Generative AI 101: Learn All About the Technology Behind ChatGPT

The foundation of Generative AI lies in neural networks, which are structures composed of interconnected nodes – symbolically similar to our neurons, although involving a lot of mathematics. These networks can identify patterns in large datasets, leading to the generation of original information and content.

Neural networks form the basis of Machine Learning and Deep Learning models. Both use algorithms to process a large amount of data, such as images, code, and texts, among others. The training of these networks occurs through adjustments to the parameters and weights of the connections between the aforementioned nodes. As a result, there is less discrepancy between the desired and predicted outputs, enabling neural networks to learn from mistakes and make more accurate predictions.

To delve a bit further into the subject, let’s talk about one of the most commonly used models within Generative AI: Generative Adversarial Networks (GANs). GANs employ two distinct neural networks – the generator and the discriminator.

• Generator Neural Network: responsible for producing synthetic data from a random input signal, usually referred to as noise. Its goal is to generate outputs that are indistinguishable from real data.
• Discriminator Neural Network: trained components designed to differentiate between real and generated data from the Generator Neural Network. They help verify the authenticity of the generated data, providing feedback for improving the generator.
• It is worth highlighting that, although the algorithms orchestrate the automation of these processes related to Generative AI, it is undeniable that a model like this is extremely complex. This complexity arises from the need to handle a large volume of data and computational resources. In other words, working with this type of technology can be a time-consuming process.