Update README overview

README.md

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 # Exercise 9: Explainable AI and Knowledge Extraction
 
 ## Overview
+The goal of this exercise is to learn how to probe what a pre-trained classifier has learned about the data it was trained on. 
 
-In this exercise we will:
-1. Use a gradient-based attribution method to try to find out what parts of an image contribute to its classification
-2. Train a CycleGAN to create counterfactual images
-3. Run a discriminative attribution from counterfactuals
+We will be working with a simple example which is a fun derivation on the MNIST dataset that you will have seen in previous exercises in this course. 
+Unlike regular MNIST, our dataset is classified not by number, but by color!
 
+![CMNIST](assets/cmnist.png)
 
+In this exercise, we will return to conventional, gradient-based attribution methods to see what they can tell us about what the classifier knows. 
+We will see that, even for such a simple problem, there is some information that these methods do not give us. 
+
+We will then train a generative adversarial network, or GAN, to try to create counterfactual images. 
+These images are modifications of the originals, which are able to fool the classifier into thinking they come from a different class!. 
+We will evaluate this GAN using our classifier; Is it really able to change an image's class in a meaningful way? 
+
+Finally, we will combine the two methods — attribution and counterfactual — to get a full explanation of what exactly it is that the classifier is doing. We will likely learn whether it can teach us anything, and whether we should trust it!
+
+If time permits, we will try to apply this all over again as a bonus exercise to a much more complex and more biologically relevant problem.
+
+![synister](assets/synister.png)
 ## Setup
 
 Before anything else, in the super-repository called `DL-MBL-2024`: