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AI Worm

Researchers have prototyped an AI-powered internet worm . The coolest thing about the prototype is that it carries its own LLM with it, and runs it on computers that have been broken into. This is the closest to John Brunner’s original 1975 conception of a computer worm that I’ve seen.

2026-06-05 原文 →
AI 资讯

How Excel is Used in Real-World Data Analysis

Introduction Excel is one of the most used tools for data analysis. It allows beginners like myself to easily clean, organize, analyze and visualize data.Excel enables users to work with large datasets and extract meaningful insights without requiring advanced technical skills. What is Excel Excel is a spreadsheet that allows you to collect, organize, analyze, calculate, and visualize data efficiently.Despite the emergence of other data analysis tools like SQL and Power BI, Excel remains one of the most widely used tools for both personal and professional data management.This can be credited to its ease of access, learning, and use. Ways Excel is used in real-world data analysis This week, I had the opportunity to explore how Excel is used in real-world data analysis.I discovered that Excel is not just a basic spreadsheet tool, but a powerful application that helps make sense of data and support decision-making. Data organization and cleaning Excel is used to structure raw data, remove duplicates, and fix errors. This improves data quality, making it easier to analyze and more reliable for decision-making.This improves data quality, making it easier to analyze and more reliable for decision-making. Financial Excel is commonly used in finance to create budgets, calculate profits and losses, and monitor expenses.It helps organizations keep accurate financial records and understand their financial situation. Business decision-making Businesses use Excel to track sales, compare performance over time, and identify trends.This helps managers understand what is working well and what needs improvement. Excel features and formulas In just a week, I have learned several Excel formulas that simplify data management and make working with data more efficient. SUM function The SUM function is used to add a range of values together in Excel, making it one of the most essential tools for quick calculations.It's used to automatically add a range of numerical values together, elimina

2026-06-05 原文 →
AI 资讯

Understanding Underfitting and Overfitting: An Introduction

Have you ever trained a model that performed beautifully on your training data but fell apart the moment it saw new data? Or perhaps you built something so simple it couldn't even learn the training data properly? These are the classic traps of overfitting and underfitting — and every machine learning practitioner runs into them. In this article, we'll cover what they are, how to detect them, how to fix them, and where the bias-variance tradeoff ties it all together — with real-world examples and code throughout. What is Model Fitting? Model fitting is the process of training a predictive model on a dataset to find the optimal parameters that best capture the underlying patterns in the data. The goal is simple: the model should generalize well to unseen data — not just memorize the training examples. There are three possible outcomes when fitting a model: Outcome Description Good fit Captures underlying patterns, generalizes well Underfitting Too simple, misses patterns even in training data Overfitting Too complex, memorizes noise, fails on new data What is Underfitting? Underfitting occurs when a model is too simple to capture the underlying patterns in the data. It performs poorly on both the training set and on new, unseen data. Think of it like this: imagine asking a child to predict house prices and they only use the rule "all houses cost $100,000." That model ignores all relevant features (size, location, age) and will be wrong almost every time. Why Does Underfitting Occur? Model is too simple : A linear model trying to fit a curved, nonlinear relationship Too few features : Important variables are left out Too much regularization : Penalizing complexity so heavily that the model can't learn anything meaningful Insufficient training : The model hasn't been trained long enough Real-World Example Suppose you're predicting whether an email is spam. If you only use the feature "email length" and ignore word content, sender, and links, your model will underfit —

2026-06-05 原文 →