A new research paper proposes a population-genetic framework to understand the evolution of artificial intelligence models. The study draws parallels between AI development practices, such as retraining models on peer outputs or averaging weights, and biological concepts like sexual and asexual reproduction. The research tests these analogies using various AI architectures, including recurrent neural networks, feedforward networks, and large language models, finding that the framework holds generally with some architecture-specific biases. AI
IMPACT This research offers a novel theoretical lens for understanding AI development, potentially guiding future model architectures and training strategies.
RANK_REASON Research paper published on arXiv proposing a new framework.
- artificial intelligence
- Fisher-Muller effect
- Giorgio F. Gilestro
- Jenkin's objection
- large-language models
- population genetics
- Wright-Fisher process
- feedforward neural network
- Recurrent Neural Networks
- variational autoencoder generators
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