Credit field


In the context of machine learning, "credit assignment" is a crucial concept that involves determining the specific contributions of various components within a system towards solving a problem. , a pioneer in AI research, emphasizes the importance of applying this principle not only within machine learning models but also in the broader field of machine learning research. He explains that understanding which elements of a system (e.g., engineers, mathematicians) are responsible for successes can optimize the entire scientific process by appropriately recognizing and incentivizing contributions. This can manifest not necessarily in monetary forms but more often in terms of reputation, which in turn can impact career opportunities and advancement within the field. The ongoing study of the history of advancements in machine learning is vital, as it can inspire new, alternative approaches and ensure a broad and informed perspective on the development of technologies 1.

Credit Assignment in ML

Jürgen explains how credit assignment is crucial in understanding the contributions of components in machine learning systems. Tim emphasizes the importance of directing resources to nodes that produce valuable insights to optimize scientific progress. Jürgen and Tim discuss the significance of studying the history of advancements to inspire alternative approaches in the field.

Machine Learning Street Talk (MLST)

Prof. Jürgen Schmidhuber - FATHER OF AI ON ITS DANGERS