This workshop aims to bridge researchers and practitioners from NLP, computer vision, music, ML, and other computational fields to create the 1st workshop on “Creative AI across Modalities”.

Amazon organizers: Jing Huang, Arindam Mandal, Haw-Shiuan Chang

Website: https://creativeai-ws.github.io

Deep neural networks (DNNs) have received tremendous attention and achieved great success in various applications, such as image and video analysis, natural language processing, recommendation systems, and drug discovery. However, inherent uncertainties derived from different root causes have been serious hurdles for DNNs to find robust and trustworthy solutions for real-world problems. A lack of consideration of such uncertainties may lead to unnecessary risk. For example, a self-driving autonomous car can misclassify a human on the road. A deep learning-based medical assistant may misdiagnose cancer as a benign tumor. Uncertainty has become increasingly important, and it has been attracting attention from academia and industry due to its increased popularity in real-world applications with uncertain concerns. It also emphasizes decision-making problems, such as autonomous driving and diagnosis systems. Therefore, the wave of research at the intersection of uncertainty reasoning and quantification in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science.

Recent advancements in AI and ML have enabled these technologies to enhance and improve our daily lives; however, these solutions are often based on simplified formulations and abstracted datasets that make them challenging to be applied in complex and personalized household domains. Furthermore, any household solution will require not only expertise across algorithmic AI but also experts in interaction, socio-technical issues, and problem space. Since the solutions touch on so many different fields, its research community is spread across different conferences. The workshop is designed to bring together interested AI experts who, while coming from different subfields, share the vision of using AI technologies to solve user problems at home. Participants of the workshop will have the opportunity to share their experience and progress in using AI technologies to assist and empower users at home as well as learn and engage with our expert speakers/panelists.

More information and submission details can be found on: https://ai4athome.github.io/

In this tutorial, we aim to introduce researchers and practitioners in the web domain to the hyperbolic equivariant of the Euclidean operations that are necessary to tackle their application to neural network architectures.

Amazon organizers: Karthik Subbian, Srinivasan H. Sengamedu

Website: https://aaai.org/Conferences/AAAI-23/aaai23tutorials

The Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-23).