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Why deep neural nets cannot ever match biological intelligence and what to do about it?

  • Danko Nikolić
Open Access
Research Article

Abstract

The recently introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory, biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent. This means that the current approaches to artificial intelligence, such as deep architectures of neural networks, will not suffice with fixed network architectures. Rather, they will need to be equipped with intelligent mechanisms that rapidly alter the architectures of those networks.

Keywords

Artificial intelligence neural networks strong artificial intelligence practopoiesis machine learning 

Notes

Acknowledgments

The author would like to thank Hrvoje Nikolić, Raul C. Muresan, Shan Yu and Matt Mahoney for valuable comments on previous versions of the manuscript.

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Copyright information

© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.DXC TechnologyFrankfurt am MainGermany
  2. 2.Frankfurt Institute for Advanced Studies (FIAS)Frankfurt/MGermany
  3. 3.Department of Psychology, Faculty of Humanities and Social SciencesUniversity of ZagrebZagrebCroatia

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