Please enable JavaScript.
Coggle requires JavaScript to display documents.
DIGITAL CULTURE 1-SATAGE 3 - Coggle Diagram
DIGITAL CULTURE 1-SATAGE 3
CULTURA DIGITAL I - ETAPA 3
Fundamentos e Historia (Págs. 105-107)
Artificial Intelligence / AI (p. 105): Branch of computer science that develops systems capable of performing tasks that normally require human intelligence.
Turing Test (1950) (p. 105): Test proposed by Alan Turing to determine whether a machine can exhibit intelligent behavior equivalent to a human.
Dartmouth Conference (1956) (p. 105): Historic workshop where the term "Artificial Intelligence" was first coined.
ELIZA (1966) (p. 105): Early natural language processing computer program simulating a psychotherapist conversationally.
Deep Blue (1997) (p. 105): IBM chess-playing computer that defeated world champion Garry Kasparov.
Data (p. 105): Information in raw form used by AI systems to learn and generate results.
Data Analysis (p. 107): Process of examining information to identify patterns or generate useful knowledge.
Automation (p. 107): Performance of tasks through technological systems with little or no human intervention.
Knowledge Extraction (p. 107): Process of retrieving structured knowledge and actionable insights from unstructured or complex data sources.
Optimization of Processes (p. 107): Practice of making a business process or technological workflow more efficient using AI and automation.
Predictive Analysis (p. 107): Use of historical data, statistical algorithms, and machine learning to estimate the likelihood of future outcomes.
Real-Time Data Analysis (p. 107): Immediate processing and evaluation of data as soon as it is collected or generated by systems.
Fraud Detection (p. 106): Identification of suspicious or irregular activities through data analysis.
Medical Imaging Analysis (p. 106): Application of AI and image recognition algorithms to interpret medical scans to assist diagnosis.
Predictive Maintenance (p. 106): Techniques that monitor equipment condition using data analysis to predict when maintenance should be performed before failures occur.
Clasificación y Herramientas (Págs. 111-131)
Narrow / Weak Artificial Intelligence (p. 111): AI systems designed and trained to perform a specific, limited task without possessing general consciousness or reasoning.
Artificial General Intelligence / AGI (p. 111): Theoretical form of AI capable of learning, understanding, and applying knowledge across a wide range of tasks at a human level.
Artificial Superintelligence / ASI (p. 112): Hypothetical system that surpasses human thinking capacities and intelligence in every aspect.
Generative AI (p. 115 / 131): Branch of AI capable of creating new digital content based on patterns learned during training.
Synthetic Content (p. 116): Material generated fully or partially through Artificial Intelligence.
Synthetic Voices (p. 131): Artificially generated human speech produced using audio AI tools and speech synthesis models.
Text AI Tool (p. 131): Tool for writing, research, translation, and problem solving.
Video and Image AI Tools (p. 131): Tools that generate and edit images and videos, create avatars, and improve media quality.
Song and Voice AI Tools (p. 131): Agents to compose music, sound effects, or convert text to voiceover.
Slides AI Tool (p. 131): Tools for creating and designing slides (Canva AI, Microsoft Copilot, Gamma, Beautiful.ai).
Storyboard AI Tool (p. 131): Tools that make illustrated stories, effects, or convert text to voiceover.
ChatGPT (p. 131): AI language model developed by OpenAI designed to understand and generate human-like text responses based on prompts.
Notebook LM (p. 131): AI-powered personalized research and note-taking assistant developed to process user-provided documents.
Recommendation Systems (p. 116): Technologies based on AI that analyze large amounts of data to suggest content, products, or services of interest to each user.
Algorithmic Personalization (p. 117): Automatic adaptation of the content shown to each person.
Behavioral Pattern (p. 117): Set of recurring actions that allow for the identification of user preferences.
Behavioral Analysis (p. 117): Examination of user actions and patterns to understand behaviors and predict future decisions or preferences.
Digital Interaction (p. 117): Action performed by a person within a platform, such as liking, commenting, or sharing content.
Digital Competencies (p. 117): Set of skills, knowledge, and attitudes required to use digital technologies safely, critically, and effectively.
Digital Inclusion (p. 117): Ensuring that all individuals and communities have equal access to digital technologies and the internet.
Digital Transformation (p. 117): Integration of digital technology into all areas of society or an organization, fundamentally changing operations.
Personalization (p. 117): Adapting content or services to the characteristics and needs of each user.
Personalized Learning (p. 117): Educational approach that uses technology to customize instruction and pacing according to each student's needs.
Machine Learning y Deep Learning (Págs. 112-115)
Machine Learning / ML (p. 112): Branch of Artificial Intelligence that utilizes algorithms capable of analyzing large amounts of information to find relationships, trends, and patterns.
Classification of Machine Learning (p. 113): Categorization of machine learning models into supervised learning, unsupervised learning, and reinforcement learning.
Supervised Learning (p. 113): Machine learning approach that works with classified or labeled data.
Unsupervised Learning (p. 113): Machine learning approach that structures unclassified data into groups with similar characteristics.
Reinforcement Learning (p. 113): Machine learning approach that learns through trial and error, punishments, and rewards based on results.
Labeled Data (p. 113): Data tagged with informative labels or target answers used to train supervised machine learning models.
Unlabeled Data (p. 113): Raw data without pre-existing labels or categories used primarily in unsupervised learning.
Regression (p. 113): Supervised learning algorithm used to predict continuous numerical values based on input features.
Association (ML) (p. 113): Technique used to discover relationships or rules between variables in large datasets.
Clustering (p. 113): Unsupervised learning technique that groups data points with similar characteristics together without prior labels.
Reduction (Dimensionality) (p. 113): Unsupervised learning process that simplifies large amounts of data into their most essential elements.
Model-Based (Reinforcement) (p. 113): Approach in reinforcement learning where the agent creates a model of the environment to predict consequences before taking an action.
Model-Free (Reinforcement) (p. 113): Reinforcement learning method where the agent associates actions directly with rewards or experiences.
Training (of a Model) (p. 114): Process through which a system learns using data to improve its performance.
Continuous Learning (p. 114): Capability of an AI system to constantly learn and improve performance from new data over time.
Deep Learning (p. 114): Specialized technique within Machine Learning that utilizes artificial neural networks with multiple layers to process complex data progressively.
Artificial Neural Network (p. 114): Computational model designed to identify patterns and relationships in large volumes of information inspired by the human brain.
AlexNet (p. 111): Convolutional neural network architecture that significantly advanced deep learning and image recognition.
Feature Extraction (p. 114): Process of transforming raw input data into a set of distinct features suitable for machine learning algorithms.
Image Recognition (p. 114): Ability of software to identify objects, places, people, or actions in digital images.
Pattern Recognition (p. 114): Automated identification of regularities and trends in data through machine learning algorithms.
Large Language Model / LLM (p. 115): Trained system for understanding and generating text in natural language.
Natural Language Processing / NLP (p. 115): Subfield of AI focused on giving computers the ability to understand, interpret, and generate human language.
Machine Translation (p. 115): Automated translation of text or speech from one natural language to another using algorithms.
Virtual Assistants (p. 115): Software applications that use natural language processing to assist users with daily tasks through voice or text.
Voice Recognition (p. 115): Technology that enables a system to decode, process, and understand spoken human speech.
Prompts e Ingeniería Crítica (Págs. 119-138)
Prompt (p. 119): Instruction or request that a person provides to an AI tool to obtain a result.
Prompt Engineering (p. 135): Process of designing clear, structured, and strategic instructions where higher specificity increases the probability of obtaining useful and precise results.
Critical Prompt Engineering (p. 135): Approach that implies analyzing generated results, questioning their validity, identifying potential errors, and reflecting on ethical implications.
Prompt Classification (name) (p. 119 / 133): Division of prompts into functional categories based on purpose: Informative, Analysis, Content Generation, Problem Solving, and Role/Context.
Informative Prompt (p. 133): Prompt whose purpose is to request explanations, definitions, or descriptions about a specific topic.
Analysis Prompt (p. 133): Prompt requesting the AI to examine information, identify patterns, compare elements, or draw conclusions based on provided data.
Content Generation Prompt (p. 133): Prompt aiming to create original texts, images, summaries, presentations, or outlines based on specific instructions.
Problem Solving Prompt (p. 133): Prompt seeking to obtain procedures, strategies, or solutions for specific situations.
Role or Context Prompt (p. 134): Prompt assigning a specific function or perspective to the AI (acting as a teacher, researcher, advisor, or specialist).
Critical Thinking (p. 135): Objective analysis and evaluation of information, results, and sources to form a reasoned judgment.
Target Audience (p. 137): Specific group of people or users for whom digital content or an AI prompt is intended.
Ética, Riesgos, Regulación y Sustentabilidad (Págs. 108-136)
Deepfake (p. 122): Content generated through AI that imitates the appearance, voice, or movement of a person with a high degree of realism.
Hyper-realistic Images (p. 122): AI-generated images that display an extraordinarily high level of detail and realism.
Disinformation / Misinformation (p. 122): False or misleading information spread intentionally (disinformation) or unintentionally (misinformation).
Algorithmic Bias (p. 108 / 110): Unfair or discriminatory result generated by an AI system due to data or criteria used in its training.
Bias Amplification (p. 108): Phenomenon where an AI model exaggerates existing biases present in its training data.
AI Hallucination (p. 123): Responses that contain incorrect, invented, or factually groundless information, yet are presented as if they were true.
Academic Dishonesty (p. 129): Used to perform activities that should reflect the student's own work, analysis, or knowledge.
Academic Integrity (p. 129): Commitment to honesty and responsibility in academic work, avoiding plagiarism or cheating.
Intellectual Honesty (p. 129): Applied ethical attitude that involves truthfulness and acknowledgment of sources in academic work.
Human-in-the-Loop (p. 129): Approach consisting of keeping people involved in the review, validation, and decision-making during the use of AI systems.
Support Tool (vs. Replacement) (p. 129): Perspective that AI should serve as an assistant to enhance human capabilities rather than replacing human effort.
Social Benefit (Ethical Principle) (p. 126): Principle stating AI must be used to improve people's quality of life and contribute to collective well-being.
Justice and Non-Discrimination (Ethical Principle) (p. 126): Principle stating AI systems must avoid reproducing prejudices or generating unfair results.
Transparency (Ethical Principle) (p. 126): Principle stating users must have access to clear information regarding when they are interacting with AI.
Privacy and Data Protection (Ethical Principle) (p. 126): Key principles establishing that intelligent systems must respect individual personal information.
Accountability (Ethical Principle) (p. 126): Principle establishing that individuals and organizations developing or utilizing AI systems must assume the consequences of their decisions.
Explainability (p. 126): Degree to which the internal mechanics and decisions of an AI model can be explained in human-understandable terms.
Automated Decision-Making (p. 127): Use of algorithms to participate in critical processes (such as personnel recruitment or credit allocation).
Digital Surveillance (p. 127): Monitoring and tracking of individuals' online activities using digital tools.
Mass Surveillance (p. 127): Intricate and widespread monitoring of an entire population or group using digital systems.
Facial Recognition (p. 127): Biometric technology that identifies or verifies a person's identity using facial features.
Job Displacement (p. 127): Replacement of human workers by automation or artificial intelligence technologies in the labor market.
EU AI Act (p. 124): Comprehensive European legal framework regulating artificial intelligence based on risk levels.
High-Risk AI Systems (p. 124): AI applications in critical areas that require strict regulation and safety measures.
Risk Analysis (p. 124): Systematic assessment of potential hazards, biases, and vulnerabilities associated with AI systems.
Risk-Based Approach (p. 124): Regulatory principle classifying AI applications according to the level of risk they pose.
Intellectual Property (p. 124): Legal rights protecting creations of the mind, artistic works, and software.
Carbon Footprint (of AI) (p. 128): Amount of greenhouse gas emissions generated directly or indirectly by AI energy consumption and data centers.
Data Centers (p. 128): Physical facilities that house computer systems and infrastructure required to process, store, and manage AI data.
Greenhouse Gas Emissions (p. 128): Gases released into the atmosphere from energy consumption during AI training and operations.
Source Triangulation (p. 136): Practice consisting of comparing information obtained from AI with various reliable and independent sources.
Source Verification (p. 136): Process of checking and confirming that the sources and references cited or generated by an AI actually exist and are credible.