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The ALL laboratory is substantially integrated with the ISI Foundation teams working on mathematical formalization and complex system science and data science applications and the development of innovative algorithms and machine learning.
As regards laboratory activities, these have been structured in such way that three normally separate research branches interact in a constructive manner:

  • Study of the traditional artificial intelligence techniques for computation, optimization and performance - Machine Learning & Deep Learning, deep neural networks, manipulating/processing of natural languages, (heavily focused in a structural sense on neurosciences especially on connectomics and cognitive and semantic functions of the cerebral cortex) and Topological Data Analysis.
  • Constructor Theory of Information, a new methodology designed to provide an integrated and unified presentation, knowledge, learning, causation, active inference and information tomography (in independent device form that aims at the complete construction of the transfer function of any type of black-box).
  • Decidability problems (in the sense of Gödel) of provability and self-consistency logic in artificial intelligence methodologies.

The ISI Foundation cultural and competence areas play a key role in this complex structure:

  • Data Mining & Machine (Deep) Learning, in its foundational and application aspects.
  • Brain, for connectomics, multi-tasking functions, semantics map.
  • Topological Data Analysis, for fundamental mathematics and applications.
  • Constructor Theory, both CT theory and its application in cognitive sciences.
  • Formal Logic, for decidability problems and axiomatization in CT categorical terms.