Videos de Ciencia
Videos sobre avances científicos y descubrimientos.
Ciencia 255 videos
Day 21 How ChatGPT Really Works: Large Language Models Explained | AI in 5
ChatGPT, Claude, Gemini — they can feel like magic. But at their core, a Large Language Model does ONE simple thing, over and over: it predicts the next token. In Day 21 we finally demystify LLMs, walk through the exact three‑step loop they run, and watch a tiny version of it work for real — using the SAME tokenizer the real GPT models use. You'll learn: what an LLM actually does (predict the next token) • what makes it "large" (huge data + billions of parameters) • the 3 steps: tokenize → predict a probability for every next token → sample → repeat • why it feels intelligent • and why it predicts plausible text rather than truly "knowing." The demo is real and runnable. 0:00 LLMs · 0:41 The one thing an LLM does · 1:20 What makes it 'large' · 2:01 Text → tokens · 2:42 Predict the next token · 3:19 Context in, token out · 3:56 Sample & repeat · 4:36 Watch it for real · 5:10 The code · 5:46 Run it · 6:28 Tokenize·predict·sample·repeat · 7:05 Know its limits · 7:45 Recap Subscribe for a new AI lesson every day. Tomorrow: how LLMs are actually trained. #AI #LLM #ChatGPT
China's INSANE Humanoid Robot Just Went on Sale — Tesla Has No Answer
Unitree's R1 humanoid robot launched at $4,900 — TIME named it a Best Invention of 2025. The G1 starts at $16,000 with 43 degrees of freedom and autonomous household task capability via open-sourced AI. Unitree shipped 5,500 units in 2025. Target for 2026: 20,000. AgiBot shipped 10,000+ humanoids by April 2026. Chinese firms hold 80% of global humanoid shipments. Tesla Optimus: not for sale, no pre-orders, no price, consumer target end of 2027 — every timeline since 2022 has slipped 12–24 months. We break down why China is shipping the humanoid future while the West is still announcing it. #HumanoidRobot #ChinaTechnology #unitreeg1
Cómo crear agentes de IA con voz y visión en Python
Aprende a construir agentes de inteligencia artificial que ven, escuchan y hablan en tiempo real usando Python. Tutorial paso a paso con Vision-Agents de Stream. En este tutorial aprenderás: - Como instalar Vision-Agents con un solo comando - Como crear un agente de voz que te escucha y te responde - Como añadir herramientas MCP para que el agente haga tareas - Como conectar visión por computadora con YOLO para que el agente vea Codigo y comandos usados en el video: - pip install vision-agents - Agent + getstream.Edge + openai.Realtime - YOLOPoseProcessor + Gemini Realtime Ideal para: desarrolladores Python, estudiantes, entusiastas de IA 🎬 Mira también: Cómo automatizar tareas complejas con agentes de IA en Python https://youtube.com/watch?v=s3PFQcxwuUU Suscribete para mas Python + IA: https://youtube.com/@lytohlgai #python #agentesia #visionagents #ia #github #opensource #devtools #realtime #computer vision
AI That Never Forgets | Dendritron Transformer Explained (The Future of LLMs)
AI That Never Forgets | Dendritron Transformer Explained (The Future of LLMs) What if AI never forgot anything it learned? In this video, we explore the groundbreaking Dendritron Transformer, a next-generation AI architecture designed to overcome one of the biggest limitations of today's Large Language Models (LLMs): catastrophic forgetting. Unlike traditional Transformer models that become static after training, the Dendritron Transformer introduces a bio-inspired internal memory system that enables continuous learning, real-time knowledge updates, and lifelong memory retention without losing previously learned information. If you're interested in Artificial Intelligence, Machine Learning, Deep Learning, Large Language Models (LLMs), AI Agents, Neural Networks, or the future of AI research, this video provides a clear and easy-to-understand explanation of one of the most exciting new AI architectures. 📌 In this video, you'll learn: ✅ What is the Dendritron Transformer? ✅ Why traditional Transformers forget information ✅ What is Catastrophic Forgetting in AI? ✅ How Continuous Learning AI works ✅ Dendritic Computation Explained ✅ Internal Memory vs KV Cache ✅ Lifelong Learning for Large Language Models ✅ Future AI Agents with Persistent Memory ✅ Real-Time Learning in Artificial Intelligence ✅ Applications in Robotics, Healthcare, Finance, Autonomous Systems, and Scientific Research 📚 Colab Notebook: https://colab.research.google.com/drive/1nao2tDffdIThxoH0Nd8_pe_5Gc3JfCZQ?usp=sharing ⭐ If you enjoy videos about Artificial Intelligence, Machine Learning, ChatGPT, OpenAI, Neural Networks, AI Agents, Python, LLMs, Deep Learning, and the latest AI breakthroughs, make sure to Subscribe and turn on notifications so you never miss future videos. 👍 Like the video if you learned something new. 💬 Comment your thoughts about the future of AI memory and continuous learning. • AI News • Machine Learning • Deep Learning • LLM Tutorials • Prompt Engineering • Generative AI • Python for AI • AI Agents • Future Technology #AI #MachineLearning #Dendritron #Transformer #DeepLearning #ContinuousLearning #nlp Dendritron Transformer, AI Memory, Artificial Intelligence, Machine Learning, Deep Learning, Transformer Architecture, Large Language Models, LLM, Continuous Learning, Lifelong Learning, Catastrophic Forgetting, Neural Networks, AI Research, AI Agents, Bio Inspired AI, Real-Time Learning, Future of AI, AI Explained, GPT Alternative, Next Generation AI, AI Architecture, Persistent Memory AI, Memory-Augmented Neural Networks, Cognitive AI, Intelligent Systems #ArtificialIntelligence #MachineLearning #DeepLearning #LLM #Transformer #AI #AIResearch #GenerativeAI #NeuralNetworks #AIAgents #ContinuousLearning #Dendritron #ArtificialGeneralIntelligence #FutureOfAI #AITechnology
GRPO Fine-Tuning with Practical | DeepSeekMath, PPO vs GRPO, Hugging Face & Unsloth
Learn GRPO (Group Relative Policy Optimization) from scratch and fine-tune an LLM using Hugging Face TRL and Unsloth. In this video, we understand how GRPO works, why it was used in DeepSeekMath, how it differs from PPO, and how language models learn from reward signals. We will also implement GRPO fine-tuning practically using Hugging Face and Unsloth. Topics covered in this video: ✅ What is GRPO? ✅ GRPO full form and intuition ✅ Quick background of DeepSeekMath ✅ DeepSeekMath training pipeline ✅ PPO vs GRPO ✅ Problems with the PPO approach ✅ Why GRPO does not require a critic/value model ✅ Group-based reward comparison in GRPO ✅ GRPO step-by-step with a simple example ✅ Reward signal vs reward model ✅ Rule-based and verifiable rewards ✅ Correctness, helpfulness and clarity reward functions ✅ LoRA-based GRPO fine-tuning ✅ GRPO practical using Hugging Face TRL ✅ GRPO practical using Unsloth ✅ Loading and testing the fine-tuned model GRPO stands for Group Relative Policy Optimization. Instead of using a separate value or critic model, GRPO generates multiple answers for the same prompt, compares their rewards within the group and improves the policy using relative performance. This video is useful for machine learning engineers, Generative AI developers, data scientists and anyone learning LLM fine-tuning, reinforcement learning, RLHF, PPO, GRPO, DeepSeek and post-training techniques. Subscribe for more videos on Generative AI, LLM fine-tuning, RAG, Agentic AI, RLHF, PPO and GRPO. Code: https://github.com/sunnysavita10/Complete-LLM-Finetuning/tree/main/LLM%20Fine-Tuning-27-GRPO #GRPO #LLMFineTuning #DeepSeek #HuggingFace #Unsloth 📌 Subscribe for more videos on: LLM Fine-Tuning, RLHF, Quantization, Hugging Face, LangChain, Agentic AI, RAG, AI Systems, and Production-Grade AI Projects. #RLHF #PreferenceAlignment #LLM #PPO #ReinforcementLearning #DPO #ORPO #Qlearning #DQN #LLMFineTuning #GenerativeAI #MachineLearning #SunnySavita #AgenticAI #LangChain #ArtificialIntelligence 📌 Keywords Covered: #MultimodalLLM #VisionLanguageModel #MultimodalFineTuning #LLMFineTuning #Unsloth #LLaVA #QwenVL #Pixtral #LlamaVision #LoRA #QLoRA #VisionEncoder #ProjectionLayer #HuggingFace #Transformers #GenerativeAI #AIForDevelopers #CustomDataset #ImageToText #AITraining #SunnySavita #SemanticSearch #RAG Multimodel RAG Playlist: https://www.youtube.com/watch?v=7CXJWnHI05w&list=PLQxDHpeGU14D6dm0rmAXhdLeLYlX2zk7p&pp=gAQBiAQB RAG detailed playlist: https://www.youtube.com/watch?v=wTVTkOb3SZc&list=PLQxDHpeGU14Blorx3Ps1eZJ4XvKET1_vx&pp=gAQBiAQB GenAI Foundation Playlist: https://www.youtube.com/watch?v=ajWheP8ZD70&list=PLQxDHpeGU14D7NiPgqxC9qhKkx4jMQcDk&pp=gAQBiAQB Connect with me on social media LinkedIn: https://www.linkedin.com/in/sunny-savita/ One-to-One Call: https://topmate.io/sunny_savita10 GitHub: https://github.com/sunnysavita10
Neural Network - How it Works
Neural Network - How it Works 📲 For More Content like this, be sure to Subscribe to our channel! ✅Thanks For Watching: Neural Network - How it Works Here at Thinking Machines, we aim to create top-quality videos about artificial intelligence, machine learning, tech documentaries, AI controversies, big tech developments, robotics, automation, future concepts, and the science shaping tomorrow—covering everything happening in the world of technology and AI. Our goal is to help you understand how AI is changing the world, uncover the stories behind major tech revolutions, and explore the ideas, companies, and breakthroughs building our future. Neural networks learn to recognize patterns by adjusting millions of mathematical connections instead of relying on manually programmed rules. The video explains how raw input data, such as image pixels, flows through layers of neurons that detect increasingly complex features before producing a final prediction. It breaks down key concepts like weights, biases, activation functions, and training in a simple, intuitive way, showing how each component contributes to the network's learning process. You'll also discover why neural networks have become the foundation of modern AI applications, from image recognition and translation to speech processing and self-driving cars. By the end, you'll understand that the power of neural networks comes not from magic or human-like thinking, but from countless simple mathematical operations working together to uncover meaningful patterns. 💻For Business Inquiries, Collaborations or Promotions, contact us at: odedschannel1@gmail.com #neuralnetworks Networks, #artificialintelligence Intelligence, #AI, #machinelearning Learning, Deep Learning, How Neural Networks Work, Neural Network Explained, AI Explained, #aivideo for Beginners, Deep Learning Tutorial, #artificialintelligencetechnology Neural Network, ANN, Weights and Biases, Activation Functions, ReLU, Sigmoid Function, Hidden Layers, Input Layer, Output Layer, Pattern Recognition, Image Recognition, Handwritten Digit Recognition, Computer Vision, AI Training, Backpropagation, Neural Network Training, Parameters, AI Models, Mathematics of AI, Modern AI, Generative AI, Large Language Models, Deep Learning Fundamentals, Data Science, Computer Science, AI Technology, AI Education, #aigenerated Concepts, How AI Learns, Pattern Detection, Predictive Models, Educational Technology, Future of AI, Thinking Machines, Tech Explained, AI Tutorial, Neural Network Basics, Machine Intelligenc
Master RAG in 6 Minutes
# RAG Explained: How AI Retrieves the Right Information Learn **Retrieval-Augmented Generation (RAG)** explained in simple terms. In this beginner-friendly tutorial, you'll understand how RAG works, why **Large Language Models (LLMs)** use it, and how AI systems retrieve relevant information before generating accurate, up-to-date responses. RAG is one of the most important technologies behind modern AI applications. It enables AI assistants to answer questions using external knowledge, documents, databases, and the latest information instead of relying only on what they learned during training. ### In this video, you'll learn: * What is Retrieval-Augmented Generation (RAG)? * Why Large Language Models (LLMs) need RAG * How RAG retrieves relevant information before generating an answer * The complete RAG architecture and workflow explained step by step * Real-world examples of RAG applications * Benefits and limitations of RAG * RAG vs Fine-Tuning: Which approach should you use? Whether you're a student, developer, AI engineer, data scientist, or machine learning enthusiast, this tutorial will help you understand one of the core building blocks of modern AI systems. This video also covers important AI concepts including: * Large Language Models (LLMs) * Generative AI * AI Agents * Vector Databases * Embeddings * Semantic Search * Knowledge Retrieval * Context Augmentation * Prompt Engineering * AI Application Development If you found this video helpful, please Like, Subscribe, and Share it with others who are learning Artificial Intelligence and Machine Learning. Subscribe for more beginner-friendly AI tutorials covering: * AI Agents * Retrieval-Augmented Generation (RAG) * Large Language Models (LLMs) * Model Context Protocol (MCP) * Prompt Engineering * Vector Databases * AI Engineering * Python for AI * Machine Learning * Generative AI * Open Source AI * AI Tools and Tutorials #RAG #AI #LLM
SIPEDAS - Mobile Apps Computer Vision AI Camera Scan Plant health 2026
SIPEDAS adalah aplikasi native android yang menggunakan Kotlin dan Dart, Tensorflow Lite sebagai Sistem Kecerdasan Buatan, dan Room Database. Untuk mendiagnosis lima penyakit utama daun cabai. TECHNOPRENEURSHIP KELOMPOK Leader and UI UX Designer Hafizh Hilman Asyhari @hafizhhasyhari Anggota Sunu | Mobile Developer (Hacker) Muh Qhofi | Consumer Behavior Analysis & Brand Designer (Hipster) Izaaz | Business (Hustler) Tri | Front End Development (Hipster) Kelas : D Mata kuliah: Technopreneurship Program Studi : S1 Teknik Informatika Dosen : Andi Dahroni Tools : Android Studio Bahasa Pemrograman: Kotlin & Dart • Artificial Intelligence: Tensorflow Lite #mobiledev #dart #kotlinmultiplatform #technopreneurship #hafizh #teknikinformatika #computerscience
Best Computer Vision Course on Coursera | Ranked for 2026
Which Computer Vision Course on Coursera Is Best? (2026 Guide) ➡️ Claim Coursera discount here: https://appunbox.com/coursera Looking for the best computer vision course on Coursera? In this video, I rank the top Computer Vision courses on Coursera based on learner ratings, hands-on projects, instructor quality, certification value, and career potential. Computer vision is one of the fastest-growing areas of artificial intelligence, powering technologies such as facial recognition, self-driving cars, medical imaging, robotics, autonomous systems, image generation, object detection, augmented reality, and intelligent surveillance. In this video, I review these top computer vision programs: • Introduction to Computer Vision and Image Processing by IBM • Computer Vision Specialization by the University of Colorado Boulder • Convolutional Neural Networks by DeepLearning.AI (part of the Deep Learning Specialization taught by Andrew Ng) These courses cover essential computer vision topics including: • Convolutional Neural Networks (CNNs) • Image Processing • Object Detection • Face Recognition • Transfer Learning • Neural Style Transfer • Vision Transformers (ViTs) • Autoencoders • TensorFlow • Python • Deep Learning Projects If you're planning to complete multiple AI or computer vision courses, check the link in the description for the latest Coursera Plus annual discount. Eligible new subscribers can currently save 40% on the annual plan, giving unlimited access to 10,000+ eligible courses, Professional Certificates, Specializations, and Guided Projects from Google, IBM, Microsoft, Meta, Stanford University, DeepLearning.AI, and many other leading organizations. In this video you'll learn: • Best computer vision course on Coursera • Best computer vision courses • Convolutional Neural Networks course review • IBM Computer Vision course review • University of Colorado Boulder Computer Vision Specialization review • Best AI courses on Coursera • Computer vision learning roadmap • Is Coursera Plus worth it? Subscribe for more Coursera reviews, AI course recommendations, deep learning tutorials, computer vision guides, and the latest Coursera discounts. **Best Computer Vision Courses on Coursera (2026 Ranked) – Video Chapters:** 00:00 – Introduction 00:16 – Top 3 Computer Vision Courses on Coursera 00:33 – #3 IBM Computer Vision and Image Processing 00:54 – #2 University of Colorado Computer Vision Specialization 01:18 – #1 DeepLearning.AI Convolutional Neural Networks 01:52 – Coursera Plus Discount and Final Recommendation #ComputerVision #ArtificialIntelligence #Coursera #DeepLearning #AndrewNg
Computer vision course | How computers see? | AI ML course in Hindi
Telegram group- https://t.me/data_dissection Full Machine Learning course in hindi- https://youtube.com/playlist?list=PLlpUUtQ9RrF4o3UTYbc4cP3NCEyE5BwX5&si=RVj6gVjE2J0XwPw8 Full Neural networks course- https://youtube.com/playlist?list=PLlpUUtQ9RrF47CpLH6PSFL2oeP3izPMhE&si=xJ5E79pugCDkYxR7 Math for ai one shot sql for data science sql for data analytics sql math for machine learning math for data science probability for machine learning probability for data science machine learning machine learning roadmap machine learning full course machine learning projects machine learning engineer roadmap machine learning tutorial machine learning playlist machine learning course machine learning interview questions machine learning machine learning full course in hindi machine learning in tamil machine learning in telugu machine learning with python Welcome to this comprehensive Machine Learning Course in Hindi . In this playlist, you'll master the essential machine learning algorithms with hands-on coding tutorials, real-world examples, and step-by-step explanations. Computer vision course OpenCV
Fundamentos conceptuales de la IA, ¿Qué es un LLM?, ¿Qué puede y qué NO puede garantizar la IA?
Que es un LLM y como funciona realmente la inteligencia artificial que usamos todos los dias? En este video te explicamos, desde cero y sin tecnicismos, los fundamentos de como funciona un modelo de lenguaje como Claude, ChatGPT o Gemini: que puede hacer, que no puede garantizar, y como diferenciarlos entre si. Vas a aprender: - Que es un LLM y por que "predice" texto en lugar de "saber" hechos - Que es un token y una ventana de contexto, explicado con analogias simples - Quien es Anthropic y que hace diferente a Claude de otros asistentes - Que hace bien la IA y que NO deberias confiarle sin verificar - Por que ocurren las alucinaciones y como detectarlas - Vocabulario esencial de IA explicado en simple: prompt, token, fine-tuning y mas Ideal para docentes, estudiantes y cualquier persona que quiera entender la inteligencia artificial de forma critica y responsable, antes de empezar a usarla. Si te sirvio, dale LIKE, SEGUIME y COMPARTI este video para que le llegue a mas personas. #InteligenciaArtificial #IA #ChatGPT #Claude #Educacion #LLM #TecnologiaEducativa #AlfabetizacionDigital