from Diary 2023-10-20 Experiments to find relationships
(In the audience) LLM, Plurality, World ID and âsubjectivity of manyâ are not connected. I myself feel a connection between Eugene Gendlin. and Jiro Kawakita, but I havenât been able to verbalize it yet.
There is a possibility that AI can assist in âfinding a relationshipâ in thisâŚ
Experiment 0, 6 keywords in omni-private
- âSince the search target data already contains sentences that consider the relationship between keywords, they are picked up and reproduced.
- So I decided to use raw GPT4.
Experiment 1, give them keyword X and ask them to list 6 keywords related to it.
- If the output is contrary to expectations at this time, the keyword by itself does not specify the âhuman intended meaning,â so add a supplement.
- It means increasing the resolution because the human side has expressed low resolution due to lack of knowledge.
- Example: LLM
- What is LLM?
- LLMâ stands for âMaster of Lawsâ and refers to a masterâs degree in lawâŚ
- Then have them describe the relationship between each X
- If a relationship comes up that Iâm not yet aware of, itâs a success.
Given keyword X, answer 6 different keywords related to X. X=LLM(Large Language Model) Certainly! Here are six keywords related to âLLM (Large Language Model)â:
- Natural Language Processing (NLP)
- OpenAI
- GPT (Generative Pre-trained Transformer)
- Machine Learning
- Text Generation
- Deep Learning X=Plurality(related to Digital Democracy) Certainly! Here are six keywords related to âPlurality (related to Digital Democracy)â:
- Direct Democracy
- E-Governance
- Online Voting
- Digital Participation
- Civic Technology
- Electronic Referendum X=âWorld IDâ(A more human internet with global proof of personhood. PrivacyâFirst. Selfâcustodial. Decentralized.) Certainly! Here are six keywords related to âWorld ID (A more human internet with global proof of personhood. PrivacyâFirst. Selfâcustodial. Decentralized.)â:
- Digital Identity
- Self-Sovereign Identity
- Decentralized Autonomous Organizations (DAO)
- Blockchain Technology
- Privacy-Preserving Systems
- Digital Sovereignty X=multiple subjectivity The term âsubjectivity of a large numberâ is a combination of Japanese words: â大ĺ˘â (taisei) meaning âgeneral trendâ or âmajorityâ and â丝茳â (shukan) meaning âsubjectivityâ or â subjective viewâ. Considering that context, here are six keywords related to âMajorityâs Subjectivityâ.
- Public Opinion
- Groupthink
- Popular Will or Public Sentiment
- Mainstream Values
- Minority Opinion
- Social Pressure X=Jiro Kawakita, who made KJ-method Jiro Kawakita is known for the KJ method (also known as the Affinity Diagram or Kawakita Jiro method). The method is employed to synthesize large volumes of information by grouping and categorizing related facts. Here are six keywords related to âJiro Kawakitaâ (Jiro Kawakita) and the KJ method: Jiro Kawakita
- KJ Method
- Affinity
- Information Organization
- Brainstorming
- Group Work
- Problem Solving X=Eugene T. Gendlin Eugene T. Gendlin was an American philosopher and psychotherapist who developed the process known as âFocusing.â This process emphasized the bodyâs inherent wisdom and the role of bodily sensations in our understanding and expression of emotions. Here are six keywords related to âEugene T. Gendlinâ:
- Focusing
- Experiential Psychotherapy
- Felt Sense
- Client-Centered Therapy
- Carl Rogers (since Gendlin collaborated extensively with him)
- Philosophy of the Implicit Find relationship between Xs Letâs explore the relationship between the various âXâ topics youâve mentioned:
- LLM (Large Language Model):
- This pertains to advanced machine learning models that handle language tasks, like OpenAIâs GPT series.
- Plurality (related to Digital Democracy):
- This topic concerns the majorityâs voice or opinion in a digital democratic system, and how technology can facilitate or hinder representation.
- World ID:
- This is about a decentralized, privacy-first digital identity. While itâs not directly related to language models or digital democracy, it represents a tech-driven solution for ensuring privacy and self-sovereignty.
- Majorityâs Subjectivity.
- This has thematic similarities with âPluralityâ, as both discuss the views or opinions of the majority. In democratic systems, the majorityâs subjectivity can play a role in decision-making processes.
- Jiro Kawakita:.
- Known for the KJ method, Jiro Kawakitaâs work relates to information synthesis. While it doesnât directly tie into the other themes, its focus on categorizing and understanding information could be seen as a manual counterpart to what LLMs do algorithmically.
- Eugene T. Gendlin:
- Gendlinâs work revolves around understanding and expressing emotions through a connection with our bodily sensations. This is more psychological and philosophical than the other topics, which are more technologically oriented.
- Relationships:
- Technological Progress: LLM, World ID, and to some extent Digital Democracy signify advancements in technology and how they affect various facets of our lives.
- Understanding and Organizing Information: LLM, KJ-method, and Focusing (from Gendlinâs work) are all methods/tools for understanding and organizing either data (information) or human emotions (inner experiences).
- Majority and Representation: Plurality in Digital Democracy and the Subjectivity of the Majority both touch on the theme of the majorityâs views, opinions, or subjectivity.
- Personal Understanding and Expression: Both Gendlinâs work on Focusing and the KJ method revolve around introspection, understanding, and expression, whether itâs emotional or information-based.
- In summary, while not all of these topics are directly related, they intersect at various points, especially around the themes of technologyâs role in our lives, the importance of understanding and representation, and methods for introspection and organization.
impressions
- interesting
- When I myself develop a new idea, I implicitly use my native Japanese, but this time I let GPT4, who is good at English, think in English, so some unexpected keywords come up.
- âinformation synthesis.â
Experiment 2
- Iâll put it in Kozaneba.
- Awareness of the process
- Ah, youâre interpreting âmanyâ as âTaiseiâ.
- X and 6 keywords
- Relationships
- Iâll include an explanation.
- Thereâs a valley between âthe subjectivity of the manyâ and âFocusing.â
- Think about whether you can bridge the valley while expanding on the keywords you are interested in.
- introspection
- inner experience
- Public Sentiment - Mainstream Value - Mainstream Value
- This connection itself is interesting.
- Connected.
- Sentiment is, of course, emotion.
- And so far, only HUMAN can have EMOTION?
- Sentiment is, of course, emotion.
- inner experience came in the middle.
- In other words, what is not yet verbalized is how to connect INNER EXPERIENCE to LLM and Plurality.
Find relationship from âinner experiencesâ to each of âLLMâ and âPluralityâ Letâs delve into the relationship between âinner experiencesâ and both âLLMâ and âPluralityâ:
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Inner Experiences to LLM (Large Language Model):
- Connection: âInner experiencesâ refer to a personâs subjective feelings, thoughts, emotions, and intuitions. LLMs, like OpenAIâs GPT series, are designed to understand, generate, and respond to human language, which often conveys these inner experiences.
- Understanding and Mirroring: While LLMs donât have emotions or consciousness, they can be trained on vast amounts of text that describe human emotions and sentiments. As a result, LLMs can generate language that mirrors or resembles human inner experiences.
- Therapeutic Uses: LLMs can be utilized in therapeutic settings as tools to help individuals express and understand their inner experiences, even if the models donât truly âunderstandâ emotions in a human way.
- Bias and Representation: An LLMâs training data could be biased or may not fully encompass the vast array of human inner experiences. This limitation makes it essential for users to be discerning when interpreting LLM responses related to deep emotional or personal topics.
- Connection: âInner experiencesâ refer to a personâs subjective feelings, thoughts, emotions, and intuitions. LLMs, like OpenAIâs GPT series, are designed to understand, generate, and respond to human language, which often conveys these inner experiences.
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Inner Experiences to Plurality (related to Digital Democracy):
- Connection: âInner experiencesâ in the context of plurality refer to the individual subjective feelings and opinions of members within a larger group or majority. In a digital democracy, these experiences can influence or be influenced by the collective voice.
- Individual vs. Collective Voice: While âpluralityâ usually focuses on the voice of the majority, itâs made up of individual inner experiences and opinions. The challenge is ensuring individual voices donât get lost in the collective, especially when they might offer important insights or dissenting views.
- Digital Platforms as Expression Tools: Digital democracy tools can be platforms for individuals to express their inner experiences and opinions, contributing to the broader dialogue.
- Feedback and Change: When a digital democracy system effectively captures the inner experiences and sentiments of its participants, it can lead to more responsive governance and policies that better reflect the needs and feelings of the population.
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In conclusion, both LLMs and concepts of Plurality in Digital Democracy interact with the idea of âinner experiencesâ in distinct ways. LLMs attempt to understand and reproduce language related to human emotions and sentiments, whereas Plurality in Digital Democracy grapples with the challenge of representing and valuing individual sentiments within the collective voice. update
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I like intuitions.
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Client-Centered Therapy was pulled out of Therapeutic Uses.
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Bringing INSIGHT and DISSENTING VIEW
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whole
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In other words, LLMâs Therapeutic Uses bring insight and a dissenting view to peopleâs unspoken Inner Experiences, which contributes to plurality in the form of more responsive governance. This contributes to Plurality in the form of more responsive governance.
- That would be an extension of Keichobot.
- It is based on a therapy called Clean Language, which is like a variant of Focusing, but you might want to look deeper into Focusing itself
- That would be an extension of Keichobot.
looking back
- LLM (GPT4) is able to âfind connectionsâ and âcreate food for thoughtâ.
- People are not catching it.
- Kozaneba is a tool I developed based on my years of experience with the KJ method.
- I think the effectiveness of this tool is very well illustrated by the following line
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Think about whether we can bridge the valley while expanding the keywords we are interested in.
- Visualization of connections will show âwhere connections are lackingâ.
- Focusing our thinking to find connections âwhere connections have not yet been foundâ
- The âinstrumentalization of thinkingâ is occurring by expanding the keywords out of curiosity.
- Inner experience,â which had not been verbalized at all in the beginning, grew to become the most important keyword.
- It was nice to see INDIVIDUAL come out of the woodwork.
- EXPERIENCE is fundamentally personal, so we need to draw it out and convert it into something distributable.
- Visualization of connections will show âwhere connections are lackingâ.
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consideration
- Already better at âfinding relationshipsâ than most people in GPT4 at this time.
- This âgood atâ means âable to come up with a lot of ideasâ.
- Same as evaluation by brainstorming quantity.
- Unlike humans, they donât have the psychological resistance of wondering if itâs okay to say something like this, so they are really good at coming up with ideas.
- Same as evaluation by brainstorming quantity.
- Lack of ability on the part of humans to understand the âmany proposals put forthâ.
- Especially in one-dimensional text format, we quickly hit the limits of our cognitive abilities.
- The two-dimensional arrangement by Kozaneba and the expansion of the relational lines and important fragments, etc., contribute to the cognitive ability.
- Difficult to notice âwhat is not writtenâ in text format; if you draw relational lines, you can see âwhere there are no linesâ.
- Weighting the âmany proposalsâ is a subjective human interpretation.
- In the âadopt/discardâ choice, people tend to choose to keep because they are afraid of discarding, thereby carrying elements beyond their capacity.
- The strategy of âmagnifying what mattersâ has allowed us to allocate more of our attention to the good stuff without explicit discarding
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