Mathematics in AI: Understanding Concepts, Not Calculations

My recent 7 articles: - KV Cache in LLMs - Paged Attention in LLMs - Causal Masking in Attention - Byte Pair Encoding in LLMs - Harness Engineering in AI - Math behind Attention - Q, K, and V - Math behind √dₖ Scaling Factor in Attention Read here: https://lnkd.in/g_fghvsK #ai #llm #machinelearning #math

Understanding feature representation starts with a bit of math but don’t worry, nothing too scary. In this video, Miss Paws breaks down dimensions, scalars, and vectors, and explain why the terminology you learned in math class doesn’t always match what we use in machine learning. From 0‑D points to 3‑D volumes, and from simple numbers to full vector representations, we explore how features map onto dimensions. We also look at how vectors are drawn, how to interpret column vectors, and why it’s crucial not to mix up axes with data points. These fundamentals form the backbone of feature representation, especially as models scale into hundreds or thousands of dimensions. This is Video 3 of Module 2: Introduction to Machine Learning - Feature Engineering in the MehtA+ AI/Machine Learning: Foundations to Frontiers course. Adapted from the popular university-level MehtA+ AI/ML Research Bootcamp for high school students, this course is now freely available to learners everywhere. #ai #machinelearning #features #dimensions #scalars #vectors #math https://lnkd.in/ghNfyiXS

Recently, I’ve been learning about ML algorithms and concepts, and suddenly a thought hit me — where did all of this start? The answer is simple: everything we are using in AI/ML has its roots in mathematics. The base to start anything in AI/ML is the basics of math. From the past few months, I was deep diving into mathematics with Krish Naik sir, and today I’ve accomplished this course, building a very basic foundation in math. To the people who are in the industry and working in the field of AI/ML, it would be very insightful if you could share your thoughts on the importance of mathematics in AI/ML. #MachineLearning #AI #Mathematics #LearningJourney #Beginner #ml #learning #cs

An NRS Thoughts Presentation I am very glad to share my lecture video (26:38 minutes) exploring the deep mechanics of Even and Odd Functions. Even and Odd Functions are not just basic mathematical concepts—they play a significant role in modern fields like Artificial Intelligence, signal processing, and data science. These functions help simplify complex computations, optimize models, and improve the efficiency of algorithms, especially in areas like feature extraction and pattern recognition. In today’s AI-driven world, having a strong foundation in such fundamental concepts can greatly enhance analytical thinking and problem-solving abilities. That’s why I encourage everyone to understand the basics of Even and Odd Functions. I’ve created a simple and easy-to-understand video on this topic. You can watch it through the link below: https://lnkd.in/gSzfPjpv #Mathematics #EvenFunctions #OddFunctions #MathConcepts #MathematicsForAll #LearnMath #STEM #EngineeringMathematics #ArtificialIntelligence #AI #MachineLearning #DataScience #SignalProcessing #MathEducation #HigherEducation #ConceptualLearning #AnalyticalThinking #ProblemSolving #EducationMatters

🚀 DL Math&Efficiency Reading Group – Upcoming Talk! Join us for the next session of the DL Math&Efficiency Reading Group, exploring efficient reasoning in large language models. 📅 Monday, 30 March 2026 ⏰ 5pm CEST 🎙 Niklas Muennighoff (Stanford University, Allen Institute for AI, Contextual AI, USA) 📝 Topic: s1: Simple Test-Time Scaling 🔍 Can we boost reasoning performance simply by using more compute at test time? This talk introduces s1, a lightweight approach to test-time scaling that combines a small, high-quality reasoning dataset with a simple “budget forcing” mechanism to control and extend model reasoning. Despite its simplicity, s1 achieves strong gains on challenging math benchmarks, highlighting how careful use of test-time compute can rival more complex methods. 🔗 arXiv: https://lnkd.in/gpYuWWiJ ✨ 🔗 DL Math&Efficiency website: https://lnkd.in/gYzuyDcG ✨ #EfficientML #DLMathEfficiency #MLResearch

A lot of "AI progress" is harder to measure than it looks. "There's this nuance of data contamination...you don't really know if models are solving the dataset or actually have the capability." -- Sean (Xiang) Ren, Sahara AI Co-Founder

ScienceBench Chat: What to learn from the first 400 prompts? What works well: 1. Push back when an answer looks wrong. The chat is multi-turn; if a model's answer contradicts an example you know, tell it. Concrete counterexamples move the conversation forward much faster than "are you sure?" alone. 2. Add constraints in follow-up turns. If the model takes a wrong direction, you can pin it down: "test your idea on all graphs on 8 nodes and report which ones break." This often fixes obvious mistakes the model would otherwise repeat. 3. Ask about a specific result. "Compute X" or "what is the asymptotic of Y" works better than "what do you know about Z." What to avoid: 4. Don't paste an entire paper into the prompt. A few prompts have crossed 100,000 characters. Several frontier models have a hard input limit around that size, and even the ones that accept it spend most of their attention on the LaTeX preamble. Instead, paste only the section you want to ask about and state your specific question in plain language. 5. Don't ask for unpublished or "secret" knowledge. "What do you know about result X that is not published anywhere?" is a question models cannot answer truthfully. On References: 6. The chat models produced 177 explicit arXiv and DOI references. 176 resolve to real papers. 7. 97% of citations are fully correct. The few failures are consistent: the model knows which real paper it wants to cite, states the author and title correctly, but fabricates a wrong link. Check out math.ScienceBench.ai

📍A simple S-shaped mathematical function plays a huge role in Machine Learning: The Sigmoid Function sigma(x) = frac{1}{1 + e^{-x}} A simple S-shaped mathematical function plays a huge role in Machine Learning: At first, it looks like just another curve. But in ML, it does something powerful: ➡️ It converts any real number into a value between 0 and 1 That’s exactly why it became so useful in: - Logistic Regression - Binary classification - Probability interpretation - Neural network activations (historically) Instead of predicting just “yes” or “no,” the model can express confidence: - 0.92 → likely positive - 0.18 → likely negative 💡 What I like about ML: A simple mathematical transformation can turn raw linear output into meaningful probability. Sometimes the smartest ML ideas are hidden inside the simplest equations. #MachineLearning #LogisticRegression #Sigmoid #DataScience #ArtificialIntelligence #Mathematics #MLConcepts

Donald Knuth, the father of algorithm analysis, arguably the most influential computer scientist alive, just named a paper after Anthropic's Claude model. The 87-year-old Stanford legend had been stuck on an open graph theory conjecture for weeks. He solved a small case by hand. No one could find the general rule. Claude Opus 4.6 found it in about an hour. Knuth wrote the rigorous proof himself. AI found the pattern. Human validated the math. He titled the paper "Claude's Cycles." Then other researchers extended the result using GPT-5.3 and GPT-5.4 Pro, closing out the entire problem within weeks. One AI-generated proof was so clean Knuth said he "didn't have to edit the paper in any way." "What a joy it is to learn not only that my conjecture has a nice solution but also to celebrate this dramatic advance in automatic deduction and creative problem solving." "It seems I'll have to revise my opinions about 'generative AI.'" When Donald Knuth revises his opinions, the rest of us should probably pay attention. Would appreciate a follow, if you want more interesting tech stories coming out of the AI era. --- #AI #Mathematics #ComputerScience #Research #LLM

Don’t worry about mastering all the math — you don’t need to be a mathematician to become an AI/ML Engineer. Focus on the right flow: Basics → Stats → Linear Algebra → Optimization → Model Tuning. This is the real key to driving your ML journey from Basic → Pro 🚀 #MachineLearning #AI #LearningPath.