Framework for Absolute Knowledge (Singularity)

We are rapidly advancing toward a world of perfect knowledge. With countless sensors gathering data increasingly ubiquitously (e.g., autonomous cars, satellite systems, drones, wearable devices, cameras), we will be able to know practically anything we wish, anytime and anywhere, as well as have the capacity to query vast data repositories for insights and answers.

The “Internet of Everything” (IoE) encompasses the progressively extensive network of intimate connectivity between devices, people, processes, and data. It is estimated that by 2025, the IoE will exceed 100 billion connected devices, with each containing a dozen or more sensors for the acquisition of data. This will culminate in a trillion-sensor economy, which enables a data revolution that is beyond our imagination.

In order to be able to render perfect knowledge (absolute knowledge), perfect knowledge may be defined as the acquisition and subsequent availability of all data associated with a given environment in order to generate accurate intelligence through the incorporation of every parameter (both visible and hidden) within that environment. To achieve technological singularity, we need to achieve the state of absolute knowledge.

Conventional robots, which are specifically developed to carry out dedicated functions including floor cleaning, restaurant assistance, game playing, and as companions, possess predefined rules within AI modules, which can perform and learn within the scope of assigned functions.

Next generation robots will have the capacity to learn on the fly using visual inputs, and perform based on particular scenarios. These evolutionary robots will make decisions and learn from outputs, in a manner similar to humans.

An optimally intelligent robot will be able to analyze data accurately in real-time, where to be accurate, it will be critical for it to consider every parameter of its environment in ultrahigh resolution. This translates to the capacity to measure every aspect of the environment, spanning direction, pressure, force, bioentities, particulates, and so on (as depicted in the illustration).

For example, an intelligent robot will have the capacity to sense deadly viruses/bacteria in the environment and subsequently alert humans. Further, it could even scan scientific literature or virtually anything on the web, and employ its sensors to quantify pressure or thermal parameters, in order to contain risks

An intelligent construction robot will have the requirement to constantly measure angles, distances, displacement, force, and other parameters to match or exceed human performance. This would require continuous data acquisition from myriad types of sensors toward the analysis of next steps in real-time. Intelligent robots demonstrate the use of multiple sensor devices for data collection, which needs to be computed in order to analyze and derive downstream actions. This feed is integrated into the motor components of the robot.

Toward the achievement of these goals using a simple strategy, we will need to develop a single standalone platform that can collect and unify data across all sensing domains, to initiate real-time learning and decision-making methods, and to coordinate with the motor elements of the machine.

The graphic below outlines the connectivity of various unique sensors that can measure energy fields, which may be combined to arrive at synthesized intelligence for automation (decisions and next actions).

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