update

people/kkersting/index.html

@@ -226,7 +226,7 @@
   co-founded the <a href=" https://www.ki-klub.de" target="_blank">KI-Klub</a>,
   connecting AI experts, media, public, and politicians, and
   published the Springer book <a href="https://link.springer.com/book/10.1007/978-3-658-26763-6" target="_blank">Wie Maschinen Lernen</a>,
-    one of the first German general introductory books on AI and, in particular, machine learning educating the promise and potential of AI to the broader society. We were also privileged enough to inform the public about AI and get our own work covered in (social) media such as New York Times, Washington Post, Financial Times, MIT Technology Review, Scientific American, ARTE, Frontiers Science Blog, Science, FAZ Digitec Podcast, KfW Podcast „Zukunft:digital“, Chainless Life Podcast, Heute Journal, Frankfurter Allgemeine Zeitung, Tagesspiegel, Handelsblatt, Frankfurter Rundschau, Zeit Campus, Heise, Spektrum der Wissenschaften, iX Magazin, Focus, among others, and an exhibition at the Nibelungen Museum in Worms, Germany, as well as a AI featured concert with the Singakademie Dresden e.V. To express my point of view on AI, I also had a regular column on AI in the German (Sunday) newspaper <a href="https://en.wikipedia.org/wiki/Die_Welt" target="_blank">Welt</a> (<a href="https://de.wikipedia.org/wiki/Welt_am_Sonntag" target="_blank">am Sonntag</a>).
+    one of the first German general introductory books on AI and, in particular, machine learning educating the promise and potential of AI to the broader society. We were also privileged enough to inform the public about AI and get our own work covered in (social) media such as New York Times, Washington Post, Financial Times, MIT Technology Review, Scientific American, ARTE, Frontiers Science Blog, Science, FAZ Digitec Podcast, KfW Podcast „Zukunft:digital“, Chainless Life Podcast, Heute Journal, Frankfurter Allgemeine Zeitung, Tagesspiegel, Handelsblatt, Frankfurter Rundschau, Süddeutsche Zeitung, Zeit Campus, Heise, Spektrum der Wissenschaften, iX Magazin, Focus, among others, and an exhibition at the Nibelungen Museum in Worms, Germany, as well as a AI featured concert with the Singakademie Dresden e.V. To express my point of view on AI, I also had a regular column on AI in the German (Sunday) newspaper <a href="https://en.wikipedia.org/wiki/Die_Welt" target="_blank">Welt</a> (<a href="https://de.wikipedia.org/wiki/Welt_am_Sonntag" target="_blank">am Sonntag</a>).
 </p>
 
 <p class="text-justify">

references.bib

@@ -11,17 +11,17 @@ @misc{friedrich2024llms
 
 @misc{skryagin2024asn,
   Anote = {./images/answer_set_networks.png},
-  title={Answer Set Networks: Casting Answer Set Programming into Deep Learning}, 
+  title={Answer Set Networks: Casting Answer Set Programming into Deep Learning},
   author={Arseny Skryagin and Daniel Ochs and Phillip Deibert and Simon Kohaut and Devendra Singh Dhami and Kristian Kersting},
-  Note={Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs of computing stable models and the CPU-bound nature of state-of-the-art solvers. To this end, we propose Answer Set Networks (ASN), a NeSy solver. Based on Graph Neural Networks (GNN), ASNs are a scalable approach to ASP-based Deep Probabilistic Logic Programming (DPPL). Specifically, we show how to translate ASPs into ASNs and demonstrate how ASNs can efficiently solve the encoded problem by leveraging GPU's batching and parallelization capabilities. Our experimental evaluations demonstrate that ASNs outperform state-of-the-art CPU-bound NeSy systems on multiple tasks. Simultaneously, we make the following two contributions based on the strengths of ASNs. Namely, we are the first to show the finetuning of Large Language Models (LLM) with DPPLs, employing ASNs to guide the training with logic. Further, we show the "constitutional navigation" of drones, i.e., encoding public aviation laws in an ASN for routing Unmanned Aerial Vehicles in uncertain environments.}, 
+  Note={Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs of computing stable models and the CPU-bound nature of state-of-the-art solvers. To this end, we propose Answer Set Networks (ASN), a NeSy solver. Based on Graph Neural Networks (GNN), ASNs are a scalable approach to ASP-based Deep Probabilistic Logic Programming (DPPL). Specifically, we show how to translate ASPs into ASNs and demonstrate how ASNs can efficiently solve the encoded problem by leveraging GPU's batching and parallelization capabilities. Our experimental evaluations demonstrate that ASNs outperform state-of-the-art CPU-bound NeSy systems on multiple tasks. Simultaneously, we make the following two contributions based on the strengths of ASNs. Namely, we are the first to show the finetuning of Large Language Models (LLM) with DPPLs, employing ASNs to guide the training with logic. Further, we show the "constitutional navigation" of drones, i.e., encoding public aviation laws in an ASN for routing Unmanned Aerial Vehicles in uncertain environments.},
   Keywords={Answer Set Programming, Deep Learning, Neuro-Symbolic AI, Large Language Models},
   Crossref={https://github.com/ml-research/answersetnetworks},
   year={2024},
   eprint={2412.14814},
   Howbulished={arXiv preprint arXiv:2412.14814},
   archivePrefix={arXiv},
   primaryClass={cs.AI},
-  url={https://arxiv.org/abs/2412.14814}, 
+  url={https://arxiv.org/abs/2412.14814},
 }
 
 @article{helff2024vlol,