Abstract
Deep learning embodied some essence of artificial intelligence, but it relied on data set and lacked migration learning ability. We should build general theorem to explain artificial intelligence from nature or human. We can treat each static data as a variable like wave-particle duality, then we can adopt idea from convolutional neural network or other machine learning algorithms to extract features from little data. This method can open up a new theory to accomplish migration learning and artificial intelligence. The theory will be supported by balance rule: everything has a gradient and tends to remain a zero gradient state, and we can connect different feature spaces by gradients.
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Li, W., Luo, G., Dai, F., Li, R. (2019). Balance Rule in Artificial Intelligence. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_24
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