Neural Network’s type of approximation math function (Cherkassky & Mulier 2007)

Abstraction and reasoning remain a challenge for artificial intelligence (AI)!

Abstraction and reasoning are elusive notions, but they may play a crucial role in artificial intelligence’ s decision making process [1]. By analogy to human brain, artificial intelligence (AI) makes decisions by “learning” from features discovered in the data (text, image, sound, sensor, video, etc.) via deep learning, which involves large artificial neural networks (ANN) with multiple layers of connected “artificial” neurons.

A biological neuron is a functional cell of the nervous system. It has dendrites that receive signals and an axon that transmits signals to another neuron
A biological neuron is a functional cell of the nervous system. It has dendrites that receive signals and an axon that transmits signals to another neuron
A biological neuron is a functional cell of the nervous system. It has dendrites that receive signals and an axon that transmits signals to another neuron - Bari (2017)
ANN — McCulloch-Pitts model neuron (i) with three inputs (dendrites) and one output (axon). The neuron receives information as the weighted sum (x=wi1x1+ wi2x2+ wi3x3) of the three inputs (x1, x2, x3), which in turn is passed through a discontinuous threshold sigmoid non-linearly to obtain a new activation (new input) value, denoted by y = f (x) = f (wi1x1+ wi2x2+ wi3x3). Golden (1996) [1] (Bari 2017)

OperAI develops IoTs with Math and AI Embedded Solutions to speed up and streamline operational processes at the edges of the cloud.