Blog Updates

Deterministic Turing Machine Neural Networks

Do we need Neural Netorks learn to program to audit the quality of their solutions?

What about Deterministic Turing Machine Neural Networks?

#imaginationHasNoLimits#DTMNN

Please leave your opinions on the comments.

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A Signal Theory approach to Knowledge maps

To reduce Uncertainty, the application of the solutions contained in the Knowledge map will be more resistent to unexepected events as the Knowledge map grows.

Imagine an Analog to Digital Converter (ADC, namely a Researcher) that processes the Signals of the Analog Social Reality and converts them using the appropriate Nyquist rate (2 times the desired Bandwith) into Signals of the Digitial Social Reality (namely a Knowledge map). https://en.wikipedia.org/wiki/Nyquist-Shannon_sampling_theorem

The signal-to-noise and distortion ratio (SINAD) would measure the quality of the Signal. https://en.wikipedia.org/wiki/SINAD

While converting from Analog to Digital, it is commonly know that the SINAD is higher (namely the quality of the Digital Signal is higher, also because it is less error-prone due to it will be computed by a Deterministic Turing Machine with very low Uncertainty).

While converting from Digital to Analog,(namely People voting and acting) the quality of the signal is guaranteed due to the Nyquist rate.

In the hypothetical case of interferences to the Analog Signal might appear there would be pre-established Control Signals in the Knowledge map (namely, sanctions and correction courses of action) to mitigate these interferences and ensure the Analogue Signal arrives correctly to Destination.

Then, the more Signals and Control Signals stored, the wider the range of effectiveness of Knowledge maps. However, Control Signals harnessing Human Autonomy should be limited and finding a trade-off enough for all the Humans to arrive to Destination while carrying the Analogue Signals will be needed.

This trade-off will be found following the Less-discriminatory Majority rule.

Probabilistic Complexity Theory

#NPcompleteEqualsP#NPNotEqualscoNP and #PNotEqualscoP!!!

What is Non-deterministic is the Environment, and any NP problem can be P with the proper reduction of Uncertainty.

With a (Knowledge) map climbing any unknown mountain is #NPEqualsP.

Do you have the same probability of losing you than reaching the top with a (Knowledge) map? Of course not. #NPNotEqualscoNP

What’s the probability of losing you in an unknown mountain with a (Knowledge) map? Less than without a proper map.

Do you have the same probability of interpreting the map correctly and incorrectly? Of course not. #PNotEqualscoP

That opens a new discipline: “Deterministic Complexity in Uncertain and Non-deterministic Environments”

#ProbabilisticComplexityTheory #SocialRealityComplexity

Citing https://en.wikipedia.org/wiki/NP-completeness

“If any NP-complete problem has a polynomial time algorithm, all problems in NP do.”

Imagine then a route map with a Hamiltonian cycle over the mountain, following the route is #P even the real problem in the Environment is #NPcomplete so #NPcompleteEqualsP if properly dealt with.

https://en.wikipedia.org/wiki/Computational_complexity_theory

Shouldn’t we try to create the proper (Knowledge) maps to improve our Society? :

First Social Law

#NPEqualsP means that #ArtificialResearch is possible as the Generation of Knowledge Maps where:

#KnowledgeMap (KM) is a (possibly partial and globally agreed) implementation of a Social Discrimination function in a Social Discrimination Space of n dimensions evaluating Uncertainty and Discrimination.

KM(Absolute Majority)=U<<1 and D>>0 what means that although it’s widely accepted it’s far from non discrimination of other (losing) preferences, candidates and the real losers of the game: Non-Politicians. Namely, you and me (We).

Towards a #WeGoverment:

Contrarily, KM(Less-discriminatory Majority) is U<<1 and D<<1. You name it. It should deserve more study (and use) as it could be the first Social Law (de facto, for the time being) in the range of KM(Gravity Law) where U=0 and D=0.

Global Reset: Keep It Simple

Open AI = Global Government.

Redefine the (Digital and “SCI”-ence) Society of XII Century just starting with “The Less Discriminatory Majority Rule”.

(“SCI” stands for “Health, Consensus and Integration”, Adapted from Spanish.)

The Less Discriminatory Majority Rule:

The methodology is sketched by voting the k-preferences of the j-less-extremist ones, having always in mind the Global Social Inclusion Goal.

Concretely,

Social Theory of Everything: (Work in Progress)

6.- Artificial Research as Open Integration of:

5.- Multi-Domain Multi-Function Abstraction and Composition in Lebesgue Spaces: Open Standardization and Integration in Social Reality.

4.- Automated Policy-making Optimization: Compromise on Social Discrimination vs. Autonomous Society.

3.- Graded Ostracism: Metrics in the Discrimination Domain.

3.2.- Graded Ostracism = Forcing demote to a sub-society in Social Power metrics.

3.1.- Metrics in the Social Power Domain: Sub-societies total order = Sub-society Utility.

2.- Social Choice Optimization: Open Standardization of Social Inclusion: Inclusive Electoral Systems.

1.- Declarative mechanism design: A testbed for Regulated Deep Learning : First Programmable Nervous System mixing Agents (Proactive Senses), Norms about Communication (Nerves) and Deep Learning (Adaptive Neurons).

0.- Normative Regulation of Open Multi-Agent Systems : First Open Standardization and Integration of Norms and Electronic Institutions.

Artificial Research

Open AI = Global Government.

Redefine the (Digital and “SCI”-ence) Society of XII Century just starting with “The Less Discriminatory Majority Rule”.

(“SCI” stands for “Health, Consensus and Integration”, Adapted from Spanish.)

The Less Discriminatory Majority Rule:

The methodology is sketched by voting the k-preferences of the j-less-extremist ones, having always in mind the Global Social Inclusion Goal.

Concretely,

Social Theory of Everything: (Work in Progress)

6.- Artificial Research as Open Integration of:

5.- Multi-Domain Multi-Function Abstraction and Composition in Lebesgue Spaces: Open Standardization and Integration in Social Reality.

4.- Automated Policy-making Optimization: Comprise on Social Discrimination vs. Autonomous Society.

3.- Graded Ostracism: Metrics in the Discrimination Domain.

3.2.- Graded Ostracism = Forcing demote to a sub-society in Social Power metrics.

3.1.- Metrics in the Social Power Domain: Sub-societies total order = Sub-society Utility.

2.- Social Choice Optimization: Open Standardization of Social Inclusion: Inclusive Electoral Systems.

1.- Declarative mechanism design: A testbed for Regulated Deep Learning : First Programmable Nervous System mixing Agents (Proactive Senses), Norms about Communication (Nerves) and Deep Learning (Adaptive Neurons).

0.- Normative Regulation of Open Multi-Agent Systems : First Open Standardization and Integration of Norms and Electronic Institutions.

Collaborative Knowledge Evolution

Collaborative Knowledge Evolution can be seen as the Open Standardization and Integration of Open Knowledge, optimized from generation to generation thanks to Human Evolution.

To start with, it might be simulated in Artificial Societies through Inclusive Social Reality implementation, and then after the required (although not precisely the desired) pass of generations it could be implemented in Research and Socio-economic Systems.

“Salud, Consenso e Integración para todos: #SCI-ence4all”.

(“Health, Consensus and Integration for everyone: #SCI-ence4all” Translated from Spanish.)

We might design the (Digital and SCI-ence) Fair Society of the XII Century from now on.

#SCI-ence4all !!! 😉