Connectionist Machine Learning

Analogous framework for understanding how higher-level information arises from lower-level components in a collective system.

Neighborhood — ranked by edge-count

concept

Collective Intelligence
associated_with
Recognition that selves are composite systems of competent parts; all intelligences are higher-level selves made of cells or components.

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Connectionist Models Of Cognitionframework0.843
Connectionist Models of Cognition and Learningframework0.843
Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism
Connectionist Learning Frameworkframework0.835
Connectionist Modelsframework0.831
Neural network models demonstrating how organized functional relationships emerge via unsupervised learning; basis for evolutionary connectionism analogy.
Data-centric machine learningconcept0.799
Approach emphasizing data quality and source identification rather than only model architecture changes.
How Do The Principles Of Connectionist Learning Andquestion0.794
Can Connectionist Models Of Individual Cognition And Learningquestion0.791
Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.775
Central claim about the power of connectionism.