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Computing with Categories in Machine Learning
Category theory has been successfully applied in various domains of science, shedding light on universal principles unifying diverse phenomena and thereby enabling knowledge transfer between them.
A Probabilistic Generative Model of Free Categories
Applied category theory has recently developed libraries for computing with morphisms in interesting categories, while machine learning has developed ways of learning programs in interesting languages.
The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence
We attempt to define what is necessary to construct an Artificial Scientist, explore and evaluate several approaches to artificial general intelligence (AGI) which may facilitate this, conclude that a unified or hybrid approach is necessary and explore two theories that satisfy this requirement to some degree.
Philosophical Specification of Empathetic Ethical Artificial Intelligence
An ethical AI must be capable of inferring unspoken rules, interpreting nuance and context, possess and be able to infer intent, and explain not just its actions but its intent.
Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI
An agent possessed of it has a rationale for its decisions in terms of that purpose, expressed in a perceptual symbol system grounded in hardware.
Feature visualization of Raman spectrum analysis with deep convolutional neural network
Common component extraction is confirmed by an evaluation of numerically mixed amino acid spectra.
