Deep learning memorization is a known phenomena, where deep learning / AI models memorize parts of their training dataset. This happens often for repeated examples, novel examples and occurs more often in overparameterized models.

This presents problems for guiding machine learning behavior, requiring much effort in guardrails and output monitoring, as well as questioning whether the models can be GDPR-compliant (i.e. the right to be forgotten).

A growing area of research on machine unlearning or machine forgetting has emerged to investigate ways a model might unlearn or forget particular memorized examples. In this talk, you'll learn about the field of machine unlearning and related topics like data anonymization to evaluate exactly what's truly unforgettable. Jokes aside: you'll have some practical take-aways to apply to your work in data and machine learning development.