# Training Data > **Machine-Evolution Nexus:** [[articles/Digital Darwinism and the Invisible World of Machine Evolution|Digital Darwinism and the Invisible World of Machine Evolution]] places this node within the history and governance of substrate-independent evolution. **Domain:** Machine Learning / Provenance **Doc Type:** Canonical Wiki Node **Maturity:** Developed ## Definition Training data are examples or observations used to fit a machine-learning model’s parameters or behavior. ## Nexus Context For protein prediction, biological databases contain sequences and structures shaped by evolutionary history. Evolution is therefore part of the data-generating process, not merely an analogy. ## Evidence Boundary Data are selected, measured and curated. Provenance, coverage, leakage, consent and historical bias constrain what a model’s performance means. ## Relationships [[wiki/Machine Learning|Machine Learning]], [[wiki/Provenance|Provenance]], [[wiki/AlphaFold|AlphaFold]], [[wiki/Public Data Infrastructure|Public Data Infrastructure]] - **Article:** [[articles/war with empire/Authorship of the West|Authorship of the West]] argues that every dataset carries civilization's disputes, because the disputes are part of the dataset. ## Simple Reminders, Quotations, and Thoughts > “Artificial intelligence will inherit more than our information; it will inherit the arguments we leave unresolved.” > **— Bryant McGill**, *Authorship of the West, September 2026* [[reminders/Machine Succession/AI Will Inherit the Arguments We Leave Unresolved by Bryant McGill|AI Will Inherit the Arguments We Leave Unresolved by Bryant McGill]] > “The machines of the future will know almost everything about our civilization, but knowledge alone will not tell them what was worth preserving.” > **— Bryant McGill**, *Authorship of the West, September 2026* [[reminders/Preservation/Machines Will Need More Than Knowledge to Know What Was Worth Preserving by Bryant McGill|Machines Will Need More Than Knowledge to Know What Was Worth Preserving by Bryant McGill]] ## Sources / Provenance - Machine-learning methodology; AlphaFold methods.