**Links to**: [[Negintelligibility]], [[Cognition]], [[Perception]], [[Knowledge]], [[Persistence]], [[Evolution]], [[Predictive processing]], [[Time]], [[Control]], [[Determinism]], [[Noise]], [[Chance]], [[Necessity]], [[Chaos]], etc. >“Any real change implies the breakup of the world as one has always known it, the loss of all that gave one an identity, the end of safety. And at such a moment, unable to see and not daring to imagine what the future will now bring forth, one clings to what one knew, or thought one knew; to what one possessed or dreamed that one possessed. Yet, it is only when a [person] is able, without bitterness or self-pity, to surrender a dream [they have] long cherished or a privilege [they had] long possessed that [they are] set free—[they have set themselves free]—for higher dreams, for greater privileges. All [...] have gone through this, go through it, each according to [their] degree, throughout their lives. [...] There is never a time in the future in which we will work out our salvation. The challenge is in the moment, the time is always now. > >J. Baldwin, “Faulker and Desegregation,” _Nobody Knows my Name: More Notes of a Native Son_, 1961. (Our adaptations). ### [[000 Postulate]]/[[000 Question]]: Prediction: once you see it, you can’t unsee it. The challenge, really, is to answer the question: what is _not_ prediction? _Article_: # What is the future? Prophecy, providence, preference, pattern, planning, prognosis, persistence, procreation: prediction. <small>Keywords: Prediction, Scientific models, Causal Inference, Knowledge, Predictive processing, Uncertainty.</small> %% Don't forget to add connection between overseeing, providence and governing, cybernetics: “The Italian title _**prov[v]editore**_ (plural _provveditori_; also known in [Greek](https://en.wikipedia.org/wiki/Greek_language "Greek language"): προνοητής, προβλεπτής; [Serbo-Croatian](https://en.wikipedia.org/wiki/Serbo-Croatian_language "Serbo-Croatian language"): _providur_), "he who sees to things" ([overseer](https://en.wiktionary.org/wiki/overseer "wikt:overseer")), was the style of various (but not all) local district governors in the extensive, mainly maritime empire of the [Republic of Venice](https://en.wikipedia.org/wiki/Republic_of_Venice). Like many political appointments, it was often held by [noblemen](https://en.wikipedia.org/wiki/Venetian_nobility "Venetian nobility") as a stage in their career, usually for a few years.” %% **Extended abstract:** If one were to summarize the impetus driving contemporary machine learning (ML) and 20th/21st century science under one heading, it would be difficult to argue against this being “prediction.” Vernacular conceptualizations of prediction have a long history: as premonition, providence, prophecy, divination, fate, destiny, etc. In comparison, its scientific conceptualization is more recent. From early cryptologist al-Kindi (801–873), through Pascal and Fermat, to today: measuring/tracking phenomena in terms of their predictability indices (by way of statistical inference and probability) is now a prominent conceptual tool spanning all scientific-modeling. In recent decades, the concept has become even more prominent by the hand of so-called artificially-intelligent technologies, many of them potentially useful for augmenting humanity’s capacity to risk-assess impending (sociopolitical, technoclimatological, etc.) crises. Given the increasing implementation of ML-techniques across scientific-modeling (Buijsman 2023), it will be argued here that a technical-philosophical conceptualization of “prediction” in the broadest sense (Sellars 1962), is needed for clarifying the driving factors behind the construction of the future.  Following predictive processing proposals (Parr, Pezzulo & Friston 2022, Clark 2023), the concept of prediction will thus be treated in the amplest sense, elucidating the generative; future-oriented functions of concepts such as _(meta)learning_: heuristics for problem-solving under uncertainty; _reliability_: striving for certainty, _causal inference_: (inventing) predictably tractable models, _robustness_: predictable foundations, _explanation_: dialogically verifying models, _trust_ and _transparency_: ensuring collaborative predictability and tractability, and _understanding_ (see _explanation_). If, roughly put, “[a]n explanation adds information to an agent’s knowledge” (Halpern & Pearl 2008), and knowledge (as a _generative model_, Parr et al 2022) and explanation (as _dialogical verification_, Dutilh Novaes 2022) are inseparable from understanding (as the process capable of revealing the _pragmatic intelligibility_ of scientific theory, de Regt 2019) then grounding these concepts under the heading of _**prediction**_ seems to provide a parsimonious vantage point from which to assess the implications of ML in scientific-modeling. However, this is where we reach an impasse. The predictive limit-point modeling seems to have reached is that, on the one hand: we have amassed complex data landscapes which can be probed for _known-unknowns_ (how to get a robot to spatiotemporally orient itself, how to predict the next word in a sentence, how to produce an image based on instructions, etc.) and on the other: many of the complex problems (probabilistic) modeling would provide meaningful answers to are intractable (van Rooij et al 2023) and/or unexplainable (Buijsman 2023). At the same time, we seem to demand the prediction-production of _unknown-unknowns_ from ML technologies (how to produce an ‘unexpected’ cultural product, how to assess black-swan scenarios, how to reveal aspects of protein folding we do not yet understand, etc.). Scientific modeling-crises reflect the impasse: from Ioannidis’ exposure (2005) that the majority of behavioral models cannot be replicated; to the translation between non-human/human animal models in pharmacology; to the metaphysical implications of modeling fluidity with the assistance of software. We thus demand predictability when we know for a _fact_ that we are subject to modeling irreducible aspects of uncertainty: this makes ML subject to high suspicion in any of its claims to model the future. Indeed: the line between suggestive/approximate modeling and self-fulfilling prophecy becomes blurred. This problem is nothing new. In philosophy, these frictions have been brought to our attention by D. Hume (induction), Hegel (who thinks abstractly?), W. James (vicious abstractionism), Nishida (subject/object), A. N. Whitehead (misplaced concreteness), G. Box (models), A. Korzybski (map-territory), and many more (Borges, Wittgenstein, Bateson, McLuhan, Ashby, the list goes on). Essentially, the gripe being a problem of the instantiation(s) of predictive legitimacy: the smoothing or generalizing tendency inherent in abstractions is a problem _vis a vis_ the evolving universe, try as we might: no event occurs twice (Heraclitus). In thinking about the current paradigm, where ML-assisted predictions imply the production of the future, not just its interpretation, this is where we ought to start thinking about the abstracting, predictive possibilities afforded by ML. Entertaining high speculation: what would it mean to expect of “AI” that it ‘understands’ this philosophical fallacy and/or predictive meta-concept of world-model frictions? In this article the proposal is made that it is at this limit-point that the concept of prediction, by becoming deconstructed and reconstructed again in a new light, might reveal something about the interesting desire for the unpredictable that seems to be inherent to the project of “AI,” and more widely: the social, historical project of cognition in general. _Article available on demand, different versions exist. Please [[ｃｏｎｔａｃｔ]] me to get a copy._ ### References <small>Box, George. “Science and statistics”. Journal of American Statistical Association 71(356), 791–799, 1976. Buijsman, Stefan. "Causal scientific explanations from machine learning." Synthese 202.6 (2023): 202. Broemeling, L. D. (2011). An Account of Early Statistical Inference in Arab Cryptology. The American Statistician, 65(4), 255–257. https://doi.org/10.1198/tas.2011.10191 Clark, Andy. The experience machine: how our minds predict and shape reality. Pantheon, 2023. de Regt, Henk W. Understanding Scientific Understanding, Oxford University Press, 2017. Dutilh Novaes, Catarina. The Dialogical Roots of Deduction: Historical, Cognitive, and Philosophical Perspectives on Reasoning, Cambridge University Press, 2020. Halpern, Joseph Y., and Judea Pearl. "Causes and explanations: A structural-model approach. Part I: Causes." The British journal for the philosophy of science, 2005.   Hegel, G. W. F. “Who Thinks Abstractly?” in Hegel: Texts and Commentary, tr. Walter Kaufmann, Garden City N.Y, Anchor Books, 113–18, 1966. Ioannidis, John P. A. “Why most published research findings are false.” _PLoS medicine_ 2.8: e124, 2005. Lange, Marc. “Hume and the Problem of Induction.” Handbook of the History of Logic. Vol. 10. North-Holland, 43-91, 2011. Murata, Yasuto. ““Fallacy of Misplaced Concreteness” and the Reality for the Sciences.” Realism for Social Sciences: A Translational Approach to Methodology. Singapore: Springer Nature Singapore, 2023. 67-81. Nishida Kitarō, Complete Works of Nishida Kitarō, Edition 2002–09, edited by A. Takeda, K. Riesenhueber, K. Kosaka & M. Fujita, Tokyo: Iwanami Shoten, 2009. Parr, Thomas, Giovanni Pezzulo, and Karl J. Friston. Active inference: the free energy principle in mind, brain, and behavior. MIT Press, 2022. Peng, Grace CY, et al. “Multiscale modeling meets machine learning: What can we learn?” Archives of Computational Methods in Engineering 28: 1017-1037, 2021. Sellars, Wilfrid. “Philosophy and the scientific image of man.” Frontiers of science and philosophy 1 (1962): 35-78. van Rooij, Iris, et al. “Reclaiming AI as a theoretical tool for cognitive science.” Psyarxiv, 2023. Winther, Rasmus Grønfeldt. “James and Dewey on abstraction.” The Pluralist 9.2 (2014): 1-28.</small> %% “the cognitive and social functions of narrative that emphasizes that narratives are _for the future_—even when they are focused on recollecting or recounting the past.” (Bouizegarene, Nabil, et al. 2024). We argue this for all semantic artifacts (as in products _and_ their noisy effects) Each word as a predictive strategy "The" designates a certain realm "Designates" - predicts "a" - limits the prediction again, into a specific realm "Certain" - makes it more specific, narrows it down "realm" - not narrow at all, too general Unconditional love: ultimate prediction: acceptance as is, also zen-mode Generativity Eleanor drage Nick chater: it's no surprise that languages share similar structures, Zf: because they reduce different uncertainties, depending on the type of environmental affordances. Sure the Greek gods reduce uncertainty, because we can model contingent behavior on the basis of a model we already know: a petty human. Sure money, real abstraction (Sohn Rethel) is a structure of uncertainty reduction Free will even, is an uncertainty reduction strategy to map the simulation that is the self forward, in predictive coherence Creativity is the name we give to negintelligibile things So is complexity, noise Nick Chater: language is specific first and general later Zf tends-Tries to reduce uncertainty Is thinking abstraction? Yes. Is abstraction prediction? Yes? Is Prediction just procreational? Maybe To end Prediction on a positive note: maybe the delayed gratification inherent to AI is also what drives it The geistig condition of being bound to things that persist beyond one Whatever the size, shape or nature of the progeny? One would guess so as people take the risk to have children every day Look into the theodicy and reflect on the idea of a god who makes choices but having already seen what is possible. Also Voltaire on this ____ For error and prediction, best formulation: “The strategy of prediction error minimization can lead to a major economy in information processing because sensations that were accurately predicted need not be further processed. In addition, the prediction errors are used to constantly improve the generative model in an iterative process. Of course, the perfect model can never be obtained, both because we live in dynamic, ever-changing environments and because we never have complete access to our environment through our senses. Thus, the current best model is not the “true” representation of the environment, but the one that yields the least prediction error relative to one’s adaptive goals or necessities.” (Bouizegarene, Nabil, et al. 2024). Which means: the modulation of possibilities. _____ Sameness: coarse graining in organisms versus computers. Computers are teleological, organisms are.open-ended ________ “Voordat ik een fout maak, maak ik die fout niet.” (J. Cruijff) Friston mortal computation: https://arxiv.org/pdf/2311.09589 use also alex: the future of copulation: https://www.glass-bead.org/article/shattering-of-the-vessels/?lang=enview add this Our central argument is that if the aim is to craft mechanistic, computational systems capable of the vast array of animal or human-like behaviors, the thrust of machine intelligence will need to change over the coming decades, similar in spirit to the call for a shift from symbolic to non-symbolic processing [107, 106, 26], which motivated classical connectionism and statistical learning. Machine intelligence research may need to focus on the processes that realize its nonlinear self-organization and efficient adaptation; such a move is towards the development of survival-oriented processing that embodies computational notions of life/mortality, a sort of naturalistic machine intelligence. Specifically, we argue that a crucial pathway — on the way to artificial general intelligence (AGI) – lies in mortal computing, a framework that we seek to develop in this article, hoping to engage researchers pursuing the sciences of the artificial [311]. ... In contrast, mortal computation means that once the hardware medium upon which a program is implemented within fails, or ‘dies’, the knowledge, behavior, and specific functionality, including its ‘quirks’, will also cease to exist. This is much akin to what would happen to the knowledge/behavior acquired/developed by a biological organism once it is no longer able to maintain itself. We argue that, at least for AGI, it may be necessary to consider mortal computation, if not entirely abandoning immortal computation. ... _intelligent behavior, whether manifest in biological or artificial form, is inextricably intertwined with persistence in the face of an environment that could bring about its end._ and compare to Yuk’s organicism claims dont forget to add to dutch version and this one: https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(23)00260-7?dgcid=raven_jbs_etoc_email something to add for sure: assisted search is doomed to fail if, precisely, what we are doing when searching is not exactly knowing what it is we will find. this is, again, not in uncritical praise of “chance encounters” and contingency, but definitely a call to awareness about: we do not know what we want, and claiming we do is fascism. Interesting idea about communicating across large distances: To play communication across very large distances one thing we might want to do is predict what the answer can be, and keep on sending messages, the other side does the same, and then hilarity and chaos ensues. Among all these answers are interesting new discoveries Light speed is the constraint, script is the prediction. If you can counteract, project, speculate: you'll be less surprised if it comes around If you can counteract, project, speculate: you'll be less surprised if it comes around Which is what makes cushy positions like philosophy PhDs unfair, because we can plan ourselves further than the rest We are out of phase with the collective Control is a particular trend or fashion we are currently undergoing in the engagement with the universe. The idea of partition between a system and another is part of the dislodging of this trend of control towards something else, but it's still not enough We can observe this tendency towards understanding intelligence as distributed and as belonging to things other than human beings. Kind of the kind of expanding of consciousness that would give rise to accepting versus fighting reality Let's start by saying this: things/predictions that have been said/made in the past have been wrong. This is this not in the spirit of producing an accurate prediction or diagnosis of the present times This is in the spirit of understanding what it means to think in terms of all these Diagnosis, opinions, etc, being predictions **{term}** {definition} # What is the future? Prophecy, providence, preference, pattern, planning, persistence, procreation: prediction. The concept of prediction has a long (i.e., _salient_ and _complex_) short-history. From its belligerent cybernetic origins in the realms of encryption and control, to the evolving paradigms of experiment-replication, translation and modeling across all sciences, to the challenging criticisms of the _planning_ zeitgeist (e.g., Agre, or as bounded rationality in Simon), all the way to current developments in cognitive theories of predictive coding/processing. In the recent decades _prediction_ has certainly become even more prominent by the hand of so-called artificially intelligent technologies, and the impending crises (sociopolitical, technoclimatological, etc.) which demand predictive risk assessment. The interesting limit-point we seem to have reached in the past few years is that, on the one hand, we have amassed complex data landscapes which are predictively probed for known-unknowns (how to get a robot to spatiotemporally direct itself, how to predict the next word in a sentence, how to produce an image based on instructions, etc.) and on the other: both the scientific and the commercial audiences seem to demand unknown-unknowns from AI technologies (how to produce an ‘unexpected’/‘surprising’ cultural product, how to interpret and produce ‘human-like’/‘creative’ texts, how to reveal aspects of protein folding we do not yet understand, etc.). In this article the proposal is made that it is at this limit-point that the concept of _prediction_, by becoming deconstructed and reconstructed again in a new light, might reveal something about the interesting desire for the _unpredictable_ that seems to be inherent to the project of “AI,” and more widely: the historical project of cognition in general. If prediction is understood as a basic, unavoidable ‘schematic’ operation of perception-cognition-action, it might become easier to reveal some of the challenges facing AI and our relationship to its development. As a conclusion we reach an untempered explore-exploit: the future as perceived through AI is as cornucopial as a beacon of abstraction, and as concrete as mindless copulous procreation. ### Introduction The concept of prediction has a long short-history. Long in the sense that much has transcurred since its semi-recent origins in the literal and metaphorical trenches of WWII. It is always worthwhile repeating how cybernetics, the science of communication and control, as well as encryption, the practice of (re/de)coding, have been driven almost exclusively by the desire to develop enemy-outsmarting and obliterating war strategies (Galison 1994, Bucher 2018, Farocki 1989). The very concept of a “black-box”, i.e.: an object enjoying a high degree of encoded unpredictability, literally refers to “a physical black box that contained war machinery and radar equipment during World War II.” (Bucher 2018, p. 42).[^1] In our current AI-obsessed context the metaphor of the black box is pervasive and it, too, _camouflages_ its much-indebted war origins. War, abstracted as two or more entities strategically predicting their control over resources, is a well-known novelty-producing imitation game. Similarly long (complex) and short (very recent), (crises of) replication and modeling across all sciences currently reflect a drive towards tempering problems of prediction: from the radical shift brought about by Ioannidis’ exposure (2005) that a majority of behavioral science cannot be predictively replicated (about 60% of all psychology studies, for example, according to Nosek et al. 2015); to the conundrum of how to accurately translate between non-human animal and human-animal models in, e.g., pharmacology (bringing into question the _relevance_ and _legitimacy_ of these studies #todo ); all the way to the metaphysical implications of modeling physical phenomena such as fluidity with the assistance of software: here, the reliability of the resemblance between the modeled and the model will necessarily be out of phase because computational models are useful because they _approximate_, but can currently not _imitate_, emergent phenomena such as fluidity. In philosophy, these frictions have been brought to our attention by D. Hume (induction), Hegel (_who_ thinks abstractly?), W. James (vicious abstraction), Nishida (subject/object), A. N. Whitehead (misplaced concreteness), G. Box (models), A. Korzybski (map-territory), and many more (Borges, Wittgenstein, Bateson, McLuhan, the list goes on and on). Essentially, the gripe being a problem of the instantiation(s) of predictive _legitimacy_: the smoothing or generalizing tendency inherent in abstractions is a problem _vis a vis_ the dynamically evolving universe, try as we might: no event occurs _twice_ (Heraclitus). In thinking about the current paradigm, where AI prediction implies the _production_ of reality, not just its interpretation, this where we might start thinking about the abstracting, predictive possibilities afforded by AI. What would it mean to expect of AI, if that is what we are after, that it doesn’t replicate this common fallacy? Or that it understands the meta-concept of world-model frictions? And, more importantly, is it possible to think _at all_ without committing (to) it? To think is to forget (to _abstract_: Lao Tse, Kant, Nietzsche, Borges) and this is something so-called AI does pretty well, albeit in a manner different from that of “humans”, which alienates us.[^11] It is well-known that the predictive capacities of AI radically differ from those “humans”.[^3] This is not “bad”—counter the usual knee-jerk reaction—this is precisely one of the main reasons we are interested in predictive AI outputs: because the amount of data that can be parsed, the ways in which it can be compressed and reinterpreted, is of scales which are not even close to conceivable by mortals. The logic of this extended-mind scenario goes back to logarithm tables and (mechanical) calculators of all kinds, revealing the allocation of memory and planning and/or predictive capacity to objects of a character _other_ than human. In the context of cybernetics, W. B. Ashby’s law of requisite variety, intended to formulate an approach to the metrics of this capacity to deal with world-model frictions: in order for a system to achieve the predictability necessary for its own persistence, it should match or surpass the contingency or variety afforded by its target environment, whatever it is that it is afforded _within_.[^10] More recently, in the context of late-early AI, Phil Agre’s criticisms of planning (e.g., “What are plans for?” with D. Chapman) argued against the installed AI-research drive which viewed planning as distinctly formulating a script of future actions in advance. Plans, instead, should be able to afford more if they are conceived of as possibilities for behavior which can be modified as a situation changes. In a completely different vein, _bounded rationality_ (Simon), is a (still much-too-rationalistic) acknowledgement of the impossibility of any _absolute_ prediction. Absolute prediction is essentially (heat) death (or at the very least: a very dark room), as we will see when discussing predictive processing below. While all of this can be taken to signal a dominating teleological bias in Western technoscience, it also signals, for the purposes of this article, an encounter with what seems to be a productive approach to cognition in general. It cannot be denied that we are creatures constrained by spacetime, and that a very salient aspect of our embedded, embodied, extended, etc. nature can be pooled within the the concept of prediction: carnal sentience as well as distanced thought. Als we het voorspellen definiëren als: adaptief leren van complexe hierarchieën van wereld-model fricties, en op basis van deze zeer dynamisch input dus blijven bestaan, valt het wel moeilijk te ontkennen dat: als we wezens zijn die begrensd worden door ruimtetijd, een zeer saillant aspect van onze ingebedde, belichaamde, uitgebreide, enz. aard samengebracht kan worden in het concept van _voorspelling_: zowel vleselijke gewaarwording als afstandelijk _armchair_-denken. Is _prediction_ a sociopolitically problematic concept because it confronts us with the permanent catastrophe that is the rehearsal of past problems (biases, assumptions, ignorances, legitimations, etc.)? Yes. Coming to terms with it might be better than hiding our heads in the sand (a.k.a., again, the _dark room_). As Marshall McLuhan desires: “Examination of the origin and development of the individual extensions of man should be preceded by a look at some general aspects of the media, or extensions of man, beginning with the never-explained numbness that each extension brings about in the individual and society.” (1964, p. 8). This numbness is inevitable, precisely because we are relegating predictive capacities to sensors beyond our bodily sensing. Two years earlier, in The Gutenberg Galaxy, he writes: “This externalization of our senses creates what de Chardin calls the “noosphere” or a technological brain for the world. Instead of tending towards a vast Alexandrian library the world has become a computer, an electronic brain, exactly as in an infantile piece of science fiction.” (p. 32). The only point of disagreement with us would be that these pieces of fiction are, actually, rather mature, they are the forward-projection of an insatiable will to persevere that extends the mind beyond its own borders, beyond what the mind itself even conceives as possible for the mind (see also: [[Negintelligibility]]). ### Predictive processing In the recent decades, the field of predictive processing, (led, citation-wise, by K. Friston, A. Clark & J. Howhy) has made significant advances in signalling the predictive dimensions of cognition. While it has not provided a formal definition of prediction beyond the logic[^5] by which it ensues,[^6] it has constructed some important abstractions that allow for an understanding of the computing of predictions across different agents (Clark 2016). Crucially, prediction does not mean planning in the sense Agre criticized: conscious organizing, judging and executing. Predictions begin at the given capacities (and thus preferences; biases) of an agent-environment coupling. Because of this, learning/adaptation is framed in terms of the parsing of changes—perhaps too hastily termed “errors” in PP, though error-minimization is also understood in the terminology of _surprise_, which is more felicitous.[^17] If an agent expects (predicts) to be homeostatic in terms of x, y or z, it will seek to actualize its preferences by advancing towards x, y, z (thereby effectuating changes in its context) or changing what it houses within itself as x, y, z (its model) on the basis of a stubborn environment which won’t comply. The reframing of perception-action under active inference we are interested here is the turning of the tables: senses do not make experience (traditional view of agents), experience is what seeks to _make sense._ The main question that active inference, the process by which predictive processing ensues, is: “How do living organisms persist while engaging in adaptive exchanges with their environment?” (Parr, Pezzulo, Friston, 2022, p. 3). Much like the phenomenon now known as reward-hacking in AI, PP emphasizes that an agent has no “reason” to accurately match up with the contingencies it encounters, because following the acceptance of witnessing existence as the result of the search-space of evolution, what is witnessed is the survival of that which has been successful in terms of reproductive persistence. The evolved “generative model” of any existing ‘living’ thing has, we must accept, persisted because it made it out the other end as a new creature.[^4] In order to exist (i.e., persist in spacetime), a system “explains” its states to itself, on the basis of what it senses as its non-self, the outside.[^8] Simplifying things to the level of a unicellular organism, it could hardly be denied that a minimal capacity to move towards and away from preferred states is what can be defined as a common denominator among all systems which persist in the face of contingency. Up the complexity a notch and you get counterfactuals/planning/reason/the simulation of a _self_: _what if we did this? What happens when…?_ Etc. This is why PP frames this phenomenon as a _generative_ model. Senses, given by whatever it is that a system is (a bacterium, a bat, a society) effectuate the possibility to _prefer_, and given these preferences an evolving (across generations or within just one) then effectuates its ever-changing generative model. Crucially: this model cannot but be probabilistic: there is never 100% certainty against a contingent reality, and whatever is sensed needs to be contemplated and explained. It’s certainly more powerful than a statistical model because it’s causal: the _correlations_ of data are explained but also the _causes_ (Da Costa). The causes may be creative, misguided inventions, but they frame what the (group of) agent(s) prefer(s) as _coherence_. Please note, that despite all the criticisms, nobody in PP claims that there is anything “correct” or “accurate” being predicted here (Parr et al. 2022, p. 22). _Prediction_ is simply the tempering texture, the _shape_ of probability,[^18] of an agent-environment coupling, where we (humans) tend to prefer to see the agent as having legitimate conceptual primacy over the environment in terms of persistence. Cognition (“the brain”, aldus Clark) is “adept at finding efficient embodied solutions that make the most of body and world” (Clark 2016, p. 300). _Voorspelling_ is onder PP de ruimtetijd-temperende textuur van een koppeling tussen agent en umwelt. Voospellende cognitie is "bedreven in het vinden van efficiënte belichaamde oplossingen die het beste halen uit lichaam en wereld" (Clark 2015, p. 300, mijn vertaling). Wat “het beste” is, in de context van AI, is natuurlijk de vraag. ### The pitfalls of lone wolves Countering brain- (and machine-)centric accounts of PP cognition, in “The Anticipating Brain is not a Scientist,” Bruineberg et al suggest a theoretical reframing of the free-energy principle as operating within loci such as singled out brains or agents. Friston’s free-energy principle proposes biological self-organization in information-theoretic terms: as hierarchical layers of Bayesian filtering: explain more #todo (https://europepmc.org/article/MED/30996493#CR32). “[T]he free-energy principle is consistent with many other accounts of self-organizing (living) systems such as synergetics (Tschacher and Haken 2007), global brain dynamics (Freeman 1987, 2000), metastability (Kelso 2012; Rabinovich et al. 2008), autopoiesis (Maturana and Varela 1980; but see Kirchhoff (2016)) and ecological psychology (Gibson 1979).” (Bruineberg et al). In active inference, the “organization and dynamics of living systems prefigures the organization and dynamics of cognitive systems ... [maintaining organic persistence is] the basis for [more abstract] complex capacities such as social cognition, cognitive control and language use” (Bruineberg et al). Because of this cross-scale intermeshing, Bruineberg et al, in an _enactivist_ reproach, suggest that the free-energy principle applies to the entire animal-environment coupling, not just the brain. Active inference, as understood by the free-energy principle, is incompatible with (Helmholtzian) unconscious inference understood as analogous to scientific hypothesis-testing. The FEP is better understood in ecological and enactive terms. The agent-organism coupling is thus ... #todo. In the paper, the authors are after showing that “the free-energy principle applies to a biological system in terms of the dynamic coupling of the organism with its environment”, they find Friston overstretches the concept of _inference_ (which, “within the Helmholtzian theory, ... is standardly understood as a probabilistic relation between prior beliefs, current evidence and posterior beliefs” ibid.). If the FEP applies to all persisting life, it should perhaps “be given a deflationary interpretation” which the authors suggest should be done “using concepts from dynamical systems theory.” Thus, if the FEP constrains Bayesian active inference, this “provides no support for a Helmholtzian inferential theory of perception. ” — this might also be, we suggest, because the minimization of free energy cannot be assumed to be the chief guiding parameter: sometimes agents maximize (_overheat_) in order to achieve a, b, or c. This idea runs in accordance with the proposal that the reason GPT-generativity is uninteresting because it is in the business of reducing entropy, and cannot produce great novelty, as it bottoms out at highly expected structures (ref.). Expectation (the generative model) meets reality somewhere between the _sensed-and-produced_ features of an input signal, whatever is preferred will lead to the suppression or salience of certain aspects.[^7] What “clashes” with expectations can be understood to change the model or lead to things like confirmation bias: _seeing what you want to see_ (which is much of what we do, all the time, or we couldn’t cope). The focus on this latter mechanism of “error-minimization” is one of the things that often distances critics of PP from its claims (as well as the use of technowords such as “agent”, “system”, etc.). Because an agent/system maintains its basic model-homestasis by insisting to itself it exists, the only thing that needs to be communicated up the predictive scale is the prediction _error_, “making it a kind of proxy ([Feldman and Friston, 2010](https://www.frontiersin.org/articles/10.3389/fpsyg.2014.01111/full#B29)) for sensory information itself.” (Rhythm and PP paper). But reframing the malemployed term “error” as signaling something functional (see e.g. Malaspina, Wilkins, Pasquinelli), what we actually should understand PP to be saying is not “_optimization_!” but rather: degrees of openness to contingency (i.e., _learning_), as many different ones as there are organisms on the planet. It is a highly perspectivist position, and follows distributed cognition in the style of enactivism, too (if we follow authors such as Bruineberg et al.). This can be put on parallel with the history of modelling/planning criticisms (Hegel, James, Whitehead, Korzibsky, Box, Agre, etc.): openness to radical contingency (i.e., entropy and ensuing complexity) means more learning. Ambiguities of all sorts _increase_ information gain, as we know. The problem is: when to stop gaining information, which is a matter of tempering scales and speeds. In terms of scales and speeds: we should note that we can go as concrete or as metaphysical as we like with prediction, and that in this article we are surfing the generalist surface (_sorry_): embodied, embedded, etc. prediction as _the capacity to persist, by latching onto the repetition-creation of patterns_. This means, for our purposes, that there is a common denominator underlying the observation that hydrogen remains hydrogen every time we observe it, in different ways, but also, perhaps: hydrogen itself is a predictive capacity of the universe (in the Whiteheadian sense). If something has the capacity to become a universal habit/general constraint: then it is a memory (at least as we perceive it!), with a noticeable _tendency_ towards the future. Remember, again: prediction is not determination, but creative approximation. It is the taking of a risk (exploring), joining patterns established in the past towards possible patterns in the future (exploiting).[^9] As you walk you set the next foot forward and incline yourself in the direction you estimate as current your gravitational model(s). As you listen to music you enjoy the (un)predictability provided by the extension of yourself towards a (group of) musicking other(s), you externalize into uncountable wavelengths extended into the environment, and many more things. All of these things are predictive: without prediction there is no coupling to the environment. Importantly: agents can only distribute themselves outward and communally, if they are capable of latching onto the evolving universe’s memory (else they die or get stuck in a dark room).[^12] In what follows we will continue to trace how this ties in with the drive for AI to predict the unpredictable (or the desire for the _unknown-unknown_). ### Exploring and exploiting An interesting tempering aspect of predictive praxis is that when something is too predictable we might not like it, and the same of course goes for what whatever we consider too unpredictable. This _Goldilocks dialectic_ occurs at all scales and in all processes, and there seems to be no general pattern as to when and where we will like unpredictability and predictability, unless we gather data over specific cases, such as in the case of slot-machines and other perhaps more intimately familiar _interact-to-refresh_ technologies such as your email interface or apps on your mobile phone. In the latter case large-scale predictive systems such as the nexus between venture capital, visionary Silicon Valley tech bros and an increasingly consuming society, all collide in a _perpetuum mobile_ which predicts itself towards the situation we now find ourselves in. Research in the mechanics of exploration-exploitation shows that we (organic evolvers with a certain brain structures) are highly attuned to reward (finding what you expected) and punishment (error signals, learning) cues (Melhorn et al., 2015, Cogliati Dezza et al. 2017). Exploration being the (evolutive, biological, predictive) strategy which seeks novelty, exploitation being the strategy which latches onto expected-produced patterns. These “strategies” are of course not mutually exclusive, and always function together, on different model-hierarchical scales. But, inevitably, we arrive again at the question of tempering explore-exploit balances. In the recent (2023) book _Technological Accidents, Accidental Technologies_, Sjoerd van Tuinen poses the question: “… {are} the events that {qualify as “technological accidents”} rather forms of functionality, which may be undesired, but not “dysfunctional” in the way that the derailment of a train appears dysfunctional?” What this question essentially asks is (at core one of **form and/or/as function**, but on its political skin it reflects): should risk or risk-avoidance be a driving target? The confusion between these two is littered over the entire biopsychosociopolitical landscape in the form of explore-exploit relationships between beings and their worlds: risk both drives and hinders speculative research, _no pain no gain_ logics of the everyday, dark room considerations in PP, etc. Tempering prediction is a collective project, but collective _alignment_ is anything but possible nor desirable. Consensus (as a very general concept which can include agreement, mutual understanding, etc.) can be said to be assumed exist across our species because of a certain symmetrical _and_ imitative pattern we are able to intuit:[^13] you look like me and therefore _probably_ think like me (Kripkenstein, though). But, as life teaches us, no two things could be more different than two perspectives, however similar. Currently in the guise of “AI,” predictive technologies have been around forever (as mentioned earlier). Insisting on our point: all technology can be said to be driven by prediction, as technology not only extends predictive human capacities, but technology is built to make things persist: fire to keep the homestasis of warmth, as does shelter, a chair the persistence of a certain posture, a book: memory, speech; a factory: repetitive gestures and products, etc. These ‘algorithms’ have grown into abstractions in the current age, as software, which has exploded exponentially in implementations with ever-more computational power. From the early days of cybernetics and planning to generative LLMs, we observe the integration of organic predictive capacities into the persistence project of creating systems that can operate independently, like organic beings, and are able to couple to their environment by predicting it. The question is: regardless of their material basis, are e.g., LLMs and humans similar types of combinatorial machines? Besides scale and speed, is one of the main functional differences the fact that human systems are (inevitably and radically) open to (functional) entropy, and often actively seek to increase it, while LLMs are actually in the business of reducing it? _The basic constraint of entropy_: #todo “To arrive at predictions, brains require something on which these predictions can be based—predictive systems require _constraints_ on the set of prior probabilities and likelihoods that should be taken into account as they finalize and settle upon a set of predictions for any given sensory-neural situation (Friston, 2003; Kemp et al., 2007; Tenenbaum et al., 2011; Blokpoel et al., 2012; Clark, 2013; Hohwy, 2013). Without such constraints, it is impossible for any intelligent system to narrow down the possibilities enough to settle on a single hypothesis or set of hypotheses (Russell and Norvig, 2010; Tenenbaum et al., 2011; Blokpoel et al., 2012)." ([[The Predictive Processing Paradigm Has Roots in Kant]], p. 5.). But entropy-minimization is not necessarily... Thinking back on Ashby’s requisite variety: if, informally, the capacity or otherwise _complexity_ of a system can be considered in terms of a metric that measures the amount of processing (_scale_: e.g., Kolmogorov, or also scale but more relative _speed_ in spacetime: e.g., computational irreducibility)[^15] needed to generate the system, then, as creatures who often seek to outlive ourselves (in progeny, in books, in AI), we are bound to find ourselves inclined towards interactions with the incommensurable: the weird, the alien, etc. This is what provides novelty in the search space for _more_. Much of what we produce as unfolding emergent events—socioeconomic crises, large corporate projects, dominating historical narratives—can be considered systemic results of these explore-exploit tendencies looped forward, beyond our capacity to tame or contemplate their unfolding. If this sounds similar to producing progeny, that is because it is. ### Alignment, imitation, sex and progeny >“Machine intelligence research may need to focus on the processes that realize its nonlinear self-organization and efficient adaptation; such a move is towards the development of survival-oriented processing that embodies computational notions of life/mortality, a sort of naturalistic machine intelligence.” Ororbia and Friston, “Mortal Computation: A Foundation for Biomimetic Intelligence,” 2023. The paradigmatic 20th century example/case of _complex prediction_ as contemplated through the lens of something _other_ than humans, is the Turing test (1950). The testing idea behind it being that _we_, humans, should (often) be able to predict whether we are speaking with a man or woman (the premise of the so-called _imitation game_ Turing was inspired by): what is being tested is the human interactor’s response to a situation, not the “imitative” capacities of the possible human-simulation #todo, we dialogically engage, like w/ ELIZA (Diane Crawfoot? And Coeckelberg: relational approaches to intelligence). This reflects, among many other things, the implication that one of the most compelling things happening in language is that it provides a predictive framework which establishes the possibility of (symmetrical, imitative) _recognition_ between those capable of using it, even when they are not physically present (in Derrida’s terminology: it is _iterable_). Of course, it also allows for the predictive exploration/exploitation of their possible asymmetries: ensuing from classifying concepts such as _gender_. One possible undercurrent that may be read into this is, precisely, the tension presented by focusing on aspects of sexual reproduction which result in _progeny_ (the perpetuation of the _thing_ in question). In “Can thought go without a body?” (1988), F. Lyotard muses on the possibility of thought going on should the Sun cease to exist (which it will), and questions the ‘positivist’ or affirmative image of thought by focusing on the labor of thought: “The unthought hurts ... we’re comfortable in what’s already thought. And thinking, which is accepting this discomfort ... [means] the unthought would have to make your machines uncomfortable ... to make their memory suffer. ... Otherwise why would they ever _start_ thinking?” (pp. 82-82). We absolutely agree with Lyotard here, if we frame things in terms of prediction: complex prediction is hard (high-exploration is energetically costly), thinking (learning) is exactly this, and to think machines that think they need preferred states in order to navigate-predict-effectuate their possible persistence. In effect, this is what we have more or less built so far, as AI: dynamic difference-sensitive engines. Lyotard goes on to suggest that this suffering-driving desire bottoms out at sexual difference in human beings, and machines which outlive the Sun should therefore be gendered, somehow: “Your thinking machines will have to be nourished not just on radiation but on irremediable gender difference.” (ibid., p. 86) So much for the end of _grand narratives_. Not a dissimilar intuition as that from Turing’s other conclusion in CMI (1950), that something like a child would actually be what we’re talking about when we want something that learns and grows as we speculatively demand of a possible thinking machine. Can we be so crass and speculate that _procreation_ (repetitive evolution at different scales) lurks behind every attempt of/at prediction?[^16] That libidinal cosmic desire grounds thought? Yes. No. Maybe. The current trends in generative AI do reflect this (not only in the realm of porn). The thing is that the “one-agent” singular being, male or female, whatever it is we are talking about, is already a cacophony of differentiations within itself. So why gender? (This question seems timely as we live in a moment during which gender has become a highly questioned classifier, one with a very boring organizing logic). It seems an interesting category to question if what we aim to predict is the _form and/or/as functionality_ of future beings, by how they might combine their distributed capacity to _differ_ from each other. Though we have been moving, as well, towards ideas of “swarm intelligence”, slime intelligence, etc., bringing attention to the emergent power of the masses. And by now we know the singular human agent is necessarily a simulation of itself, because it abstracts an apparently linear self-narrative from billions of inputs (imagine having permanent conscious access to all the states of your cells, including gut flora, etc.). The same with a group of agents: the narratives here being habits such as gender, history, ideology, etc. In current times it might be more fun to say that in order to _think_, it is not this exploration-exploitation of difference-similarity dialectic through sex/gender that we are always after, it is rather perspectival communism that learns how to die (because heat death _is_ coming). But not all will vibe with this, which is precisely the point if we want combinatorial variety. To return to the perpetuation of whatever it is that is going on: perhaps the _creabilia bias_ of creativity as fertility, as procreation, also has the unconscious pitfall of thinking of the alien-creative in AI as a species so specialized it cannot procreate (if the _genus_ is what would give the _general_ to AGI, and species procreatively specialize, perhaps what we are scared of is that misaligned AI will be a _mule_). This _Frankenstein syndrome_-like (Falk 2021) misalignment fear is certainly the repressed fantasy of doomsayers: it will be different from us (so much so that we can’t even have sex with it).[^14] Relatedly, at least in terms of language as one of the current AI foci: thought effectuated through language coheres with other thought effectuated through language because there’s procreative compatibility, fertility _in_ language (iterability). This is the reason we (seem to) share symbols, habits, thinkers, etc., as dynamically changing but temporarily grounding points of reference. When something falls beyond the scope of linguistic intelligibility, while it may be incredibly interesting—legible, useful, valid, etc.—it is often a mule (e.g., _Finnegans Wake_).^[M. McLuhan thought of Joyce as the writer of “the most luminous analogical order for the unique experience of that age.” McLuhan notes, on _Finnegans Wake_, that this is the type of work he considered his own books as relevant to, in terms of providing a creative learning window. McLuhan’s own books should “enable a man to read and enjoy Finnegans Wake.” (Cited in Chrystall 2008, p. 103). McLuhan, known to be a prolific media predictor, predicted the wonderfully contrived, inaccessible uniqueness of the Joycean mule.] LLMs enjoy this closed-universe specialization, too, they are not only hermetic but also difficult to interact with (however convincing the chatbot interface). However, we should not forget that, as we are increasingly having (linguistic) sex with robots and enjoy AI-generated porn, much in the same way that you will know a word by the company it keeps (Firth 1957, Derrida 1978 in Salmon 2020), we may be turning ourselves into mules, slowly but surely. It is perhaps this that Lyotard feared. “God is dead, and yet metaphysics continues.” (Salmon 2020, p. 111). ### Limit/aporia/catastrophe point But if we desire novelty as radical unpredictability (again: tempered by prior preferences which are way beyond our access: the universal habits which led us here): in what ways do we desire it? We can assess different circumstances: unpredictability in times of foraging for food, all the way to the reward mechanisms of slot-machines and mobile phones, to the contemplation of protein-folding and back. The interesting limit or catastrophe point we seem to have reached in the past few years/decades is that, on the one hand: we have amassed complex data landscapes which are effectively predictively probed for _known-unknowns_ (we tend to call this science) but on the other: both the scientific and the commercial audiences seem to demand _unknown-unknowns_ from AI technologies. It seems, somehow, that scaling up the speed and size of things has also created a systemic _metareward function_: we desire a pattern we cannot predict ourselves, but we would also like an explanation (pattern) of how a certain prediction was reached (because we are used to things being, well, predictable). One of the challenges which the field of AI is faced with today is the fact that tracing the logic through complex, multidimensional data landscapes is precisely what we need automated prediction for. But, so long as it remains opaque, it will remain both fascinating and unconvincing, somehow. These problems pertaining to e.g., (un)supervised learning, human-in-the-loop, _orthogonality_, etc. are due to the fact that “Algorithms are made to be opaque—not just to protect businesses but due to the technical necessity of handling the complexity of the system.” (Bucher 2018, p. 41) This is a somewhat familiar trope in critical (new) media studies, and while “transparency is not just an end in itself,” transparency is still seen as a necessary condition for greater intelligibility (Pasquale, 2015: 8).” (ibid.). Transparency and intelligibility are two sides of the same predictive coin: transparency implies that an intelligible logic can be traced, where tracing implies prediction (if we know _how_ x, y or z happen we will know how it can(not) happen again). The problem of the “black-box” (which Bucher tackles in her 2018 book) is a problem of prediction: “The concept of the black box has become a catch-all for all the things we (seemingly) cannot know.” (ibid. p. 42). The aporia or limit in this moving-goalposts or AI effect-style logic, is there because we want both opaqueness and transparency (exploration and exploitation): once we understand something it is predictable and thus can be added to the list of exploitative things no longer interesting for explorative generativity. The problem of prediction as prescription is obvious, especially in the case where we are unable to trace where and how a certain result emerged: in weather-modeling we may be able to observe the consequences of a failed prediction, but in legal cases where AI-assistance is already implemented (Ashley 2019) “will this person commit an offense again?” the prediction-prescription line is incredibly blurred. Clearly, these are problems which exist across the board and before/beyond AI, but the AI-lens we now seem to evaluate everything through allows for some scrutiny with regard to bias. Bias, unavoidable as it is, presents us with the challenge that all decision-making is interested and invested in particular realms. Dreams of absolute prediction are certainly dreams of absolute control. The concept of prediction though, if thoroughly accepted as a matter-of-fact in all perception-cognition-action (this trinity not presented in any particular order), can improve our self-understanding as embedded within myriads of predictive, distributed schemas: from self-identity all the way to political alliances. If I know I want to predict how things will behave, and I also know I enjoy a certain degree of unpredictability, these things can be discussed generatively. The conversation that goes: > A: “why did you betray my trust?” > B: “I did not, I thought you were OK with what I did!” becomes elucidated in a different light when we understand what underlies it, in terms of prediction: >A: your behavior has had the consequence that I have to drastically adjust my model of our relationship/you, i.e.: my way of being able to predict your behavior. >B: my behavior functioned within the parameters of what I had as a predictive model of our relationship/you. What changes here? Well, for starters we sound like robots. Surprise, surprise. But: we are able to discuss each other as distributed (amongst each other, collectively: _with_ and _for_ each other) collections of models (assumptions, etc.) rather than reduce each other to errors (you are at fault of x, y or z). These methods are tried-and-proven in the context of psychology: “frame discussions around what is it that _you_ perceived as bothersome, etc. rather than blame the other party”, etc. In the context of AI: ask what the model wants of you, rather than what you want of the model. This should be paramount (we should know better) now that we exist in a world of “free” services (ChatGPT and the rest) which are sourcing us for their predictive (capital) gains. Moreover, in the context of a transparency and auditing paradigm, where all efforts seem geared towards the allocation of blame, responsibility, etc. we might be better off challenging said notions ### Conclusion Prediction does not equal restriction, reduction or negation, it contains these characteristics as features --> explain why, #todo. Basically because prediction involves a complex dance with surprise-appraisal, affordance exploitation-exploration, hence “bias” (as the paradigmatic virtue-signaling mechanism of our era) is not this negative condition, Kahnemann-style, but a given, filter condition, a condition, simply, the reason people are interesting, conversations are combinatorially novel: procreative. Prediction is only negatively reductive/oppressive when it is oppressive/negatively reductive, becoming aware of our incessant drive to predict (from mindreading to weather-modeling) actually probes our unavoidably predictive biases and reveals the possibility of a more social landscape where consensus, dialogue, understanding and collectivity can be _explored_ rather than _assumed_ to exist. In relation to value-driven capital and the death-drive we could make a final note, treated at large by Samo and by Sami: >“Freudo-Marxism, the “Faustian conflict” that Marx localized between the capitalist’s drive for enjoyment and the drive of capital’s enjoyment has been discussed as an opposition between independent forces. The two drives that Freud introduced in Beyond the Pleasure Principle (1920), death drive and life drive(s), could in fact be read as a stable opposition in the sense of an eternal conflict between Eros and Thanatos. However, in the Lacanian reading of Freud, as indicated by Tomšič, this duality is internal to the split nature of the sexual drive itself.” Khatib, 2021, p. 103 Surplus value, in the context AI as progeny, is complicated by this idea in the context of securing a future, since ... “In actual capitalism, there is no such thing as simple commodity exchange where money only functions as an external mediator of exchange. As soon as we are in a society in which “the capitalist mode of production prevails” (Marx 1990: 123), money functions as capital too. That is to say, the method of money consists in treating ‘money as money’ in its pure (and fetishistic) self- relation, and in treating commodities as the fleeting, transitory materializations and embodiments of value.” (ibid.). So the predictive reliance on money as the object of attainment (money can be understood as a technology which greatly reduces uncertainty in the context in which is can be exchanged for_anything_ ), can be understood as a similar type of predictive reliance on AI as the “anything” machine. Money is, taking a historical wide angle lens view, an important predecessor to the abstraction that is AI. Machines talking to machines scenario. #todo _____ ### Extra notes #todo [[Active-reactive conundrum]], [[Active inference]] [[Granger causality]] and prediction correlation. #lookinto [[Time]] A[[Symmetry]], trajectory of birds in a flock, the way that A transfers information to B, without that information being in A in the first place. Is related to [[Transfer entropy]] Compare to [[Catarina Dutihl Novaes]] dialogical roots of deduction. _____ ### Bibliography #todo don’t forget to add these to main PhD biblio Agre, Philip. _Computation and human experience_. Cambridge University Press, 1997. Agre, Philip E., and David Chapman. "What are plans for?." _Robotics and autonomous systems_ 6.1-2 (1990): 17-34. Ashby, W. Ross, and Jeffrey Goldstein. "Variety, constraint, and the law of requisite variety." _Emergence: Complexity and Organization_ 13.1/2 1968 (2011): 190. Ashley, Kevin D. “A brief history of the changing roles of case prediction in AI and law.” _Law Context: A Socio-Legal J._ 36: 93, 2019. Bucher, Taina. _If... then: Algorithmic power and politics_. Oxford University Press, 2018. Brouwer, J., and S. Van Tuinen. "Technological Accidents, Accidental Technology." (2023). Bruineberg, J., Kiverstein, J. & Rietveld, E. “The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective.” Synthese, 195(6):2417-2444, Oct. 2016. https://europepmc.org/article/MED/30996493 Cogliati Dezza, Irene, et al. "Learning the value of information and reward over time when solving exploration-exploitation problems." _Scientific reports_ 7.1 (2017): 16919. Clark, Andy. _Surfing uncertainty: Prediction, action, and the embodied mind_. Oxford University Press, 2015. Clark, Andy. _The experience machine: How our minds predict and shape reality_. Pantheon, 2023. Da Costa, Lancelot, et al. "Neural dynamics under active inference: Plausibility and efficiency of information processing." _Entropy_ 23.4 (2021): 454. Davies, D. Rhodri, Cian R. Wilson, and Stephan C. Kramer. "Fluidity: A fully unstructured anisotropic adaptive mesh computational modeling framework for geodynamics." _Geochemistry, Geophysics, Geosystems_ 12.6 (2011). de Jager, Sonia. "Semantic Noise and Conceptual Stagnation in Natural Language Processing." _Angelaki_ 28.3 (2023): 111-132. Derrida, ‘Structure, Sign and Play in the Discourse of the Human Sciences’, 1978. Dreber, Anna, et al. "Using prediction markets to estimate the reproducibility of scientific research." _Proceedings of the National Academy of Sciences_ 112.50 (2015): 15343-15347. Galison, Peter. “The ontology of the enemy: Norbert Wiener and the cybernetic vision.” _Critical inquiry_ 21.1: 228-266, 1994. Falk, M. “Artificial Stupidity”, _Interdisciplinary Science Reviews_ 46.1-2: 36-52, 2021. Farocki, Harun. Images of the World and the Inscription of War, 1989. Friston 2007 https://europepmc.org/article/MED/30996493#CR32 Szucs, Denes, and John PA Ioannidis. "Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature." _PLoS biology_ 15.3 (2017): e2000797. Juarrero, Alicia. _Context changes everything: how constraints create coherence_. MIT Press, 2023. Love, Nigel. "The linguistic thought of JR Firth." _Stellenbosch Papers in Linguistics_ 15.1 (1986): 31-60. Lyotard, F. “Can Thought go on without a Body?” (Translated by Bruce Boone and Lee Hildreth). In: *Discourse*, Vol. 11, No. 1, BODY // MASQUERADE (Fall-Winter 1988-89), pp. 74-87, 1988. Mak, Isabella WY, Nathan Evaniew, and Michelle Ghert. "Lost in translation: animal models and clinical trials in cancer treatment." _American journal of translational research_ 6.2 (2014): 114. Matthews, Robert AJ. "Medical progress depends on animal models-doesn't it?." _Journal of the Royal Society of Medicine_ 101.2 (2008): 95-98. Mehlhorn, Katja, et al. "Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures." _Decision_ 2.3 (2015): 191. Parr, T. Pezzulo, G. & Friston, K. _Active Inference_, MIT Press, 2022 Rieder, Bernhard. _Engines of order: A mechanology of algorithmic techniques_. Amsterdam University Press, 2020. Salmon, P. _An Event, Perhaps: A Biography of Jacques Derrida._ 2020 Simon, Bounded rationality Stringhi, Elisabetta. “Hallucinating (or poorly fed) LLMs? The problem of data accuracy.” _i-lex_ 16.2: 54-63, 2023. Turing, Alan, “Computing Machinery and Intelligence”, _Mind_, LIX (236): 433–460, October 1950. Vasil, Jared, et al. "A world unto itself: human communication as active inference." _Frontiers in psychology_ 11 (2020): 417. Bouizegarene, Nabil, et al. “Narrative as active inference: an integrative account of cognitive and social functions in adaptation.” _Frontiers in Psychology_ 15 (2024): 1345480. Vuust, P. & Witek, M. A. G. “Rhythmic complexity and predictive coding: a novel approach to modeling rhythm and meter perception in music”, Front. Psychol., 01 October 2014, Sec. Auditory Cognitive Neuroscience Volume 5 - 2014 [https://doi.org/10.3389/fpsyg.2014.01111](https://doi.org/10.3389/fpsyg.2014.01111) ### Footnotes [^1]: Bucher quotes von Hilgers (2011), who “describes how the black box initially referred to a “black” box that had been sent from the British to the {United States (original says “Americans”)} as part of the so-called Tizard Mission, ... This black box, which was sent to the radiation lab at MIT, contained another black box, the Magnetron. During wartime, crucial technologies had to be made opaque in case they fell into enemy hands. Conversely, if confronted with an enemy’s black box, one would have to assume that the box might contain a self-destruct device, making it dangerous to open. As a consequence, what emerged was a culture of secrecy or what Galison (1994) has termed “radar philosophy,” a model of thought that paved the way for the emergence of cybernetics and the analysis and design of complex “man-machine” systems. The black box readily became a metaphor for the secret, hidden, and unknown.” [^3]: Although “what humans can do” is far from having been understood, subject of another paper on NLP: de Jager, Sonia. "Semantic Noise and Conceptual Stagnation in Natural Language Processing." _Angelaki_ 28.3 (2023): 111-132. [^4]: The tacit drive towards immortality inherent in the AI project signals (which is also, before “AI” the very business of culture, of language), among other things, the desire for more-than-organic, vastly outlasting progeny. More on this later on. [^5]: That is, Bayesian _active inference_ and/or the _Free Energy Principle_. See: [^6]: Providing an absolute explicitation, which applies across the board, would imply formalizing the generative model of any predictive agent and thus solve many of the things science/humanity at large seems to desire: absolute _providence_. [^7]: “The relationship between bottom-up (input) and top-down (prediction) processes is entirely mutually dependent, and the comparison between them is essential to the system, since a variety of environmental causes can theoretically result in similar sensory input (e.g., a cat vs. an image of a cat). ... In this way, the causes of sensations are not solely backtracked from the sensory input, but also inferred and anticipated based on contextual cues and previous sensations. Thus, perception is a process that is mutually manifested between the perceiver and the environment, reflecting the bottom-up/top-down reciprocity...” (Rhythm PP paper). [^8]: This, in our preferred framing, brings attention to the possibility of proposing a radically distributed agent (over groups, environment, etc.) and not an image of a lone agent voluntarist against the background of an inert world. [^9]: Etymonline.com: “from Latin _explicitum_ “a thing settled, ended, or displayed,” noun use of neuter of _explicitus_, past participle of _explicare_ "unfold, unroll, disentangle," from ex "out" (see _ex_-) + _plicare_ “to fold” (from PIE root _plek_- “to plait”).” Again: this footnote is meant to steer the reader away from the negative connotations of _exploitation_ and highlight the predictive aspects in its _unfolding_ history, as related to _explanation_: a modeling impetus. [^10]: Persistence being “stability over time (Pascal and Pross 2015) ... constraint regimes endure longer than their moment to moment realizations ... This persistence holds even if the possibility landscapes that contain those interdependencies turn increasingly rugged over the entity’s existence or lifespan. ... What persists in individual realizations is not the material substrate of concrete particulars but the stored information embodied in constraint regimes.” (Juarrero 2023, p. 129). [^11]: Kripkenstein note. [^12]: The dark room: explain. [^13]: Symmetry as in: we look similar, and imitative as in: anthropomorphizing is also a major aspect of how we engage with the world. Optimistically: this is empathy with all that is _other_, pessimistically: this is making the world conform to our own image. [^14]: This may appear laughable on the surface, at least it does to me, but it’s a common internet trope in the realms of Reddit. Reddit is, coincidentally, a gigantic chunk of all LLM training data. “WebTex2”, owned by Open AI and “built on every webpage linked to Reddit in all posts that received at least 3 “Karma” votes.” (Stringhi 2023). [^15]: Explain Kolmogorov “iterability” and Wolfram’s principle. [^16]: Please note by focusing on procreation I am sidelining the question of sexual pleasure and desire, which can be part of the phenomenon of progeny, but also not. [^17]: “Active Inference is a normative framework to characterize Bayes-optimal behavior and cognition in living organisms. Its normative character is evinced in the idea that all facets of behavior and cognition in living organisms follow a unique imperative: minimizing the surprise of their sensory observations. Surprise has to be interpreted in a technical sense: it measures how much an agent’s current sensory observations differ from its preferred sensory observations—that is, those that preserve its integrity (e.g., for a fish, being in the water). Importantly, minimizing surprise is not something that can be done by passively observing the environment: rather, agents must adaptively control their action-perception loops to solicit desired sensory observations. This is the active bit of Active Inference.” (Pezzulo, Parr, Friston 2022, p. 6). In essence, thus, we are talking about a measure of _difference_, which this project deals with in the sense of chasing after the question: _is difference reducible to something more foundational than itself?_ [^18]: Explain here what is meant by the “shape” of (a) probability in geometric terms.