How Generative AI works: a deep dive into randomization

Generative artificial intelligence represents one of the most fascinating innovations in modern technology. It leverages machine learning models, such as deep neural networks, to create original content like texts, images, music, and even code. But how does it actually work? And how does randomization play a crucial role? Let’s find out together.

The Essence of Generative AI: Language Models

At the core of generative AI are advanced natural language processing (NLP) models, such as GPT (Generative Pre-trained Transformer), which power tools like ChatGPT. These models are trained on vast datasets composed of billions of words, drawn from books, articles, websites, and other sources. During training, AI learns to recognize patterns and implicit linguistic rules without explicit programming.

The model then processes user inputs to generate coherent responses. For example, a prompt like "Explain generative artificial intelligence" will be interpreted based on its context, producing a detailed and targeted explanation.

The Role of Randomization: Why Do Responses Vary?

One of the most fascinating aspects of generative AI is its ability to provide different responses even to the same input. This variability depends on several factors, including:

Internal Randomization:

Models like ChatGPT incorporate a controlled level of randomness. This allows the system to explore various plausible options and choose among them. For instance, if you ask ChatGPT to describe generative AI multiple times, you might receive similar responses but with nuanced differences.

Temperature Settings:

The temperature is a parameter that controls the level of creativity in responses. Lower values (e.g., 0.2) lead the model to choose more predictable and precise responses, while higher values (e.g., 0.8 or 1.0) increase randomness, resulting in more imaginative or unexpected outputs. This is especially noticeable in conversations with models like ChatGPT.

Perplexity and Coherence:

Perplexity is a technical term that measures how predictable a sequence of words appears to a language model. In practice, it gauges the model’s ability to calculate the likelihood of certain words occurring based on context. Low perplexity values indicate the model finds the text highly consistent with its training data, while higher values suggest greater uncertainty. Tools like Perplexity.ai, which are based on generative models, tend to optimize text coherence by reducing randomness to provide more accurate and informative responses.

A Practical Example: ChatGPT vs. Perplexity

Let’s compare how ChatGPT and Perplexity respond to the same question:

• Prompt: "Explain how randomization works in generative AI models."
• ChatGPT’s Response (with high temperature):
  "Randomization in generative AI models enables the creation of unique responses for each request. By incorporating a controlled level of randomness, AI explores multiple possibilities, making every interaction diverse and often enriched with creative or unexpected details."
• Perplexity.ai’s Response:
  "Randomization in generative artificial intelligence models allows for response variation through probabilistic mechanisms, often controlled by parameters like temperature. This process ensures output diversity while maintaining coherence based on training data."

In this comparison, ChatGPT emphasizes creativity and linguistic variations, while Perplexity focuses on clarity and technical precision.