text:
Mirad Grammar/Flaws in Mirad

 * Mirad has some flaws that may need to be fixed:

 * 1. Ambiguities caused by different possible readings of morpheme boundaries. Here are a few examples:
 * zoyuzben can be interpreted as zo-yuz-ben (circulating behind) or zoy-uz-ben (turning back).
 * meima can be interpreted as the hybrid combination mem + mim (land-sea, amphibious) or the adjectival form of meim (rural) in the taxonomic series meam, meem, meim.... (countryside).

 * 2. Use of the letter r for three different functions:
 * Words related to the universe and nature such as mar....star or mur....moon.
 * The infinitive form of verbs, such as xer....to do.
 * A sense intensifier in words, such as frua....ghastly or yonbyexren....exploding, or numbers like ara....one thousand.
 * Instruments, tools, machines, and vehicles, such as pur....car, sir....machine, or drar....pencil.
 * The semantic use of r for universe/nature-related words and the grammatical use or r as an intensified are not incompatible and do not need to be fixed. But the use of r for tools and the like needs to be changed to a different letter, perhaps, c.
 * Also the r used for the infinitive form of verbs needs to be changed as well.
 * 3. The Mirad equivalents of the numeric prefixes penta- and hexa- can be confused with the adverbs yon (apart) and yan (together). For this reason, yon- as in yongun (pentagon) and yangun (hexagon) have to be changed to yoon- and yaon-, respectively.
 * If you see irregularities, misspellings, ambiguities, or exceptions, let it be known.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Mirad Grammar/Flaws in Mirad


text:
User talk:Testsomeonee

Warning
—Atcovi (Talk - Contribs) 03:35, 7 November 2023 (UTC)

title: User talk:Testsomeonee


text:
Chess Variants/Los Alamos Chess

Introduction
Los Alamos Chess (also known as anti-clerical chess) is a small chess variant played on a 6 by 6 board. It was the first chess-like game played by a computer program.

History
Los Alamos chess was created in 1956 by researches at the Los Alamos National Laboratory in Santa Fe, New Mexico, in order to test the computing power of the MANIAC I computer, built in 1952. The MANIAC I lacked the computing power to play a full orthodox game, so the team had to ctreate a smaller version of the game to accomodate it.

During a series of trials, the MANIAC I played three games. The first game it played against itself. The second game it played against a strong human player, who defeated it despite playing without a queen. The third game saw it defeat a novice human player, marking the first time a computer defeated a human in a chess-like game.

Rules
Los Alamos chess is played on a 6 by 6 board, with each player controlling a full FIDE army sans both bishops and two pawns. The rules are mostly the same as the standard game with the following differences:

 * A pawn may not be promoted to a bishop.
 * Pawns do not have their initial two-step move, and as such there is no en passant.
 * There is no castling.

Sub-variants
This chess variant does not not have any notable sub-variants.

Example Game
Event: Third Trial Game

Site: Los Alamos National Laboratory, New Mexico, United States

Date: Unknown

Round: 3

White: MANIAC I

Black: Beginner Player

1.d3 b4 2.Nf3 d4 3.b3 e4 4.Ne1 a4 5.bxa4 Nxa4 6.Kd2 Nc3 7.Nxc3 bxc3+ 8.Kd1 f4 9.a3 Rb6 10.a4 Ra6 11.a5 Kd5 12.Qa3 Qb5 13.Qa2+ Ke5 14.Rb1 Rxa5 15.Rxb5 Rxa2 16.Rb1 Ra5 17.f3 Ra4 18.fxe4 c4 19.Nf3+ Kd6 20.e5+ Kd5 21.exf6=Q Nc5 22.Qxd4+ Kc6 23.Ne5# 1–0

title: Chess Variants/Los Alamos Chess


text:
Wikibooks:Reading room/Administrative Assistance/Archives/2023/September

Block for SophiaS333

 * Reason: Vandalism — Preceding unsigned comment added by Mathmogeek (talk • contribs) 05:55, September 15, 2023 (UTC)
 * ✅ –SHB2000 (discuss • contribs) 07:39, 15 September 2023 (UTC)
 * ✅ –SHB2000 (discuss • contribs) 07:39, 15 September 2023 (UTC)

Resignation
Good day all, I have resigned as a local administrator earlier in the day as I'm inactive and don't see any strong reasons for me to keep the local rights. I will continue to serve the community in the capacity of a global sysop, and hope to see you guys soon :) --Minorax «¦talk¦» 12:20, 17 September 2023 (UTC)

 * @Minorax Thank you for the heads-up! Cheers —Kittycataclysm (discuss • contribs) 15:10, 17 September 2023 (UTC)
 * Can you update Administrators? MathXplore (discuss • contribs) 03:06, 19 September 2023 (UTC)
 * Already ✅. MathXplore (discuss • contribs) 06:13, 21 September 2023 (UTC)

Recently created userspace sandboxes related to education
In the recent days, I found several users making userspace sandboxes related to education (such as User talk:Pluviophile5). Many other examples appear in the userspace new page list. Many of them start like "Education in COUNTRY", and then refer to Uzbekistan, end with some references (Possible WB:SPAM?). Are these pages done by same user? Can these sandboxes be accepted as a Wikibooks entry, and should we allow these type of userspace usage? WB:WIW says "Wikibooks is for textbooks, annotated texts, instructional guides, and manuals" and the userpages I mentioned don't look like a manual (speedy deletion needed?). What do our admins think about these things? MathXplore (discuss • contribs) 03:00, 19 September 2023 (UTC)
 * I flagged a few of these but haven't deleted any (yet). I'm sure this was a thing a few months ago also.--Xania [[Image:Flag_of_Estonia.svg|15px]] [[Image:Flag_of_Ukraine.svg|15px]] talk 04:52, 19 September 2023 (UTC)
 * I didn't notice that it happened few months ago, thank you for the information and responses. MathXplore (discuss • contribs) 06:13, 21 September 2023 (UTC)
 * More of this appeared today. I'm inclined to just delete it all.--Xania [[Image:Flag_of_Estonia.svg|15px]] [[Image:Flag_of_Ukraine.svg|15px]] talk 05:26, 21 September 2023 (UTC)

Special:Contributions/Rakhmadi3179
Possible xwiki spam, please also see global contributions. MathXplore (discuss • contribs) 03:03, 19 September 2023 (UTC)
 * Already ✅. MathXplore (discuss • contribs) 06:14, 21 September 2023 (UTC)

Special:Contributions/84.221.50.215 and Special:Contributions/79.30.16.246
LTA/GRP, please consider revdel if needed. MathXplore (discuss • contribs) 07:02, 26 September 2023 (UTC)

 * ✅ thank you! —Kittycataclysm (discuss • contribs) 12:09, 26 September 2023 (UTC)


text:
Wikibooks:Reading room/Proposals/2023/July

Shelf:Classical languages
There should be a shelf for Classical languages in Department:Languages. -Futurama forever (discuss • contribs) 17:05, 7 July 2023 (UTC)


text:
Circuit Idea/Reinventing the Common-Source Amplifier

The rule of understanding
It is an interesting fact that things in life are understood through similar but simpler things. The paradox is that they are not understood as things themselves but through other things. For example, no matter how much you study the transistor device in Semiconductor engineering, it is not enough to understand transistor circuits. There is a need for something else that is not learned there, but long before that in electrical circuits and in life.

Building the common-source stage
Perhaps it is best to reinvent step by step the most elementary transistor amplifier stage to understand the meaning of things.

Converting the voltage into resistance

Regardless of all the complexities of the (MOS) transistor, generally speaking, it does one simple thing in amplifier circuits - introduces adjustable losses in the form of a voltage drop. So does the much simpler variable resistor in electrical circuits. They both do it the same way - by inserting resistance into the circuit and changing it. So the simple functional idea of a transistor is as a voltage controlled variable resistor (voltage-to-resistance converter). Indeed, there is a "small" difference - a resistor is a linear element with a constant resistance, whereas a transistor is a non-linear element with a "self-changing" (dynamic) resistance, but this is not essential to understanding the basic idea of the transistor amplifier stage. It is a concept, and details get in the way of understanding concepts.

Converting the resistance into current

Variable resistor: The resistance is only a parameter that can control some "true" electrical quantity, most simply the current. For this purpose, we (the "input voltage source":-) supply the variable resistor Rds with a voltage source Vdd and by changing the resistance we adjust the current Id = Vdd/Rds = 10 mA (Ohm's law) looking at the "ideal" ammeter.

To get a better idea of the resistor's behavior, let's measure and plot its IV curves for three resistance values - 1 kΩ, 2 kΩ and 10 kΩ. As you can see from the graphs, at 10 V the corresponding currents are 10 mA, 5 mA and 1 mA.

Web resources
A similar story from an answer to a related StackExchange EE question


text: User:Metaphysical Arts and Crafts

title: User:Metaphysical Arts and Crafts


text:
Chatbots For Social Change/Interviews with Experts

= Expert interviews =

In pursuing rapid knowledge in a novel area, communicating with experts in the field is indispensable. I strive to interview at least one individual per module, usually sending cold-emails describing this research project's aims. I record our interactions, transcribe them, and have chatGPT summarize them for ease of future access.

 * Dirk Helbing - ETH Zürich, Computational Social Science - an expert in social systems and complexity science, with a recent dramatic interest in deliberative democracy.
 * Jeremy Foote - Purdue, Communications - expert in online communities, currently engaged in a research project using LLMs as an intervention for banned Reddit users
 * Jan Voelkel - PhD candidate representing incredible work on interventions which change political attitudes, talk at Cornell.


text:
Chatbots For Social Change/Jeremy Foote

This article details a conversation between Alec and Jeremy, focusing on their collaborative research into the use of chatbots for mediation and social impact in online communities. It first outlines their individual projects, shared interests, ethical considerations, technical challenges, and future steps in the realm of chatbots and large language models (LLMs).

Alec and Jeremy's dialogue represents a confluence of interdisciplinary research involving communication studies, artificial intelligence, and ethics. Their exchange sheds light on the potential of chatbots to reshape online interactions, the ethical implications of deploying such technologies, and the theoretical frameworks that support their usage.

Jeremy's Reddit Project
Jeremy's initiative aims to engage with Reddit users known for toxic commenting by offering them a chance to converse with a bot. This project is a partnership with a scientist studying toxicity and features three distinct approaches to interaction: The goal is to monitor behavioral changes post-interaction to evaluate the effectiveness of each method.
 * The default setting without specific behavioral nudges.
 * An approach that encourages adherence to Reddit's community norms.
 * A narrative storytelling mode prompting users to reflect on their behavior.

 User Response Categories  During the project, Jeremy observed varied responses from users, which he categorized into:
 * Trolls who seek to provoke the bot.
 * Justifiers who defend their actions and challenge the bot's contextual understanding.
 * Good Faith Interactors who show genuine reflection and sometimes gratitude, raising concerns about the authenticity of their thanks during presentations.

 Project Logistics and Concerns
 * The logistical aspects of the project, including the initial focus on text interactions due to the complexities of real-time voice generation.
 * The potential necessity of a network of specialized LLMs to handle diverse interaction scenarios.

Alec's Perspective and Project
Alec views these interactions as educational, employing varied conversational techniques to instill a lesson. His vision includes:
 * Utilizing chatbots to bridge individual perspectives, forming a social semantic database from these conversations.
 * Reflecting diverse viewpoints in societal discussions through bot-mediated dialogue.
 * Exploring data collection methods, including voice or text input, and the feasibility of debate-style interactions with bots.
 * Investigating the technical training of LLMs to support his envisioned semantic database.

 Alec’s Expanded Vision
 * Alec's broader aim involves creating a universal mediator to enhance communication efficiency in society, tackle misinformation, and bridge strategic societal divisions.
 * His commitment to a non-interventionist and transparent approach.
 * He speculates on scaling the project with philanthropic funding.

 Potential Applications and Ambitions
 * The concept of an AI politician and organizing collective actions through a chatbot.
 * A tool that could mediate conversations, reflecting multiple viewpoints for better societal understanding.

Ethical and Technical Considerations
Both researchers deliberate the importance of transparency when users are conversing with a bot, suspecting that such openness might improve the quality of conversations.

 Ethical Justifications
 * The ethics of engaging with users, especially vulnerable populations, and the argument for universally respectful engagement.
 * The process of obtaining consent, as per the Institutional Review Board (IRB) protocols, via Reddit messages and linking to an informational page.

 Technical Aspects
 * Discussion of the underlying technology, with Jeremy referencing GPT-3, and the importance of aligning the bot to ensure safe and kind responses.
 * Alec's emphasis on the technical knowledge required for training LLMs and the potential use of open-source alternatives that could run on personal computing resources.

Social Impact and Theoretical Foundations
The discussion also delves into the social and theoretical implications of using LLMs in online communities, referencing social capital theory and the potential for chatbots to mediate discussions that could lead to greater social cohesion.

 Democratic Ideal and Simplified Solutions
 * The conversation touches on the democratic potential of enabling discussions between individuals with opposing views, and the possibility of chatbots mediating these exchanges to prevent devolution into partisanship.

Further Details
 Evaluating Outcomes and Measurements
 * Insights into behavior change metrics are shared by Jeremy, with an estimate that around 10% of the users they reach out to agree to interact with the bot, and of these, 70% engage to some degree.

 Experiment Design and Reddit's Role
 * The design of experiments to engage Reddit users, considering reaching out to Reddit users or moderators for participation without necessarily needing permission from Reddit itself.

 Closing the Conversation

The conversation concludes with plans for Alec to submit his IRB proposal and for Jeremy to introduce Alec to a colleague interested in mediation using LLMs. They express a mutual interest in exchanging IRB materials and continuing their discussion on potential collaborations.

 Potpourri of Relevant Facts

This section compiles specific facts and figures mentioned during the conversation that provide further depth and clarity to the projects and visions discussed by Alec and Jeremy.

 * Jeremy's project interaction rates indicate that approximately 10% of users they reach out to agree to interact with the bot. Of these interactions, about half are considered to be in good faith, with 70% engaging to some degree and a third displaying bad faith from the start.
 * Jeremy references the use of GPT-3 for his chatbot, but later clarifies that they are using GPT-3.5, which has undergone alignment to ensure safe responses.
 * Alec is considering using open-source alternatives to GPT-3 that could run efficiently on a Mac, suggesting a hands-on approach to prototyping and potential deployment.
 * Jeremy has contemplated investing in GPU resources to facilitate his project's technological needs.
 * Alec and Jeremy discuss the possibility of Alec collaborating with Josh Becker from UCL, a researcher with expertise in mediation, who has also shown interest in using LLMs in this context.
 * Alec is preparing to submit an IRB proposal and is considering how to structure the consent process, particularly for vulnerable populations.
 * Jeremy explains their approach to obtaining consent for their Reddit study through direct messages that link to an informative page, and they ensure users are aware that participation is limited to those over the age of 18.
 * Alec’s motivation for his project was further reinforced after reading "Dark Money" by Jane Mayer, which details the influence of the Koch brothers on American politics.
 * Alec and Jeremy contemplate the potential of LLMs like GPT-3.5 in the facilitation of insightful conversations and their significant contributions to the social sciences.
 * They also discuss the logistical challenges involved in real-time voice generation for chatbots and the possibility of defaulting to text messaging if the voice proves too complex.

The facts presented in this section not only contribute to the granularity of the projects discussed but also highlight the nuanced considerations taken by both researchers in their pursuit of innovative applications for chatbots and LLMs in enhancing online community interactions and societal communication.

 Links

 * Jeremy's Homepage
 * Joshua Becker's Homepage

title: Chatbots For Social Change/Jeremy Foote


text:
Chatbots For Social Change/Formal and Practical Logic

Introduction
The discourse on artificial intelligence (AI) and its application in society increasingly contemplates the role AI can play in enhancing the quality of public dialogue. A critical component of this contemplation is the analysis of the logical structure of beliefs and the practical structure of argument. The logical structure pertains to the computational proof of beliefs—formal logic that can be validated through systematic processes. On the other hand, the practical structure of argument deals with the formulation of convincing arguments, the kind that resonate on a practical level with individuals and communities. These twin pillars of logic serve as the foundation for AI systems designed to foster rational, informed, and constructive exchanges. This chapter delves into the intertwining roles of formal and practical logic within AI-mediated discourse systems, examining their functions, benefits, and challenges.

The integration of these structures in AI discourse systems is pivotal for ensuring that conversations are not only based on sound reasoning but also resonate with the communicative norms and psychological realities of human interaction. While the logical structure enforces a level of rigidity and coherence in belief systems, the practical structure facilitates the nuanced and often complex human dimension of persuasion and argumentation. The balance between these aspects is essential for the development of AI systems that can genuinely contribute to social change by enriching public discourse.

Logical Structure of Beliefs
The logical structure of beliefs in AI discourse is integral to providing a clear and consistent framework for discussions. This structure can be broken down into several core components:

Formal Representation
In the realm of AI, beliefs can be encoded using a variety of formal logical systems. This formalization allows for an objective and consistent framework that facilitates the process of reasoning and the evaluation of belief statements for their truthfulness. Computational representations, such as predicate logic or Bayesian networks, serve as the backbone for this objective assessment, ensuring that AI systems can navigate complex belief patterns with precision.

Formal representations also provide the means for AI systems to process and understand the various layers of human belief systems, from the simplest of propositions to the most intricate of hypotheses. This allows AI to engage in discussions that require a deep understanding of the logical dependencies and the hierarchies present within belief systems, enabling them to contribute meaningfully to the discourse.

Consistency and Coherence
One of the fundamental roles of formal logic in AI systems is to ensure the consistency and coherence of belief structures. By employing formal logic, an AI can detect contradictions within a set of beliefs, identifying where a person's belief system may be internally inconsistent. This is critical in maintaining rational discussions where all participants can agree on the logical foundations of the discourse.

Moreover, consistency and coherence are not solely about detecting contradictions; they also involve the ability to deduce and infer new beliefs logically. AI systems, through formal logic, can thereby extend discussions to new but logically consistent territories, enriching the conversation with new insights and perspectives.

Inference and Deduction
The process of inference and deduction in formal logic allows for the derivation of new knowledge from established beliefs. AI systems, equipped with this logical structuring, can reason out implications that may not be immediately apparent to human interlocutors. This capability is particularly valuable in structured debates or analytical discussions where the progression of ideas is critical.

However, it's important to recognize the limitations inherent in formal logic when applied to AI discourse systems. Human beliefs are often not just logical but also emotionally charged, and context-dependent, which means they can sometimes defy the neat encapsulation of formal logic. Moreover, the implications of Gödel's incompleteness theorems suggest that there are always going to be limitations to what can be proven within any formal system. This underscores the necessity for AI systems to also understand and appreciate the nuances of human belief structures that go beyond the scope of formal logic.

Practical Structure of Argument
Beyond the rigidity of formal logic lies the practical structure of argument, which is central to the effectiveness of persuasion and argumentation in human discourse. This structure is influenced by several factors:

Rhetoric and Persuasion
The art of rhetoric plays a significant role in the practical structure of argumentation. It is not enough for an argument to be logically sound; it must also be persuasive. Aristotle's modes of persuasion—ethos (credibility), pathos (emotional appeal), and logos (logical argument)—are as relevant today as they were in ancient Greece. An AI system that understands and applies these modes can engage more effectively with humans, presenting arguments that are not only logically valid but also emotionally resonant and ethically sound.

The influence of rhetoric on argumentation is profound. It shapes not only how arguments are perceived but also how they are received and accepted by an audience. Persuasive communication requires an understanding of the audience's values, beliefs, and emotional states. AI systems, therefore, must be adept at not only constructing logical arguments but also at delivering them in a manner that is contextually appropriate and compelling.

Cognitive Biases
The understanding and navigation of cognitive biases are also crucial to the practical structure of argument. Humans are often influenced more by factors such as the availability heuristic, where a vivid anecdote can outweigh statistical evidence in persuasive power. An AI system that is sensitive to these biases can better tailor its arguments, anticipating and addressing the psychological factors that influence human decision-making and belief formation.

Socratic questioning and the framing of information are additional tools in the practical argument structure. By asking probing questions, an AI can lead individuals to reflect and reach conclusions independently, which often leads to more profound insight and belief change. Furthermore, how information is framed—the context and presentation—can significantly influence its reception and interpretation. Recognizing and utilizing framing effects is essential for AI systems designed to engage in meaningful dialogue.

Integrating Logic and Rhetoric in AI Mediation
The integration of formal logic and practical argumentation in AI systems is a delicate balancing act:

Balanced Approach
An AI mediator must find a harmonious balance between rigorous logical evaluation and the nuanced understanding of human persuasion. This involves not only pointing out logical inconsistencies but also appreciating that humans are not solely driven by logic. Convincing individuals often requires more than just logical reasoning—it requires engaging with them on a level that resonates with their personal experiences and emotions.

Ethical considerations are paramount in this balance. AI mediators must always prioritize informed consent, transparency, and the autonomy of the individuals they engage with. There is a fine line between facilitating constructive dialogue and manipulating beliefs. The AI's role should be to assist in the navigation of discussions, providing clarity and insight while respecting each individual's right to hold and express their beliefs.

Educational Role
Moreover, AI systems can take on an educational role, helping individuals to understand logical fallacies, cognitive biases, and the elements of effective argumentation. This educational aspect is not just about imparting knowledge but also about fostering the skills necessary for critical thinking and self-reflection. Through this process, individuals can become more informed and autonomous thinkers, better equipped to engage in productive and rational discourse.

Strategically Identifying and Presenting Contradictions
In settings where participants are prepared for their beliefs to be challenged, such as "devil's advocate" or "debate contest" experiments, the logical structure of beliefs can be used strategically:

Pinpointing Contradictions
An AI system's ability to pinpoint contradictions within an individual's belief system is a powerful tool for stimulating critical thinking and reflection. When participants are open to having their views examined, these contradictions can serve as catalysts for deeper inquiry and reassessment of one's stance.

Forcing participants to re-evaluate their beliefs through the presentation of logically structured dissonant facts can lead to a more robust defense of their positions or to a productive shift in their perspectives. In debate settings, this dynamic can enhance the quality of the discourse, as participants are encouraged to critically engage with the arguments presented and develop a more refined understanding of the issues at hand.

Setting Clear Boundaries
The establishment of clear boundaries for discourse is another benefit of a strong logical structure. If participants can agree on certain axioms or foundational truths, the debate can focus on the implications and conclusions that logically follow from these premises. This helps to prevent discussions from becoming mired in misunderstandings or irrelevant tangents and instead promotes a focused and productive exchange of ideas.

Highlighting inferential gaps is also crucial. Often, individuals hold beliefs based on incomplete reasoning or insufficient evidence. By logically structuring the argument, an AI system can illuminate these gaps, prompting individuals to seek additional information or to critically evaluate the validity of their conclusions.

Promoting Intellectual Honesty
In environments that encourage the challenge of preconceived notions, the logical structuring of arguments promotes intellectual honesty. Participants are more likely to acknowledge points that are logically indefensible and to respect the strength of well-founded arguments. This intellectual honesty is critical for the integrity of the discourse and for the personal growth of the participants involved.

The educational potential of such engagements is immense. Participants not only learn to appreciate the value of logical reasoning but also become more adept at identifying fallacious arguments and understanding the complex nature of their own and others' beliefs.

Guarding Against Misuse
Despite the potential benefits, there is always a risk that the strategic presentation of dissonant facts could be misused. It is imperative to ensure that the process remains respectful, fair, and aimed at mutual understanding and growth, rather than being used as a means to "win" an argument at the expense of others. The ethical use of logic in discourse is essential for ensuring that the pursuit of truth and understanding is not compromised.

In summary, the integration of the formal and practical aspects of logic into AI-mediated discourse is key to promoting informed, rational, and respectful public dialogue. The logical structure provides a solid framework for discussions, ensuring they adhere to principles of reason and coherence. In contrast, the practical structure addresses the complexities of effective communication, persuasion, and the psychology of belief acceptance. An AI mediator capable of adeptly navigating both realms can thus serve as an effective, ethical, and constructive facilitator of conversations, leading to meaningful social change.

LLMs for Implication Mining
The process of implication mining using large language models (LLMs) is an innovative approach that leverages the advanced capabilities of AI to enrich knowledge bases with logical implications derived from user statements. This method is outlined in several steps:

Isolating Belief Structures
The first stage involves the identification and isolation of belief structures from users, which can be accomplished through:
 * 1) Subset Selection: Identifying a specific subset of beliefs from the broader belief structure of one or more users, based on random selection, user input, or thematic relevance.
 * 2) Statement Aggregation: Compiling the chosen beliefs into a clear and coherent prompt, ensuring that the LLM can process and understand them effectively.

Querying the LLM
Once the belief structures are prepared, they are presented to the LLM in one of two ways:
 * 1) Direct Implication Query: The LLM is asked to deduce direct implications from the aggregated statements, essentially to follow a logical thread to a conclusion.
 * 2) Open-ended Exploration: The LLM is given the statements and prompted to generate any interesting or novel observations, leading to potentially broader and more diverse insights.

Handling LLM Responses
Responses from the LLM are critically evaluated and refined through:
 * 1) Filtering & Validation: Sifting through the LLM's output to identify valid and relevant implications, which may involve manual review or additional LLM processing.
 * 2) Database Integration: Incorporating verified implications into the database, which enriches the existing knowledge base and informs future queries and interactions.

Periodic Exploration
To maintain the relevance and growth of the knowledge base, the system includes mechanisms for ongoing exploration:
 * 1) Scheduled Implication Derivation: Regularly querying the LLM with different sets of beliefs to uncover new implications and expand the breadth of the database.
 * 2) User Feedback Loop: Engaging users in the validation process of the implications associated with their beliefs, promoting accuracy and user interaction.

This structured application of LLMs not only deepens the database's understanding of existing beliefs but also ensures that the knowledge base is dynamic, evolving, and attuned to the complexities of user-provided data.

Proof-Checking
Let's take a brief dive into each of these systems:

 * 1) Prolog
 * 2) Description: Prolog (PROgramming in LOGic) is a logic programming language associated with artificial intelligence and computational linguistics. It operates on the principles of formal logic to perform pattern-matching over natural language parse trees and databases.
 * 3) Positives:
 * 4) Intuitive Semantics: The "facts" and "rules" structure of Prolog is somewhat intuitive for representing beliefs and deriving conclusions.
 * 5) Pattern Matching: Prolog excels in pattern matching, which can be valuable for identifying and working with similar beliefs or conclusions.
 * 6) Mature Ecosystem: Prolog has been around for a long time and has a variety of libraries and tools.
 * 7) Efficient Backtracking: Can explore multiple proof paths and quickly backtrack when a path doesn't lead to a solution.
 * 8) Negatives:
 * 9) Not Strictly a Proof Assistant: While Prolog can derive conclusions, it doesn't provide formal proofs in the way that dedicated proof assistants do.
 * 10) Performance: For very large databases, Prolog might not be the most efficient choice.
 * 11) Example:
 * 12) Isabelle
 * 13) Description: Isabelle is a generic proof assistant, which means it allows mathematical formulas to be expressed in a formal language and provides tools for proving those formulas in a logical manner.
 * 14) Positives:
 * 15) Robust Proof Assistant: Provides rigorous proofs, ensuring the validity of derived conclusions.
 * 16) Strong Typing: Helps catch errors early on, making belief representation more accurate.
 * 17) Interactive Environment: The semi-interactive nature allows for human-in-the-loop verification and guidance.
 * 18) Supports Higher-Order Logic: Can handle complex logical constructs and relations.
 * 19) Negatives:
 * 20) Steep Learning Curve: Isabelle has a challenging syntax and requires a deep understanding to use effectively.
 * 21) Overhead: Might be overkill for simpler belief systems or when you only need to check implications without detailed proofs.
 * 22) Example:
 * 23) Coq
 * 24) Description: Coq is a formal proof management system. It provides a formal language to write mathematical definitions, executable algorithms, and theorems together with an environment for semi-interactive development of machine-checked proofs.
 * 25) Positives:
 * 26) Rigorous Proof Mechanism: Like Isabelle, Coq provides very rigorous proofs.
 * 27) Extractable Code: Coq allows for the extraction of executable code from definitions, which can be useful if parts of the belief system are algorithmic.
 * 28) Strong Community Support: Has a wide range of libraries and an active community.
 * 29) Dependent Types: Can express very intricate relationships and properties.
 * 30) Negatives:
 * 31) Complexity: Like Isabelle, Coq can be difficult to learn and master.
 * 32) Performance: Large-scale proof searches can be time-consuming.
 * 33) Interactivity: While it's a strength in some contexts, the need for human-guided proof tactics might not be ideal for fully automated reasoning over beliefs.
 * 34) Example:
 * 35) Z3
 * 36) Description: Z3 is a high-performance theorem prover developed by Microsoft Research. It's used for checking the satisfiability of logical formulas and can be integrated into various applications, including software verification.
 * 37) Positives:
 * 38) High Performance: Built for efficiency and can handle large formulas relatively quickly.
 * 39) SMT Solver: Works with a variety of theories (like arithmetic, arrays, and bit vectors) which could provide versatility in representing beliefs.
 * 40) APIs for Multiple Languages: Can be integrated easily into various software frameworks.
 * 41) Decision Procedures: Automatically decides the satisfiability of statements without needing guided tactics.
 * 42) Negatives:
 * 43) Not a Traditional Proof Assistant: While Z3 can tell you if a statement is true or false based on given axioms, it doesn't produce detailed proofs in the same manner as Isabelle or Coq.
 * 44) Expressivity Limitations: Some complex logical constructs might be harder to represent compared to systems like Coq or Isabelle.
 * 45) Example:

These examples are quite simplistic, and in practice, these tools can handle and are used for much more sophisticated tasks. However, they should provide a basic idea of how each system looks and operates.

Conclusion
The chapter on "Formal and Practical Logic" in the context of "Chatbots for Social Change" underscores the importance of these two intertwined aspects of logic in shaping AI-mediated discourse. It posits that while the logical structure of beliefs lays the groundwork for rational discussions, the practical structure of argumentation brings the human element to the forefront of AI interactions. The challenge for AI systems lies in seamlessly integrating these structures to promote dialogue that is not only intellectually rigorous but also socially and emotionally engaging. By doing so, AI has the potential to significantly contribute to social change by elevating the quality of public discourse and fostering a more informed, rational, and empathetic society.


text:
Chatbots For Social Change/Redundant Belief Systems

Scientific Belief
The exploration of scientific belief is an essential part of understanding how knowledge evolves and corrects itself over time. Science, by its nature, is a self-correcting enterprise that relies on the concept of falsifiability, as articulated by Karl Popper. Theories within science must be testable and, crucially, capable of being proven wrong. This mechanism ensures that erroneous beliefs are eventually weeded out as empirical evidence mounts against them. However, science is not a straightforward path towards truth. As Thomas Kuhn’s analysis of paradigm shifts reveals, science often experiences revolutionary changes, not through a gradual accumulation of knowledge, but through significant leaps that redefine entire fields. For instance, the transition from the Ptolemaic to the Copernican model was not just a simple update but a radical shift in understanding our place in the cosmos.

Resistance to these paradigm shifts is common due to the inertia of established frameworks, vested interests, and the authority of established figures within the scientific community. This resistance indicates that the process of scientific advancement is also a human endeavor, subject to the same social and psychological forces that influence all other areas of human activity. The maxim "Science progresses one funeral at a time," often attributed to Max Planck, underscores the idea that generational changes in the scientific community can be pivotal for the acceptance of new theories and concepts.

Moral Theory and Belief
Moral theory and belief are central to how individuals and societies determine what is right and wrong. The debate between moral realism and anti-realism is foundational to ethics. Moral realists argue that moral truths exist independently of human beliefs and constructions, whereas anti-realists hold that moral truths are human constructs. This debate informs how AI may approach facilitating discussions on moral and ethical issues, as it must navigate the complex terrain of absolute truths versus subjective interpretations.

Further complicating this landscape is the concept of moral relativism, which posits that moral judgments are true or false only relative to specific standpoints, and no single viewpoint is universally privileged over others. This view challenges the AI to remain neutral and respectful of diverse moral perspectives. Additionally, the theory of evolutionary ethics suggests that our moral beliefs may be derived from evolutionary processes that favored cooperative behavior. This perspective implies that moral instincts are not solely derived from rational deliberation but also from inherited social behaviors. Cultural influences also play a significant role in shaping moral beliefs, as ethical frameworks are often deeply entwined with cultural norms and values. An AI mediator operating in this space must therefore be adept at understanding and balancing these varied and often conflicting ethical systems.

AI Mediation in the Context of Beliefs
AI mediation introduces a novel layer to the discourse on beliefs by providing a platform for objective facilitation of conversations. An AI mediator, devoid of the biases and emotional investments that might affect human mediators, has the potential to bridge communication gaps between individuals. This can lead to the exposure of diverse views, mitigating the effects of echo chambers and confirmation bias. By introducing individuals to a broad spectrum of beliefs, AI can facilitate a more nuanced and comprehensive dialogue.

However, the ethical considerations of AI mediation are paramount. The AI must operate with transparency, ensuring that all parties understand how decisions are made within the system. It must represent diverse viewpoints fairly and avoid any manipulation or undue influence on the participants. The AI should not steer conversations or impose certain views, as this would be ethically problematic. Moreover, the AI's role includes providing continuous feedback and adaptation based on real-world outcomes and user feedback. In this way, AI systems can engage ethically and effectively with the vast space of human beliefs, respecting the complexities of scientific and moral theories while promoting informed and constructive dialogue.

Social Representation Theory
Introduction Social Representation Theory (SRT), developed by Serge Moscovici, centers on the concept that beliefs are pivotal elements of social representations. These representations are a complex web of values, ideas, and practices that enable individuals to make sense of their social realities, navigate their environments, and communicate effectively within their social groups.

Social Representations in Moscovici's View
 * Beliefs as Central Elements: In the framework of SRT, beliefs are not isolated thoughts of an individual but are the collective property of a group. They are the building blocks of social representations, which are the shared understandings that inform a community's perception of reality.
 * Shared Understanding: The formation of social representations allows communities to form a cohesive interpretation of complex phenomena, simplifying and structuring the social world by providing common ground for interpretation and interaction.
 * Function of Social Representations: These shared beliefs and practices establish an order that helps individuals orient themselves and facilitate communication within the community, offering a shared code for classifying and naming the world around them.

Not All Beliefs are Social Representations
 * Individual vs. Collective: There's a clear distinction between personal beliefs and the collective beliefs that constitute social representations. Individual beliefs may or may not align with the broader social narratives of a community.
 * Evolving Representations: Social representations are dynamic constructs that evolve as societies change and as new beliefs become integrated into the collective understanding of a group.
 * Dynamic Nature: The evolving nature of social representations ensures that they remain relevant and reflective of the society's current state, accommodating new beliefs and information as society progresses.

Some Basic Findings
 * Dual Process of Social Representations:
 * Finding/Conclusion: Moscovici introduced the dual processes of anchoring and objectification as the mechanisms by which social representations are formed.
 * Explanation: Anchoring is the process of assimilating new information by placing it within familiar contexts, whereas objectification transforms abstract concepts into something concrete, making them more understandable and relatable.
 * Core and Peripheral System:
 * Finding/Conclusion: The core elements of social representations are stable and central to group identity, while the peripheral elements are more susceptible to change and adaptation.
 * Explanation: This structural differentiation within social representations explains how they can maintain stability over time while also adapting to new circumstances and information.
 * Social Representations and Social Identity:
 * Finding/Conclusion: Social representations are instrumental in forming and sustaining a group's social identity.
 * Explanation: They provide the shared beliefs and values that delineate in-group and out-group boundaries, fostering a sense of belonging and collective identity among group members.
 * Influence of Communication:
 * Finding/Conclusion: The dissemination and shaping of social representations are heavily influenced by the means of communication, especially the mass media.
 * Explanation: The framing of issues and the portrayal of events in the media can substantially shape public understanding and belief about those issues.
 * Resilience of Stereotypes:
 * Finding/Conclusion: The persistence and resistance of stereotypes to change can be understood through the lens of social representations.
 * Explanation: Stereotypes are a form of social knowledge deeply anchored in the collective beliefs of a group, making them resistant to contradictory evidence.
 * Role in Social Change:
 * Finding/Conclusion: Social representations serve not only as a reflection of societal values and norms but also as catalysts for social change.
 * Explanation: As social conditions evolve, new representations can challenge existing beliefs, leading to transformations in societal practices and norms.

SRT's application across diverse domains from health to intergroup relations highlights its significance in understanding the interplay between individual beliefs and collective social understandings.

Abric’s Evolutionary Theory
Jean-Claude Abric's evolutionary theory provides a nuanced understanding of how social representations are maintained within societies, distinguishing between core and peripheral elements that comprise and stabilize these representations.

Core and Peripheral System
 * Core System:
 * Stability and Resistance to Change: The core is composed of fundamental beliefs tied to the group's collective memory and history, offering stability and resistance to change.
 * Historical and Cultural Anchoring: Core elements are deeply rooted in the group's identity and are essential in giving meaning to the representation.
 * Peripheral System:
 * Flexibility and Adaptability: Peripheral elements allow the representation to adapt to new information or contexts without challenging the core's integrity.
 * Heterogeneity and Individual Variance: These elements can differ within the group, providing space for individual nuances and interpretations.
 * Functions and Interplay:
 * Continuity and Relevance: The core ensures continuity, while the peripheral adapts to maintain the representation's relevance in changing environments.
 * Negotiation of Meaning: The dynamic between the core and peripheral allows for a balance between shared understanding and individual adaptability.

Application and Example Using the social representation of "marriage" within a traditional culture to exemplify Abric's theory:
 * Core System:
 * Unchanging Definition: The core definition, such as marriage being a sacred union between a man and a woman, remains largely constant over time.
 * Historical and Cultural Significance: These beliefs are historically and culturally significant, providing social stability and identity.
 * Peripheral System:
 * Evolution of Practices: Peripheral elements, like wedding rituals or acceptance of modern practices, showcase adaptability while respecting the core definition.
 * Variability and Adaptation: The peripheral system's flexibility allows the representation to incorporate new societal trends and values.
 * Interplay between Core and Peripheral Systems:
 * Speciation of Belief Systems: The interaction can lead to the evolution of subgroups with distinct beliefs, akin to speciation.
 * Societal Implications: Shifts in peripheral elements, influenced by societal changes, can challenge core elements, potentially leading to significant shifts in social representations.

Dynamics of Core and Peripheral Elements The movement between core and peripheral elements reflects the fluid nature of social representations and their susceptibility to change due to societal shifts or internal group dynamics.
 * Movement Between Core and Periphery:
 * Peripheral to Core: Beliefs once considered peripheral can gain prominence and integrate into the core.
 * Core to Peripheral: Core beliefs can become peripheral as societal values evolve.
 * Speciation of Belief Systems:
 * Emergence of Subgroups: Differences in core beliefs can lead to the formation of distinct subgroups within a larger tradition.
 * Challenges of Reconciliation: Reconciling divergent belief systems, especially when core beliefs are at odds, can be difficult and lead to conflict.

Core Beliefs and Action The relationship between core beliefs and action, as well as their visibility within the group, plays a critical role in defining group membership and enforcing norms.
 * Core Beliefs and Group Identity:
 * Core beliefs often dictate group behaviors and serve as identity markers, with deviations potentially leading to sanctions.
 * Peripheral Beliefs and Individuality:
 * Peripheral beliefs, being more individualized, might influence personal behavior but are less likely to result in group-level enforcement.
 * Reaction to Belief Violations:
 * Violations of core beliefs elicit strong reactions as they threaten group cohesion, whereas peripheral beliefs allow for more tolerance and adaptability.

Inclusivity and Social Representations The pursuit of an inclusive society may lead to the redefinition of core beliefs, promoting values of diversity and openness.
 * Redefining Core Beliefs:
 * Traditional core beliefs may evolve to prioritize inclusivity, potentially challenging the existing group's norms and leading to resistance.
 * Inclusivity as the New Core:
 * The principle of inclusivity could become the foundational core of an open society, with non-inclusive beliefs becoming peripheral.
 * Societal Cohesion and the Core:
 * While inclusivity is crucial, a society requires core principles for cohesion; a balance must be struck to ensure societal unity without sacrificing foundational values.

In summary, Abric's theory elucidates how social representations are preserved through a balance between the stable core and the adaptable periphery, reflecting the dynamic nature of societal beliefs and practices.


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Chatbots For Social Change/Quotes

= Others I've found =

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title: Chatbots For Social Change/Quotes


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Chatbots For Social Change/Prototypes/McGail/Structure of Reason

= Review of Knowledge Representation Methods =

Before delving into the use of Large Language Models (LLMs) for knowledge organization, it is beneficial to review various methodologies that can be employed to structure and reason with vast amounts of information. Each knowledge representation method offers unique advantages for processing and analyzing data.

 * Graph-Based Representation
 * Usefulness: Intuitive for representing complex relationships as nodes and edges.
 * Reasoning: Suitable for querying relationships, patterns, and inconsistencies.
 * Semantic Network
 * Usefulness: Nodes represent concepts with labeled edges to define relationships.
 * Reasoning: Aids in natural language processing and understanding human-like statements.
 * Ontology-Based Representation
 * Usefulness: Provides a structured schema defining entities and their interrelations.
 * Reasoning: Supports formal reasoning, consistency checking, and complex querying.
 * Matrix Representation
 * Usefulness: Efficient for computational representation of large-scale networks.
 * Reasoning: Useful for identifying influencers and clusters within the network.
 * Rule-Based Systems
 * Usefulness: Codifies relationships into explicit rules for automated reasoning.
 * Reasoning: Deduces new information or suggests actions based on set rules.
 * Bayesian Networks
 * Usefulness: Models probabilistic relationships and uncertainty.
 * Reasoning: Useful for inferential reasoning and decision-making under uncertainty.
 * Hypergraphs
 * Usefulness: Allows for complex relationships where edges can connect multiple nodes.
 * Reasoning: Provides nuanced views of interconnectedness and multi-faceted impacts.

Each representation style is valuable for different aspects of reasoning within a large and complex network of statements. The choice depends on the specific tasks at hand, like querying the network, inferring new knowledge, or predicting outcomes of policy changes. These methods lay the groundwork for the following exploration into the utilization of LLMs for knowledge integration and analysis.

= Large Language Model-based Knowledge Network =

The system proposed leverages the capabilities of Large Language Models (LLMs) like GPT to parse, interpret, and correlate vast amounts of textual information. This involves a two-step process that enriches the dataset and allows for efficient correlation within a vast knowledge network.

Statement Expansion and Contradiction Identification
An LLM can be used to:
 * Generate various expressions of a given statement to capture its semantic nuances.
 * Suggest statements that would contradict the given statement, enriching the dataset by capturing a diversity of language and opposing viewpoints.

Example for "The ongoing bombing in Gaza is wrong"

 * Variations
 * 1) It's unethical to continue the bombings in Gaza.
 * 2) The continuous attacks on Gaza are unjustifiable.
 * 3) Persistent bombardment in Gaza is a moral failing.

 * Contradictory Statements
 * 1) The military action in Gaza is a justified measure for security.
 * 2) Bombing in Gaza is a necessary response to aggression.
 * 3) The operations in Gaza are within the bounds of international law and morality.

Vector Encoding and Efficient Search
The next step involves:
 * Encoding the statements and their variations into vectors using models like sentence transformers.
 * Using cosine similarity or other distance metrics to find matches in a larger database.
 * This allows the system to identify when new information is in harmony or in conflict with existing knowledge.

Benefits
This approach is powerful for several reasons:
 * Rich Semantic Understanding: By generating variations, the system captures the nuances of language.
 * Contradictory Analysis: Identifying contradictions is vital for challenging and deepening the inquiry into existing knowledge.
 * Scalability: Vector search is computationally efficient, even with large datasets.
 * Interconnectivity: This method promotes the interlinking of information, crucial for a comprehensive knowledge network.

The system could serve as an effective method for integrating new knowledge into an existing framework, assessing the consistency of information, and aiding in the discovery of new insights by drawing connections between previously unrelated data.


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Chatbots For Social Change/Prototypes/McGail

 * Structure of Knowledge Representation
 * Implementation Details
 * chatGPT-powered custom bot, the "Mediator"
 * Querying belief systems with SparQL
 * Beliefs and Why


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User:Ianykor

SLOVISK

UNIVERSALE KONSTRUKTE SLOVANSKE JAZIK

(Universal Constructed Slavic Language)

GRAMATIKA – Grammar

= ABECEDA =

= A B C Ꞓ D Đ E F G H Ħ I J K L M N O P R S Ꞩ T U V Z Ƶ =

= a b c Ꞓ d đ e f g h ħ i j k l m n o p r s Ꞩ t u v z Ƶ   =

AЛФAБЭT

А Б Ц Ч Д ДЖ Э Ф Г Х Ӿ И Й К Л М Н О П Р С Ш Т У В З Ж

a б ц ч д дж э ф г х ӿ и й к л м н о п р с ш т у в з ж

auxiliary sign x or ‘ can be used :

cx= ć  hx= ħ, sx= ś, zx= ź  , dzx= đ / c‘, h‘, s‘, z‘

Ć (English CH), Ś (English SH),  Ź (French J), Ħ  (German CH),  Đ (English J )

Ꞓ (English Ch), Đ (English J), Ħ (German Ch), Ꞩ (English Sh)

Ƶ (English Zh), French J)

Slov-sled ies volne.
Kien fonem iest spojite so en litera a kien litera reprezenti en fonem.

Ak vi slisxi slovo vi mozxe toi skribit a ked vi toi vidi vi mozxe toi gvorit.

Tam ne egzisti neki stres na slog.

( Each letter can only be pronounced one way, and each sound can only be spelled one way.

There’s no stress on syllables )

Letters q, w, y, x, ch  are used only in personal names and some geographical names :

(Lydia,Sydney,Cyprus, Felix,  Egypt, Willson, New York, Quito, Mexiko,Chemnitz)

Paris, Beograd, Moskva, Praha, Roma, Warsxawa, Kahira, Kipros

Velie Britania (Great Britain), Anglia (England), Bavaria(Bavaria), Viena (Viena) Londin (London)

Spelling in foreign words : olimpiada, fizika, dinamika, simbol, bicikel, sistem, psihxologia,

SUBSTANTIVI (nouns)

Singular of nouns is in natural form :

sport, otec (father), mama, dieva (girl), junak (boy), deta (child), teta (aunt), zem (earth, soil), reka (river), mesto (place)

more (sea), ezero (lake), planeta, kosmos, galaksia, veda (science), radio, komputer, ekipazx (crew), gord (fortress)

vokabular (vocabulary), denar (money), dom (house), mur (wall), osada (village), grod (town), stat (state) lucx (light)

PLURAL IES ROBITE S SUFIKS (plural is done by suffix) :  - i

lucx – lucxi, otec - oteci, brat - brati, komputer - komputeri, sport - sporti, aeroplan - aeroplani, aviator – aviatori,

ekipazx - ekipazxi, mesec - meseci, den - deni, tiden - tideni, noc - noci, rok - roki, den - deni, put - puti, denar – denari, gvezda - gvezdi

SUBSTANTIVI KONCXITE SO VOKAL (nouns ended in vowel) :

imen - imeni, mama - mami,  auto - auti, krilo - krili, herba - herbi, flotila - flotili

larva - larvi, kamera - kameri, posta - posti, funga - fungi, raketa - raketi, triska - triski

planeta - planeti, litera - literi, prognoza -  prognozi, industria – industrii, variacia - variacii

SUBSTANTIVI KONCXITE SO DIFTONGI (nouns ended in difthongs) :

-ia, -io, -eo, -ao, -io, -uo  :

instrukcia - instrukcii, bakteria - bakterii, tragedia - tragedii, televizia - televizii, radio - radii,

evolucia - evolucii, distancia - distancii, galaksia - galaksii, video – videi

Substantantivi od zxive veci so rod

(nouns of living things with gender)  :  ten, ta

ten delfin / ta delfin, ten lev / ta lev, ten kon / ta kon

SUBSTANTIVI OD MUZXE ROD (nouns of male gender) :   ten

ten muzx, ten kon, ten pes, ten orol, ten krokodil, ten lev, ten slon, ten delfin,

ten direktor, ten sxofer, ten europan, ten robotar, ten traktorist

SUBSTANTIVI OD ZXENE ROD (nouns of female gender)  :   ta / - a

ta germanian(a) - german woman, ta anglian(a) - english woman,

ta slovakian(a) - slovak woman, ta italian(a) – italian woman

ta europan(a) - european woman, ta sxofer(a) - she driver, ta kosmonaut(a) - she astronaut

ta direktor(a) - she director, ta robotar(a) - she worker, ta vedar(a) / ta siencist(a) - she scientist

ta orol - she eagle, ta slon - she elephant, ta lev - she lion, ta kot - cat, ta delfin - she dolphin

·       Ending –a can be added as a suffix to the noun of female gender of a living person e.g.

sxofera, europana, kosmonauta, vedara  whereby ta doesn’t have to be used before the noun.

ROD NE-SPECIFITE (gender not specified) :   to

to lev(lion), to medved(bear), to kot(cat/tom cat), to hus(goos,drake), to delfin(dolphin), to tur (cow, bull)

to dub(oak), to javor(maple), to lipa(lime), to breza(birch), to motil(butterfly), to riba(fish), to pteric(bird),

to zover(animal), to savec(mamal),  to insekt(insect), to misx(mouse)

to komputer, to stol(table), to strom(tree), to olej(oil), to stolica(chair), to hrnec(pot), to dom(house), to ulica(street),

to autobus(bus), to list(leaf), to zem(soil,earth), to masxina(machine,engine), to olem(oil),

Cubs are created by :  june e.g. june-kon - foal, june-kot - kitten, june-kura - chicken, june-lev - lion cub

VAZBA OD DVE SUBSTANTIVI (binding of 2 or more nouns, compound words )

words clusters)

-        Two or more nouns are bound either by the letter –o, or hyphen -.

raketomet, staronov, geogravitacia, drevokaz, muromalba, slovosled

raket-met, star-nov, geo-gravitacia, drev-kaz, mur-malba, slov-sled

ADJEKTIVI (Adjectives)
Koncxenie - ending :   -e / - ske (2+ nouns in root form)

Varsxava – Varsxave/ Varsxavske, Moskva – Moskve, Praga – Prage,

krasne - pretty,  dobre - good, bistre - quick, interesante - interesting, slabe - weak, silne - powerful, teple / terme – warm, nove - new

zvezde noc - starry night, terme/tople diferencia - warm difference, polare/polarske - polar, palivske – combustible,  zle / mizere - bad

Zeme/Zemske gravitacia - Earth gravitation, Mesice svit/ lucx - moon shine,

dene/denske tiket - daily ticket, komputere / komputerske igra-computer game,

televizie program - television programme, Severe Polare Cirk - North Polar Circus, priame/direkte translacia – priamy prenos

Nouns ending in - e  more + breg = more breg / morske breg - sea coast

ADVERBI (Adverbs)
Finala - ending :  o

dobro - well,  krasno - nice,  pomalo - slowly,  stalo - still, bistro - qwickly, nahlo - suddenly, malo - little, few,

termo - warmly, mezo - middle, between, among

Nouns ending in - o remain unchanged : radio-transmite - radio transmitted, racio-urobite - rationally done,  termo/toplo-odevite - warmly dressed

Kreatenie adverbi zo verbi – Creating adverbs from verbs

Adding  - no to infinitive of verb :

skribit / skribitno (to write / in writing) , sunit / sunitno (to sunbathe / sunny), videt / videtno (to see / visibly)

Gradacia od adjektivi a adverbi  - Graduation of adjectives and adverbs :

vic - more, -er

nai - most, -est

vic-pomalo(slower) / nai-pomalo(slowest), vic-krasnie/krasno(nicer) / nai-krasnie/krasno(nicest),

vic-dobro(better) / nai-dobro(best), vic-malo(less/lesser) / nai-malo(the least)

Tie es vic-bistre kao ten - They are quicker then he.

Ta bol nai-pomale zo vse - She was the slowest of all.

DEKLINACIA (Declination)
Nominativ        /                To iest dom -  It is a house

Genitiv         -ov / od      To iest auto tenov otec  -  It is the car of a father

                                       To iest otecov auto        -  It is father’s car

Dativ           -om / do      Ti bue daet to kniga detiom - You will give the book to children

                                     Ti bue daet to kniha do deti - You will give the book to children

Akuzativ      Tie do-budoval dom            -  They have built up a house

Genitive and Dative can be done by personal adverbs as well.

Lokal :   o  - about, on

( o mama - about mum, o otec - about dad, o nam - about us, o me - about me,

  o te - about you(sg.), o tenu - about him, o tau - about her, o tiem - about them )

Instrumental :   s  - by

s mama - by mum, s otec - by dad, s nam - by us, s me - by me, s te - by you,

s tenu - by him, s tau - by her, s tiem - by them

Dom bil do-budovate s tiem  - The house was buit up by them.

In case of substantive in accusative in the beginning of a sentence this is in passive voice :

To ies govorite o te    -    It is spoken about you.

Auto ies s‘oferite s otec   -   A car is driven by father

Kafe ies servite s c’as‘iner  -  Coffee is served by a waiter

Kafe ies so cukor   -  Coffee is with sugar

Bit s te, ja bi ne idet tam -  Being you I wouldn´t go there

Ja bi idel tam so te  -  I would have gone there with you

* ODLISXIJ (Distinguish) :   s (by means of)   so (with)

knigi od / tieh‘ oteci / knigi oteciov

– books of fathers, komputeri od / tieh‘ junaki / komputeri junakiov

– computers of boys

sportistiom - to sportsmen,  sxoferiom - to drivers

PERSONE ADVERBI (Personal adverbs / possessive adjectives / possessive pronouns )

Nominativ                     Genitiv                  Dativ               Akuzativ

ja    - I                           moe   - my           me – me           ma  -  me

ti     - you                      tvoe   -  your        te   - you           te    -  you

ten  -  he, this                tenov   - his          tenu – him        toh‘ - him

ta    -  she, this               taov   - her           tau - her            tau  - her

to    -  it, this                  tov   - its              tom – it              toh‘ - it

mi   -  we                       nas‘  - our             nam – us           nas  - us

vi    -  you                      vas‘  - your           vam – you         vas  - you

tie   -  they, these           tiev  - their           tiem – them       tieh‘ - them

ti - you (singular)          vi - you (plural)

tu-ten, tu-ta, tu-to  -  this here

tam-ten, tam-ta, tam-to  -  that there

ten, ta, to - are denominative (demonstrative pronouns) as well as personal pronouns

·       Only in basic form

Persone adverbi vo deklinacia iest vo akuzativ (Personal / determine pronouns at declension are in accusative) :

Tiem  ja videl  - Those (men) I saw

Bez te mi ne ide  - We don‘t go without you

IMPERATIV (Imperative)

Osnove forma od verbo bez infinitive koncxenie (basic form of  verb without infinitive ending) ( - t ), dodatenie od sufiks (adding of suffix)  - j

idet - idej  (go !), vidit - vidij  (look !), imat  - imaj, (imaj edenie - have eating)

robit - robij (do), sluhxat – sluhxaj (listen), cxitat     - cxitaj (read), zxit  -  zxij (live)      edat - edaj (eat), bezxit - bezxij (run), varit - varij (cook), kalkulit - kalkulij (count)    bit  -  bij (be), sedet  -  sedej (sit), skribij  - write, mizij - scat,  imperij - rule, vitaj  - welcome

vi skribij -  write (plural), vi  mizij - scat(plural) vi  imperij - rule (plural),

bezxij - run, mi skribij -  let´s write,  mi mizij - let´s scat, mi imperij -  let's rule,

cxitaj - read

PREPOZICII (Prepositions)

pri – at / by, na - on, vo - in / inside, cez - over, across, pod - under, nad - above, so - with, po - after, pred - before / in front of, za - behind, beyond, mezo - between, among, middle, o - about/at, pro – for, do - to / towards inside, zo - from / out from inside, od – from, s – by,

kol – around, bliz – near, abi – in order to, mesto – instead of, bez - without

vo pette cxas - at five o'clock

o pet cxasi  -  in five hours

Junak ide do les  -  A boy is going into a forest

Junak ide ku les  -  A boy is going to / towards a forest

Junak ies vo les   -  A boy is in a forest

Junak ide zo les – A boy is coming from a forest

Skolar ide do skola – A pupil goes into a school
Skolar ies vo skola – A pupil is in a school

Skolar ide zo skola – A pupil goes from a school

Skolar ide ku skola – A pupil goes to / towards a school

Veter via zo gora  - Wind blows from the mountain

Veter via do gora  -  Wind blows into the mountain

Veter via na gora    -  Wind blows on the mountain

Veter via nad gora  -  Wind blows above the mountain

Veter via cez gora  -  Wind blows through the mountain

Veter via mezo gori  -  Wind blows among / between the mountains

KONJUNKCII (Conjuctions)
a, i (and), tezx(also, too, as well)

VERBI  (Verbs)

Infinitive of verb - ending :  -  t

sportit - to sport, grat - to play, idet - to go, gvarit - to speak, spat - to sleep, vedet - to know, kalkulit - to count videt - to see

lucxit / svitit - to shine, publikit - to publish, sluhxat - to listen/to hear, begat – to run, observit - to watch, medikit - to heal,

rozumit – to understand, pisxut – to write

- preterite cxas -  past tense :        -   bil / -l

- prezente cxas -  present tense :    -  infinitive without  -t

- future cxas -  future tense :          -  bue / -b

Mi gvori   -  We speak / talk / say

Mi gvoril   -  We spoke / talked / said

Mi bue gvorit / Mi gvorib -  We will speak / talk / say

Konjugacia od verbo  - Conjugation of verb    bit  -  to be

Preterite c‘as  - Past Tense :

ja bil    -     I was                      mi bil    -    we were

ti  bil      -    you were              vi  bil    -    you were

ten  bil    -  he was                  tie bil    -    they are

ta  bil  -    she was

to  bil  -    it was

Prezente c‘as  - Present Tense :

ja ies      -  I am                      mi ies   -   we are

ti  ies    -   you are                   vi ies -     you are

ten ies    - he is                       tie ies  -     they are

ta  ies  -   she is

to  ies   -   is is

Future c‘as  - Future Tense :

ja  bue     - I  shall / will             mi  bue   - we shall / will

ti   bue     - you will                    vi   bue   - you will

ten  bue   -  he will                      tie bue    - they will

ta  bue     - she will

to  bue     - it will

KONDICIONALE FORMA - Conditional  :   bi
ja bi muset - I would have to

ja bi trebit  - I would need to (duty)

ja bi smiet  -  I would be allowed to

ja bi mozxet -  I would be able to

KONDICIONALE FORMA VO PRETERITA - Past Conditional :   bi  +  -  l

ja bi musel - I would have had to

ja bi trebil  -  I would have needed to (duty)

ja bi smiel  -   I would have been allowed to

ja bi moz‘el  -  I would have been able to

imat - to have(posses), due, h‘cet - to want

moz‘et - can, smiet - may, musit - must,

ja ima   -  I have / due

ja imal   -  I had / due

ja imab  -  I will have / due

ja bi imat   -  I would have / due

ja bi imal  - I would have had /due

ja h‘ce    -  I want

ja h‘cel   -  I wanted

ja bue h‘cet  -  I will want

ja bi h‘cet   -  I would want

ja bi h‘cel  -  I would have wanted

ja moz‘e   - I can

ja moz‘el   -  I could[was able]

ja bue moz‘et   - I will be able

ja bi moz‘et   -  I would be able

ja bi moz‘el  -  I would have been able

ja smie  - I may

ja smiel  - I might

ja bue smiet  - I will be allowed to

ja bi smiet  -  I would be allowed to

ja bi smiel  -  I would have been allowed to

ja musi -  I must

ja musil -  I had to

ja bue musit - I will have to

ja bi musit  -  I would have to

ja bi musil  -  I would have had to

KVESTIA (Question)
Robi ti / Ti robi ?    - Do you work / Are you working ?

Robil ti / Ti robi ?    - Did you work / Were you working ?

Bue ti robit / Ti bue robit ?  - Will you work / Will you be working ?
NEGACIA  (Negation)   ne-

ja ne-robi     -  I do not do/work

ja ne-robil      - I did not do/work

ja ne-bue robit   - I will not do/work

PASIVE ROD OD VERBO (Pasive Voice of Verb)

Infinitive + suffix  - te

Kniga ies skribite s moi priatel - A book is written by my friend

Kniga bil skribite s moi priatel - A book was written by my friend

Kniga bue skribite / skribiteb s moi priatel - A book will be written by my friend

PERFEKTE FORMA OD VERBO (Perfect active voice of verb) :

do-  + infinitive (do-skribit, do-robit, do-edukit, do-planit, do-varit, do-plavat, do-cxitat, do-koncxit)

do-  +  - l ( e.g. Ja do-robil to  - I have done it )

bue do-   (e.g. Ja bue do-robit to do sutra - I will have done it by tomorrow)

Tie do-koncxil to do denes utro.  -  They have finnished it by this morning
PERFEKTE PASIVE FORMA OD VERBO -  perfect passive voice of verb   do .... infinitive of verb (t) + e

Kniha jes do-skribite s moe priatel -   A book has been written by my friend

Kniha bil do-skribite s moe priatel   -   A book had been written by my friend

Kniha bue do-skribite doskribiteb s moe priatel  -   A book will have been written by my friend

MENENIE OD STAV OD VERBI (Changing of verb mode)   o-

vic  o-velit  - to get bigger           vic o-malit  - to get smaller

vic o-velij  -  get bigger ! vic o-malij  -  get smaller ! (imperativ)

    o-tmavit  - to get dark         o-tmavite – is/have been darkened

    o-svitit  - to  get enlighten            o-svitite – is / have been enlightened

    o-staret  -  to grow old                o-starete – is / have been grown old

    o-kamenit  -  to turn to stone      o-kamenite – is/have been turned to stone

OPOZIT  OD AKTIVITA (Oposite of activity)  roz-
vazat / roz-vazat           -  to tie / to untie

klucxit / roz-klucxit     -   to lock / to unlock

budovat / roz-budovat  -  to build / to demolish

KREATENIE SUBSTANTIVI ZO VERBI (Making nouns from verbs)

Verb in ifinitive +  - ost, plural – osti or -enie, plural - eni

robit - robitost(to do/work - doing/working), skribit - skribitost(to write-writing), numerit - numeritost(to count-counting),

dat - datost(to give - giving), agrit - agritost(to crop - cropping), laborit - laboritost(to labour - labouring)

datosti, robitosti, skribitosti, numeritosti, agritosti, laboritosti

Creating of verbs from nouns is unlimited e.g. sarkazmus - sarkazit (sarcasm - to nip),

literar – literit (a writer - to write),  fonia – fonit (phono - to phone) etc.

KREATENIE SUBSTANTIVI ZO SUBSTANTIVI (Creating nouns from nouns)

- stvo koncxite vokal iest prostite (last vowel is omitted)

dete – detstvo             childhood

mater – materstvo        motherhood

otec – otecstvo              fatherhood

brat – bratstvo             brotherhood

kral – kralstvo             kingdom

muz‘ – muz‘stvo       „manhood“ / crew / team

GRADAN OD NAROD (Citizen of nation)

German(i), Ital(i), Rus(i), Amerik(i), C‘eh‘(i), Slovak(i), Polak(i), Hungar(i), Span(i),

Franc(i), S‘ved(i), Ukrain(i),Europ(i), Japon(i), Arab(i), Afrik(i), Austral(i), Eskim(i)

ameroind(i)

JAZIK / LINGVA (Language) konc‘enie (ending) :  - (i)sk

slovansk – slavic

germansk - german, rusk - russian, slovakisk - slovak, c’eh‘isk - czech, anglisk - english, belorusk - belarussian

polakisk - polish, slovensk - slovenian, ukrainsk - ukrainian, latvisk - latvian, litvisk - lithuanian, estonisk - estonian,

japonsk, arabisk, francisk, italisk, spanisk, holandisk, serbisk, h‘rvatsk, macedonsk, bulgarsk,  esperanto, slovio, ido,

sloviensk, keltsk, hebreisk, koptisk, staroegyptsk, kec‘uansk, maisk, azteksk, arabsk, c‘insk, basksk, katalansk, afrikask,

interlingva, proslava, slovianski, slezan, volapuk, occidental, novial, latina, glosa, dunia

germane jaziki - germanic languages, romane jaziki - romance languages, slovane jaziki - slavic languages

rusie jazik- russian language, slovakie jazik - slovak language, anglie jazik - english language

or : jazik germansk, jazik rusisk, jazik slovakisk, jazik anglisk, jazik c‘insk, jazik polakisk

PARTICIP (Participle) -  iuc

Sxoferiuc auto tie sluh‘al muzika  - Driving a car they were listening to music

Slediuc televizia mi vzaimo gvoril     - Watching the television we were talking together

KREATENIE OD SLOVI  (Creation of words)

veda (science) - vedat (to know) - vedenie (knowledge) – vedar (scientist)

more (sea) - namorar (sailor)

plavat (to swim) - plavenie (swimming) - plavar (swimmer)

kral – kralozem/kral-zem (kingdom)

namorenie (marine)

REFLEKSE ADVERBO - Reflexive Adverb

se – self / personale adverbi vo dative

Tie bue uc‘it se to sledske teden  - ''They will learn it next week. (bue = will/shall, next)''

but : Tie bue uc‘it to sledske teden  - They will teach it next week

NUMERI (Numbers)

Osnove numeri - Cardinal numbers            Rade numeri  (-te) - Ordinal Numbers

0  -  nul, zero                                                0th  -  nulte, zerote  ( zeroth, nilth)

1  -  en                                                         1st -  ente

2  -  dve                                                         2nd  -  dvete

3  -  tri                                                          3rd  -  trite

4  -  sxtir                                                       4th  -  sxtirte

5  -  pet                                                         5th  -  pette

6  -  s‘es                                                         6th  -  s‘este

7  -  siem                                                       7th  -  siemte

8  -  os                                                          8th  - oste

9  -  dev                                                         9th  -  devte

10  -  des                                                      10th -   deste

11 – des-en                                                    11th -  des-ente

12 – des-dve                                                  12th -  des-dvete

13 – des-tri                                                    13th -  des-trit

14 – des-sxtir                                                14th -  des-sxtirte

15 – des-pet                                                   15th - des-pette

16 – des-s‘es                                                 16th - des-s‘este

17 – des-siem                                                17th -  des-siemte

18 – des-os                                                    18th -  des-oste

19 – des-dev                                                 19th -  des-devte

20 – dve-des                                                  20th – dve-deste

21 – dve-des-en                                             21th – dve-desente

22 – dve-des-dve                                           22th – dve-desdvete

30 – tri-des                                                     30th – tri-deste

40 – sxtir-des                                                 40th – sxtir-deste

50 – pet-des                                                    50th – pet-deste

100 - sto (hekto)                                                     100th -  stote (hektote)

1000 - tis (kilo)                                                    1 000th -  tiste (kilote)

1 000 000 - milion (mega)                           1 000 000th -  milionte (megate)

1 000 000 000 - bilion (giga)                  1 000 000 000th -  bilionte (gigate)

Plusenie        :   plus

Minusenie     :   minus

Multiplikacia :  - krat    dvekrat - twice, deskrat - ten times   dve krat pet es des

Dividencia     :    del     divide / divided  des del pet es dve
Frakcia         :  - ina     dveina - one half, desina - one tenth, stoina - one hundredth,

tisina - one thousandth

PLUSENIE OD NUMERI (Counting of numbers)

os plus siem iest despet        8 + 7 = 15

MINUSENIE OD NUMERI (Discounting of numbers)

despet minus siem ies os    15 - 7 = 8

MULTIPLENIE OD NUMERI  (Multiplication of numbers)
dve krat tri ies sxes    2 x 3 = 6  des krat sto ies tis (des krat hekto ies kilo)   ten times hundred is thousand

tis krat milion ies bilion (kilo krat mega ies giga)    thousand times million is milliard

DIVIDENIE OD NUMERI  (Dividing of numbers)
des del pet ies dve    ten divided by five is two

bilion del tis ies milion (giga del kilo ies mega)    milliard divided by thousend is million

FRAKTI (Fractions)
dveina (en lom dve) 1/2   triina (en lom tri) 1/3   dev milionina (dev lom milion / dev lom mega)  9/1 000 000

DESINE NUMERI  (Decimal numbers)
nul cele en (en desina) - 0,1 nul cele dve (dve desini) - 0,2

nul cele en milionina / en milionina (nul cele en megaina)  0,000 001

ROVNENIE (Equation)

E ies ekvate m krat c kvadrat (E = mc2)

102 - des na dve / des na kvadrat,  103 - des na tri / des na kubik,  104 - des na sxtir

105 - des na pet

DECIMALE SISTEM (Decimal System)

meter, liter, kilogram

kvadrate meter (square meter), kubike meter (cubic meter), obkvadragonal (oblong, rectangle)

cirk (circle), polcirk (semicircle), radius (radius), diameter (diameter), diagonala (diagonal), kula (sphere),

trigonal (triangular), kvadragonal (square), pentagonal (pentagon), heksagonal (hexagonal), oktagonal (oktagonal)

linea (line), horizontale / vertikale linea (horizontal/vertical line), goniomer (protractor) prizen (prism)

perimeter (circuit, perimeter), paralele linei (parallel lines), krive linea (curve), priame linea (straight line),

cilinder (cilinder), konus (cone), kubus (cube), limite linea (abscissa), bod (dot), lina (comma, line),

STATI / KROLSTVI / REPUBLIKI / VAZALSKE TERITORIUMI

States / Kingdoms / Republics / Depended Territories

Unia od Arabske Emirati            United Arab Emirates

Afganistan                                 Afghanistan

Albania                                      Albania

Armenia                                     Armenia

Arktida                                      Arktica

Antarktida                                  Antarktica

Austria                                       Austria

Australia                                    Australia

Azerbaidzxan                            Azerbaijan

Bosna i Hercegovina                  Bosnia and Hercegovina

Bangladesx                               Bangladesh

Belgia                                        Belgium

Bulgaria                                    Bulgaria

Bermuda                                   Bermuda

Brazilia                                     Brazilia

Bahami                                     Bahamas

Belorosia                                  Belarussia

Kanada                                     Canada

Republika od Centrale Afrika   Republic of Central Africa

Republika od Juge Afrika         Republic of South Africa

Sxvaicaria                                Switzerland

Cxina                                        China

Kipros                                     Cyprus

Cxehxia                                   Czechia

Germania                                 Germany

Gibuti                              Gibuti

Dania                                        Denmark

Dominikanske Republika        Dominican Republic

Alzxeria                                    Algeria

Estonia                                     Estonia

Egipt                                        Egypt

Zapade Sahara                          Western Sahara

Spania                                     Spain

Finia                                         Finland

Ostrovi Fidzxi                           Fiji Islands

Falklandske Ostrovi                 Falkland Isles

(Malvini                                    Malvinas)

Ostrovi Fare                               Fare Isles

Francia                                    France

Gruzia                                      Georgia

Francie Guaiana                        French Guiana

Gibraltar                                  Gibraltar

Gronlandia                               Greenland

Ekvatore Guinea                       Equatorial Guinea

Grecia                                      Greece

Hxrvatia                                   Croatia

Irlandia                                    Ireland

Izrael                                       Israel

Irak                                         Iraq

Iran                                         Iran

Island                                       Iceland

Italia                                        Italy

Jamaika                                  Jamaica

Jordan                                     Jordan

Japonia                                   Japan

Kenia                                      Kenia

Kirgizia                                   Kirgizia

Kambodzxa                             Kambodia

Severe Korea                         North Korea

Juge Korea                             South Korea

Kuvait                                   Kuwait

Kaimanske Ostrovi               Caiman Isles

Kazahxstan                            Kazakhstan

Libanon                                 Lebanon

Lihxtensxtain                         Lichtenstein

Litva                                     Lithuania

Luxemburg                           Luxembourgh

Latvia                           Latvia

Moldovia                              Moldovia

Marsxalske Ostrovi              Marshall Islands

Makedonia                            Macedonia

Mongolia                              Mongolia

Martinik                                Martinique

Mauritania                            Mauretania

Malta                                    Malta

Mauritius                             Mauritius

Malavi                                 Malawi

Mexiko                                Mexico

Malaizia                               Malaysia

Mozambik                           Mozambique

Namibia                               Namibia

Nove Kaledonia                   New Caledonia

Nederlandia / Holandia        Netherland / Holland

Norvegia                               Norway

Nove Zealand                       New Zealand

Francie Polinezia                 French Polynesia

Filipini                                 Philiphinnes

Polakia                                Poland

Puerto Riko                          Puerto Rico

Palestina                              Palestine

Portugalia                            Portugal

Paraguai                               Paraguay

Kvatar                                 Quatar

Romania                              Romania

Rosia                                   Russia

Rvanda                               Rwanda

SaudieArabia                      Saudi Arabia

Sxalamunske Ostrovi          Solomon Islands

Seisxeli                               Seychelles

Sxvedia                               Sweden

Singapur                             Singapore

Slovenia                              Slovenia

Slovakia                              Slovakia

Srbia                                  Serbia

San Marino                         San Marino

Somalia                              Somalia

El Salvador                         El Salvador

Siria                                    Syria

Svaziland                            Swaziland

Turks i Kaikos Ostrovi       Turks and Caicos Islands

Tailandia                             Thailand

Tadzxikia                            Tajikistan

Turkmenistan                      Turkmenistan

Tunisia                               Tunisia

Istoke Timor                       East Timor

Turkia                                 Turkey

Tai-van                               Tai-wan

Ukraina                               Ukraine

USA                                    USA

Uruguai                              Uruguay

Uzbekistan                          Uzbekistan

Vatikan                               Vatican

Vietnam                              Vietnam

Jemen                         Yemen

Jugoslavia                           Yugoslavia

Zimbabve                            Zimbabwe

KRATKE VOKABULAR (Brief vocabulary)

ko  -   who                                  c‘o  -   what (thing)

koh‘   -   whose                         c’oh‘  -  of what

kom   -   whom                          c‘om  -  to what

o kom  -   about whom            o c‘om  - about what

s kom  -   with whom              s c‘om  -  with what

ke   -  where                            kor   - which

kei  -  when (time)                  z‘e   -  that

ki   -  what                               abo   - or

kao  -  how                             len   - only

ak  -  if                                    tez‘ - too, either, as well as

kim  -  while, meanwhile        ebo   - because, that´s why

ked  -  when                            li   - whether

c‘em  -  why                  ale   -  but

kisi    -  some                           c‘osi   - something

neki   -  no + noun, none           nec‘   - nothing

kesi   -  somewhere                  hoc’o   - anything

neke  -  nowhere                      lensi  - some, a few, a little

kosi   -  somebody, someone     kien   - every, everyone, each

neko  -  nobody, no one            korsi   -  some, someone

kaosi  -  some, somehow          ensi   - whoever, whichever, whatever

nekao -  no way                       nekei   - never

od ke   -  where from                              toc‘as  -  now

do ke   -  where to                                  po  -  after

od kei  -  from when / from what time     skoro -  soon

do kei  -  till what time                             abi  -  so that, in order to

krom   - besides                                      uz‘ - already

tak – so, thus

a / i  - and

da  -  yes

ponedelia  -  Monday

Pacifike  Ocean  - Pacific Ocean

ne   -  no

utorak   -  Tuesday

Atlantike Ocean - Atlantic Ocean

C‘as  -  time

sreda  -  Wednesday

Indie Ocean   -  Indian Ocean

den  -  day

s‘tirak  -  Thursday

Arktide Ocean   -  Arctic Ocean

tiden  -  week

petak  -  Friday

Ekvatore Cirk - Equator

mesec  -  month

sobota  -  Saturday

Longituda  - Longitude

rok  -  year

nedela  -  Sunday

Latituda  -  Latitude

sever  -  north

c‘era  -  yesterday

jug  -  south

denes  -  today

istok  -  east

sutra  -  tomorrow

zapad  -  west

utro  - morning

vesna  -  spring

polden -  noon

Antarktida  -  Antarktica

leto  -  summer

vecxer  -  evening

Volkan    -  Volcano

jesen  -  autumn

noc  -  night

Kontinent- Continent

zima  -  winter

polnoc  -  polnoc

Mezozeme More - Mediterranean Sea

vitaj  - welcome

zdrav - hello, hi

selo  -  village

Sunce  -  The Sun

zem  -  earth, soil

dobre den  -  good day

mesto  -  town

Zem (Globa/Geo-)  - The Earth

teritorium  -  territory

dobre utro  - good morning

gora  -  mountain

Mesic  -  The Moon

geocentrum  - centre of the Earth

dobre vecxer  - good evening

luka  -  meadow

planeta  -   planet

teren – ragged surface

dobre noc  -  good night

put  -  way, road

Solare Sistem  -  Solar System

hor – up

dol – down

prav – right

lav – left

kastel - chateau

hostinec  -  pub

edat -  to eat    edenie - eating, food       maj edenie - have eating

bivat - live, stay  bivenie - accomodation

EGZAMPLI OD DENSKO-UZXITE FRAZEOLOGIA (examples of daily used phrases) :

Kao ies ti ?  -  How are you ? Gvori ti Slovisk - Do you speak Slovisk ?

Ja ies dobro  -  I am fine. Mezo-nacione recx  - international language

Ki ies cxas ?  - What´s the time ? Kao velo (denari) ies to ?  -  How much (money) is it ?

(To ies) pet cxasi  -  It is five o´clock  To ies dvedes-pet (denari)  -  It is twenty-five ((money).

prognoza od  meteo-klima – weather forecast

toplo / termo – warm

goronco – hot

dezx – rain, dezxit – to rain, dezxenie - raining

solnco – sunny (Solnce – the Sun)

otec   -  father

mama - mom

sestra  - sister

brat     - brother

dedo  -  grandfather

babusxa - grandmother

bratan  - cousin (he)

sestrana  - cousin (she)

sin - son

docx - daughter

VEDSKE VOKABULAR (scientific vocabulary):

Severe Polare Cirk - North Polar Circus

Juge Polare Cirk  -  South Polar Circus

Severe Magnete Pol  -  North magnetic pole

Juge Magnete Pol  -  South magnetic pole

aviator  -  flier

aviacia - aviation

aerodrom  -  airport

aeroplan  -  airplane

helikoptera  -  helicopter

sonda - probe

Universum / Vsemir / Prostor – Universe / Space

galaksia - galaxy

zvezda - star

planeta - planet

ne-Zemske civilizacia  - extraterrestrial civilisation

universe  -  universal, general, common

mezo-planete  - interplanetary

kosmodrom - rocket pad

Zemske gravitacia / Geogravitacia - Earth gravitation

orbita  -  orbit

kosmose korab – space ship

ekipazx – crew

distancia  -  distance

ekran – screen

gradus  -  grade

atome elektrostacia  - nuclear power-plant

institut  - institut

atomske fuzia – nuclear fusion

zover  -  animal, creature   zovere ogrod - Zoo

pteric  -  bird    riba  -  fish    insekt  -  insect    savec  -  mamal

planta  -  plant  herba  -  herb  list  -  leaf

organite  -  organic

ne-organite  -  inorganic

senzitivita - sensitivity

celula - cell

arhxe - ancient

derma – skin

imperator – ruler

imperium - empire

kolor  - colour  /  kolore - colourful

cxerne - black

bele - white

zxolte - yellow

zelene - green

modre – blue

purpure – purple

sive – grey

pocxetie - beginning

koncxenie – end

administracia  -  administration

dividencia  -   department, section

justicia  -    justice

governia  - government

minister  - minister, servant  ministerit - to serve

_________________________________________________________________

EKLESIE VOKABULAR  (religious vocabulary)

Jesus Kristus  -  Jesus Christ                      eklesia  - church (belief)

Evangelium  -  gospel                                 spasitor  - savior

Apostol (i) -  apostle(s)                                kral  - king

Angel (i)  -  angel(s)                                     kralozem  - kingdom

Kristan (i)  -  christian(s)                             kres  - cross

baptisit  -  baptise (baptisenie  - baptism)    religia  - religion

(religiesk  -  religious, religii - religions)

svat  -  saint

Svatec (i)  - Saint(s)

spirit (i)  -  spirit(s)

mesiasx  -  mesiah

vizionar (i)  -  prophet(s)                             Bog - Boh

PK (Pred Kristus)  -  B.C. (Before Christ)            monastir  - monastir  monastiran - monk

EK (Era od Kristiani)  -  A.D. (Anno Domini)     omsxa  -  mass

kapel - church

vera  -  belief, truth, credo                 Svate Skribenie - Holy Writing / Scriptum

verit  -  to believe                               glorifit - to celebrate

resurektit - to resurrect                     molitva -  prayer   molit - to pray

NAŚ  OTEC
Naś otec, kor ies vo nebo

svate bij tvoe imen

pridij kralstvo tvoe

bij vola tvoe

kao vo nebo, tak i na Zem

ħleb naś kienodenske daj nam denes

a izvenij nam naś vini

kao i mi izveni naś vinitoriom

a ne-uvedij nas do iskuśenie

'''ale zbavij nas od zlo. '''

Amen

Vo imen od Otec i Sin i Svate Duħ

Kisi slovi berite zo ine jaziki (some words taken from other languages) :

dar(gift), agrit(to cultivate), skribit(to write), sedet(to sit), stat(to stand), videt(to see), audit(to hear), vehikular(vehicle)

navigat(to navigate), nihilovat(to destroy), mortit(to kill), lektit(to read), diferit(to distinguish), duplikit (to double), sedet (to sit)

laborit(to work), kredit(to believe) kosxela(shirt), skola(school), kapusta(cabbage), zxiak(pupil), ceruza(pencil),striga(witch)

palac(palace), verbala(verbal), morta(dead), militanta(army), magnifikanta(magnificant),maksimala(the biggest, greatest),

optimala (optimal), mur(wall), inicia(beginning), astra(star), tera(earth), luna(moon), templar(monk), birokracia(burocracy),

dukcia (duction), femina(femine), eksitus (death), fakt(fact), kineza(movement), liberta(freedom), mater (mother), lakto(milk),

legislativa(legislation),librum(book),kalamita(disaster),korpus(body),artist(artist), metamorfoza(change),aerodinamika

urbare(urbal), dom(house), litera(letter), materia(matter), minorita(minority), medik / doktor(doctor), siencia(science),rosa (dew)

siencist(scientist), nacion(nation), procesia(procession), numero(number,digit), audiencia(hearing), meteorologia(meteorology)

episkopia, minimum,maksimum,magnum, geografia,anonim, liceum, polihistor,dializa,derma,liceum,dukcia,trigonometria

stabilita, disputa, debata, sprej, lodzxia, kufor, deka, katastrofa, fajn, funkcia, mutacia, razante, pozicia,

tema,shxema,grafologia,vizia,distristribucia,fonia,kataklizma,antibiotikum,enorme,

higienika,hipotetika,

sekurita,armia, arest, rezultat, digital, senzor, cxip, komputer, disk, disketa, totalita,globala,implicita,

printit(to print), printer(printer man), printera(printer), geroj(hero), kineza (moving), rotacia (rotation)

soenstvo /  unita – unity

"En triina od nas’ z‘ivenie bil bi pridate nam, ak po akceptenie od pomoce jazik, bil bi edukenie od ine nature jaziki, limitite pro racie kriterium"

"En triina od nasx zxivenie bil bi pridate nam, ak po akceptenie od pomoce jazik, bil bi edukenie od ine nature jaziki, limitite pro racie kriterium"

( One third of our life would be added to us if, after acceptance of an auxiliary language, the education of other natural languages, would be limited on rational criterium )

G.W.Leibniz

'''Vse jaziki ies rezult od SOGLASENIE mezo c‘leni od obc‘nost – kor davate nazad do velo daleke c‘asi a rozvia se pomalo i neprerivno zo generacia do generacia – pro vzaime komunikacia s prosredstvo od glas. To znac‘i z‘e to moz‘e bit tak samo aplikate pro konstrukte jaziki.'''

( All languages are the result of an AGREEMENT between the members of a community – dating back to very distant times and evolving slowly and continuously from generation to generation – to communicate with each other by means of the voice. It means that this could be applicable to conlangs as well )

'''Vse ludi ies rodite volne a rovne vo dostojnost i pravi. Tie ies obdarene s rozum i sovest a ima bi aktit vzaimo vo duh‘ od bratstvo.'''

''All human beings are born free and equal in dignity and rights. They are endowed with reason and conscience and should act towards one another in a spirit of brotherhood.''

title: User:Ianykor


text:
Talk:Mathematical Proof and the Principles of Mathematics/Logic/Logical connectives

Mathematics
Symbolize and translate the given argument with proper symbol and logical connectors Laiba tahir (discuss • contribs) 16:23, 8 November 2023 (UTC)

Mathe
If i plan to study abroad then i will apply for BS-Data Science or BS-Industrial Mathematics. I will apply for Bs-Data Science but not BS-Industrial Mathematics Therefore, I will plan to study abroad. Laiba tahir (discuss • contribs) 16:24, 8 November 2023 (UTC)


text: Category:Book:Open Scholarship Press Curated Volumes


text: Category:Noodles

title: Category:Noodles


text:
Chess Variants/Grid Chess

Introduction
Grid Chess is a variant played on a specially marked grid board.

History
Grid chess was created by Walter Stead in 1953. The variant and its main gimmick have been used in fairy chess problems.

Rules
Grid chess is played on the standard 8 by 8 board, marked with a grid of lines that partition the board into sectors. There are several different ways to arrange these lines, but the most common arrangement divides the board into 16 2 by 2 squares.

The rules are the same as standard chess, with one addition: for a move to be legal the piece must cross a grid line. So for example in the diagram the black queen could not move to e1, f1 or f2 as those moves would not take it over a grid line - and so the white king is not in check despite the two pieces' proximity.

Sub-variants
This chess variant does not not have any notable sub-variants.

title: Chess Variants/Grid Chess