Kirtiman Gopanayak's Blog

A Comprehensive Guide to Theories of Truth A Comprehensive Guide to Theories of Truth: Definitions, Formulations, and Examples The concept of truth has been a cornerstone of philosophical inquiry for millennia. Different theories attempt to explain what makes a statement true, each offering unique perspectives on the relationship between language, reality, and knowledge. Below, we explore eight prominent theories of truth, providing definitions, formulaic representations, and illustrative examples for each. 1. Correspondence Theory of Truth Definition: The Correspondence Theory posits that truth is determined by how accurately a statement aligns with objective reality. A proposition is true if it corresponds to a fact or state of affairs in the world. A statement P is true ⇔ P corresponds to a fact in reality. Example: The statement “Grass is green” is true if and only if grass, in the physical world, exhibits the...

Thought Experiments Parts-2 Thought Experiments: A Comprehensive Guide Thought experiments serve as powerful cognitive tools, enabling us to probe hypothetical scenarios, refine theories, and navigate complex philosophical and scientific questions. By engaging with counterfactuals, predictive models, and retroductive inferences, these mental exercises transcend mere speculation, offering structured pathways to innovation and discovery. This blog post unpacks seven key types of thought experiments—predictive, retroductive, hindcasting, backcasting, semifactual, counterfactual, and prefactual—providing explanations, examples, and formulaic frameworks for constructing them. Predictive Thought Experiments Explanation Predictive thought experiments extrapolate future outcomes by analyzing current trends, causal relationships, and existing data. These experiments test the logical consistency of theor...

A Comprehensive Framework for Formulating Questions Using Taxonomy A Comprehensive Framework for Formulating Questions Using Taxonomy The ability to craft precise, targeted questions is fundamental to effective pedagogy and critical thinking development. This report presents a systematic approach to constructing questions using Bloom's Taxonomy, organized around twenty core verbs that span cognitive complexity levels. Grounded in educational psychology research, this framework enables educators to design assessments and discussions that progressively develop higher-order thinking skills. Cognitive Foundations of Question Design Hierarchical Structure of Bloom's Taxonomy Bloom's Taxonomy organizes cognitive processes into six hierarchical levels, each representing increased intellectual demand: Remember : Retrieving factual knowledge Understand : Interpreting meaning Apply : Using informatio...

Advanced Techniques and Practices in Prompt Engineering Advanced Techniques and Practices in Prompt Engineering: A Comprehensive Analysis Introduction Prompt engineering has emerged as a critical discipline in optimizing interactions with large language models (LLMs), enabling precise control over outputs through structured instructions, examples, and constraints. This report systematically examines key prompting techniques, best practices, reliability improvements, hyperparameters, and common pitfalls in LLM applications, supported by practical examples and empirical evidence from industry research. Prompting Techniques Role Prompting Definition: Assigning a specific persona or expertise to guide the model’s responses. Example: "Act as a constitutional lawyer analyzing this privacy clause for compliance risks" demonstrates how role specialization improves domain-specific accuracy. Chain-of-Though...

Generative AI Blog Post Understanding Generative AI 1. Generative vs Discriminative AI Models Q1: What distinguishes generative AI models from discriminative ones? A1: Generative models (e.g., VAEs, GANs) learn the joint probability distribution $$ P(X,Y) $$ to generate new data resembling training data. Discriminative models (e.g., logistic regression, SVMs) learn $$ P(Y|X) $$ to classify or predict labels. Generative models excel in creativity (e.g., art generation), while discriminative models prioritize accuracy in classification tasks. 2. Rule-based vs Probabilistic Approaches Q2: How do rule-based systems differ from probabilistic models like VAEs? A2: Rule-based systems use predefined if-then logic (deterministic), ideal for structured tasks like grading. Probabilistic models (e.g., VAEs) leverage statistical distributions to handle uncertainty, generating data via latent space sampling. VAEs merge deep le...

Statistical Biases Explained Understanding Statistical Biases Self-Selection Bias Occurs when individuals choose whether to participate in a study, potentially skewing results. Example: A voluntary online survey about internet usage attracts mostly tech-savvy respondents. Exclusion Bias Systematic omission of certain groups from a study, leading to unrepresentative results. Example: A health study excludes participants with pre-existing conditions, potentially missing important data. Attribution Bias Tendency to explain behaviors based on internal characteristics rather than external factors. Example: A manager attributes an employee's poor performance to laziness without considering workload or ...

Some Decision Making Paradoxes Exploring Paradoxes 1) Hedgehog's Paradox The challenge of human intimacy. A group of hedgehogs huddle for warmth but must keep distance to avoid hurting each other with their spines. 2) Inventor's Paradox Solving a more general problem is often easier than solving a specific one. Proving a mathematical theorem for all numbers can be simpler than proving it for a specific case. 3) Newcomb's Paradox A conflict between two principles of rational decision-making. Choosing between one opaque box or two boxes, where the contents depend on a predictor's forecast of your choice. 4) Paradox of Tolerance Unlimited tolerance may lead to the extinction of tolerance. A tolerant s...