Abstract
Lifelong Learning in the context of Artificial Intelligence is a new paradigm that is still in its infancy. It refers to agents that are able to learn continuously, accumulating the knowledge learned in previous tasks and using it to help future learning. In this position paper we depart from the focus on learning new tasks and instead take a stance from the perspective of the life-cycle of intelligent software. We propose to focus lifelong learning research on autonomous intelligent systems that sustain their performance after deployment in production across time without the need of machine learning experts. This perspective is being applied to three European projects funded under the CHIST-ERA framework on several domains of application.
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Notes
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Also called continual learning [2], among other terminological options.
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Acknowledgements
This work has been partially funded by the ALLIES, DELTA and LIHLITH projects supported by the EU ERA-Net CHIST-ERA and the Spanish Research Agency (LIHLITH, PCIN-2017-118; DELTA, PCIN-2017-082).
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Agirre, E., Jonsson, A., Larcher, A. (2021). Framing Lifelong Learning as Autonomous Deployment: Tune Once Live Forever. In: Marchi, E., Siniscalchi, S.M., Cumani, S., Salerno, V.M., Li, H. (eds) Increasing Naturalness and Flexibility in Spoken Dialogue Interaction. Lecture Notes in Electrical Engineering, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-15-9323-9_29
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DOI: https://doi.org/10.1007/978-981-15-9323-9_29
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