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ZhiZhi Gewu

Harari's Nexus: Information, Networks, and the AI Revolution

Harari's Nexus: Information, Networks, and the AI Revolution

Yuval Noah Harari's Nexus offers a compelling new lens for understanding human history and our current predicament: the analysis of information networks. Harari argues that understanding how information connects people and things, rather than focusing solely on technological advancements, is key. He emphasizes that information's power

Planning for Planning

Planning for Planning

As 2024 draws to a close, many companies are immersed in strategic planning for the coming year. I've recently participated in several such meetings and have found myself reflecting on the differences between corporate and individual planning. This reflection has led me to consider how we can apply

How I Built a Slack Anki Reminder Bot in Record Time (Thanks, ChatGPT!)

How I Built a Slack Anki Reminder Bot in Record Time (Thanks, ChatGPT!)

When I decided to create a Slack bot to remind me of my due Anki flashcards, I didn’t find the concept intimidating. The steps—accessing Anki’s data, posting to Slack, and setting up a cron job—were all within my capability. However, I’m not a seasoned software

The cost of building is the lowest it has ever been

The cost of building is the lowest it has ever been

AI tools have opened up a world of possibilities, enabling us to accomplish tasks more efficiently and creatively. Here are some of the exciting things I've tried recently with AI: 1. Create a Webpage with Just a Chatbox Using tools like Cursor, you can: * Build a webpage by

Bayes’ Rule Demystified: Five Intuitive Perspectives

Bayes’ Rule Demystified: Five Intuitive Perspectives

Example Consider the following scenario: * The probability that a person has a disease is 0.01. * The probability that a person with the disease tests positive is 0.90. * The probability that a person without the disease tests positive is 0.10. Question: What is the probability that a person

Beyond the Numbers: A Practitioner's Guide to Meaningful Data Analysis

Beyond the Numbers: A Practitioner's Guide to Meaningful Data Analysis

This article presents my personal perspective on approaching data analysis. While there are many methodologies and best practices in the field, the following framework represents my thoughts on providing a structured approach to guide your analytical process. These views are based on my experience and should be taken as one

Why Supervised Learning Works?

Why Supervised Learning Works?

In essence, "Low Training Error + Sufficient Data Relative to Model Complexity → Low Test Error". Supervised learning must work on the SAME distribution. Key Concepts * Training Error (\(\text{Train}_S(f)\)): The error of a model \(f\) evaluated on the training dataset \(S\). * Test Error (\(\text{Test}_D(f)