R.I.P.
π»
Ghosted
On the Parallels Between Evolutionary Theory and the State of AI
May 13, 2025 Β· Declared Dead Β· π GECCO Companion
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zeki Doruk Erden, Boi Faltings
arXiv ID
2505.23774
Category
q-bio.NC
Cross-listed
cs.LG,
cs.NE,
nlin.AO
Citations
2
Venue
GECCO Companion
Last Checked
3 months ago
Abstract
This article critically examines the foundational principles of contemporary AI methods, exploring the limitations that hinder its potential. We draw parallels between the modern AI landscape and the 20th-century Modern Synthesis in evolutionary biology, and highlight how advancements in evolutionary theory that augmented the Modern Synthesis, particularly those of Evolutionary Developmental Biology, offer insights that can inform a new design paradigm for AI. By synthesizing findings across AI and evolutionary theory, we propose a pathway to overcome existing limitations, enabling AI to achieve its aspirational goals.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-bio.NC
R.I.P.
π»
Ghosted
SuperSpike: Supervised learning in multi-layer spiking neural networks
R.I.P.
π»
Ghosted
Generic decoding of seen and imagined objects using hierarchical visual features
R.I.P.
π»
Ghosted
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
R.I.P.
π»
Ghosted
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
R.I.P.
π»
Ghosted
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted