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34th Brazilian Conference on Intelligent Systems (BRACIS)<\/h2>\n
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BRACIS 2024 Call for Papers – The 34th Brazilian Conference on Intelligent Systems (BRACIS) will be held in Bel\u00e9m, Brazil, from November 17-21, 2024. This is Brazil’s major AI and CI event. It invites the submission of research papers on various AI topics. Key dates: submission by June 13, 2024; notifications by August 10, 2024; and camera-ready copies by August 20, 2024.<\/p>\n
The conference features a double-anonymous review process and introduces four tracks, including a new “Published Papers in Top-Venues” track. Best papers may be invited for special issue publications. Submissions are via JEMS<\/a>. The website with more information about the event will be published soon. More details are as follows.<\/p>\n The Program Committee of the 34th Brazilian Conference on Intelligent Systems (BRACIS) invites submissions of original research papers for the conference to be held in Bel\u00e9m, PA, Brazil \u2013\u00a0** \u2013 from November 17th to 21st, 2024.<\/p>\n BRACIS is the most important event in Brazil for researchers interested in publishing significant and novel results related to Artificial and Computational Intelligence.<\/p>\n The Brazilian Conference on Intelligent Systems (BRACIS) originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (21 editions), and the Brazilian Symposium on Neural Networks – SBRN (12 editions).<\/p>\n BRACIS, which previously had 12 editions, will now be recognized as the 34th edition when considering its history and the 21 editions of SBIA. The 34th BRACIS plays a pivotal role in AI in Brazil, serving as a hub for promoting theoretical concepts and applications in Artificial and Computational Intelligence.<\/p>\n The event fosters a space for exchanging scientific ideas among researchers, practitioners, scientists, and engineers working toward advancing Artificial and Computational Intelligence science.<\/p>\n This aligns with the goals of other major international conferences proposed in a similar period of time in the history of AI, such as the 37th AAAI, 32nd IJCAI, and 37th NeurIPS (formerly called NIPS). The 33 previous editions of BRACIS highlight the pioneering of the Brazilian AI Community.<\/p>\n BRACIS works: <\/strong>https:\/\/drive.google.com\/drive\/folders\/1WaGDb_3rEzs38MXeRXYyVVB7b0M0CBVC?usp=sharing<\/a><\/p>\n Plenary Speakers<\/b><\/p>\n <\/p>\n Title:<\/strong> Revealing the Parametric Knowledge of Language Models<\/p>\n Abstract:<\/strong> Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant challenges for understanding a model’s inner workings and further for updating or correcting this embedded knowledge without the significant cost of retraining. Moreover, when using these language models for knowledge-intensive language understanding tasks, LMs have to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated knowledge. Nevertheless, studies indicate that LMs often ignore the provided context as it can be in conflict with the pre-existing LM’s memory learned during pre-training. Conflicting knowledge can also already be present in the LM’s parameters, termed intra-memory conflict. This underscores the importance of unveiling exactly what knowledge is stored and its association with specific model components, and how this knowledge is used for downstream tasks. In this talk, I will present our research on evaluating the knowledge present in LMs, through a unified knowledge attribution framework, as well as diagnostic tests that can reveal knowledge conflicts.<\/p>\n Bio:<\/strong> Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science, where she heads the Copenhagen Natural Language Understanding research group as well as the Natural Language Processing section. Her main research interests are fair and accountable NLP, including challenges such as explainability, factuality and bias detection. Prior to starting a faculty position, she was a postdoctoral researcher at University College London, and before that a PhD student at the University of Sheffield. In October 2022, Isabelle Augenstein became Denmark\u2019s youngest ever female full professor. She currently holds a prestigious ERC Starting Grant on ‘Explainable and Robust Automatic Fact Checking’, as well as the Danish equivalent of that, a DFF Sapere Aude Research Leader fellowship on ‘Learning to Explain Attitudes on Social Media\u2019. She is a member of the Royal Danish Academy of Sciences and Letters, and President of SIGDAT, which organises the EMNLP conference series.<\/p>\n <\/p>\n Elias Bareinboim<\/strong><\/p>\n Title: <\/strong>Towards Causal-based Artificial Intelligence<\/p>\n Bio: <\/strong>Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on causal and counterfactual inference and their applications to artificial intelligence, machine learning, and data science in biomedical and social domains. In particular, Bareinboim is a leading proponent and driving force behind the causal-based approach to AI, which he argues is essential for achieving more general forms of AI and capabilities such as decision-making (including reinforcement learning), explainability (including fairness analysis), and generalizability. His scientific contributions include the first general solution to the problem of ‘data fusion,’ providing practical methods for combining data generated under different experimental conditions and affected by various biases. Bareinboim currently serves as the editor-in-chief of the Journal of Causal Inference (JCI), the first journal dedicated to causal inference research, and as an action editor of the Journal of Machine Learning Research (JMLR), the premier journal focused on machine learning. He received his Ph.D. from the University of California, Los Angeles, where he was advised by Professor Judea Pearl. Recognized by IEEE as one of ‘AI’s 10 to Watch’, Bareinboim has also received several awards, including the NSF CAREER Award, ONR Young Investigator Award, DARPA Young Faculty Award, Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award. His research has been funded by grants and gifts from public agencies and private institutions, including the NSF, ONR, AFOSR, DARPA, the DoE, NIH, Amazon, JP Morgan, and the Alfred P. Sloan Foundation.<\/p>\n <\/p>\n <\/p>\n Please note that this year, we are inaugurating the track submissions. See further details in the Submission Details section.<\/em><\/strong><\/p>\n <\/em><\/strong><\/p>\n Program Committee:<\/b><\/strong><\/p>\n![\"\"](\"https:\/\/bracis.sbc.org.br\/2024\/wp-content\/uploads\/2024\/10\/WhatsApp-Image-2024-10-14-at-11.01.21-300x300.jpeg\")
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