ETR technology

From black-box prediction to causal, traceable and adaptive intelligence.‍

Our foundational ETR (Emotional Theory of Rationality) cognitive architecture is a CELL-based design, focused on combining adaptability, resource efficiency and native traceability and explainability.

A pentadimensional cube representing the AAGI core of the ETR Technology from The Mindkind.

The Black Box problem, solved by architecture

The Black Box is one of the most significant open problems in AI: the opacity of machine learning models whose decision-making processes are difficult to interpret.

Rule-based systems are explicit and auditable, but they become rigid when conditions or combinations were not anticipated.

Statistical and neural systems adapt and capture non-linear relationships, but their internal representations are difficult to inspect and audit.

In critical or regulated environments, high average accuracy is not enough when operators cannot reconstruct why a decision was made or how the system will react to a novel condition.

Why now: auditability has deadlines

EU AI Act high-risk requirements make traceability, logging, technical documentation and human oversight procurement issues, not optional extras (Digital Omnibus, Reg. (EU) 2026/1744, in force 27 Jul 2026):

2 Dec 2027
Standalone high-risk systems (Annex III).

2 Aug 2028
AI embedded in regulated products (Annex I: medical devices, machinery, vehicles).

The solution: ETR architecture

ETR does not add an explanation layer on top of an opaque model.
Events, relations, decisions and consequences are stored in an inspectable (glass-box) shared knowledge graph as the system operates. Traceability is native to the ETR architecture, not reconstructed after the fact like post-hoc XAI (SHAP, LIME…).

The 'Black Box' issue arises because we don't know where or how AI-specific knowledge is stored (correlations).

Trustworthiness, or the 'Black Swan' phenomenon, is a concern with statistical models, as they can generate nonsensical responses (Long Tails) when confronted with the unknown.

The challenge of adaptability, as it is incapable of conceptualizing and extrapolating available knowledge to address new problems.

The efficiency challenge, as it requires substantial amounts of data, time, energy, and computational resources for learning.

Architecture

The ETR architecture: five differentiating principles

Bio-inspired by a neuroscientific research made by the founder since 2005, it has been scientifically supported by peer-review (Garcés & Finkel 2019), and funded by R&D public grants such as NEOTEC 2021, INVESTIGO 2022, CPP 2022 from Spain's Science Ministery, phase 2 EIC Accelerator, NextGEN (€1.4M since 2021)

1 · Energy/time as the organising constraints

Intelligence treated as efficient resource allocation under energy/time scarcity: what deserves attention, computation and action.

2 · CELL-based architecture

Small adaptive units process bounded information locally and interact through a common mechanism.

3 · Emergent complexity

Global behaviour is not fully programmed in one model; it emerges from the dynamic interaction of multiple CELLs.

4 · Shared Knowledge Graph

Events, relations, decisions and consequences are stored in an inspectable shared space. Traceability is native to the architecture, not reconstructed after the fact like post-hoc XAI (SHAP, LIME…).

5 · Emotions = functional optimisation elements

In ETR, emotions are not feelings: they are the optimisation mechanism that evaluates criticality, prioritises stimuli and allocates scarce cognitive resources (Garcés & Finkel, 2019).

A knowledge graph of concepts, actions and causality of ETR Cognitive Architecture.

Our "Special Sauce"

Evolution-inspired capability levels

Evolution-inspired, not biologically simulated: each level is a measurable capability, a benchmark and a commercial use case.

ETR 0.5 covers WORM and FLY in productised form, with native traceability across both. BEE — operational causality — is our first commercial milestone (release ETR 0.6, 2027).
‍
Expected progression BEE (2027) → MOUSE (2028) → PRIMATE (2029) → HUMAN-level general cognition (2030).

Level

Maintain critical variables and prioritise responses under changing inputs.

First commercial milestone · 2027 (ETR 0.6)

Productised (ETR 0.5)

General cognition

Open-ended generalisation, language, metacognition and cross-domain reasoning.

AGI target: 2030

Operational causality

Test action–consequence relationships, adapt continuously, remain efficient and traceable.

Productised (ETR 0.5)

WORM

FLY

BEE

Reactive regulation

Adaptive learning

MOUSE

PRIMATE

HUMAN

Abstraction

Transfer useful structure across tasks; cooperate and coordinate across CELL groups.

Context + planning

Episodic context, persistent goals, multi-step planning and anticipation.

2029 — unlocks B2B/B2B2C at consumer scale

2028 — unlocks autonomous systems

Learn non-linear action–outcome associations and update behaviour from experience.

Capability

What the system must do

Status

302
neurons in C. elegans. Its full wiring diagram was mapped in 1986 (White et al., Philos. Trans. R. Soc. B), yet predicting behaviour from that structure alone remains a scientific challenge.

Evolutionary insight:
Knowing the parts is not understanding the system

The lesson behind ETR: complex, effective and efficient behaviour emerges from dynamic interaction, context and regulation, not only from adding more components.

302
neurons in C. elegans. Its full wiring diagram was mapped in 1986 (White et al., Philos. Trans. R. Soc. B), yet predicting behaviour from that structure alone remains a scientific challenge.

86 Bn
neurons in a human brain…

20 W
…running on the power of a light bulb.

There are infinite applications and we discover more every single day

ETR 0.5 Fullytrazable & explainable
Machine LearningTechnology

Our ETR 0.5 technology offers a solution to the Black Box problem by providing fully traceable and explainable machine learning capabilities. This enables integration into decision-critical systems or systems requiring full explainability, especially in applications such as medicine, finance, industry, or justice.

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Meet your multidevice fully "Alive Intelligence"

Envision your personal Artificial General Intelligence that seamlessly accompanies you in your daily interactions with the digital realm, effortlessly integrated into your life, enhancing your experiences and simplifying your tasks.

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Videogames

We make sense of the digital environments by bringing Avatars and NPCs to life with human-like capabilities.

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Intelligent Robotics

Developing the first AAGI operating system that fully enables robots to assist us in tasks that were previously limited to humans.

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Really Autonomous Space-Explorers

Fully AAGI-powered autonomous space robots, initiating exploration and exploitation of extraterrestrial environments prior to human arrival.

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Our human-like AI technology for autonomous machines has garnered significant recognition and public funding from EU, national and regional governments.