Data Scaling

Scaling law optimizing compute vs data.

Cost to run models in production.

Cost of model training.

Competitive advantage from proprietary models/data.

Compromising AI systems via libraries, models, or datasets.

Embedding signals to prove model ownership.

Detecting unauthorized model outputs or data leaks.

Increasing model capacity via compute.