Videos de Ciencia
Videos sobre avances científicos y descubrimientos.
Ciencia 255 videos
11. Variational Autoencoders (VAEs) Explained Clearly | From Basics to Latent Space & KL Divergence
Variational Autoencoders (VAEs) are one of the most important concepts in modern deep learning and generative AI. In this video, you will learn how VAEs work from first principles—without confusion, fluff, or unnecessary complexity. We begin by revisiting traditional autoencoders and identifying their limitations in generative tasks. Then, we build a strong conceptual understanding of VAEs, including probabilistic encoding, latent space representation, and the powerful idea of learning data distributions instead of fixed mappings. This video explains key concepts such as: The difference between autoencoders and variational autoencoders Why VAEs model latent variables as probability distributions The role of mean (μ) and standard deviation (σ) The reparameterization trick and why it is essential Understanding KL Divergence in a simple and intuitive way How VAEs enable data generation, interpolation, and representation learning Real-world applications in AI, including image generation, anomaly detection, and healthcare By the end of this video, you will have a clear, intuitive, and practical understanding of VAEs, making it easier to implement and apply them in research or real-world AI systems. This content is especially useful for: Students and researchers in Machine Learning and AI Data Scientists and Deep Learning practitioners Anyone preparing for interviews or academic projects in generative models 🚀 What You’ll Learn ✔ Variational Autoencoders (VAEs) from scratch ✔ Latent space and probabilistic modeling ✔ Mathematical intuition behind KL divergence ✔ Differences between VAEs and GANs ✔ Applications of VAEs in modern AI 📌 Why This Topic Matters VAEs are foundational to many advanced generative models and are widely used in fields like computer vision, NLP, healthcare AI, and scientific simulations. Understanding VAEs gives you a strong edge in mastering generative AI systems. ⚠️ Disclaimer This video is created purely for educational and knowledge-building purposes. The content is AI-generated, and while efforts have been made to ensure accuracy, some information may be incomplete or incorrect. Viewers are encouraged to verify facts and concepts from reliable sources before applying them in academic or professional work. #VariationalAutoencoder #VAE #DeepLearning #GenerativeAI #MachineLearning #LatentSpace #ArtificialIntelligence #NeuralNetworks #AIExplained #DataScience #KLdivergence #Autoencoder #AIeducation #LearnAI #TechEducation Variational Autoencoder, VAE explained, VAE tutorial, deep learning VAE, generative AI models, autoencoder vs VAE, KL divergence explained, latent space machine learning, probabilistic models AI, reparameterization trick, neural networks tutorial, AI for beginners, advanced machine learning, generative models explained, data science AI concepts, VAE applications, AI education content, machine learning lecture, NotebookLM AI video, deep learning concepts 2026
La IA que HABLA como UN HUMANO: ¿CÓMO FUNCIONA por DENTRO? | LLM PARTE 1
En este video analizaremos cómo funcionan los modelos de lenguaje que están revolucionando el mundo, desde los conceptos más básicos hasta la arquitectura que les dio vida. Sin tecnicismos, con ejemplos del día a día, para que lo entienda hasta tu abuela. Esta es la primera parte de una serie donde iremos paso a paso, desde lo más fundamental, conectando ideas cotidianas con las redes neuronales, hasta llegar a entender qué hay detrás de tecnologías como ChatGPT o Claude. 🌐 Sígueme en otras redes: 🔴 YouTube: / https://www.youtube.com/@kunSure%C3%B1o 📱 Instagram: https://www.instagram.com/kunsurenio 📱 TikTok: https://www.tiktok.com/@kunsurenio 💻Twitter/X: https://x.com/CumlouderSanti 🎧 Spotify: https://open.spotify.com/ 💼 LinkedIn: https://kunsureniorbg.github.io/porfolio/en/ Contacto profesional/sponsor: poncearrochasanti@gmail.com Si te gusta el contenido, no olvides darle Like, Hype, suscribirte y activar la campanita 🔔 para no perderte nada de lo que viene.
👉 “Ciudadanía Digital e Inteligencia Artificial 🌐 Seguridad, Ética y Riesgos Online”
Vivimos conectados. Redes sociales, inteligencia artificial, plataformas digitales y aplicaciones forman parte de nuestra vida cotidiana… pero ¿sabemos realmente cómo protegernos y actuar de forma responsable en Internet? En este video exploramos los conceptos fundamentales de la ciudadanía digital responsable, la seguridad de la información y los desafíos éticos de la inteligencia artificial en el mundo actual. A lo largo del contenido, aprenderás cómo funciona la protección de datos digitales y por qué la seguridad informática se basa en tres pilares esenciales: – Confidencialidad – Integridad – Disponibilidad Explicamos de forma clara cómo estos principios ayudan a proteger información personal, cuentas, archivos y sistemas digitales frente a ataques o accesos no autorizados. También analizamos la importancia de cuidar nuestra identidad digital, entendiendo que cada publicación, comentario o interacción deja una huella en Internet que puede afectar nuestra privacidad, reputación y seguridad futura. El video incluye ejemplos reales y situaciones cotidianas relacionadas con riesgos digitales como: – Phishing – Robo de datos – Estafas online – Ciberacoso – Suplantación de identidad – Manipulación de información Además, reflexionamos sobre cómo desarrollar pensamiento crítico frente a la enorme cantidad de contenido que circula en redes sociales y plataformas digitales. Uno de los temas centrales del video son las cinco leyes de la Alfabetización Mediática e Informacional (AMI) propuestas por la UNESCO, fundamentales para aprender a consumir, analizar y compartir información de manera responsable en la era digital. También exploramos los desafíos éticos de la inteligencia artificial: – Sesgo algorítmico – Automatización de decisiones – Manipulación de contenido – Falta de transparencia – Necesidad de supervisión humana Analizamos cómo los algoritmos pueden influir en lo que vemos en Internet y por qué es importante que las personas mantengan el control crítico sobre las decisiones tomadas por sistemas inteligentes. Este contenido está pensado para estudiantes, docentes, familias y cualquier persona interesada en comprender cómo convivir de forma segura y ética en un mundo cada vez más digitalizado. Gracias al uso de inteligencia artificial, organizamos la información de forma dinámica, clara y accesible para facilitar el aprendizaje y la reflexión. En este video vas a encontrar: – Ciudadanía digital responsable – Seguridad de la información – Confidencialidad, integridad y disponibilidad – Protección de identidad digital – Prevención de phishing y ciberacoso – Leyes AMI de la UNESCO – Ética e inteligencia artificial – Sesgo algorítmico y supervisión humana Te recomendamos ver el video completo y compartirlo para ayudar a construir una cultura digital más segura, crítica y responsable. Si te interesan temas de tecnología, inteligencia artificial, educación digital y ciberseguridad, suscribite al canal y activá las notificaciones. También podés dejar en los comentarios qué tema tecnológico te gustaría aprender en próximos videos. La tecnología avanza rápido… pero la responsabilidad digital debe avanzar todavía más.
¡Redes Neuronales que Explican sus Decisiones! El Diccionario Interpretable en Sparse Coding
Las redes neuronales artificiales, especialmente los modelos de aprendizaje profundo, a menudo son consideradas "cajas negras". Esto se debe a que su funcionamiento interno y la forma en que representan los datos son increíblemente complejos y difíciles de interpretar para los humanos. No podemos entender fácilmente por qué toman una decisión específica, lo cual es un problema fundamental cuando queremos confiar en sistemas de inteligencia artificial para tareas críticas. Este estudio presenta un enfoque novedoso para entrenar una red neuronal de una manera que sus componentes internos sean comprensibles, inspirándose en los mecanismos de representación y aprendizaje del cerebro de los mamíferos. Utilizando una técnica llamada "codificación dispersa" (sparse coding), el modelo aprende un "diccionario" de elementos que son más fáciles de entender. A diferencia de los modelos tradicionales, este método obliga a la red a ser selectiva, utilizando solo unos pocos elementos para explicar una entrada de datos, de forma similar a como nuestro cerebro procesa la información de manera eficiente. Los resultados demuestran que el modelo de codificación dispersa ofrece beneficios tanto cualitativos como cuantitativos en la interpretación en comparación con modelos equivalentes como los autoencoders convolucionales. Las representaciones internas aprendidas son mucho más claras y selectivas, lo que permite a los investigadores entender la contribución de cada neurona a la decisión final del sistema. Este trabajo es un paso adelante hacia la creación de una inteligencia artificial más transparente y fiable. Link al paper: https://arxiv.org/pdf/2011.11805 Autores del estudio: Edward Kim, Connor Onweller, Andrew O'Brien, Kathleen McCoy Apoyanos en https://www.patreon.com/audioarxiv Unete en https://discord.gg/vKRmFhg4YQ #Ciencias de la computación #InteligenciaArtificial #MachineLearning #RedesNeuronales #DeepLearning #IAExplicable
JAKA PI – O NOVO ROBÔ CHINES QUE PROMETE REVOLUCIONAR A ROBÓTICA
O JAKA Pi, desenvolvido pela JAKA Robotics em Xangai, foi apresentado ao mundo. Com apenas 1,22 metro de altura e 42 quilogramas, ele combina dois sistemas de computação separados: um para raciocínio com inteligência artificial, outro para controle de movimento em tempo real — uma arquitetura inspirada na separação entre o cérebro e o cerebelo humano. Neste vídeo, você vai entender como funciona essa "arquitetura de fusão cerebral", por que ela é relevante, quais são as capacidades reais e os limites honestos do JAKA Pi, e o que esse lançamento revela sobre a estratégia da China para dominar a robótica humanoide nos próximos anos. Pandaily "JAKA Robotics Unveils Compact 1.22m Humanoid Robot JAKA Pi for Education and Service" https://pandaily.com/jaka-robotics-jaka-pi-jun2026 TechCrunch "Why China's humanoid robot industry is winning the early market" https://techcrunch.com/2026/02/28/why-chinas-humanoid-robot-industry-is-winning-the-early-market/ MERICS (Mercator Institute for China Studies) "Embodied AI: China's ambitious path to transform its robotics industry" https://merics.org/en/report/embodied-ai-chinas-ambitious-path-transform-its-robotics-industry Carnegie Endowment for International Peace "Embodied AI: China's Big Bet on Smart Robots" https://carnegieendowment.org/research/2025/11/embodied-ai-china-smart-robots TrendForce "China's Humanoid Robot Output to Surge 94% in 2026; Unitree and AgiBot to Capture Nearly 80% Market Share" https://www.trendforce.com/presscenter/news/20260409-13007.html TechNode "Shanghai Jiao Tong University launches China's first undergraduate major in embodied AI" https://technode.com/2025/12/01/shanghai-jiao-tong-university-launches-chinas-first-undergraduate-major-in-embodied-ai/ The AI Insider "China releases national standards for humanoid robotics and embodied AI" https://theaiinsider.tech/2026/03/01/china-releases-national-standards-for-humanoid-robotics-and-embodied-ai/ JAKA Robotics "About JAKA" https://www.jaka.com/en/about CKGSB Knowledge "China's Robotics Revolution: AI and Embodied Intelligence in Industry" (entrevista com Li Mingyang, fundador da JAKA Robotics) https://english.ckgsb.edu.cn/knowledge/article/ai-driven-robotics-and-china-future-industries/ #RoboHumanoide #Robotica #InteligenciaIncorporada
How LLMs Actually Generate Text (Every Dev Needs to See This)
Every day, millions of people use ChatGPT, Claude, and Grok—but very few understand what is actually happening behind the blinking cursor. Did you know the model has no idea what it's going to say next? In this video, we break down the exact 5-step process of how Large Language Models (LLMs) generate text, from the moment you hit "send" to the final output. We move past the magic and dive into the mechanism so you can become a better AI builder. You’ll learn exactly how AI reads text, how it understands context, and why "hallucinations" actually happen. 👇 What You Will Learn (Chapters): 0:00 - The Illusion of AI (No Hidden Script) 0:28 - The 5 Steps of LLM Text Generation 0:54 - Step 1: Tokenization (How Models Read) 1:56 - Step 2: Embeddings (Mapping Meaning & Context) 3:11 - Step 3: Transformers & The Attention Mechanism 4:40 - Step 4: Probabilities (Logits & Softmax) 5:40 - Step 5: Sampling (Greedy Decoding, Temperature & Top-P) 6:52 - Autoregressive Generation (The Loop) 7:50 - Why AI Hallucinates (Mechanism, Not Magic) 8:50 - Summary: Becoming an AI Builder If you want to understand the architecture of modern AI, hit the LIKE button and SUBSCRIBE for more deep dives into machine learning and software engineering. #ChatGPT #LLM #MachineLearning #ArtificialIntelligence #Transformers #OpenAI #TechEducation #LLM #HowAIWorks #ChatGPT #MachineLearning #ArtificialIntelligence #NeuralNetworks #Transformers #TechExplained #Programming #OpenAI #Claude 🏷️Keywords ChatGPT, OpenAI, Large Language Models, LLM, Claude, Grok, Artificial Intelligence, AI explained, Machine Learning, Neural Networks, Deep Learning, Generative AI, generative text, Specific & Technical Tags: Tokenization, AI tokens explained, Word embeddings, Transformer model explained, Attention mechanism AI, Self attention, Softmax function, AI logits, Top-p sampling, Nucleus sampling, AI Temperature setting, Greedy decoding, Autoregressive models, Llama 3, GPT-4, Long-Tail/Search Query Tags: How does ChatGPT work, How large language models work, What is a token in AI, Transformer neural network explained simply, How AI generates text, Why does AI hallucinate, ChatGPT temperature explained, How to write better AI prompts, Mechanism not magic, Learn AI for beginners, How to build with LLMs, AI context window explained,
Computer Vision Explained in 60 Sec 👁️🤖 | #AI Terminology #11 #ComputerVision #CareerClust #Shorts
👁️ What is Computer Vision? Computer Vision is a field of Artificial Intelligence that enables computers to see, analyze, and understand images and videos just like humans. In this short video, you'll learn: ✅ What Computer Vision is ✅ How it works ✅ Real-world applications ✅ Why it's important in AI Examples include: 🚗 Self-driving cars 📷 Face recognition 🏥 Medical image analysis 🛒 Smart retail systems 📚 AI Terminology Series – Part 11 Follow CareerClust for simple and practical AI concepts explained in under a minute. #CareerClust #ComputerVision #AI #ArtificialIntelligence #MachineLearning #DeepLearning #AITerminology #GenerativeAI #DataScience #Technology #LearnAI #AIForBeginners #TechShorts #Innovation #FutureTech #YouTubeShorts #Shorts
AI, Computer Vision & ML Training Program | IIT Hyderabad AI Summer School 2026 |
🚀 IIT Hyderabad AI Summer School 2026 | Artificial Intelligence, Computer Vision & Machine Learning In this video, Dr. Satish IRSE explains an exciting AI learning opportunity related to IIT Hyderabad's rapidly growing Artificial Intelligence ecosystem. If you are interested in Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, Deep Learning, Data Science, Robotics, Autonomous Systems, and Future Technologies, this program can help strengthen your profile. IIT Hyderabad is home to India's first dedicated Department of Artificial Intelligence and offers strong exposure to AI research and industry applications. 🎯 Key Learning Areas ✅ Artificial Intelligence Fundamentals ✅ Machine Learning Algorithms ✅ Computer Vision Applications ✅ Deep Learning Concepts ✅ AI Research Trends ✅ Image Processing & Pattern Recognition ✅ Real-world AI Projects ✅ Python for AI & ML IIT Hyderabad's AI ecosystem focuses on domains such as autonomous systems, healthcare AI, natural language processing, computer vision, and industry-driven innovation. Students also benefit from internship and research opportunities. 🎓 Who Can Apply? ✔ B.E. / B.Tech Students ✔ M.Tech Students ✔ B.Sc Students ✔ M.Sc Students ✔ Research Scholars ✔ Faculty Members ✔ AI Enthusiasts Programs in AI, Machine Learning and Computer Vision summer schools are generally designed for UG/PG students, researchers, faculty members and professionals interested in AI technologies. 🔥 Why Students Should Join? ✔ Learn from IIT Faculty & Researchers ✔ Build AI & ML Skills ✔ Explore Computer Vision Applications ✔ Gain Research Exposure ✔ Improve Higher Studies Profile ✔ Enhance Placement Opportunities ✔ Network with AI Community 📚 Trending Technologies Covered • Artificial Intelligence • Machine Learning • Computer Vision • Deep Learning • Neural Networks • Data Science • Robotics • Autonomous Systems • Generative AI • Large Language Models (LLMs) The AI and ML ecosystem at IIT Hyderabad also includes advanced work in autonomous systems, perception, computer vision and machine learning research. In this video Dr. Satish IRSE explains: ✔ Eligibility ✔ Course Benefits ✔ AI Career Scope ✔ Computer Vision Applications ✔ Machine Learning Opportunities ✔ Future AI Jobs ✔ How Students Can Benefit #IITHyderabad #ArtificialIntelligence #MachineLearning #ComputerVision #AI2026 #DeepLearning #DataScience #GenerativeAI #Robotics #EngineeringStudents #TechCareers #FutureSkills #AITraining #MachineLearningCourse #CareerGuidance #DrSatishIRSE #AIJobs #Technology #StudentOpportunities #IIT
¿Cómo funciona ChatGPT?
Anaizo cómo funciona ChatGPT desde una perspectiva técnica y divulgativa. Revisamos cómo se entrenan los grandes modelos de lenguaje, de qué manera procesan texto y por qué pueden producir respuestas que muchas veces resultan sorprendentemente coherentes. • Qué es un gran modelo de lenguaje (LLM) • Cómo aprende una inteligencia artificial como ChatGPT • Qué ocurre cuando ingresamos un prompt • Por qué la IA puede generar texto con apariencia humana • Cuáles son sus fortalezas y limitaciones #ChatGPT #InteligenciaArtificial #ModelosDeLenguaje #TransformacionDigital #TecnologiasDisruptivas Si valoras el análisis riguroso de las tecnologías que están redefiniendo nuestro tiempo, suscríbete a Ovejas Eléctricas, activa las notificaciones y comparte este video. Tu participación ayuda a seguir generando contenido que conecta ciencia, tecnología y sociedad desde una mirada crítica y fundamentada.
Diagnósticos más rápidos: Doctor explica los beneficios de la Inteligencia Artificial en la salud
En una nueva edición de Entrevistas CNN, conversamos con el doctor y jefe de Informática Biomédica de la Clínica Alemana, Jaime de los Hoyos, sobre cómo se puede aprovechar la inteligencia artificial (IA) en la salud. Para mayor información sobre este tema ingresa a https://www.cnnchile.com #BreakingNews #Noticias #Información #EnVivo
How Modern AI Systems Actually Work: RAG, Tool Calling & LLMs
In this MizuFlow.ai Foundation of Finance episode, Sung Lee, CFA, CPA, CA, explains the core architectural principles behind modern enterprise AI systems. Many organizations focus exclusively on the Large Language Model itself. However, real business value comes from combining: • LLM reasoning • enterprise data • tool calling • APIs • Retrieval Augmented Generation (RAG) • structured outputs • workflow orchestration This session explores how these components work together to create intelligent systems capable of solving real-world business problems. The objective is to help finance, accounting, and technology professionals understand how enterprise AI moves beyond simple chat interfaces into fully integrated operational platforms. 🧠 What This Video Covers Enterprise AI Is More Than an LLM A common misconception is that AI equals the model. In reality: Model ≠ System The model provides: • reasoning • language understanding • planning • decision support The application provides: • actions • integrations • workflows • business execution Real enterprise AI requires both. Local Inference vs Cloud AI The session compares two primary deployment approaches. Local Inference Models run on: • local servers • private infrastructure • enterprise-controlled environments Advantages: ✅ Privacy ✅ Data sovereignty ✅ Lower long-term inference costs Challenges: ❌ Hardware requirements ❌ Maintenance complexity ❌ Potentially weaker models Cloud APIs Examples include: • OpenAI • Gemini • Anthropic Advantages: ✅ State-of-the-art models ✅ Rapid deployment ✅ Minimal infrastructure Challenges: ❌ Ongoing API costs ❌ Data governance considerations ❌ Third-party dependencies The Role of Tool Calling A major theme throughout the module is: Reasoning vs Action The LLM performs reasoning. The application performs actions. Examples include: • database queries • ERP updates • report generation • sending emails • running calculations Through tool calling, AI becomes capable of interacting with real-world systems. Retrieval Augmented Generation (RAG) Enterprise AI systems often require access to information that was never included during model training. RAG solves this challenge. Question ↓ Document Retrieval ↓ Relevant Context ↓ LLM Reasoning ↓ Answer This enables AI to work with: • accounting policies • contracts • financial statements • internal procedures • audit documentation while reducing hallucinations. Structured Outputs The module explains why enterprise systems require: Structured Outputs rather than unpredictable text. Examples include: • JSON • XML • predefined schemas This allows software systems to reliably process AI-generated outputs. Example: { "customer": "ABC Corporation", "risk_score": 8.4, "action_required": true } Structured outputs are essential for automation. Learned Weights & Inference Engines The session also clarifies key technical concepts. Learned Weights The knowledge stored inside the model. These represent billions of learned relationships developed during training. Inference Engine The runtime environment responsible for: • executing the model • generating responses • serving predictions The inference engine transforms static model weights into useful business outputs. Finance & Accounting Applications These architectural components support: Financial Reporting Agents • retrieve supporting schedules • generate commentary • draft disclosures AP Automation Systems • OCR extraction • vendor validation • workflow routing • ERP integration FP&A Platforms • scenario analysis • forecasting • variance explanations • executive reporting Enterprise Knowledge Systems • policy search • tax research • accounting guidance retrieval • regulatory interpretation 🚀 Why This Matters The future of AI is not: Question → Answer The future is: Question ↓ RAG ↓ Reasoning ↓ Tool Calling ↓ Business Action ↓ Human Review This is the foundation of modern enterprise intelligence systems. DISCLAIMER & LIABILITY NOTICE: The content in this video is for educational and informational purposes only. It does not constitute financial, accounting, tax, or legal advice. No Professional Relationship: Watching this video or interacting in the comments does not create a CPA-Client or fiduciary relationship between you and Sung Lee. Software & Tools: Any code, software, or tools mentioned (including https://www.google.com/search?q=Katchiflow.com) are provided "as-is" for demonstration and drafting purposes only. Outputs should not be relied upon for tax or statutory reporting without independent verification by a qualified professional.