From VentureBeat, August 26:
Inbreeding refers to genomic corruption when members of a population reproduce with other members who are too genetically similar. This often leads to offspring with significant health problems and other deformities because it amplifies the expression of recessive genes. When inbreeding is widespread — as it can be in modern livestock production — the entire gene pool can be degraded over time, amplifying deformities as the population gets less and less diverse.
In the world of generative AI, a similar problem exists, potentially threatening the long-term effectiveness of AI systems and the diversity of human culture. From an evolutionary perspective, first generation large language models (LLMs) and other gen AI systems were trained on a relatively clean “gene pool” of human artifacts, using massive quantities of textual, visual and audio content to represent the essence of our cultural sensibilities.
But as the internet gets flooded with AI-generated artifacts, there is a significant risk that new AI systems will train on datasets that include large quantities of AI-created content. This content is not direct human culture, but emulated human culture with varying levels of distortion, thereby corrupting the “gene pool” through inbreeding. And as gen AI systems increase in use, this problem will only accelerate. After all, newer AI systems that are trained on copies of human culture will fill the world with increasingly distorted artifacts, causing the next generation of AI systems to train on copies of copies of human culture, and so on.
Degrading gen AI systems, distorting human culture
I refer to this emerging problem as “Generative Inbreeding,” and I worry about two troubling consequences. First, there is the potential degradation of gen AI systems, as inbreeding reduces their ability to accurately represent human language, culture and artifacts. Second, there is the distortion of human culture by inbred AI systems that increasingly introduce “deformities” into our cultural gene pool that don’t actually represent our collective sensibilities....
....MUCH MORE
If interested see also: "When AI Is Trained on AI-Generated Data, Strange Things Start to Happen" and "Artificial Data To Train Artificial intelligence"
