Videos de Ciencia
Videos sobre avances científicos y descubrimientos.
Ciencia 128 videos
Aprenda Visão Computacional: A Habilidade Mais Valiosa da IA
🚀 Aprenda Visão Computacional: A Habilidade Mais Valiosa da IA Você já imaginou ensinar um computador a enxergar, reconhecer pessoas, identificar objetos e tomar decisões automaticamente? Neste vídeo eu apresento o treinamento completo de Visão Computacional da Sara Educação e mostro por dentro da plataforma tudo o que você vai aprender para dominar uma das áreas que mais crescem no mundo da Inteligência Artificial. Você vai conhecer: ✅ Como funciona a plataforma de ensino ✅ Os módulos do treinamento ✅ As tecnologias utilizadas ✅ Projetos práticos desenvolvidos durante as aulas ✅ Reconhecimento facial ✅ Detecção de objetos ✅ Rastreamento de pessoas e veículos ✅ Processamento de imagens em tempo real ✅ Inteligência Artificial aplicada à visão computacional A Visão Computacional está presente em: 🚗 Carros autônomos 🏭 Indústria 4.0 📹 Sistemas de monitoramento inteligente 🤖 Robótica 🛒 Varejo inteligente 🏥 Área médica 📦 Logística automatizada Se você trabalha com eletrônica, programação, automação, robótica ou inteligência artificial, esta pode ser uma das habilidades mais valiosas para os próximos anos. 🔥 Conheça o treinamento completo de Visão Computacional da Sara Educação: ➡ https://saraeducacao.com.br 📲 Acompanhe mais conteúdos: Instagram: https://instagram.com/sara.educacao YouTube: https://youtube.com/@Sara.Educacao WhatsApp: (54) 99245-0330 E-mail: contato@saraeducacao.com.br 🔎 PERGUNTAS FREQUENTES (SEO) 🔹 O que é Visão Computacional? 🔹 Como aprender Visão Computacional do zero? 🔹 Como funciona o reconhecimento facial? 🔹 O que é OpenCV? 🔹 Como detectar objetos usando Inteligência Artificial? 🔹 Vale a pena estudar Visão Computacional em 2026? 🔹 Como criar sistemas que reconhecem pessoas? 🔹 Como funciona a IA que identifica imagens? 🔹 Qual a diferença entre IA e Visão Computacional? 🔹 Como trabalhar com Inteligência Artificial aplicada a imagens? 🔹 Como entrar no mercado de Visão Computacional? 🔹 Quanto ganha um profissional de Visão Computacional? 🔹 Como criar projetos de reconhecimento facial? 🔹 Como desenvolver sistemas inteligentes com câmeras? 🔹 Como funciona o curso de Visão Computacional da Sara Educação? #VisaoComputacional #InteligenciaArtificial #OpenCV #IA #MachineLearning #DeepLearning #Python #Robotica #Automacao #Eletronica #SaraEducacao #Tecnologia #Industria40 #ComputerVision #ArtificialIntelligence
04 How Large Language Models (LLMs) Works? | All about LLMs | What are Tokens & Context Length?
Generative AI | LLM | GenAI | NN | Large Language Models ⏰ Scheduled to be Public from Members Only on 01st Jun 2026 16:00 HRS IST ⏰ ===== In this video, you will learn ===== What is Large Language Model? What is LLM? How LLMs work? Next Token Prediction in LLM, What are Tokens and Context Length? Importance on Tokens in LLM, Different Sampling Controls, LLM Personas and Prompts, Probability Distribution for LLMs ===== Chapters ===== 00:00 - Introduction 00:27 - What are Large Language Models or LLMs? 03:44 - How Large is Large in LLMs? 05:44 - Transformers 08:07 - What are Tokens and their Importance in LLM? 08:18 - What is Vocabulary in LLM? 14:27 - Probability Distribution for Tokens 19:01 - Sampling Controls - Temperature, Top-p 25:51 - Auto-Regressive Generation Loop 28:53 - How LLMs preserves meaning? 30:58 - How LLMs are Trained? 33:15 - What is Fine Tuning? 34:28 - LLM Personas/Roles and Prompts 36:55 - What is Context Length? 39:01 - Model Knowledge Cutoff and Hallucination 41:23 - Open and Closed LLM Models 42:36 - Reasoning Models 43:24 - Multimodal Models ===== Links ===== Google's "Attention is all You Need" Paper - https://arxiv.org/pdf/1706.03762 Groq Cloud - https://console.groq.com/home GPT Tokenizer - https://platform.openai.com/tokenizer ===== Other Playlists ===== Checkout all other playlists on Data Engineering 👇🏻 https://www.youtube.com/@easewithdata/playlists ===== GitHub Repo ===== https://github.com/subhamkharwal ===== Connect with ME ===== LinkedIn - https://www.linkedin.com/in/subhamkharwal Medium - https://subhamkharwal.medium.com ===== Hashtags ==== #genai #dataengineering #python #agenticai #aiagents #aiagent #nn #neuralnetworks
Chinese Robots in Barcelona: Shanghai is leaving Tesla behind!
While the West is waiting for the next Tesla Optimus update and watching Boston Dynamics demos in labs, humanoids from China are already out on the streets of Europe. 🤖🇪🇺 In this video, we look at the reality behind the robotics race. AGIBOT, a company founded just a few years ago, managed to secure a massive share of the global humanoid market in 2025. Now, their machines are appearing in Europe—not just as exhibition concepts, but as working assets you can actually rent. What we analyze in this episode: The European Foothold: How AGIBOT rapidly established a physical presence in Germany and Spain in early 2026 while Western competitors focused on domestic markets. The Reality of Western Tech: Where do Tesla Optimus Gen 3, Boston Dynamics Atlas, and 1X NEO actually stand regarding mass production and European deliveries? The Business Formula: The background of co-founder Peng Zhihui, backing from major tech giants, and how a "data factory" model is scaling their AI. Hardware in Action: A look at the X2 model, its algorithmic decision-making, and its real-world viability for the service sector. The Challenges Ahead: Morgan Stanley’s warnings of an industry shakeout, short battery life, and how the upcoming EU AI Act might impact Chinese suppliers. Is the West genuinely playing catch-up, or is this early market penetration just a temporary lead? Let’s look at the numbers, the tech, and the strategy behind @AGIBOT Official. #AGIBOT #HumanoidRobots #Robotics #AI #TeslaOptimus #BostonDynamics #EmbodiedAI #TechIndustry #FutureOfWork 👉For business inquiries: info.prorobots@gmail.com ✅ Instagram: https://www.instagram.com/pro_robots While the U.S. awaits Tesla Optimus, the future of robotics is already present on Barcelona's waterfront with AGI Bot robots. These artificial intelligence-powered humanoid robot units are not just for show; they're actively being rented out for commercial purposes, demonstrating advanced automation technology. AGI Bot robots have already made Guinness World Records for their capabilities, including covering long distances and performing intricate dances. This marks a significant step in the practical application of industrial robots outside of traditional settings. 🤖 #Robotics #ArtificialIntelligence #HumanoidRobot #AutomationTechnology #IndustrialRobots
Alinear y Memorizar: La Clave del Aprendizaje en Redes Neuronales Profundas
Este estudio explora una alternativa eficiente y biológicamente plausible a la retropropagación para entrenar redes neuronales profundas, conocida como Alineación de Retroalimentación Directa (DFA). A pesar de su éxito en modelos como los Transformers, la DFA falla notablemente en redes convolucionales, y este trabajo busca desentrañar el porqué de esta discrepancia. La investigación propone una nueva teoría que describe el aprendizaje con algoritmos de alineación de retroalimentación. Se demuestra que el aprendizaje se desarrolla en dos fases distintas: una fase inicial de 'alineación', donde el modelo ajusta sus pesos para alinear el gradiente aproximado con el gradiente real de la función de pérdida, seguida de una fase de 'memorización', en la que el modelo se centra en ajustar los datos para minimizar el error. Este proceso de 'alinear y luego memorizar' no solo explica por qué la DFA converge naturalmente a soluciones que maximizan la alineación del gradiente, sino que también ofrece una explicación para su fallo en las redes neuronales convolucionales. Los hallazgos, respaldados por experimentos numéricos, muestran cómo este mecanismo opera secuencialmente desde las capas inferiores hasta las superiores de la red, abriendo nuevas vías para entender y mejorar los algoritmos de aprendizaje profundo. Link al paper: https://arxiv.org/pdf/2011.12428 Autores del estudio: Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt Apoyanos en https://www.patreon.com/audioarxiv Unete en https://discord.gg/vKRmFhg4YQ #Ciencia de la computación #InteligenciaArtificial #MachineLearning #RedesNeuronales #DeepLearning #FeedbackAlignment
¿Cómo Funciona ChatGPT? La Explicación Más Clara de los LLM, Transformers y Embeddings
¿Qué es un LLM? ¿Cómo funciona ChatGPT? ¿Qué son los Transformers, los Embeddings y el mecanismo de atención? En este vídeo explico de forma sencilla y visual cómo funciona la inteligencia artificial generativa detrás de ChatGPT, Claude, Gemini, Grok, DeepSeek y otros modelos de lenguaje modernos. Aprenderás: ✅ Qué es un LLM (Large Language Model) ✅ Cómo aprende un modelo de IA ✅ Qué diferencia a ChatGPT, Gemini, Claude o DeepSeek ✅ Qué son los embeddings o representaciones vectoriales ✅ Cómo funciona el mecanismo de atención (Attention) ✅ Qué son los Transformers ✅ Cómo se procesa una pregunta desde que la escribes hasta que recibes una respuesta ✅ Por qué los LLM no memorizan respuestas, sino que predicen la siguiente palabra Este vídeo está pensado para principiantes, estudiantes, profesionales y cualquier persona que quiera entender cómo funciona realmente la inteligencia artificial sin necesidad de conocimientos técnicos previos. Capítulos: 00:00 Introducción 00:45 ¿Qué es un LLM? 03:15 Qué hace diferente a un modelo de IA 06:20 Capacidades de los LLM 07:45 Qué son los Embeddings 10:30 El mecanismo de Atención 13:20 Cómo funciona un LLM paso a paso 17:00 Conclusión Si te interesa la Inteligencia Artificial, los LLM, ChatGPT, los agentes de IA, RAG, automatización, Machine Learning y el futuro de la tecnología, suscríbete al canal. #ChatGPT #InteligenciaArtificial #LLM #Transformers #Embeddings #IA #MachineLearning #DeepLearning #Gemini #Claude #DeepSeek #GenerativeAI #AI
VTU ML BCS602 | Artificial Neuron Model & ANN Structure | Module 4 | Important
Welcome to Express VTU 4 All 🎓 In this video, we explain a very important Artificial Neural Network (ANN) theory question from Module–04: Neural Networks for VTU (BCS602). This question is frequently asked in VTU examinations and is a guaranteed 8–10 mark scoring question. 📌 Exact Question Covered Explain the simple model of an Artificial Neuron along with the Artificial Neural Network (ANN) structure. 🧠 What You Will Learn ✔ What is an Artificial Neuron ✔ Biological Neuron vs Artificial Neuron ✔ Components of Artificial Neuron ✔ Weighted Sum Calculation ✔ Activation Function ✔ Output Generation Process ✔ Structure of Artificial Neural Networks ✔ Input, Hidden, and Output Layers ✔ How to write answers in VTU exam format 📘 Introduction to Artificial Neuron An Artificial Neuron is the basic processing unit of an Artificial Neural Network (ANN). It receives input signals, processes them using weights and activation functions, and produces an output. Artificial neurons are inspired by the working of biological neurons in the human brain. 🧾 Simple Model of Artificial Neuron An artificial neuron consists of: 👉 1. Inputs (x₁, x₂, x₃, ...) Input values received from data. 👉 2. Weights (w₁, w₂, w₃, ...) Each input is assigned a weight representing its importance. 👉 3. Summation Unit Calculates weighted sum: z= i=1 ∑ n w i x i +b 👉 4. Bias (b) Additional parameter used to improve learning capability. 👉 5. Activation Function Converts weighted sum into output. Examples: Sigmoid ReLU Tanh 👉 6. Output (y) Final result produced by the neuron. 🌟 Artificial Neuron Diagram x1 ──(w1)──┐ x2 ──(w2)──┼──► Σ + b ──► Activation Function ──► Output y x3 ──(w3)──┘ 📘 Artificial Neural Network (ANN) Structure An Artificial Neural Network consists of interconnected neurons arranged in layers. 👉 1. Input Layer Receives input data No computation performed Example: Age, Salary, Marks 👉 2. Hidden Layer(s) Performs computations Extracts patterns and features Example: Feature learning 👉 3. Output Layer Produces final prediction Example: Pass/Fail, Yes/No 🌟 ANN Structure Diagram Input Layer Hidden Layer Output Layer x1 ● ─────┐ ├──► ● ───┐ x2 ● ─────┤ ├──► ● (Output) ├──► ● ───┘ x3 ● ─────┘ 🎯 Working of ANN Step 1: Input data enters the network. Step 2: Weights are applied to inputs. Step 3: Weighted sum is computed. Step 4: Activation function processes the result. Step 5: Output is generated. Step 6: Weights are adjusted during training. ✅ Advantages of ANN ✔ Learns complex patterns ✔ Handles nonlinear data ✔ High prediction accuracy ✔ Self-learning capability 🎯 Exam Writing Strategy (VERY IMPORTANT) ✔ Define Artificial Neuron first ✔ Draw neuron diagram neatly ✔ Explain each component separately ✔ Draw ANN structure diagram ✔ Explain Input, Hidden, and Output layers 👉 This ensures full 10 marks 📊 Marks Weightage ✅ Usually asked for 8–10 Marks ✅ Theory + Diagram question ✅ Frequently repeated in VTU exams 🚀 Why This Question Is Important ✔ Foundation of Deep Learning ✔ Core concept of Neural Networks ✔ Frequently asked in VTU exams ✔ Important for AI and ML interviews 📚 Subject Details 📌 Subject: Machine Learning 📌 Subject Code: BCS602 📌 Module: 04 – Artificial Neural Networks 📌 University: VTU (CBCS Scheme) 📲 Free Notes & Updates Join Telegram for ML notes + important questions 👇 🔗 https://t.me/vtu4all artificial neuron model VTU artificial neural network structure ANN explained BCS602 machine learning module 4 neural networks VTU ML important questions #VTU #BCS602 #MachineLearning #ArtificialNeuron #ANN #NeuralNetworks #DeepLearning #VTUExams 👉 Watch till the end to master Artificial Neural Networks and learn how to draw ANN diagrams perfectly for full marks in VTU exams.
SESGOS #19 - Marina Rojo: Medicina, datos y diagnóstico en tiempos de IA
En este episodio de Sesgos conversamos con Marina Rojo, médica y directora del Laboratorio de Innovación Tecnológica en Salud Pública de la Facultad de Medicina de la UBA, sobre inteligencia artificial aplicada a salud, sesgos en los algoritmos, regulación, privacidad de los datos médicos y el impacto de estas tecnologías en el trabajo profesional.
La Inteligencia Artificial nos dejará sin futuro: ¡¡¡Así podría pasar!!!
La IA (Inteligencia Artificial) ya no es solo una herramienta: se está organizando. En este video explico por qué se hacen experimentos como Moltbook, el auge de agentes autónomos, y fallos reales de las IA como apagones y algoritmos con poco acierto. ¿Estamos preparados para este cambio tecnológico? ¿Qué puedes hacer hoy para proteger tu identidad digital, tu trabajo y la democracia? Suscríbete y activa la campana para no perderte nuestros análisis semanales. 00:00 Introducción de IA y por qué importa 05:16 Qué es un bot, un agente y una API 11:13 El caso Moltbook: qué pasó y por qué es alarmante 14:11 Cómo las IAs replican sesgos y cámaras de eco 19:36 Hackers y la IA 24:42 El peor escenario de la IA 26:25 ¿Qué podemos hacer? (seguimiento humano, regulación, educación) Conviértete en miembro de este canal para disfrutar de ventajas: https://www.youtube.com/channel/UCmHv37aBniCJK0ihYmHDqAg/join 💟 SÍGUEME EN Instagram 👉 https://www.instagram.com/gabriel.bulgakov/ Facebook: https://www.facebook.com/topdeimpacto TOP DE IMPACTO Gabriel Bulgakov
Did China Actually Create an Artificial Womb Robot? | The Shocking Truth
Did China Actually Create an Artificial Womb Robot? | The Shocking Truth Did China's Kaiwa Technology really unveil a $14,000 humanoid robot with an artificial womb at the 2025 World Robot Conference? In this video, we fact-check the viral claims surrounding artificial wombs (ectogenesis). While scientists at the Children's Hospital of Philadelphia successfully kept a lamb alive in an artificial womb back in 2017, replicating the entire 9-month human gestation period inside a machine presents massive biological and ethical challenges—such as replicating maternal hormones and navigating global 14-day limit laws on human embryos. Watch till the end to find out if this technology is the next giant leap for humanity, or just another AI-generated hoax. Don't forget to like, share, and subscribe to Hidden Science for more simplified science and global updates. Disclaimer: The information presented in this video is gathered from public news sources, scientific journals, and tech publications for educational and informational purposes. This video does not provide medical advice. Visuals used may include AI-generated concepts to represent speculative technology. #ArtificialWomb #Biotechnology #FutureTech #ScienceFactCheck
La verdad sobre la IA: Lo que necesitas saber (Explicación para principiantes)
¿Qué es realmente la Inteligencia Artificial? Más allá del ruido mediático y la ciencia ficción, la IA es la herramienta tecnológica más transformadora de nuestra era. En este video desglosamos el concepto desde cero, de forma clara, precisa y con un enfoque estrictamente profesional, sin tecnicismos innecesarios. Aprenderás la diferencia exacta entre Machine Learning y Deep Learning, cómo funcionan las redes neuronales y qué es la IA Generativa, todo explicado de forma detallada y estructurada para que domines los conceptos esenciales de inmediato. También analizamos los límites técnicos actuales, como los sesgos de datos y las llamadas "alucinaciones", para que comprendas el panorama real con criterio propio. Si quieres dominar los fundamentos de la tecnología que está redefiniendo la productividad y el futuro del trabajo, este video es el punto de partida que necesitas. #inteligenciaartificial #ia #gemini #claude #chatgpt #explicaciondefinitiva #guiadefinitivo