Generative AI creates new text, images, audio, video, and code by learning patterns from massive datasets and then sampling plausible outputs—most commonly with transformers for language and diffusion for visuals—refined by feedback and grounded in up‑to‑date information when needed.
The core idea
- Models learn a data distribution during training and then generate fresh samples that fit that distribution, like predicting the next word or denoising an image from noise.
- Large language models perform next‑token prediction with transformers: self‑attention weighs relationships among all tokens to produce coherent sequences.
Key model families
- Transformers (text/code): stack layers of self‑attention and feed‑forward networks to model long‑range dependencies; dominate chatbots and coding assistants.
- Diffusion models (images/video): start with random noise and iteratively remove it to render the requested scene; prized for stability and controllability.
- GANs and VAEs: adversarial training and latent‑space modeling still power tasks like image enhancement, stylization, and anomaly simulation.
How outputs are sampled
- Decoding strategies shape creativity: greedy vs. nucleus/top‑k sampling and temperature control the balance between accuracy and novelty in generated text.
- For visuals, guidance scales and denoising steps tune fidelity vs. creativity, trading speed for detail.
Making models useful and safe
- RLHF and DPO align behavior with human preferences, teaching models to be helpful, harmless, and honest by learning from curated comparisons.
- Retrieval‑augmented generation keeps answers factual by fetching documents at query time and conditioning outputs on them, avoiding full retraining for changing knowledge.
Multimodal systems
- New models understand and generate across text, images, audio, and video, enabling assistants that can read screens, parse forms, describe scenes, and follow voice instructions.
- Mixture‑of‑Experts and sparse activation techniques improve efficiency by activating only relevant experts for each token or region.
When to fine‑tune vs. use RAG
- Fine‑tune when tone, style, or task format must match your brand or domain tightly for long periods.
- Prefer RAG when facts change often or data must stay private; you keep the base model intact and swap or update the knowledge source.
Limits and failure modes
- Hallucinations occur when models overgeneralize beyond training; guard with grounding, validation, and evaluation rubrics.
- Bias and privacy risks stem from training data; mitigate via data curation, red‑teaming, and privacy‑preserving techniques.
Bottom line: machines “create” by learning the statistical structure of data and sampling from it—transformers write, diffusion paints, and alignment plus retrieval make results useful, controllable, and current for real‑world tasks.
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