We invite high-high quality paper submissions of a theoretical and experimental character on generative AI subjects including, although not limited to, the following:
Nevertheless, these improvements also provide forth important issues for example moral factors, details privateness concerns, and potential biases. This workshop aims to take a look at how generative AI may be correctly and responsibly integrated into instruction, making sure that its Gains are maximized even though mitigating affiliated hazards.
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The leading objective of the workshop would be to push this re- lookup direction forward by: (one) internet hosting a set of talks on this subject matter.
A workshop on Bias in AI was held on August 18, 2020. A draft report which includes details about discussions has actually been posted. A recording of the function are available within the occasion page.
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There isn't any formal proceedings. Recognized papers and posters are going to be published over the workshop’s Internet site, In case the authors comply with the release in their manuscripts.
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This calendar year the AICS workshop emphasis will likely be over the “Stability of AI-enabled Programs,” concentrating on the rising threats concentrating on these technologies along with the advanced strategies necessary to safeguard them.
The afternoon session will take a look at VRD comprehending, with subject areas covering doc framework comprehension, structure parsing, and semantic extraction from elaborate reviews and forms. By engaging investigation shows, invited talks, along with a panel dialogue, this workshop aims to bridge the hole among textual and Visible doc processing, fostering interdisciplinary collaborations.
We want to Categorical our sincere gratitude to our technological method committee for generously volunteering their time and abilities to review submissions for our workshop.
The Synthetic Intelligence with Causal Technologies (AICT) workshop aims to debate the latest innovations in causal methodology, like novel causal discovery and causal inference methods, and also techniques for downstream causal duties which include causal illustration Understanding, causal reinforcement Discovering, causal fairness, and so on. We will likely investigate how these advancements within the causal Group can lead to unique subfields of AI like recommender systems, purely natural language processing, Laptop eyesight, and so on.
How can we embed a notion of uncertainty inside of AI/ML to quantify the importance of the here discovery? How can we guarantee Honest reproducibility of these discoveries? Which interpretable AI/ML strategies can develop immediate explanations of such discoveries?
Two different types of papers are welcomed: Investigate papers (approximately four pages in AAAI structure, excluding references). Papers currently approved at prime ML and AI conferences in the last year.