divided into six tokens: ["Chat", "G", "PT", " is", " great", "!"].
The reason for this is that the model tokenizes text based on a pre-defined vocabulary that it learned during its training process. This vocabulary includes common words, but also individual characters and common substrings. The model uses this vocabulary to break down the input text into tokens that it can process.
The number of tokens in a text can be different from the number of words. For example, the word "ChatGPT" is one word, but it's tokenized into three tokens: "Chat", "G", and "PT". Having that in mind, our diagram looks more like this in real life:
In conclusion, the ChatGPT model can be envisaged as a sophisticated guessing engine that leverages a trained neural network to anticipate the next token in a sequence. The neural network, analogous to our park filled with its myriad paths, comprises a complex web of parameters, including weights, which act as markers to guide these predictions. It mirrors the intricate landscape of human language by identifying well-worn routes (common sequences) and less-trodden paths (rare sequences) and utilizing them to make contextually relevant predictions. Yet, it's crucial to remember that while ChatGPT's outputs can be impressively accurate and diverse, they're the result of intricate pattern recognition, rather than genuine understanding. The journey through the park of language, for ChatGPT, is a constant guessing game of anticipating what comes next, guided by the footprints left by countless previous journeys.