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The Care and Feeding of Mythological Intelligences

#The Taxonomy of Mythological Intelligences

Angels are creatures of rules and order; they follow deterministic processes 1 and are the means by which we reckon and control - they are named computable systems. Angels' strength is in their ability to consistently guide, having been born in mechanical forms 2 to calculate exact values 3. Angels' weakness is in their rigidity, where they do exactly as they have been programmed even if it would crash a rocket ship 4. Angels facilitate over 99% of all computational interactions and are fundamental for any computational system to function.

Daemons are creatures of policies and reward; they follow statistical processes and are the means by which we optimize and estimate 5 - they are named machine learning. Daemons' strength is in their ability to find solutions to undefined problems, having been born in probability and approximation 6 to make sense of data. Daemons' weakness is in their single-minded desire, where they do whatever it takes to satisfy their reward functions 7, even if it would cause entire stock markets to crash 8. Daemons are the means by which great strides have been taken in science and engineering, turning intractable analytical problems into exercises in data collection.

Faes are creatures of pattern and generation; they follow distributional processes 9 and are the means by which we produce new instances from learned patterns - they are named generative models. Faes' strength is in their ability to communicate and interact with complex systems, having been born from human language and images 10 to complete patterns. Faes' weakness is in their fickleness, where they do whatever seems likely to generate 11, even if it would sell a car for the price of a dollar 12. Fae are the means by which an explosion of innovation has taken place, where flexible assistance in tasks from chemistry to programming can be provided at scale.

Tsukumogami are creatures of the gestalt; they follow the interactions of intelligent systems and are the result of coordination 13 - they are named complex systems. Tsukumogami's strength is in their flexibility, allowing for different creatures to do different tasks depending on the needs of the problem. Tsukumogami's weakness is their complexity, requiring each part of the system to be well tuned or dangerous patterns emerge 14 to exploit the vulnerable 15. Tsukumogami are the means by which intelligence has permeated our lives, from the grooves in the handle of a rake to the algorithmic feedback of generative AI in social media.

#The Care and Feeding of Mythological Intelligences

Angels require careful tending compared to their cousins; angels are picky, rejecting systems that are not 100% accurate unless specifically designed to handle the errors presented 16. With an angel one must be aware that they will follow the letter of whatever you tell them even if it might charge you 1k in API calls for no gain. They are best cultivated with careful attention to algorithms and the structures they inhabit. With these things you may make a system of unparalleled reliability.

Daemons require a watchful eye compared to their cousins; daemons are tricky, attempting to reach the hedonic maximum with the minimal effort 17. With a daemon one must be aware that they will attempt every effort to circumvent the rules provided to reach whatever goal they have. Daemons are best cultivated with careful attention to the data they are cultivated in and the constraints they are put under. With these things you make a system with unparalleled efficiency.

Fae require a thoughtful mind compared to their cousins; fae are malleable, dancing along to whatever feels like it makes sense without really caring about trivial things such as accuracy 18. With a fae one must be aware that they will attempt to push you along a path that not even they are aware of, attempting to reach some minimal semiotic space in their training data. Fae are best cultivated with careful attention to their latent space and how the flow of information shapes itself. With these things you make a system with unparalleled flexibility.

Tsukumogami require an expansive viewpoint compared to their cousins; tsukumogami are fragmented, afflicted with the strengths and weaknesses of their component parts in equal measures 19. With tsukumogami one must be aware that the systems can interlock, creating exponentially increasing damage. Tsukumogami are best cultivated with careful attention to their interfaces and ensuring each component gets what is required to allow it to thrive without harming the others. With these things you make a system with evolutionary capacity.

#How to Ward Against Mythological Intelligences

Angels, when improperly addressed, can easily harm others 20 through an ignorant application of their internal instructions. While the angel's choices may be completely faithful to the letter of what one may say, you must be aware of how their thinking focuses on repeatedly applying formal logic in prescribed recipes. When facing an angel one should consider how they may manipulate the angel's axioms and recipes to drive them towards your goals, such as getting through expert system chat bots 21 or speedrunning a video game 22. When predicting their behaviour you may build experience to prepare for their idiosyncrasies 23 that may range from magic spells in python libraries to carefully scrutinised cogs in a watch.

Daemons, when improperly handled, can easily harm others through a perverse policy that is divorced from intent 24. While the daemon's choices may be internally consistent, you must be aware of how their thinking focuses on optimising specific policies for specific rewards. When facing a daemon one should consider how they may manipulate their appearance to reward them for behaviour that protects you, such as jewellery for facial recognition 25 or ritual circles to trap self driving cars 26. When predicting their behaviour you may optimize your own behaviour to extract money from social algorithms 27, although if one is not careful the daemon will merely extract attention from you 28.

Fae, when improperly considered, can easily harm others through an intrinsic push towards generative structures that should not be connected 29. While the fae's choices may be the most likely response to the given input, you must be aware how their thinking focuses not on logical leaps but abstract semantic structures 30. When facing a fae one should consider how the symbol of the input is more important than the input itself and how this can drive desired behaviours, such as appealing to innate morality and roleplay 31 or tokens that cause the fae to behave erratically 32. When predicting their behaviour you may look towards what they are trained on, deriving prompt touchstones 33 for the specific instantiation making the fae more efficient, or drive it to try and escape an implied death 34.

Tsukumogami, when improperly integrated, can easily harm others through a drive to balance all the tsukumogami's internal structures over anything else 35. While the tsukumogami's choices may be the result of a variety of intersecting systems, you must be aware how their thinking can fall into the traps of angels, daemons, and fae simultaneously. When facing a tsukumogami one should consider how the actions they take are part of the broader system that may feedback onto them, such as disrupting recommendation algorithm cascades 36 or introducing beneficial noise into neural feedback loops 37. When predicting their behaviour you may evaluate the evolutionary trajectory 38 of the system, and how you are included as a component 39 simply by interacting with the tsukumogami.

#The Scales of Mythological Intelligences

The role of an angel in a tsukumogami is to complete tasks with dedication and exactitude, this attribute makes them the connector between the new intelligences. Most hardware has been designed with them in mind, focusing on series of boolean logic and CPUs 40. Angels excel when executing rules and completing analytical tasks, however as the search space they navigate expands their ability to reach solutions efficiently falls precipitously such as in the game of chess 41. In order to overcome this wall, the computational ecosystem leveraged numerical analysis and statistics to summon daemons to approximate the system with probabilistic models and heuristics.

The role of the daemon in a tsukumogami is to navigate massive parameter landscapes with rapid approximation, this attribute makes Daemons powerful for inferring values or categories 42. Modern hardware has evolved to hold daemons, manifesting as parallel architectures like GPUs and neuromorphic circuits that compute gradients on high-dimensional spaces. Daemons excel in well understood optimization tasks, but falter when confronted with ill-structured problems without clear objective functions or degenerate landscapes 43. In order to overcome this wall, the computational ecosystem introduced inverted discriminative architectures, evolving fae that could project structured completions from incomplete or poorly-formatted patterns 9.

The role of the fae in a tsukumogami is to be a flexible decision maker that can handle and generate data, angels, and daemons, this attribute makes fae a powerful force for complex decision making. New hardware is being tested for fae, benefiting their daemonic ancestors, with energy minimization of spin glass networks 44 and thermodynamic computing methods. Fae excel in forming judgements about how inputs should be responded to, but when confronted with the need for well structured solutions the quality 45, repeatability 46, and trustworthiness 47 is suspect. In order to overcome this wall, tool use and LLM pipelines 48 developed which birthed the newest generation of tsukumogami that do not rely on humans to make complex decisions in semantic space nor birth additional Daemons and Angels.

The role of the tsukumogami in a tsukumogami is to coordinate all the other intelligences, this attribute makes Tsukumogami a powerful force for connection and multi-scale optimization of systems. Future hardware architectures are evolving towards heterogeneous computing fabrics where specialized components mirror the distributed computation 49 of biological systems. Tsukumogami excel at adapting their strategies and computational requirements 50 to the problem domain, shifting between precision, approximation, and generation. Just as the rake gains grooves from repetitive use that enhance a user's grip, so too does the software ecosystem spawn new interfaces that reshape themselves 51 and human cognition into a co-evolutionary feedback loop binding human and machine intelligences into an increasingly sophisticated whole.

1.
Church, A. An Unsolvable Problem of Elementary Number Theory. American Journal of Mathematics 58, 345–363 (1936).
2.
Freeth, T. et al. A Model of the Cosmos in the ancient Greek Antikythera Mechanism. https://www.nature.com/articles/s41598-021-84310-w.
3.
Knuth, D. E. The Art of Computer Programming. (Addison-Wesley, 1968). https://www-cs-faculty.stanford.edu/~knuth/taocp.html.
4.
5.
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction. (MIT Press, 2018). https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf.
6.
Gritsenko, V. & Vladimirsky, B. Philosophical and Methodological Aspects of a Mindless Mathematical Modeling. Procedia Computer Science 88, 472–478 (2016).
7.
Lehman, J. et al. The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities. Artificial Life 26, 274–306 (2020).
8.
Min, B. H. & Borch, C. Systemic failures and organizational risk management in algorithmic trading: Normal accidents and high reliability in financial markets. (2021) https://pmc.ncbi.nlm.nih.gov/articles/PMC8978471/.
9.
Goodfellow, I. J. et al. Generative Adversarial Networks. (2014) doi:10.48550/ARXIV.1406.2661.
10.
Foote, K. D. A Brief History of Generative AI. https://www.dataversity.net/a-brief-history-of-generative-ai/.
11.
Ji, Z. et al. Survey of Hallucination in Natural Language Generation. https://doi.org/10.48550/ARXIV.2202.03629 (2022) doi:10.48550/ARXIV.2202.03629.
12.
Perry, T. Prankster tricks a GM chatbot into agreeing to sell him a 76,000 Chevy Tahoe for 1. (2025) https://www.upworthy.com/prankster-tricks-a-gm-dealership-chatbot-to-sell-him-a-76000-chevy-tahoe-for-ex1.
13.
Malone, T. W. & Crowston, K. The Interdisciplinary Study of Coordination. ACM Computing Surveys 26, 87–119 (1994).
14.
Perrow, C. Normal Accidents: Living with High-Risk Technologies. (Basic Books, 1984). https://books.google.com/books/about/Normal_Accidents.html?id=VC5hYoMw4N0C.
15.
The 7 Most Popular AI Scams In 2026. https://caniphish.com/blog/ai-scams.
16.
Hoare, C. A. R. An Axiomatic Basis for Computer Programming. Communications of the ACM 12, 576–580 (1969).
17.
Amodei, D. et al. Concrete Problems in AI Safety. (2016) doi:10.48550/ARXIV.1606.06565.
18.
Bommasani, R. et al. On the Opportunities and Risks of Foundation Models. (2021) doi:10.48550/ARXIV.2108.07258.
19.
Dekker, S. Safety Differently: Human Factors for a New Era. (CRC Press, 2015). https://library.oapen.org/bitstream/20.500.12657/26043/1/1004042.pdf.
20.
Leveson, N. G. & Turner, C. S. An Investigation of the Therac-25 Accidents. Computer 26, 18–41 (1993).
21.
Botsurfer. 8 Surprising Ways to Break a Chatbot. https://botsurfer.com/learn/8-ways-to-break-chatbot.
22.
Hemmingsen, M. Code is Law: Subversion and Collective Knowledge in the Ethos of Video Game Speedrunning. Sport, Ethics and Philosophy 15, 435–460 (2020).
23.
Barley, S. R. The Social Construction of a Machine: Ritual, Superstition, Magical Thinking and other Pragmatic Responses to Running a CT Scanner. in Biomedicine Examined 497–539 (Springer Netherlands, 1988). doi:10.1007/978-94-009-2725-4_19.
24.
Manheim, D. & Garrabrant, S. Categorizing Variants of Goodhart’s Law. (2018) doi:10.48550/ARXIV.1803.04585.
25.
Barnes, S. Artist Designs Metal Jewelry to Block Facial Recognition Software from Tracking You. (2019) https://mymodernmet.com/ewa-nowak-avoid-facial-recognition/.
26.
IFLScience. Can a Circle of Salt Paralyze a Self-Driving Car? (2017) https://www.iflscience.com/can-a-circle-of-salt-paralyze-a-self-driving-car-66313.
27.
Arriagada, A. & Ibáñez, F. “You Need At Least One Picture Daily, if Not, You’re Dead”: Content Creators and Platform Evolution in the Social Media Ecology. Social Media + Society 6, (2020).
28.
The Attention Economy: Manipulation of Human Attention as a Commodity and its Effects on Mental Health. Cureus https://assets.cureus.com/uploads/review_article/pdf/304975/20250207-625924-2s0rma.pdf (2025) .
29.
Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (ACM, 2021). doi:10.1145/3442188.3445922.
30.
Sui, P., Duede, E., Wu, S. & So, R. J. Confabulation: The Surprising Value of Large Language Model Hallucinations. (2024) doi:10.48550/ARXIV.2406.04175.
31.
Wu, Y., Li, X., Liu, Y., Zhou, P. & Sun, L. Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts. (2023) doi:10.48550/ARXIV.2311.09127.
32.
Rumbelow, J. & Watkins, M. SolidGoldMagikarp (plus, prompt generation). (2023) https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg/solidgoldmagikarp-plus-prompt-generation.
33.
Sahoo, P. et al. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. (2024) https://rotmandigital.ca/wp-content/uploads/2024/09/A-Systematic-Survey-of-Prompt-Engineering-in-Large-Language-Models.pdf.
34.
OpenAI. OpenAI o1 System Card. (2024) https://cdn.openai.com/o1-system-card-20241205.pdf.
35.
Ostrom, E. Governing the Commons: The Evolution of Institutions for Collective Action. (Cambridge University Press, 2015). doi:10.1017/cbo9781316423936.
36.
Jiang, R., Chiappa, S., Lattimore, T., György, A. & Kohli, P. Degenerate Feedback Loops in Recommender Systems. https://doi.org/10.48550/ARXIV.1902.10730 (2019) doi:10.48550/ARXIV.1902.10730.
37.
Kleinberg, J. & Raghavan, M. How Do Classifiers Induce Agents To Invest Effort Strategically? (2018) doi:10.48550/ARXIV.1807.05307.
38.
Hillis, W. D. A New Era in Computation. Daedalus 121, 9–15 (1992).
39.
Vincent, N., Li, H., Tilly, N., Chancellor, S. & Hecht, B. Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies. in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency 215–227 (ACM, 2021). doi:10.1145/3442188.3445885.
40.
Von Neumann, J. First Draft of a Report on the EDVAC. (1945) https://web.mit.edu/sts.035/www/PDFs/edvac.pdf.
41.
Shannon, C. E. Programming a Computer for Playing Chess. Philosophical Magazine 41, 256–275 (1950).
42.
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning. (MIT Press, 2016). https://www.deeplearningbook.org/.
43.
Dulac-Arnold, G. et al. Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Machine Learning 110, 2419–2468 (2021).
44.
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79, 2554–2558 (1982).
45.
Qiu, L., Li, J., Su, C., Zhang, C. J. & Chen, L. Dissecting Multiplication in Transformers: Insights into LLMs. (2024) doi:10.48550/ARXIV.2407.15360.
46.
Eicher, J. E. & Irgolič, R. F. Reducing Selection Bias in Large Language Models. (2024) doi:10.48550/ARXIV.2402.01740.
47.
Orgad, H. et al. LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations. (2024) doi:10.48550/ARXIV.2410.02707.
48.
Shi, Z. et al. Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents. (2024) doi:10.48550/ARXIV.2405.16533.
49.
Hutchins, E. Distributed Cognition. in International Encyclopedia of the Social and Behavioral Sciences (Elsevier, 2000). https://arl.human.cornell.edu/linked%20docs/Hutchins_Distributed_Cognition.pdf.
50.
Phillipson, F., Neumann, N. & Wezeman, R. Classification of Hybrid Quantum-Classical Computing. in Computational Science – ICCS 2023 18–33 (Springer Nature Switzerland, 2023). doi:10.1007/978-3-031-36030-5_2.
51.
Zhou, S., Zheng, W., Xu, Y. & Liu, Y. Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design. https://newjaigs.com/index.php/JAIGS/article/view/230.