Squashing the past on the future. Ecological systems for modelling the complexities of the ancient Near East

Prof. Marco Ramazzotti – (Archaeology and History of the Ancient Near East –La Sapienza University , Rome and Research Fellows of the Semeion) hald a conference at COLLÈGE DE FRANCE (PARIS) on 12 July, from the Title:
“Squashing the past on the future. Ecological systems for modelling the complexities of the ancient Near East”.
For the analysis work were applied Semeion Artificial Intelligence systems.
The conference was held as part of a workshop entitled: “Digital practices in Western Asiatic Studies: experiments and advances” organized for the conference:  65ᵉ Rencontre Assyriologique Internationale.


The semiotic and logicist encoding of the spatial-temporal archaeological, historical, geographical and anthropological records can be considered an ideal-typical representation of the contextual reality inspired by the human reasoning and thus also an artificial adaptive membrane interposed between the observer / researcher and the past. Nowadays, these epistemic networks are semantic segmentations and can undergo interrogation processes through the most advanced analytical tools for learning and modelling complex data-set configurations. Encoding the epistemic contexts of the past and simulating the dynamic and complex behaviour of the high variability of the natural and cultural factors in artificial membranes thus conceived equals tracking down, selecting, and separately recreating a wide variety of functions associating variables, a wide variety of inferences controlling their semantic structure, and an equally wide variety of causes producing their transformation. In the present contribution, we explored the application possibilities of Ecological Systems (ES) to simulate the complexities  of different ancient near eastern archaeological, anthropological and epipgraphical contexts. ES composed of different neural networks, evolutionary algorithms, dynamic associative memories, auto encoder that work with different mathematics independently and simultaneously on the same data set, or on different data sets that will be made to interact.