Why Your Brilliant AI Agent Might Be Your Biggest Risk (And How to Fix That)
Author(s): MahendraMedapati Originally published on Towards AI. Why Your Brilliant AI Agent Might Be Your Biggest Risk (And How to Fix That) Picture this: You’ve just deployed a shiny new AI agent to handle customer orders. It’s fast, it’s smart, and for …
Visualizing Risk: A Latent World Model for Financial Crisis Hedging
Author(s): Chase Metoyer Originally published on Towards AI. Introduction Financial markets have traditionally been understood through parametric models and stochastic calculus. From Black-Scholes to Heston, quantitative finance relies on mathematical frameworks that treat volatility either as a scalar parameter or as an …
![Why Recommendation Systems Are Structurally Different from Deep Learning[1/2]](https://miro.medium.com/v2/resize:fit:653/1*uMKlK0Fp87Hh1jtBdlKzQA.png "Why Recommendation Systems Are Structurally Different from Deep Learning[1/2]")
Why Recommendation Systems Are Structurally Different from Deep Learning[1/2]
Author(s): NP_123 Originally published on Towards AI. Why One-Hot Features and Naïve MLPs Fail This article is Part 1 of a two-part series on why recommendation systems require fundamentally different modeling assumptions from standard deep learning. 📌 TL;DR Recommendation systems are not …
AI’s Cold War: The Infrastructure Race from Greenland to Orbit
Author(s): Eray Alguzey Originally published on Towards AI. The Hidden Energy Bill of Artificial Intelligence In a hyperscale data center in rural Virginia, forty cents of every dollar spent goes to a single task: keeping the machines from melting. This isn’t a …
Deploying a TensorFlow Model with TensorFlow Serving and Docker
Author(s): Samith Chimminiyan Originally published on Towards AI. TensorFlow Serving is a powerful tool for deploying machine learning models in a production environment. It allows for easy scaling and management of models, as well as the ability to serve multiple models at …
Writing Tools for Your Agents: A Complete Guide
Author(s): Yashod Perera Originally published on Towards AI. This is the era of Agentic AI, where everyone is writing their agents with tools. But are we writing tools correctly? What is a tool? What are the best practices? If you are having …
Building a Self-Updating Knowledge Graph From Meeting Notes With LLM Extraction and Neo4j
Author(s): Cocoindex Originally published on Towards AI. Transform unstructured meeting notes into a queryable knowledge graph with incremental updates — no full reprocessing required. Meeting notes are goldmines of organizational intelligence. They capture decisions, action items, participant information, and the relationships between …
Why Most RAG Projects Fail in Production (and How to Build One That Doesn’t)
Author(s): Bran Kop, Engineer @Conformal, Founder of aiHQ Originally published on Towards AI. Enterprise AI discussions often begin with a diagram that looks almost perfect in its simplicity: Documents → Vector Database → LLM → Answers It is not wrong. It is …
The Builder’s Notes: No-Show Rate Costs Practices $150K/Year — Here’s the Automation That Pays Back in 2 Months
Author(s): Piyoosh Rai Originally published on Towards AI. Manual reminder calls: 260 hours of staff time quarterly, 38% failure rate, $6,500 cost. Automated SMS reminders: 3 seconds per patient, 98% delivery rate, $0.02 cost. The family practice in this case study recovered …
Synthetic Data That Behaves: A Practical Guide to Generating Realistic Healthcare-Like Data Without Violating Privacy
Author(s): Abhishek Yadav Originally published on Towards AI. A hands-on guide to building synthetic data that looks, feels, and behaves like the real world without privacy risk Photo by Luke Chesser on Unsplash Healthcare organizations sit on treasure chests of data be …
Recent Posts
