Learn AI Image Generation Terms with Claude and ChatGPT

image generated with AI

Last month, I asked ChatGPT to “Explain the top 10 terms in LLMs to a non technical audience with funny examples.”

Since my children have really enjoyed using generative AI for images, I thought I’d try it with Claude and ChatGPT to “Explain the top 10 terms in AI for image generation to a non technical audience with funny examples.”

I hope you enjoy it as much as I enjoyed the LLM terms explained by ChatGPT.

Here’s an explanation of the top 10 terms in AI for image generation, tailored for a non-technical audience with funny examples:

Diffusion Models: Imagine a bunch of tiny, confused artists randomly throwing paint at a canvas. Over time, they start to communicate and work together, refining the image until it looks like something recognizable, like a cat wearing a top hat.

Latent Space: It’s like a secret underground club where all the cool AI models hang out and share their abstract ideas. They don’t invite us humans because we wouldn’t understand their quirky sense of humor.

Generative Adversarial Networks (GANs): Picture two AI models engaged in an intense art competition. One tries to create realistic images, while the other plays the role of a grumpy art critic, constantly judging and forcing the first model to improve its skills.

Style Transfer: It’s like giving your pet rock a makeover by applying the style of a famous painting. Suddenly, your dull gray rock looks like it belongs in a Van Gogh masterpiece.

Convolutional Neural Networks (CNNs): These networks are like a group of nosy neighbors peeking through different windows to gather information about an image. They gossip and share their findings to understand the bigger picture.

Autoencoders: Imagine a machine that loves to play “telephone.” It takes an image, whispers its essence to itself, and then tries to recreate the original image from the whispered message. The results can be hilarious and sometimes eerily accurate.

Variational Autoencoders (VAEs): These models are like abstract artists who create their own interpretations of reality. They learn to compress images into a secret language and then use that language to generate new, dream-like versions of the original.

Transformer Models: Picture a group of robots playing a game of “pass the message.” Each robot adds its own unique twist to the image before passing it along, resulting in a wild and unpredictable final creation.

Upscaling: It’s like using a magic magnifying glass that not only makes an image bigger but also fills in the missing details. Suddenly, your pixelated stick figure drawing turns into a high-resolution masterpiece.

Inpainting: Imagine you have a family photo with your annoying cousin in it. Inpainting is like having a skilled artist magically remove your cousin from the picture and seamlessly fill in the background, as if they were never there.

These AI image generation terms might seem complex, but they all work together in amusing and creative ways to produce stunning and sometimes hilarious visual creations.

I hope this helps to explain some of the the technical terms used in generative AI for images!

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

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