Artificial Intelligence – The Mindfire Foundation and Other Initiatives
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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.
- Bohannon J (2016) Who’s the Michael Jordan of computer science? New tool ranks researchers’ influence. Science | AAAS. http://www.sciencemag.org/news/2016/04/who-s-michael-jordan-computer-science-new-tool-ranks-researchers-influence. Created: 19.04.2016. Retrieved: 13.08.2018.
- 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. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/how-artificial-intelligence-can-deliver-real-value-to-companies. Created: 06.2017. Retrieved: 19.06.2018.
- Dillet R (2018) France wants to become an artificial intelligence hub. TechCrunch. http://social.techcrunch.com/2018/03/29/france-wants-to-become-an-artificial-intelligence-hub. Created: 29.03.2018. Retrieved: 13.08.2018.
- Dutton T (2018) An Overview of National AI Strategies. Politics + AI. https://medium.com/politics-ai/an-overview-of-national-ai-strategies-2a70ec6edfd. Created: 28.06.2018. Retrieved: 02.08.2018.
- Ford A (2018) CLAIRE – a new European confederation for AI research | Science, Technology & the Future. Science Technology Future. http://www.scifuture.org/claire-a-new-european-confederation-for-ai-research. Created: 26.06.2018. Retrieved: 13.08.2018.
- 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. https://spectrum.ieee.org/robotics/artificial-intelligence/machinelearning-maestro-michael-jordan-on-the-delusions-of-big-data-and-other-huge-engineering-efforts. Created: 20.10.2014. Retrieved: 25.06.2018.
- Haykin S (1994) Neural Networks: A Comprehensive Foundation. Prentice Hall PTR.Google Scholar
- Ito J (2016) Design and Science. Journal of Design and Science.Google Scholar
- Ito J (2012) Antidisciplinary. Joi Ito’s Web. https://joi.ito.com/weblog/2014/10/02/antidisciplinar.html. Created: 02.10.2012. Retrieved: 26.06.2018.
- 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
- Karch T, Kaja A, Luo Y (2018) Covington Artificial Intelligence Update: China’s Vision for The Next Generation of AI. Inside Privacy. https://www.insideprivacy.com/artificial-intelligence/chinas-vision-for-the-next-generation-of-ai. Created: 24.03.2018. Retrieved: 13.08.2018.
- Lake BM, Ullman TD, Tenenbaum JB, Gershman SJ (2017) Building machines that learn and think like people. Behavioral and Brain Sciences, 40.Google Scholar
- Lanier J (2003) Why Gordian software has convinced me to believe to the reality of cats and apples. Edge.org. https://www.edge.org/conversation/why-gordian-software-has-convinced-me-to-believe-in-the-reality-of-cats-and-apples. Created: 18.11.2003. Retrieved: 26.07.2018.
- LeVine S (2017) Artificial intelligence pioneer says we need to start over. Axios. https://www.axios.com/artificial-intelligence-pioneer-says-we-need-to-start-over-1513305524-f619efbd-9db0-4947-a9b2-7a4c310a28fe.html. Created: 15.09.2017. Retrieved: 25.06.2018.
- Ling J (2001) Power of a Human Brain. The Physics Factbook. https://hypertextbook.com/facts/2001/JacquelineLing.shtml. Created: 2001. Retrieved: 19.06.2018.
- Malone TW (2018) How Human-Computer ‘Superminds’ Are Redefining the Future of Work. MIT Sloan Management Review, 59(4):34–41.Google Scholar
- Malsburg C (2018) The Neural Code: Roadblock on the way to AI. Platonite. https://platonite.com/the-neural-code-roadblock-on-the-way-to-ai/ Created: 20.02.2018. Retrieved: 26.07.2018.
- McCarthy J, Minsky M, Rochester N, Shannon CE (1955) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.Google Scholar
- Mitchell TM (1997) Machine Learning, McGraw-Hill series in computer science. McGraw-Hill, New York.Google Scholar
- Moravec H (1988) Mind children: The future of robot and human intelligence. Harvard University Press.Google Scholar
- Ng A (2016) What does Andrew Ng think about Deep Learning? – Quora. https://www.quora.com/What-does-Andrew-Ng-think-about-Deep-Learning. Created: 03.02.2016. Retrieved: 25.06.2018.
- Picard RW (1997) Affective computing. MIT Press.Google Scholar
- Ramchandani J (2017) What is ‘transdisciplinary’? We Learn, We Grow. https://medium.com/we-learn-we-grow/what-is-transdisciplinary-13c16eacf57d. Created: 24.01.2017. Retrieved: 16.08.2018.
- Ramesh P (2018) SingularityNET and Mindfire unite talents to explore artificial intelligence. Packt Hub. https://hub.packtpub.com/singularitynet-and-mindfire-unite-talents-to-explore-artificial-intelligence. Created: 09.08.2018. Retrieved: 13.08.2018.
- Sieber L (2018) White Paper. Mindfire Global. https://mindfire.global/wp-content/uploads/2018/08/Mindfire-Whitepaper-V5.pdf. Created: 08.2018. Retrieved: 27.08.2018.
- Stark L (2018) Canada’s risky bet on AI. The Globe and Mail.Google Scholar
- 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
- 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
- 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
- Tenenbaum JB (1999) Bayesian Modeling of Human Concept Learning, Advances in neural information processing system, 59–65.Google Scholar
- Urech M (2017) Cognitive Computing und künstliche Intelligenz am 17. CNO-Panel in Bern. Netzwoche. https://www.netzwoche.ch/news/2017-11-01/cognitive-computing-und-kunstliche-intelligenz-am-17-cno-panel-in-bern. Created: 01.11.2017. Retrieved: 13.08.2018.
- Weidman S (2018) The 3 Tricks That Made AlphaGo Zero Work. Hacker Noon. https://hackernoon.com/the-3-tricks-that-made-alphago-zero-work-f3d47b6686ef. Created: 07.01.2018. Retrieved: 19.06.2018.