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An Adaptive Vision Architecture for AGI Systems

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Artificial General Intelligence (AGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13921))

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Abstract

This paper presents an unsupervised object detection system which can offline-learn generic visual features via Siamese neural network, yet is able to learn new object classes at run-time with a prototype learning approach applied on the latent representations. The operating requirements of this system feature bounded processing time per frame, while dealing with a fixed amount of available memory. This system works under the Assumption of Insufficient Knowledge and Resource and is hence operating in real-time and open to new information which can arrive at any time, as systems such as NARS and AERA ideally also demand for perception.

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Notes

  1. 1.

    This is also referred to lifelong or incremental learning in Machine Learning [14].

  2. 2.

    Will be extended to a more complicated kernel and in multiple directions.

  3. 3.

    source code and the neural network models, which are trained using the Backpropagation algorithm, can be found at https://github.com/PtrMan/23R.

  4. 4.

    https://github.com/nim-lang/Nim.

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Acknowledgements

Special thanks to Patrick Hammer, Tony Lofthouse and Robert Johansson for valuable discussions.

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Authors and Affiliations

  1. Department of Psychology, Stockholm University, Stockholm, Sweden

    Robert Wünsche

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  1. Robert Wünsche
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Correspondence to Robert Wünsche .

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Editors and Affiliations

  1. Department of Psychology, Stockholm University, Stockholm, Sweden

    Patrick Hammer

  2. Örebro University, Örebro, Sweden

    Marjan Alirezaie

  3. University of Gothenburg, Gothenburg, Sweden

    Claes Strannegård

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Wünsche, R. (2023). An Adaptive Vision Architecture for AGI Systems. In: Hammer, P., Alirezaie, M., Strannegård, C. (eds) Artificial General Intelligence. AGI 2023. Lecture Notes in Computer Science(), vol 13921. Springer, Cham. https://doi.org/10.1007/978-3-031-33469-6_34

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  • DOI: https://doi.org/10.1007/978-3-031-33469-6_34

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-33469-6

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