Digital inbreeding: is AI overlooking a basic principle of biology?
The concept of digital inbreeding is a recent analogy applied to artificial intelligence. It refers to the fact that AIs feed and train on content generated by other AIs, creating a kind of closed loop, which causes a progressive and irremediable degradation in quality, leading to loss of accuracy and reliability...
We all know that, in nature, inbreeding can lead to serious genetic deficiencies, reducing the resilience of species and even increasing their vulnerability. Reproduction between closely related individuals ultimately leads to an accumulation of negative traits.
In the case of AI, this basic principle of biology could potentially lead to a deterioration in the quality and diversity of the content generated. By feeding on repetitive data cycles and feedback processes, AI generates amplification loops that lead to a loss of diversity and heterogeneity. Could training AI on the content of other AIs ultimately lead to an impoverishment of its own resource? Quite possibly.
Today, many researchers no longer hesitate to draw an analogy with inbreeding in biology, which shows how systems that feed on their own or similar productions end up losing diversity and robustness.
The term “Habsburg AI” has even been proposed by some specialists to describe this digital inbreeding, in reference to the Habsburg dynasty known for its inbreeding practices.
"Digital inbreeding”, on the other hand, raises concerns about the potential impoverishment of AI algorithms and the compromise of their long-term effectiveness.
(MH with CM with LM - Sources: Olivier Hamant, Journal du Geek, Centaure Marketing/Illustration picture: Gerd Altmann via Pixabay)