** # Intelligence: A Comprehensive Hierarchical Map Across Human, Biological, Artificial, and Collective Systems ## Executive summary Intelligence is commonly treated as a system’s capacity to learn from experience, reason, and adapt effectively to its environment, but formal definitions vary across psychology, neuroscience, AI, and organizational science. [[1] ](https://www.apa.org/topics/intelligence)A useful cross-domain lens is the “agent in an environment” framing: intelligence manifests as goal-directed behavior under uncertainty, constrained by computation, data, and time. [[2] ](https://arxiv.org/abs/0712.3329)Human intelligence research has developed robust measurement traditions (e.g., WAIS/WISC/Raven/Stanford–Binet) and hierarchical models (e.g., general intelligence g, broad abilities such as fluid/crystallized intelligence), while debates persist about interpretation, fairness, and societal use. [[3] ](https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Adult-Intelligence-Scale-%7C-Fifth-Edition/p/P100071002?srsltid=AfmBOoqwlbxzjL8PJFFfVuytBPBPDY2w0IHuSyLLzcxrX9ummmQ3PHDg)Neuroscience links cognitive performance to distributed brain networks (e.g., prefrontal–parietal systems, intrinsic connectivity networks) and learning mechanisms (e.g., synaptic plasticity; dopamine-like reward prediction errors consistent with reinforcement-learning theory). [[4] ](https://pubmed.ncbi.nlm.nih.gov/17655784/)Artificial intelligence spans symbolic approaches (search, planning), statistical learning (deep neural networks), reinforcement learning, and modern foundation-model paradigms; evaluation increasingly relies on multi-benchmark suites (GLUE, MMLU, BIG-bench, HELM) and multi-metric assessment (accuracy, calibration, robustness, fairness, toxicity, efficiency). [[5] ](https://people.stfx.ca/jdelamer/courses/csci-564/_downloads/b2220c66675ddde471ca1795147b8e86/A_Formal_Basis_for_the_Heuristic_Determination_of_Minimum_Cost_Paths.pdf)Collective intelligence emerges in human groups, organizations, markets, and swarms; empirically, groups can show stable performance differences (a “collective intelligence” factor c), while engineered swarm intelligence inspires optimization algorithms (PSO, ACO). [[6] ](https://pubmed.ncbi.nlm.nih.gov/20929725/)Ethics and governance increasingly shape “intelligence” research and deployment: major frameworks and instruments include the OECD AI Principles, UNESCO’s AI ethics recommendation, NIST’s AI RMF, and the EU AI Act’s risk-based regulatory model and implementation guidance. [[7]](https://www.oecd.org/en/topics/ai-principles.html) ## Scope and assumptions This map treats “intelligence” primarily as adaptive information processing and goal-directed capability (not just “IQ”), spanning humans, machines, organisms, and collectives. [[8] ](https://www.apa.org/topics/intelligence)“Artificial intelligence” is treated as a family of computational methods for perception, learning, reasoning, planning, and action, historically anchored by the Turing test debate and the Dartmouth proposal that coined “artificial intelligence.” [[9] ](https://courses.cs.umbc.edu/471/papers/turing.pdf)“Collective intelligence” is treated as group-level intelligent behavior (humans and/or computers) that can exceed individual performance under certain conditions and task/ecology structures. [[10] ](https://cci.mit.edu/)“Biological intelligence” is treated as adaptive behavior and control across multiple biological scales, including neural and non-neural systems when supported by peer-reviewed literature (e.g., slime mold decision-making, bacterial quorum sensing, immune cognition metaphors, plant intelligence debates). [[11] ](https://www.nature.com/articles/35035159)A recurring organizing principle is that intelligence is constrained by information, computation, and environment structure (e.g., “no free lunch” theorems; bias–variance; complexity limits), so “better” algorithms depend on assumptions and task distributions. [[12] ](https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf)Polysemy note: “intelligence” also means state/organizational information activities (e.g., national security intelligence), which is conceptually distinct from cognitive ability but shares themes of uncertainty reduction and decision support. [[13]](https://www.cia.gov/resources/csi/static/cc27ce9b678dc69d4bdeef410feffa20/Article-New-Approach-to-Old-Question-Sep-2023.pdf) A compact conceptual map (not exhaustive) of how the report’s branches relate is summarized below. [[14]](https://arxiv.org/abs/0712.3329) flowchart TD   INT[Intelligence (umbrella)] --> DEF[Definitions & desiderata]   INT --> HUM[Human intelligence]   INT --> BIO[Biological intelligence]   INT --> AI[Artificial intelligence]   INT --> COL[Collective & hybrid intelligence]   DEF --> INFO[Information & uncertainty]   DEF --> COMP[Computation & complexity]   DEF --> GOAL[Goals, rewards, values]   HUM --> PSY[Psychometrics & measurement]   HUM --> COG[Cognitive mechanisms]   HUM --> NEU[Neuroscience & biology]   BIO --> EVOL[Evolution & adaptation]   BIO --> NONN[Non-neural cognition debates]   AI --> SYM[Symbolic AI]   AI --> STAT[Statistical ML & deep learning]   AI --> RL[Reinforcement learning]   AI --> EVAL[Evaluation & benchmarks]   COL --> GRP[Groups & organizations]   COL --> SWARM[Swarm intelligence]   COL --> TEAM[Human-AI teams]   INT --> ETH[Ethics, governance, and impact] ## Conceptual foundations - Intelligence (general construct) — A system’s capacity to learn from experience, reason, and adapt effectively to its environment to achieve goals under constraints. [[15]](https://www.apa.org/topics/intelligence) - Imitation game and “Turing test” framing — A behavioral-evaluation proposal that shifts “Can machines think?” into a practical test of indistinguishable language behavior under interrogation. [[16]](https://courses.cs.umbc.edu/471/papers/turing.pdf) - Dartmouth AI conjecture — A founding research claim that aspects of learning and intelligence can be precisely described such that a machine can simulate them, motivating AI as an engineering/science program. [[17]](https://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf) - Universal (machine) intelligence measure — A formal proposal to quantify intelligence as expected performance across environments weighted by simplicity (an algorithmic-information prior), linking intelligence to universal induction and optimal agents. [[18]](https://arxiv.org/abs/0712.3329) - Agent–environment loop — A general modeling pattern where an agent selects actions based on observations/history, influencing future observations and rewards. [[19]](https://incompleteideas.net/book/the-book-2nd.html) - Reward prediction and reinforcement framing — A normative account of adaptive behavior in which agents learn to maximize cumulative reward through interaction. [[20]](https://incompleteideas.net/book/the-book-2nd.html) - Bounded rationality — A theory that real decision-making is rational only relative to cognitive/computational limits and environmental structure, not ideal optimization. [[21]](https://iiif.library.cmu.edu/file/Simon_box00063_fld04838_bdl0001_doc0001/Simon_box00063_fld04838_bdl0001_doc0001.pdf) - Information theory (entropy) — A mathematical framework for quantifying uncertainty and communication limits via entropy and related quantities. [[22]](https://ia803209.us.archive.org/27/items/bstj27-3-379/bstj27-3-379_text.pdf) - Cybernetics — A foundational interdisciplinary study of control and communication in animals and machines, emphasizing feedback and regulation. [[23]](https://direct.mit.edu/books/oa-monograph/4581/Cybernetics-or-Control-and-Communication-in-the) - Computational levels of analysis — A methodological stance distinguishing what problem is being solved (computational), how it is solved (algorithmic), and how it is physically realized (implementation). [[24]](https://people.ciirc.cvut.cz/~hlavac/pub/MiscTextForStudents/1982MarrDavidVisionBook.pdf) - Computational complexity constraints — A body of results showing that many problem classes are intractable in worst case (e.g., NP-completeness), shaping feasible intelligence strategies. [[25]](https://perso.limos.fr/~palafour/PAPERS/PDF/Garey-Johnson79.pdf) - No Free Lunch (NFL) theorems for optimization — Formal results showing that averaged uniformly over problems, no optimizer outperforms any other, implying performance gains require inductive bias and task structure. [[26]](https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf) - Bias–variance dilemma — A core statistical learning tradeoff between underfitting (bias) and overfitting (variance) that shapes model selection and generalization. [[27]](https://doursat.free.fr/docs/Geman_Bienenstock_Doursat_1992_bv_NeurComp.pdf) - Curiosity and intrinsic motivation as “compression progress” — A formal proposal that exploration and “interestingness” can be modeled via intrinsic reward for improving predictive/compressive models of data. [[28]](https://arxiv.org/abs/0812.4360) - Predictive processing / free-energy principle — A unifying theoretical lens modeling perception and action as minimizing variational free energy (prediction error under a generative model). [[29]](https://www.nature.com/articles/nrn2787) ### Comparative table of widely used intelligence definitions and evaluation perspectives |Perspective|Working definition (one sentence)|Typical evaluation approach|Representative sources| |---|---|---|---| |Psychology (everyday/APA)|Intelligence is the ability to derive information, learn from experience, adapt to the environment, and use thought and reason effectively.|Psychometrics: reliability/validity, factor models, standardized tests.|[[30]](https://www.apa.org/topics/intelligence)| |Behavioral test framing (Turing)|Machine intelligence can be operationalized via behavior indistinguishable from humans in dialogue under interrogation.|Human-judged conversational indistinguishability.|[[16]](https://courses.cs.umbc.edu/471/papers/turing.pdf)| |AI/AGI formalization (Legg–Hutter)|Intelligence is performance across a wide range of environments, formalizable via a simplicity-weighted expectation over tasks.|Theory-driven measure + broad benchmark families.|[[18]](https://arxiv.org/abs/0712.3329)| |Cognitive neuroscience / network view|Intelligence differences partly reflect variation in distributed brain networks supporting control, working memory, and integration.|Neuroimaging and cognitive tasks; network models.|[[31]](https://pubmed.ncbi.nlm.nih.gov/17655784/)| |Collective intelligence|Groups can exhibit stable differences in effectiveness across tasks (“c”), partly influenced by interaction patterns and social sensitivity.|Group task batteries; collaboration metrics.|[[32]](https://pubmed.ncbi.nlm.nih.gov/20929725/)| |Governance and risk framing|“Intelligent systems” must be evaluated for harms and trustworthiness across metrics beyond accuracy (e.g., robustness, fairness, transparency).|Risk management frameworks and compliance controls.|[[33]](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf)| ## Human intelligence - Psychometrics — The scientific measurement of psychological traits (including intelligence) using quantified tests, models, and validation evidence. [[34]](https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf) - General intelligence (g) — A statistical factor capturing the positive correlations among diverse cognitive tests, often modeled as the apex of hierarchical ability structures. [[35]](https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf) - Mainstream psychometric consensus statement (historical artifact) — A widely cited 1990s-era summary asserting that intelligence is measurable and tests can be reliable/valid, while remaining embedded in contentious public debates. [[36]](https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf) - Fluid vs crystallized intelligence — A distinction between reasoning/problem-solving in novel situations (fluid) and accumulated knowledge and skills (crystallized). [[37]](https://pmc.ncbi.nlm.nih.gov/articles/PMC5156710/) - Parieto-frontal integration theory (P-FIT) — A neurocognitive model proposing that intelligence differences relate to efficiency/integration across distributed frontal–parietal brain systems. [[38]](https://pubmed.ncbi.nlm.nih.gov/17655784/) - Working memory (Baddeley–Hitch model) — A multi-component model of temporary storage and control (central executive plus specialized buffers) supporting complex cognition. [[39]](https://app.nova.edu/toolbox/instructionalproducts/edd8124/fall11/1974-Baddeley-and-Hitch.pdf) - Episodic buffer — A proposed working-memory component that integrates/binds information across modalities and long-term memory into coherent episodes. [[40]](https://home.csulb.edu/~cwallis/382/readings/482/baddeley.pdf) - Executive functions (unity/diversity) — A family of control processes (e.g., inhibition, updating, shifting) that are separable yet correlated and predictive of complex task performance. [[41]](https://www.researchgate.net/profile/Ryan-Van-Patten/post/What_are_proper_tasks_to_estimate_executive_functions_and_resourcefulness_in_children/attachment/59d6372d79197b80779948cc/AS%3A391842349764610%401470433904539/download/Miyake%2Bet%2Bal.%2B2000.pdf) - Executive attention view of working memory — A theory treating working-memory capacity as attention control under interference, linking it to fluid intelligence. [[42]](https://journals.sagepub.com/doi/10.1111/1467-8721.00160) - Cognitive control (PFC theory) — A theory that prefrontal cortex supports goal-directed behavior via active maintenance of task-relevant representations. [[43]](https://www.annualreviews.org/content/journals/10.1146/annurev.neuro.24.1.167) - Default mode network (DMN) — A network showing characteristic activity patterns during rest and deactivation during many goal-directed tasks, relevant to baseline brain function. [[44]](https://www.pnas.org/doi/10.1073/pnas.98.2.676) - Salience vs executive control networks — Intrinsic connectivity networks dissociable in function, often tied to interoception/selection (salience) vs control/working-memory demands (executive control). [[45]](https://www.jneurosci.org/content/27/9/2349.short) - Heuristics and biases — A program showing systematic deviations from ideal probabilistic reasoning under uncertainty due to cognitive heuristics. [[46]](https://sites.socsci.uci.edu/~bskyrms/bio/readings/tversky_k_heuristics_biases.pdf) - Bounded rationality in human choice — A formal approach to decision-making that explicitly models internal computational limits as constraints on rational behavior. [[47]](https://iiif.library.cmu.edu/file/Simon_box00063_fld04838_bdl0001_doc0001/Simon_box00063_fld04838_bdl0001_doc0001.pdf) - Synaptic learning postulate (Hebbian learning) — A foundational idea linking learning to changes in synaptic strength driven by correlated activity. [[48]](https://pure.mpg.de/pubman/item/item_2346268_3/component/file_2346267/Hebb_1949_The_Organization_of_Behavior.pdf) - Long-term potentiation (LTP) — An experimentally observed long-lasting increase in synaptic efficacy widely treated as a candidate cellular mechanism for learning and memory. [[49]](https://pmc.ncbi.nlm.nih.gov/articles/PMC1350458/) - Hippocampal role in memory (H.M. era) — Evidence from medial temporal lobe lesions showing severe anterograde memory impairment, motivating modern memory systems theory. [[50]](https://pmc.ncbi.nlm.nih.gov/articles/PMC497229/) - Dopamine reward prediction error — Evidence that midbrain dopamine signals resemble temporal-difference prediction errors central to reinforcement learning theory. [[51]](https://folia.unifr.ch/global/documents/242358) - Rescorla–Wagner learning model — A classical conditioning model where learning is driven by prediction error between expected and obtained reinforcement. [[52]](https://www.columbia.edu/~rk566/Session4/Theory%20of%20Pavlovian%20Conditioning.pdf) - Heritability and genetics of intelligence — Behavioral genetics findings that intelligence differences are substantially heritable and highly polygenic, shaping modern “gene-hunting” and predictive models. [[53]](https://pubmed.ncbi.nlm.nih.gov/25224258/) - Large-scale GWAS of intelligence — Genome-wide studies identifying many loci associated with intelligence and suggesting enrichment in brain-related pathways (with substantial remaining unexplained variance). [[54]](https://pubmed.ncbi.nlm.nih.gov/29942086/) - Flynn effect — Documented multi-decade generational rises in IQ test performance across many countries, with ongoing debates about causes and recent trend reversals in some populations. [[55]](https://www.iapsych.com/iqmr/fe/LinkedDocuments/flynn1987.pdf) - Cognitive development (Piaget) — A stage-based theory proposing qualitative shifts in children’s reasoning capacities across sensorimotor to formal operational stages. [[56]](https://books.google.com/books/about/The_Origins_of_Intelligence_in_Children.html?id=H7MkAQAAMAAJ) - Multiple intelligences (Gardner) — A theory proposing multiple relatively distinct “intelligences” (e.g., linguistic, spatial), influential in education but debated in psychometrics. [[57]](https://books.google.com/books/about/Frames_of_Mind.html?id=ObgOAAAAQAAJ) - Triarchic theory (Sternberg) — A theory arguing intelligence comprises analytical, creative, and practical components beyond traditional IQ framing. [[58]](https://assets.cambridge.org/97805212/78911/excerpt/9780521278911_excerpt.pdf) - Emotional intelligence (ability model) — A framework treating emotion reasoning and regulation as measurable abilities that can predict some social outcomes beyond personality. [[59]](https://journals.sagepub.com/doi/10.2190/DUGG-P24E-52WK-6CDG) - Theory of mind (ToM) — The capacity to attribute mental states to self and others to predict behavior, originally posed as a comparative cognition question in chimpanzees. [[60]](https://carta.anthropogeny.org/sites/default/files/file_fields/event/premack_and_woodruff_1978.pdf) - Reading the Mind in the Eyes Test — A widely used adult mentalizing measure assessing inference of mental states from eye-region facial cues. [[61]](https://docs.autismresearchcentre.com/papers/2001_BCetal_adulteyes.pdf) - Social brain hypothesis — An evolutionary hypothesis linking primate brain expansion to the demands of managing complex social relationships. [[62]](https://cognitionandculture.net/wp-content/uploads/Evolutionary-Anthropology-1998-Dunbar-The-social-brain-hypothesis.pdf) - Cultural origins of cognition — A view emphasizing uniquely human cooperative communication, shared intentionality, and culture as drivers of cognitive specialization. [[63]](https://www.hup.harvard.edu/books/9780674005822) ### Table of major human intelligence measurement tools and what they operationalize |Instrument / construct|One-sentence operational target|Typical outputs|Notes on usage|Representative sources| |---|---|---|---|---| |WAIS-5|Adult cognitive ability across multiple domains aggregated into index scores and a full-scale IQ.|FSIQ and index scores.|Widely used in clinical and organizational contexts; 2024 publication date listed by publisher.|[[64]](https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Adult-Intelligence-Scale-%7C-Fifth-Edition/p/P100071002?srsltid=AfmBOoqwlbxzjL8PJFFfVuytBPBPDY2w0IHuSyLLzcxrX9ummmQ3PHDg)| |WISC-V|Child cognitive ability and domain index scores used for educational and clinical assessment.|Index scores; composite IQ.|Common in psychoeducational assessment and learning support planning.|[[65]](https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Intelligence-Scale-for-Children-%7C-Fifth-Edition-/p/100000771?srsltid=AfmBOop6HmxwFAegdkP6w7uL84WPBQyZOeANNTYAcW8rQabq9eFGidj5)| |Stanford–Binet 5 (SB-5)|Lifespan assessment spanning broad cognitive factors aligned with fluid reasoning, knowledge, quantitative reasoning, visual–spatial processing, and working memory.|Composite and factor scores.|Often used for giftedness and developmental assessment.|[[66]](https://www.wpspublish.com/sb-5-stanford-binet-intelligence-scales-fifth-edition)| |Raven’s Progressive Matrices (Raven’s 2 / APM)|Nonverbal abstract reasoning and pattern completion often treated as a proxy for fluid intelligence.|Standard scores / percentiles.|Designed to reduce language demands; multiple forms exist.|[[67]](https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Raven%E2%80%99s-Progressive-Matrices-Second-Edition-%7C-Raven%27s-2/p/100001960?srsltid=AfmBOoqgmJy1kAgRKaG1WDURRGWBoN514rPtZJmjbE1Mx-qrF1rsgEZG)| |Working memory construct (Baddeley–Hitch)|Temporary maintenance/manipulation enabling reasoning, comprehension, and control.|Task battery scores.|Strong links to executive control theories and fluid intelligence research lines.|[[68]](https://app.nova.edu/toolbox/instructionalproducts/edd8124/fall11/1974-Baddeley-and-Hitch.pdf)| |Executive functions (Miyake factors)|Core control functions (inhibition/shifting/updating) supporting goal-directed behavior.|Latent factors from task batteries.|Separability with shared variance (“unity and diversity”) is empirically supported.|[[41]](https://www.researchgate.net/profile/Ryan-Van-Patten/post/What_are_proper_tasks_to_estimate_executive_functions_and_resourcefulness_in_children/attachment/59d6372d79197b80779948cc/AS%3A391842349764610%401470433904539/download/Miyake%2Bet%2Bal.%2B2000.pdf)| |ToM / mentalizing (Eyes test)|Inference of others’ mental states from minimal social cues.|Accuracy-based mentalizing score.|Used in autism/social cognition research with known psychometric caveats.|[[61]](https://docs.autismresearchcentre.com/papers/2001_BCetal_adulteyes.pdf)| ## Biological intelligence - Evolutionary adaptation lens — Biological intelligence can be treated as adaptive fit between organism strategies and ecological demands across evolutionary time. [[69]](https://cognitionandculture.net/wp-content/uploads/Evolutionary-Anthropology-1998-Dunbar-The-social-brain-hypothesis.pdf) - Neural intelligence (vertebrate/mammalian) — Intelligence in animals with nervous systems depends on perception, memory, valuation, and control circuits shaped by plasticity and learning rules. [[70]](https://pmc.ncbi.nlm.nih.gov/articles/PMC1350458/) - Social cognition in primates — Social reasoning capacities (e.g., ToM-like competencies) are studied via comparative methods, often showing partial but not fully human-like abilities. [[71]](https://www.eva.mpg.de/documents/Elsevier/Call_Does_TrendsCogSci_2008_1554401.pdf) - Dopaminergic learning mechanisms — Reward-related dopaminergic activity aligns with prediction-error learning and provides a biological bridge to reinforcement learning models. [[72]](https://folia.unifr.ch/global/documents/242358) - Memory systems specialization — Hippocampal and medial temporal structures are central for forming new episodic memories, as shown by lesion evidence. [[73]](https://pmc.ncbi.nlm.nih.gov/articles/PMC497229/) - Large-scale brain networks — Intrinsic networks (DMN, salience, executive control) describe recurring functional organization relevant to attention, control, and cognition. [[74]](https://www.pnas.org/doi/10.1073/pnas.98.2.676) - Basal cognition (conceptual program) — A research program exploring cognition-like properties (learning, decision, inference) in systems without neurons, often emphasizing minimal mechanisms. [[75]](https://pmc.ncbi.nlm.nih.gov/articles/PMC10770251/) - Slime mold problem solving (Physarum maze) — Experiments show Physarum polycephalum can form efficient paths through mazes, motivating “computation by morphology” models. [[76]](https://www.nature.com/articles/35035159) - Physarum decision-making review — A synthesis arguing that Physarum supports multi-objective foraging decisions and may serve as a “model brain” for minimal decision processes. [[77]](https://pubmed.ncbi.nlm.nih.gov/26189159/) - Bacterial communication and coordination — Bacteria coordinate via chemical signaling (including quorum sensing), enabling colony-level behaviors that resemble distributed decision-making. [[78]](https://www.annualreviews.org/content/journals/10.1146/annurev.cellbio.21.012704.131001) - Quorum sensing (review) — A canonical review describing architectures of bacterial cell–cell communication and how it regulates group behaviors. [[79]](https://www.annualreviews.org/content/journals/10.1146/annurev.cellbio.21.012704.131001) - “Bacterial linguistic communication” hypothesis — A controversial framing that interprets bacterial signaling and genomic plasticity as a form of social intelligence and shared “interpretation” of cues. [[80]](https://pubmed.ncbi.nlm.nih.gov/15276612/) - Immune system as cognitive system — A theoretical proposal that immune behavior can be interpreted via a “cognitive paradigm” using internal “images” such as self/nonself. [[81]](https://pubmed.ncbi.nlm.nih.gov/1463581/) - Plant intelligence (overview) — A debated position arguing that plant adaptive behavior and signaling can be framed as intelligence linked to fitness and problem solving. [[82]](https://academic.oup.com/bioscience/article/66/7/542/2463205) - Plant intelligence foundations (Royal Society) — A review positioning plant intelligence research as a maturing but controversial line since early 2000s debates. [[83]](https://royalsocietypublishing.org/rsfs/article/7/3/20160098/64153/The-foundations-of-plant-intelligencePlant) - Critiques of plant intelligence — A counter-position arguing that key concepts (e.g., individuality) make “intelligence” a misleading label for many plant processes. [[84]](https://www.tandfonline.com/doi/pdf/10.4161/psb.4.5.8276) - Plants-are-intelligent argument (philosophy-of-biology angle) — An argument defending plant intelligence claims and summarizing critiques, highlighting conceptual stakes and empirical examples. [[85]](https://pmc.ncbi.nlm.nih.gov/articles/PMC6948212/) ## Artificial intelligence - Artificial intelligence (field definition by founding proposal) — AI is a research program aiming to model/simulate aspects of learning and intelligence in machines via precise descriptions and implementations. [[86]](https://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf) - Symbolic search (A*) — A graph-search algorithm that uses admissible heuristics to find optimal paths efficiently, foundational for planning and problem solving. [[87]](https://people.stfx.ca/jdelamer/courses/csci-564/_downloads/b2220c66675ddde471ca1795147b8e86/A_Formal_Basis_for_the_Heuristic_Determination_of_Minimum_Cost_Paths.pdf) - Symbolic planning (STRIPS) — A planning system representing world states with logical predicates and searching operator sequences to satisfy goals. [[88]](https://ai.stanford.edu/~nilsson/OnlinePubs-Nils/PublishedPapers/strips.pdf) - Perceptron (early neural model) — A linear-threshold learning model historically central to early neural network research and debates about representational limits. [[89]](https://www.academia.edu/60542953/The_perceptron_a_probabilistic_model_for_information_storage_and_organization_in_the_brain) - Backpropagation (modern neural training catalyst) — A method for learning internal representations by propagating error gradients through multilayer networks. - Universal approximation (theoretical result) — A result showing certain neural network classes can approximate broad families of functions, supporting expressive-power claims. [[90]](https://www.researchgate.net/publication/397842321_Perceptron?utm_source=chatgpt.com) - Convolutional neural networks (CNNs) — Architectures using local receptive fields and weight sharing that achieved strong document recognition and later large-scale vision performance. [[91]](https://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf) - ImageNet dataset milestone — A large-scale hierarchical image database that catalyzed modern computer vision benchmarking and representation learning. [[92]](https://www.image-net.org/static_files/papers/imagenet_cvpr09.pdf) - AlexNet milestone — A deep CNN trained at scale that marked a major leap in ImageNet classification and accelerated deep learning adoption. [[93]](https://proceedings.neurips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - Transformer architecture — A sequence model using self-attention to enable parallelizable learning and long-range dependency handling, foundational for modern NLP. [[94]](https://rodsmith.nz/wp-content/uploads/Minsky-and-Papert-Perceptrons.pdf) - BERT-style bidirectional pretraining — A pretraining approach producing deep bidirectional language representations that improved performance across NLU tasks. [[95]](https://arxiv.org/abs/1810.04805) - GPT-style generative pretraining — A paradigm using generative pretraining for broad language understanding and transfer. [[96]](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) - Scaling and few-shot learning (GPT-3) — Evidence that scaling language models can induce strong task-agnostic few-shot performance across many benchmarks. [[97]](https://arxiv.org/pdf/2005.14165) - Reinforcement learning (RL) textbook framing — A foundational synthesis of RL concepts including value functions, policy optimization, and temporal-difference learning. [[98]](https://incompleteideas.net/book/the-book-2nd.html) - Q-learning — A temporal-difference control method with convergence guarantees in tabular Markovian settings under standard sampling conditions. [[99]](https://link.springer.com/article/10.1007/BF00992698) - Deep Q-Networks (DQN) — A system combining deep neural function approximation with Q-learning to achieve strong Atari performance from pixels. [[100]](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf) - Proximal Policy Optimization (PPO) — A policy-gradient family using clipped surrogate objectives to stabilize updates with strong empirical performance. [[101]](https://arxiv.org/pdf/1707.06347) - Deep RL from human preferences — A method learning reward models from pairwise human comparisons to train RL agents without explicit reward functions. [[102]](https://arxiv.org/abs/1706.03741) - InstructGPT / RLHF — A demonstration that fine-tuning language models with human feedback can improve instruction-following, reduce toxicity, and improve helpfulness (with tradeoffs). [[103]](https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf) - Constitutional AI — A method using AI feedback guided by a “constitution” of rules/principles to reduce harmful behavior with reduced reliance on human labels. [[104]](https://www-cdn.anthropic.com/7512771452629584566b6303311496c262da1006/Anthropic_ConstitutionalAI_v2.pdf) - Variational autoencoders (VAE) — Latent-variable generative models trained via variational inference with a reparameterization trick enabling scalable optimization. [[105]](https://arxiv.org/abs/1312.6114) - Generative adversarial networks (GANs) — A generative framework training a generator and discriminator in a minimax game to learn data distributions. [[106]](https://papers.neurips.cc/paper/5423-generative-adversarial-nets.pdf) - Diffusion models (DDPM lineage) — Generative models learning to reverse a gradual noise process, achieving high-quality synthesis in modern settings. [[107]](https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf) - Adversarial examples (robustness challenge) — Evidence that small worst-case perturbations can reliably fool many neural networks, motivating adversarial training and robustness science. [[108]](https://arxiv.org/pdf/1412.6572) - Common corruption robustness (ImageNet-C/P) — Benchmarks evaluating classifier stability to realistic corruptions and perturbations beyond worst-case adversarial noise. [[109]](https://openreview.net/pdf?id=HJz6tiCqYm) - Uncertainty via dropout (Bayesian approximation) — A theory interpreting dropout training as approximate Bayesian inference, enabling uncertainty estimates in deep learning. [[110]](https://proceedings.mlr.press/v48/gal16.pdf) - Meta-learning (MAML) — A method training model parameters for rapid adaptation to new tasks with few gradient steps. [[111]](https://arxiv.org/pdf/1703.03400) - Interpretability as a science (position paper) — A call for rigorous definitions and evaluation protocols for interpretability, warning that “interpretability” is context-dependent and under-specified. [[112]](https://arxiv.org/abs/1702.08608) - LIME (local surrogate explanations) — A method approximating a classifier locally with an interpretable model to explain individual predictions. [[113]](https://arxiv.org/abs/1602.04938) - SHAP (Shapley additive explanations) — A unified feature-attribution framework with axiomatic grounding connecting to Shapley values. [[114]](https://proceedings.neurips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf) - Fairness through awareness — A framework for fair classification based on a task-specific similarity metric, emphasizing individual-level fairness constraints. [[115]](https://www.cs.toronto.edu/~toni/Papers/awareness.pdf) - Equality of opportunity — A fairness criterion targeting error-rate disparities (e.g., true positive rates) across sensitive groups and methods to post-process predictors. [[116]](https://arxiv.org/pdf/1610.02413) - Differential privacy — A formal privacy guarantee limiting how much any single individual’s data can affect outputs, enabling principled privacy-preserving analysis. [[117]](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/dwork.pdf) - Big data disparate impact analysis — A legal/technical argument that data mining can inherit societal bias and create discriminatory outcomes even without explicit intent. [[118]](https://www.cs.yale.edu/homes/jf/BarocasSelbst.pdf) - No Free Lunch (AI design implication) — A formal reason that “general intelligence” requires carefully formalized priors/inductive biases rather than expecting one method to dominate everywhere. [[119]](https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf) - Cognitive architectures (ACT-R) — A computational theory and modeling framework for human cognition aiming to explain how knowledge and mechanisms produce behavior. [[120]](https://act-r.psy.cmu.edu/) - Cognitive architectures (Soar) — A general cognitive architecture for intelligent behavior emphasizing unified mechanisms, problem spaces, and learning from experience. [[121]](https://soar.eecs.umich.edu/) ### AI evaluation and measurement map (benchmarks, metrics, and desiderata) - Benchmark suites (why they exist) — Benchmarks operationalize “capabilities” through task sets but also create incentives and blind spots, so multiple benchmarks and metrics are needed. [[122]](https://crfm.stanford.edu/helm/) - GLUE — A multi-task NLU benchmark designed to measure cross-task generality and support diagnostic linguistic evaluation. [[123]](https://arxiv.org/abs/1804.07461) - MMLU — A multi-domain test of academic/professional knowledge and problem solving across dozens of subjects. [[124]](https://arxiv.org/abs/2009.03300) - BIG-bench — A large task suite designed to probe beyond-current capabilities, including reasoning and bias-related tasks and emergent behaviors with scale. [[125]](https://arxiv.org/abs/2206.04615) - HELM — A “living” evaluation framework emphasizing multi-metric assessment (accuracy, calibration, robustness, fairness, bias, toxicity, efficiency) across scenarios. [[126]](https://crfm.stanford.edu/helm/) |Evaluation dimension|What it intends to measure (one sentence)|Example operationalization|Representative sources| |---|---|---|---| |Accuracy / task performance|Correctness on task-defined outputs under benchmark conditions.|Classification accuracy; exact match; BLEU-like measures.|[[127]](https://arxiv.org/abs/1804.07461)| |Calibration|Whether predicted probabilities or confidence meaningfully match empirical correctness rates.|Reliability diagrams; ECE; risk–coverage tradeoffs in selective prediction.|[[122]](https://crfm.stanford.edu/helm/)| |Robustness|Stability under distribution shift, corruptions, or adversarial perturbations.|ImageNet-C/P; adversarial accuracy.|[[128]](https://openreview.net/pdf?id=HJz6tiCqYm)| |Uncertainty estimation|Whether models express uncertainty when appropriate, supporting safer decisions.|Bayesian approximations (dropout) and predictive uncertainty metrics.|[[110]](https://proceedings.mlr.press/v48/gal16.pdf)| |Interpretability|Human-usable explanations for model behavior suitable for a given context.|Local explanations (LIME); axiomatic attributions (SHAP).|[[129]](https://arxiv.org/abs/1602.04938)| |Fairness / non-discrimination|Equal treatment or equal error properties across groups or individuals.|Equality of opportunity; individual fairness constraints.|[[130]](https://arxiv.org/pdf/1610.02413)| |Privacy|Limiting information leakage about individuals in data.|Differential privacy guarantees.|[[117]](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/dwork.pdf)| A canonical agent–environment loop for reinforcement learning (and many control-like intelligence models) is summarized below. [[20]](https://incompleteideas.net/book/the-book-2nd.html) flowchart LR   ENV[Environment] -->|observation o_t| AG[Agent]   AG -->|action a_t| ENV   ENV -->|reward r_t| AG   AG -->|policy/value update| AG ## Collective and hybrid intelligence - Collective intelligence (definition by mission) — A field studying how people and computers can be connected so that—collectively—they act more intelligently than any individual alone. [[131]](https://cci.mit.edu/) - Collective intelligence factor (c) — Empirical evidence that groups show stable performance differences across task batteries analogous to individual g, partly tied to interaction patterns and social sensitivity. [[132]](https://pubmed.ncbi.nlm.nih.gov/20929725/) - Organizational/team collective intelligence (review framing) — A synthesis analyzing mechanisms (e.g., equality in turn-taking) and interventions that can improve group problem solving. [[133]](https://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Collective_Intelligence/Woolley_Aggarwal_Malone_Collective%20Intelligence%20in%20Teams%20and%20Organizations.pdf) - Wisdom-of-crowds thesis — A popular-level argument that under certain conditions (diversity, independence, aggregation) groups can outperform individuals in estimation and judgment. [[134]](https://sentry.rmu.edu/SentryHTML/pdf/lib_finn_DISC8710_wisdom_of_crowds.pdf) - Swarm intelligence (book-level formalization) — A framework translating social insect/self-organization principles into engineered algorithms for optimization and control. [[135]](https://global.oup.com/academic/product/swarm-intelligence-9780195131598) - Particle swarm optimization (PSO) — An optimization method inspired by social flocking behavior, using populations (“particles”) that move through a search space by combining individual and social signals. [[136]](https://www.cs.tufts.edu/comp/150GA/homeworks/hw3/_reading6%201995%20particle%20swarming.pdf) - Ant colony optimization (ACO) metaheuristic — A family of stochastic combinatorial optimization methods inspired by pheromone-mediated path finding in ants. [[137]](https://web2.qatar.cmu.edu/~gdicaro/15382/additional/aco-book.pdf) - Hybrid intelligence (human–AI teaming) — A design goal in which human judgment and machine pattern recognition/computation are combined, requiring careful evaluation across accuracy and risk dimensions. [[138]](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf) - Human feedback as a collective intelligence mechanism — RL from human preferences operationalizes a “collective” supervisory signal distributed across evaluators rather than encoded rewards. [[102]](https://arxiv.org/abs/1706.03741) - Large-scale benchmark authorship as collective intelligence — BIG-bench operationalizes collective scientific judgment by aggregating tasks from hundreds of contributors to probe model capabilities. [[139]](https://arxiv.org/abs/2206.04615) ## Ethics, governance, and societal impacts - OECD AI Principles — Government-adopted principles promoting innovative and trustworthy AI that respects human rights and democratic values. [[140]](https://www.oecd.org/en/topics/ai-principles.html) - UNESCO Recommendation on AI Ethics — A global normative instrument emphasizing human dignity, rights, transparency, and oversight for AI systems. [[141]](https://unesdoc.unesco.org/ark%3A/48223/pf0000380455) - NIST AI Risk Management Framework (AI RMF 1.0) — A voluntary framework for managing AI risks to individuals, organizations, and society across the AI lifecycle. [[142]](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf) - NIST Generative AI profile (companion resource) — A cross-sectoral profile linking generative AI risks and controls to the AI RMF structure. [[143]](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf) - EU AI Act (risk-based governance overview) — An EU framework describing prohibited practices, high-risk systems, transparency obligations, and governance structures with phased timelines. [[144]](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai) - EU AI Act implementation guidance (prohibited practices) — Commission guidance elaborating prohibited AI practices with legal explanations and practical examples. [[145]](https://ai-act-service-desk.ec.europa.eu/sites/default/files/2025-08/guidelines_on_prohibited_artificial_intelligence_practices_established_by_regulation_eu_20241689_ai_act_english_ied3r5nwo50xggpcfmwckm3nuc_112367-1.PDF) - Fairness constraints in ML (core technical lineage) — Seminal definitions and methods (individual fairness; equality of opportunity) formalize discrimination criteria and mitigation strategies. [[146]](https://www.cs.toronto.edu/~toni/Papers/awareness.pdf) - Privacy protection (differential privacy) — A formal privacy standard enabling bounded leakage guarantees, now central to responsible data use in intelligent systems. [[117]](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/dwork.pdf) - Transparency and explainability tooling — Explanation methods (LIME/SHAP) operationalize interpretability but require rigorous evaluation and context-specific justification. [[129]](https://arxiv.org/abs/1602.04938) - Robustness and safety arguments — Adversarial vulnerability and distribution shift motivate robustness benchmarks and defenses as safety-critical evaluation components. [[147]](https://arxiv.org/pdf/1412.6572) - “Intelligence” as state activity (policy polysemy) — National security intelligence definitions emphasize secret state activities to understand and influence threats, intersecting with AI governance through surveillance and analysis capabilities. [[148]](https://www.cia.gov/resources/csi/static/cc27ce9b678dc69d4bdeef410feffa20/Article-New-Approach-to-Old-Question-Sep-2023.pdf) ### Governance frameworks comparison table |Framework / instrument|Primary purpose (one sentence)|Typical users|Notable emphasis|Representative sources| |---|---|---|---|---| |OECD AI Principles|Establish shared democratic values-based principles for trustworthy AI policy and practice.|Governments, policymakers, industry.|Human rights and democratic values; practical flexibility.|[[140]](https://www.oecd.org/en/topics/ai-principles.html)| |UNESCO AI ethics recommendation|Provide global normative guidance for ethical AI anchored in rights, dignity, and oversight.|Member states, regulators, civil society.|Human dignity, transparency, fairness, human oversight.|[[141]](https://unesdoc.unesco.org/ark%3A/48223/pf0000380455)| |NIST AI RMF 1.0|Offer structured risk management for AI systems across lifecycle processes and outcomes.|Organizations building/deploying AI.|Risk identification/management; trustworthy AI characteristics.|[[142]](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf)| |EU AI Act overview|Regulate AI uses via risk tiers (prohibited, high-risk, transparency, minimal risk) and governance controls.|EU providers/deployers; regulators.|Risk-based constraints; phased compliance timelines; enforcement.|[[144]](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai)| |EU AI Act prohibited-practices guidance|Clarify interpretation of prohibited AI practices for compliance.|Providers, deployers, enforcement bodies.|Practical examples and legal explanations of bans.|[[149]](https://ai-act-service-desk.ec.europa.eu/sites/default/files/2025-08/guidelines_on_prohibited_artificial_intelligence_practices_established_by_regulation_eu_20241689_ai_act_english_ied3r5nwo50xggpcfmwckm3nuc_112367-1.PDF)| --- [[1]](https://www.apa.org/topics/intelligence) [[8]](https://www.apa.org/topics/intelligence) [[15]](https://www.apa.org/topics/intelligence) [[30]](https://www.apa.org/topics/intelligence) https://www.apa.org/topics/intelligence [https://www.apa.org/topics/intelligence](https://www.apa.org/topics/intelligence) [[2]](https://arxiv.org/abs/0712.3329) [[14]](https://arxiv.org/abs/0712.3329) [[18]](https://arxiv.org/abs/0712.3329) https://arxiv.org/abs/0712.3329 [https://arxiv.org/abs/0712.3329](https://arxiv.org/abs/0712.3329) [[3]](https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Adult-Intelligence-Scale-%7C-Fifth-Edition/p/P100071002?srsltid=AfmBOoqwlbxzjL8PJFFfVuytBPBPDY2w0IHuSyLLzcxrX9ummmQ3PHDg) [[64]](https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Adult-Intelligence-Scale-%7C-Fifth-Edition/p/P100071002?srsltid=AfmBOoqwlbxzjL8PJFFfVuytBPBPDY2w0IHuSyLLzcxrX9ummmQ3PHDg) https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Adult-Intelligence-Scale-%7C-Fifth-Edition/p/P100071002?srsltid=AfmBOoqwlbxzjL8PJFFfVuytBPBPDY2w0IHuSyLLzcxrX9ummmQ3PHDg [https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Adult-Intelligence-Scale-%7C-Fifth-Edition/p/P100071002?srsltid=AfmBOoqwlbxzjL8PJFFfVuytBPBPDY2w0IHuSyLLzcxrX9ummmQ3PHDg](https://www.pearsonassessments.com/en-us/Store/Professional-Assessments/Cognition-%26-Neuro/Wechsler-Adult-Intelligence-Scale-%7C-Fifth-Edition/p/P100071002?srsltid=AfmBOoqwlbxzjL8PJFFfVuytBPBPDY2w0IHuSyLLzcxrX9ummmQ3PHDg) [[4]](https://pubmed.ncbi.nlm.nih.gov/17655784/) [[31]](https://pubmed.ncbi.nlm.nih.gov/17655784/) [[38]](https://pubmed.ncbi.nlm.nih.gov/17655784/) https://pubmed.ncbi.nlm.nih.gov/17655784/ [https://pubmed.ncbi.nlm.nih.gov/17655784/](https://pubmed.ncbi.nlm.nih.gov/17655784/) [[5]](https://people.stfx.ca/jdelamer/courses/csci-564/_downloads/b2220c66675ddde471ca1795147b8e86/A_Formal_Basis_for_the_Heuristic_Determination_of_Minimum_Cost_Paths.pdf) [[87]](https://people.stfx.ca/jdelamer/courses/csci-564/_downloads/b2220c66675ddde471ca1795147b8e86/A_Formal_Basis_for_the_Heuristic_Determination_of_Minimum_Cost_Paths.pdf) https://people.stfx.ca/jdelamer/courses/csci-564/_downloads/b2220c66675ddde471ca1795147b8e86/A_Formal_Basis_for_the_Heuristic_Determination_of_Minimum_Cost_Paths.pdf [https://people.stfx.ca/jdelamer/courses/csci-564/_downloads/b2220c66675ddde471ca1795147b8e86/A_Formal_Basis_for_the_Heuristic_Determination_of_Minimum_Cost_Paths.pdf](https://people.stfx.ca/jdelamer/courses/csci-564/_downloads/b2220c66675ddde471ca1795147b8e86/A_Formal_Basis_for_the_Heuristic_Determination_of_Minimum_Cost_Paths.pdf) [[6]](https://pubmed.ncbi.nlm.nih.gov/20929725/) [[32]](https://pubmed.ncbi.nlm.nih.gov/20929725/) [[132]](https://pubmed.ncbi.nlm.nih.gov/20929725/) https://pubmed.ncbi.nlm.nih.gov/20929725/ [https://pubmed.ncbi.nlm.nih.gov/20929725/](https://pubmed.ncbi.nlm.nih.gov/20929725/) [[7]](https://www.oecd.org/en/topics/ai-principles.html) [[140]](https://www.oecd.org/en/topics/ai-principles.html) https://www.oecd.org/en/topics/ai-principles.html [https://www.oecd.org/en/topics/ai-principles.html](https://www.oecd.org/en/topics/ai-principles.html) [[9]](https://courses.cs.umbc.edu/471/papers/turing.pdf) [[16]](https://courses.cs.umbc.edu/471/papers/turing.pdf) https://courses.cs.umbc.edu/471/papers/turing.pdf [https://courses.cs.umbc.edu/471/papers/turing.pdf](https://courses.cs.umbc.edu/471/papers/turing.pdf) [[10]](https://cci.mit.edu/) [[131]](https://cci.mit.edu/) https://cci.mit.edu/ [https://cci.mit.edu/](https://cci.mit.edu/) [[11]](https://www.nature.com/articles/35035159) [[76]](https://www.nature.com/articles/35035159) https://www.nature.com/articles/35035159 [https://www.nature.com/articles/35035159](https://www.nature.com/articles/35035159) [[12]](https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf) [[26]](https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf) [[119]](https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf) https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf [https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf](https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf) [[13]](https://www.cia.gov/resources/csi/static/cc27ce9b678dc69d4bdeef410feffa20/Article-New-Approach-to-Old-Question-Sep-2023.pdf) [[148]](https://www.cia.gov/resources/csi/static/cc27ce9b678dc69d4bdeef410feffa20/Article-New-Approach-to-Old-Question-Sep-2023.pdf) https://www.cia.gov/resources/csi/static/cc27ce9b678dc69d4bdeef410feffa20/Article-New-Approach-to-Old-Question-Sep-2023.pdf [https://www.cia.gov/resources/csi/static/cc27ce9b678dc69d4bdeef410feffa20/Article-New-Approach-to-Old-Question-Sep-2023.pdf](https://www.cia.gov/resources/csi/static/cc27ce9b678dc69d4bdeef410feffa20/Article-New-Approach-to-Old-Question-Sep-2023.pdf) [[17]](https://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf) [[86]](https://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf) https://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf [https://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf](https://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf) [[19]](https://incompleteideas.net/book/the-book-2nd.html) [[20]](https://incompleteideas.net/book/the-book-2nd.html) [[98]](https://incompleteideas.net/book/the-book-2nd.html) https://incompleteideas.net/book/the-book-2nd.html [https://incompleteideas.net/book/the-book-2nd.html](https://incompleteideas.net/book/the-book-2nd.html) [[21]](https://iiif.library.cmu.edu/file/Simon_box00063_fld04838_bdl0001_doc0001/Simon_box00063_fld04838_bdl0001_doc0001.pdf) [[47]](https://iiif.library.cmu.edu/file/Simon_box00063_fld04838_bdl0001_doc0001/Simon_box00063_fld04838_bdl0001_doc0001.pdf) https://iiif.library.cmu.edu/file/Simon_box00063_fld04838_bdl0001_doc0001/Simon_box00063_fld04838_bdl0001_doc0001.pdf [https://iiif.library.cmu.edu/file/Simon_box00063_fld04838_bdl0001_doc0001/Simon_box00063_fld04838_bdl0001_doc0001.pdf](https://iiif.library.cmu.edu/file/Simon_box00063_fld04838_bdl0001_doc0001/Simon_box00063_fld04838_bdl0001_doc0001.pdf) [[22]](https://ia803209.us.archive.org/27/items/bstj27-3-379/bstj27-3-379_text.pdf) https://ia803209.us.archive.org/27/items/bstj27-3-379/bstj27-3-379_text.pdf [https://ia803209.us.archive.org/27/items/bstj27-3-379/bstj27-3-379_text.pdf](https://ia803209.us.archive.org/27/items/bstj27-3-379/bstj27-3-379_text.pdf) [[23]](https://direct.mit.edu/books/oa-monograph/4581/Cybernetics-or-Control-and-Communication-in-the) https://direct.mit.edu/books/oa-monograph/4581/Cybernetics-or-Control-and-Communication-in-the [https://direct.mit.edu/books/oa-monograph/4581/Cybernetics-or-Control-and-Communication-in-the](https://direct.mit.edu/books/oa-monograph/4581/Cybernetics-or-Control-and-Communication-in-the) [[24]](https://people.ciirc.cvut.cz/~hlavac/pub/MiscTextForStudents/1982MarrDavidVisionBook.pdf) https://people.ciirc.cvut.cz/~hlavac/pub/MiscTextForStudents/1982MarrDavidVisionBook.pdf [https://people.ciirc.cvut.cz/~hlavac/pub/MiscTextForStudents/1982MarrDavidVisionBook.pdf](https://people.ciirc.cvut.cz/~hlavac/pub/MiscTextForStudents/1982MarrDavidVisionBook.pdf) [[25]](https://perso.limos.fr/~palafour/PAPERS/PDF/Garey-Johnson79.pdf) https://perso.limos.fr/~palafour/PAPERS/PDF/Garey-Johnson79.pdf [https://perso.limos.fr/~palafour/PAPERS/PDF/Garey-Johnson79.pdf](https://perso.limos.fr/~palafour/PAPERS/PDF/Garey-Johnson79.pdf) [[27]](https://doursat.free.fr/docs/Geman_Bienenstock_Doursat_1992_bv_NeurComp.pdf) https://doursat.free.fr/docs/Geman_Bienenstock_Doursat_1992_bv_NeurComp.pdf [https://doursat.free.fr/docs/Geman_Bienenstock_Doursat_1992_bv_NeurComp.pdf](https://doursat.free.fr/docs/Geman_Bienenstock_Doursat_1992_bv_NeurComp.pdf) [[28]](https://arxiv.org/abs/0812.4360) https://arxiv.org/abs/0812.4360 [https://arxiv.org/abs/0812.4360](https://arxiv.org/abs/0812.4360) [[29]](https://www.nature.com/articles/nrn2787) https://www.nature.com/articles/nrn2787 [https://www.nature.com/articles/nrn2787](https://www.nature.com/articles/nrn2787) [[33]](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf) [[138]](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf) [[142]](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf) https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf [https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf](https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf) [[34]](https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf) [[35]](https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf) [[36]](https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf) https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf [https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf](https://www1.udel.edu/educ/gottfredson/reprints/1997mainstream.pdf) [[37]](https://pmc.ncbi.nlm.nih.gov/articles/PMC5156710/) https://pmc.ncbi.nlm.nih.gov/articles/PMC5156710/ [https://pmc.ncbi.nlm.nih.gov/articles/PMC5156710/](https://pmc.ncbi.nlm.nih.gov/articles/PMC5156710/) [[39]](https://app.nova.edu/toolbox/instructionalproducts/edd8124/fall11/1974-Baddeley-and-Hitch.pdf) 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