Artificial Intelligence

*The Ai Engineering Paradox: Evolve or Expire.* 🔥 The biggest challenge in AI Engineering today? You learn a technique, and in four weeks, it’s obsolete. This isn’t an exaggeration. It’s the reality of AI’s rapid evolution. - 📌 *Remember OpenAI's GPT-3 fine-tuning?* People spent months learning it—only for GPT-4’s API with function calling and RAG-based solutions to render most of that effort unnecessary. - 📌 𝐘𝐎𝐋𝐎 𝐟𝐨𝐫 𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧? You mastered YOLOv4, but by the time you deployed it, YOLOv8 and SAM (Segment Anything Model) were already redefining the space. - 📌 *𝐀𝐮𝐭𝐨𝐌𝐋 & 𝐀𝐈 𝐂𝐨𝐝𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐨𝐫𝐬* Spent months mastering hyperparameter tuning? Now, tools like AutoGPT and Deepseek solve it with a single prompt. 👉  *The lesson?* AI is no longer a skill you "master"; it's a skill you continuously evolve. You are either on the bleeding edge or left behind. 🔥 How do you keep up? 𝐇𝐚𝐧𝐝𝐬-𝐨𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐬: Reading isn’t enough; apply new AI models quickly. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦: Be active in AI communities, research papers, and GitHub repos. 𝐅𝐢𝐫𝐬𝐭-𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Instead of just learning tools, understand core ML concepts. ⚡ Adaptability > Knowledge in AI Engineering. What’s the last AI technique you learned that became obsolete too fast? 🚀 Join discussion: https://www.linkedin.com/posts/nirajlunavat_ai-learnai-ailearning-activity-7297835217610801152-xDeG

*DeepSeek is one of the most powerful AI tool right now.* But almost no one knows how to use it for learning. Here's a complete cheatsheet to master any topic, skill or subject in minutes with DeepSeek for free: (Save this cheatsheet and get started) Let me show you how to use it: ➝ *Act as a [ROLE]* Tell DeepSeek to be your tutor, essay reviewer, exam question generator, or even a debate coach. ➝ *Show as [FORMAT]* Get responses as bullet points, mind maps, real-life case studies, or even Socratic Q&A. ➝ *Set Restrictions* Force DeepSeek to use only academic sources, explain in 100 words, or simplify for a 10-year-old. ➝ *Create a [TASK]* Ask for a study guide, flashcards, research summaries or critical thinking questions. Example Prompt: "Act as an experienced professor in physics. Create a structured study plan for quantum mechanics covering 4 weeks. Provide essay openings, simplify explanations for a 10-year-old & include real-world applications." Use this, and you’ll learn anything 10x faster.

*Is Computer Vision Being Overshadowed?* It’s been almost a year since major publications and magazines stopped covering Computer Vision, the buzz has shifted entirely to Gen AI, DeepSeek, and LLMs. But CV isn’t dead. Far from it. I’ve been deep in some exciting advancements that push the boundaries of vision intelligence: 1)Vision-Language Models (VLMs) – Bridging the gap between text and vision for richer AI understanding. 2)Agentic Object Detection – Moving beyond traditional detection with reasoning-driven AI for human-like precision no custom training needed. *Breakthrough Research at NeurIPS:* 1)No "Zero-Shot" Without Exponential Data – Vishaal Udandarao, University of Tuebingen 2)Understanding Bias in Large-Scale Visual Datasets – Boya Zeng, University of Pennsylvania 3)Map It Anywhere: Empowering BEV Map Prediction – Cherie Ho, Omar Alama & Jiaye Zou, Carnegie Mellon University Are you still following developments in Computer Vision, or has your focus shifted

*Introducing YOLOv12: Attention-Centric Real-Time Object Detectors* 🚀 The Future of Real-Time Object Detection is here. YOLOv12 takes real-time detection to the next level by integrating attention mechanisms while maintaining the speed of CNN-based models. This breakthrough closes the gap between accuracy and latency, making it a game-changer for AI applications. 🔥 *𝐖𝐡𝐲 𝐘𝐎𝐋𝐎𝐯12 𝐒𝐭𝐚𝐧𝐝𝐬 𝐎𝐮𝐭* ✅ First-ever attention-centric YOLO with CNN-level speed ✅ Unmatched balance between performance and efficiency ✅ Outperforms YOLOv7, YOLOv8, and Gold-YOLO across all scales 🚀 *𝐊𝐞𝐲 𝐀𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬* ⚡️ YOLOv12-N: 40.6% mAP at 1.64ms latency on T4 GPU → +2.1% mAP over YOLOv10-N ⚡️ YOLOv12-S: 42% faster than RT-DETR-R18 with only 36% compute & 45% parameters ⚡️ Best-in-class latency-accuracy and FLOPs-accuracy trade-offs With YOLOv12, real-time object detection is faster, smarter, and more efficient than ever. Get ready to explore the next evolution.🚀🔍 https://www.arxiv.org/abs/2502.12524 Checkout: https://www.linkedin.com/posts/nirajlunavat_yolov12-activity-7298360460993568769-eHiK

*Master AI in 2025 – A Quick Roadmap* 🚀 AI can be overwhelming, but following a structured path makes it easier. Here’s the roadmap: *1. Build Strong Foundations* Learn Python, data structures, linear algebra, statistics & version control before diving into AI. *2. Work with Data* Clean, preprocess & visualize datasets using Pandas, Seaborn, and Matplotlib for hands-on experience. *3. Master Machine Learning* Understand supervised & unsupervised learning, regression, decision trees & implement models with Scikit-Learn. *4. Explore Deep Learning* Learn neural networks, CNNs, RNNs, and Transformers using TensorFlow & PyTorch for AI applications. *5. Choose an AI Specialization* Focus on NLP, computer vision, reinforcement learning, or AI in business and healthcare. *6. Learn Large Language Models (LLMs)* Work with GPT, LLaMA, fine-tuning, Retrieval-Augmented Generation (RAG), and AI APIs. *7. Master AI Deployment & MLOps* Deploy models using Flask, FastAPI, Docker, Kubernetes, and automate pipelines. Join: https://t.me/Artificial_intelligence_in

*What AI engineers think they do:* ✍️ Design a clear prompt for the LLM. 🔗 Connect the LLM to the frontend. 🛋️ Sit back while the LLM does its magic. *What AI engineers actually do:* ✏️ Change the prompt because, for some reason, the LLM is giving you the recipe to create pancakes 🥞 ⚖️ Turn down the content filtering because the LLM is acting more prudent than the Pope ✝️ 📏 Ask the LLM to be more concise because you don’t want to read a 2-page monologue 📜 🔄 Deploy a second LLM because you’ve already reached the token limit (was the token limit always that low? 🤔) 🔠 Put the most important rules in UPPERCASE so the LLM knows you’re being serious 😤 🚀 Bump up the API version (because you heard the newest API is much better 🔥) 🔒 Add more rules to the prompt because god forbid you get prompt hacked 👀 🔄 Go back to the original prompt because now your LLM is confused. 😀😅😂

*Andrej Karpathy’s Ultimate Guide to LLMs – A Must-Watch!* If you want to master LLM, Andrej Karpathy has released one of the most comprehensive deep dives into the subject. In just 3.5 hours, he unpacks architecture, training & real-world impact like never before. 🔥 *Why this Tutorial Stands Out?* 1️⃣ *Evolution of Language Models:* Karpathy traces the journey from basic statistical methods to Transformer-based neural networks. Learn how LLMs process vast datasets to generate human-like text, translate languages & write code. 2️⃣ *Breaking Down the Black Box:* Ever wondered how LLMs really work? Karpathy simplifies attention mechanisms, tokenization, and large-scale training while tackling bias, ethics & fine-tuning for specialized tasks. 3️⃣ *Real-World Impact:* LLMs are transforming healthcare, finance, entertainment. Discover how they enhance services, optimize processes & drive innovation. 4️⃣ *Crystal-Clear Explanations:* Karpathy’s teaching style is unmatched makes it easy to learn. https://youtu.be/7xTGNNLPyMI?si

*Multi-agents AI: what exactly is it? (Part 1)* But first, why do we need it Most AIs today still fall into one of two categories: 1- Over-reliant on a single large model → prone to mistakes, loops, and unpredictable behavior. 2- Predefined workflows → more reliable but rigid and hard to scale. Neither truly enables AI to handle real tasks independently. Multi-agent AI takes a different approach. Instead of one AI doing everything, multiple specialized agents work together dynamically to complete tasks efficiently. One might gather information, another analyzes it, and another takes action—they communicate, adjust plans, and track progress, just like a well-coordinated team. Here’s how it happens/ tech breakdown: 1️⃣ Role Assignment & Task Delegation At the core of any multi-agent system, there’s usually an Orchestrator Agent (or Coordinator). This agent is responsible for: Breaking down the task; Deciding which agents are needed; Delegating work based on agent capabilities 2️⃣ Communication & Information Sharing Agents exchange data through APIs, message passing, or shared memory. This allows them to: - Share insights in real time - Adjust workflows dynamically based on new information 3️⃣ Reflection & Self-Correction Unlike single-agent AI, multi-agent systems track progress and self-correct using: - Task Ledgers (tracking what’s been done vs. what’s left) - Feedback Loops (agents double-check their work) - Dynamic Replanning (if an approach fails, agents adjust strategy) 4️⃣ Multi-LLM & Specialized AI Models Instead of using one large LLM for everything, multi-agent AI systems combine: - A generalist LLM for reasoning and orchestration - Small fine-tuned models for specialized tasks 5️⃣ Execution & Continuous Learning Once agents complete a task, multi-agent systems don’t just stop—they learn from each execution to improve performance. And this isn’t theoretical, it’s already happening. A few examples: 🚗 Tesla’s Full Self-Driving (vision, path planning, and decision-making agents working together) 💰 Goldman Sachs AI Trading (market analysis, risk management, and execution agents) 🔬 Recursion AI in drug discovery (analyzing biological data, predicting drug interactions, and optimizing trials)

*SQL IS DEAD!!* Uber just unveiled QueryGPT, an AI tool that translates natural language into SQL queries. ASK QueryGPT "How many trips were completed by Teslas in Seattle yesterday?" and getting the exact SQL query in seconds no manual coding required. *Is this the end of SQL?* For years, SQL has been a core skill for data professionals. But with AI tools like QueryGPT automating complex queries, the game is changing. So, is learning SQL still worth it? Or will AI make writing queries obsolete? Here's my take: 1/ 𝗦𝗤𝗟 𝗶𝘀𝗻’𝘁 𝗱𝗲𝗮𝗱 𝗶𝘁’𝘀 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴. AI tools can speed up query writing, but understanding data structures, optimization, and debugging still require human expertise. 2/ 𝗔𝗜 𝗮𝘀 𝗮 𝗰𝗼-𝗽𝗶𝗹𝗼𝘁, 𝗻𝗼𝘁 𝗮 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁. Just like calculators didn’t kill math, AI won’t kill SQL but it will redefine how we interact with data. 3/ 𝗞𝗻𝗼𝘄𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗼 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗦𝗤𝗟 𝘁𝗼𝗼𝗹𝘀. Instead of memorizing syntax, the real skill will be knowing how to prompt AI effectively and validate results.

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