What is Generative AI?
Generative AI is artificial intelligence that creates content (like text, images, audio, video, and even code) based on patterns discovered in large data sets. Unlike other AI systems, which classify, predict, or recommend, generative AI creates original outputs that are similar to an original created by a person.
Generative AI can be used in many applications, including chatbots, image generators, music composers, and code assistants. Importantly, the model’s outputs may seem random, but they fall within certain probability models that work to identify and mimic the patterns, style, structure, and semantics of the data used to train the model.
How does Generative AI work?
Generative AI learns patterns from large datasets in the training phase. It doesn’t memorize examples but captures statistical relationships (i.e., grammar and context in text, pixel arrangement in images, or notes in music) in mathematical representations called embeddings. Models like Transformers, GANs, VAEs, and diffusion models process the data and learn the structure, style, and relationships of content.
When used to create content, the AI model requires a prompt or “seed,” and it produces new content one element at a time, or predicts the “next” element (words, pixels, sounds) based on those probabilities. If the outputs appear coherent and resemble human-made content, the model performs to some degree of success. Controlled randomness throughout generation allows for variability, while the outputs are still anchored in learned patterns from training.
What are the types of Generative AI?
Generative AI can be categorized by the techniques and outputs it produces. The main types include:
- Generative Adversarial Networks (GANs): Two models (a generator and a discriminator) work against each other, producing highly realistic images, videos, and synthetic data.
- Variational Autoencoders (VAEs): Used to generate data by encoding inputs into a latent space and decoding them back into new variations, often applied in image synthesis.
- Transformer-based Models: Large language and multimodal models (e.g., GPT, LLaMA, DALL·E, Stable Diffusion) that generate text, images, or code by predicting tokens based on context.
- Diffusion Models: A newer class of generative models that progressively refine noisy data into high-quality outputs, widely used in image generation.
- Audio/Music Generators: Specialized models like Jukebox or MusicLM that create original soundtracks, speech, or effects.
Is there a difference between AI and generative AI?
Artificial Intelligence (AI) is a broad category of systems designed to simulate human-like intelligence to perform functions. These functions include classification, prediction, optimization, or decision-making. Generative AI is an area of focus of AI where the emphasis is on generative content rather than just analyzing or responding.
For example, a standard AI model may be able to identify whether an email was spam or not spam, while a generative AI model might be able to write a brand-new email that mirrors the style of the earlier email. Simply, AI is completing intelligent computing tasks; generative AI is building original data, in imaginative forms, from processes based on iteration and patterning.