Mihai Datcu, Gottfried Schwarz and Corneliu Octavian Dumitru (February 25th 2020). Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures, Recent Trends in Artificial Neural Networks - from Training to Prediction, Ali Sadollah and Carlos M. Travieso-Gonzalez, IntechOpen, DOI: 10.5772/intechopen.90910. 

Xiying Li and Zhihao Zhou (June 23rd 2019). Object Re-Identification Based on Deep Learning, Visual Object Tracking with Deep Neural Networks, Pier Luigi Mazzeo, Srinivasan Ramakrishnan and Paolo Spagnolo, IntechOpen, DOI: 10.5772/intechopen.86564. 

Austria

Explainable AI is rapidly becoming extremely important. Whilst classic rule-based approaches of early AI have been comprehensible &quot;glass-box&quot; approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. The introduction of statistical/probabilistic learning methods has made AI increasingly successful. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models, they still lack explicit declarative knowledge. For example, words are mapped to high-dimensional vectors, making them unintelligible to humans. What we need in the future are context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. This talk presents some recent approaches on how to understand why a machine decision has been reached, making results re-traceable, explainable and comprehensible on demand and paving the way for an expert in the loop to augment human intelligence with artificial intelligence.

IJCCI 2019

From Machine Learning to Explainable AI and Beyond

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation. IJCCI is composed of three co-located conferences, each specialized in at least one of the aforementioned main knowledge areas.

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Explainable AI is rapidly becoming extremely important. Whilst classic rule-based approaches of early AI have been comprehensible "glass-box" approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. The introduction of statistical/probabilistic learning methods has made AI increasingly successful. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models, they still lack explicit declarative knowledge. For example, words are mapped to high-dimensional vectors, making them unintelligible to humans. What we need in the future are context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. This talk presents some recent approaches on how to understand why a machine decision has been reached, making results re-traceable, explainable and comprehensible on demand and paving the way for an expert in the loop to augment human intelligence with artificial intelligence.

keynote

The growing complexity of nowadays distributed systems becomes a critical issue.  Distributed software systems are forced to integrate other software systems and components that are often not reliable, exhibit bad performance, and are sometimes unavailable. Such software is typically characterized by a huge problem size concerning number of computers, clients, requests and size of queries, autonomy and heterogeneity of participating organizations, and dynamic changes of the environment. To cope with unforeseen dynamics in the environment and vast number of unpredictable dependencies on participating components, there is a demand on self-organizing approaches.

From Machine Learning to Explainable AI and Beyond

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