The .serva Standard

One Primitive for All AI Cost Reduced, Barriers Removed

Vector symbolic sub-objects classifiers as manifold analogues

Leveraging Conscious and Nonconscious Learning for Efficient AI

The work provided here attempts to link the neural basis of nonconscious and conscious learning while providing early results for a learning protocol more similar to human brains than is currently available.

Preserving Knowledge in Simulated Behavioral Action Loops

The importance of investigating what types of architectures are well suited for preserving knowledge lies in constructing artificial learning systems that can not only provide utility in our daily lives, but also give insight into our understanding of human learning and intelligence.

Gluing Neural Networks Symbolically Through Hyperdimensional Computing

Symbolic representation and learning with hyperdimensional computing

Learning sensorimotor control with neuromorphic sensors: Toward hyperdimensional active perception