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.

Prefer to learn by …

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.

Prefer …

So far in this series on memorization in deep learning, you've learned how massively repeated text and images incentivize training data memorization, but that's not the only training data that machine learning models memorize. Let's take a look at another proven memorization: novel examples.

Prefer to learn by video? This …

In this article in the deep learning memorization series, you'll learn how one part of memorization happens -- highly repeated data from the "head" of the long-tailed distribution.

Prefer to learn by video? This post is summarized on Probably Private's YouTube.

Recall from the data collection article that some examples are …

In this article in the series on machine learning memorization, you'll dive deeper into how typical machine learning training and evaluation happens, a crucial step in ensuring the machine learning model actually "learns" something. Let's review the steps that lead up to training a deep learning model.

High-level steps to …

In this series, you've learned a bit about how data is collected for machine learning, but what happens next? You need to turn the collected data -- images, text, video, audio or even just a spreadsheet -- into numbers that can be learned by a model. How does this happen?

You probably are already aware that many machine learning datasets come from scraped internet data. Maybe you received the infamous GPT response: "Please note that my knowledge is limited to information available up until September 2021." You might have also read fear-mongering opinions and articles that companies will "run out …

When's the last time that ChatGPT parroted someone else's words to you? Or the last time a diffusion model you used recreated someone's art, someone's photo, someone's face? Has Copilot given you someone else's code without permission or attribution? If this happened, how would you know for sure?

In this …