Uncertainty and Trust Estimation in Incrementally Learning Function Approximation

Buschermöhle, Andreas; Schoenke, Jan; Brockmann, Werner

doi:10.1007/978-3-642-31709-5_4

Cited by 3 publications

(1 citation statement)

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“…One key aspect of Agri-Gaia with respect to providing data for AI at scale is the generation of synthetic data for training AI models in order to leverage existing high-quality real data. Here, we want to complement this line of work by developing a uniform uncertainty management embedded in the proposed AI and data streaming platform to support the generation of 'AI ready data' and find an application-agnostic data quality indication, cf., [13,14,28] for according related work about uncertainty estimation, architecture concepts and robust self-optimization. The importance of handling uncertain data is inherent to AI applications, but the current balance between the effort for handling data and tuning the model puts a disproportional weight on data handling.…”

Section: Related Projectsmentioning

confidence: 99%

Gaia-AgStream: An Explainable AI Platform for Mining Complex Data Streams in Agriculture

Schoenke¹,

Aschenbruck

Interdonato

et al. 2021

Communications in Computer and Information Science

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We present a position paper about our concept for an artificial intelligence (AI) and data streaming platform for the agricultural sector. The goal of our project is to support agroecology in terms of carbon farming and biodiversity protection by providing an AI and data streaming platform called Gaia-AgStream that accelerates the adoption of AI in agriculture and is directly usable by farmers as well as agricultural companies in general. The technical innovations we propose focus on smart sensor networks, unified uncertainty management, explainable AI, root cause analysis and hybrid AI approaches. Our AI and data streaming platform concept contributes to the European open data infrastructure project Gaia-X in terms of interoperability for data and AI models as well as data sovereignty and AI infrastructure. Our envisioned platform and the developed AI components for carbon farming and biodiversity will enable farmers to adopt sustainable and resilient production methods while establishing new and diverse revenue streams by monetizing carbon sequestration and AI ready data streams. The open and federated platform concept allows to bring together research, industry, agricultural start-ups and farmers in order to form sustainable innovation networks. We describe core concepts and architecture of our proposed approach in these contexts, outline practical use cases for our platform and finally outline challenges and future prospects.

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Section: Related Projectsmentioning

confidence: 99%