University of Colorado Denver, USA
STILMAN Advanced Strategies, USA
I will introduce the audience to the results of our investigation of the structure of the Primary Language of the human brain (as suggested by J. von Neumann in 1957). According to his hypothesis, the Primary Language empowers all the human symbolic languages and sciences. Our hypothesis goes further. We assume that the Primary Language is the Language of Visual streams, i.e., it is based on mental dynamic images (movies), visual streams, so that those streams drive reasoning, reading, writing, and translation and serve as a different form, the foundation, for all the sciences. (Note that, according to our hypothesis, the Primary Language is not a language in mathematical sense.) We are interested in revealing the detailed nature of the Primary Language by investigating ancient algorithms, crucial for development of humanity. It is likely that such ancient algorithms should be powered by the Primary Language directly and, thus, should utilize symbolic reasoning as auxiliary because it is located in the neocortex, a component of the human brain that had yet to be developed at that time. Our contention is that the hypothetical Algorithm of Discovery (AD) must be one of such algorithms. We assume that there is a universal AD driving all the innovations and, certainly, the advances in all sciences. By tracing and replaying various discoveries, we revealed the dynamics of visual streams, especially, the means for focusing streams in desired direction.
In the first part of the tutorial, I will introduce various types of visual streams including communication and internal streams as well as mundane and science streams. The communication streams include expression and impression streams. The expression streams pass information from the internal streams to the outer world via converting it into the strings of symbols and pictures. The science streams may generate new knowledge because they include the discovery streams controlled by the Algorithm of Discovery. The streams may initiate thought experiments, program them, and execute them in due course. The streams are focused employing various themes including proximity and mosaic reasoning. In the second part of this tutorial, I will replay three discoveries employing the AD. These examples demonstrate communication between the primary and conventional science. Each example consists of a series of thought experiments that turn a piece of the primary science to the secondary one, the conventional science. Those examples include discovery of the structure of DNA (in Molecular Biology), discovery of the grammar of shortest trajectories (in Linguistic Geometry), and discovery of the formulas of differentiation (in Differential Calculus).
References (journal papers and book chapters):
- Stilman, B., From Primary to Conventional Science, V. Sgurev, V. Piuri, and V. Jotsov (Eds.), in Novel Applications of Intelligent Systems, Computational Intelligence Series, pp. 1-54, Springer Int., Switzerland, 2017 (in press).
- Stilman, B., Discoveries on Demand, Int. J. of Design & Nature and Ecodynamics, Vol.11, No.4, pp. 495-507, 2016.
- Stilman, B., Mosaic Reasoning for Discoveries, J. of Artificial Intelligence and Soft Computing Research, Vol. 3, No. 3, pp. 147-173., 2013 (published in 2014).
- Stilman, B., Proximity Reasoning for Discoveries, Int. J. of Machine Learning and Cybernetics, Springer, (DOI) 10.1007/s13042-014-0249-x, 31 p., 2014.
- Stilman, B., Visual Reasoning for Discoveries, Int. J. of Machine Learning and Cybernetics, Springer, (DOI): 10.1007/s13042-013-0189-x, 23 p., 2013.
- Stilman, B., Discovering the Discovery of the Hierarchy of Formal Languages, Int. J. of Machine Learning and Cybernetics, Springer, (DOI) 10.1007/s13042-012-0146-0, 25 p., 2012.
- Stilman, B., Discovering the Discovery of the No-Search Approach, Int. J. of Machine Learning and Cybernetics, Springer, (DOI) 10.1007/s13042-012-0127-3, 27 p., 2012. Printed in 2014, Vol. 5, No. 2, pp. 165-191.
- Stilman, B., Discovering the Discovery of Linguistic Geometry, Int. J. of Machine Learning and Cybernetics, Springer, (DOI) 10.1007/s13042-012-0114-8, 20 p., 2012. Printed in 2013, Vol. 4, No. 6, pp. 575-594.
More comprehensive list of publications including conference papers can be found in my Resume.