How Can Generative AI Make New Things?

Session 2 of 3

Tuesday Session 1: How Can Generative AI Make New Things?

Thursday Session 3: When Should We Trust AI?

Jon Ippolito

Professor of New Media
Director, Digital Curation graduate program
The University of Maine
LearnWithAI.org

🐦/🧵 @jonippolito 🐘 @jonippolito@digipres.club 🦋 @jonippolito.bsky.social 🔗 https://www.linkedin.com/in/jonippolito

Today's menu

Creativity through averaging

Connect Random Things exercise

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Learning about LLMs through image generators

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Showcasing output variability
Demonstrating AI's probabilistic character
Illustrating AI's dependence on model
Calling attention to AI fabrications
Recognizing beauty stereotypes
The benefit of seeing "averaged" imagery
The challenge of visualizing "averaged" language

Generative AI creates by averaging

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Angelo Ippolito, Berkeley #37, collage
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George Legrady, AI-generated collages

What's behind the magic?

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How image generators depend on word associations

How image generators create hybrid images

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Associating images with words

Uploading images
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Viewing images
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Generating hybrid images

If generative AI creates by "averaging," can we just average the relevant images in our dataset to create new hybrids?

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AI can't generate hybrid images just by averaging pixels ☹️

A better approach to generating hybrid images

The solution turns out to be related to the problem of removing noise from a photo 🤔

The reason is that the best de-noising algorithms work by segmenting the image into shapes.

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Finding noise means finding shapes

A de-noising algorithm can find shapes in noise 💪

💡 So instead of averaging the original photos, we could

  1. Add noise to both images
  2. Average the noisy results
  3. De-noise to find new shapes
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The transporter analogy

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An infamous Star Trek episode about a transporter malfunction offers an analogy:

Creating a hybrid of two people is easier while their molecules are scrambled than in their original form.

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Results of averaging noisy images

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Back to title screen

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