ETR 0.5
Fully trazable & explainable
ML Technology

The Black Box is one of the most significant open problems in the field of AI. It refers to the opacity of certain Machine Learning (ML) models, especially deep neural networks. In these, decisions and predictions are made through complex internal processes that are difficult to interpret, making it hard to understand how and why a model reaches a specific conclusion. This presents challenges for adoption of ML technology in critical systems.

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

The Challenge

ETR 0.5 ML Technology: all for one and one for all

Our ETR 0.5 Technology combines the advantages of two complementary ML technologies: the Neural Networks and the Decision Trees & Random Forest. It learns from each interaction, dynamically accumulating knowledge that can be applied across various applicatons, enhancing its adaptability and effectiveness.

ETR 0.5 vs Neural Networks

Neural Networks are capable to detect the non-linear combined effects of low importance variables, but their decisions are not traceable nor explainable (Black Box)

TRACEABILITY
ETR 0.5
provides continuous information about which factors and how they influence the final decision, whereas Neural Networks are black-box models with zero traceability
ADAPTABILITY
ETR 0.5 does not need to beretrained if a new feature becomes availablefor prediction. In contrast, if a neural network has been trained with alimited number of inputs, it cannot continue training by adding a new one. Itwould need to be retrained from scratch.
TRANSFER-LEARNING
ETR 0.5
can easily transfer knowledge from one system to another or the removal of features without retraining, thus making it highly versatile for dealing with highly changing environments.

ETR 0.5 vs Decision Trees & Random Forest

which allow to understand what are the main variables that are taking into account to make the proposed decisions:

CROSSED-VARIABLES INTERACTIONS
Decision Trees and Random Forests
are traceable but will never identify the importance of combined features XY unless X and Y have a significant individual impact on the model.
ETR 0.5 captures complex relationships hidden in the data much better, which can have a high impact in non-linear systems.
FLEXIBILITY & CUSTOMIZATION
ETR 0.5
offers greater control over algorithm decision-making, allowing parameter adjustments to optimize the model and find the best configuration for the specific problem.
SCALABILITY AND TRANSFER-LEARNING (FUSSION AND RETRAINING)
ETR 0.5
is easily scalable. Adding new features to the model does not require retraining from scratch. Also, it is possible to easily merge knowledge acquired by two different ETR systems trained separately.
CONTINUOUS PROCESSING AND REINFORCEMENT LEARNING
ETR 0.5,
Like other machine learning algorithms, can process continuous inputs andadapt to dynamic environments. Its adaptability makes it perfect for solvingproblems where data entrance changes over time.

System Parameters

These are the parameters defining potential adaptation of this tool todifferent applications:

Input Variables:
ETR 0.5 handles a large and dynamic set of input variables without the need for retraining. This is especially useful in environments where the data features are constantly evolving or where new variables are discovered over time.

Unlike neural networks or decision trees, which may struggle with scalability or require full retraining to accommodate new inputs, ETR 0.5 can natively support an expanding input space.

Computational Resources:
ETR 0.5 is resource-efficient both in training and operation. It supports modular updates without the heavy computational cost of retraining whole models from scratch.

This makes it well-suited for environments with limited hardware or strict energy/resource budgets (e.g., embedded systems or edge devices), unlike many traditional ML models that require large-scale GPU clusters.

Maximum Response Time :
Designed for deployment in decision-critical systems (e.g., finance, aerospace, or justice), ETR 0.5 is optimized for fast, deterministic response times once trained.

It enables real-time decision-making with a traceable logic path, offering significant advantages over black-box models like deep neural networks, where inference speed can be high but explainability is low.

Degrees of Freedom:
Degrees of freedom refer to the variety and complexity of outputs or actions the model must handle. ETR 0.5 supports high-dimensional output spaces and can manage complex multi-variable decision scenarios.

With its cognitive architecture, ETR 0.5 can set objectives, evaluate strategies, and generate alternatives, providing intelligent action across varied contexts—something traditional decision trees or static rule-based systems are not designed for.

What we have already acomplished

We, at The Mindkind, have already invested a lot of our own resources and also received a series of public R+D+i grants, tax benefits, and financing:

650 K€
Self Funding
1,2 M€
R+D+i Public grants and loans
200 K€
Bank loans and credit

Contact us and learn more

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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 intodecision-critical systems or systems requiring full explainability, especially in applications such as medicine, finance, industry, or justice.

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