update 2024 banner image

Author: fredhohman

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@@ -123,14 +123,14 @@ <h2>7<sup>th</sup> Workshop on <br> <b>Visualization for AI Explainability</b></
 
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             <div class="figure-caption">Example interactive visualization articles that explain general concepts and communicate experimental insights when playing with AI models.
             (a) <a href="https://distill.pub/2019/visual-exploration-gaussian-processes/" target="_blank">A Visual Exploration of Gaussian Processes</a> by Görtler, Kehlbeck, and Deussen (<a href="https://visxai.io/2018">VISxAI 2018</a>);
             (b) <a href="https://pair.withgoogle.com/explorables/fill-in-the-blank/" target="_blank">What Have Language Models Learned?</a> by Adam Pearce (<a href="https://visxai.io/2021">VISxAI 2021</a>);
             (c) <a href="https://theo-jaunet.github.io/MemoryReduction/" target="_blank">What if we Reduce the Memory of an Artificial Doom Player?</a> by Jaunet, Vuillemot, and Wolf (<a href="https://visxai.io/2019">VISxAI 2019</a>);
             (d) <a href="https://k-means-explorable.vercel.app/" target="_blank">K-Means Clustering: An Explorable Explainer</a> by Yi Zhe Ang (<a href="https://visxai.io/2022">VISxAI 2022</a>);
             (e) <a href="https://tiga1231.github.io/umap-tour/" target="_blank">Comparing DNNs with UMAP Tour</a> by Li and Scheidegger (<a href="https://visxai.io/2020">VISxAI 2020</a>);
-            (f) <a href="https://parametric.press/issue-01/the-myth-of-the-impartial-machine/" target="_blank">The Myth of the Impartial Machine</a> by Feng and Wu (<a href="https://parametric.press/">Parametric Press</a>);
+            (f) <a href="https://www.cs.brandeis.edu/~dylan/pac_learning/" target="_blank">PAC Learning Or: Why We Should (and Shouldn't) Trust Machine Learning </a> by Cashman (<a href="https://visxai.io/2023">VISxAI 2023</a>);
             (g) <a href="http://formafluens.io/client/mix10.html">FormaFluens Data Experiment</a> by Strobelt, Phibbs, and Martino.
             (h) <a href="https://idyll.pub/post/visxai-dimensionality-reduction-1dbad0a67a092b007c526a45/">The Beginner's Guide to Dimensionality Reduction</a> by Conlen and Hohman (<a href="https://visxai.io/2018">VISxAI 2018</a>).
             </div>