Abstract
While AI is expanding to many systems and services from search engines to online retail, a revolution is needed, to produce rapid, reliable “AI everywhere” applications by “continuous, cross-domain learning”. We introduce Synthesizable Artificial Intelligence, and discuss its uniqueness by its five advanced “abilities”; (1) continuous learning after training by “connecting the dots”; (2) measuring quality of success; (3) correcting concept drift; (4) “self-correcting” for new paradigms; and (5) retroactively applying new learning for development of “long-term self-learning”. SAI can retroactively apply new concepts to old examples, “self-learning” in a new way by considering recent experiences similar to the human experience. We demonstrate its current and future applications in transferring seamlessly from one domain to another, and show its use in commercial applications, including engine sound analysis, providing real-time indications of potential engine failure.
The authors acknowledge the sponsorship of NASA Ames Research Center, US Department of Agriculture, National Science Foundation, US Department of Defense and the US Army Research Lab in their research.
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Notes
- 1.
While deep learning techniques have eliminated the need for automatically extracting features, they have been shown not to work well, for example, for texture datasets where the inherent dimensionality of the data is high [2].
- 2.
Some individual pieces of the puzzle are already developed in subfields of AI like active learning and transfer learning.
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Mukhopadhyay, S., Iyengar, S.S., Madni, A.M., Di Biano, R. (2019). The Next Generation of Artificial Intelligence: Synthesizable AI. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_50
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