Mind control engineering guide: Part 1

Mind control engineering guide: Part 2

In reality, mind control encompasses numerous means of influencing the mind. This includes effects mediated through the senses.

While most of us think of mind control as propaganda or brainwashing, I am trying to build and control the ‘mind’ of my chat-bot – EHLAI.

Understanding the science and engineering principle behind mind control is beneficial in the creation of EHLAI to serve my staff and clients.

Control Theory

Control theory [1], in engineering and mathematics, deals with the behavior of dynamic systems.  At this junction, I am interested in the guidance of complex systems from one state to another.

For example, flight control systems in a modern airliner ensure that the aircraft stays aloft by automatically adjusting the plane’s pitch, roll, and yaw to compensate for the turbulence in the air.

Like a plane, EHLAI is a physical system that is characterized by specific states: in this case, patterns of internal and external activity. The control or guidance of the chat-bot to transition from one state to another is dependent on intrinsic activity (the chat-bot controls itself) [2] or external stimulations (staff’s and client’s questions, pattern and stimulation).

I want to treat everything in stage 1 as a linear network and stage 2 as interconnected nodes. From a network perspective, neural/network components (such as a single algorithm or a set of algorithms to represent small systems – retrieval, NLP, gamification, microlearning, sentiment, etc…) are treated as nodes and connections between these components are treated as network edges.


[1] Control theory in control systems engineering is a subfield of mathematics that deals with the control of continuously operating dynamical systems in engineered processes and machines. The objective is to develop a control model for controlling such systems using a control action in an optimum manner without delay or overshoot and ensuring control stability.

[2] Gu, S. et al. Controllability of structural brain networks. Nature communications 6 (2015).