NCA-GENM Flashcards

Train with labeled input-output pairs and optimi

Generate outputs token-by-token conditioned on prior context.

Generate by iterative denoising from noise toward structured output.

Learn compressed latent representation with probabilistic decoding.

Generator/discriminator competition for synthetic realism.

Encode source signal then decode target sequence/signal.

Shared attention mechanics speciali

Each modality has different structure, noise profile, and annotation cost.

Where and how modality streams are combined in the pipeline.

Learns alignment between modality tokens/regions/segments.

Shared semantic vector space enabling cross-modal retrieval.

Reduce modality gap so semantically similar items are close.

Practical application patterns likely to appear in scenario questions.

Speech-to-text conversion pipeline and quality constraints.

Text-to-waveform synthesis with intelligibility and prosody goals.

Review this concept in the chapter content.

Assign one or more labels to an image.

Predict object classes and locations.

Predict pixel-level masks for regions or objects.

Build representation vectors for downstream tasks.

Local hierarchical features versus global attention-based context.

Align vision and text in shared semantic space.

Core mechanism behind image-text retrieval systems.

Low-latency speech I/O loop for natural interaction.

Mapping speech dynamics to facial/body expression.

Audio-driven facial motion generation concept.

Visual output stack for believable interaction.

Decompose speech, reasoning, animation, and rendering services.

End-to-end orchestration across multimodal components.

Each modality has its own quality and normali

Controlled comparison under realistic usage conditions.

Systematic search across training/inference settings.

Better reliability when data is constrained.

Reproducible baseline-vs-candidate evaluation.

Runtime gates that decide deployment viability.

Review this concept in the chapter content.

Expose model behavior safely and consistently to clients.

Host versioned models with predictable runtime behavior.

Separate concerns for scale, resilience, and ownership clarity.

Interface tradeoffs by latency, typing, and ecosystem fit.

Automate validation and release with model-aware checks.

Review this concept in the chapter content.

Uneven outcomes caused by data, model, or pipeline behavior.

Plausible but unsupported outputs.

Policy and control layers reducing harmful behavior.

Controls for sensitive data access, retention, and exposure.

Risk-aware release and monitoring process.

Ownership, auditability, and accountability framework.

Enterprise software platform for AI lifecycle and operations.

Digital human and conversational avatar capability ecosystem.

GPU compute foundation for accelerated AI workloads.

Engine/runtime optimi