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Search 3,717 documents across 34 fields — every claim tier-rated by evidence

3,717 documents 34 sections 47,686 citations 34,596+ keywords indexed 4 evidence tiers

55 results for "learning" — page 1 of 3

ZD_2_16 Credible Information & Computation

ZD_2_16 — Federated Learning & Privacy-Preserving ML

Federated learning (FL) is a machine learning paradigm in which a model is trained across multiple decentralized devices or servers holding local data samples, without exchanging the raw data — the model comes to the dat

federated learning privacy-preserving machine learning differential privacy Google Brendan McMahan data privacy

ZD_2_11 Verified Information & Computation

ZD_2_11 — Reinforcement Learning: Agents, Rewards, and Sequential Decision-Making

Reinforcement learning (RL) is a paradigm of machine learning in which an agent learns to make sequential decisions by interacting with an environment, receiving rewards (or penalties) for its actions, and adjusting its

reinforcement learning MDP Q-learning policy gradient AlphaGo reward

V_4_19 Verified Mathematics & Information

V_4_19 — Machine Learning Mathematics: Neural Networks, Optimization, and Learning Theory

Machine learning mathematics — the theoretical foundations underlying the training, generalization, and behavior of learning algorithms — spans statistical learning theory, optimization, approximation theory, information

machine learning neural network deep learning gradient descent backpropagation transformer

K_4_20 Verified Consciousness

K_4_20 — Non-Neural Learning: Slime Molds, Plants, Bacterial Adaptation

Learning — modifying behavior based on experience — was long thought to require a nervous system. The last twenty years of basal-cognition research have empirically falsified this assumption. Single-celled slime molds (P

basal cognition non-neural learning habituation Physarum polycephalum Mimosa pudica plant memory

G_1_08 Verified Modern Frameworks

G_1_08 — Machine Learning in Archaeology — Pattern Recognition in the Past

Machine learning (ML) — the subset of artificial intelligence in which algorithms learn patterns from data rather than being explicitly programmed — is transforming archaeological practice across every stage of research:

machine learning artificial intelligence deep learning neural network convolutional neural network CNN

T_1_09 Psychology & Social

T_1_09 — Psychology of Learning and Conditioning

Learning — relatively permanent changes in behavior or behavioral potential resulting from experience — is the foundational process of behavioral adaptation. Three paradigms dominate: classical conditioning (Pavlov, 1927

learning psychology classical conditioning Pavlov operant conditioning Skinner reinforcement

ZD_2_01 Information & Computation

ZD_2_01 — Machine Learning Mathematics

Machine learning — the science of algorithms that improve through experience — rests on a rich mathematical foundation spanning optimization, statistics, linear algebra, probability, and functional analysis. The core mat

machine learning gradient descent backpropagation neural network statistical learning theory VC dimension

S_1_11 Verified Future Technology

S_1_11 — Machine Learning and Deep Learning

Machine learning (ML) is the subfield of AI in which systems learn patterns from data rather than being explicitly programmed. Deep learning uses artificial neural networks with many layers (hence "deep") to learn hierar

machine learning deep learning neural networks artificial intelligence convolutional neural networks CNN

V_4_27 Verified Mathematics & Information

V_4_27 — Bayesian Inference: Probabilistic Reasoning from Bayes to Machine Learning

Bayesian inference — the mathematical framework for updating beliefs in light of evidence — has become the dominant paradigm in statistics, machine learning, cognitive science, and philosophy of science. Named after Reve

bayesian inference bayes theorem probability prior posterior machine learning

G_2_12 Credible Modern Frameworks

G_2_12 — Cultural Evolutionary Theory — Boyd, Richerson, and Henrich

Cultural evolutionary theory — developed primarily by Robert Boyd, Peter Richerson, and Joseph Henrich — provides a rigorous, formally modeled framework for understanding how cultural traits (beliefs, practices, technolo

cultural evolution dual inheritance gene-culture coevolution social learning imitation prestige bias

ZD_2_04 Verified Information & Computation

ZD_2_04 — Computer Vision and Image Processing

Computer vision — enabling machines to interpret and understand visual information from the world — has progressed from hand-crafted feature engineering to the deep learning revolution that now approaches or exceeds huma

computer vision image processing convolutional neural network object detection image classification edge detection

ZD_2_10 Verified Information & Computation

ZD_2_10 — Speech Recognition and Synthesis: From Acoustic Models to Neural Voice Generation

Speech recognition (Automatic Speech Recognition — ASR) and speech synthesis (Text-to-Speech — TTS) are complementary technologies that bridge human spoken language and machine processing. ASR converts spoken audio into

speech recognition ASR text-to-speech TTS voice assistant Whisper

ZD_2_13 Verified Information & Computation

ZD_2_13 — Explainable AI: Interpretability, Trust, and the Black Box Problem

Explainable AI (XAI) is the field concerned with making artificial intelligence systems — particularly complex machine learning models — understandable to humans. As AI systems increasingly make or influence high-stakes

explainable AI XAI interpretability LIME SHAP black box

L_5_07 Verified Genetics & Origins

L_5_07 — Genetics of Speech and Language: Beyond FOXP2

Language is humanity's most distinctive cognitive ability — and identifying its genetic basis has been a central goal of human genetics and neuroscience since the discovery of the KE family and the FOXP2 gene. The KE fam

FOXP2 language genetics speech CNTNAP2 SRPX2 ATP2C2

S_1_16 Verified Future Technology

S_1_16 — Large Language Models: Architecture, Capabilities, and Societal Impact

Large Language Models (LLMs) are neural networks with billions to trillions of parameters, trained on massive text corpora to predict the next token in a sequence. Built on the transformer architecture introduced by Vasw

large language models LLM GPT transformer BERT natural language processing

ZF_5_22 Verified Oceanography

ZF_5_22 — Cetacean Cognition: Marine Mammal Intelligence and Problem-Solving

Cetaceans (whales, dolphins, porpoises) display a suite of cognitive capacities that meet or exceed those of great apes on multiple comparative measures, despite an evolutionary lineage independent from primate cognition

cetacean cognition dolphin intelligence whale culture mirror self-recognition vocal learning signature whistle

K_3_10 Consciousness

K_3_10 — Fetal and Infant Consciousness

The question of when consciousness emerges during human development — whether prenatally, at birth, or gradually through infancy — is one of the most consequential in consciousness studies, with direct implications for f

fetal consciousness infant consciousness neonatal consciousness prenatal awareness fetal pain cortical development

K_3_14 Credible Consciousness

K_3_14 — Consciousness in Octopuses and Distributed Nervous Systems

Octopuses (Octopus vulgaris, O. bimaculoides, Abdopus aculeatus, and ~300 other species in order Octopoda) represent perhaps the most profound natural experiment in the evolution of consciousness: they are the most cogni

octopus cephalopod consciousness distributed nervous system invertebrate cognition mollusc

K_3_07 Consciousness

K_3_07 — Evolution of Consciousness

The question of when, how, and why consciousness evolved is one of the deepest unsolved problems at the intersection of biology, neuroscience, and philosophy. Two major recent proposals have attempted to identify the evo

evolution of consciousness consciousness origins sentience evolution Cambrian consciousness nervous system evolution neural correlates evolution

Y_1_02 Altered States

Y_1_02 — Morphic Resonance and Sheldrake's Hypothesis

Morphic resonance is a hypothesis proposed by biologist Rupert Sheldrake (b. 1942, Cambridge-trained plant physiologist) that proposes nature operates by habits, not fixed laws, and that organisms and systems are influen

morphic resonance Rupert Sheldrake morphogenetic field formative causation habits of nature collective memory

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BROWSE BY SECTION — 3717 documents across 34 fields

A Foundations 112 docs B Beings & Entities 114 docs C Global Traditions 116 docs D Sites & Artifacts 116 docs E Cataclysms & Chronology 109 docs F Lost Connections 111 docs G Modern Frameworks 99 docs H Suppression & Thesis 95 docs I UAP Disclosure 87 docs J Ancient Technology 103 docs K Consciousness 100 docs L Genetics & Origins 92 docs M Forbidden Archaeology 103 docs N Secret Societies 87 docs O Earth Anomalies 108 docs P Philosophy & Meaning 104 docs Q Cosmology & Physics 100 docs R Biology & Evolution 94 docs S Future Technology 100 docs T Psychology & Social 112 docs U Art, Music & Culture 122 docs V Mathematics & Information 87 docs W World Civilizations 140 docs X Medicine & Healing 126 docs Y Altered States 113 docs Z Molecular Biology 112 docs ZA Physics & Quantum 117 docs ZB Ecology & Biology 118 docs ZC Social Science 111 docs ZD Information & Computation 95 docs ZE Ethics & Applied Philosophy 99 docs ZF Oceanography 105 docs ZG Linguistics & Communication 110 docs ZH Archaeoastronomy 114 docs