In the past, machine learning and decision-making have been treated as independent research areas. However, with the increasing emphasis on human-centered AI, there has been a growing interest in the understanding of how these two research fields interplay and can be jointly addressed to propose novel technological solutions having humans at their center.
By offering a large number of highly diverse resources, learning platforms have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promoted a significant growth of machine learning and knowledge discovery approaches and have opened up new opportunities for supporting and assessing educational experiences in a data-driven fashion.
The increasing amount of learning data, originated from a variety of learning contexts (e.g., Massive Open Online Courses - MOOCs, intelligent tutoring systems, and flipped classroom courses) is soliciting interesting educational research questions. Notable research directions include exploring and understanding how students learn and how people teach, as well as developing strategies for supporting learners and teachers effectively.
As cities worldwide are becoming increasingly interconnected and technologically advanced, it is crucial to explore the implications of algorithmic decision-making and AI applications on urban environments. The proposed workshop aims to delve into the profound influence of algorithms (AI in particular), platforms and services on the structure and dynamics of the urban ecosystem.
The 2024 edition of the SoBigData summer school will focus on how data can be used for social good, and this topic will be explored around five different thematic areas: European Framework and Communities, Trustworthy and Ethical AI, Ecology and Green development, Health, Technology and AI.
Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. Therefore IDA accepts all inspiring papers for both presentation and publication. In order to create an open atmosphere that encourages discussion, the IDA symposium is intentionally small-scale and single-track.
Lunedì 15 aprile 2024 nell'Aula Magna Nuova della Sapienza a Pisa si svolgerà una Giornata di studi sul tema de "Le regole dell’Intelligenza Artificiale".
L'evento é organizzato insieme ai Dottorati in Scienze giuridiche, Intelligenza artificiale nonché quello in studi religiosi e con il progetto FAIR.
From spatial to spatio-temporal and, then, to mobility data. So, what’s next? It is the rise of mobility-aware integrated Big Data analytics. The Big Mobility Data Analytics (BMDA) workshop series, started in 2018 with EDBT Conference, aims at bringing together experts in the field from academia, industry and research labs to discuss the lessons they have learned over the years, to demonstrate what they have achieved so far, and to plan for the future of mobility.
Aula Master, Officine Garibaldi, via Gioberti 39, Pisa, Italy
Description:
In questo intervento di training e disseminazione, verrà illustrata agli studenti di PhD in Intelligenza Artificiale e Informatica di diverse istituzioni (UniPi, SNS, CNR) il funzionamento dell’infrastruttura di ricerca SoBigData RI.
Verranno illustrate le potenzialità e le opportunità messe a disposizione agli studenti dall’infrastruttura e come questi possano interagire e accrescere la loro ricerca.
Verranno inoltre illustrate le modalità per accedere a risorse di calcolo offerte dall’infrastruttura.
Interverranno:
The First Workshop on Human-centered Artificial Intelligence focuses on the study of Artificial Intelligence systems that cooperate synergistically, proactively and purposefully with humans, amplifying instead of replacing human intelligence. Human-centered AI research aims for AI systems that work together with humans, emphasizing the need for adaptive, collaborative, responsible, interactive and coevolving intelligent human-AI ecosystems.
Humans, by nature, are said to be social, enthusiastic living beings. Interacting and discussing with people is crucial to them as food, water, and shelter are for their survival. While face-to-face communication has proven to enhance the quality of a person’s life, the effects of online interactions and discussions on individuals and society are more blurred and still widely debated.
The National Ph.D. in Artificial Intelligence has a new cohort of Ph.D. students is starting his path (39th cycle).
As usual, the staff has organized a Welcome Meeting to allow the new students meet the board members and the "old" students.
The 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) is organized by AIxIA (Associazione Italiana per l’Intelligenza Artificiale) , which is a non-profit scientific society founded in 1988 and devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.
In the past, machine learning and decision-making have been treated as independent research areas. However, with the increasing emphasis on human-centered AI, there has been a growing interest in combining these two areas. Researchers have explored approaches that aim to complement human decision-making rather than replace it, as well as strategies that leverage machine predictions to improve overall decision-making performance.
In the past decade, machine learning based decision systems have been widely used in a wide range of application domains, like credit score, insurance risk, and health monitoring, in which accuracy is of the utmost importance. Although the support of these systems has an immense potential to improve the decision in different fields, their use may present ethical and legal risks, such as codifying biases, jeopardizing transparency and privacy, and reducing accountability. Unfortunately, these risks arise in different applications.
By offering a large number of highly diverse resources, learning platforms have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promoted a significant growth of machine learning and knowledge discovery approaches and have opened up new opportunities for supporting and assessing educational experiences in a data-driven fashion.
