Kishore Pocham’s Post

Everyone is talking about AI. But let’s be real: for many, it’s still a jungle of buzzwords. So I put together a quick guide carousel: my own short notes on the most essential AI terms. Inside you’ll find a mix of: 🔹 Foundations → AI, ML, Deep Learning, Transformers 🔹 Core Tech → LLMs, Multimodal, Generative AI 🔹 Concepts → Dataset, Generalization, Hallucination 🔹 Tools → ChatGPT, Copilot, MidJourney, Runway 🔹 Principles → Responsible AI 🔹 Next Frontier → AI Agents Of course, there are many more terms out there — but I tried to capture the ones I believe everyone should at least recognize. 📌 A short, no-fluff “cheat sheet” to help you not feel lost in AI conversations. 👉 Which of these concepts was new to you — and which tools do you actually use? #AI #ArtificialIntelligence #GenerativeAI #DigitalTransformation #Innovation #Leadership

Demystifying AI: Traditional ML vs. Generative AI Are you leveraging the right AI for your task? Understanding the core differences between Traditional Machine Learning (ML) and Generative AI is key to effective implementation. Traditional ML excels at analyzing data to predict outcomes, classify information, or detect patterns – think fraud detection or customer churn analysis. Generative AI, conversely, creates new content like text, images, or code by learning from vast datasets. Grasping this distinction empowers you to select the optimal AI approach, design more robust solutions, and communicate AI's value accurately to stakeholders. This knowledge is crucial for making informed strategic decisions and driving innovation. What are your go-to applications for each type of AI? Share your insights below! #AIML #MachineLearning #GenerativeAI #AIStrategy #TechEducation #ProfessionalDevelopment [Machine learning and generative AI: What are they good for](https://lnkd.in/gJrAtD5N)

Day 2 – AI, ML & Generative AI: Clearing the Fog Everywhere we look, terms like AI, ML, and Generative AI are thrown around. But are they the same? Not really. Here’s the simple breakdown: Artificial Intelligence (AI): The big umbrella — machines that mimic human intelligence (reasoning, problem-solving, decision-making). Machine Learning (ML): A subset of AI — machines learn patterns from data and improve over time (e.g., spam filters, recommendation engines). Generative AI (GenAI): A newer subset — not just learning, but creating (text, images, code, music). Think ChatGPT, DALL·E, or GitHub Copilot. In short: AI = Intelligence | ML = Learning | GenAI = Creating The real magic is how these build on each other to transform the way we work, innovate, and even express creativity. As we move deeper into this 30-day journey, I’ll keep simplifying AI concepts so they’re not just buzzwords, but tools we can actually understand and apply. Join me in this learning adventure, where sharing knowledge becomes a two-way street of growth and enlightenment. Let's share in comments when you hear “AI,” what’s the first thing that comes to mind — intelligence, learning, or creativity? #30DaysOfAIWithSudipta #ArtificialIntelligence #MachineLearning #GenerativeAI #AIInnovation #FutureOfWork

09.22.2025) 【Use case of AI: 04】 This is to share use cases of AI (machine learning and deep learning) one by one, for those who are wondering how AI products and services are different and helping people better. 🤖 “Using the windows-comparidon type Gen AIs for your brainstorming” 天秤AI (Tenbin stands for a balance scale) https://lnkd.in/g48iKbZW When I brainstorm, I do not use only one Gen AI but use max.6 AIs at one time. I recommend you to do the same using this kind of App showing multiple answers from different and latest AIs. Have a nice day, thinking AI is better to people than before. 🌏 #天秤AI #brainstorming #aiusecase #internationalcommunication #globalcommunication

🚀 Building Your First AI Agent Many people think creating an AI Agent requires complex coding or a deep AI background. The truth is, with today’s tools, anyone can start experimenting. 👉 The key is not perfection from day one, but starting small, learning fast, and improving along the way. We are entering an era where AI Agents will become teammates, not just tools. #AI #AgenticAI #GenerativeAI #DigitalTransformation #FutureOfWork

Day 11 of AI Series 🤖 AI is fascinating, but here’s the catch: it’s not magic — it’s patterns, data, and patience. Today, I spent time understanding how small tweaks in a model or data can completely change outcomes. It reminded me that in AI, attention to detail matters as much as big ideas. Learning AI isn’t about rushing to build the next big thing; it’s about exploring, experimenting, and iterating — every day. Small steps today lead to breakthroughs tomorrow. 💡 #AI #MachineLearning #DataScience #AISeries #ContinuousLearning

Beyond the Hype: My Hands-On AI Journey Starts Here I've been reading about AI trends and technologies for a few days and have come to a conclusion: AI won't replace you, but someone using AI will. Learning AI doesn't only mean you create your domain artifacts with the help of AI tools. Rather, I would like to focus on implementing AI in the core process itself for better decision-making and efficiency. I've explored and implemented Ollama, an open-source tool that helps run AI models locally. I explored models like: - Phi 4 with 14B parameters - Mistral with 7B parameters - Neural Chat with 7B parameters - DeepSeek-R1 with 7B parameters - LLaVA with 7B parameters Among the list above, LLaVA is a multimodal model (text + images), while the others are unimodal (text only). For those who don't know what "parameters" means, an easy definition would be: The more parameters a model has, the more capable it is of understanding, evaluating, reasoning, and reporting results. There are pros and cons to higher and lower-parameter models. One of the most common cons of a higher-parameter model is overfitting. Stay tuned to learn more about the pros and cons of these models, their specific use cases, and how to treat overfitting and underfitting. Feel free to connect with me for simplified resources to learn and grow with me on my AI learning journey. #AI #MachineLearning #LLMs #Ollama #OpenSource #TechTrends #Learning

Unlabeled data → Pretrain → Foundation model → Adapt → Broad range of general tasks Tasks: Text generation Text summarization Information extraction Image generation Chatbot Question answering 🧠 How do Foundation Models (FMs) work? Foundation Models are trained on vast amounts of unlabeled data. ➡️ First, they are pretrained to learn patterns, context, and relationships. ➡️ Then, they can be adapted for a wide range of tasks, such as: ✍️ Text generation 📄 Text summarization 🔍 Information extraction 🎨 Image generation 🤖 Chatbots ❓ Question answering This adaptability makes FMs far more powerful than traditional ML models—acting as the backbone of Generative AI. 💡 The future of AI will be shaped by how well we learn to leverage, adapt, and optimize these models. 👉 What’s one FM-powered application you find most exciting? #FoundationModels #GenerativeAI #AI #MachineLearning #FutureOfWork

Just wrapped up an intense but exciting dive into Generative AI with Outskill 🚀 It was an eye-opening 2-day experience — filled with fresh ideas, practical tools, and plenty of “aha!” moments that showed me just how powerful AI can be. Looking forward to exploring further, learning more and seeing how AI will continue to shape the future of work and problem-solving. #AI #GenerativeAI #Upskilling #LifelongLearning

"An hour of learning, a wealth of insights — Generative AI explained! 💡" Just wrapped up the LinkedIn Learning course – "What is Generative AI?" (1 hr 3 min) 🎉 In that one hour, I got a clear picture of what GenAI really is, how it works, and why it’s such a game-changer. From text-to-image tools to GANs and even the future of jobs with AI — this course covered it all. Honestly, it was a powerful boost to my understanding 💡 If you’re curious about Generative AI and don’t know where to start, I’d definitely recommend this as a beginner-friendly resource! #Learning #GenerativeAI #ArtificialIntelligence #AI #linkedinlearning