Videos de artificial intelligence
Videos etiquetados con "artificial intelligence"
artificial intelligence 41 videos
Google Gemini 3.5 Pro Explained | 2M Tokens, Deep Think & GPT-6 Comparison
Link to our newsletter: https://bitbiased.ai/ Google's Gemini 3.5 Pro is rumored to launch with a massive 2 million token context window, a new Deep Think reasoning mode, and significantly cheaper API pricing. But are these leaks actually as groundbreaking as everyone claims? In this video, we break down: ✅ Gemini 3.5 Pro leaks explained ✅ 2 Million Token Context Window ✅ Deep Think reasoning mode ✅ API pricing and why it matters ✅ Gemini 3.5 Pro vs GPT-6 ✅ Gemini 3.5 Pro vs Claude Fable 5.1 ✅ Google AI strategy in 2026 ✅ What actually deserves the hype Instead of repeating AI rumors, this video separates confirmed information from speculation and explains what Google's strategy could mean for developers, businesses, and AI enthusiasts. If you're following GPT-6, OpenAI, Google DeepMind, Anthropic, Claude, or the future of generative AI, this video is for you. Timestamps 00:00 Introduction 01:23 What's Actually Being Claimed 02:48 The Two Million Tokens Everyone's Chasing 04:54 The Price Is The Real Story 06:55 Deep Think & The Paywall 08:37 Gemini 3.5 Pro vs GPT-6 & Claude Fable 5.1 10:43 What Would Actually Earn The Hype 12:21 Final Thoughts Subscribe for weekly AI news, model comparisons, benchmark analysis, and practical insights. #Gemini35Pro #googleai #gpt6 #openai #artificialintelligence #deepthinking #ai #claude #googledeepmind #machinelearning
The AI That's Quietly Replacing Millions of Jobs (And Most People Don't Notice)
What if AI isn't replacing jobs the way you think? While most people imagine robots taking over factories, today's AI is transforming offices, spreadsheets, customer service, and even white-collar work—often without anyone noticing. Instead of dramatic layoffs, many companies are simply hiring fewer people as AI takes over routine tasks. In this documentary-style video, we explore: * How automation evolved from factory robots to generative AI * Why AI is changing the job market faster than ever * Which careers are most at risk * Why entry-level jobs are disappearing * The new opportunities AI is creating * How to prepare for the future of work Based on research from the World Economic Forum, labor market reports, and academic studies, this video separates hype from reality and explains what the AI revolution really means for your career. If you're interested in AI, future technology, automation, business, economics, and the future of work, subscribe to FUTURECTH for weekly documentary-style videos exploring the technologies shaping tomorrow. **Disclaimer:** This video is created for educational and informational purposes. Visuals may include licensed stock footage and AI-generated illustrations. All trademarks and copyrighted materials belong to their respective owners. #AI #ArtificialIntelligence #FutureOfWork #Automation #FutureTech #Technology #Jobs #Career #Business #Productivity #GenerativeAI #MachineLearning #OfficeJobs #Innovation #FUTURECTH
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
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
How AI Makes Images From Pure Noise (Diffusion, Explained)
You type a few words and — seconds later — a picture appears that has never existed anywhere in the world. No clip art, no copy-paste, no artist. So how does an AI actually paint something from nothing? The answer is genuinely strange: it doesn't start with a blank canvas. It starts with a screen full of pure random noise — TV static — and removes it. This is the clearest possible explanation of diffusion, the idea behind DALL·E, Midjourney, and Stable Diffusion. No math required. We start with the twist that trips everyone up: image models don't "draw." They begin from a field of random static and, step by step, strip the noise away until a picture that was hiding underneath comes into focus. Creation by removing randomness. Then we unpack how that's even possible: • Trained in reverse. During training the model takes millions of real photos and slowly adds noise to each one, watching it dissolve into static. Do that a billion times and it learns to predict the exact noise that was added at every step. • The whole trick. If you can predict the noise that was added, you can subtract it. So to create a brand-new image, the model just runs the process backwards — from static, back toward a picture. • The denoise loop. Look at the noisy image, predict the noise, subtract a little, repeat — 20 to 50 times — each pass a little sharper, until only the image is left. • What decides the picture? Pure noise could become anything — a face, a forest, a bowl of soup. So your prompt gets turned into numbers the model understands, and at every single denoising step it nudges the guess toward your words. "A sunset over the mountains" pulls the noise, bit by bit, toward exactly that. The mental model to walk away with: the AI is a sculptor, the block of marble is pure noise, and your prompt is the chisel — every step chips a little randomness away until your image is all that remains. Chapters: 0:00 The picture that never existed 0:15 What AI image tools actually do 0:30 The twist: it starts with static 0:52 Watch noise become an image 1:11 How it learned — by destroying images 1:34 Adding noise, step by step 1:51 The trick: predict, then subtract 2:07 Denoising in a loop 2:26 But what decides the picture? 2:45 Your prompt steers every step 3:07 The mental model: a sculptor 3:24 Recap 3:45 Subscribe Making sense of AI, one concept at a time. Subscribe → @watchsuperintelligence #AI #Diffusion #StableDiffusion #Midjourney #DALLE #AIart #GenerativeAI #TextToImage #AIexplained #MachineLearning #ArtificialIntelligence #HowAIWorks
What is a Neural Network? | Neural Network Explained for Beginners | @quicklearnerss
🧠 Neural Networks are the foundation of modern Artificial Intelligence, Machine Learning, and Deep Learning. In this beginner-friendly tutorial, you'll learn how Artificial Neural Networks (ANN) work using simple language, real-life examples, and easy-to-understand animations. Whether you're a Computer Science student, engineering student, AI enthusiast, or preparing for placements and interviews, this video will help you understand Neural Networks from scratch. 📌 In this video, you'll learn: ✔ What is a Neural Network? ✔ Why Neural Networks are important? ✔ Biological Neuron vs Artificial Neuron ✔ Structure of an Artificial Neural Network ✔ Input Layer, Hidden Layer & Output Layer ✔ Weights, Bias, and Activation Function ✔ Feed Forward Process ✔ Training a Neural Network ✔ Backpropagation (Basic Introduction) ✔ Real-life Applications of Neural Networks 🎯 This video is perfect for: • B.Tech / BCA / MCA Students • AI & Machine Learning Beginners • Deep Learning Beginners • Placement Preparation • University Exam Preparation • GATE Aspirants • Anyone curious about Artificial Intelligence ━━━━━━━━━━━━━━━━━━━━ 📚 Prerequisites: Basic understanding of mathematics is helpful but not required. ━━━━━━━━━━━━━━━━━━━━ 🔥 Related Videos: ▶ Artificial Intelligence Complete Playlist ▶ Machine Learning for Beginners ▶ Deep Learning Tutorial ▶ Perceptron Explained ▶ Activation Functions Explained ▶ Machine Learning Roadmap ━━━━━━━━━━━━━━━━━━━━ 💻 Technologies Discussed: Artificial Intelligence Machine Learning Deep Learning Artificial Neural Networks Perceptron Activation Functions Backpropagation ━━━━━━━━━━━━━━━━━━━━ 👍 If you found this video helpful: ✔ Like the video ✔ Share it with your friends ✔ Subscribe for more AI and Computer Science tutorials ✔ Turn on the notification bell 🔔 #NeuralNetwork #ArtificialIntelligence #MachineLearning #DeepLearning #AI #ANN #DataScience #ComputerScience #AIForBeginners #DeepLearningTutorial
Large Language Models (LLMs) Explained | No Coding Required | Vishwa sir | Tab 47
#llm #machinelearning #softwareengineering Want to understand how ChatGPT, Claude, Gemini, and other modern AI models actually work? Join this LIVE LLM Masterclass where we'll break down Large Language Models (LLMs) in the simplest possible way—no coding or programming experience required. Whether you're a student, working professional, AI enthusiast, or someone planning to build AI applications in the future, this live session will help you understand the core concepts behind today's most powerful AI systems. 🔗 Ready to build a career in AI? Join upGrad's Artificial Intelligence Courses and start learning from industry experts: https://bit.ly/4uRBTFU What you'll learn: What are Large Language Models (LLMs)? How ChatGPT and modern AI models work Tokens, embeddings, transformers, and attention explained Training vs inference Prompt engineering fundamentals Open-source vs closed-source LLMs Real-world applications of LLMs How LLMs power AI Agents, RAG, copilots, and automation The best roadmap to start learning Generative AI
🤯 How AI Creates Images From Pure Noise! (Stable Diffusion Explained)
🤯 How can AI create breathtaking images from just a few words? In this video, you'll discover the fascinating technology behind Stable Diffusion, DALL·E, Midjourney, and modern AI image generators. Learn step by step how Diffusion Models work: ✅ What is a Diffusion Model? ✅ Forward Process (Adding Noise) ✅ Reverse Process (Denoising) ✅ U-Net Neural Networks ✅ Latent Space & VAE ✅ CLIP and Text Prompts ✅ Stable Diffusion Architecture ✅ How AI Turns Text Into Images Whether you're interested in Artificial Intelligence, Machine Learning, Generative AI, or Digital Art, this video will help you understand one of the most revolutionary technologies of our time. 🔥 Don't forget to Like, Subscribe, and turn on Notifications for more AI, Machine Learning, and Technology content. #StableDiffusion #ArtificialIntelligence #GenerativeAI #MachineLearning #AIImages #StableDiffusion #DiffusionModels #AI #ArtificialIntelligence #GenerativeAI #MachineLearning #DeepLearning #AIArt #Midjourney #Dalle #TextToImage #NeuralNetworks #ComputerVision #FutureTech #TechExplained
What is an LLM?
An LLM is a model trained on massive amounts of text that learns how words relate to each other. This lesson covers how text becomes tokens, how the model generates a response one token at a time, and why the transformer architecture is what makes modern LLMs so effective. 🖊️ Learning objectives: - What tokens are and why models use them - How auto-regressive next-token prediction works - What the transformer brings to the picture Every token requires a full pass of calculations across billions of parameters. Multiply that by thousands of users sending requests at once and you start to see why inference speed becomes a hard engineering problem. For more resources, you may check out our blog here, where you will find information on: - What is AI inference? Meaning, benefits and how it works - Inference speed or throughput? With RDUs, you don't have to choose #AI #LLM #Tokens #Transformers #SambaNova
How to Choose the Best AI Image Generator (Midjourney vs DALL-E vs Stable Diffusion) (2026)
In this video: How to Choose the Best AI Image Generator (Midjourney vs DALL-E vs Stable Diffusion) (2026) Subscribe and hit the bell for new videos every week! 🔗 TOOLS & RESOURCES: • Claude AI (free): https://claude.ai • ChatGPT: https://chat.openai.com • Canva (free — join link): https://www.canva.com/join/myv-gzr-ptz • Notion (free): https://notion.so • Zapier (free tier): https://zapier.com • NordVPN (67% off): https://go.nordvpn.net/aff_c?offer_id=15&aff_id=823d06043aa7fa9cfa5d7c3185cc204b99c104c7412d135e41dcb31207afe3b7 • SafetyWing Insurance: https://safetywing.com/?referenceID=26525115&utm_source=26525115&utm_medium=Ambassador • Hostinger (web hosting): https://www.hostinger.com/it?REFERRALCODE=KZXALF199EZ9 📥 FREE DOWNLOAD — AI Tools Cheat Sheet 2026: https://scalabletools.gumroad.com/l/oruyf 🚀 Want AI to run your entire business? Try S.C.A.L.A.: https://get-scala.com/?utm_source=youtube&utm_medium=aitoollab&utm_campaign=video 📧 Sponsorships & collabs: sponsor@get-scala.com ━━━━━━━━━━━━━━━━━━━━━ 🔔 Subscribe for daily AI tool reviews! 👍 Like this video if it helped you! ━━━━━━━━━━━━━━━━━━━━━ #AITools #AI #Productivity #AI2026 #TechReview #productivity #tutorial #2026
How to Generate AI Images: GANs, Stable Diffusion & Visual Language Models Explained in 8 Minutes.
How does AI create images that look real—even when the people, places, or scenes never existed? In this video, Luka Anicin explores the evolution of AI image generation, from Generative Adversarial Networks (GANs) to Diffusion Models like Stable Diffusion, DALL·E, and Midjourney, and finally to Visual Language Models (VLMs) that can both generate and understand images. You'll learn how GANs sparked the first wave of realistic AI-generated images, why diffusion models became the dominant approach for modern image generation, and how Visual Language Models are pushing AI beyond image creation into image understanding and multimodal reasoning. Topics covered: • What GANs (Generative Adversarial Networks) are and how they work • Why ThisPersonDoesNotExist became a breakthrough AI demonstration • How Diffusion Models generate images from noise • Stable Diffusion, DALL·E, and Midjourney explained • What Visual Language Models (VLMs) are • How AI combines image understanding and language generation • The strengths and limitations of GANs, Diffusion Models, and VLMs • The future of multimodal AI systems Whether you're studying Generative AI, Machine Learning, Computer Vision, or AI Engineering, this video will help you understand the technologies powering today's most advanced image-generation systems. @Saras_AI_Institute
Only 25% of Jobs Are at Real Risk — Is Yours One of Them?
5 jobs are proving far more resistant to AI than the headlines suggest. While companies like Oracle are cutting thousands of positions and AI capabilities continue to improve, the real data tells a much more nuanced story. Discover what Anthropic, the World Economic Forum, BCG, and the OECD reveal about the future of work, artificial intelligence, automation, and the careers most likely to thrive in the AI era. 🚀 Subscribe to the channel: youtube.com/@TechRushByte?sub_confirmation=1 — Turn on notifications so you don't miss the latest updates on technology, AI breakthroughs, and future innovations! Will AI really replace every profession? Or are some careers becoming even more valuable as artificial intelligence evolves? In this video, we analyze the latest research from Anthropic, BCG, the OECD, and the World Economic Forum to separate fear from reality. You'll discover why AI adoption in the real world is happening much more slowly than the headlines suggest, why replacing an employee is far more complex than deploying an AI model, and what the data actually says about the future of work. We also explore the five careers that remain among the most resilient in the age of AI. From mental health professionals and skilled trades to senior executives, teachers, and healthcare workers, each profession shares human qualities that today's most advanced AI systems still struggle to replicate: trust, adaptability, judgment, empathy, accountability, and decision-making in unpredictable situations. Rather than focusing on speculation, this video examines real-world evidence. We explain the difference between AI capability and AI adoption, why businesses are integrating AI more as an augmentation tool than a replacement, and how professionals can use artificial intelligence to increase productivity instead of competing against it. If you follow AI news, ChatGPT, Claude AI, OpenAI, future technology, machine learning, automation, career development, and technology trends, this analysis provides practical insights into one of the biggest questions facing today's workforce. Whether you're a student choosing a career, a professional adapting to AI, or simply curious about the future of work, this video will help you understand where human skills continue to create lasting value. Hashtags: #AI #ArtificialIntelligence #FutureOfWork #Automation #Technology #Innovation #ChatGPT #ClaudeAI #OpenAI #MachineLearning #Career #Jobs #FutureTech #DigitalTransformation #TechNews Keywords: AI jobs, jobs AI cannot replace, future of work, artificial intelligence jobs, Anthropic research, Claude AI, ChatGPT, OpenAI, AI careers, automation, technology trends, machine learning, future technology, AI adoption, AI productivity, jobs safe from AI, resilient careers, career development, World Economic Forum AI, OECD AI, BCG AI, human skills, digital transformation, AI news, technology news, workforce trends, AI employment, AI impact on jobs, future careers, professional development Timestamps: 00:00 – AI Is Already Replacing Jobs 00:55 – The Question Everyone Is Asking 02:12 – What Anthropic's Research Really Found 03:40 – Why AI Adoption Is Slower Than You Think 05:02 – Job #1: Mental Health Professionals 06:08 – Job #2: Skilled Trades 07:08 – Job #3: Senior Leadership 08:00 – Job #4: Teachers 08:48 – Job #5: Healthcare Professionals 09:42 – The Biggest Myth About AI and Jobs 10:48 – The Skills That Will Matter Most in the AI Era