How does Gen AI work?

 

Here is what ChatGPT has to say:

Generative AI, also known as generative modeling, is a field of artificial intelligence that focuses on creating models capable of generating new and original content. Generative AI techniques work by learning patterns and structures from a given dataset and then using that knowledge to create new data that resembles the training data.

Here's a simplified overview of how generative AI works:

1. Data collection: A large dataset of examples is collected, containing the type of data the model is intended to generate. For instance, if the goal is to generate images of cats, a dataset of cat images would be gathered.

2. Training the model: A generative AI model, such as a Generative Adversarial Network (GAN) or a Variational Autoencoder (VAE), is created and trained using the collected dataset. GANs consist of two components: a generator and a discriminator. The generator creates new samples, while the discriminator evaluates the authenticity of those samples. The two components are trained together, with the generator aiming to produce increasingly realistic samples to fool the discriminator.

3. Learning patterns and structures: During training, the model learns the patterns, features, and statistical properties of the training data. It learns to capture the underlying distribution of the data, enabling it to generate new examples that resemble the original dataset.

4. Generating new content: Once the model is trained, it can generate new content by providing it with a starting point or a random input. For example, if it is an image generator, you might input a random vector, and the model would generate an image based on that vector. The generated output is a creative synthesis based on what the model has learned from the training data.

5. Iterative refinement: Generative AI models can be further fine-tuned and refined over multiple training iterations to improve the quality and diversity of the generated content. This process typically involves adjusting the model's architecture, hyperparameters, or training techniques.

It's important to note that generative AI models are limited to generating content based on the patterns and examples they were trained on. They do not possess true creativity or understanding of the content they generate, and the generated outputs are based on statistical patterns learned from the training data.

Generative AI has applications in various domains, including image synthesis, text generation, music composition, video creation, and more.