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  1. RELIGION FROM SCIENTIFIC POINT OF VIEW Arizona State University 14 Oct., 2008 Dr. Leonid Perlovsky Harvard University and AFRL

  2. OUTLINE • Science and religion today • Artificial intelligence difficulties since the 1950s and logic • Dynamic logic, the mind, and the knowledge instinct • Higher cognitive functions • Beautiful (scientific explanation) • Sublime (scientific explanation) • GOD (scientific explanation)

  3. SCIENCE AND RELIGION • Can scientific and religious views be reconciled? • Many scientists wrote that scientific discoveries in physics, molecular biology, evolution, and cosmology do not contradict the main tenets of the world’s religions • Einstein: • “Everyone who is seriously involved in the pursuit of science becomes convinced that a spirit is manifest in the laws of the Universe.” • Jung: • Schism between science and religion points to a psychosis of contemporary collective psyche, • survival of culture demands repairing this schism

  4. CURRENT UNDERSTANDING • There is no scientific theory, explaining religion • From a review (in my words): • I hoped that your book will help me to enter my classroom without leaving my religious beliefs at the doorstep. I hoped that I’ll be able to enter my church without leaving at the doorstep my intellectual integrity. I hoped for too much. • I attempt to outline directions to unifying science and religion • Not any specific religion • Scientific foundations for emotions of religiously sublime

  5. DIFFICULTIES OF AI SINCE the1950s • Cognition involves evaluating large numbers of combinations • Pixels -> objects -> scenes • Combinations of 100 elements are 100100 • This number is larger than the size of the Universe • > all the events in the Universe during its entire life • Combinatorial Complexity (CC) • A general problem (since the 1950s) • AI, recognition, language… • Statistical, neural networks, rule systems…

  6. CC vs. LOGIC • CC is related to formal logic • Gödel proved that logic is “illogical,” “inconsistent” (1930s) • CC is Gödel's “incompleteness” in a finite system • Logic pervades all algorithms • rule systems, fuzzy systems (degree of fuzziness), pattern recognition, neural networks (training uses logic)

  7. DYNAMIC LOGIC • Dynamic Logic: “from vague to crisp” • initial vague concepts (thoughts, decisions, plans) dynamically evolve into crisp concepts (formal-logic) • Overcomes CC • Experimentally proved recently in brain neuro-imaging • The brain works “from vague to crisp” • Vague are also less conscious

  8. OUTLINE Science and religion today Artificial intelligence difficulties since the 1950s and logic Dynamic logic, the mind, and the knowledge instinct Higher cognitive functions Beautiful (scientific explanation) Sublime (scientific explanation) GOD (scientific explanation)

  9. STRUCTURE OF THE MIND • Concepts • Models of objects, their relations, and situations • Evolved to satisfy instincts • Instincts • Internal sensors (e.g. sugar level in blood) • Emotions • Neural signals connecting instincts and concepts • e.g. a hungry person sees food all around • Behavior • Models of goals (desires) and muscle-movement… • Hierarchy • Concept-models and behavior-models are organized in a “loose” hierarchy

  10. THE KNOWLEDGE INSTINCT • Model-concepts always have to be adapted • lighting, surrounding, new objects and situations • even when there is no concrete “bodily” needs • Instinct for knowledge and understanding • Increase similarity between models and the world • Emotions related to the knowledge instinct • Satisfaction or dissatisfaction • change in similarity between models and world • Related not to bodily instincts • harmony or disharmony (knowledge-world): aesthetic emotion

  11. CAUSALITY VS. TELEOLOGY • The knowledge instinct is a teleological principle • The mind and human evolution has a purpose: increase of knowledge • Evolution is moved by a “final cause” • The knowledge instinct is mathematically equivalent to dynamic logic • Teleology = causal dynamics • Scientific causality = Intelligent design • Basic physical laws at the elementary level are the same • Hamiltonian dynamics = minimization of Lagrangian (energy minimization) • But for complex systems KI is a revolutionary change - Law of Entropy: evolution toward chaos (thermal death) - Law of the KI: evolution toward knowledge

  12. OUTLINE Science and religion today Artificial intelligence difficulties since the 1950s and logic Dynamic logic, the mind, and the knowledge instinct - Engineering applications Higher cognitive functions Beautiful (scientific explanation) Sublime (scientific explanation) GOD (scientific explanation)

  13. APPLICATIONS • Many applications • Signals processing and object recognition • Financial market predictions • Market crash on 9/11 predicted a week ahead • Internet search engines • Based on text understanding • Semantic Web

  14. PERCEPTION: PATTERNS BELOW NOISE Three objects in noise object 1 object 2 object 3 SCR - 0.70 dB -1.98 dB -0.73 dB 3 Object Image 3 Object Image + Clutter y y x x

  15. IMAGE PATTERNS BELOW NOISE DL starts with uncertain knowledge, and similar to human mind converges rapidly on exact solution y x

  16. b d a c h e f g IMAGE PATTERNS BELOW NOISE 3 objects, 10,000 data points, signal-to-noise, S/N ~ 0.5 Complexity: Logical~MN ~105000; DL ~ 106, Improvement in S/N about 100 times

  17. OUTLINE Science and religion today Artificial intelligence difficulties since the 1950s and logic Dynamic logic, the mind, and the knowledge instinct Higher cognitive functions Beautiful (scientific explanation) Sublime (scientific explanation) GOD (scientific explanation)

  18. Action/Adaptation Similarity measures Models Action/Adaptation meanings Similarity measures Models situations objects HIGHER COGNITIVE FUNCTIONS • Abstract concept-models are at higher levels of the hierarchy • Higher level concepts are general, vague, less conscious • Higher levels unify lower-level knowledge • Purpose of higher-level concepts: make meanings of lower-level knowledge

  19. BEAUTY • The highest aesthetic emotion, beautiful • improvement of the highest models (at the top of the hierarchy) • feel emotion of beautiful • Beautiful “reminds” us of our purposiveness • the “top” model unifies all our knowledge • vague, rarely consciously perceived • we perceive it as our life’s purpose (Kant: “aimless purposiveness”) • what makes us different from a piece of rock? • Beauty is separate from sex • sex uses all our abilities, including beauty

  20. RELIGIOUSLY SUBLIME • Beautiful • Emotion related to improvement of the highest concept-model of understanding of our meaning and purpose • Sublime • Emotion related to improvement of the highest concept-model of behavior toward making our lives meaningful and purposeful • Can we do this? When we feel we can, we feel emotion of sublime. • Ten commandments? • Maimonides (12th century): God demands from us thinking on our own, but we are incapable, therefore, he gave us ten commandments

  21. GODFROM PURELY SCIENTIFIC VIEW • The highest concept(s) in our mind • Vague, not differentiated, not separated from emotions, not conscious • They do not belong to our conscious psyche (“I”) • We do not “owe” them • They direct our lives • They “owe” us • We perceive them as active source of will outside of our selves • Agents with will and purpose • This is what traditionally is called GOD • C. Jung warned against “psychologizing” unconscious • Science tread on incomputable, infinite, unconscious, mysterious

  22. FUTURE DIRECTIONSresearch, predictions and testing of NMF/DL • Brain neuroimaging • Cognitive and emotional hierarchy • Higher concepts and emotions • Conscious vs. unconscious • History of cultures, historical linguistics, and psycholinguistics • Correlate evolution of religions, languages, consciousness, and cultures • Measure emotionality of various languages in labs and correlated with religious and cultural evolution • Mathematical development • Joint evolution of language and cognition • DL in the hierarchy, evolution of the higher models • Emotionality in computer models of evolution of languages and cultures • Music • Direct effect on emotions • Concurrent evolution of music, consciousness, religions, and cultures • Improve human condition around the globe • Develop predictive cultural models, integrate spiritual and material causes • Identify language and music effects that can advance consciousness, reduce religious intolerance, and tensions • Diagnose cultural states (up, down, stagnation), measure Differentiation, Synthesis, Hierarchy 22

  23. BACKUP • Combinatorial Complexity since the 1950s • Aristotle vs. Gödel • Language, cognition, and cultures • Role of music in evolution of cognition and cultures • Predictions and testing 23 16-Sep-05

  24. COMBINATORIAL COMPLEXITY SINCE the 1950s • CC was encountered for over 50 years • Statistical pattern recognition and neural networks: CC of learning requirements • Rule systems and AI, in the presence of variability : CC of rules • Minsky 1960s: Artificial Intelligence • Chomsky 1957: language mechanisms are rule systems • Model-based systems, with adaptive models: CC of computations • Chomsky 1981: language mechanisms are model-based (rules and parameters) • Current ontologies, “semantic web” are rule-systems • Evolvable ontologies : present challenge

  25. ARISTOTLE VS. GÖDEL logic, forms, and language • Aristotle • Logic: a supreme way of argument • Forms: representations in the mind • Form-as-potentiality evolves into form-as-actuality • Potentialities are illogical, actualities are logical (Dynamic Logic) • Language and thinking are closely linked • From Boole to Russell: formalization of logic • Logicians eliminated from logic uncertainty of language • Hilbert: formalize rules of mathematical proofs forever • Gödel (the 1930s) • Logic is not consistent • Aristotle and Alexander the Great

  26. LANGUAGE vs. COGNITION • “Nativists”, - since the 1950s - Language is a separate mind mechanism (Chomsky) - Pinker: language instinct • “Cognitivists”, - since the 1970s • Language jointly with cognition • Talmy, Elman, Tomasello… • “Evolutionists”, - since the 1980s • Language transmission between generations • Hurford, Kirby, Cangelosi… • NMF / DL was extended to language ~ 2000 • Co-evolution of language and cognition

  27. INTEGRATEDLANGUAGE AND COGNITION • Where language and cognition come together? • A fuzzy concept m has linguistic and cognitive-sensory models • Mm = { Mmcognitive,Mmlanguage}; • Language and cognition are fused at fuzzy pre-conceptual level • before concepts are learned • Language and cognition • Initial models are vague fuzzy blobs • Language models have empty “slots” for cognitive model (objects and situations) • Language participates in cognition and v.v. • L & C help learning and understanding each other • Help associating signals, words, models, and behavior

  28. Action Action Similarity Similarity grounded in language Action Action Similarity Similarity grounded in language grounded in real-world objects SYMBOLIC ABILITY • Integrated hierarchies of Cognition and Language • High level cognition is only possible due to language language cognition M M M M

  29. EVOLUTION OF CULTURES • The knowledge instinct • Two mechanisms: differentiation and synthesis • Differentiation • At every level of the hierarchy: more detailed concepts • Separate concepts from emotions • Synthesis • Connect knowledge to life • Connect concepts and emotions • Connect language and cognition • Connect high and low: concepts acquire meaning at the next level 29 16-Sep-05

  30. EMOTIONS IN LANGUAGE • Animal vocal tract • controlled by old (limbic) emotional system • involuntary • Human vocal tract • controlled by two emotional centers: limbic and cortex • Involuntary and voluntary • Human voice determines emotional content of cultures • Emotionality of language is in its sound: melody of speech 30 16-Sep-05

  31. LANGUAGEEMOTIONS AND CUTURES • Conceptual content of culture: words, phrases • Easily borrowed among cultures • Emotional content of culture • In voice sound (melody of speech) • Determined by grammar • Cannot be borrowed among cultures • English language (Diff. > Synthesis) • Weak connection between conceptual and emotional (since 15 c) • Pragmatic, high culture, but may lead to identity crisis • Arabic language (Synthesis > Diff.) • Strong connection between conceptual and emotional • Cultural immobility, but strong feel of identity (synthesis) 31 16-Sep-05

  32. MODELS OF CULTURAL EVOLUTION • Differentiation, D, synthesis, S, hierarchy, H dD/dt = a D G(S); G(S) = (S - S0) exp(-(S-S0) / S1) dS/dt = -bD + dH H = H0 + e*t

  33. DYNAMIC CULTURE Average synthesis, high differentiation; oscillating solution Knowledge accumulates; no stability

  34. TRADITIONAL CULTURE High synthesis, low differentiation; stable solution Stagnation, stability increases

  35. INTERACTING CULTURES • Two cultures • dynamic and traditional • slow exchange by D and S dDk/dt = ak Dk G(Sk) + xkDk dSk/dt = -bkDk + dkHk + ykSk Hk = H0k + ek*t

  36. INTERACTING CULTURES • Early: Dynamic culture affects traditional culture, no reciprocity • Later: 2 dynamic cultures stabilize each other Knowledge accumulation + stability

  37. PUBLICATIONS 300 publications 3 books OXFORD UNIVERSITY PRESS (2001; 3rd printing) 2007: Neurodynamics of High Cognitive Functions with Prof. Kozma, Springer Sapient Systems with Prof. Mayorga, Springer 2008: The Knowledge Instinct Basic Books

  38. ROLE OF MUSIC IN EVOLUTION OF THE MIND • Melody of human voice contains vital information • About people’s world views and mutual compatibility • Exploits mechanical properties of human inner ear • Consonances and dissonances • Tonal system evolved (14th to 19th c.) for • Differentiation of emotions • Synthesis of conceptual and emotional • Bach integrates personal concerns with “the highest” • Pop-song is a mechanism of synthesis • Integrates conceptual (lyric) and emotional (melody) • Also, differentiates emotions • Bach concerns are too complex for many everyday needs • Human consciousness requires synthesis immediately • Rap is a simplified, but powerful mechanism of synthesis • Exactly like ancient Greek dithyrambs of Dionysian cult

  39. SCIENCE VS. RELIGION • Science causal mechanisms • Religion teleology (purpose) • Wrong! • In basic physics causality and teleology are equivalent • The principle of minimal energy is teleological • More general, min. Lagrangian • The knowledge instinct • Teleological principle in evolution of the mind and culture • Dynamic logic is a causal law equivalent to the KI • Causality and teleology are equivalent 39 16-Sep-05

  40. PREDICTIONS AND TESTING of NMF/DL theory of the mind • Experimental testing • Neural, psychological, and psycholinguistic labs • Simulation of multi-agent evolving systems • Instinctual learning mechanisms • Ongoing and future research: • similarity measure as a foundation of knowledge and language instincts • mechanisms of model parameterization and parameter adaptation • dynamics of fuzziness during perception/cognition/learning • mechanisms of language and cognition integration • emotionality of languages and cultures • mechanisms of differentiation and synthesis • mechanisms of cultural evolution • role of music in synthesis and in cultural evolution

  41. NEURAL MODELING FIELDSbasic two-layer mechanism: from signals to concepts • Bottom-up signals • Pixels or samples (from sensor or retina) x(n), n = 1,…,N • Top-down concept-models Mm(Sm,n), parameters Sm, m = 1, …; • Models predict expected signals from objects

  42. THE KNOWLEDGE INSTINCT MATH. • The knowledge instinct = maximization of similarity between signals and models • Similarity between signals and models, L • L = l ({x}) = l (x(n)) • l (x(n)) = r(m) l (x(n) | Mm(Sm,n)) • l (x(n) | Mm(Sm,n)) is a conditional similarity for x(n) given m • {n} are not independent, M(n) may depend on n’ • CC: L contains MN items: all associations of pixels and models (LOGIC)

  43. DYNAMIC LOGIC (DL) non-combinatorial solution • Start with a set of signals and unknown object-models • any parameter values Sm • associate object-model with its contents (signal composition) • (1) f(m|n) = r(m) l (n|m) /r(m') l (n|m') • Improve parameter estimation • (2) Sm = Sm + a f(m|n) [ln l (n|m)/Mm]*[Mm/Sm] • (adetermines speed of convergence) • learn signal-contents of objects • Continue iterations (1)-(2). Theorem: MF is a convergingsystem - similarity increases on each iteration - aesthetic emotion is positive during learning