How to Build a Knowledge Graph in the Age of LLMs
Author(s): Michael Shapiro MD MSc Originally published on Towards AI. How to Build a Knowledge Graph in the Age of LLMs In recent years, LLMs have transformed the way we do almost everything. Knowledge graphs(KGs) have been there since the digital revolution …
Beyond Associations: Reinforcement Learning for Sequential Market Basket Decisions
Author(s): Shenggang Li Originally published on Towards AI. Clustered contextual bandits and tabular Q-learning with off-policy evaluation on real-world retail logs Traditional market basket analysis (MBA) explains what tends to co-occur, but it does not decide what to do next. This paper …
LLMs Don’t Need Search Engines: They Can Search Their Own Brains
Author(s): MKWriteshere Originally published on Towards AI. SSRL Framework Proves AI Models Already Contain the Knowledge They Keep Looking Up We’ve been training AI to ask Google for answers when we should have been teaching it to remember what it already knows. …
Remember That Free Google CLI That Killed Cursor? It Just Got 10x Better
Author(s): Poojan Vig Originally published on Towards AI. Remember That Free Google CLI That Killed Cursor? It Just Got 10x Better The thousands of developers ditching Cursor and Windsurf overnight. Two months ago, I wrote about how Google’s free Gemini CLI was …
Human in the loop AI Workflows using Langgraph
Author(s): Aayushi_Sharma Originally published on Towards AI. 🔥What if you could pause an autonomous AI agent mid-task and steer it in the right direction — live? As AI agents become more powerful and autonomous, the need for human-in-the-loop (HITL) systems has never …
“Unlock the power of Principal Component Analysis (PCA) with this step-by-step guide. Explore dimensionality reduction and data insights with clarity and ease.”
Author(s): Ajay Kumar mahto Originally published on Towards AI. A Step-by-Step Journey Through Dimensionality Reduction and Data Exploration In simple terms, PCA (Principal Component Analysis) is a technique used to simplify and understand complex data. It takes a dataset with many variables …
Fine-Tuning LLMs: From Zero to Hero with Python & Ollama 🚀
Author(s): MahendraMedapati Originally published on Towards AI. Ever wondered how to make AI models actually useful for YOUR specific needs? Let me show you how I went from confused beginner to fine-tuning wizard in just one weekend! Picture this: You’re trying to …
Bank Wealth Planning — Dynamic AI “Broker Guider” Platform
Author(s): Shenggang Li Originally published on Towards AI. Real-time, constraint-aware portfolio rebalancing for advisors and clients This platform adjusts portfolio weights to each investor’s goals while honoring risk, liquidity, turnover, and tax rules. It ingests client profiles and live market data, then …
I Ran OpenAI’s New Open Model on My Laptop to Extract Medical Data — Here’s What Happened
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. Testing privacy-first healthcare AI with OpenAI’s first open-weight models OpenAI just released its first family of open-weight models, and I couldn’t resist testing them on one of healthcare’s trickiest problems: extracting structured data …
How to Build a Knowledge Graph in the Age of LLMs
Author(s): Michael Shapiro MD MSc Originally published on Towards AI. How to Build a Knowledge Graph in the Age of LLMs In recent years, LLMs have transformed the way we do almost everything. Knowledge graphs(KGs) have been there since the digital revolution …
Hybrid Time-Series Forecasting with LangGraph, Prophet & Large Language Models (LLMs)
Author(s): Vikram Bhat Originally published on Towards AI. Build a Local, Explainable Pipeline with Parallel Models, Automated Evaluation (RMSE/MAE/SMAPE), and Agent Explanations. Forecasting demand has always been a tricky mix of art and science. On one hand, classical statistical models like Prophet …
How to Perform Effective Data Cleaning for Machine Learning
Author(s): Eivind Kjosbakken Originally published on Towards AI. Learn how you can improve your machine learning models using effective data cleaning Data cleaning is arguably the most important step you can perform in your machine-learning pipeline. Without data, your model algorithm improvements …
The Hidden Potential of RAG Pipelines in Augmented Analytics for Non-Experts
Author(s): Daksh Trehan Originally published on Towards AI. Bridging the Gap: How RAG Enables Scalable Insights Without Deep Technical Expertise in 2025 Picture this: A business analyst sifting through terabytes of IoT-generated data, expected to deliver insights on device performance trends. With …
Microsoft Just Solved AI’s Biggest Problem: Why Magentic-UI Changes Everything
Author(s): MKWriteshere Originally published on Towards AI. How human-AI collaboration beats pure automation every time with 71% better results Your AI assistant decides your quarterly budget spreadsheet needs “optimization” and helpfully converts all your financial data into interpretive haikus. Image Generated by …
Important LLM Papers for the Week From 28/07 to 02/08
Author(s): Youssef Hosni Originally published on Towards AI. Stay Updated with Recent Large Language Models Research Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers must stay informed about the latest …
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