Generative AI

Diffusion and flow-matching samplers can represent rich terminal laws but require iterative inference with many neural function evaluations and task-specific guidance. Generative AI is distinguished from earlier rule-based AI by its ability to produce adaptive, context-sensitive content and more naturalistic interaction. AI-generated messages are created with ChatGPT using the same topics, instructions and prompts as the human Content Team. Under a matched 60-second wall-clock training budget, MNO achieves materially lower W2 than the diffusion baseline while evaluating approximately 3× faster. MNO's advantage over iterative samplers is specific to applications requiring mean and covariance fields rather than full sample diversity. Generative AI encompasses large language models, multimodal systems, synthetic voice, conversational agents, avatars, and other tools capable of producing novel outputs such as text, speech, and images. No validated search filters currently exist for generative AI, requiring custom search term development for the review. The diffusion baseline's W2 does not improve substantially with more NFE steps, indicating the model is undertrained relative to MNO u…