Recent years have shown that trees, especially in big cities, have to face strenuous conditions due to missing rainfalls, rising temperatures and long-lasting droughts. But also environmental urban factors such as sealed surfaces, diverse soil types, dog excrements and reflecting house facades impact the tree’s wellbeing. In our talk, we will introduce you to the publicly funded project ‘QTrees’ and to our methods that we use to connect human and botanical intelligences with AI to implement solutions to support the effective watering and care of city trees. Furthermore, we show how the usage of machine learning can bridge the gap between local IoT sensors and physics-informed soil water balance models.
Artificial Intelligence (AI) and Machine Learning (ML) provide powerful tools to tackle climate change in various applications: They can support climate change mitigation, for instance, by helping reduce greenhouse gas emissions within various applications. They can help to adapt to a changing climate and even advance climate science itself. However, AI and ML are not silver bullets and can always only be one part of the solution. This talk provides an overview of the strengths and weaknesses of ML, some example applications and recurring themes.
Artificial Intelligence (AI) and Machine Learning (ML) provide powerful tools to tackle climate change in various applications: They can support climate change mitigation, for instance, by helping reduce greenhouse gas emissions within various applications. They can help to adapt to a changing climate and even advance climate science itself. However, AI and ML are not silver bullets and can always only be one part of the solution. This talk provides an overview of the strengths and weaknesses of ML, some example applications and recurring themes.
Die Künstliche Intelligenz (KI) bietet leistungsstarke Werkzeuge um den Klimawandel in verschiedenen Anwendungen anzugehen – aber sie ist kein Allheilmittel. Sie kann Maßnahmen in der Abschwächung des Klimawandels unterstützen, beispielsweise durch die Reduzierung von Treibhausgasemissionen in verschiedenen Anwendungen im Energie-, Transport-, Industrie oder Gebäudesektor. Sie kann aber auch die Anpassung an ein sich veränderndes Klima unterstützen, wie z.B. in Frühwarnsystemen für Naturkatastrophen. Weiterhin kann die KI die Klimaforschung selbst unterstützen, indem beispielsweise komplexe Simulationsmodelle beschleunigt werden. Allerdings: Die KI für sich ist ein Werkzeugkasten. Falsch eingesetzt, können die Werkzeuge dem Klima auch schaden! Um das zu vermeiden, sollten KI-Anwendungen in Zusammenarbeit und im ständigen Austausch mit denen entwickelt werden, die die Technologie nutzen oder anderweitig von ihr betroffen sind, um unvorhergesehene Auswirkungen und Nachteile zu vermeiden.
AI provides powerful tools to tackle climate change in various applications – but it is not a silver bullet. It can support the mitigation of climate change, for instance, by helping reduce greenhouse gas emissions within various applications. It can support adapting to a changing climate. AI can even support climate science itself. However, it can also be used to harm the climate. To avoid that, AI applications should be developed in collaboration and ongoing exchange with the communities that will use or are otherwise affected by the technology to avoid unforeseen impacts and drawbacks.
This expanding use of AI systems raises discussions about their risks, such as non-transparent decision-making processes, discrimination, increasing energy consumption and greenhouse gas emissions or increasing consumption, e.g., through personalised advertising. This talk presents preliminary insights from the project “SustAIn – Sustainability Index for Artificial Intelligence”, which aims at developing a comprehensive framework for assessing AI systems from a sustainability perspective. We outline conceptual considerations for establishing a comprehensive set of criteria that can be deployed in order to minimise negative social, ecological and economic impacts of AI-Systems and their applications. To develop a comprehensive assessment framework, we distinguish between the different scopes of impacts related to AI and we define sustainable AI maturity levels. We outline how we can define measures for each of the above criteria that need to be implemented to enhance the sustainability of AI systems.
Vorstellung des Projektes neue Berliner Luft.
Artificial Intelligence (AI) and Machine Learning (ML) provide powerful tools to tackle climate change in various applications: They can support climate change mitigation, for instance, by helping reduce greenhouse gas emissions within various applications. They can help to adapt to a changing climate and even advance climate science itself. However, AI and ML are not silver bullets and can always only be one part of the solution. This talk provides an overview of the strengths and weaknesses of ML, some example applications and recurring themes.
The Open Online Data Meetup (OODM) is an online-based meetup series which provides the space to share interesting insights and entertaining stories from the field of data science with other people from the community. OODM is organized by the CorrelAid Education Team together with CorrelAidX Bremen.
Wir zeigen, dass klassische Analysemethoden, die auf der Euklidischen Distanz und dem Arithmetischen Mittel beruhen für die Analyse von Smart Meter Daten unter gewissen Umständen zu ungewünschten Ergebnissen führen. Für Smart Meter Daten wurde daher die LPI-Distanz vorgestellt. In WindNODE wurden Algorithmen entwickelt, die die LPI-Distanz für Prognosen oder Cluster-Methoden nutzbar machen. Dabei müssen die unterschiedlichen Ergebnisse in der Datenanalyse im Kontext des eigentlichen Problems evaluiert werden. So zeigen wir, dass die Wahl der „besten“ Prognose vom Optimierungsziel abhängt.