Differential privacy influenced both privacy attacks and defenses you've investigated in this series on AI/ML memorization. You might be wondering: what exactly is differential privacy when it's applied to deep learning? And can it address the problem of memorization?

Are you a visual learner? There's a YouTube video on …

In the past articles, you've been exploring the field of machine unlearning, investigating if you can surgically remove memorized or learned data from models without retraining them from scratch or from an earlier checkpoint.

Unlearning is one proposed solution to the AI/ML memorization problem explored in this multi-article series …

Building on our understanding of machine unlearning and its varied definitions, in this article you'll learn common approaches to implementing unlearning. To effectively use these approaches, you'll first want to define what unlearning definition and measurement fits your needs.

In current unlearning research, there are three main categories of unlearning …

Machine unlearning sounds pretty cool. It is the idea that you can remove information from a trained model at will. If this was possible, you'd be able to edit out things you don't want the model to know, from criminal behavior, racialized slurs to private information. It would solve many …

You've probably at some point heard the term "guardrails" when talking about security or safety in AI systems like LLMs or multi-modal models (i.e. models that include and produce multiple modalities, like speech and image, videos, image and text).

Are you a visual learner? There's a YouTube video for …

One common way to control memorization in today's deep learning systems is to fix the problem by building software around it. This software can also be used to deal with other undesired behavior, like producing hate speech or mentioning criminal activities.

Are you a visual learner? There's a YouTube video …

In this article series, you've been able to investigate memorization in AI/deep learning systems -- often via interesting attack vectors. In security modeling, it's useful to explicitly define the threats you are defending against, so you can both discuss and address them and compare potential interventions.

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In this article, the last in the problem exploration section of the series, you'll explore adversarial machine learning - or how to trick a deep learning system.

Adversarial examples demonstrate a different way to look at deep learning memorization and generalization. They can show us how important the learned decision space …

At this point in reading the article series on AI/ML memorization you might be wondering, how did the field get so far without addressing the memorization problem? How did seminal papers like Zhang et al's Understanding Deep Learning Requires Rethinking Generalization not fundamentally change machine learning research? And maybe …

You've heard claims that we will "run out of data" to train AI systems. Why is that? In this article in the series on machine learning memorization you'll explore model size as a factor in memorization and the trend for bigger models as a general problem in machine learning.

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