Original article can be found here (source): Artificial Intelligence on Medium
Sense Theory. Sense Antiderivative
Like each neuron of the human brain may be connected to up to 10,000 other neurons, passing signals to each other via as many as 1,000 trillion synaptic connections, in Sense Theory there is a possibility for connecting over 1,000 trillion heterogeneous objects. An object in Sense Theory is like a neuron in the human brain. Properties of the object are like dendrites of the neuron. Changing object in the process of addition or deletion of its properties is like forming a new knowledge in the process of synaptic connections of two or more neurons. In Sense Theory, we introduced a mechanism for determining possible semantic relationships between objects by connecting-disconnecting different properties. This mechanism is Sense Integral. In this article, we describe one of the instruments, sense antiderivative, that sheds light on the nature of forming new knowledge in the field of Artificial Intelligence.
In traditional mathematics, the antiderivative of a function of a single variable, for example, measures the area under the curve of the function. In Sense Theory, the antiderivative of a sense function  determines a possible new knowledge (or describing current one more deeply) by the addition or deletion of the properties. It also clearly shows sense associations between the properties of different objects.
Unlike traditional integral calculus where infinitesimals used, in Sense Theory, we operate sets (finite or infinite) of possible properties of NoSense Set (Object No-Sense Set) for zero-object (‘s).
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