Generative AI
Generative AI
Generative AI (Artificial Intelligence) refers to a class of AI systems that can create new content—such as text, images, audio, code, video, and more—based on the data they’ve been trained on. Unlike traditional AI that classifies or predicts based on existing inputs, generative AI produces entirely new outputs that resemble human-made content.
How Does Generative AI Work?
Generative AI models are trained using machine learning, particularly a subset called deep learning. The most common architectures include:
Transformer Models
- Examples: GPT (Generative Pre-trained Transformer), BERT, T5
- Trained on massive datasets of text and learn to predict the next word in a sentence.
- Used for tasks like text generation, summarization, translation.
Generative Adversarial Networks (GANs)
- Consist of a generator and a discriminator.
- The generator creates fake data, the discriminator evaluates it.
- Used mainly in image and video generation.
Variational Autoencoders (VAEs)
- Learn the underlying distribution of input data and generate new samples from it.
- Used in image synthesis, anomaly detection.
Diffusion Models
- Generate images by reversing a noise process.
- Power tools like DALL·E 2, Midjourney, and Stable Diffusion.
Examples of Generative AI in Action
| Type of Content | Tool or Model | Output |
|---|---|---|
| Text | ChatGPT, Claude, Gemini | Emails, essays, blogs, chat responses |
| Images | DALL·E, Midjourney, Stable Diffusion | Artwork, product designs, concept visuals |
| Code | GitHub Copilot, CodeWhisperer | Programming code, scripts, documentation |
| Audio | ElevenLabs, Google MusicLM | Voice clones, music generation |
| Video | Sora (OpenAI), RunwayML, Pika | Short videos, animations, synthetic actors |
| 3D Models | NVIDIA GET3D, OpenAI Shap-E | 3D object creation for games, AR/VR |
| Data | Synthetic data generation tools | Privacy-safe datasets for training/testing |
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