According to our research, following the Gödel’s incompletude demonstration theorem, we demonstrated that GDPR rules complexity decomposed into prime number and "prime of april" sequence are incomplete in the sense of axiom “incompletude”. We also demonstrate that there does not exist any Axiom that can reduce its mathematical complexity. And following the San Diego University Neuroscience lab, demonstrating that our brain can be mathematically explained, we prove that GDPR data protection legal rules are mathematically too complex for a human brain to process. In a further opening, we demonstrate that potentially, even computers are not able to process GDPR complex rules completely. The whole industry needs to wait until the development of Quantum computers.
Summary of the research
Decomposition of GDPR rules and axioms into Prime numbers and Prime of April numbers, according to Gödel’s Methodology
In our paper, we decomposed all the GDPR rules and axioms into prime numbers and tried to determine of a GDPR rule can be explained with a sufficient set of axioms. We then treated the existing dependencies of these axioms following the well-established methodology for the typical unification of access points and redundancy.
Gödel’s incompleteness theorems are among the most important results in modern logic, and have deep implications for various issues. His incompleteness theorems meant there can be no mathematical theory of everything, no unification of what’s provable and what’s true. What mathematicians can prove depends on their starting assumptions, not on any fundamental ground truth from which all answers spring.
They concern the limits of provability in formal axiomatic theories. The first incompleteness theorem states that in any consistent formal system F within which a certain amount of arithmetic can be carried out, there are statements of the language of F which can neither be proved nor disproved in
F . According to the second incompleteness theorem, such a formal system cannot prove that the system itself is consistent (assuming it is indeed consistent). These results have had a great impact on the philosophy of mathematics and logic. There have been attempts to apply the results also in other areas of philosophy such as the philosophy of mind, but these attempted applications are more controversial. The present entry on GDPR applies the two incompleteness theorems to recent data protection rules and various issues surrounding them.
. The GDPR’s computation of parabolic is then described in the following way:
Comparing Brain mathematical modelisation with small neural network and GDPR rules dependency mathematical model
We used the foundings of the University of California, demonstrating that the brain performance is equivalent to “the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans”. Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in wholebrain imaging, they examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. They
show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, they compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In their experiments, they found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.
Their conclusion is categoric :
“We can replicate the brain mathematically using local and small neural networks in graph model, but not the GDPR rules entanglement since our current technologies are not powerful enough to handle such a complexity. Quantum computing may be the solution
lawyers and mathematicians for applied law are waiting for”, said the great mathematician Primero Avrilo from San Diego University Neuroscience lab when summarizing its last publication.