Symbolic Manipulation: an Anthropological Perspective

The aim of this article is to elucidate the anthropological conditions of possibility for computing. I will first show how symbolic manipulation is constitutive of hominization. Secondly, I will examine the gradual widening of the gearing between the sensorial world (signifiers) and the intelligible world (concepts) during cultural evolution. Following on from these analyses, I’ll comment some of the main features of contemporary digital civilization, including the development of generative artificial intelligence.

Towards Reasonable Agents

How can we graft symbolic encoding and decoding capabilities onto a neural model that can initially only recognize and generate sensory forms or aggregates of signifiers? This challenge is reminiscent of the process of hominization – when biological neural networks became capable of manipulating symbolic systems – which is not to my displeasure.

THE DIGITAL PUBLIC SPHERE

Let’s think about the­ new digital public sphere. I will begin by discussing the anthropological and demographic context of the public sphere shift into the digital environment. Then I will analyze the original forms of memory and communication supported by the new medium. I will then evoke the figures of domination and alienation specific toLire la suite « THE DIGITAL PUBLIC SPHERE »

Semantics, artificial intelligence and collective intelligence

How does language work? On the receiving end, we hear a sequence of sounds that we translate into a network of concepts. This is what we call « understanding the meaning » of a statement we hear. On the transmit side, we have in mind a network of concepts – a meaning to be conveyed – that we translate into a sequence of sounds.

IEML: Towards a Paradigm Shift in Artificial Intelligence

IEML has the expressive power of a natural language and the syntax of a regular language. Its semantics are unambiguous and computable because they are an explicit function of its syntax. A neuro-semantic architecture based on IEML combines the strengths of neural AI and classical symbolic AI while enabling integration of knowledge through an interoperable computing of semantics.