{"id":1484,"date":"2025-04-16T12:26:30","date_gmt":"2025-04-16T15:26:30","guid":{"rendered":"https:\/\/bracis.sbc.org.br\/2025\/?page_id=1484"},"modified":"2025-08-18T09:30:13","modified_gmt":"2025-08-18T12:30:13","slug":"kdmile-2025","status":"publish","type":"page","link":"https:\/\/bracis.sbc.org.br\/2025\/kdmile-2025\/","title":{"rendered":"KDMiLe 2025"},"content":{"rendered":"\t\t
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KDMiLe 2025<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The\u00a0Symposium on Knowledge Discovery, Mining and Learning (KDMiLe)\u00a0aims at integrating researchers, practitioners, developers, students, and users to present their research results, discuss ideas, and exchange techniques, tools, and practical experiences \u2013 related to the Data Mining and Machine Learning areas.\u00a0
KDMiLe originated from WAAMD (Workshop em Algoritmos e Aplica\u00e7\u00f5es de Minera\u00e7\u00e3o de Dados) that occurred during five years \u2013 2005 to 2009 \u2013 as a Workshop of the Brazilian Symposium on Databases (SBBD). Since 2013, KDMiLe has been organized alternatively in conjunction with the Brazilian Conference on Intelligent Systems (BRACIS) and the Brazilian Symposium on Databases (SBBD).<\/p>

This year, 2025, in its thirteenth edition, KDMiLe will be held in Fortaleza, Cear\u00e1, from September 29 to October 2, in conjunction with the Brazilian Symposium on Databases (SBBD) and the Brazilian Conference on Intelligent Systems (BRACIS). This year KDMiLe is being organized by Federal University of Cear\u00e1.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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SPEAKER<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Isaac Triguero<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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University of Granada, Spain<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Title<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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From Traditional AI to General Purpose Artificial Intelligence Systems: A Journey Towards Open-World Intelligence<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Abstract<\/strong><\/p>

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them. The term General Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. Whilst we might still be far from achieving that, GPAIS is a reality and sitting at the forefront of AI research.
In this talk, I will present a definition for GPAIS that allows for a gradual differentiation among types of GPAIS according to their properties and limitations. We will distinguish between closed-world and open-world GPAIS, characterising their degree of autonomy and ability based on several factors such as adaptation to new tasks, competence in domains not intentionally trained for, ability to learn from few data, or proactive acknowledgement of their own limitations. To walk you through the transition from traditional AI to GPAIS, I will start off talking about some interdisciplinary projects I have worked on, including big data applications in collaboration with industry partners such as Unilever and E.ON, spanning sectors like energy, transportation, and healthcare. I will then shift focus to my latest research on generalisable models that may help accelerate AI adoption. Finally, I will emphasise the critical need for responsible and trustworthy AI development as we navigate this evolving landscape.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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IMPORTANT DATES<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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