How to Explain Quantum Entanglement to Any Audience

Transformers & Attention: The Brain of AI Scenario: How does ChatGPT know “it” in a sentence refers to the “ball” and not the “dog”? Definition: Transformer architecture uses self-attention to link context. Analogy: A teacher scanning all students—focusing more on the one raising a hand. Real-Time Example: Sentence: “The dog chased the ball because it was fast.” 👉 Attention links “it” → “ball”. Flow: Tokens → Attention Layer → Context-aware Representations Tips: Transformers process tokens in parallel, unlike RNNs. Memory Trick: Transformer = Multi-focus camera lens. Interview Q: Why transformers > RNNs? ➡️ Because they capture global context in parallel. Conclusion: Attention is the magic trick—AI doesn’t just read, it understands context.

Lesson no. 2: How do we know how to ask questions? One of the things I noticed last term was how often the quality of the answer depends on the quality of the question. In this era of ChatGPT and other AI tools, this has become even more crucial. Students are learning that it is not enough to ask a question - it is about asking it in a way that reveals what they really need to know. The same applies in management, research, or even in everyday conversations. For me, as a lecturer, it is a reminder to pay attention not just to the answers I give, but to the questions being asked, and sometimes, to help refine those questions before trying to respond. Look at the video I’ve attached to this post so you can see what ChatGPT itself said when I asked, “What's the best way to ask you a question or write a prompt?” What helps you to ask better questions in class, at work, or in life? London College of Contemporary Arts (LCCA) Global University Systems

Hello there, I just used Chatgpt  to understand piping as a concept.  I understood that piping simply means sending the result of one command as an input to another command.  An example that I explored is ls | grep “file” | wc -l. As explained to me by the AI,  ls: list all the files in the directories Grep “file”: sorts only the file with the letters “file” Wc -l: counts the number of files in the directory with the name “file”. Before the prompting, I specified my learning style i.e. Visual-INTP learner and provided the prompt to explain the concept using the example I provided above. The result is a series of notes tailored to my learning style and it was really helpful compared to the generic approach I’m used to. I highly recommend this approach.  #ALX_SE #ALX_FE alx_africa

ChatGPT doesn’t think, it predicts. One of the biggest misconceptions I see when teaching people is the belief that AI “remembers” or “considers” facts like a human would. When a model like ChatGPT, Gemini, or Claude generates text, it’s not reasoning about truth. It’s doing math to predict the most likely next chunk of text--like an extremely advanced autocomplete. That misunderstanding is the root of so much frustration in the workplace: - Engineers trusting false directions and increasing ticket resolution times. - Leaders seeing budgets wasted on “AI pilots” that stall - Customers losing confidence when answers don’t line up The first breakthrough for everyone I work with is this: once you really understand how the model works, you can start to shape its output with prompts, guardrails, and workflows. That’s when reliability goes up and frustration goes down.

**Ready to unlock the true power of ChatGPT?** **Prompt engineering is the game-changer every AI user must master!** As a video editor starting out in the tech world, diving into prompt engineering opened my eyes to the magic behind interactions with AI. Here are my key takeaways: 👇 ⭐️ Understanding the components of a good prompt is essential for getting precise, helpful AI responses. 🛠️ Hands-on practice with prompt crafting helps avoid common errors and limitations that can slow you down. 💡 Learning how to define a prompt clearly can transform AI from a tool into a creative partner. 🎯 Advanced prompting techniques open doors to tackling complex scenarios with generative AI. 🚀 Applications of prompt engineering span many fields—even creative video editing workflows. Prompt engineering is not just about asking questions—it's about engineering the conversation for maximum impact and productivity. How do you think mastering prompt engineering could improve your daily work or creative process?

OpenAI JUST released how people are using chatgpt I immediately tossed that study into NotebookLM by Google Why, you ask? NotebookLM will bankrupt universities. This is the best learning tool I have seen in Decades. A few stats: 1. The context window of 1 source is 500,000 tokens (Compared to ChatGPT Plus of 32,000) 2. You can add 50 sources to 1 Notebook to analyze all at once. 3. You can generate a video overview, audio overview, study guide, and mind map analyzing all the sources. Literally you can apply this to any skill in the world. I'm using this now to extract atomic skills and create skill trees, Then building a 14-day plan: daily objectives, 1–2 labs, reading/watch list (with timestamps), a 30-min ‘teach-back’ task, and a pass/fail rubric. Keeping total learning time ≤120 minutes. Time to become superhuman. If you're interested in AI and how to use it, I talk about it a ton so give me a follow Kyler Stocks🗣️

OpenAI launches Agent Mode. Since July 2025, ChatGPT is no longer just a Q&A tool — it’s a true work assistant. With Agent Mode, it can: 1. Search the web in real time 2. Draft emails and documents 3. Build slides and reports 4. Even run code and automate tasks Implications: 1. Productivity shift – Routine admin and research tasks move from humans to AI. 2. Education dilemma – Great for teachers and researchers, but raises integrity concerns. 3. Future of work – The line between “assistant” and “colleague” gets thinner. 💡 The question isn’t just what AI can do, but how we integrate it responsibly. Question for you: Would you trust an AI agent to draft, execute, and deliver work without review?

Looking at this fascinating chart from Daily Chartbook! 📊 ChatGPT usage peaked during the final weeks of the college semester (late May), then dropped significantly once summer break began in early June. This really highlights how integral AI tools have become to academic workflows. Students aren't just casually experimenting - they're relying on ChatGPT as a core part of their study and research process.

🚀 Five Golden Rules for Prompting 🚀 AI is only as good as the prompts we design. Whether you’re using ChatGPT, Claude, Gemini, or any other LLM, mastering prompt engineering can make the difference between generic answers and business-changing insights. Here are the five golden rules I always teach: 🔍 Clarity – Be precise, avoid ambiguity. 📄 Context & Constraints – Provide background, goals, and boundaries. 🛡️ Role Assignment – Shape tone and expertise with role-based instructions. 💡 Few-Shot Examples – Show what “good” looks like with examples. 🔧 Iterate & Test – Great prompts evolve—refine, experiment, optimize. 🎓 Want to learn how to apply these rules in real-world scenarios? Check out the Prompt Engineering for Everyone course by TheDBAdmin. https://lnkd.in/dz7Cjdr2 You’ll gain: ✅ Hands-on labs & exercises ✅ Business use cases for AI in HR, Sales, Marketing, and IT ✅ Frameworks to design prompts that deliver consistent, high-quality results ✨ Think of this as upgrading from “asking questions” to “designing intelligent workflows.” 👉 Curious: Which of the five rules do you think is the hardest to master? #PromptEngineering #AICourses #ArtificialIntelligence #ChatGPT #GenerativeAI #AITraining #Upskilling #FutureOfWork #LLM #BusinessAutomation #TheDBAdmin