How does Generative AI create images, text, and videos?

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Generative AI creates images, text, and videos using deep learning models, primarily based on neural networks such as Generative Adversarial Networks (GANs) and Transformer architectures like GPT (for text) and DALL·E (for images). These models are trained on large datasets, learning patterns, structures, and correlations within the data.

1. Images: For image generation, models like DALL·E use a type of neural network that learns to generate images based on text prompts. It does this by encoding the relationship between words and visual elements. The model generates an image pixel by pixel, using learned features like shapes, textures, and color distributions that align with the textual input.

2. Text: Text-based generative models, like GPT, learn language patterns, grammar, and context from vast amounts of text data. When given a prompt, the model predicts the most probable next word or sequence based on context, generating coherent and contextually relevant sentences or paragraphs. GPT’s architecture allows it to understand and produce human-like text.

3. Videos: Video generation, like with tools such as Sora, extends the principles of image generation but adds the complexity of time and motion. The model learns how visual elements change over time, generating frame sequences that align with a given prompt. The AI learns to predict the temporal transitions between frames, incorporating movement, lighting, and continuity in scenes.

In all cases, generative models rely on extensive training datasets and computational power to synthesize new, often creative outputs that closely mimic the characteristics of the data they've been trained on.

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