Thinking·OS

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Explore the knowledge graph

Machine learning is a Algorithm. Activate to inspect this relation.Machine learning depends on Pattern. Activate to inspect this relation.Machine learning depends on Probability. Activate to inspect this relation.Training data enables Machine learning. Activate to inspect this relation.Machine learning enables Generalization. Activate to inspect this relation.Overfitting suppresses Generalization. Activate to inspect this relation.Overfitting depends on Training data. Activate to inspect this relation.Model bias is derived from Training data. Activate to inspect this relation.Feedback influences Machine learning. Activate to inspect this relation.Machine learning depends on Optimization. Activate to inspect this relation.Supervised learning is part of Machine learning. Activate to inspect this relation.Unsupervised learning is a Machine learning. Activate to inspect this relation.Unsupervised learning is part of Machine learning. Activate to inspect this relation.Reinforcement learning is a Machine learning. Activate to inspect this relation.Reinforcement learning is part of Machine learning. Activate to inspect this relation.Gradient descent applies to Optimization. Activate to inspect this relation.Gradient descent depends on Training data. Activate to inspect this relation.Supervised learning depends on Training data. Activate to inspect this relation.Supervised learning enables Generalization. Activate to inspect this relation.Training dataMachine learningOverfittingModel biasSupervised learningGradient descentGeneralizationAlgorithmPatternOptimizationFeedbackProbabilityUnsupervised learningReinforcement learning
Relationship types
Legend
  • Focused concept
  • Connected concept
  • Arrow points from cause / source to effect / target
  • A line with no arrow is a two-way relationship
  • Node colour marks the concept’s primary discipline
14 concepts19 relationships28 disciplines4 relation families

Training data

Open concept →

At a glance

The examples a machine-learning model learns from; their quality and coverage shape everything it can do.

Disciplines
Machine Learning · Computer Science · Statistics
Role in the graph
Cross-disciplinary bridge — reaches Artificial Intelligence, Data Science, Ethics, Optimization, Public Policy
Relationships
5 · 2 relation families
Mental models
1

Insights from this view

Structural observations about the concepts shown here — descriptions of this graph, not claims about the world.

  • This view connects 28 disciplines: Algorithms, Artificial Intelligence, Biology, Business, Cognitive Science, Computer Science, Data Science, Design, Discrete Mathematics, Economics, Education, Educational Science, Engineering, Ethics, Human-Computer Interaction, Logic, Machine Learning, Mathematical Modelling, Mathematics, Optimization, Probability, Public Policy, Software Engineering, Statistics, Systems Biology, Systems Engineering, Systems Science, Theory of Computation.
  • Training data is a bridge concept — viewed here through Computer Science, Machine Learning, Statistics.
  • The connections here span 4 relation families.
  • Information explains 4 concepts in this view (Algorithms, Artificial Intelligence, Computer Science, Data Science, Discrete Mathematics, Logic, Machine Learning, Mathematics, Software Engineering, Statistics, Theory of Computation).

Relationships as a list

The focused concept’s relationships. Pick another concept in the graph above to update this list.

Explore through a different lens

A lens is a deterministic projection of the graph. Pick a discipline, thinking pattern or journey to reframe the whole view.

By discipline

ChemistryBiologyPhysicsMathematicsComputer ScienceGeologyMineralogyEconomics

By thinking pattern

Bayesian updatingCause and effectCompoundingConservationConstraintsDefense in depthDiffusion & spreadEmergenceEnergy transformationEntropy & irreversibilityEquilibriumFeedback loopIncentivesInformationLevels & hierarchyModularityNetworksOptimizationOscillationProbabilityRecursion & self-similarityScaleSignal vs noiseSource–pathway–receptorStocks and flowsSymmetry & invarianceThresholds & tipping pointsTrade-offsTriangulationVariation & selectionWaves

By journey

From a Leaf to the ClimateWhy Music Moves UsHow Machines LearnReasoning about a Leaf and the Climate

Concept collections

Concept collections are curated lenses onto the fabric — themed sets of ideas that recur across disciplines. They are not journeys; they are a way to read the graph.

About this view

What this is

Start from one concept and expand outward. The view never shows everything at once — click a node to refocus, filter by relationship type, or switch to an accessible list.

One fabric

3750 concepts and 5051 typed relations form one connected component — no isolated silo.

How to read it

Focus a concept, or apply a lens (discipline, mental model, journey) to see only the threads that matter.