diff --git a/examples/tutorials/neurips_2022/README.md b/examples/tutorials/neurips_2022/README.md
index 303be4ed..14cbf7c5 100644
--- a/examples/tutorials/neurips_2022/README.md
+++ b/examples/tutorials/neurips_2022/README.md
@@ -3,15 +3,16 @@
## **NeurIPS'22 Tutorial**
### Monday, November 28th
9:30 a.m. - 12:25 p.m. CST
-
+
### Motivation
Graphs are general data structures that can represent information from a variety
of domains (social, biomedical, online transactions, and many more). Graph
-Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models
-for learning from graph data and inferring missing information (such as
-predicting labels of nodes or imputing missing edges).
+Neural Networks (GNNs) are an exciting way to use graph structured data inside
+neural network models that have recently exploded in popularity. However,
+implementing GNNs and running GNNs on large (and complex) datasets still raises
+a number of challenges for machine learning platforms.
#### Goals