Abstract
In recent years, AI based on deep learning has achieved tremendous success in specialized tasks such as speech recognition, machine translation, and the detection of tumors in medical images. Despite these successes there are also some clear signs of the limitations of the current state-of-the-art in AI. For example, biases in AI-enabled face recognition and predictive policing have shown that prejudice in AI systems is a real problem that must be solved. In this position paper, we argue that current AI needs to be developed along four dimensions to become more generally applicable and trustworthy: environment, purpose, collaboration, and governance. Hybrid AI offers the potential for advancements along these four dimensions by combining two different paradigms in AI: knowledge-based reasoning and optimization, and data-driven machine learning.
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This position paper was funded by the TNO Early Research Program Hybrid AI.
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Huizing, A., Veenman, C., Neerincx, M., Dijk, J. (2021). Hybrid AI: The Way Forward in AI by Developing Four Dimensions. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_6
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