Abstract
Serendipitous discovery, invention or artistic creation are among the most exciting and utmost relevant phenomena strongly related to human learning. At the moment, there are very few measurable criteria helping to understand and foster serendipity. In other papers [1,2,3] we have presented, discussed and exemplified a new paradigm/model/method/system/environment – called ViewpointS – that represents our efforts to overcome many current existing limitations in generic Information Systems or search engines (e.g.: Google) as well as in other social media (e.g.: recommender systems) offering information retrieval solutions based on the proximity of available resources. We also have also exposed how ViewpointS may facilitate serendipitous discovery in an unprecedented way. In this paper, we wish to further motivate this last conjecture by proposing to explore two main research directions that did not convey sufficient attention by previous researchers (in particular those active in recommender systems): 1. assessing brain states in order to understand and forecast serendipitous human learning events triggered by emotions; 2. enhancing collective wisdom, since Human-Computer Interactions do not occur today between a human and a single machine (or algorithm), but within a community of humans and machines that continuously update “knowledge” beyond the scene. Both directions (assessment of brain states, collective wisdom) are currently on separate ways; we propose to combine them within one unified approach called ViewpointS.
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Notes
- 1.
This paper has the objective to stimulate reflections about an ambitious, but equally modest and feasible, collaborative, large, long term research project for understanding, forecasting and stimulating serendipitous learning, i.e.: discovering, inventing, creating. At the same time we claim this project to be a concrete step towards digital sovereignty since it enables to personalize -thus control and trust- effectively the individual and collective information spaces, differently from current search engines, social networks and recommender systems.
- 2.
From: https://en.wikipedia.org/wiki/Learning: Humans learn before birth and continue until death as a consequence of ongoing interactions between people and their environment.
- 3.
Rationalism and empiricism exist and balance each other since centuries in the philosophy of Science, as much as idealism and materialism. In the brain, logical thinking is associated to emotions; in Society it occurs probably the same. It is not rational to negate the interest of serendipitous events and the associated processes.
- 4.
Margaret Boden is a major actor in the historical development of Artificial Intelligence.
- 5.
This section is a revised, annotated version of the similar section of another paper. On purpose, we use the same very simple example in order to support quite different conjectures about the properties of ViewpointS; in this case serendipity.
- 6.
This is obviously an extremely simplified tutoring system!
- 7.
This is obviously an oversimplification: in the real prototype available, the KM is visualized and the notion of proximity (short path) is reified by real distances in the visualized graph. Another way to convey the same message without visualization is to give a list of answers in order of increasing distance. In this case the difference with Google’s answers is evident.
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Cerri, S.A., Lemoisson, P. (2020). Serendipitous Learning Fostered by Brain State Assessment and Collective Wisdom. In: Frasson, C., Bamidis, P., Vlamos, P. (eds) Brain Function Assessment in Learning. BFAL 2020. Lecture Notes in Computer Science(), vol 12462. Springer, Cham. https://doi.org/10.1007/978-3-030-60735-7_14
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