Artificial Intelligence – The Mindfire Foundation and Other Initiatives

  • Moreno ColomboEmail author
  • Edy Portmann
  • Pascal Kaufmann
Part of the Edition Informatik Spektrum book series (EIS)


Artificial intelligence being one of the buzzwords of the decade, with an increasingly growing number of new initiatives and investments, the scope of this article is that of investigating the current situation of research in this field. In particular the reasons why experts believe that research in artificial intelligence is currently stuck and other of its problems, as well as some possible solutions are analyzed. Moreover, a framework describing the fundamental building blocks of AI initiatives, based on an analysis of already existing solutions, is defined, and the innovative structure and ideas of one of those initiatives, the Mindfire Foundation, are presented in detail. The Mindfire Foundation is a non-profit organization with the goal of understanding and replicating the human mind, with a focus on application solving problems affecting humanity. To allow this, its fulcrum is an innovative blockchain-based system providing incentivization of transdisciplinary and antidisciplinary collaborations, combined with a solid framework for the handling of ethical and regulatory problems.


  1. Bohannon J (2016) Who’s the Michael Jordan of computer science? New tool ranks researchers’ influence. Science | AAAS. Created: 19.04.2016. Retrieved: 13.08.2018.
  2. Bughin J, Hazan E, Ramaswamy S, Chui M, Allas T, Dahlström P, Henke N, Trench M (2017) How artificial intelligence can deliver real value to companies | McKinsey & Company. Created: 06.2017. Retrieved: 19.06.2018.
  3. Carew AL, Wickson F (2010) The TD Wheel: A heuristic to shape, support and evaluate transdisciplinary research. Futures, 42(10):1146–1155.CrossRefGoogle Scholar
  4. Cortes C, Vapnik V (1995) Support-vector networks. Machine learning, 20(3):273–297.zbMATHGoogle Scholar
  5. Dillet R (2018) France wants to become an artificial intelligence hub. TechCrunch. Created: 29.03.2018. Retrieved: 13.08.2018.
  6. Dutton T (2018) An Overview of National AI Strategies. Politics + AI. Created: 28.06.2018. Retrieved: 02.08.2018.
  7. Ford A (2018) CLAIRE – a new European confederation for AI research | Science, Technology & the Future. Science Technology Future. Created: 26.06.2018. Retrieved: 13.08.2018.
  8. Gomes L (2014) Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts. IEEE Spectrum: Technology, Engineering, and Science News. Created: 20.10.2014. Retrieved: 25.06.2018.
  9. Haykin S (1994) Neural Networks: A Comprehensive Foundation. Prentice Hall PTR.Google Scholar
  10. Ito J (2016) Design and Science. Journal of Design and Science.Google Scholar
  11. Ito J (2012) Antidisciplinary. Joi Ito’s Web. Created: 02.10.2012. Retrieved: 26.06.2018.
  12. Jahn T (2008) Transdisciplinarity in the practice of research. Transdisziplinäre Forschung: Integrative Forschungsprozesse verstehen und bewerten. Campus Verlag, Frankfurt/Main, Germany, 21–37.Google Scholar
  13. Karch T, Kaja A, Luo Y (2018) Covington Artificial Intelligence Update: China’s Vision for The Next Generation of AI. Inside Privacy. Created: 24.03.2018. Retrieved: 13.08.2018.
  14. Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science, 350(6266):1332–1338.MathSciNetCrossRefGoogle Scholar
  15. Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ (2017) Building machines that learn and think like people. Behavioral and Brain Sciences, 40.Google Scholar
  16. Lang DJ, Wiek A, Bergmann M, Stauffacher M, Martens P, Moll P, Swilling M, Thomas CJ (2012) Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustainability Science, 7(S1):25–43.CrossRefGoogle Scholar
  17. Lanier J (2003) Why Gordian software has convinced me to believe to the reality of cats and apples. Created: 18.11.2003. Retrieved: 26.07.2018.
  18. LeVine S (2017) Artificial intelligence pioneer says we need to start over. Axios. Created: 15.09.2017. Retrieved: 25.06.2018.
  19. Ling J (2001) Power of a Human Brain. The Physics Factbook. Created: 2001. Retrieved: 19.06.2018.
  20. Malone TW (2018) How Human-Computer ‘Superminds’ Are Redefining the Future of Work. MIT Sloan Management Review, 59(4):34–41.Google Scholar
  21. Malsburg C (2018) The Neural Code: Roadblock on the way to AI. Platonite. Created: 20.02.2018. Retrieved: 26.07.2018.
  22. McCarthy J, Minsky M, Rochester N, Shannon CE (1955) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.Google Scholar
  23. Mitchell TM (1997) Machine Learning, McGraw-Hill series in computer science. McGraw-Hill, New York.Google Scholar
  24. Moravec H (1988) Mind children: The future of robot and human intelligence. Harvard University Press.Google Scholar
  25. Ng A (2016) What does Andrew Ng think about Deep Learning? – Quora. Created: 03.02.2016. Retrieved: 25.06.2018.
  26. Peterson LE (2009) K-nearest neighbor. Scholarpedia, 4(2):1883.CrossRefGoogle Scholar
  27. Picard RW (1997) Affective computing. MIT Press.Google Scholar
  28. Pohl C (2008) From science to policy through transdisciplinary research. Environmental Science & Policy, 11(1):46–53.CrossRefGoogle Scholar
  29. Ramchandani J (2017) What is ‘transdisciplinary’? We Learn, We Grow. Created: 24.01.2017. Retrieved: 16.08.2018.
  30. Ramesh P (2018) SingularityNET and Mindfire unite talents to explore artificial intelligence. Packt Hub. Created: 09.08.2018. Retrieved: 13.08.2018.
  31. Sieber L (2018) White Paper. Mindfire Global. Created: 08.2018. Retrieved: 27.08.2018.
  32. Stark L (2018) Canada’s risky bet on AI. The Globe and Mail.Google Scholar
  33. Stauffacher M, Spreng D, Flüeler T, Scholz RW (2012) Learning from the Transdisciplinary Case Study Approach: A Functional-Dynamic Approach to Collaboration Among Diverse Actors in Applied Energy Settings. In: P. Krütli, T. Flüeler, D.L. Goldblatt, J. Minsch (Eds.), Tackling Long-Term Global Energy Problems. Springer Netherlands, Dordrecht, 227–245.Google Scholar
  34. Stokols D, P. Moser R, Hall K, Feng A (2010) Evaluating Cross-Disciplinary Team Science Initiatives: Conceptual, Methodological, and Translational Perspectives, Oxford handbook on interdisciplinarity. Oxford University Press, New York.Google Scholar
  35. Talwar S, Wiek A, Robinson J (2011) User engagement in sustainability research. Science and Public Policy, 38(5):379–390.CrossRefGoogle Scholar
  36. Taniguchi H, Sato H, Shirakawa T (2018) A machine learning model with human cognitive biases capable of learning from small and biased datasets. Scientific Reports, 8(1).Google Scholar
  37. Tenenbaum JB (1999) Bayesian Modeling of Human Concept Learning, Advances in neural information processing system, 59–65.Google Scholar
  38. Turing AM (1950) Computing Machinery and Intelligence. Mind, 49:433–460.MathSciNetCrossRefGoogle Scholar
  39. Urech M (2017) Cognitive Computing und künstliche Intelligenz am 17. CNO-Panel in Bern. Netzwoche. Created: 01.11.2017. Retrieved: 13.08.2018.
  40. Weidman S (2018) The 3 Tricks That Made AlphaGo Zero Work. Hacker Noon. Created: 07.01.2018. Retrieved: 19.06.2018.
  41. Zucker D (2012) Developing Your Career in an Age of Team Science. Journal of Investigative Medicine, 60:779–784.CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  1. 1.Human-IST InstituteUniversity of FribourgFribourgSwitzerland
  2. 2.Mindfire FoundationZugSwitzerland

Personalised recommendations