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Dear subscribers,

I believe that AI will dramatically lower the cost of creation in the next few years.

If you’re a creator, consider giving AI tools a try to learn how they can help you create better content. To guide you through this, I’ll expand on my AI art post to explore:

1. How does AI work anyway?

2. How is AI lowering the cost of creation?

3. How can creators get started with AI?

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How does AI work anyway?

I think every creator should have a basic understanding of how AI works. I’m far from an expert, but let’s start with a few definitions:

1. AI is when machines demonstrate intelligence at the human level and beyond.

2. Machine learning (ML) is how machines learn to be intelligent by identifying patterns in data. ML models have two artifacts:

   1. Label: The output that the machine wants to predict (e.g., video watch time).

   2. Features: Inputs to achieve the output (e.g., video views and likes).

At a high level, machine learning works in 3 steps:

1. Prepare data: Machines need lots of quality data to learn. For example, to convert text to images, a ML model needs to learn from millions of images with text labels. ML engineers typically spend 80% of their time manually cleaning the data in a process called feature engineering.

2. Train model: Next, ML engineers split the data into a training set and a test set. The machine uses the training set to build the model and then uses the test set to improve the model’s accuracy. The model’s algorithms can be:

   1. Simple like a linear regression (e.g., a person’s weight = 80 + 2 * height). The model will adjust the feature weights (e.g., change 2 to 1.5) through repeat iterations to more accurately predict the label (output).

   2. Complex like a neural network. The model will not only assign weights to features but also create new features automatically. Most models based on images or natural language use neural networks.

3. Build user experience: After training the model, the team needs to build a UX where people can supply inputs to get their desired output. How the model works is a black box even to ML engineers, so the user experience needs to be clear, believable, and actionable.

Ok, that’s a mouthful. If you remember one thing, it’s this:

Machines need large quality data sets to learn. They get better by identifying patterns and predicting outcomes through repeat iterations.

If you want to learn more, I highly recommend Google’s ML crash course. You don’t need to know how to code to understand the basics.

How is AI lowering the cost of creation?

Many creative pursuits have large quality data sets for AI to learn from:

Writing

OpenAI’s GPT-3 is trained off 1 trillion words from Common Crawl (a non-profit that scrapes billions of webpages monthly), books, and Wikipedia. Since the average book has 100K words, GPT-3 has essentially read ten million books.

It’s no wonder that GPT-3 can easily generate essays from prompts like:

“Write a scary essay about how my dog ate my homework.”

Peter Yang @petergyang

An AI essay about how my dog ate my homework

3:00 PM ∙ Sep 24, 2022

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12,639Likes1,212Retweets

Art and Digital Media

Stable Diffusion is trained off 5 billion image-text pairs (images that have HTML alt-text attributes). Unlike other AI models, Stable Diffusion was open sourced to the public. Since then, there has been a Cambrian explosion of innovation built on top of the model. It has been used for:

1. Text to video (Meta also announced their closed version recently)

2. Interior design

3. CG avatars

4. Replacing the background in film clips

5. Apparel and shoe design

This pace of innovation shows no signs of slowing down.

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Scott Lighthiser @LighthiserScott

@StableDiffusion Img2Img x #ebsynth x @koe_recast TEST

#stablediffusion #AIart