As science progresses in its understanding of the brain, new models of cognition are needed to make use of both modern advancements and the philosophical puzzles that they bring.  The Blyth Institute has been pioneering a new model for understanding cognitive processes based on Alan Turing’s work on non-algorithmic operators.  This has contributed both to the understanding of natural as well as artificial intelligence.

Technical Papers

• Bartlett, Jonathan. 2023. “Causal Capabilities of Teleology and Teleonomy in Life and Evolution.” Organon F 30(3):222-254. DOI 10.31577/orgf.2023.30301.
• Holloway, Eric. 2021. “2D Puzzle Visualizations of Boolean Formulae.” Communications of the Blyth Institute 3(2):29-33.
• Holloway, Eric. 2020. “Empirical Active Information.” Communications of the Blyth Institute 2(2):47-48.
• Bartlett, Jonathan. 2020. “Active Information is a Specified Complexity Model.” Communications of the Blyth Institute 2(2):40-41.
• Holloway, Eric. 2020. “Evolution in the Valley of Illusions.” Communications of the Blyth Institute 2(2):41-44.
• Holloway, Eric. 2020. “Independence Conservation and Evolutionary Algorithms.” Communications of the Blyth Institute: 2(1):32-35.
• Holloway, Eric. 2020. “Improving XGBoost with Imagination Sampling.” Communications of the Blyth Institute: 2(1):3-6.
• Bartlett, Jonathan. 2020. “Two Methods of Calculating Axiom Size.” Communications of the Blyth Institute 2(1):25-27.
• Bartlett, Jonathan and Eric Holloway, 2019.  “Generalized Information: A Straightforward Method for Judging Machine Learning Models.”  Communications of the Blyth Institute 1(2):13-22.
• Holloway, Eric.  2019.  “The Unlearnable Checkerboard Pattern.”  Communications of the Blyth Institute 1(2):78-80.
• Holloway, Eric.  2019.  “Creativity and Machines.”  Communications of the Blyth Institute 1(1):13-16.
• Holloway, Eric.  2019.  “The Wealth Pies of Plato’s Library.”  Communications of the Blyth Institute 1(1):53-4.
• Holloway, Eric and Robert Marks II.  2018.  “Observation of Unbounded Novelty in Evolutionary Algorithms is Unknowable.”  In Rutkowski et al (eds.), Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science, vol 10841:395-404.
• Bartlett, Jonathan.  2017.  “Describable but not Predictable: Mathematical Modeling and Non-Naturalistic Causation.”  In Jonathan Bartlett and Eric Holloway (eds.), Naturalism and Its Alternatives in Scientific Methodologies (pp. 113-127).  Broken Arrow, OK: Blyth Institute Press.
• Holloway, Eric and Robert Marks II.  2016.  “High Dimensional Human Guided Machine Learning.”  arXiv:1609.00904.
• Bartlett, Jonathan.  2016.  “Review: Knowledge and Christian Belief.” Journal of Interdisciplinary Studies 28(1):186-188.
• Bartlett, Jonathan.  2014.  “Using Turing Oracles in Cognitive Models of Problem-Solving.”  In Jonathan Bartlett, Dominic Halsmer, and Mark R. Hall (eds.), Engineering and the Ultimate: An Interdisciplinary Investigation of Order and Design in Nature and Craft (pp. 99-122).  Broken Arrow, OK: Blyth Institute Press.
• Bartlett, Jonathan.  2014.  “Calculating Software Complexity Using the Halting Problem.”  In Jonathan Bartlett, Dominic Halsmer, and Mark R. Hall (eds.), Engineering and the Ultimate: An Interdisciplinary Investigation of Order and Design in Nature and Craft (pp. 123-130).  Broken Arrow, OK: Blyth Institute Press.
• Bartlett, Jonathan.  2014.  “Introduction.”  In Jonathan Bartlett, Dominic Halsmer, and Mark R. Hall (eds.), Engineering and the Ultimate: An Interdisciplinary Investigation of Order and Design in Nature and Craft (pp. 1-8).  Broken Arrow, OK: Blyth Institute Press.
• Bartlett, Jonathan.  2010.  “Developing an Approach to Non-Physical Cognitive Causation in a Creation Perspective”  Occasional Papers of the BSG 17:3. (Poster, Slides)
• Bartlett, Jonathan.  2010.  “A Critique of Nonreductive Physicalism”  MTS Integrative Paper.