AI Without Networks

Abstract

Contemporary Artificial Intelligence (AI) stands on two legs: large training data corpora and many-parameter artificial neural networks (ANNs). The data corpora are needed to represent the complexity and heterogeneity of the world. The role of the networks is less transparent due to the obscure dependence of the network parameters and outputs on the training data and inputs. This raises problems, ranging from technical-scientific to legal-ethical. We hypothesize that a transparent approach to machine learning is possible without using networks at all. By generalizing a parameter-free, statistically consistent data interpolation method, which we analyze theoretically in detail, we develop a framework for generative modeling. Given the growing usage of machine learning techniques in science, we demonstrate this framework with an example from the field of animal behavior. We applied this generative Hilbert framework to the trajectories of small groups of swimming fish. The framework outperforms previously developed state-of-the-art traditional mathematical behavioral models and contemporary ANN-based models in reproducing naturalistic behaviors. We do not suggest that the proposed framework will outperform networks in all applications, as overparameterized networks can interpolate. However, our framework is theoretically sound, transparent, deterministic and parameter free: it does not require any compute-expensive training, does not involve optimization, has no model selection, and is easily reproduced and ported. We also propose an easily computed method of credit assignment based on this framework that could help address ethical-legal challenges raised by generative AI.