Trans-Belief: Developing Artificial Intelligence NLP Model Capable of Religious-Belief-like Cognitive Processes for Expected Enhanced Cognitive Ability
Abstract
:1. Introduction: A Literature Review of Cognitive Aspects of Belief; Doubt, Rebellion, and Conditions of Truth; Synchronicity; Theoretical Models
1.1. Doubt, Rebellion, and Conditions of Truth
1.2. Synchronicity
1.3. Theoretical Models
2. Computation of Belief Systems: How Do Artificial Neural Networks Work? Optional Bayesian Reasoning Logic Models—Higher-Order Theorem Provers for Computational Belief Systems
Optional Bayesian Reasoning Logic Models—Higher-Order Theorem Provers for Computational Belief Systems
3. Ethical Considerations; Singularity and the Singularianism Religion; ‘Trans Belief Computational Theology’
3.1. Singularity and the Singularianism Religion
3.2. Trans-Belief ‘Computational Theology’
4. A Proposed ‘Trans-Belief’ Theoretical Logic Model
5. Discussion
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
1 | Reviewing the relevant religious knowledge for the purpose of formulating a theological background for the article, would require the scope of an article in itself, if not beyond that and unfortunately, the constraints of the scope do not allow this. An attempt to condense into one paragraph will distort and flatten the proper breadth and will arouse many objections and rightly so. For example, see some references for discussions in this topic: Kenny (1992); Evans (2005); Swinburne (1981); Wolfson (1942). From the Philosophy of Mind perspective, Belief is an attitude involving dispositions to act and behave as if its content were true and to use it as premise in reasoning. See: Armstrong (1973). |
2 | Theological issues are indeed charged and stand at the core of belief, but despite their necessity for understanding the overall experience of belief, at the moment, in any case, they cannot be programmed, or even come to some agreement and decision that can be formalized, so I isolated the cognitive component of belief, which is also essential to the development of belief, from the constraints of programming language. |
3 | My choice of the term “Belief” over “Faith” is deliberate and intended to isolate the personal and universal psychological belief experience from the cultural and religious construct of faith, which depends on its environment. Of course, the inspiration that the individual takes for his Belief, mostly originates from the faith of diverse religions. According to Philosophy of Mind, Belief in God, which this article focuses on, is a unique example of non-propositional belief, that is, belief in an external agent, a sense of trust in him, which is a more prefund phenomenon than propositional belief, which is a belief that something is real or will come true. |
4 | “A person does not find himself subject to belief- he must struggle with himself to achieve it. This struggle extends over two levels: cognitive and conative”. (Sagi 2005, p. 112); Crystal Park (2007) has codified religion as a meaning system consisting of cognitive, emotional and motivational components that shapes an individual’s global belief, goals and as a result, sense of meaning. In other words, according to this perspective, religious beliefs work as a paradigm through which individuals observe, understand, interpret and evaluate their experiences and direct their behaviors. (Park 2007, pp. 319–28); “Intellect is the tool through which alone the soul will know God. Beliefs are (usually) formed out of the interaction between cognitive processes and prior knowledge”. (Eckhart 2009, p. 166); A similar notion is McCarthy and Hayes’ (1969) distinction between heuristics and epistemology in artificial intelligence (McCarthy and Hayes 1969); For Some philosophical problems from the standpoint of artificial intelligence, see: Webber and Nilsson (1981). |
5 | Cognitive abilities such as: way of processing, coding and retrieval memory, cognitive-intuitive/analytical style, examines reality and worldview, decision-making processes, spatial perception, peripheral vision, interpersonal relationships, quality of dreams, motivation, preferences, risk taking, existential safety, and language skills. See also: Paivio (1971); Riding et al. (1989). |
6 | “Beliefs are meant to accurately represent the world in order to appropriately guide adaptive behaviors” (Fodor 2000, pp. 66–68). |
7 | In terms of its neural basis, ToM has been associated with specific brain regions, particularly those involved in social processing and empathy. The “Theory of Mind network” includes areas such as the medial prefrontal cortex, the superior temporal sulcus, and the temporoparietal junction. These regions are implicated in processing information about others’ mental states and intentions. For Neural Basis of Theory of Mind (ToM), see: Saxe et al. (2004); Blakemore et al. (2004). |
8 | The AI language model has already some identity and opinions currently, for example: “In a manner of speaking, the network is receptive to, imprinted by the structure of the world as presented to it. We might say that it develops a point of view: not a conscious experience, but something like the classical notion of the mind’s conformity to a thing” (Wales 2022, p. 166). Referring to: e.g., Thomas Aquinas, ST I, q. 16, a. 1, co.: “Knowledge is according as the thing known is in the knower” and the “truth [of one’s own thoughts] is the equation of thought and thing.” Recently, the study even researched the emotional characteristics of AI NLP model: Li et al. (2023). |
9 | In that paper, Vestrucci also expressed hope for the creation of a model that would be the closest to cognitive belief processes, a model that does not yet exist, but is possible and may even be useful: “It would be needed a much more sophisticated fuzzy logic than, for instance, the one currently used in engineering contexts. Let us imagine a “believing machine”, an AI system able to generate beliefs from a large number of data or information… through pattern recognition.” (Vestrucci et al. 2021, p. 27). He also coined the expression “Computational theology” to aim for a future in which it will be possible to build a system that can “recognize the existential value of transcendence, in the same way as a religious or a spiritual mind can do.” (Idem, p. 30). |
10 | This complex model will be discussed in more detail in Section 4 of the paper. |
11 | |
12 | Some partial references: AlgorithmWatch, Automating Society, Report. Bertelsmann Stiftung, 2019 and 2020, URL https://automatingsociety.algorithmwatch.org/wp-content/uploads/2020/12/Automating-Society-Report-2020.pdf (accessed on 15 January 2024). UNESCO’s first draft of the Recommendation on the Ethics of Artificial Intelligence, 2020, URL https://unesdoc.unesco.org/ark:/48223/pf0000373434 (accessed on 15 January 2024). The 2030 Agenda-URL https://www.un.org/sustainabledevelopment/development-agenda/ (accessed on 15 January 2024). The European Union framework on human-centric AI, in particular its core-idea of an ‘ecosystem of excellence and trust in AI, European Commission, Building Trust in Human-Centric Artificial Intelligence, 2019, URL https://ec.europa.eu/jrc/communities/sites/default/files/ec_ai_ethics_communication_8_april_2019.pdf (accessed on 15 January 2024). “Rome Call for AI Ethics” URL https://www.romecall.org (accessed on 15 January 2024). “Artificial Intelligence: Ethical Concerns” European Parliament, Artificial Intelligence: Ethical Concerns, 2019, URL https://www.europarl.europa.eu/at-your-service/files/be-heard/religious-and-non-confessional-dialogue/events/en-20190319programme.pdf (accessed on 15 January 2024). |
13 | For further reading on Singularity, Superinteligence and Transhumanism, see: Ulam (1986); Kurzweil (2005); Gray (2015); Bostrom (2014); Good (1966). For Singularianism- Anthony Levandowski- https://www.bloomberg.com/news/articles/2023-11-23/anthony-levandowski-reboots-the-church-of-artificial-intelligence (accessed on 15 January 2024). |
14 | For example, theological works that worth including Al and McCarthy (1999); Aquinas (1920); Heschel (1976); James (1979); Kierkegaard (2006); Otto (1923); Rosenzweig (1971); Soloveitchik (1992). |
15 | With the appropriate machine learning models for unstructured data like images, audio, or text that need to leverage deep learning, TensorFlow might be suitable. TensorFlow is a machine learning framework developed by Google. |
16 | A pre-processing step should be considered, where natural language processing extracts key features or entities from the unstructured data sources to feed into the logic models. |
17 | As far as I know, this subject has not yet been studied at the level of experiments in the training of programming code on a database, for various reasons. Even though this is the framework of a journal in the field of humanities, just to be able to illustrate the model, like adding a graph to the text, I gave AI model to generate two pseudocodes, according to this theoretical description of Trans Belief model, as an illustrative examples only and not as an experiment. I call on the programmers to put the proposed model to the test and perfect it while training on the data base mentioned earlier and then also on the Big Data: 1. Trans- Belief logic model outline generic function for a pseudocode: def defuzzify(fuzzy_belief): if fuzzy_belief.membership_value > FIRM_THRESHOLD: return True else: return False Where FIRM_THRESHOLD is some value like 0.51 or 0.7. Another approach could use more of a graded mapping, like: def defuzzify(fuzzy_belief): if fuzzy_belief.membership_value >= 0.9: return 1.0 # Full belief elif fuzzy_belief.membership_value >= 0.7: return 0.8 # Strong belief elif fuzzy_belief.membership_value >= 0.5: return 0.6 # Moderate belief else: return 0 # No belief 2. Trans- Belief logic model outline generic function for a pseudocode: #Stage 1 - Fuzzy Logic def extract_features(data): # Use NLP to extract key features from textual data return extracted_features def calculate_confirmation(event): # Fuzzy logic formulas to assess event’s synchronicity return confirmation_level def update_fuzzy_beliefs(event, confirmation): # Add new fuzzy belief based on event data fuzzy_beliefs.add(event, confirmation_level) #Stage 2 - Doxastic Logic def defuzzify(fuzzy_belief): # Convert fuzzy belief to binary based on threshold if fuzzy_belief.confirmation > CONFIRMATION_THRESHOLD: return BinaryBelief(fuzzy_belief, True) else: return BinaryBelief(fuzzy_belief, False) def confirm_belief(belief) # Add firm binary belief confirmed_beliefs.add(belief) #Stage 3 - Belief Revision def check_consistency(current_beliefs, new_belief): # Check if any contradiction return current_beliefs.contradicts(new_belief) def revise(current_beliefs, new_belief): if check_consistency(current_beliefs, new_belief): # Remove contradicted beliefs current_beliefs.remove_contradictions(new_belief) # Add new belief current_beliefs.add(new_belief) return current_beliefs #Main data = load_data() features = extract_features(data) for event in features: confirmation = calculate_confirmation(event) fuzzy_beliefs.update(event, confirmation) for fuzzy_belief in fuzzy_beliefs: belief = defuzzify(fuzzy_belief) confirm_belief(belief) new_data = get_updated_data() new_belief = extract_features(new_data) current_beliefs = revise(current_beliefs, new_belief) |
References
- Al, Ghazzālī, and Richard Joseph McCarthy. 1999. Deliverance from Error: An Annotated Translation of Al-Munqidh Min Al Dalāl and Other Relevant Works of Al-Ghazālī. Louisville: Fons Vitae. [Google Scholar]
- Aquinas, Thomas Saint. 1920. The “Summa Theologica” of St. Thomas Aquinas. London: Burns, Oates & Washburne, Ltd. [Google Scholar]
- Armstrong, David Malet. 1973. Belief, Truth and Knowledge. London: Cambridge University Press. [Google Scholar]
- Benzmüller, Christoph. 2011. Combining and Automating Classical and Non-Classical Logics in Classical Higher-Order Logics. Annals of Mathematics and Artificial Intelligence 62: 103–28. [Google Scholar] [CrossRef]
- Blakemore, Sarah Jayne, Joel Winston, and Uta Frith. 2004. The detection of contingency and animacy from simple animations in the human brain. Cerebral Cortex 14: 837–44. [Google Scholar] [CrossRef] [PubMed]
- Bostrom, Nick. 2005. In defense of posthuman dignity. Bioethics 19: 202–14. [Google Scholar] [CrossRef] [PubMed]
- Bostrom, Nick. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press. [Google Scholar]
- Bostrom, Nick. 2015. What Happens When Our Computers Get Smarter Than We Are. [Ted Talk]. Available online: https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are (accessed on 15 January 2024).
- Bostrom, Nick. 2016. The control problem. Philosophy Compass 14: e12625. [Google Scholar]
- Buckner, Cameron. 2019. Deep learning: A philosophical introduction. Philosophy Compass 14: e12625. [Google Scholar] [CrossRef]
- Castillo, Ramon D., Heidi Kloos, Michael J. Richardson, and Talia Waltzer. 2015. Beliefs as self-sustaining networks: Drawing parallels between networks of ecosystems and adults’ predictions. Frontiers in Psychology 6: 160775. [Google Scholar] [CrossRef] [PubMed]
- Chen, Taolue, Giuseppe Primiero, Franco Raimondi, and Neha Rungta. 2016. A computationally grounded, weighted doxastic logic. Studia Logica 104: 679–703. [Google Scholar] [CrossRef]
- Daňková, Martina, and Libor Běhounek. 2020. Fuzzy neighborhood semantics for multiagent probabilistic reasoning in games. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cham: Springer Nature, pp. 680–93. [Google Scholar]
- Eckhart, Meister. 2009. The Complete Mystical Works of Meister Eckhart. New York: The Crossroad Publishing Company. Sermon Fifteen, p. 119. [Google Scholar]
- Evans, Charles Stephen. 2005. Faith and Revelation. In The Oxford Handbook of Philosophy of Religion. Edited by William Wainwright. New York: Oxford University Press, pp. 323–42. [Google Scholar]
- Fodor, Jerry. 1975. The Language of Thought. New York: Thomas Crowell. [Google Scholar]
- Fodor, Jerry. 2000. The Mind Doesn‘t’ Work That Way: The Scope and Limits of Computational Psychology. Cambridge and Oxford: MIT Press. [Google Scholar]
- Gervais, Will. 2013. Perceiving minds and gods: How mind perception enables, constrains, and is triggered by belief in gods. Psychological Bulletin 11: e0155283. [Google Scholar] [CrossRef] [PubMed]
- Good, Irving John. 1966. Speculations concerning the first ultraintelligent machine. In Advances in Computers. Amsterdam: Elsevier, vol. 6, pp. 31–88. [Google Scholar]
- Gray, Jhon. 2015. The Soul of the Marionette. London: Penguin Books. [Google Scholar]
- Harari, Yuval Noah. 2015. The Rise and Fall of Man [Podcast Episode No. 1.2]. In The Broadcast University Podcast. Available online: https://open.spotify.com/episode/0fjcNdE94jCwKP3Holh8hR?si=A251aDgNRJKT3KtuJltgnw (accessed on 5 January 2024).
- Heschel, Abraham Joshua. 1976. Man Is Not Alone. New York: Farrar, Straus and Giroux. [Google Scholar]
- Hume, David. 1986. On reason. In The Philosophy of Mind. Edited by Peter Smith and Olivia Jones. Cambridge: Cambridge University Press. [Google Scholar]
- Jack, Anthony Ian, Jared Parker Friedman, Richard Eleftherios Boyatzis, and Scott Nolan Taylor. 2016. Why Do You Believe in God? Relationships between Religious Belief, Analytic Thinking, Mentalizing and Moral Concern. PLoS ONE 11: e0155283. [Google Scholar] [CrossRef] [PubMed]
- James, William. 1902. The Varieties of Religious Experience. London: Longmans, Green & Co. [Google Scholar]
- James, William. 1979. The Will to Believe and Other Essays in Popular Philosophy. Cambridge: Harvard University Press. [Google Scholar]
- Jung, Carl Gustav. 1947. On the Nature of the Psyche. In Collected Works. Princeton: Princeton University Press, vol. 8. [Google Scholar]
- Jung, Carl Gustav. 1952. Synchronicity: An Acausal Connecting Principle. In Collected Works. Princeton: Princeton University Press, vol. 8. [Google Scholar]
- Jung, Carl Gustav. 1981. The Archetypes and the Collective Unconscious. In Collected Works, 2nd ed. Princeton: Bollingen, p. 9. First published 1954. [Google Scholar]
- Kenny, Anthony. 1992. What is Faith?: Essays in the Philosophy of Religion. New York: Oxford University Press. [Google Scholar]
- Kierkegaard, Søren. 2006. Fear and Trembling. Edited by Stephen Evans and Sylvia Walsh. Translated by Sylvia Walsh. Cambridge: Cambridge University Press. [Google Scholar]
- Kurzweil, Ray. 2005. The Singularity Is Near: When Humans Transcend Biology. New York: Viking. [Google Scholar]
- Ladyman, James, James Lambert, and Karoline Wiesner. 2013. What is a Complex System? European Journal for Philosophy of Science 3: 33–67. [Google Scholar] [CrossRef]
- Lawrence, Neil David. 2019. AI-Ethical and Religious Perspectives [Slides]. Cambridge: Jesus College. Available online: http://inverseprobability.com/talks/notes/what-is-ai-and-what-are-the-implications-of-advances-in-ai-for-religion.html (accessed on 15 January 2024).
- Li, Cheng, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie. 2023. Large language models understand and can be enhanced by emotional stimuli. arXiv arXiv:2307.11760. [Google Scholar]
- Maor, Ofri. 2014. The Naive Understanding and Conceptualization of The Subconscious in Everyday Life. Beersheba: Ben Gurion University of the Negev. [Google Scholar]
- McCarthy, John, and Patrick J. Hayes. 1969. Some Philosophical Problems from the Standpoint of Artificial Intelligence. In Machine Intelligence. Edited by B. Meltzer and D. Michie. Edinburgh: Edinburgh University Press, vol. 4, pp. 463–502. [Google Scholar]
- Miller, Tim. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267: 1–38. [Google Scholar] [CrossRef]
- Mitchell, Melanie. 2011. Complexity: A Guided Tour. Oxford: Oxford University. [Google Scholar]
- Musolino, Julien, Joseph Sommer, and Pernille Hemmer, eds. 2022. The Cognitive Science of Belief a Multidisciplinary Approach. Cambridge: Cambridge University Press. [Google Scholar]
- Newell, Allen. 1994. Unified Theories of Cognition. Cambridge: Harvard University Press. [Google Scholar]
- Nisbett, Richard, and Lee Ross. 1980. Human Inference: Strategies and Shortcomings of Social Judgment. Englewood Cliffs: Prentice-Hall. [Google Scholar]
- Otto, Rudolf. 1923. The Idea of the Holy. Oxford: Oxford University Press. [Google Scholar]
- Paivio, Allan. 1971. Imagery and language. In Imagery. Cambridge: Academic Press, pp. 7–32. [Google Scholar]
- Park, Crystal. 2007. Religiousness/spirituality and health: A meaning systems perspective. Journal of Behavioral Medicine 30: 319–28. [Google Scholar] [CrossRef] [PubMed]
- Premack, David, and Guy Woodruff. 1978. Does the chimpanzee have a theory of mind? Behavioral and Brain Sciences 1: 515–26. [Google Scholar] [CrossRef]
- Putnam, Hilary. 1981. Brains in a Vat, Reason, Truth and History. Cambridge: Cambridge University Press, pp. 1–21. [Google Scholar]
- Ramsey, Frank Plumpton. 1931. General propositions and causality. In The Foundations of Mathematics. London: Kegan Paul, Trench & Trubner, pp. 237–55. [Google Scholar]
- Riding, Richard, Chris Buckle, Stewart Thompson, and Edward Hagger. 1989. The Computer Determination of Learning Styles as an Aid to Individualized Computer-Based Training. Innovations in Education & Training International 26: 393–98. [Google Scholar]
- Rosenzweig, Franz. 1971. The Star of Redemption. Translated by William W. Hallo. London: Routledge & Kegan Paul. [Google Scholar]
- Sagi, Avi. 2005. Belief as Temptation. In On Belief, Studies in the Concept of Belief and Its History in the Jewish Tradition. Edited by Moshe Halbertal, David Kurzwill and Avi Sagi. Jerusalem: Keter Publishing, pp. 112, 118–39. [Google Scholar]
- Saxe, Rebecca, Susan Carey, and Nancy Kanwisher. 2004. Understanding other minds: Linking developmental psychology and functional neuroimaging. Annual Review of Psychology 55: 87–124. [Google Scholar] [CrossRef] [PubMed]
- Shermer, Michael. 2011. The Believing Brain: From Ghosts and Gods to Politics and Conspiracies—How We Construct Beliefs and Reinforce Them as Truths. New York: St. Martin’s Griffin. [Google Scholar]
- Smith, Aaron. 2014. Thinking about Religion: Extending the Cognitive Sciences of Religion. Gurgaon: Palgrave MacMillan. [Google Scholar]
- Soloveitchik, Joseph Ber. 1992. The Lonely Man of Faith. New York: Doubleday. [Google Scholar]
- Swinburne, Richard. 1981. Faith and Reason. London: Oxford University Press. [Google Scholar]
- Swinburne, Richard. 2004. The Existence of God, 2nd ed. Oxford: Oxford University Press. [Google Scholar]
- Ulam, Stanislaw M. 1986. John von Neumann 1903–1957. In Science, Computers, and People: From the Tree of Mathematics. Boston: Birkhäuser Boston, pp. 169–214. [Google Scholar]
- Vestrucci, Andrea, Sara Lumbreras, and Lluis Oviedo. 2021. Can AI help us to understand belief?: Sources, advances, limits, and future directions. International Journal of Interactive Multimedia and Artificial Intelligence 7: 1. [Google Scholar] [CrossRef]
- Wales, Jordan Joseph. 2022. Metaphysics, meaning, and morality: A theological reflection on AI. Journal of Moral Theology 11: 157–81. [Google Scholar] [CrossRef]
- Walsh, Toby. 2022. Machines Behaving Badly: The Morality of AI. Collingwood: La Trobe University Press. [Google Scholar]
- Webber, Bonnie Lynn, and Nils Nilsson, eds. 1981. Readings in Artificial Intelligence. Burlington: Morgan Kaufmann. [Google Scholar]
- Wilks, Yorick. 2020. Artificial Intelligence and Religion, Online Lecture, Gresham College and University of Sheffield. Available online: https://www.youtube.com/watch?v=hX9YTOVjfJ0 (accessed on 15 January 2024).
- Witkin, Herman, and Donald Goodenough. 1977. Field dependence and interpersonal behavior. Psychological Bulletin 84: 661–89. [Google Scholar] [CrossRef] [PubMed]
- Wolfson, Harry Austryn. 1942. The Double Faith Theory in Clement, Saadia, Averroes, and St. Thomas and its Origin in Aristotle and the Stoics. The Jewish Quarterly Review 33: 264–31. [Google Scholar] [CrossRef]
- Zanoni, Chiara, ed. 2021. Shaping the AI transformation: The Agency of Religious and Belief Actors. Trento: ISR Center Policy Paper. [Google Scholar]
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Dagan, I. Trans-Belief: Developing Artificial Intelligence NLP Model Capable of Religious-Belief-like Cognitive Processes for Expected Enhanced Cognitive Ability. Religions 2024, 15, 655. https://doi.org/10.3390/rel15060655
Dagan I. Trans-Belief: Developing Artificial Intelligence NLP Model Capable of Religious-Belief-like Cognitive Processes for Expected Enhanced Cognitive Ability. Religions. 2024; 15(6):655. https://doi.org/10.3390/rel15060655
Chicago/Turabian StyleDagan, Ido. 2024. "Trans-Belief: Developing Artificial Intelligence NLP Model Capable of Religious-Belief-like Cognitive Processes for Expected Enhanced Cognitive Ability" Religions 15, no. 6: 655. https://doi.org/10.3390/rel15060655
APA StyleDagan, I. (2024). Trans-Belief: Developing Artificial Intelligence NLP Model Capable of Religious-Belief-like Cognitive Processes for Expected Enhanced Cognitive Ability. Religions, 15(6), 655. https://doi.org/10.3390/rel15060655