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Paris
Legal data mining, machine learning and visualization
The aim of the conference is to structure a conversation on both the fundamental and practical issues on legal data mining and machine learning between scientists and professionals from artificial Intelligence, data science, law, and logic. The Legal Data Mining, Machine Learning and Visualization conference will explore the specific technical challenges from data mining and machine learning technique addressing together practical and legal theoretical issues. It is an opportunity for computer scientists to showcase and explore in conversation with lawyers further developments in AI and data-mining applied to the legal domains. Legal academics specializing in the interface of law and AI are given the opportunity to articulate the challenges of automated functions in law including in natural language processing applied to law, information extraction from legal databases and texts and data mining applied for legal analytics.
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Ljubljana
Conference, symposium - Thought
Repetition/s: Performance and Philosophy in Ljubljana
Contemporary developments in the increasingly intertwined fields of philosophy and performance call for a renewed inquiry into the question of repetition. With its unique critique of ideology arising from a synthesis of German Idealism and Lacanian psychoanalysis, the Ljubljana School (Dolar, Zupančič, Žižek et al.) continues to furnish important theorisations of repetition and performance as they pertain to subjectivity and the political. One of the primary aims of “Repetition/s” will be to investigate and develop the usefulness of the Ljubljana School’s theorisations for the emerging field of Performance Philosophy.
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Rabat
Conference, symposium - Economy
Open data, big data : quelles valeurs, quels enjeux ?
Cette cinquième conférence « Document numérique et société », organisée en partenariat avec l'École des sciences de l'information de Rabat, favorisera les observations et les retours d’expériences mais aussi les réflexions théoriques et critiques sur les modèles que l'open data et le big data suscitent et plus particulièrement ceux qui renouvellent le champ des sciences de l’information et de la communication.
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