The value of generative AI is more than ChatGPT

How generative AI works

Whether you're an AI enthusiast or just curious about the technology shaping our future, this post will give you valuable insights into the fascinating world of generative AI.

Generative AI is a buzzword, but what's really going on under the hood? In this post, we’ll break down the basics of neural networks and dive into the world of transformer models. We’ll explore how computers “see” images and understand language, making sense of key concepts like tokenization, attention mechanisms, and contextual understanding.

You’ll learn how these models don’t just predict based on simple probabilities—they mimic understanding through pattern recognition. We’ll also touch on how these techniques extend beyond text to revolutionize image and video processing.

This blog post is part of a series where we showcase that generative AI is much more than just ChatGPT. We’ll cover:

·     How Generative AI works?

·     How conversations in ChatGPT works (To be released)

·     How to improve your ChatGPT results (To be released)

·     How agentic solutions unleash the full power of AI  (To be released)

Interested? Follow us on LinkedIn to stay updated on new posts!

Understanding Neural Networks: The Foundation of AI

At the core of any generative AI tool is a neural network. Think of it as the brain behind the magic. To understand generative AI, let’s first get a grasp on how neural networks work.

Neural networks are simplified models of how the human brain functions. They consist of neurons (nodes) connected in an organized manner. Just like in the brain, some neurons are more strongly connected to each other than others. The strength of these connections is called the weight.

A great way to visualize how it works in practice is by looking at a convolutional neural network (CNN), an older model architecture used in vision based systems. These models classify images, such as determining if a photo contains a cat. The image is fed into the network, and each pixel color is assigned to a neuron. As the signal moves through the network, simple patterns are recognized, and deeper layers identify more complex features like eyes or ears. Finally, the network concludes whether the image contains a cat.

The “magic” happens in the weights.Randomly connected nodes would produce random results, but carefully chosen weights enable the network to perform useful tasks.

How AI Learns: Training Neural Networks

So, how do we figure out which weights to choose? Manually adjusting them would take forever, so we use training methods to automate the process. Here are two common methods:

·     Supervised Learning: The model is trained with examples where the correct outcome is known. The model’s weights are adjusted to maximize accuracy.

·     Unsupervised Learning: The model is trained on data without explicit labels. It identifies patterns or relationships within the data on its own, adjusting its parameters to capture the structure of the data.

Transformer Models: Understanding Text

So far, we’ve looked at image-based neural networks, but how do neural networks handle text? Text doesn’t have pixels, so how do they generate text? Enter transformer models, a recent neural network architecture designed to tackle this problem.

Input and Output

Since text doesn’t contain pixels, it’s represented differently in the model. Instead of using whole words, text is broken down into smaller pieces called tokens. This allows the model to handle any text, even words it hasn’t seen before.

The model predicts the next token in a sequence based on the input text. It continues generating tokens (one token at a time) until it reaches an “end token,” which stops the process. You can see this in action when you watch ChatGPT generate text, one word at a time.

Attention mechanism

What makes transformers special is their use of attention mechanisms. Instead of treating all tokens equally, transformers focus on the most relevant ones to predict the next word.

For example, if the model is predicting the next word in the sentence "The cat sat on the _," it focuses on words like "cat" and "sat," which give strong clues about what might come next.

Attention helps the model understand the context, making its predictions more accurate and coherent.

Just Probabilities?

You might be wondering if these models are just using probabilities to guess the next word. Not quite. While probabilities play a role, the magic lies in how transformers analyze the entire context of the input to make predictions.

During training, models aren't just learning to predict based on simple probabilities. They’re being pushed to improve their prediction skills by understanding the context and patterns in the data. This allows them to generate more coherent and contextually accurate text. Essentially, these models mimic understanding by recognizing and applying the patterns they’ve learned from vast amounts of training data.

Beyond Text: Other Transformer Models

Transformers aren’t just for text—they’ve also revolutionized fields like image and video processing. In these areas, transformers use visual patches as tokens, enabling them to handle different types of visual data. For example, they can take noisy images or video frames and apply denoising techniques to produce clear, coherent visuals. This technique is used in image and video generation.

Looking ahead, transformers are expected to excel in multimodal learning, seamlessly integrating text, images, and audio.They’ll also advance real-time processing in areas like autonomous systems and robotics, making them a key driver of AI innovation across diverse applications.

If you’re interested in learning more, follow us on LinkedIn to be notified about future posts where we’ll dive deeper into the value of Generative AI in businesses.