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This is not a paper about the current state of chemistry but rather the author’s perspective on a strategy for the future of chemistry and other scientific and technical disciplines. This future vision is based on synergies of natural sciences and technical disciplines on one side and the science of information, i.e., informatics, on the other. It relates the author’s interactions during 2003-2023 in professional collaborations with Alain Krief, now Emeritus Professor at the University of Namur. It is argued that the scientific value of Prof. Krief’s work - particularly related to his well-known interest in “chemical knowledge transfer” - extends beyond the scientific-technical domain of chemistry to include a much broader area of natural sciences and associated technologies. Prof. Krief’s signal contribution has resulted from his profound interest in information, knowledge, and human learning, which has been at the core of our exchanges during the last 20 years.

International audience; 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 [1-3] 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 "knowl-edge" 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.

International audience; Collective intelligence is one major outcome of the digital revolution , but this outcome is hardly evaluated. By implementing a topological knowledge graph (KG) in the metaphor of a brain, the ViewpointS approach attempts to trace and assess the dynamics of collaborative knowledge building. Our approach relies on a bipartite graph of resources (agents, documents, topics) and time stamped "viewpoints" emitted by human or artificial agents. These viewpoints are typed (logical, mining, subjective). User agents feed the graph with resources and viewpoints and exploit maps where resources are linked by "synapses" aggregating the viewpoints. They reversely emit feedback viewpoints which tighten or loosen the synapses along the knowledge paths. Shared knowledge is continuously elicited against the individual "systems of values" along the agents' exploitation/feedback loops. This selection process implements a rudimentary form of collective intelligence, which we assess through innovative metrics. In this paper, we present the exploitation/feedback loops in detail. We expose the mechanism underlying the reinforcement along the knowledge paths and introduce a new measure called Multi Paths Proximity inspired from the parallel neural circuits in the brain. Then we present the Web prototype VWA implementing the ViewpointS approach and set a small experiment assessing collective knowledge building on top of the exploitation/feedback loops.