The conversation surrounding artificial intelligence is evolving, highlighting the necessity for a deeper scientific understanding of its training dynamics.
AI models are not fixed entities; they represent snapshots of processes that change over time, influenced by data, objectives, and architectural choices.
This perspective encourages researchers and practitioners to consider the complexities involved in AI development rather than relying solely on post-hoc fixes.