8-Chems

Neuro-Symbolic Integration: Enhancing LLMs with Knowledge Graphs Abstract Large Language Models (LLMs) have revolutionized natural language processing, achieving remarkable success in tasks like text generation and question answering. However, their reasoning capabilities are constrained by hallucinations—generating plausible but factually incorrect outputs—and limited parametric memory, which hampers their ability to maintain context over long interactions or perform complex multi-step reasoning. This article synthesizes insights from 2024-2025 surveys on neuro-symbolic artificial intelligence, focusing on integrating LLMs with Knowledge Graphs (KGs) to enhance factual grounding, reasoning, and knowledge management in real-world applications. We explore methodologies for knowledge extraction, representation, reasoning, and dynamic updating, emphasizing bidirectional synergies where LLMs automate KG construction and KGs improve LLM reasoni...

Introducing Hugging Face: Your Gateway to Cutting-Edge Machine Learning Hugging Face has emerged as a powerhouse in the machine learning (ML) community, championing open-source solutions to democratize artificial intelligence. With a mission to advance AI through open science, Hugging Face offers a suite of powerful libraries and tools that simplify the development, training, and deployment of state-of-the-art ML models. Whether you're a researcher, developer, or enthusiast, Hugging Face provides accessible, high-quality resources to bring your ML projects to life. In this post, we’ll explore Hugging Face’s core ML libraries, their functionalities, how you can leverage them for your projects, and an example of using an advanced image-to-image model, along with links for further exploration. What is Hugging Face? Hugging Face is an open-source platform that provides tools, libraries, and a collaborative hub for building, sharing, and deploying...

Understanding and Using the Generalized Pareto Distribution (GPD) The Generalized Pareto Distribution (GPD) is a probability distribution used in Extreme Value Theory to model values that exceed a certain high threshold. It is widely used in finance, insurance, hydrology, and environmental science. 📘 What is the GPD? The GPD models the distribution of excess values over a threshold. That is, if we set a threshold u , the GPD models the distribution of X − u | X > u . 🔣 Probability Density Function (PDF) f(x) = (1 / σ) * (1 + ξ * x / σ)^(-1/ξ - 1) ξ : Shape parameter (controls the heaviness of the tail) σ : Scale parameter (spread) Support: x ≥ 0 if ξ ≥ 0 ; 0 ≤ x ≤ -σ/ξ if ξ < 0 🛠️ Fitting GPD to Synthetic Insurance Claims (Python Example) Let’s simulate a small dataset of insurance claims, set a threshold, and fit a Generalized Pareto Distribution using scipy . 🔢 Step 1: Simulate Data import numpy as np import matplotlib.py...

Risk Management for Data Scientists in Insurance and Finance Risk management is a cornerstone of the insurance and finance industries, where uncertainty shapes every decision. For data scientists, this domain offers a dynamic playground to apply statistical modeling, machine learning, and predictive analytics to mitigate uncertainties and optimize outcomes. This blog post provides a detailed, hands-on learning roadmap for aspiring risk analysts, enriched with practical examples, Python code snippets, and recommended libraries. ✨ Why Risk Management Matters In insurance and finance, decisions like issuing loans, underwriting policies, or managing investment portfolios hinge on balancing potential gains against losses. Effective risk management quantifies these uncertainties, enabling informed decisions, regulatory compliance, and stakeholder trust. Data scientists play a pivotal role by leveraging tools like Python’s pandas , scikit-learn ...