Neural Erosion: Emulating Controlled Neurodegeneration and Aging in AI
  Systems
Antonios Alexos Yu-Dai Tsai Ian Domingo Maryam Pishgar Pierre Baldi
Creating controlled methods to simulate neurodegeneration in artificial
intelligence (AI) is crucial for applications that emulate brain function
decline and cognitive disorders. We use IQ tests performed by Large Language
Models (LLMs) and, more specifically, the LLaMA 2 to introduce the concept of
``neural erosion." This deliberate erosion involves ablating synapses or
neurons, or adding Gaussian noise during or after training, resulting in a
controlled progressive decline in the LLMs' performance. We are able to
describe the neurodegeneration in the IQ tests and show that the LLM first
loses its mathematical abilities and then its linguistic abilities, while
further losing its ability to understand the questions. To the best of our
knowledge, this is the first work that models neurodegeneration with text data,
compared to other works that operate in the computer vision domain. Finally, we
draw similarities between our study and cognitive decline clinical studies
involving test subjects. We find that with the application of neurodegenerative
methods, LLMs lose abstract thinking abilities, followed by mathematical
degradation, and ultimately, a loss in linguistic ability, responding to
prompts incoherently. These findings are in accordance with human studies.