Preprint Article Version 1 This version is not peer-reviewed

Superintelligent Deep Learning Artificial Neural Networks

Version 1 : Received: 17 December 2019 / Approved: 19 December 2019 / Online: 19 December 2019 (13:28:49 CET)

How to cite: Adamu, J. Superintelligent Deep Learning Artificial Neural Networks. Preprints 2019, 2019120263 (doi: 10.20944/preprints201912.0263.v1). Adamu, J. Superintelligent Deep Learning Artificial Neural Networks. Preprints 2019, 2019120263 (doi: 10.20944/preprints201912.0263.v1).

Abstract

Activation Functions are crucial parts of the Deep Learning Artificial Neural Networks. From the Biological point of view, a neuron is just a node with many inputs and one output. A neural network consists of many interconnected neurons. It is a “simple” device that receives data at the input and provides a response. The function of neurons is to process and transmit information; the neuron is the basic unit in the nervous system. Carly Vandergriendt (2018) stated the human brain at birth consists of an estimated 100 billion Neurons. The ability of a machine to mimic human intelligence is called Machine Learning. Deep Learning Artificial Neural Networks was designed to work like a human brain with the aid of arbitrary choice of Non-linear Activation Functions. Currently, there is no rule of thumb on the choice of Activation Functions, “Try out different things and see what combinations lead to the best performance”, however, sincerely; the choice of Activation Functions should not be Trial and error. Jamilu (2019) proposed that Activation Functions shall be emanated from AI-ML-Purified Data Set and its choice shall satisfy Jameel’s ANNAF Stochastic and or Deterministic Criterion. The objectives of this paper are to propose instances where Deep Learning Artificial Neural Networks are SUPERINTELLIGENT. Using Jameel’s ANNAF Stochastic and or Deterministic Criterion, the paper proposed four classes where Deep Learning Artificial Neural Networks are Superintelligent namely; Stochastic Superintelligent, Deterministic Superintelligent, and Stochastic-Deterministic 1st and 2nd Levels Superintelligence. Also, a Normal Probabilistic-Deterministic case was proposed.

Subject Areas

Stochastic Superintelligent;, Deterministic Superintelligent; Neuron; Stochastic Activation Function; Deterministic Activation Function; Probabilistic; Jameel’s ANNAF Criterion

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