What the new wave of machine learning libraries means for SEO, marketing

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What the new wave of machine learning libraries means for SEO, marketing

When many of us think of algorithms and machine learning models, we think of Google.

And really, who can blame us? We’re marketers, and many of us SEOs. We can’t help ourselves.

But there is a lot going on in and out of the Googleplex right now, and it’s becoming increasingly important that we keep up.

In this article, we’ll dive into some new and exciting technologies. We’ll cover some of the current uses where applicable, then move on to discuss where I see the technology going in the near-to-mid future and how it’ll impact marketers.

So let’s dive in – starting with arguably my favorite “new release.”

1. Stable Diffusion

Stable Diffusion is a text-to-image model built by Stability AI. In essence, with it, you can generate some amazing images from text prompts.

The model is open source and public, meaning you can get your hands on it easily (on GitHub) and build a variety of tools or applications to suit your needs. 

Here are a couple of examples of it in action.

This shoe doesn’t exist.

And Johnny Depp never did a photo shoot like this, nor did anyone put in the painstaking effort to create this in PhotoShop. In fact, it only took me a few minutes of prompt engineering to create.

Prompt engineering is basically just playing around with different words, word orders and syntax to generate the type of image you want.

For those interested, you can play with Stable Diffusion yourself here. I should note, you do need to authenticate yourself, either by creating an account or with Google or Discord, but it’s well worth it. 😊

If you’d like to see Stable Diffusion running with the code (but without having to write the code), I’ve created a Colab here.

Stable Diffusion is already being used to create images for ads (I know… I’ve used it myself) and websites (did you like the featured image for this article?) so the current use case makes itself.

There’s already a PhotoShop plugin you can download from here to integrate Stable Diffusion images directly into your work more easily.

The obvious questions

This brings up some obvious questions such as who owns the rights to the work? It turns out, you can’t copyright your images because they’re not really yours and immediately become public domain.

How about the issue with me having created an image of a person without their consent? What if I had them holding a product? Or worse, if I can’t own the copyright as it’s not mine, how responsible am I for other images that might be produced?

I’m not going to head down the ethical rabbit hole with you here, but there is a lot to consider.

Thinking purely as a marketer, if you build your ad campaign on Stable Diffusion-generated images, they can be taken and reused by your competitors and there’s nothing you can do about it.

Down the road

Last spring, we saw the rise of text-to-image models with DALL-E Mini (now Craiyon). You can play around with that model here.

Stable Diffusion is a leap forward. Assuming things continue to progress along the same line in the months and years ahead, I predict that we’ll evolve quickly into video generation from text, including the creation of video tutorials from text instructions.

Additionally, I imagine we’ll soon see automated WordPress plugins that create images for the site based on the surrounding content.

But more interesting perhaps are some commercial opportunities Sergey Galkin captures brilliantly in this video tweet:

What if we imagined what shopping could be like with the technology we already have today?

• Images generated by #AI from text
• #AR clothes fitting
• Voice assistant

In seconds we automatically create a customized product for the customer. What do you think? pic.twitter.com/Evv1uV8yZD

— Sergey Galkin (@sergeyglkn) September 19, 2022

It’s worth noting that OpenAI has also produced DALL-E 2 which is arguably superior in quality, but it’s not open source and thus less versatile and more expensive.

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This content was originally published here.