feat(uai-2023): enhance category and tutorial video details

- Add a comprehensive description for the UAI 2023 conference.
- Include a URL for the conference website.
- Improve the formatting of the tutorial video description for clarity.

Author: ELC

uai-2023/category.json

@@ -1,3 +1,5 @@
 {
-  "title": "UAI 2023 (Pittsburgh)"
-}
+  "description": "Conference of Uncertainty in Artificial Intelligence held in Pittsburgh in 2023. The Conference on Uncertainty in Artificial Intelligence (UAI) is one of the premier international conferences on research related to knowledge representation, learning, and reasoning in the presence of uncertainty. UAI is supported by the Association for Uncertainty in Artificial Intelligence (AUAI).",
+  "url": "https://www.auai.org/uai2020/",
+  "title": "UAI 2023"
+}

uai-2023/videos/uai-2023-tutorial-data-compression-with-machine-learning.json

@@ -1,5 +1,5 @@
 {
-  "description": "\"Data Compression With Machine Learning\"\nKaren Ullrich, Yibo Yang, Stephan Man\n The efficient communication of information is an application with enormous societal and environmental impact, and stands to benefit from the machine learning revolution seen in other fields. Through this tutorial, we hope to disseminate the ideas of information theory and compression to a broad audience, overview the core methodologies in learning-based compression (i.e., neural compression), and present the relevant technical challenges and open problems defining a new frontier for probabilistic machine learning.",
+  "description": "Data Compression With Machine Learning \n Karen Ullrich, Yibo Yang, Stephan Man \n The efficient communication of information is an application with enormous societal and environmental impact, and stands to benefit from the machine learning revolution seen in other fields. Through this tutorial, we hope to disseminate the ideas of information theory and compression to a broad audience, overview the core methodologies in learning-based compression (i.e., neural compression), and present the relevant technical challenges and open problems defining a new frontier for probabilistic machine learning.",
   "duration": 7209,
   "language": "eng",
   "recorded": "2023-07-31",