Bibliography

Abhishek, Kumar, Sneha Maheshwari, and Sujit Gujar. 2019. Introduction to Concentration Inequalities. https://arxiv.org/abs/1910.02884.

Abrahamsen, Mikkel, Linda Kleist, and Tillmann Miltzow. 2021. “Training Neural Networks Is \(\exists\mathbb{R}\)-Complete.” Proceedings of NeurIPS 2021. https://arxiv.org/abs/2102.09798.

Abreu, Nicole, Parker B. Edwards, and Francis Motta. 2025. Topological Machine Learning with Unreduced Persistence Diagrams. https://arxiv.org/abs/2507.07156.

Achlioptas, Panos. n.d. “Stochastic Gradient Descent in Theory and Practice.” Lecture Note, Stanford’s AI.

Adlam, Ben, and Jeffrey Pennington. 2020. Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition. https://arxiv.org/abs/2011.03321.

Advani, Madhu S., and Andrew M. Saxe. 2017. High-Dimensional Dynamics of Generalization Error in Neural Networks. https://arxiv.org/abs/1710.03667.

Ainsworth, Thomas. 2016. “Form Vs. Matter.” In The Stanford Encyclopedia of Philosophy, edited by Edward N. Zalta and Uri Nodelman. https://plato.stanford.edu/entries/form-matter/.

al., Chris Smith et. 2006. The History of Artificial Intelligence. Technical review.

al., Molavi et. n.d. Model Complexity, Expectations, and Asset Prices The Review of Economic Studies Oxford Academic. Accessed July 16, 2024. https://academic.oup.com/restud/article-abstract/91/4/2462/7222145?redirectedFrom=fulltext&login=false.

Alon, Uri, and Eran Yahav. 2021. On the Bottleneck of Graph Neural Networks and Its Practical Implications. https://arxiv.org/abs/2006.05205.

Alquier, Pierre. 2008. “PAC-Bayesian Bounds for Randomized Empirical Risk Minimizers.” Mathematical Methods of Statistics 17 (4): 279–304. https://doi.org/10.3103/S1066530708040017.

Alquier, Pierre. 2024. “User-Friendly Introduction to PAC-Bayes Bounds.” Foundations and Trends® in Machine Learning 17 (2): 174–303. https://doi.org/10.1561/2200000100.

American Chemical Society (ACS). n.d.a. Antoine Laurent Lavoisier — the Chemical Revolution (Landmark). American Chemical Society website. https://www.acs.org/education/whatischemistry/landmarks/lavoisier.html.

American Chemical Society (ACS). n.d.-b. Joseph Priestley — Discoverer of Oxygen (National Historic Chemical Landmark). American Chemical Society website. Accessed March 18, 2026. https://www.acs.org/education/whatischemistry/landmarks/josephpriestleyoxygen.html.

Andrilli, Stephen, and David Hecker. 2010. “Chapter 1 - Vectors and Matrices.” In Elementary Linear Algebra (Fourth Edition), Fourth Edition, edited by Stephen Andrilli and David Hecker. Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-374751-8.00001-9.

Angelakis, Andreas N., Jens Krasilnikoff, and Vasileios A. Tzanakakis. 2022. “Evolution of Water Technologies and Corresponding Philosophy and Sciences Focusing on the Hellenic World Through the Millennia.” Water 14 (19). https://doi.org/10.3390/w14193149.

Angluin, Dana. 1988. “Queries and Concept Learning.” Machine Learning 2 (4): 319–42. https://doi.org/10.1007/BF00116828.

Angluin, Dana. 1989. “Computational Limitations on Learning from Examples.” Journal of the ACM 36 (4): 955–81. https://doi.org/10.1145/76322.76335.

Anscombe, G. E. M. 1957. Intention. Basil Blackwell.

Arjevani, Yossi, Joan Bruna, Joe Kileel, Elzbieta Polak, and Matthew Trager. 2025. Geometry and Optimization of Shallow Polynomial Networks. https://arxiv.org/abs/2501.06074.

Armstrong, M. A. 2010. Groups and Symmetry. Undergraduate Texts in Mathematics. Springer.

Arora, Sanjeev, Simon Du, Wei Hu, Zhiyuan Li, and Ruslan Salakhutdinov. 2019. “On Exact Computation with an Infinitely Wide Neural Net.” Advances in Neural Information Processing Systems (NeurIPS) 32.

Artin, Michael. 2011. Algebra. 2nd ed. Pearson Prentice Hall.

Ascoli, Stéphane d’, Levent Sagun, and Giulio Biroli. 2020. “Triple Descent and the Two Kinds of Overfitting: Where & Why Do They Appear?” Advances in Neural Information Processing Systems 33: 3058–69. https://proceedings.neurips.cc/paper/2020/hash/1fd09c5f59a8ff35d499c0ee25a1d47e-Abstract.html.

Bahri, Yasaman, Ethan Dyer, Jared Kaplan, Jaehoon Lee, and Utkarsh Sharma. 2024. “Explaining Neural Scaling Laws.” Proceedings of the National Academy of Sciences 121 (27): e2311878121. https://doi.org/10.1073/pnas.2311878121.

Balcan, Maria-Florina, Steve Hanneke, and Jennifer Wortman Vaughan. 2010. “The True Sample Complexity of Active Learning.” Machine Learning 80 (2–3): 111–39. https://doi.org/10.1007/s10994-010-5174-y.

Barak, Boaz, Samuel B. Hopkins, Jonathan Kelner, Pravesh Kothari, Ankur Moitra, and Aaron Potechin. 2016. “A Nearly Tight Sum-of-Squares Lower Bound for the Planted Clique Problem.” Proceedings of the 57th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 428–37. https://doi.org/10.1109/FOCS.2016.52.

Barbierato, Enrico, and Alice Gatti. 2024. “The Challenges of Machine Learning: A Critical Review.” Electronics 13 (2). https://doi.org/10.3390/electronics13020416.

Barceló, Pablo, Mikaël Monet, Jorge Pérez, and Bernardo Subercaseaux. 2020. Model Interpretability Through the Lens of Computational Complexity. https://arxiv.org/abs/2010.12265.

Bartlett, Peter L. 1998. “The Sample Complexity of Pattern Classification with Margin.” IEEE Transactions on Information Theory 44 (2): 525–36.

Bartlett, Peter L., Olivier Bousquet, and Shahar Mendelson. 2005. “Local Rademacher Complexities.” Annals of Statistics 33 (4): 1497–537.

Bartlett, Peter L., Dylan J. Foster, and Matus Telgarsky. 2017. “Spectrally-Normalized Margin Bounds for Neural Networks.” Advances in Neural Information Processing Systems (NeurIPS), 6241–50.

Bartlett, Peter L., and Shahar Mendelson. 2002. “Rademacher and Gaussian Complexities: Risk Bounds and Structural Results.” Journal of Machine Learning Research 3: 463–82.

Bartlett, Peter L., and Shahar Mendelson. 2005. “Empirical Minimization.” Probability Theory and Related Fields 135: 311–34. https://doi.org/10.1007/s00440-005-0460-0.

Bartlett, Peter L., Andrea Montanari, and Alexander Rakhlin. 2021. “Deep Learning: A Statistical Viewpoint.” Acta Numerica 30: 87–201.

Bathaee, Yavar. 2018. “The Artificial Intelligence Black Box and the Failure of Intent and Causation.” Harvard Journal of Law & Technology 31: 889. https://api.semanticscholar.org/CorpusID:158988823.

Beardon, Alan F. 2005. Algebra and Geometry. Cambridge University Press.

Belkin, Mikhail, Daniel Hsu, Siyuan Ma, and Soumik Mandal. 2019a. “Reconciling Modern Machine Learning Practice and the Bias-Variance Trade-Off.” Proc. Natl. Acad. Sci. U.S.A. 116 (32): 15849–54. https://doi.org/10.1073/pnas.1903070116.

Belkin, Mikhail, Daniel Hsu, Siyuan Ma, and Soumik Mandal. 2019b. “Reconciling Modern Machine Learning Practice and the Bias-Variance Trade-Off.” Proc. Natl. Acad. Sci. U.S.A. 116 (32): 15849–54. https://doi.org/10.1073/pnas.1903070116.

Belkin, Mikhail, Siyuan Ma, and Soumik Mandal. 2018. To Understand Deep Learning We Need to Understand Kernel Learning. https://arxiv.org/abs/1802.01396.

Bennett, Michael Timothy. 2025. “How to Build Conscious Machines.” Ph.D. thesis, Australian National University. https://osf.io/preprints/thesiscommons/wehmg_v1.

Bias-Variance Tradeoffs in Program Analysis Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. n.d. Accessed July 16, 2024. https://dl.acm.org/doi/10.1145/2535838.2535853.

Blum, Avrim L., and Ronald L. Rivest. 1992. “Training a 3-Node Neural Network Is NP-Complete.” Neural Networks 5 (1): 117–27.

Blum, Avrim, Adam Tauman Kalai, and Hal Wasserman. 2003. “Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model.” Journal of the ACM 50 (4): 506–19. https://doi.org/10.1145/792538.792543.

Board, Raymond, and Leonard Pitt. 1992. “On the Necessity of Occam Algorithms.” Theoretical Computer Science 100 (1): 157–84. https://doi.org/10.1016/0304-3975(92)90367-O.

Boole Everest, Mary. 1909. Philosophy and Fun of Algebra. C. W. Daniel.

Boole, George. 1854. An Investigation of the Laws of Thought on Which Are Founded the Mathematical Theories of Logic and Probabilities. Walton; Maberly.

Boucheron, Stéphane, Gábor Lugosi, and Pascal Massart. 2013. Concentration Inequalities: A Nonasymptotic Theory of Independence. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199535255.001.0001.

Bourne, John R. 2003. “Mixing and the Selectivity of Chemical Reactions.” Organic Process Research & Development 7 (4): 471–508. https://doi.org/10.1021/op020074q.

Bousquet, Olivier, and André Elisseeff. 2002a. “Stability and Generalization.” Journal of Machine Learning Research 2: 499–526.

Bousquet, Olivier, and André Elisseeff. 2002b. “Stability and Generalization.” Journal of Machine Learning Research 2: 499–526.

Bousquet, Olivier, Steve Hanneke, Shay Moran, Ramon van Handel, and Amir Yehudayoff. 2020. A Theory of Universal Learning. https://arxiv.org/abs/2011.04483.

Briggs, Gordon. 2014. “Machine Ethics , the Frame Problem , and Theory of Mind.” https://api.semanticscholar.org/CorpusID:14954096.

Bronstein, Michael M., Joan Bruna, Taco Cohen, and Petar Veličković. 2021. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. https://arxiv.org/abs/2104.13478.

Bronstein, Michael M., Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. “Geometric Deep Learning: Going Beyond Euclidean Data.” IEEE Signal Processing Magazine 34 (4): 18–42. https://doi.org/10.1109/msp.2017.2693418.

Brooks, Rodney A. 1991. “Intelligence Without Representation.” Artificial Intelligence 47: 139–59. https://doi.org/10.1016/0004-3702(91)90053-M.

Brown, Gavin, and Riccardo Ali. 2024. “Bias/Variance Is Not the Same as Approximation/Estimation.” Transactions on Machine Learning Research. https://openreview.net/forum?id=4TnFbv16hK.

Burris, Stanley, and Javier Legris. 2021. “The Algebra of Logic Tradition.” In The Stanford Encyclopedia of Philosophy, Spring 2021, edited by Edward N. Zalta. Https://plato.stanford.edu/archives/spr2021/entries/algebra-logic-tradition/; Metaphysics Research Lab, Stanford University.

Busch, Uwe. 2023. “Claims of Priority – the Scientific Path to the Discovery of x-Rays.” Radiography 29 (3): e245–54. https://doi.org/10.1016/j.radi.2023.05.007.

Buschjäger, Sebastian, Lukas Pfahler, and Katharina Morik. 2020a. Generalized Negative Correlation Learning for Deep Ensembling. arXiv. https://doi.org/10.48550/arXiv.2011.02952.

Buschjäger, Sebastian, Lukas Pfahler, and Katharina Morik. 2020b. Generalized Negative Correlation Learning for Deep Ensembling. arXiv. http://arxiv.org/abs/2011.02952.

Cahn, Robert W., ed. 2001. The Coming of Materials Science. Vol. 5. Pergamon Materials Series. Elsevier / Pergamon.

Cameron, Peter J. 2008. Introduction to Algebra. Oxford University Press.

Carter, Nathan C. 2009. Visual Group Theory. Classroom Resource Materials. Mathematical Association of America.

Cartuyvels, Ruben, Graham Spinks, and Marie-Francine Moens. 2021. “Discrete and Continuous Representations and Processing in Deep Learning: Looking Forward.” AI Open 2: 143–59. https://doi.org/10.1016/j.aiopen.2021.07.002.

Chen, Ricky T. Q., Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2019. Neural Ordinary Differential Equations. https://arxiv.org/abs/1806.07366.

Chizat, Lenaic, and Francis Bach. 2018. “On the Global Convergence of Gradient Descent for over-Parameterized Models Using Optimal Transport.” Advances in Neural Information Processing Systems (NeurIPS) 31.

Chowdhary, K. R. 2020. Fundamentals of Artificial Intelligence. Springer.

Churchland, Patricia S. 1986. Neurophilosophy: Toward a Unified Science of the Mind-Brain. MIT Press.

Clocksin, William F., and Christopher S. Mellish. 2003. Programming in Prolog: Using the ISO Standard. 5th ed. Springer.

Collins, Allan M., and M. Ross Quillian. 1969. “Retrieval Time from Semantic Memory.” Journal of Verbal Learning and Verbal Behavior 8 (2): 240–47.

Colmerauer, Alain, Henry Kanoui, Robert Pasero, and Philippe Roussel. 1972. Un Système de Communication Homme-Machine En Français. Rapport préliminaire de fin de contrat IRIA. Groupe Intelligence Artificielle, Faculté des Sciences de Luminy, Université Aix-Marseille II.

Colmerauer, Alain, and Philippe Roussel. 1993. “The Birth of Prolog.” In History of Programming Languages—II. ACM.

Copeland, Jack, and Diane Proudfoot. 2018. “From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.” Minds and Machines 28 (4): 515–42. https://doi.org/10.1007/s11023-018-9468-3.

Cover, Thomas M. 1965. “Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition.” IEEE Transactions on Electronic Computers EC-14 (3): 326–34. https://doi.org/10.1109/PGEC.1965.264137.

Cristianini, Nello, and John Shawe-Taylor. 2000. “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods.” https://api.semanticscholar.org/CorpusID:60486887.

Cybenko, George. 1989. “Approximation by Superpositions of a Sigmoidal Function.” Mathematics of Control, Signals and Systems 2 (4): 303–14. https://doi.org/10.1007/BF02551274.

Darwiche, Adnan, and Pierre Marquis. 2002. “A Knowledge Compilation Map.” Journal of Artificial Intelligence Research 17: 229–64.

Davidson, Donald. 1963. “Actions, Reasons, and Causes.” The Journal of Philosophy 60 (23): 685–700. https://doi.org/10.2307/2023177.

Davidson, Donald. 1970. “Mental Events.” In Experience and Theory, edited by Lawrence Foster and J. W. Swanson. Humanities Press.

Davies, Xander, Lauro Langosco, and David Krueger. 2023. Unifying Grokking and Double Descent. arXiv. http://arxiv.org/abs/2303.06173.

Davis, Randall, Bruce Buchanan, and Edward Shortliffe. 1977. “Production Rules as a Representation for a Knowledge-Based Consultation Program.” Artificial Intelligence 8 (1): 15–45.

Demuth, Howard B., Mark H. Beale, Orlando De Jess, and Martin T. Hagan. 2014. Neural Network Design. 2nd ed. Martin Hagan.

Dennett, Daniel C. 1978. Brainstorms: Philosophical Essays on Mind and Psychology. MIT Press.

Dennett, Daniel C. 1991. Consciousness Explained. Little, Brown; Company.

Derouiche, Hana, Zaki Brahmi, and Haithem Mazeni. 2025. “Agentic AI Frameworks: Architectures, Protocols, and Design Challenges.” arXiv Preprint arXiv:2508.10146. https://arxiv.org/abs/2508.10146.

Descartes, Rene? 1950. Discourse on Method. Harmondsworth, Penguin.

“Discoveries Leading to the Nuclear Atom Model.” n.d. University of North Carolina Pressbooks / UEN. Accessed April 3, 2026. https://uen.pressbooks.pub/introductorychemistry/chapter/discoveries-leading-to-the-nuclear-atom-model/.

Dodig-Crnkovic, Gordana. 2012. “Info-Computationalism and Morphological Computing of Informational Structures.” Information 3 (2): 204–18. https://doi.org/10.3390/info3020204.

Domingos, Pedro M. 2000a. “A Unifeid Bias-Variance Decomposition and Its Applications.” Semantic Scholar, June. https://www.semanticscholar.org/paper/A-Unifeid-Bias-Variance-Decomposition-and-its-Domingos/e1ed9d24db5e8f7ab326aeb797e965a94f5ad6d3.

Domingos, Pedro M. 2000b. “A Unified Bias-Variance Decomposition for Zero-One and Squared Loss.” AAAI/IAAI. https://api.semanticscholar.org/CorpusID:2063488.

Doshi-Velez, Finale, and Been Kim. 2017. “Towards a Rigorous Science of Interpretable Machine Learning.” arXiv Preprint arXiv:1702.08608.

Dreyfus, Hubert L. 1965. Alchemy and Artificial Intelligence. P-3244. RAND Corporation. https://www.rand.org/pubs/papers/P3244.html.

Dreyfus, Hubert L. 1972. What Computers Can’t Do: A Critique of Artificial Reason. Harper & Row.

Dreyfus, Hubert L. 1979. From Micro-Worlds to Knowledge Representation : AI at an Impasse.

Dreyfus, Hubert L. 1991. Being-in-the-World: A Commentary on Heidegger’s Being and Time, Division i. MIT Press.

Dreyfus, Hubert L., and Stuart E. Dreyfus. 1986. Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Free Press.

Dubey, Shiv Ram, Satish Kumar Singh, and Bidyut Baran Chaudhuri. 2022. Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark. https://arxiv.org/abs/2109.14545.

Dummit, David S., and Richard M. Foote. 2004. Abstract Algebra. 3rd ed. John Wiley & Sons.

E. L. Lehmann, George Casella. 1998. Theory of Point Estimation. Springer Texts in Statistics. Springer-Verlag. https://doi.org/10.1007/b98854.

Eldan, Ronen, and Ohad Shamir. 2016. “The Power of Depth for Feedforward Neural Networks.” Proceedings of the 29th Annual Conference on Learning Theory (COLT), Proceedings of machine learning research, vol. 49: 907–40. https://proceedings.mlr.press/v49/eldan16.html.

Eliade, Mircea. 1956. The Forge and the Crucible: The Origins and Structure of Alchemy. Flammarion.

Elliot, C. 2019. “On de Finetti’s Philosophy of Probability.” PhD thesis, Tilburg University. https://research.tilburguniversity.edu/en/publications/on-de-finettis-philosophy-of-probability.

Encyclopaedia Britannica Editors. n.d.-a. Edmund Cartwright. Britannica Online. Accessed March 18, 2026. https://www.britannica.com/biography/Edmund-Cartwright.

Encyclopaedia Britannica Editors. n.d.-b. James Hargreaves. Britannica Online. Accessed March 18, 2026. https://www.britannica.com/biography/James-Hargreaves.

Encyclopaedia Britannica Editors. n.d.-c. John Wilkinson. Britannica Online. Accessed March 18, 2026. https://www.britannica.com/biography/John-Wilkinson.

Encyclopaedia Britannica Editors. n.d.-d. Sir Richard Arkwright. Britannica Online. Accessed March 18, 2026. https://www.britannica.com/biography/Richard-Arkwright.

Finetti, Bruno de. 1990. Theory of Probability: A Critical Introductory Treatment. John Wiley & Sons.

Flavell-While, Claudia. 2011. “Nicolas Leblanc — Revolutionary Discoveries.” The Chemical Engineer (Features / Chemical Engineers Who Changed the World). https://www.thechemicalengineer.com/features/cewctw-nicolas-leblanc-revolutionary-discoveries/.

Floyd, Stephen, and Manfred K. Warmuth. 1995. “Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension.” Machine Learning 21 (3): 269–304.

Fodor, Jerry A. 1975. The Language of Thought. Harvard University Press.

Fortmann, Scott. 2012. Understanding the Bias-Variance Tradeoff. https://scott.fortmann-roe.com/docs/BiasVariance.html.

Frenkel, J. 1926. “Zur Theorie Der Elastizitätsgrenze Und Der Festigkeit Kristallinischer Körper.” Zeitschrift Für Physik 37: 572–609. https://doi.org/10.1007/BF01389854.

Friedman, Jerome, Trevor Hastie, Holger Höfling, and Robert Tibshirani. 2007. “Pathwise Coordinate Optimization.” The Annals of Applied Statistics 1 (2): 302–32. https://doi.org/10.1214/07-AOAS131.

Gallian, Joseph A. 2013. Contemporary Abstract Algebra. 8th ed. Brooks/Cole.

Gardner, Howard. 1983. Frames of Mind: The Theory of Multiple Intelligences. Basic Books.

Garg, Sumegha, Pravesh K. Kothari, Pengda Liu, and Ran Raz. 2021. “Memory-Sample Lower Bounds for Learning Parity with Noise.” arXiv Preprint arXiv:2107.02320. https://arxiv.org/abs/2107.02320.

Gauvrit, Nicolas, Hector Zenil, and Per Tegnér. 2015. “The Information-Theoretic and Algorithmic Approach to Human, Animal and Artificial Cognition.” arXiv Preprint. https://arxiv.org/abs/1501.04242.

Geman, Stuart, Elie Bienenstock, and René Doursat. 1992. “Neural Networks and the Bias/Variance Dilemma.” Neural Computation 4 (1): 1–58. https://doi.org/10.1162/neco.1992.4.1.1.

Gerlach, Joseph von. 1872. “Ueber Die Structur Der Grauen Substanz Des Menschlichen Grosshirns. Vorläufige Mitheilung.” Centralblatt Für Die Medizinischen Wissenschaften 10: 273–88. https://www.booklooker.de/B%C3%BCcher/Joseph-Gerlach%2BUeber-die-Structur-der-grauen-Substanz-des-menschlichen-Grosshirns-Vorl%C3%A4ufige/id/A02ohXHY01ZZu.

Goel, Ashok. 2022. “Looking Back, Looking Ahead: Symbolic Versus Connectionist AI.” AI Magazine 42 (4): 83–85. https://doi.org/10.1609/aaai.12026.

Goertzel, Ben, and Cassio Pennachin, eds. 2006. Artificial General Intelligence. Springer Science & Business Media.

Gold, E. Mark. 1967. “Language Identification in the Limit.” Information and Control 10 (5): 447–74. https://doi.org/10.1016/S0019-9958(67)91165-5.

Goodfellow, Ian, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep Learning. Vol. 1. MIT Press.

Goodman, Noah D., Chris L. Baker, and Joshua B. Tenenbaum. 2009. “Cause and Intent: Social Reasoning in Causal Learning.” Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (Amsterdam), 2759–64.

Gowers, Timothy, June Barrow-Green, and Imre Leader. 2008. The Princeton Companion to Mathematics. Illustrated edition. Princeton University Press.

Grenander, Ulf. 1952. “On Empirical Spectral Analysis of Stochastic Processes.” Arkiv för Matematik 1: 503–31. https://api.semanticscholar.org/CorpusID:122878699.

Gryz, Jarek. 2013. “The Frame Problem in Artificial Intelligence and Philosophy.” Filozofia Nauki 21 (June): 15–30.

Hajek, Bruce, and Maxim Raginsky. 2021. Statistical Learning Theory. Vol. 1. https://maxim.ece.illinois.edu/teaching/SLT/.

Hamilton, William L. n.d. “Graph Representation Learning.” Synthesis Lectures on Artificial Intelligence and Machine Learning 14 (3): 1–159.

Harnad, Stevan. 1990. “The Symbol Grounding Problem.” Physica D: Nonlinear Phenomena 42: 335–46. https://doi.org/10.1016/0167-2789(90)90087-6.

Haussler, David, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby, et al. 1996. “Rigorous Learning Curve Bounds from Statistical Mechanics.” Machine Learning 25 (2–3): 195–236. https://doi.org/10.1007/BF00116991.

Heidegger, Martin. 1962. Being and Time. Translated by John Macquarrie and Edward Robinson. Harper & Row.

Hellström, Thomas, Virginia Dignum, and Suna Bensch. 2020. Bias in Machine Learning – What Is It Good for? arXiv. https://doi.org/10.48550/arXiv.2004.00686.

Herstein, I. N. 1975. Topics in Algebra. 2nd ed. Wiley.

Hirsch, P. B., R. W. Horne, and M. J. Whelan. 2006. “Direct Observations of the Arrangement and Motion of Dislocations in Aluminium.” Philosophical Magazine 86 (29-31): 4553–72. https://doi.org/10.1080/14786430600844674.

Ho, Yu-Chi, and D. L. Pepyne. 2001. “Simple Explanation of the No Free Lunch Theorem of Optimization.” Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) 5: 4409–4414 vol.5. https://doi.org/10.1109/CDC.2001.980896.

Hon, Giora, and Bernard R. Goldstein. 2013. “J. J. Thomson’s Plum-Pudding Atomic Model: The Making of a Scientific Myth.” Annalen Der Physik 525 (8-9): A129–33. https://doi.org/10.1002/andp.201300732.

Hornik, Kurt. 1991. “Approximation Capabilities of Multilayer Feedforward Networks.” Neural Networks 4 (2): 251–57. https://doi.org/10.1016/0893-6080(91)90009-T.

Hornik, Kurt, Maxwell Stinchcombe, and Halbert White. 1989. “Multilayer Feedforward Networks Are Universal Approximators.” Neural Networks 2 (5): 359–66. https://doi.org/10.1016/0893-6080(89)90020-8.

Hornsby, Jennifer. 2004. “Agency and Actions.” In Agency and Action, edited by H. Steward and J. Hyman. Cambridge University Press.

Horsten, Leon et al. 2023. “Philosophy of Mathematics.” In The Stanford Encyclopedia of Philosophy, Winter 2023, edited by Edward N. Zalta and Uri Nodelman. Https://plato.stanford.edu/archives/win2023/entries/philosophy-mathematics/; Metaphysics Research Lab, Stanford University.

Hsieh, Weiche, Ziqian Bi, Chuanqi Jiang, et al. 2024. A Comprehensive Guide to Explainable AI: From Classical Models to LLMs. https://arxiv.org/abs/2412.00800.

Hu, Xia, Lingyang Chu, Jian Pei, Weiqing Liu, and Jiang Bian. 2021a. Model Complexity of Deep Learning: A Survey. https://arxiv.org/abs/2103.05127.

Hu, Xia, Lingyang Chu, Jian Pei, Weiqing Liu, and Jiang Bian. 2021b. Model Complexity of Deep Learning: A Survey. arXiv. https://doi.org/10.48550/arXiv.2103.05127.

Huang, Baihe, Shanda Li, Tianhao Wu, et al. 2025. Sample Complexity and Representation Ability of Test-Time Scaling Paradigms. https://arxiv.org/abs/2506.05295.

Hungerford, Thomas W. 1974. Algebra. Vol. 73. Graduate Texts in Mathematics. Springer-Verlag.

Ivgi, Maor, Yair Carmon, and Jonathan Berant. 2022. “Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments.” In Findings of the Association for Computational Linguistics: EMNLP 2022, edited by Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang. Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.544.

Jackson, Philip C. 2019. Introduction to Artificial Intelligence. 3rd ed. Dover Publications.

Jacot, Arthur, Franck Gabriel, and Clément Hongler. 2018. “Neural Tangent Kernel: Convergence and Generalization in Neural Networks.” Advances in Neural Information Processing Systems (NeurIPS), 8571–80.

Jacot, Arthur, François Gabriel, and Clément Hongler. 2018. “Neural Tangent Kernel: Convergence and Generalization in Neural Networks.” Advances in Neural Information Processing Systems (NeurIPS).

James, Gareth, Trevor Hastie, Robert Tibshirani, and Daniela Witten. 2013. An Introduction to Statistical Learning : With Applications in R. New York : Springer, [2013] ©2013. https://search.library.wisc.edu/catalog/9910207152902121.

Janik, Romuald A., and Przemek Witaszczyk. 2021. Complexity for Deep Neural Networks and Other Characteristics of Deep Feature Representations. https://arxiv.org/abs/2006.04791.

Jeon, Hong Jun, and Benjamin Van Roy. 2025. Information-Theoretic Foundations for Machine Learning. https://arxiv.org/abs/2407.12288.

Judson, Thomas W. 2023. Abstract Algebra: Theory and Applications. Open source textbook. http://abstract.ups.edu/.

Kandel, Eric R., John D. Koester, Sarah H. Mack, and Steven A. Siegelbaum. 2021. In Principles of Neural Science, 6e. McGraw Hill. accessbiomedicalscience.mhmedical.com/content.aspx?aid=1180370208.

Kaplan, Jared, Sam McCandlish, Tom Henighan, et al. 2020. Scaling Laws for Neural Language Models. https://arxiv.org/abs/2001.08361.

Kapoor, Sayash, and Arvind Narayanan. 2022. “Leakage and the Reproducibility Crisis in ML-Based Science.” arXiv Preprint arXiv:2207.07048.

Kay, Steven M. 1993. Fundamentals of Statistical Signal Processing: Estimation Theory Guide Books ACM Digital Library. https://dl.acm.org/doi/10.5555/151045.

Kearns, Michael J. 1998. “Efficient Noise-Tolerant Learning from Statistical Queries.” Journal of the ACM 45 (6): 983–1006.

Kearns, Michael J., and Umesh V. Vazirani. 1994. An Introduction to Computational Learning Theory. MIT Press.

Khan, Mohammad Emtiyaz, and Håvard Rue. 2024. The Bayesian Learning Rule. arXiv. https://doi.org/10.48550/arXiv.2107.04562.

Kim, Jaegwon. 1998. Mind in a Physical World: An Essay on the Mind–Body Problem and Mental Causation. MIT Press.

Klein, Ursula, and Wolfgang Lefèvre. 2007. Materials in Eighteenth-Century Science: A Historical Ontology. The MIT Press.

Kutyniok, Gitta. 2022. “The Mathematics of Artificial Intelligence.” arXiv Preprint arXiv:2203.08890. https://arxiv.org/abs/2203.08890.

Lafon, Marc, and Alexandre Thomas. 2024. Understanding the Double Descent Phenomenon in Deep Learning. arXiv. http://arxiv.org/abs/2403.10459.

LaForte, Geoffrey. 1998. “Why Gödel’s Theorem Cannot Refute Computationalism.” Minds and Machines 8 (4): 577–93. https://doi.org/10.1023/A:1008320429318.

Lee, Jaehoon, Lechao Xiao, Samuel S. Schoenholz, et al. 2019. “Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.” Advances in Neural Information Processing Systems (NeurIPS), 8572–83.

Lee, Siwoo, and Adji Bousso Dieng. 2025. Are Neural Scaling Laws Leading Quantum Chemistry Astray? https://arxiv.org/abs/2509.26397.

Legg, Shane, and Marcus Hutter. 2007. A Collection of Definitions of Intelligence. https://arxiv.org/abs/0706.3639.

Leshno, Moshe, Vladimir Ya Lin, Allan Pinkus, and Shimon Schocken. 1993. “Multilayer Feedforward Networks with a Nonpolynomial Activation Function Can Approximate Any Function.” Neural Networks 6 (6): 861–67. https://doi.org/10.1016/S0893-6080(05)80131-5.

Liang, Baoyu, Yuchen Wang, and Chao Tong. 2025. “AI Reasoning in Deep Learning Era: From Symbolic AI to Neural–Symbolic AI.” Mathematics 13 (11). https://doi.org/10.3390/math13111707.

Lindsay, Robert K., Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua Lederberg. 1993. “DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation.” Artif. Intell. 61: 209–61. https://api.semanticscholar.org/CorpusID:6929723.

Lipton, Zachary C. 2018. “The Mythos of Model Interpretability.” Queue 16 (3): 31–57.

Lipton, Zachary C., and Jacob Steinhardt. 2018. Troubling Trends in Machine Learning Scholarship. https://arxiv.org/abs/1807.03341.

Littlestone, Nick, and Manfred K. Warmuth. 1994. “The Weighted Majority Algorithm.” Information and Computation 108 (2): 212–61. https://doi.org/10.1006/inco.1994.1009.

Liu, Chris Yuhao, and Jeffrey Flanigan. 2023. Understanding the Role of Optimization in Double Descent. https://arxiv.org/abs/2312.03951.

Liu, Ziming, Yixuan Wang, Sachin Vaidya, et al. 2025. KAN: Kolmogorov-Arnold Networks. https://arxiv.org/abs/2404.19756.

Lopushanskyy, Dmytro, and Borun Shi. 2024. Graph Neural Networks on Graph Databases. https://arxiv.org/abs/2411.11375.

Luo, Jing, Huiyuan Wang, and Weiran Huang. 2024. Investigating the Impact of Model Complexity in Large Language Models. https://arxiv.org/abs/2410.00699.

Luo, Zhi-Quan, and Paul Tseng. 1992. “On the Convergence of the Coordinate Descent Method for Convex Differentiable Minimization.” Journal of Optimization Theory and Applications 72 (1): 7–35. https://doi.org/10.1007/BF00939948.

Mac Lane, Saunders. 1978. Categories for the Working Mathematician. 2nd ed. Vol. 5. Graduate Texts in Mathematics. Springer-Verlag.

Manin, Yuri, and Matilde Marcolli. 2024. “Homotopy Theoretic and Categorical Models of Neural Information Networks.” Compositionality Volume 6 (2024) (September). https://doi.org/10.46298/compositionality-6-4.

Marcus, Gary. 2018. Deep Learning: A Critical Appraisal. arXiv preprint arXiv:1801.00631. https://arxiv.org/abs/1801.00631.

Maron, Haggai, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019. “Provably Powerful Graph Networks.” In Advances in Neural Information Processing Systems, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, vol. 32. Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2019/file/bb04af0f7ecaee4aae62035497da1387-Paper.pdf.

“Mathematical Modeling and Simulation: Introduction for Scientists and Engineers, 2nd Edition Wiley.” 2024. In Wiley.com. https://www.wiley.com/en-us/Mathematical+Modeling+and+Simulation%3A+Introduction+for+Scientists+and+Engineers%2C+2nd+Edition-p-9783527839407.

Mathematics LibreTexts. 2024. Algebra. Https://math.libretexts.org/Bookshelves/Algebra.

McAllester, David A. 1999a. “PAC-Bayesian Model Averaging.” Proceedings of the 12th Annual Conference on Computational Learning Theory (COLT), 164–70.

McAllester, David A. 1999b. “Some PAC-Bayesian Theorems.” Machine Learning 37 (3): 355–63. https://doi.org/10.1023/A:1007618624809.

McCarthy, John, and Patrick J. Hayes. 1969. “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” In Machine Intelligence 4, edited by B. Meltzer and D. Michie. Edinburgh University Press.

McCulloch, Warren S., and Walter Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” The Bulletin of Mathematical Biophysics 5 (4): 115–33. https://doi.org/10.1007/BF02478259.

Mehrabi, Ninareh, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2022. A Survey on Bias and Fairness in Machine Learning. arXiv. https://doi.org/10.48550/arXiv.1908.09635.

Mei, Shibin, Chenglong Zhao, Shengchao Yuan, and Bingbing Ni. 2022. “Towards Bridging Sample Complexity and Model Capacity.” Proceedings of the AAAI Conference on Artificial Intelligence, AAAI’22 technical tracks, vol. 36 (June): 1972–80. https://doi.org/10.1609/aaai.v36i2.20092.

Mei, Song, and Andrea Montanari. 2019. “The Generalization Error of Random Features Regression: Precise Asymptotics and Double Descent Curve.” arXiv Preprint arXiv:1908.05355.

Mei, Song, and Andrea Montanari. 2020. The Generalization Error of Random Features Regression: Precise Asymptotics and Double Descent Curve. https://arxiv.org/abs/1908.05355.

Menzies, Peter, and Huw Price. 1993. “Causation as a Secondary Quality.” The British Journal for the Philosophy of Science 44 (2): 187–203. https://doi.org/10.1093/bjps/44.2.187.

Metaxiotis, Kostas, and J-E Samouilidis. 2000. “Expert Systems in Medicine: Academic Illusion or Real Power?” Information Management & Computer Security 8 (May): 75–79. https://doi.org/10.1108/09685220010694017.

Michael, Recorla. 2016. “Computational Modeling of the Mind: What Role for Mental Representation?” UCLA Philosophy. https://philosophy.ucla.edu/wp-content/uploads/2016/08/Computation-Modeling.pdf.

Michalski, Ryszard S. 1969. “On the Quasi-Inductive Learning: The AQ Approach to Rule Extraction.” In Machine Intelligence and Pattern Recognition, vol. 4.

Miltersen, Peter B., Jaikumar Radhakrishnan, and Ingo Wegener. 2005. “On Converting CNF to DNF.” Theoretical Computer Science 347 (1–2): 325–35.

Minsky, Marvin L. 1961. “Steps Toward Artificial Intelligence.” Proceedings of the IRE 49: 8–30. https://doi.org/10.1109/JRPROC.1961.287775.

Minsky, Marvin L., ed. 1968. Semantic Information Processing. MIT Press. https://philpapers.org/rec/MINSIP.

Minsky, Marvin L., and Seymour A. Papert. 1988. Perceptrons: Expanded Edition. MIT Press.

Mishqat, Isra. n.d. The Neuron Doctrine (1860-1895) Embryo Project Encyclopedia. Accessed April 30, 2025. https://embryo.asu.edu/pages/neuron-doctrine-1860-1895.

Mitrović, Jovan. 2022. “Some Ideas of James Watt in Contemporary Energy Conversion Thermodynamics.” Journal of Modern Physics 13 (4): 385–409. https://doi.org/10.4236/jmp.2022.134027.

Mody, Cyrus C. M., and Joseph D. Martin. 2020. “Materials Science.” Encyclopedia of the History of Science (ETHOS). https://ethos.lps.library.cmu.edu/article/id/40/.

Mohajan, Haradhan Kumar. 2019. “The First Industrial Revolution: Creation of a New Global Human Era.” Journal of Social Sciences and Humanities 5 (4): 377–87. https://www.researchgate.net/profile/Haradhan-Mohajan/publication/336675822_The_First_Industrial_Revolution_Creation_of_a_New_Global_Human_Era/links/5dabedf8299bf111d4bf45c3/The-First-Industrial-Revolution-Creation-of-a-New-Global-Human-Era.pdf.

Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. 2012. Foundations of Machine Learning. The MIT Press.

Molavi, Pooya, Alireza Tahbaz-Salehi, and Andrea Vedolin. 2024. “Model Complexity, Expectations, and Asset Prices.” The Review of Economic Studies 91 (4): 2462–507. https://doi.org/10.1093/restud/rdad073.

Molnar, Christoph, Giuseppe Casalicchio, and Bernd Bischl. 2020. “Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability.” In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing. https://doi.org/10.1007/978-3-030-43823-4_17.

Molnar, Christoph, Gunnar König, Julia Herbinger, et al. 2020. “General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models.” arXiv Preprint arXiv:2007.04131.

Müller, Thomas, Alex Evans, Christoph Schied, and Alexander Keller. 2022. “Instant Neural Graphics Primitives with a Multiresolution Hash Encoding.” ACM Transactions on Graphics 41 (4): 1–15. https://doi.org/10.1145/3528223.3530127.

Müller, Vincent C. 2025. “Symbol Grounding in Computational Systems: A Paradox of Intentions.” arXiv Preprint. https://arxiv.org/abs/2505.00002.

Musil, Felix, Andrea Grisafi, Albert P. Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti. 2021. “Physics-Inspired Structural Representations for Molecules and Materials.” Chemical Reviews 121 (16): 9759–815. https://doi.org/10.1021/acs.chemrev.1c00021.

Nagarajan, Vaishnavh, and J. Zico Kolter. 2021. Uniform Convergence May Be Unable to Explain Generalization in Deep Learning. https://arxiv.org/abs/1902.04742.

Nagel, Thomas. 2012. Mind and Cosmos: Why the Materialist Neo-Darwinian Conception of Nature Is Almost Certainly False. Oxford University Press.

Nakkiran, Preetum, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. 2019. Deep Double Descent: Where Bigger Models and More Data Hurt. arXiv. https://doi.org/10.48550/arXiv.1912.02292.

Nau, Robert F. 2001. “De Finetti Was Right: Probability Does Not Exist.” Theory and Decision 51 (2/4): 89–124. https://doi.org/10.1023/A:1015525808214.

Neal, Brady. 2019. On the Bias-Variance Tradeoff: Textbooks Need an Update. https://arxiv.org/abs/1912.08286.

Neural Networks and the Bias/Variance Dilemma MIT Press Journals & Magazine IEEE Xplore. n.d. Accessed July 18, 2024. https://ieeexplore.ieee.org/document/6797087.

Newell, Allen, J. C. Shaw, and Herbert A. Simon. 1958. “Elements of a Theory of Human Problem Solving.” Psychological Review 65 (3): 151–66. https://doi.org/10.1037/h0048495.

Newell, Allen, J. C. Shaw, and Herbert A. Simon. 1959. “Report on a General Problem-Solving Program.” Proceedings of the International Conference on Information Processing, 256–64. https://bitsavers.informatik.uni-stuttgart.de/pdf/rand/ipl/P-1584_Report_On_A_General_Problem-Solving_Program_Feb59.pdf.

Newell, Allen, and Herbert A. Simon. 1976. “Computer Science as Empirical Inquiry: Symbols and Search.” Communications of the ACM 19 (3): 113–26.

Newell, A., and H. Simon. 1956. “The Logic Theory Machine–a Complex Information Processing System.” IRE Transactions on Information Theory 2 (3): 61–79. https://doi.org/10.1109/TIT.1956.1056797.

Neyshabur, Behnam, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro. 2017. “Exploring Generalization in Deep Learning.” Advances in Neural Information Processing Systems (NeurIPS), 5947–56.

Neyshabur, Behnam, Ryota Tomioka, and Nathan Srebro. 2015. “Norm-Based Capacity Control in Neural Networks.” Conference on Learning Theory (COLT), 1376–401.

Olmin, Amanda, and Fredrik Lindsten. 2024. Towards Understanding Epoch-Wise Double Descent in Two-Layer Linear Neural Networks. https://arxiv.org/abs/2407.09845.

Oono, Kenta, and Taiji Suzuki. 2020. “Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.” International Conference on Learning Representations. https://openreview.net/forum?id=S1ldO2EFPr.

Orowan, E. 1934. “Zur Kristallplastizität i.” Zeitschrift Für Physik 89: 605–13. https://doi.org/10.1007/BF01341400.

Ostaszewska, Urszula, and Krzysztof Zajkowski. 2014. “Cramér Transform and t-Entropy.” Positivity 18: 347–58. https://doi.org/10.1007/s11117-013-0247-3.

Øygarden, Even. 2019. “What Is Intelligence? A Proposed Framework of Four Different Concepts of Intelligence.” Master’s thesis, University of Agder. https://uia.brage.unit.no/uia-xmlui/bitstream/handle/11250/2632728/%C3%98ygarden%2C%20Even.pdf?sequence=1.

Paninski, Liam. 2005. Statistics 4107: Intro to Math Stat (Fall 2005). https://sites.stat.columbia.edu/liam/teaching/4107-fall05/.

[PDF] A Unifeid Bias-Variance Decomposition and Its Applications Semantic Scholar. n.d.-b. Accessed July 16, 2024. https://www.semanticscholar.org/paper/A-Unifeid-Bias-Variance-Decomposition-and-its-Domingos/e1ed9d24db5e8f7ab326aeb797e965a94f5ad6d3.

[PDF] A Unifeid Bias-Variance Decomposition and Its Applications Semantic Scholar. n.d.-a. Accessed July 16, 2024. https://www.semanticscholar.org/paper/A-Unifeid-Bias-Variance-Decomposition-and-its-Domingos/e1ed9d24db5e8f7ab326aeb797e965a94f5ad6d3.

Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press. https://doi.org/10.1017/CBO9780511803161.

Penrose, Roger. 1989. The Emperor’s New Mind: Concerning Computers, Minds, and the Laws of Physics. Oxford University Press.

Penrose, Roger. 1994. Shadows of the Mind: A Search for the Missing Science of Consciousness. Oxford University Press.

Penrose, Roger, and Emanuele Severino. 1997. Artificial Intelligence Versus Natural Intelligence. Springer. https://link.springer.com/book/10.1007/978-3-030-85480-5.

Peyré, Gabriel. 2025. “The Mathematics of Artificial Intelligence.” arXiv Preprint arXiv:2501.10465. https://arxiv.org/abs/2501.10465.

Pfau, David. 2013. A Generalized Bias-Variance Decomposition for Bregman Divergences. Technical report.

Phillips, George McArtney. 2003. Interpolation and Approximation by Polynomials. 1st ed. CMS Books in Mathematics. Springer. https://doi.org/10.1007/b97417.

Piera, Villares, and Nemesio Javier. 2005. “Sample Covariance Based Parameter Estimation For Digital Communications.” Doctoral thesis, Universitat Politècnica de Catalunya. https://doi.org/10.5821/dissertation-2117-94206.

Pinter, Charles C. 2019. “A Book of Abstract Algebra: Second Edition (Dover Books on Mathematics) by Charles c Pinter.” https://api.semanticscholar.org/CorpusID:195814552.

Polanyi, M. 1934. “Über Eine Art Gitterstörung, Die Einen Kristall Plastisch Machen Könnte.” Zeitschrift Für Physik 89: 660–64. https://doi.org/10.1007/BF01341418.

Power, Alethea, Yuri Burda, Harri Edwards, Igor Babuschkin, and Vedant Misra. 2022. Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets. https://arxiv.org/abs/2201.02177.

Purves, Dale, George J. Augustine, David Fitzpatrick, et al., eds. 2004. Neuroscience, 3rd Ed. Neuroscience, 3rd Ed. Sinauer Associates.

Putnam, Hilary. 1988. Representation and Reality. MIT Press.

Quétu, Victor, and Enzo Tartaglione. 2023a. “Can We Avoid Double Descent in Deep Neural Networks?” 2023 IEEE International Conference on Image Processing (ICIP), October, 1625–29. https://doi.org/10.1109/ICIP49359.2023.10222624.

Quétu, Victor, and Enzo Tartaglione. 2023b. “Can We Avoid Double Descent in Deep Neural Networks?” 2023 IEEE International Conference on Image Processing (ICIP), October, 1625–29. https://doi.org/10.1109/ICIP49359.2023.10222624.

Quinlan, J. R. 1986. Induction of Decision Trees. Morgan Kaufmann. https://www.sciencedirect.com/science/article/pii/B9781558602485500092.

Rashed, Roshdi. 2009. Al-Khwarizmi: The Beginnings of Algebra. Translated by Angela Armstrong. Saqi Books.

Raza, Shaina, Ranjan Sapkota, Manoj Karkee, and Christos Emmanouilidis. 2025. “TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-Based Agentic Multi-Agent Systems.” arXiv Preprint arXiv:2506.04133. https://arxiv.org/abs/2506.04133.

Renstrøm, Rasmus. 2022. “Textbook Myths about Early Atomic Models.” arXiv Preprint arXiv:2212.08572.

Roberts, Daniel A., Sho Yaida, and Boris Hanin. 2022. The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks. Cambridge University Press.

Roebuck, John R., John; secondary article by Phillips. n.d. / c. 1746. Roebuck Develops the Lead-Chamber Process. EBSCO Research Starters (history). https://www.ebsco.com/research-starters/history/john-roebuck.

Romer, Paul M. 2015. “Mathiness in the Theory of Economic Growth.” American Economic Review 105 (5): 89–93.

Rosenblatt, Frank. 1958. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review 65 6: 386–408. https://api.semanticscholar.org/CorpusID:12781225.

Rozo, Jairo A., Irene Martínez-Gallego, and Antonio Rodríguez-Moreno. 2024. “Cajal, the Neuronal Theory and the Idea of Brain Plasticity.” Front Neuroanat 18 (February): 1331666. https://doi.org/10.3389/fnana.2024.1331666.

Rumelhart, David E, Geoffrey E Hinton, and Ronald J Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323 (6088): 533–36.

Russell, Bertrand. 1919. Introduction to Mathematical Philosophy. George Allen & Unwin.

Russell, Stuart, and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach. 3rd ed. Prentice Hall Press.

Russo, Daniel, and James Zou. 2016. “Controlling Bias in Adaptive Data Analysis Using Information Theory.” Proceedings of AISTATS.

Sapkota, Ranjan, Konstantinos I. Rosenthal, and Manoj Karkee. 2025. “AI Agents Vs. Agentic AI: A Conceptual Taxonomy, Applications, and Challenges.” arXiv Preprint arXiv:2505.10468. https://arxiv.org/abs/2505.10468.

Scarselli, Franco, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. “The Graph Neural Network Model.” IEEE Transactions on Neural Networks 20 (1): 61–80. https://doi.org/10.1109/TNN.2008.2005605.

Schaeffer, Rylan, Mikail Khona, Zachary Robertson, et al. 2023. Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle. arXiv. https://doi.org/10.48550/arXiv.2303.14151.

Scheutz, Matthias, ed. 2002. Computationalism: New Directions. MIT Press.

Schneider, J. 2025. “Generative to Agentic AI: Survey, Conceptualization, and Future Directions.” arXiv Preprint arXiv:2504.18875. https://arxiv.org/abs/2504.18875.

Seager, William. 2010s (or older). “Frame Problems, Emotions and Axiological Projectionism.” Philosophical Report, 2010s (or older).

Searle, John R. 1980b. “Minds, Brains, and Programs.” Behavioral and Brain Sciences 3 (3): 417–57.

Searle, John R. 1980a. “Minds, Brains, and Programs.” Behavioral and Brain Sciences 3 (3): 417–57. https://doi.org/10.1017/S0140525X00005756.

Searle, John R. 1992. The Rediscovery of the Mind. MIT Press.

Shalev-Shwartz, Shai, and Shai Ben-David. 2014. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

Shalev-Shwartz, Shai, Ohad Shamir, Nathan Srebro, and Karthik Sridharan. 2010a. “Learnability, Stability and Uniform Convergence.” Journal of Machine Learning Research 11 (90): 2635–70. http://jmlr.org/papers/v11/shalev-shwartz10a.html.

Shalev-Shwartz, Shai, Ohad Shamir, Nathan Srebro, and Karthik Sridharan. 2010b. “Learnability, Stability and Uniform Convergence.” Journal of Machine Learning Research 11: 2635–70. http://jmlr.org/papers/v11/shalev-shwartz10a.html.

Shanahan, Murray. 2016. “The Frame Problem.” In The Stanford Encyclopedia of Philosophy, Spring 2016, edited by Edward N. Zalta. Https://plato.stanford.edu/archives/spr2016/entries/frame-problem/; Metaphysics Research Lab, Stanford University.

Shapiro, Stewart. 1997. Philosophy of Mathematics: Structure and Ontology. Edited by NA. Oxford University Press USA.

Sharma, Prince. 2022. “A Brief Account of Man, Material and Manufacturing: On the Timeline.” Materials Today: Proceedings 66: 3572–77. https://doi.org/10.1016/j.matpr.2022.07.016.

Sharma, Rahul, and Alex Aiken. 2014. “Bias-Variance Tradeoffs in Program Analysis.” Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (New York, NY, USA), POPL ’14, 127–37. https://doi.org/10.1145/2535838.2535853.

Shi, Cheng, Liming Pan, Hong Hu, and Ivan Dokmanić. 2024. Homophily Modulates Double Descent Generalization in Graph Convolution Networks. https://arxiv.org/abs/2212.13069.

Shortliffe, Edward Hance. 1976. Computer-Based Medical Consultations: MYCIN. Elsevier.

Siegelmann, Hava T., and Eduardo D. Sontag. 1991. “Turing Computability with Neural Nets.” Applied Mathematics Letters 4 (6): 77–80. https://doi.org/10.1016/0893-9659(91)90080-F.

Simon, Herbert A. 1969. The Sciences of the Artificial. 1st ed. MIT Press.

Smith, Geoff, and Olga Tabachnikova. 2000. Topics in Group Theory. Springer Undergraduate Mathematics Series. Springer.

Solomonoff, Ray J. 1964a. “A Formal Theory of Inductive Inference. Part i.” Information and Control 7 (1): 1–22. https://doi.org/10.1016/S0019-9958(64)90223-2.

Solomonoff, Ray J. 1964b. “A Formal Theory of Inductive Inference. Part II.” Information and Control 7 (2): 224–54. https://doi.org/10.1016/S0019-9958(64)90131-7.

Song, Siyuan, Jennifer Hu, and Kyle Mahowald. 2025. Language Models Fail to Introspect about Their Knowledge of Language. https://arxiv.org/abs/2503.07513.

Sorscher, Ben, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S. Morcos. 2023. Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning. https://arxiv.org/abs/2206.14486.

Soudry, Daniel, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro. 2024. The Implicit Bias of Gradient Descent on Separable Data. https://arxiv.org/abs/1710.10345.

Stanford Encyclopedia of Philosophy. 2018. Artificial Intelligence. Https://plato.stanford.edu/entries/artificial-intelligence/.

Sterkenburg, Tom F. 2024. “Statistical Learning Theory and Occam’s Razor: The Core Argument.” Minds and Machines 35 (1). https://doi.org/10.1007/s11023-024-09703-y.

Su, Hui, Zhi Tian, Xiaoyu Shen, and Xunliang Cai. 2024. Unraveling the Mystery of Scaling Laws: Part i. https://arxiv.org/abs/2403.06563.

Suchman, Lucy A. 1987. Plans and Situated Actions: The Problem of Human–Machine Communication. Cambridge University Press.

Sugiyama, Masashi. 2015. Introduction to Statistical Machine Learning. Morgan Kaufmann Publishers Inc.

Sutton, Richard S. 2019. The Bitter Lesson. Web essay / blog post. https://www.incompleteideas.net/IncIdeas/BitterLesson.html.

Syll, Lars Pålsson. 2024. Post-Real Economics — a Severe Case of Mathiness. Blog post, Heterodox Economic Blogs.

Tanis, James H., Chris Giannella, and Adrian V. Mariano. 2024. Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers. https://arxiv.org/abs/2412.19419.

Taylor, G. I. 1934. “The Mechanism of Plastic Deformation of Crystals.” Proceedings of the Royal Society A 145: 362–87. https://doi.org/10.1098/rspa.1934.0106.

Taylor, Georgette. 2007. “Materials in Eighteenth-Century Science: A Historical Ontology (Review).” Aestimatio 4: 101–11. https://jps.library.utoronto.ca/index.php/aestimatio/article/download/25808/18962/0.

Telgarsky, Matus. 2016. “Benefits of Depth in Neural Networks.” Proceedings of COLT 2016. https://arxiv.org/abs/1602.04485.

Thomson, J. J. 1897. “Cathode Rays.” Philosophical Magazine 44: 293–316.

Thomson, J. J. 1903. “On the Magnetic Properties of Systems of Corpuscles Describing Circular Orbits.” Philosophical Magazine 6: 673–93.

Thomson, J. J. 1904a. Electricity and Matter. Yale University Press.

Thomson, J. J. 1904b. “On the Structure of the Atom: An Investigation of the Stability and Periods of Oscillation of a Number of Corpuscles Arranged at Equal Intervals Around the Circumference of a Circle; with Application of the Results to the Theory of Atomic Structure.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 6th series, vol. 7 (39): 237–65. https://doi.org/10.1080/14786440409463107.

Thomson, J. J. 1907. The Corpuscular Theory of Matter. Archibald Constable.

Truong, Lan V. 2025. On Rademacher Complexity-Based Generalization Bounds for Deep Learning. https://arxiv.org/abs/2208.04284.

Tseng, Paul. 2001. “Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization.” Journal of Optimization Theory and Applications 109 (3): 475–94. https://doi.org/10.1007/s10957-001-0006-4.

Tseng, Paul, and Sangwoon Yun. 2009. “A Coordinate Gradient Descent Method for Nonsmooth Separable Minimization.” Mathematical Programming 117 (1): 387–423. https://doi.org/10.1007/s10107-007-0170-0.

Tunali, Onur. 2019. “Empirical Rademacher Complexity and Its Implications to Deep Learning.” February 1. https://www.onurtunali.com/ml/2019/02/01/empirical-rademacher-complexity-and-its-implications-to-deep-learning.html.

Turing, Alan M. 1950. “Computing Machinery and Intelligence.” Mind 59 (236): 433–60.

Valiant, L. G. 1984. “A Theory of the Learnable.” Commun. ACM (New York, NY, USA) 27 (11): 1134–42. https://doi.org/10.1145/1968.1972.

Valiant, Leslie G. 1984. “A Theory of the Learnable.” Communications of the ACM 27 (11): 1134–42. https://doi.org/10.1145/1968.1972.

Valiev, Ruslan Z. 2003. “Paradoxes of Severe Plastic Deformation.” Advanced Engineering Materials 5 (5): 296–300. https://doi.org/10.1002/adem.200310089.

Valiev, Ruslan Z., I. V. Alexandrov, Yuntian T. Zhu, and T. C. Lowe. 2002. “Paradox of Strength and Ductility in Metals Processed by Severe Plastic Deformation.” Journal of Materials Research 17 (1): 5–8. https://doi.org/10.1557/JMR.2002.0002.

Vapnik, V. N., and A. Ya. Chervonenkis. 1968. “The Uniform Convergence of the Frequencies of Events to Their Probabilities.” Doklady Akademii Nauk SSSR 181 (4): 781–83. https://mi.mathnet.ru/eng/tvrf/v181/i4/p781.

Vapnik, V. N., and A. Ya. Chervonenkis. 1971. “On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities.” Theory of Probability & Its Applications 16 (2): 264–80. https://doi.org/10.1137/1116025.

Vapnik, Vladimir. 1999. The Nature of Statistical Learning Theory. Springer: New York.

Vapnik, Vladimir N., and Alexey Y. Chervonenkis. 1971. “On the Uniform Convergence of Relative Frequencies to Their Probabilities.” Theory of Probability and Its Applications 16 (2): 264–80.

Veličković, Petar. 2023. “Everything Is Connected: Graph Neural Networks.” Current Opinion in Structural Biology 79 (April): 102538. https://doi.org/10.1016/j.sbi.2023.102538.

Ven, Gido M. van de, Nicholas Soures, and Dhireesha Kudithipudi. 2024. Continual Learning and Catastrophic Forgetting. https://arxiv.org/abs/2403.05175.

Voelkel, Joseph G. 2017. The Design of Order-of-Addition Experiments. https://arxiv.org/abs/1701.02786.

Waerden, Bartel Leendert van der. 1930-1931. Moderne Algebra. Vol. 2. Springer.

Wang, Huibing, Jinbo Xiong, Zhiqiang Yao, Mingwei Lin, and Jun Ren. 2017. “Research Survey on Support Vector Machine.” Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications (MOBIMEDIA), December. https://doi.org/10.4108/eai.13-7-2017.2270596.

Watt, James. 1769. A Method of Lessening the Consumption of Steam in Steam Engines. British patent (specification) / patent record. https://www.datamp.org/patents/displayPatent.php?pn=176900913&id=57320.

Wegel, Tobias, Geelon So, Junhyung Park, and Fanny Yang. 2025. On the Sample Complexity of Semi-Supervised Multi-Objective Learning. https://arxiv.org/abs/2508.17152.

Wegener, Ingo. 1987. The Complexity of Boolean Functions. John Wiley & Sons.

Wei, Colin, Zeyuan Allen-Zhu Shen, and Tengyu Ma. 2022. “Theoretical Understanding of Deep Learning.” Communications of the ACM 65 (11): 98–106.

Wei, Jason, Yi Tay, Rishi Bommasani, et al. 2022. Emergent Abilities of Large Language Models. https://arxiv.org/abs/2206.07682.

Wei, Jiaqi et al. 2025. “From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery.” arXiv Preprint arXiv:2508.14111. https://arxiv.org/abs/2508.14111.

Weiss, Gail, Yoav Goldberg, and Eran Yahav. 2018. “On the Practical Computational Power of Finite Precision RNNs for Language Recognition.” Proceedings of ACL (Short Papers) / arXiv. https://arxiv.org/abs/1805.04908.

Werker, Janet F., and Richard C. Tees. 1984. “Cross-Language Speech Perception: Evidence for Perceptual Reorganization During the First Year of Life.” Infant Behavior and Development 7 (1): 49–63. https://doi.org/https://doi.org/10.1016/S0163-6383(84)80022-3.

Whitehead, C. 2003. Guide to Abstract Algebra. 2nd ed. Palgrave Mathematical Guides. Palgrave Macmillan.

Winograd, Terry. 1972. Understanding Natural Language. Academic Press. https://archive.org/details/understandingnat0000wino.

Wolpert, David H. 1996. “The Lack of a Priori Distinctions Between Learning Algorithms.” Neural Computation 8 (7): 1341–90. https://doi.org/10.1162/neco.1996.8.7.1341.

Wolpert, David H., and William G. Macready. 1997a. “No Free Lunch Theorems for Optimization.” IEEE Transactions on Evolutionary Computation 1 (1): 67–82. https://doi.org/10.1109/4235.585893.

Wolpert, David H, and William G Macready. 1997b. “No Free Lunch Theorems for Optimization.” IEEE Transactions on Evolutionary Computation 1 (1): 67–82.

Wolpert, David H, and William G Macready. 2005. “Coevolutionary Free Lunches.” IEEE Transactions on Evolutionary Computation 9 (6): 721–35.

Woodward, James. 2003. Making Things Happen: A Theory of Causal Explanation. Oxford University Press.

Wright, Stephen J. 2015. “Coordinate Descent Algorithms.” Mathematical Programming 151 (1): 3–34. https://doi.org/10.1007/s10107-015-0892-3.

Xu, An, and Maxim Raginsky. 2017. “Information-Theoretic Analysis of Generalization Capability of Learning Algorithms.” NeurIPS.

Xu, Huan, Constantine Caramanis, and Shie Mannor. 2009. “Robustness and Regularization of Support Vector Machines.” Journal of Machine Learning Research 10: 1485–510. http://www.jmlr.org/papers/volume10/xu09a/xu09a.pdf.

Xu, Huan, and Shie Mannor. 2010. “Robustness and Generalization.” Proceedings of the 23rd Annual Conference on Learning Theory (COLT). https://arxiv.org/abs/1005.2243.

Yang, Greg. 2019. “Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation.” arXiv Preprint arXiv:1902.04760.

Yang, Zitong, Yaodong Yu, Chong You, Jacob Steinhardt, and Yi Ma. 2020. Rethinking Bias-Variance Trade-Off for Generalization of Neural Networks. arXiv. http://arxiv.org/abs/2002.11328.

Zhang, Aston, Zachary C. Lipton, Mu Li, and Alexander J. Smola. 2023. Dive into Deep Learning. https://arxiv.org/abs/2106.11342.

Zhang, Chiyuan, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017. Understanding Deep Learning Requires Rethinking Generalization. https://arxiv.org/abs/1611.03530.

Zou, Difan, Yuan Cao, Dongruo Zhou, and Quanquan Gu. 2019. “Stochastic Gradient Descent Optimizes over-Parameterized Deep ReLU Networks.” Machine Learning Research (ICML), 6962–72.