AI vs ML: Chess Genius vs YouTube Detective

🧠 The Rat that Learns Faster than your Dashboard At last week’s AI Gatherings Montréal, Google DeepMind's Pablo Samuel Castro unpacked AlphaEvolve, an 𝘓𝘓𝘔-𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘦𝘷𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘢𝘳𝘺 𝘤𝘰𝘥𝘪𝘯𝘨 𝘢𝘨𝘦𝘯𝘵 that discovers cognitive models automatically. 𝗧𝗵𝗲 𝗵𝗼𝗼𝗸? Thirsty rat. Many doors. Water, sometimes. ~700 trials later it’s not guessing, it’s optimizing. (Yes, rats did reinforcement learning before it was cool.) 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘄? Discovery cycle → weeks, not years. 𝗛𝗼𝘄? Evolves thousands of tiny Python “cognitive programs,” scores them, and keeps the best. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? ⤷ Accuracy and explainability. Predicts the next action and surfaces the why (forgetting, exploration, credit assignment). ⤷ Built to travel. One framework that holds across humans, rats, and optogenetically modified fruit flies. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? ⤷ Behavioral health: detect relapse/stress via learning-loop signatures. ⤷ Adaptive learning: instruction that adapts to cognition. ⤷ Markets: model how traders learn → anticipate regime shifts beyond price patterns. ⤷ Autonomy: systems that progress automation → agency → adaptation. 𝗦𝗼 𝘄𝗵𝗮𝘁? If AI can model 𝘩𝘰𝘸 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘩𝘢𝘱𝘱𝘦𝘯𝘴, productivity tools become discovery engines, compounding insight across product, science, and strategy. And Montréal is one of the few global hubs where AI × biology × cognition truly converge. — What would you track as a live learning KPI and how would you validate it in prod? — If Splinter ran PMF, which metric would he ship first? — Dot-com déjà vu or S-curve compounding? PS. If your A/B test needs a third arm, you’re not failing, you’re evolving. #AI #DeepMind #AIAgents #ReinforcementLearning #MontrealAI

My Machine Learning class sometimes gets a little math-heavy and I have to dig deep into all my high school brain cells to keep up 😅 The other day, my professor asked: “What’s *temperature* in LLMs?” At first, I thought, wait, that’s not math….that’s physics, right? So I did some digging and found out that *temperature* actually controls how LLMs generate their output, basically, how "creative" or "predictable" their answers are. That was something completely new to learn. Now, as I’m learning more about prompt engineering, I realized that depending on what kind of answer I want, technical, balanced, or creative, I can actually tweak the *temperature* through my prompt style. Here are some simple, reusable prompts you can use to control the *temperature* of your AI responses: Low Temperature (Precise/ Factual) : → Act as a precise and analytical expert. Give a concise, step-by-step answer with no creative elaboration. Focus only on accuracy and clarity. Medium Temperature (Balanced / Conversational) : → Act as a professional but friendly expert. Explain the concept clearly and conversationally with examples if helpful. Keep a balanced tone – not too formal, not too casual. High Temperature (Creative / Expressive) : → Act as a creative thinker or storyteller. Be imaginative, expressive, and take creative freedom. Use vivid imagery or unexpected ideas. Dual Mode (Precision + Creativity) → First, give me a concise and factual explanation. Then, give me a creative or analogy-based version of the same idea. Small tweaks in how you ask can make big changes in how AI answers. Learning this made me appreciate how much prompt design feels like a blend of logic, language, and creativity. #MachineLearning #LLM #AI #PromptEngineering #LearningJourney

I’ll be honest, this week tested my patience. No shiny models, no cool architectures, no instant results. Just reasoning, debugging, and a lot of quiet thinking. In Andrew Ng’s Advanced Learning Algorithms (Course 2, Week 3), I entered the phase of machine learning that challenges not your coding, but your clarity. It’s where you stop chasing accuracy and start asking why. Why did my model fail? Why does accuracy look good, but performance doesn’t? Why does “smart” AI still make silly mistakes? This week was about bias and variance, regularization, error analysis, and model evaluation — topics that don’t look glamorous but quietly shape the reliability of every AI system. They taught me to stop guessing and start diagnosing. I also learned how powerful data can be. A small improvement in how you collect, clean, or represent it can outperform even the most advanced neural network. It reminded me that AI isn’t just math, it’s judgment, discipline, and design. If Week 1 was exciting and Week 2 was creative, Week 3 was about maturity, learning to think like an engineer, not just a model builder. It wasn’t easy, but it left me with one clear insight: Better data, thoughtful design, and honest evaluation, that’s where real intelligence begins. #MachineLearning #DeepLearning #AI #DataScience #AndrewNg #EthicalAI #LearningJourney #ArtificialIntelligence #Mathematics #Education

🚀 Sunday Learning Recap: Mastering Prompt Engineering with Sir Ali Jawwad Sunday 2–5 PM class with Sir Ali Jawwad was a deep dive into the art and science of prompt engineering — the skill every AI enthusiast needs in 2025. We explored: Top-K and Top-P (Nucleus Sampling) — how tuning them changes how creative or focused AI responses become. 🧩 The fundamentals of prompting: Zero-shot prompts → when the model gets no examples One-shot prompts → when we show one example Few-shot prompts → when we show multiple examples System prompts → to define the model’s behavior or role Role prompts → to give personality or perspective What I loved most was learning how to combine these techniques to get more accurate, human-like, and creative AI outputs. Prompt engineering isn’t just about asking questions — it’s about communicating with AI like a developer, teacher, and designer all at once. 💡 I’m excited to keep experimenting and sharing what I learn next! 💭 Have you ever tried few-shot or role-based prompting in your AI projects? #AI #PromptEngineering #LearningJourney #ArtificialIntelligence #AIEducation #TopK #TopP #MachineLearning #OpenAI #AIinPakistan

✨ “Machines are not born intelligent they learn.” That single idea forms the foundation of everything we call Artificial Intelligence today. Over the past few weeks, I’ve been diving deep into one of the most fascinating parts of AI Machine Learning and the more I learn, the more I realize how close it brings machines to human-like thinking. 🧠 Machine Learning is not a single concept it’s an entire ecosystem of learning techniques, each with its own unique way of understanding the world through data. 🔹 Supervised Learning : where the model learns like a student guided by a teacher, using labeled examples to make accurate predictions. 🔹 Unsupervised Learning : where there’s no teacher, and the algorithm explores on its own to find hidden patterns and structures. 🔹 Reinforcement Learning : the art of trial and reward, where machines learn strategies by interacting with their environment (just like how we learn from experience). 🔹 Semi-Supervised Learning : the perfect blend of both worlds, leveraging small amounts of labeled data with large amounts of unlabeled data to make smarter predictions. Each of these techniques has a different purpose from diagnosing diseases and detecting fraud to powering self-driving cars, recommendation systems, and even game AI. 🎮🚗 What amazes me most is how ML is transforming every industry helping systems not just analyze data but understand, adapt, and evolve with it. The deeper I go, the clearer it becomes: 👉 Machine Learning isn’t just about programming models it’s about teaching them to learn from experience, just as we do. If you’re someone who’s exploring AI, I’d say this don’t just learn the algorithms; learn the philosophy behind them. Because behind every dataset, every prediction, and every pattern, there’s a story waiting to be uncovered. 📊✨ #MachineLearning #ArtificialIntelligence #DataScience #AI #LearningNeverStops #Innovation #Technology

Stop Ignoring Your ML Foundations! GenAI is just the next evolution. 🛑 Many developers are rushing to master Prompt Engineering and RAG, but hitting a wall because their core ML/AI fundamentals are shaky. GenAI didn't invent intelligence; it built on decades of work. Here’s the core challenge: Without understanding why a model behaves the way it does, you can't truly optimize, fine-tune, or debug it at a professional level. You need to refresh the foundational concepts. Think of it as your GenAI "System Design" checklist: Attention & Transformers: The backbone of LLMs. Do you know exactly how self-attention works and what multi-head attention buys you? It's not just a black box. The Loss Function: This is the model's objective. Understanding its role in training (Pre-training vs. Fine-tuning) is crucial for knowing how to influence a model's output beyond basic prompting. Reinforcement Learning from Human Feedback (RLHF): This is the magic that makes an LLM helpful. Know the role of the Reward Model and why it's a game-changer for alignment. Embeddings & Vector Databases: The secret to custom, real-time knowledge. Your core concepts of vector space, distance metrics (like Cosine Similarity), and dimensionality reduction are more relevant than ever. 💡 Shortcut Tip: Don't re-read the entire textbooks. Focus your refresh on the 'Deep Learning' and 'Sequence Modeling' chapters. Everything from RNNs to LSTMs was a stepping stone to the Transformer. Seeing the evolution makes the current architecture click. What core ML concept did you have to re-learn or refresh to better understand GenAI? Share your "Aha!" moment in the comments! 👇 #SystemDesign #AI #MachineLearning #GenAI #DeveloperTools #LearningJourney

My current favorite #AI x #Climate theme is using AI leaderboards to track climate emissions of AI. For those (like me) who didn't know what leaderboards even were, here is a good primer (https://lnkd.in/gbMbAfhY) It wasn't until I started working at an AI company, that I realised just how important they were. The TLDR for me was that if it's not on a leaderboard, people that are actually building AI tools aren't going to see it. I think that if we are actually going to start tracking carbon emmissions of AI, it HAS to be done through leaderboards. If anyone is interested, I've basically used SciSpace to conduct a full literature review on the topic, to see where we stand. You can find the deep dive here: https://lnkd.in/gNhFbK8M Let me know if this is useful for people- I was actually impressed to find that there's been a lot done since the last time I looked. And I think I'll probably be doing a deep dive on some of these papers moving forward. But if you want access to the entire thing, it's on the link above.

🧠 The biggest mindset shift in my AI journey wasn’t about accuracy — it was about purpose. When I first started building models, I’d obsess over getting the perfect F1-score or minimizing loss to the last decimal. But the truth? None of that matters if your model doesn’t solve a real problem. Over the past months, I’ve learned to think less like a student and more like an engineer: ⚙️ Understand the business context before writing code. 📊 Focus on interpretability — knowing why the model makes a decision. 🚀 Build for reliability, not perfection — deploy small, learn fast, iterate faster. Looking back at my older projects now, every “mistake” I made taught me something that books never could. If you’re working in AI/ML — what’s one lesson your early projects taught you the hard way? 👇 Let’s make this a thread of real experiences instead of curated success stories. #AI #MachineLearning #GenAI #LangChain #LLM #DeepLearning #DataScience #AICommunity #Learning #TechJourney

✨𝐘𝐨𝐮 𝐃𝐨𝐧’𝐭 𝐇𝐚𝐯𝐞 𝐭𝐨 𝐁𝐞 𝐚𝐧 𝐄𝐱𝐩𝐞𝐫𝐭 𝐭𝐨 𝐒𝐭𝐚𝐫𝐭 When I first started exploring AI, I honestly thought it was just for engineers or tech people. But the more I learn, the more I realize — it’s not about coding or complex algorithms. It’s about curiosity. If you can learn to ask good questions, you can use AI. I used to feel intimidated by all the “AI hype.” Now I see it as a tool for creativity, learning, and growth. My biggest lesson so far: you don’t have to be an expert to start — you just have to be curious enough to try. 🌱 #AIForEveryone #LearningJourney #CuriosityDriven #ContinuousLearning #GrowthMindset #LearningInPublic

" Vibecoding " — the word keeps popping up everywhere lately. Out of curiosity, I decided to look it up... and the realization hit hard. I’ve been using tools like Replit, Bolt, and Cursor all along — without realizing they’re part of the vibecoding ecosystem! These tools simplify the coding experience by blending AI assistance and intuitive workflows — helping us stay in flow while building or analyzing. As someone in the Data Science field, this really struck me. We often rely on tools and automations daily — from notebooks to AI code assistants — but rarely stop to think about how they shape our learning and creativity. This made me realize that understanding the “why” behind what we use is just as important as mastering the “how.” To explore the concept further, I built a simple Vibecoding Hub — a small project experimenting with the intersection of AI, creativity, and coding flow. 🔗 Check it out here: https://lnkd.in/emf7HkZJ Every day is a chance to learn, unlearn, and upgrade — and sometimes, the biggest lessons come from a simple realization. #Vibecoding #DataScience #AI #ContinuousLearning #Innovation #TechJourney #DeveloperTools #LearningEveryday