Currently, we are dealing with a very limited set of activation functions such as Sigmoid, ReLu, Leaky ReLu among others. These activation functions used in the existing digital brain neural network systems are chosen using assumption with the help of “trial and error” approach. However, they do not ethically and appropriately establish any relationship with the referenced AI datasets. Jamilu (2019) proposed that a digital brain should have at least 2000 to 100 billion distinct activation functions implying distinct artificial neurons satisfies Jameel’s criterion(s) for it to normally mimic the human brain. The objectives of this paper are to propose a theorem called “Digital Brain Completeness Theorem”, “superintelligent digital brain neural network systems” and why it is tremendously important to have an extremely huge distinct activation functions implying distinct artificial neurons in a digital brain just like in the case of its counterpart for it to function rationally.
The existing neural networks activation functions, the Sigmoid among others is the only set uses across various applications of NNs such as microscopy, neuromorphic, optical, robotics, finance and transportation. Only one set applies to different areas of application. Also, these activation functions’ selections are based on trial and error, neither emanate from the AI and or training datasets, nor from the testing data. This formed NNs’ Black-box. Jamilu (2019) proposed that strong links between the AI and or training datasets and activation functions must be established. This is to replace the NNs’ Black-box models with the models rely much less on experts’ assumptions, and much more on input AI and or training datasets, time change and specific area of application. Thus, Jamilu (2019) proposed Criterion(s) for the rational selection of activation functions. The paper is to use superintelligent NNs for stock price predictions, portfolio optimization, and general application approaches to shed light on the paper’ title.
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