Deep Learning

Learning hierarchical representations of non-decomposable functions; proposed as formal equivalent to ETI process.

Neighborhood — ranked by edge-count

paper

thinker

framework

Evolutionary Connectionism
associated_withimplements
Proposed framework translating connectionist learning principles into natural selection domain to explain ETIs.
Connectionist Models of Cognition and Learning
extends
Models where intelligence arises from organisation of connections between simple processing units, used as basis for evolutionary connectionism

community

claim

method

Back-propagation
implements
Standard learning algorithm for deep neural networks that propagates error signals to adjust weights; lacks convergence guarantee for non-linearly separable functions

concept

hypothesis

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.

Deep Reinforcement Learningmethod0.877
AI training method inspired by behaviorism, used for autonomous cars and drones; cited as bioinspired success
Deep Researchconcept0.839
The task of autonomously reasoning through complex questions requiring extensive search and reasoning over multiple sources, combining web browsing and code execution.
Deep Integrationconcept0.821
Methodological approach privileging no single discipline; facilitates mutually informed explorative conversation on equal footing across frameworks
DeepMindinstitute0.819
Developed AlphaGo and AlphaCode.
Learningconcept0.804
Inference of parameters encoding contingencies of the world (e.g., likelihood matrix A) at slower timescale than perception.
Google DeepMindinstitute0.797
AI company mentioned as seeking research scientist on machine cognition, consciousness; also created AlphaStar, AdA, etc.
Convolutional Neural Networksmethod0.795
Biologically-inspired AI architecture cited as a successful example of bioinspiration from visual cortex organization
Deep Optimizationframework0.792