Videos de AI
Videos etiquetados con "AI"
AI 44 videos
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
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
Gemini、韓国AI検索でChatGPTを逆転…利用率... #Shorts
#AI #人工知能 #ChatGPT #Gemini #Claude #OpenAI #テクノロジ #ITニュス #生成AI #テック #AIニュス #最新情報 #Apple #Google #Microsoft #ガジェット #テクノロジニュス #AI #人工知能 #ChatGPT #Gemini #Claude #OpenAI #テクノロジー #ITニュース #生成AI #テック #AIニュース #最新情報 #Apple #Google #Microsoft #ガジェット #テクノロジーニュース
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
【革命】Geminiの使い方が変わる。1行足すだけでプロンプト作成が完全自動化する神ワザ #プロンプト#gemini #ai活用
【Gemini】プロンプト作成はAIに丸投げ！1行で自動化する魔法の「使い方」徹底解説 Googleの最新AIをマスター！初心者でも今日から実践できる、Gemini（ジェミニ）の究極の使い方を伝授します！ 「プロンプトをどう書けばいいか分からない…」と悩むのはもう終わりです。 本動画では、Geminiに情報を渡すだけで「精度の高いプロンプト」を自動生成させる裏技を紹介。AI初心者の方でも、動画内で紹介する「魔法の1行」をコピー＆ペーストするだけで、プロンプト作成のプロになれます。AIを使いこなして、仕事や日常のタスクを劇的に効率化しましょう。 【目次】 0:00 イントロ 0:12 AIでプロンプト作成を自動化する方法（魔法の1行とは？） 1:00 【実践1】夜ご飯のレシピ作成：適当な材料メモがレシピに！ 5:14 【実践2】会議の議事録作成：プロの事務局レベルの構成術 6:43 【実践3】カフェの集客戦略：専門的なマーケティング視点を導入 11:39 プロンプト自動化のポイントと注意点 12:18 チャンネル登録・AIテクニックの再生リスト紹介 🏫メンバーシップ入会はこちらから！ https://www.youtube.com/channel/UCMZNTpLvBFc6eAzsqXQlerQ/join 🎥プロンプト学習用動画 ・自分専用の「プロンプト自動生成AI」を5分で作る方法 https://youtu.be/7YxTMUmxezs ・Geminiプロンプトの書き方完全ガイド！神回答を引き出す5つの基本構成【AI初心者必見】 https://youtu.be/oQKcBsnHyh0 ・Gemini神プロンプト7選｜初心者でも明日から仕事で使えるAI活用術【2026年最新】 https://youtu.be/xWWaLhN5kig ・【脱初心者】AIへの指示出しがうまくいく初心者向けプロンプト5選！ https://youtu.be/R2DujXPhiIA 📚 AI学習を頑張りたい人向けの「再生リスト」 ・【基本】AI初心者向け講座. Gemini/NotebookLM https://www.youtube.com/playlist?list=PLv8b5HyHhh0JxE_pqxH7jCyKn6KCpm6Lj ・【入門】AIプロンプト作り方基本講座！初心者がうまく指示を出すコツ https://www.youtube.com/playlist?list=PLv8b5HyHhh0Jr24yxCgyHDxHwYLv33Mrn 📖 このスクールで学べること 当チャンネルでは、AIの基本から最新の便利ツールまで、初心者の方がつまずかないように丁寧な「授業」を行います。 【主な授業内容】 Geminiの基本： Googleの最新AIを使いこなす第一歩 NotebookLMの活用： 膨大な情報を整理し、学習効率を上げる方法 プロンプトの書き方： AIから最高の回答を引き出す「指示」のコツ 実践授業： AIを日常や仕事に応用する具体的なテクニック 最新トレンド： AIの最新情報を分かりやすくお届け 🎓 目指すのは「自分で使える」へのステップアップ 私の目標は、視聴者の皆様が「なんとなく分かる」という状態から、「自分の力で使いこなせる」ようになるまで寄り添うことです。 お茶でも飲みながら、リラックスして授業に参加してくださいね。 私と一緒に、新しいデジタルの扉を楽しく開いていきましょう！ チャンネル登録という名の「入学」を、心よりお待ちしております😺🎓 #gemini3 #プロンプト #ai活用 #初心者
😎 Claude 입문 강의! ChatGPT만 쓰던 사람이라면 꼭 보세요 #소이랩왕초 #claude #antropic
😎 [ 왕초 ] 와 함께하는 '나도 잘 할꾸야' Claude~ 요즘 정말 많이 들리는 AI, Claude! 도대체 ChatGPT랑 뭐가 다르고, 왜 개발자들이 많이 사용할까요? + Claude는 뭔가요? + ChatGPT와 뭐가 다른가요? + 실제로 무엇을 할 수 있나요? + 비용은 얼마나 드나요? 🚀소이랩 7번째 신작! 온라인 강의 (오픈) '프로에게 배우는 CLOUD COMFY' : https://fastcampus.co.kr/data_online_cloud 🚀소이랩 6번째 온라인 강의 (완결) '프로에게 배우는 나노바나나!' : https://fastcampus.co.kr/data_online_kontext 💬카카오톡 실시간 채팅 채널 - 소이랩 [생생정보통] : https://open.kakao.com/o/gs60FZgh 📢콘텐츠 개발 및 교육문의 : contact@soylab.ai - 00:13 Intro 00:31 클로드가 뭔가요? 06:36 클로드의 현재? 09:09 뭘 할 수 있나요? 10:51 가격 14:43 Outro - 콘텐츠 개발 및 교육문의 : contact@soylab.ai 카카오톡 실시간 채팅 채널 - 소이랩 [생생정보통] : https://open.kakao.com/o/gs60FZgh #claude #anthropic #chatgpt #gpt #ai #생성형AI #AI입문 #왕초보 #AI기초 #MCP #클로드 #클로드AI #AI에이전트 #바이브코딩 #VibeCoding #cursor #codex #openai #개발AI #AI자동화 #stablediffusion #comfyui #소이랩 #soylab
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
Spring AI 2.0: Custom Advisors for Tool & Token Logging
Ever wondered which tools your LLM is actually using and how many tokens each call is consuming? Spring AI Advisors give you AOP-like superpowers for your AI interactions, letting you intercept, log, and monitor every call to your language model. In this video, we explore Spring AI's Advisor API by building two custom advisors from scratch. The first is an AvailableToolsLoggingAdvisor that shows which tools are visible to the model before a call and which ones were actually invoked after. The second is a TokenCounterAdvisor that tracks prompt tokens, completion tokens, and total tokens with running totals across multiple calls. Along the way, you'll learn how advisors work as before/after interceptors for LLM calls, how to use the ChatResponse metadata, and how to wire tools into your Spring AI chat client. - Understand what Spring AI Advisors are and how they act as AOP for LLM calls - Build an AvailableToolsLoggingAdvisor to log which tools are loaded and which are invoked - Build a TokenCounterAdvisor to track prompt, completion, and total token usage per call - Learn how to use ChatResponse metadata for usage statistics - Wire up custom tools (like a DateTime tool) and see them in action with advisors 0:00 - Intro - Why I needed custom advisors 1:10 - What are Spring AI Advisors? 2:30 - Spring AI Reference Docs & Built-in Advisors 3:30 - Project setup on start.spring.io 4:30 - Configuring API key and model 5:15 - Creating the ChatController and ChatResponse 6:45 - Understanding token usage from ChatResponse 8:00 - Adding a DateTime tool for LLM calls 10:00 - Building the AvailableToolsLoggingAdvisor (before) 13:30 - Adding the after method to log invoked tools 16:30 - Testing the AvailableToolsLoggingAdvisor 17:45 - Building the TokenCounterAdvisor 20:30 - Testing the TokenCounterAdvisor 21:30 - Wrap up & what's next (Tool Searching) 🔗Resources & Links mentioned in this video: Spring AI Reference Documentation - Advisors: https://docs.spring.io/spring-ai/reference/api/advisors.html Spring Initializr: https://start.spring.io Dan Vega's Spring AI Workshop (GitHub): https://github.com/danvega/spring-ai-workshop 👋🏻Connect with me: Website: https://www.danvega.dev Twitter: https://twitter.com/therealdanvega Github: https://github.com/danvega LinkedIn: https://www.linkedin.com/in/danvega Newsletter: https://www.danvega.dev/newsletter SUBSCRIBE TO MY CHANNEL: http://bit.ly/2re4GH0 ❤️
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
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 AI Steals? Borrow? Creates? Images From Text
Ever wondered how typed words instantly turn into stunning AI images? 🤖✨ In this video, we break down the mind-blowing science behind text-to-image AI (like Midjourney, DALL-E, and Stable Diffusion) in simple terms anyone can understand! From decoding your text prompts to understanding how "Diffusion Models" actually paint pixels out of random static noise, we pull back the curtain on how artificial intelligence "thinks" in pictures. No coding experience required! If this video helped you understand AI a bit better, smash that LIKE button and SUBSCRIBE for more simple tech breakdowns! 🔔What’s the craziest prompt you’ve ever given an AI? Drop it in the comments below! 👇
Googleが4,000億円で取り戻した天才は、なぜ2年... #Shorts
#AI #人工知能 #ChatGPT #Gemini #Claude #OpenAI #テクノロジ #ITニュス #生成AI #テック #AIニュス #最新情報 #Apple #Google #Microsoft #ガジェット #テクノロジニュス #AI #人工知能 #ChatGPT #Gemini #Claude #OpenAI #テクノロジー #ITニュース #生成AI #テック #AIニュース #最新情報 #Apple #Google #Microsoft #ガジェット #テクノロジーニュース