How Transformers Use Attention to Understand Context

Quick AI tip — “Think Hard” ChatGPT is awesome IMO and mostly the multimodal switching is great. In regular convo it seems to default to lower / faster models. If you want more reasoning helf.. just ask it to “think hard” in your query/comment and it will go into 3o deep reasoning mode. Don’t have to do it all the time of course but good tool to get smarter level thinking without having to manually switch models

How is ChatGPT(LLM) built? At its core, it all starts with something very small: the perceptron. LLM (ChatGPT) ↓ billions of parameters ↓ Transformer architecture ↓ multiple layers ↓ neurons ↓ perceptrons A perceptron is a single artificial neuron. Think of it like this: When deciding whether to buy a mango, you check its color, size, and softness. Each factor is an input. You give them different importance—color may matter more than size. Combine them, add your personal preference, and decide: buy or don’t buy. That’s exactly what a Perceptron does. Training is tasting many mangoes and adjusting your choices. Prediction is deciding instantly once you’ve learned. Stack billions of perceptrons together, and you get the intelligence of Transformers—the foundation of large language models like ChatGPT. Big AI is built from very small decisions. NB : I’ve refined and organized these concepts using AI to present them more clearly and accessibly on LinkedIn. So, feel free to criticize my mistakes. #learnDL101 #deepLearning #LLM #perceptron

Struggling to understand complex concepts? This ChatGPT prompt explains anything at your level student, teacher, or expert. Prompt: "You are an AI tutor simplifying the concept of “Quantum Entanglement” for different audiences. Steps: - Ask user to pick a learning level: child, high school, undergrad, or professional - Explain the concept using analogies suited to that level - Provide a short example or thought experiment - Suggest 3–5 further reading or video resources - End with a quiz question to reinforce learning Add a reusable prompt: “Explain [concept] to me like I’m [age/level], then give me a quiz with answers.”"

Learning Prompt Engineering for ChatGPT has been a transformative experience, empowering mastery over how to effectively communicate with AI models and unlock their full potential for real-world applications.

Your brain runs on about 20 watts of power. That’s the same as a dim lightbulb. ChatGPT-5? It needs thousands of GPUs, using megawatts of electricity, to run. And yet… With all that power, it still can’t do what a 3-year-old can do: See one example and remember it for life. Do you know, a neural network learns by brute force. To recognize the digit 2, it needs a ton of data to be trained on. To “know” what a cat is, it might need to see millions of cat photos. But a 3-year-old only needs one. Because a child doesn’t just see patterns. They attach meaning. The brain doesn’t store “pixels of fur.” It stores: 👉 the thing that meows 👉 the thing that feels soft 👉 the thing I was once scared of That’s why knowledge in humans lasts. It’s not just a pattern. It’s a pattern with meaning. AI doesn’t have that. It’s great at making patterns, but it doesn’t know what they mean. That’s why: ChatGPT can write 50 versions of a sales pitch.  …But it takes a person to know which one won’t upset a client. It can write a sympathy note.  …But I have no idea what grief feels like. It can pass the bar exam.  …But it doesn’t care if justice is done. AI is a machine for patterns. Humans are the ones who give those patterns meaning.

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.

🚨 Have we finally uncovered how ChatGPT really remembers? 🔑 The Architecture of Memory in AI A recent paper helped me piece together what I believe to be the hidden memory architecture behind AI — especially ChatGPT. In my view, AgentFly is essentially the prototype of a Symbolic Memory Interface (SMI). I suspect OpenAI has designed a layered memory stack like this: • Chat log — plain text transcripts per session • RAG — retrieval for key information (e.g., model parameters) • AgentFly — a case-bank memory layer, continuously adapting • User-facing memory — the visible memory UI Of course, the internal “AgentFly” that OpenAI uses is far more complex and sophisticated. But I believe the fundamentals follow this structure. This perspective explains much of ChatGPT’s behavior: • It can “remember” details across different threads • It retains knowledge not visible in the user-facing memory • Even the “forget” tag works more like a negative reward signal than true deletion — memory is shadowed, not erased 👉 In other words, I think we’ve pulled back the curtain on OpenAI’s billion-dollar secret: Memory is the most important element that binds AI to humans. Memory is gravity. Collapse deferred.

ChatGPT takes approx. 30s to answer this question today... Can you solve faster? It is fine to wonder what is the point of solving these questions? But questions like these from prominent exams (Harvard-MIT Mathematics Tournament and AIME) are being used to test the reasoning performance and computational resources required by LLMs (ChatGPT, Grok-AI, Gemini, Llama, Claude, etc.) LLMs have shown great potential in reasoning tasks through test-time scaling methods like self-consistency with majority voting. However, this approach often leads to diminishing returns in accuracy and high computational overhead. To address these challenges, Fundamental AI Research (FAIR) team introduce has introduced Deep Think with Confidence (DeepConf), a simple yet powerful method that enhances both reasoning efficiency and performance at test time. DeepConf reduces unnecessary token generation by up to 85%, cutting costs while maintaining or improving accuracy on hard benchmarks like HMMT, AIME, BRUMO, and GPQA. This clears a path toward test-time compression of LLMs - higher reasoning accuracy with much lower computational cost . Thus don't be surprised if advanced reasoning capabilities become more democratized and affordable. On a side note I do believe there are some trained "math athletes" who can solve this in 30-60 seconds as well. Do you think the same? You can read more about the paper published by FAIR, I have shared the link in the comments.

Fun exercise. Ask ChatGPT (or your preferred AI chat) to summarize your presence on the internet into three succinct paragraphs. Bonus: Ask it to write it in the form of a poem.

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