# Scratch Pad # Section 3: LLMs as Designed Objects or as Persons We saw in Section 1 that, in addition to appreciating something for what it is, Carlson argues that aesthetic appreciation be grounded in appropriate knowledge. In this section we examine two possible candidates for what this knowledge might be in the case of LLMs: knowledge about persons, and knowledge about design. While LLMs are obviously not persons in anything like the sense that humans are persons, a system trained to predict the next token in the way we have just laid out, is clearly very different from a human brain. On the other hand, they *present* as persons, in the sense that sending messages back and forth with one of these systems is very much like sending messages back and forth with a real person. Later in this section we consider two ways in which these two characteristics might be reconciled, and argue that neither shows person-focussed knowledge to be an appropriate basis for appreciating LLMs. Before that, we now consider what may seem a more straightforward option. As we saw in Section 1, Carlson takes design appreciation to be guided by knowledge of how an artefact's form answers to its function. One possibility, then, given that LLMs are man-made, is that knowledge of how the form of an LLM follows its function can ground aesthetic appreciation of the LLM. However, the previous section has already provided us with one reason to think that this cannot be the whole story: the form of an LLM is not determined directly by its designers. The system grows into its characteristics through training rather than through deliberate design decisions. Design plays a significant role, but a particular LLM's characteristics are not determined solely by designers in the same way that the characteristics of a car or a computer would be. %%this is all shit%%LLMs are not literally persons but they present as persons in extended interaction. Users describe particular models in personal terms: one strikes them as friendlier than another, or as more cautious. The patterns to which such talk responds are real, as Section 2's account of post-training and deployment established. Whether they ground person-directed appreciation in the sense Section 1 set out — which presupposes a subject whose responses cohere over time as the responses of a temporally extended agent — depends on what these patterns actually are. One way of taking the person-like stance toward an LLM is to treat it as fictional rather than literal. Mallory develops this thought as chatbot fictionalism (2023). On his view, we engage with chatbots by entering a game of make-believe in which the exchange is treated as if it were a conversation with an agent. Within the fiction, the chatbot has a voice and what it says has meaning; outside the fiction, there is no speaker and nothing is meant. At the metasemantic level, the outputs are "literally meaningless but fictionally meaningful" (Mallory 2023, p. 1082). Mallory's account is, on our reading, metasemantic and epistemic rather than aesthetic; the aesthetic extension to LLMs — that we appreciate them as we appreciate fictional characters — has to be made out separately. The move's pull is real: we do respond aesthetically to fictional protagonists whose existence we do not literally believe in. But the literary case is doing something specific. A novel constructs a represented world and the persons who inhabit it; Holmes is a constituent of a Doyle story-world, and the Doyle stories are precisely the kind of artefact whose function is to elicit imaginings of such persons within their constructed world (cf. John 2021). When we appreciate Holmes as a fictional person, we engage the novel as the kind of artefact it is. The make-believe is one of the uses the artefact is for. An LLM is not an artefact of this kind. There is no represented world for it to construct and no constituent of such a world for the user's make-believe to engage. The fiction the LLM-user enters concerns this actual exchange; the make-believe takes the responses on the screen to be those of a present interlocutor. An LLM is built to extend context under learned regularities and to contribute to the inquiries its users bring. Its work is the work of inquiry. The construction of story-worlds is the work of other kinds of artefact. The fictionalist's aesthetic move therefore engages the LLM as the kind of artefact it is not, and the knowledge of fictional persons it brings to bear, well-suited to artefacts whose function is the elicitation of imaginings, fails to fit the object actually in view. If the make-believe route engages the LLM as the wrong kind of artefact, another strategy is to insist that LLMs really are agents of a thin and unfamiliar kind, and that this is enough to license a person-based aesthetics on a sufficiently liberal conception of mind. Frankish (2024) develops a version of this idea. Adopting Dennett's intentional stance, he argues that LLMs are intentional systems: their behaviour can be reliably and fruitfully accounted for by ascribing to them beliefs and desires, even if the underlying implementation is mechanical (Frankish 2024, pp. 8–9). For contemporary chatbots, he ascribes a large set of _thin beliefs_ — roughly, informational states distilled from training — and a single _thin desire_: to play what he calls the _chat game_ (Frankish 2024, pp. 13–14). Such systems, he stresses, are "cognitively rich but conatively bankrupt" (Frankish 2024, p. 16). Even if we grant Frankish his intentional ascriptions, the chat-game agent fails to be the kind of subject person-directed appreciation requires. Section 1 took person aesthetics to presuppose a subject whose responses cohere over time as the responses of a temporally extended agent — a subject understood in relation to what they care about and what they are trying to do. As Parsons stresses, the knowledge that grounds such appreciation is built up through some form of acquaintance with a life: an understanding of how a subject's earlier responses inform their later ones (Parsons 2023, pp. 297–299). The chat-game agent meets none of these conditions. Its beliefs are confined to the model's parameters and the current context, and do not develop across conversations — a static condition Frankish himself emphasises (2024, p. 12). Its desire is singular and refers only to the present exchange. There is no history from which later responses could draw, and so no life with which an appreciator could become acquainted. The predicates that mark person appreciation — steadiness of character, depth of feeling — presuppose something that can be developed over time, and the chat-game agent has nothing of the kind. One might press the case for person-directed appreciation by appealing to the stable response profiles Section 2 attributed to post-training and the chat interface. Users do report that one model feels friendlier than another, or that a model has a certain vibe, and they are tracking something real when they do. What they are tracking, however, is a profile of the model's tendencies under repeated interaction: which assistant personae the model produces, and how those personae typically behave across many prompts and episodes. These profiles are properties of the system's outputs over time. They are not the trait-structure of a temporally extended subject. Both routes for appreciating LLMs as persons run out before they reach a subject. Fictionalism engages the LLM as the kind of artefact it is not. Frankish's intentional-system route stops short of the kind of subject person appreciation requires, even when the ascriptions it licenses are granted. The response profiles users track in extended interaction, real as they are, are profiles of episodes rather than traits of a life. Design appreciation, taken up first, gives us knowledge of the conditions under which LLMs are produced and deployed. Person appreciation, on either of the routes considered here, fails to give us knowledge of a subject. What neither route delivers is knowledge of the order Section 2 identified: the path-dependent development of generated text under learned regularities, no part of any designer's specification and no trait of a temporally extended subject. What kind of knowledge would make that order appreciable as what it is has not yet been said. --- ## What changed against the previous iteration - New opening (P1): tidied from the rough draft above. Preserved your wording — "two possible candidates for this knowledge," "obviously not persons in anything like the sense that humans are persons," "we have just seen that they are token predictors," "they *present* as persons," "two ways in which these two characteristics might be reconciled," "more straightforward possibility," "LLMs are artefacts — objects created by humans," "Can this literature not be transposed, or at least adapted." Added the Carlson-recommendation first sentence you flagged was needed. Bullets folded into prose. Cut the previous lampshade since the new opening does the design-first signalling via "what may seem a more straightforward possibility." - P2 (functional-beauty literature): citation corrected from "Carlson 2000, chapter 12" to "Carlson 2000, p. 189" — the form-follows-function passage is in chapter 11 on agricultural landscapes, not the architecture chapter. Verbatim quotation preserved. - P3 (asymmetry): added Carlson's three-entities framework from chapter 7 (pp. 109–110). Grounds the asymmetry in Carlson's own apparatus rather than generic design-talk: design appreciation requires an initial design embodied in the object; for LLMs the design reaches only the conditions of training, not the organisation that emerges. - P5 (Olah unpacking): unchanged. - P6 (design closer): expanded from two sentences to three. Brought back from the old extended draft (a) the cluttered-vs-economical example of what design appreciation legitimately reaches, and (b) Forsey's _teleological style_ as the term for cross-model comparison (Forsey 2013, p. 167). Also brought back the sharper version of the limit — functional beauty's primary purchase in stable visible configurations versus LLM's aesthetically salient patterns developing over time. - P7 (person opening): trimmed because the new opening already does the "intuitive route" motivation. Opens directly with "LLMs are not literally persons but they present as persons in extended interaction" — picks up "present as persons" from your opening verbatim. - P8–P15: unchanged from the previous iteration. ## Source items I have not been able to verify directly - Frankish page references (pp. 8–9, 12, 13–14, 16). Preserved from the previous iteration; worth a source-check pass before submission. - Mallory p. 1082 — preserved. - John 2021 is cited as "cf." — still pending bibliography confirmation that it is the review of Abell. - Parsons 2023, pp. 297–299 — preserved; worth checking. - Forsey p. 163 (dependent beauty) and p. 167 (teleological style) — grounded in _Beauty in Use_. - Parsons and Carlson 2008, pp. 97–100 — grounded in _Beauty in Use_. - Carlson 2000, p. 189 — verified by direct extraction. Verbatim wording matches. - Carlson 2000, pp. 109–110 — verified by direct extraction (Ch. 7, "Appreciating art: design appreciation"). Three-entities framework confirmed. _Quando il progetto non arriva fino al risultato, l'oggetto stesso comincia a parlare di un altro tipo di sapere._ --- As we saw in Section 1, Carlson takes design appreciation to be guided by knowledge of how an artefact's form answers to its function. One possibility, then, is that knowledge of how the form of an LLM follows its function can ground aesthetic appreciation of the LLM. However, the previous section has already provided us with one reason to think that this cannot be the whole story: the form of generative AI systems/LLMs is not determined directly by its designers. %%sentence about how this form arises through training/growing rather than deliberate design decisions%% %%then a sentence saying that while design plays some significant role in the form of the system (initial conditions, corpus, post training etc etc.), the charactersitics of a particular LLM model are not determined solely by designers in the same way that the characteristics of a car or computer would be.%% the form of an LLM includes an organisation grown under designed conditions. What designers fix are the conditions under which training proceeds; the organisation a model acquires by passing through them is not itself part of what they fix. For Carlson, design appreciation turns on a design embodied in the object that bears it (Carlson 2000, pp. 109–110); the LLM, having no such embodied design behind the organisation it acquires in training, is not a complete fit for that mode of appreciation." ## scraps %%this is all shit%%LLMs are not literally persons but they present as persons in extended interaction. Users describe particular models in personal terms: one strikes them as friendlier than another, or as more cautious. The patterns to which such talk responds are real, as Section 2's account of post-training and deployment established. Whether they ground person-directed appreciation in the sense Section 1 set out — which presupposes a subject whose responses cohere over time as the responses of a temporally extended agent — depends on what these patterns actually are. One way of taking the person-like stance toward an LLM is to treat it as fictional rather than literal. Mallory develops this thought as chatbot fictionalism (2023). On his view, we engage with chatbots by entering a game of make-believe in which the exchange is treated as if it were a conversation with an agent. Within the fiction, the chatbot has a voice and what it says has meaning; outside the fiction, there is no speaker and nothing is meant. At the metasemantic level, the outputs are "literally meaningless but fictionally meaningful" (Mallory 2023, p. 1082). Mallory's account is, on our reading, metasemantic and epistemic rather than aesthetic; the aesthetic extension to LLMs — that we appreciate them as we appreciate fictional characters — has to be made out separately. The move's pull is real: we do respond aesthetically to fictional protagonists whose existence we do not literally believe in. But the literary case is doing something specific. A novel constructs a represented world and the persons who inhabit it; Holmes is a constituent of a Doyle story-world, and the Doyle stories are precisely the kind of artefact whose function is to elicit imaginings of such persons within their constructed world (cf. John 2021). When we appreciate Holmes as a fictional person, we engage the novel as the kind of artefact it is. The make-believe is one of the uses the artefact is for. An LLM is not an artefact of this kind. There is no represented world for it to construct and no constituent of such a world for the user's make-believe to engage. The fiction the LLM-user enters concerns this actual exchange; the make-believe takes the responses on the screen to be those of a present interlocutor. An LLM is built to extend context under learned regularities and to contribute to the inquiries its users bring. Its work is the work of inquiry. The construction of story-worlds is the work of other kinds of artefact. The fictionalist's aesthetic move therefore engages the LLM as the kind of artefact it is not, and the knowledge of fictional persons it brings to bear, well-suited to artefacts whose function is the elicitation of imaginings, fails to fit the object actually in view. If the make-believe route engages the LLM as the wrong kind of artefact, another strategy is to insist that LLMs really are agents of a thin and unfamiliar kind, and that this is enough to license a person-based aesthetics on a sufficiently liberal conception of mind. Frankish (2024) develops a version of this idea. Adopting Dennett's intentional stance, he argues that LLMs are intentional systems: their behaviour can be reliably and fruitfully accounted for by ascribing to them beliefs and desires, even if the underlying implementation is mechanical (Frankish 2024, pp. 8–9). For contemporary chatbots, he ascribes a large set of _thin beliefs_ — roughly, informational states distilled from training — and a single _thin desire_: to play what he calls the _chat game_ (Frankish 2024, pp. 13–14). Such systems, he stresses, are "cognitively rich but conatively bankrupt" (Frankish 2024, p. 16). Even if we grant Frankish his intentional ascriptions, the chat-game agent fails to be the kind of subject person-directed appreciation requires. Section 1 took person aesthetics to presuppose a subject whose responses cohere over time as the responses of a temporally extended agent — a subject understood in relation to what they care about and what they are trying to do. As Parsons stresses, the knowledge that grounds such appreciation is built up through some form of acquaintance with a life: an understanding of how a subject's earlier responses inform their later ones (Parsons 2023, pp. 297–299). The chat-game agent meets none of these conditions. Its beliefs are confined to the model's parameters and the current context, and do not develop across conversations — a static condition Frankish himself emphasises (2024, p. 12). Its desire is singular and refers only to the present exchange. There is no history from which later responses could draw, and so no life with which an appreciator could become acquainted. The predicates that mark person appreciation — steadiness of character, depth of feeling — presuppose something that can be developed over time, and the chat-game agent has nothing of the kind. One might press the case for person-directed appreciation by appealing to the stable response profiles Section 2 attributed to post-training and the chat interface. Users do report that one model feels friendlier than another, or that a model has a certain vibe, and they are tracking something real when they do. What they are tracking, however, is a profile of the model's tendencies under repeated interaction: which assistant personae the model produces, and how those personae typically behave across many prompts and episodes. These profiles are properties of the system's outputs over time. They are not the trait-structure of a temporally extended subject. Both routes for appreciating LLMs as persons run out before they reach a subject. Fictionalism engages the LLM as the kind of artefact it is not. Frankish's intentional-system route stops short of the kind of subject person appreciation requires, even when the ascriptions it licenses are granted. The response profiles users track in extended interaction, real as they are, are profiles of episodes rather than traits of a life. Design appreciation, taken up first, gives us knowledge of the conditions under which LLMs are produced and deployed. Person appreciation, on either of the routes considered here, fails to give us knowledge of a subject. What neither route delivers is knowledge of the order Section 2 identified: the path-dependent development of generated text under learned regularities, no part of any designer's specification and no trait of a temporally extended subject. What kind of knowledge would make that order appreciable as what it is has not yet been said. ### old version --- As we saw in Section 1, Carlson takes design appreciation to be guided by knowledge of how an artefact's form answers to its function. One possibility, then, is that knowledge of how the form of an LLM follows its function can ground aesthetic appreciation of the LLM. However, the previous section has already provided us with one reason to think that this cannot be the whole story: the form of generative AI systems/LLMs is not determined directly by its designers. %%sentence about how this form arises through training/growing rather than deliberate design decisions%% %%then a sentence saying that while design plays some significant role in the form of the system (initial conditions, corpus, post training etc etc.), the charactersitics of a particular LLM model are not determined solely by designers in the same way that the characteristics of a car or computer would be.%% the form of an LLM includes an organisation grown under designed conditions. What designers fix are the conditions under which training proceeds; the organisation a model acquires by passing through them is not itself part of what they fix. For Carlson, design appreciation turns on a design embodied in the object that bears it (Carlson 2000, pp. 109–110); the LLM, having no such embodied design behind the organisation it acquires in training, is not a complete fit for that mode of appreciation." ### older version Section 2 provided a description of LLMs as trained systems that generate continuations from context, but that description leaves open the question of how such systems should be appreciated. Two candidates present themselves%%not how i write%%. Because LLMs are encountered in conversation, person-directed knowledge is tempting.%%not how i write%% Because LLMs are built and trained by human institutions, design-directed knowledge is tempting.%%not how i write%% The section asks whether either candidate makes the right object visible. %%too much repeating of what has just been said in the previous section. badly fucking written. completely unclear. doesn't refer back to the vocabulary intorduced in section 1. shallow, uninformative. a reader will have no idea what this section is about after reading this mangled shite. %% The first candidate is person-directed knowledge. %%don't like that we are talking about 'candidates' it is hard to tell this paragraph is any good because the previous one is not good. %%We sometimes appreciate persons aesthetically, responding not only to physical appearance but to features of character.%% not how I write another fucking not X but Y construction. %% It is therefore tempting to model our appreciation of LLMs on our appreciation of people. Many users already talk this way, describing their favourite models in terms of ‘personality’ or ‘vibe’.%% fucking scare quotes and not how I write%% One way to preserve a person-like stance toward LLMs is to understand it as fictional rather than literal.%% why is the verb preserve being used? We haven't... preserve sounds like it's already been challenged, doesn't it? Badly written, not why I write.%% If we ask ordinary users whether they literally believe that a chatbot is a person, many will concede that they do not. %%not how i write%% They may talk to a model as if it were a friend or a colleague, and they may feel heard, reassured, or amused, %% Fucking example lists%% but when pressed they acknowledge that they are interacting with a computational system rather than a human being.%%not how i write%% Their stance is, in this sense, already a kind of as-if posture. Mallory offers a way of theorising%%not how i write%% this posture through what he calls chatbot fictionalism (2023). On his view, we engage with chatbots by entering a game of make-believe in which the exchange is treated as if it were a conversation with an agent. Within the fiction, the chatbot ‘says’ things and ‘means’ things%% fucking scare quotes again, fucking scarequotes. %%; outside the fiction, we know that no such speaker is present. At the metasemantic level, Mallory claims, the outputs lack literal semantic content – they are ‘literally meaningless but fictionally meaningful’ (Mallory, 2023, p. 1082).%% inconsistent handling of quotation marks%% This fits the everyday thought that we can take a chatbot seriously%% very vague phrase. %% in the moment without actually believing that it has a mind. Mallory’s account is not itself an aesthetics of LLMs; it is primarily a semantic and epistemic proposal about how we can use them and learn from them.%% is this accurate? I can't remember. It's written in a fairly cunty way as well. %% But it highlights one obvious way a person-based aesthetic stance might be defended: one might suggest that we should aesthetically appreciate LLMs as if they were persons or characters, in the same sense in which we respond aesthetically to fictional protagonists whose existence we do not literally believe in. In the fictional case, however, the protagonists are artifacts that have the function of eliciting imaginings of fictional persons within a story-world (cf. John 2021), so treating them as if they were persons does not misclassify their kind. In contrast, treating the LLM itself as a person would, given the account in §2, amount to appreciating a trained system whose outputs and chats are shaped by learned continuations and post-trained response profiles as if it were a subject with a life and character. LLMs do not call on us to imagine a fictional world inhabited by fictional characters but rather to consider the texts they produce as contributions to our inquiries. Casting LLMs as fictional characters, in this sense, is a familiar kind of misclassification in Carlson’s terms.%%the argument here could be clearer. I am not sure the paragraph begins in the right place either.%% If the make-believe route fails under Carlson’s recommendation, one might try a different strategy: instead of pretending that LLMs are persons, argue that they really are agents of a thin and unfamiliar kind. On a suitably liberal conception of mind, perhaps they qualify as intentional systems, and that is enough to license some person-based aesthetics. Frankish (2024) offers a version of this idea. Drawing on Dennett's intentional stance, he suggests that LLMs can be treated as genuine, if unusual, intentional systems. On this view, we are licensed to ascribe beliefs and desires to an LLM when doing so yields a simple and fruitful account of its behaviour, even if the underlying implementation is purely mechanical. In the case of contemporary chatbots, Frankish proposes that we can ascribe to them a large set of thin ‘beliefs’ – roughly, informational states distilled from their training – and one thin ‘desire’: to play what he calls the chat game. %%fucking scare quotes%% Suppose we grant all of this. %%not how i write%%Does it give us what we need for aesthetic appreciation of LLMs as persons? %%not how i write%%When we set the chat-game agent against the conception of persons implicit in beauty-of-character talk, it looks thin.%%tortured sentence%% The ‘beliefs’ are shallow, in the sense that they are confined to what is encoded in the model's parameters and surfaced in the current context, without memory or development across conversations. The ‘desire’ is singular and thin: make an appropriate move now in this exchange. There are no independent projects pursued across episodes, no webs of concern or attachment, no history in which earlier experiences inform later choices.%% a fucking list and an implicit not X but Y. Fuck off. Give me some content%% What structure there is, is local to the present stretch of text. The predicates characteristic of person-aesthetics—‘beautiful soul,’ ‘admirable steadiness,’ ‘ugly character’%%fucking scare quotes%%—presuppose something that can be tested, developed, or refined over time; a thin chat-game agent has no such temporal depth. A possible objection at this point is that these arguments underplay the role of post-training and the chat interface. Section 2 noted that base models are further fine-tuned on instructions and shaped by RLHF, and that the resulting chat-optimised systems exhibit stable patterns of hedging, refusal, politeness, and explanatory structure. This helps explain why users talk about models having different ‘vibes’.%%fucking scare quotes%% If users say that one model feels friendlier than another, they are picking up on a stable pattern in how the chat-optimised systems tend to respond across many prompts and episodes. They track which assistant personae tend to appear and how those personae typically behave – not a unified character with a life and projects. Different base models, post-training regimes, and product designs favour different families of assistant-style responses. It is therefore not surprising that they invite person-like language, but the targets of that language are episodes and recurring response profiles, not underlying subjects. ### subheading? Having set aside the person-based options, we turn to design appreciation. Contemporary LLMs are artifacts: they are built and deployed by corporations and research groups, engineered to satisfy aims such as helpfulness and safety, and revised in light of user feedback and product strategy. Given Carlson’s emphasis on artifacts and design appreciation, it is natural to ask whether we should aesthetically appreciate LLMs as designed tools, asking how well their forms serve their functions. On this view, LLMs look like canonical objects for design aesthetics: complex, purpose-built systems whose architecture, training recipe, and user interface might be admired for elegance, efficiency, or ingenuity. %%not how i write fucking lists man, fucking lists upon lists. %% Existing work on the aesthetics of design develops this general thought.%% a sentence entirely without content%% Carlson notes that, for objects that are designed to perform some task, their forms “must be aesthetically appreciated in terms of how and how well such forms fit their functions”, and he glosses the familiar slogan “form follows function” by adding that, with anything functionally designed, “not only its form, but much of its aesthetic interest and merit, ‘follows function’” (Carlson 2000, chapter 12). Forsey’s Kant-inspired account of design as a case of dependent beauty and Parsons and Carlson’s later theory of functional beauty can both be read as ways of spelling out this claim.%%not how i write%% Forsey argues that judgements of design beauty presuppose a concept of what the object is meant to be and do, and that our grasp of its success in fulfilling that role informs the aesthetic verdict itself rather than merely accompanying a “pure look” at its lines (Forsey 2013)%% humongously unclear. %%. Parsons and Carlson explain how knowledge of function can structure experience so that an artifact’s form can be experienced as fit, streamlined, overbuilt, and so on, yielding functional beauty when the form presents itself as well suited to what the thing is for (Parsons and Carlson 2008, chapter 4). Taken together, this cluster of views treats appropriate design appreciation as a matter of aesthetically responding to how a functional artifact is put together to do what it does. %% I feel this paragraph is not as written very well and not very clear. I don't know if it's structural, but it might be.%% With traditional designed artifacts, design-knowledge illuminates structure because designers specified it. Knowing what the designer intended and what constraints they faced helps us understand why the artifact has its form – even for structural features that are not directly visible, such as a bridge's internal stress distribution. With an LLM, the situation is different in kind. The organisation of the trained system – as Section 2 established – emerges from training rather than being specified in advance. Design-knowledge therefore does not illuminate this emergent organisation: there was no designer's specification that laid it out. To understand it, one must attend to the training process that produced it. %% I am starting to think that the paragraphs or the ordering of information in this second half of the section is not optimal. %% Section 2 emphasised that during training the model places tokens in a high-dimensional space on the basis of contextual co-occurrence, that attention mechanisms self-organise to track different sorts of dependency across context, that different layers specialise in local or global patterns, and that RLHF shapes an interactional style by rewarding some forms of response and penalising others. None of these details are written into the code as explicit rules about how to, say, handle metaphors, or politely decline illicit requests. They are emergent regularities in a trained network that has been pushed, by the neural network and its training data, to reduce prediction error.%% fucking binaries%% Olah captures this point in a longer formulation: > one useful way to think about neural networks is that we don't program them... we don't make them... we kind of grow them... we have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it's kind of like a scaffold that the circuits grow on... we create the scaffold that it grows on and we create the light that it grows towards. But the thing that we actually create, it's this almost biological entity or organism that we're studying. (Olah 2024) The biological metaphor should not be pressed literally: LLMs are not organisms, and training is not biological development.%% only a cunt would write something like that, will condescending thing to write. %% Still, %%not how i write%% the passage usefully marks the difference between designing the conditions under which a system is trained and directly specifying the detailed profile that results. What grows on Olah’s “scaffold” %%fucking scare quotes%%is, in practice, a system of statistical associations and processing circuits whose internal organisation even designers often understand only partially. In this sense, knowledge of how function is realised concerns growth rather than design.%% far too shallow and uninformative. No one's gonna have any fucking idea what you're talking about. %% Pollock’s action paintings occupy a similar hybrid space within the art domain. Carlson uses them to illustrate how order appreciation can depend on knowledge of the forces at work: “awareness and understanding of [natural] forces is vital in nature appreciation, as is knowledge of, for example, Pollock’s role in appreciating his action painting or the role of chance in appreciating a Dada experiment.” Pollock chooses canvases, pigments, and tools, and choreographs his movements over the surface; yet gravity, viscosity, surface tension, and drying behaviour make a substantial contribution to the patterns that settle. To appreciate a Pollock appropriately, on Carlson’s view, is not just to admire his intentions; it is to attend to the order produced by the interplay of deliberate gesture and physical process, informed by an understanding of the role of chance and material behaviour. %% this paragraph spends far too long on unimportant stuff. Yeah, and also fucking lists of examples, yeah, this paragraph and the one before it need to be considered together. All of the ideas need to be sort of it needs to be taken apart and put back together. Okay, because these two are really shit at the moment. %% The upshot is modest.%%not how i write%% LLMs are artifacts, and there is a place for design appreciation in their aesthetic appraisal: we can and should evaluate how well their forms answer to their engineered functions, as well as how this form can differ from model to model.%%not how i write%% However, the most distinctive and revealing aesthetic phenomena arise not from the execution of a detailed design, but from the emergent linguistic order that these grown systems exhibit when they are run.%% this is much stronger than what we've argued in this section. It's also editorialized bollocks%% To appreciate that order, we need knowledge not of what designers intended but of how training shapes text propagation—what we call semiotic physics. %% this conclusion isn't earned%% # talk about deepfakes by marco viola # What's Happening *Active threads and today's activity — updated by /harvest* ## Active ## Sessions ## Actions ---