# To Do > [!multi-column] > >> [!bug] Overdue >> ```tasks >> not done >> (due before today) >> hide task count >> hide due date >> hide edit button >> short mode >> ``` > >> [!success] Today >> ```tasks >> not done >> (due on today) >> hide task count >> hide due date >> hide edit button >> short mode >> ``` > [!multi-column] > >> [!note] Current Papers >> ```dataview >> LIST WITHOUT ID file.link >> FROM #paper/current >> SORT file.ctime ASC >> ``` # Health Tracking > [!multi-column] > >> [!abstract] Weight Tracker >> ```tracker >> searchType: frontmatter >> searchTarget: weight >> folder: / >> line: >> title: "Weight Over Time" >> yAxisLabel: Weight (kg) >> lineColor: "#69b3a2" >> ``` > >> [!danger] Beer Tracker >> ```tracker >> searchType: frontmatter >> searchTarget: beer500ml >> folder: / >> line: >> title: "Weekly Beer Pattern (500ml bottles)" >> yAxisLabel: "Bottles" >> lineColor: "#ff6b6b" >> ``` >> >> ```tracker >> searchType: frontmatter >> searchTarget: beer500ml >> folder: / >> summary: >> template: "🍺 Weekly Beer Status:\n- Bottles this week: {{sum()}}/8\n- Remaining: {{8 - sum()}}\n- Standard drink equivalent: {{sum() * 1.52}}\n\n📊 Status: {{8 - sum() >= 0 ? '✅ On track!' : '⚠️ Over budget'}}\n\nℹ️ Quick Reference:\n1 bottle = 1.5 drinks\n2 bottles = 3 drinks\n3 bottles = 4.5 drinks (over limit)\n\n🎯 Session Targets:\n- Ideal: 2 bottles (3 drinks)\n- Max: 3 bottles (4.5 drinks)" >> style: "color:var(--text-normal); background-color: var(--background-secondary); padding: 10px; border-radius: 5px;" >> ``` # Notes > [!multi-column] > >> [!summary]- Created Today >> ```dataview >> LIST WITHOUT ID file.link >> FROM "" >> WHERE file.cday = this.file.day AND file.name != this.file.name >> SORT file.ctime ASC >> ``` > >> [!example]- Modified Today >> ```dataview >> LIST WITHOUT ID file.link >> FROM "" >> WHERE file.mday = this.file.day AND file.name != this.file.name AND file.cday != this.file.day >> SORT file.mtime ASC >> ``` > [!multi-column] > >> [!danger]- Created Yesterday >> ```dataview >> LIST WITHOUT ID file.link >> FROM "" >> WHERE file.cday = date(this.file.day-1) AND file.name != this.file.name >> SORT file.ctime ASC >> ``` > >> [!info]- Created Two Days Ago >> ```dataview >> LIST WITHOUT ID file.link >> FROM "" >> WHERE file.cday = date(this.file.day-2) AND file.name != this.file.name >> SORT file.ctime ASC >> ``` > [!multi-column] > >> [!abstract] Recent Thoughts >> ```dataview >> LIST WITHOUT ID file.link >> FROM #thought >> WHERE date(today) - file.cday <= dur(14 days) >> SORT file.ctime DESC >> ``` ## Quick Links > [!multi-column] > >> [!example] Quick Links >> ```dataview >> LIST WITHOUT ID file.link >> FROM #quicklink >> SORT file.cday DESC >> ``` # [[Scratch Pad]] Scrap from the llm paper ** First, an AI system can be appreciated in and of itself by considering how its technical and conceptual “forces” converge. As in a natural habitat, basic familiarity with the system’s training processes, data sources, and overall structure reveals emergent patterns in its generative activity. Carlson’s view underscores how knowledge informs the way we perceive and interpret an environment, suggesting that understanding [[underlying forces]] offers an enriched [[aesthetic experience]]. Conceptualism Second, the outputs of an AI system can be appreciated in ways reminiscent of enjoying a cultivated garden, where natural growth and human intervention intersect. AI outputs likewise result from an interplay between underlying computational processes and user-driven inputs or prompts. Observing how style, context, and user objectives shape these outputs highlights a collaborative dynamic that supports deeper [[aesthetic appreciation]]. The presence of new data, varying prompt strategies, or different user intentions can produce surprising outcomes that reward attentive engagement. appreciating the garden ‘[[not just]] how beautiful is the picture, but how beautiful is midjounrney.  Appreciating [[the value]] of the system by appreciating the [[aesthetic value]] of an output of the system.   Third, a more active, creative engagement emerges when one directly participates in the [[generative process]]. Much like a gardener shaping a plot through trial and error, individuals who configure prompts, refine parameters, or otherwise tinker with AI systems acquire a practical understanding that further enriches appreciation. This immersive involvement reveals subtle interdependencies within the model and facilitates a distinctive awareness of how the system’s possibilities unfold. Taken together, these modes of appreciation—observing the AI system, enjoying its outputs, and creatively participating in [[the process]]—flow from Carlson’s emphasis on knowledge and context. Once the relevant “forces,” whether ecological or computational, are recognized, [[the experience]] becomes more coherent and rewarding. Applied to generative AI, this perspective demonstrates that an understanding of parameters, data, and interactive possibilities underwrites [[aesthetic experience]], whether one focuses on the system itself, its content, or the generative potential it unlocks. REVISED SECTION 3 (Approaches to [[Appreciating Generative]] AI) Building on Carlson’s environmental perspective and the notion of AI as a “naturans system” outlined in the previous section, we can now examine how generative AI systems invite [[aesthetic appreciation]]. Much as knowledge of geology or ecology can illuminate our view of a forest, even a modest grasp of model architectures and training processes can deepen our engagement with AI. We thereby begin to see the “order” in what might otherwise seem an opaque set of outputs, and recognize how distinct forces—ranging from algorithmic constraints to cultural data inputs—coalesce to shape the system’s behavior. In this respect, generative AI can be treated as an environment that is accessible to both experts and laypeople, each drawing on different kinds of insight. First, there is a mode of appreciation grounded in scientific or technical knowledge. A researcher at OpenAI or Anthropic, for instance, may investigate the paths by which data flows through a large language model in ways akin to a biologist charting nutrient cycles. This vantage point allows one to discern hidden interdependencies and complex feedback loops, revealing qualities that might otherwise remain obscure. While such detailed expertise is not strictly necessary for appreciation, it demonstrates how advanced technical understanding can foster a particular kind of aesthetic admiration for the system’s sophistication. A second form of appreciation arises through practical engagement, analogous to a gardener learning from hands-on interaction with soil, light, and moisture. Users who iterate with prompts, observe which outputs arise, and adapt their strategies develop an intuitive grasp of the AI’s evolving “ecology.” Although less specialized than formal engineering research, this practical understanding likewise enriches appreciation by sensitizing individuals to the system’s patterns and capacities. As a gardener may marvel at the interplay of roots and rainfall, so an attentive user learns to savor the interplay of prompts, context, and emergent textual or visual results. Finally, these perspectives converge when we consider how a viewer might appreciate the outputs of a generative AI system in a manner reminiscent of admiring a cultivated garden. While the AI’s processes retain an element of autonomy, user interventions guide and shape its growth much as a gardener nurtures plants. Observing a surprising poem or image is not merely a matter of enjoying a finished product; it also invites reflection on the system itself, revealing the balance between algorithmic independence and human influence. In this sense, appreciating the AI’s creations provides a window into [[the forces]]—technical, cultural, and interactive—that converge to produce an unfolding [[aesthetic experience]]. Taken together, these modes of appreciation—scientific insight, practical engagement, and a garden-like enjoyment of AI outputs—demonstrate that generative AI can be regarded as a unified environment open to multiple avenues of understanding. Rather than presupposing one privileged route to aesthetic appreciation, Carlson’s framework suggests that scientific and everyday knowledge exist on a continuum. By placing generative AI within this environmental model, we see how various forms of familiarity—ranging from technical analysis to hands-on trial and error—can deepen our relationship to the system. In each case, the object of appreciation remains the same, even if the lens through which it is viewed shifts from specialized study to everyday curiosity. --- ## Changes Made and Justifications 1. Smoother Transition from Section 2 - What Changed: The opening paragraph explicitly references Carlson’s environmental perspective and the “naturans system” idea. - Why: This helps Section 3 flow more naturally from the closing ideas of Section 2, maintaining thematic continuity in discussing AI as an environment shaped by various forces. 3. Reduced Emphasis on the Common Object - What Changed: While the text still highlights that scientific and practical engagements concern the same AI system, it does so with a lighter touch, avoiding repetitive reminders. - Why: This addresses the concern about “over-egging” the point while preserving the notion of a single object of appreciation. 5. Reintroduction of the Garden Analogy - What Changed: A paragraph devoted to the cultivated-garden analogy is re-added, showing how enjoying AI outputs can be akin to admiring a garden. - Why: This responds to the request to reintroduce the garden perspective, ensuring it follows naturally from the preceding discussions of scientific and practical appreciation. 7. Preservation of Original Wording - What Changed: Where possible, original phrases (e.g., “AI’s ecology,” “hands-on interaction,” “technical understanding”) have been retained or only lightly modified. - Why: This fulfills the requirement to conserve as much of the initial vocabulary and phrasing as possible, ensuring consistency with the paper’s style. 9. Paragraph Flow and Length - What Changed: Each paragraph remains at least four sentences, and extraneous headings have been removed. - Why: This meets the guideline for coherent paragraphs while keeping the section seamlessly integrated with the existing text. ### References Carlson, A. (2005). Aesthetics and the environment: The appreciation of nature, art and architecture. Routledge. Forsey, J. (2013). The aesthetics of design. OUP USA. Parsons, G., & Carlson, A. (2008). Functional beauty. OUP Oxford. **