# AlteregoNets: a way to human augmentation

## TL;DR

AlterEgoNets are personalized neural networks that simulate an individual's internal thoughts and feelings about objects through a dynamic narrative stream, offering a novel approach to human augmentation.

## Contribution

The paper introduces AlterEgoNets, a new person-dependent network architecture that models internal human representations and perceptions through iterative cycles, enabling personalized simulation of thoughts.

## Key findings

- Proposes a novel network architecture for human perception modeling.
- Demonstrates potential for personalized human augmentation.
- Introduces an algorithmic scheme mimicking perception cycles.

## Abstract

A person dependent network, called an AlterEgo net, is proposed for development. The networks are created per person. It receives at input an object descriptions and outputs a simulation of the internal person's representation of the objects. The network generates a textual stream resembling the narrative stream of consciousness depicting multitudinous thoughts and feelings related to a perceived object. In this way, the object is described not by a 'static' set of its properties, like a dictionary, but by the stream of words and word combinations referring to the object. The network simulates a person's dialogue with a representation of the object. It is based on an introduced algorithmic scheme, where perception is modeled by two interacting iterative cycles, reminding one respectively the forward and backward propagation executed at training convolution neural networks. The 'forward' iterations generate a stream representing the 'internal world' of a human. The 'backward' iterations generate a stream representing an internal representation of the object. People perceive the world differently. Tuning AlterEgo nets to a specific person or group of persons, will allow simulation of their thoughts and feelings. Thereby these nets is potentially a new human augmentation technology for various applications.