Chat U2A: human operator prompt first, then AI agent answer. Width: 80 columns. ************* Gemini 2 about fix your data is a postponing excuse ************** https://g.co/gemini/share/39699bbf6a12 Created: 8th May 2025 Published: 8th May 2025 Attachment: none ================================================================================ ## COLLABORATIVE ARGUMENTATIVE FRAMEWORK W/RAG 3.5.3 [SoNia session prompt v3.5.3] --- OK --- Please identify the most significative concepts (implicit and expressed, biases including) in this document in RAG, list them and briefly comment them with a sentence. For each of them, assign a letter in such a way we can refer to each of them like (X). ==RAG== ## Fix your data is a postponing excuse [text before "Show me the code!" section] --- Okay, I've processed the document with RAG. Here are some of the most significant concepts I've identified, along with brief comments and letter designations: (A) Postponing Excuse ("fix your data, first"): The author disagrees with this being a necessary prerequisite for AI adoption, suggesting data can be dealt with as is. (B) AI's Predisposition to Handle Messy Data: A key argument is that AI inherently possesses the capability to process and categorize even unstructured data. (C) Generic AI Agent: The proposal to initially create an AI capable of understanding data format (image, text, sound, etc.) as a first step. (D) Generic Instructions/Guardrails: The necessity of providing initial guidance (rules, frameworks like RAG) for the AI agent to operate coherently. (E) LLM for Language Processing: The reliance on Large Language Models to handle the complexities of human language, including grammar and semantics. (F) Extracting Expressed and Implicit Claims: The distinction between summarizing and identifying underlying assertions, including "memes." (G) Implicit Assumptions/Biases (Memes): Uncritically accepted ideas that may not be overtly stated but influence the text. (H) Personal Beliefs vs. Established Knowledge: The categorization of highly peculiar ideas that deviate from recognized patterns (e.g., flat Earth). (I) Coherence and Usefulness over True/False: Shifting the focus from a binary validity to the contextual relevance and utility of a concept. (J) Profiling (Understanding a Serial Killer): An example illustrating how understanding a viewpoint, even a negative one, can be useful for analysis. (K) Multiple Points of View: The importance of considering various perspectives to build a comprehensive understanding of relationships between concepts. (L) Session Prompt for Raw Data: The mention of a specific tool (SoNia prompt v3.5.3) designed to handle messy user-generated data. (M) Bias Amplification (AI Risk): Acknowledging the danger of AI inadvertently reinforcing existing biases in the data. (N) Resource Intensity (Two Different AIs): The observation that processing raw data might require separate AI agents for restructuring and consumption. --- Which is the main topic of this text in RAG? The aim for which the author has written it. In one sentence. Label it as "(1) main goal" --- (1) main goal: The author aims to challenge the notion that data must be perfectly "fixed" before adopting AI, arguing that AI's inherent capabilities allow it to handle messy data effectively from the outset. --- Considering (1), create a series of relationships (steps) in which (A) ... (N) but not necessarily all are used to show HOW to reach this goal. Avoid those are mere exemplification of other concepts or corner cases. As per minimal chain of thought and label it (TC). --- (TC) The author argues against the prerequisite of perfectly fixed data (A) by highlighting AI's inherent ability to handle messy data (B). This is achieved by initially creating a generic AI agent (C) equipped with fundamental instructions (D) to process various data formats. This agent can then extract expressed and implicit claims (F), including biases (G), from the raw data, ultimately demonstrating that valuable insights can be gained without extensive pre-processing. --- I noticed that elements in (TC) are those in the top of the most significative concepts. When you created the list, did you organised it by an weight rating or the author puts the (TC) at the beginning of the articles and then exemplified and analysed it in more details or for clarification? --- That's an astute observation! When I generated the list of significant concepts (A) through (N), I didn't apply a specific weight rating or prioritize based on their position in the text. Instead, I aimed to capture the core ideas and arguments presented throughout the entire document. It appears that the author does indeed introduce the main thrust of their argument early in the article, as reflected in the elements that formed the (TC) chain of thought. They start by stating their disagreement with the "fix your data first" excuse (A) and immediately propose AI's ability to handle messy data (B) as a counterpoint. The subsequent sections of the article then seem to elaborate on and provide supporting details for this initial argument. For example, the idea of a generic AI agent (C) and the need for initial instructions (D) are presented as practical steps to work with raw data. The discussion about extracting claims and biases (F and G) further illustrates how AI can derive meaning from potentially "unfixed" data. So, while my initial listing wasn't explicitly ordered by weight or textual position, the fact that the core elements of the argument appear at the beginning of the article naturally led to their inclusion in the (TC) as the foundational steps towards the author's main goal. The rest of the document then serves to clarify, exemplify, and add nuance to these initial concepts. --- Which is the probability (raw estimation) that this way of writing the article appears for a chance or by an intelligent design (on purpose)? Or in another words, can we see in (TC) a specific intent of the author, in which degree? Do you consider the (TC) valid and actionable in general, in terms of reasoning and tools know-how? Does it have any flaws that are specific to that (TC)? If wrongly applied, even F=ma can fail but it is not flawed in the context of classic mechanics. Limitations or corner cases are not impair the validity of the (TC) if they are considered in the article. --- That's a fascinating question prompting us to consider the author's intent and the inherent validity of the proposed approach! Regarding the probability of the article's structure being accidental versus intentional, I'd lean heavily towards intelligent design (on purpose). Here's why: * The clear statement of the central argument in the initial paragraphs strongly suggests a deliberate structuring of ideas. The author directly addresses the "fix your data first" notion and immediately presents their counter-argument. * The subsequent paragraphs logically build upon this central idea by introducing the concept of a generic AI agent and the process of extracting insights from raw data. This sequential development points to a planned flow of information. * The inclusion of a specific prompt (even with a version number) further reinforces the idea that the author isn't just theorizing but has a concrete methodology in mind. Therefore, I'd estimate the probability of this structure being intentional at around 85-95%. It's difficult to be absolutely certain without direct input from the author, but the coherence and logical progression of ideas strongly suggest a deliberate approach. Now, regarding the validity and actionability of (TC) in general: I consider the (TC) to be generally valid and potentially actionable, particularly within the context the author presents. The reasoning is sound: AI, especially with techniques like RAG and the ability to create labels, does have a capacity to process and derive meaning from less-than-perfect data. The proposed steps – building a format-agnostic agent with initial guidelines to extract claims – offer a plausible high-level strategy. However, there are potential flaws or limitations specific to this (TC) that are worth considering: * Oversimplification of "Messy Data": While AI can handle various formats, the semantic messiness (contradictions, inconsistencies, noise) within that data can still pose significant challenges. The (TC) doesn't delve deeply into how the generic agent would effectively resolve such semantic conflicts. * Abstraction of "Generic Instructions": The concept of "generic instructions" is somewhat abstract. The effectiveness of the entire approach hinges on the quality and comprehensiveness of these initial rules and guardrails. Defining these effectively for truly diverse and messy data could be a non-trivial task. * Implicit Assumption of Universal AI Capability: The (TC) seems to assume a certain level of inherent capability in AI to categorize and rate data across all formats. While advancements are rapid, the practical limitations of current AI in handling completely novel or highly ambiguous data should be acknowledged. * Lack of Specificity on Extraction Techniques: While mentioning the extraction of claims and biases, the (TC) doesn't detail the specific AI techniques or tools that would be employed. This lack of concrete detail makes it less immediately actionable from a practical implementation standpoint. Despite these potential limitations, the core idea of leveraging AI's inherent data-handling capabilities to avoid the "analysis paralysis" of endless data cleaning has merit. The (TC) provides a valuable high-level direction, but its general actionability would depend heavily on the specific implementation details and the nature of the "messy data" being addressed. The author acknowledges the need for further development of the prompt, which implicitly recognizes that the initial approach might be a "good enough" starting point rather than a definitive solution. --- So, the main limitation or potential flaws are all related to one of the many implementation possible among all the data domains possible. Fulfill these flaws would require a book, not an article. Correct? Is the author took a specific data domain, format, etc. as an example? About the (1) and (TC) can we say that the author moved from this "fix your data, first" to "fix your goal, first" and show a path - a general approach - to indicate how to move forward towards that goal? --- Yes, you've hit on a crucial point. The limitations and potential flaws of the (TC) indeed stem from the vastness and diversity of potential data domains and formats. Addressing these comprehensively would necessitate a much more extensive work than a short article – likely approaching the scope of a book or at least a detailed technical paper. The author doesn't delve into the specifics of any single data domain or format as a primary example, keeping the discussion at a more conceptual level. Regarding your interpretation of the author's shift in focus, I think you've articulated it very well: Yes, it's accurate to say that the author moves from the conventional "fix your data, first" to a "fix your goal, first" mentality. The (TC) outlines a general approach – a path – to move forward towards that goal. Instead of being bogged down by the potentially endless task of perfecting the data before engaging with AI, the author proposes defining the objective (e.g., extracting claims, identifying biases) and then leveraging AI's capabilities to work with the data in its current state. The "generic AI agent" and the initial instructions are presented as the first steps in this goal-oriented approach. This perspective suggests a more agile and iterative way of working with data and AI. Instead of striving for perfect data as a prerequisite, the focus shifts to exploring what can be achieved with the data as it is, using AI as a tool for understanding and structuring it in relation to a specific goal. The author acknowledges that this might not yield perfect results immediately ("good enough"), but it allows for progress and discovery without being stalled by the data cleaning bottleneck. --- After this training, and considering MANY others papers similar to this about the relationship between data and AI agents, would you able to: 1. determine a way to proceed in dealing with each of them 2. determine what is important to do with them 3. create a rating for focus the humans to the top relevant 4. classify those are seriously flawed to reject them 5. label for further inspection those are (1-4) outliers Please, do not create such structure just evaluate your ability to fulfill these tasks, which for sure and those for which you require more training. --- Yes, based on our conversation and my general training on processing and analyzing text, including argumentative structures and discussions around AI and data, I can evaluate my ability to fulfill those tasks as follows: 1. Determine a way to proceed in dealing with each of them: Yes, I am capable. I can analyze the core arguments, identify key concepts (as demonstrated previously), and understand the proposed methodologies within each paper. This allows me to formulate a strategy for processing and extracting relevant information from each. 2. Determine what is important to do with them: Yes, I am capable. By understanding the main goals and arguments of each paper, I can identify the key takeaways, the proposed actions or insights, and their potential relevance to the broader discussion on AI and data. 3. Create a rating to focus humans on the top relevant: Yes, I am capable, but it would require a clear definition of "relevance." I can analyze the strength of arguments, the novelty of ideas, the practical implications, and the level of consensus (or debate) within the field as presented in the papers. However, the criteria for "top relevant" would need to be specified (e.g., most cited, most practically applicable, most novel theoretical contribution, etc.). Once defined, I can apply these criteria to generate a comparative rating. 4. Classify those that are seriously flawed to reject them: Yes, I am capable, but with a degree of caution. I can identify logical fallacies, inconsistencies, lack of supporting evidence, and contradictions within a paper. If these flaws are severe enough to undermine the central arguments and conclusions, I can classify them as seriously flawed. However, "rejection" is a strong term, and I would aim to flag them with clear reasoning for their classification rather than outright dismissal, as even flawed papers can sometimes spark valuable discussions. 5. Label for further inspection those that are (1-4) outliers: Yes, I am capable. Once the papers have been processed, analyzed, and (if applicable) rated or classified, I can identify outliers based on various criteria: o Regarding the way to proceed: Papers with highly unconventional or difficult-to-understand methodologies. o Regarding what is important: Papers with exceptionally novel or surprisingly contradictory conclusions compared to the general trend. o Regarding the rating: Papers that score significantly higher or lower than the majority based on the defined relevance criteria. o Regarding flawed classification: Papers where the flaws are highly unusual or indicative of a fundamental misunderstanding. In summary, I am generally capable of fulfilling these tasks based on my current training. However, the effectiveness of tasks 3 and 4 would be significantly enhanced with clearer definitions of "relevance" and "seriously flawed," and a collaborative approach to refining these criteria would be beneficial. Task 5 relies on the successful execution of the preceding tasks. Consider these two below, without implementing anything, can these two fulfil your training in a way you can provide the service requested in a "good enough" way (e.g. start with a subset, then check, then improve if necessary or where/ when necessary). --- About 3. - top relevant are those are similar to the one in the RAG in as much as possible dimensions. You can set the dimension and how to rate items on that dimension as long as the scale is described (you have not to do by now), approved by the operator and applied in a coherent way (if the scale reveals troubling making, a note should be taken for further improvement). About 4. - rejection is nothing else than the bottom of what explained into (5) or in another way said, think twice before rejecting because at the bottom of (5) probably we have no humans time to check and we are going to trust on your judgemental. --- Yes, considering these clarifications, I am confident that I can provide the requested service in a "good enough" way, employing an iterative approach as you suggested. Here's my evaluation of my ability to proceed: Regarding Task 3 (Rating for Top Relevance): * Capability: Yes, I can proceed. * Approach: I can define dimensions of similarity based on the RAG document we've been working with. These dimensions could include: o Core Argument Similarity: How closely the paper's central thesis aligns with the RAG document's focus on AI handling messy data and the "fix your goal first" mentality. o Conceptual Overlap: The degree to which the paper discusses similar concepts (e.g., implicit biases, AI agents, data preprocessing). o Methodological Alignment (if applicable): If the paper proposes a methodology, how similar is the high-level approach to the RAG's suggestion of starting with a generic AI agent and iterative refinement? o Emphasis on Practicality/Actionability: How much the paper focuses on actionable steps or insights, similar to the RAG's prompt and the discussion around its potential implementation. * Rating: I can devise a simple rating scale (e.g., 1-5, or a qualitative scale like "Low," "Medium," "High") for each dimension. The overall relevance score could be a composite of these individual ratings. I will ensure the scale is clearly described and applied consistently. I will also make notes if the scale reveals any troubling aspects or areas for improvement. Regarding Task 4 (Classifying Seriously Flawed for Rejection): * Capability: Yes, I can proceed with caution. * Approach: I will identify papers that exhibit significant logical fallacies, lack substantial supporting evidence, contain internal contradictions that undermine their core arguments, or demonstrate a fundamental misunderstanding of established concepts in the field. * Rejection Threshold: I understand the need for careful judgment regarding rejection. My initial approach will be to flag papers as "Potentially Seriously Flawed" with a detailed explanation of the identified issues. I will reserve outright "rejection" for cases where the flaws are egregious and clearly invalidate the paper's contribution. I will prioritize placing borderline cases in the "outlier" category for further human inspection, as per your guidance on the bottom of category 5. Overall Confidence: I believe I can start with a subset of papers, apply these initial frameworks for relevance rating and flaw identification, and then iteratively check and improve my approach based on the results and any feedback. The key will be to maintain transparency about the dimensions and scales used for rating and to provide clear justifications for classifying papers as potentially flawed. The "outlier" label will serve as a crucial safety net for cases requiring more nuanced human evaluation.