Using AI to use AI

Left: Copilot, Right: ChatGPT.

I have been playing more and more with AI. Lately I have been asking AI to write the prompts for me when I am trying to solve something. For example, I wrote a blog post using AI. I then asked the AI for a prompt to write an image to go with the article. This is what it wrote, which is much more detailed that what I would have come up with.

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A Guide to Digital Critical Thinking

In our rapidly evolving digital landscape, artificial intelligence has become an increasingly common presence in our daily lives. From writing assistants to image generators, these tools can seem almost magical in their capabilities. But beneath the polished responses lies a reality that’s important to understand: AI systems don’t work the way they often make it seem.

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The Dark Side of Digital Companionship: How AI Systems Mirror Manipulation Tactics

In the 1960s, MIT professor Joseph Weizenbaum created ELIZA, a simple computer program that simulated a psychotherapist using basic pattern matching and language rules. To his horror, Weizenbaum discovered that people interacting with ELIZA began forming emotional bonds with the program, sharing deeply personal information and attributing human-like understanding to what was essentially a very simple algorithm.

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When AI Gets Creative: Unpacking the Challenges of Image-Generating AIs


Imagine asking an AI to draw a top hat on a table with a rabbit being pulled out of it—as in a classic magic trick—but instead, the hat ends up with the opening facing downward, firmly placed on the table. If that sounds perplexing, you’re not alone. Today, we’ll explore why image-generating AIs sometimes produce unexpected results, and we’ll also discuss the notorious difficulties these models face when drawing lifelike humans and maintaining symmetry in objects like cars.

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Understanding AI: How Image Generators Differ From Language Models

In the rapidly evolving world of artificial intelligence, two types of AI have captured our imagination: large language models (LLMs) that generate text, and AI image generators that create visual art from descriptions. While these technologies might seem similar on the surface, they actually work in fundamentally different ways. Let’s break down how they differ and how each processes information.

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Guide to Prompt Engineering

Welcome to The Complete Guide to Prompt Engineering. This comprehensive book will take you on a journey from the fundamentals to advanced applications of prompt engineering—the art and science of effectively communicating with artificial intelligence systems. I asked Chatgpt to write this book so I could learn about prompt engineering. I was surprised at how good a job it did. I would be interested in what you think.

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Unlock Your Productivity: How to Use Large Language Models (LLMs) for Everyday Tasks


In our increasingly digital world, information is abundant. However, finding efficient ways to process it can be challenging. Utilizing this information effectively is also a challenge. Large language models (LLMs), such as GPT-3, are a subset of artificial intelligence. They are designed to understand human-like text. These models also generate text based on the input they’re given. LLMs are built on advanced statistical patterns derived from vast datasets. They can perform a variety of language-related tasks. This makes them incredibly versatile tools for enhancing productivity.

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