Geometry of Concept

I find this article fascinating and orthogoanally relevant to AIKR as I
understand it
it provides an interesting direction imho

The Geometry of Concept Learning
https://www.biorxiv.org/content/10.1101/2021.03.21.436284v1.full.pdf

* Abstract *Understanding the neural basis of our remarkable cognitive
capacity to accurately learn novel highdimensional naturalistic concepts
from just one or a few sensory experiences constitutes a fundamental
problem. We propose a simple, biologically plausible, mathematically
tractable, and computationally powerful neural mechanism for few-shot
learning of naturalistic concepts. We posit that the concepts we can learn
given few examples are defined by tightly circumscribed manifolds in the
neural firing rate space of higher order sensory areas. We further posit
that a single plastic downstream neuron can learn such concepts from few
examples using a simple plasticity rule. We demonstrate the computational
power of our simple proposal by showing it can achieve high few-shot
learning accuracy on natural visual concepts using both macaque
inferotemporal cortex representations and deep neural network models of
these representations, and can even learn novel visual concepts specified
only through language descriptions. Moreover, we develop a mathematical
theory of few-shot learning that links neurophysiology to behavior by
delineating several fundamental and measurable geometric properties of
high-dimensional neural representations that can accurately predict the
few-shot learning performance of naturalistic concepts across all our
experiments. We discuss several implications of our theory for past and
future studies in neuroscience, psychology and machine learning.