Almost Timely News, June 25, 2023: When Should You Use Generative AI?

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Almost Timely News: When Should You Use Generative AI? (2023-06-25)

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What’s On My Mind: When Should You Use Generative AI?

Today, let’s talk about when to use or not use generative AI to create content. There are three sets of factors we need to consider to make this decision.

First, is the effort AI-assisted or AI-led? This makes a difference – is the final product ultimately made by humans or machines?

Second, is the task at hand generative or comparative? Generative AI – both large language models that power tools like ChatGPT and image tools like Stable Diffusion – are better at one versus the other.

Third, is the content being created a commodity or is it premium?

These are the three tests. Let’s explore what each means.

AI-Assisted Versus AI-Led

This first test is fairly straightforward. AI-assisted content is when you ask an AI model to help you create, but you, the human, are ultimately the creator. Examples of AI-assisted content would be things like writing an outline, brainstorming, giving suggestions, asking advice, etc. AI is the helper, and you are the do-er.

AI-led content is content in which the machine’s output is a substantial part of the final product. Examples of AI-led content would be writing a detailed prompt that the machine creates a blog post for, or creating a series of images used in a slide deck, or writing a jingle that you use in a video. You are the supervisor and AI is the worker, but the final product is largely the worker’s product.

Why does this distinction matter? The main reason here is intellectual property. Laws vary from jurisdiction to jurisdiction; in the USA where I’m based, the US Copyright and Patent Office has ruled that AI-led content is ineligible for copyright. Copyright only applies to works created by humans – a precedent set in Naruto v Slater in 2018.. If the planned content is intended to be valuable – meaning you would enforce intellectual property rights if someone else copied it – then your work should be AI-assisted instead of AI-led.

Here’s a simple example, to disambiguate this. If you ask a tool like Google Bard or ChatGPT to write you an outline for a blog post about marketing, and then you write the blog post, the finished work is human-led. AI may have assisted with the ideas, but ideas are ineligible for copyright anyway. The final work is human-made, and thus can be copyrighted and protected.

If you give ChatGPT an outline and tell it to write the blog post, the finished work is AI-led – and that means it is ineligible for copyright. A competitor or some rando on the Internet could take the work in whole and copy paste it to their blog with no consequences because that work is not protected, at least under USA law.

So, that’s the first test.

Generative Versus Comparative

The second test is what kind of work you’re asking AI to do. In general, today’s generative AI tools are much better at comparative efforts than generative efforts. What does this mean? In my talk on AI, I outline 6 categories of tasks generative AI (specifically large language models, but some of it does apply to image and audio generation as well) are good at: summarization, extraction, rewriting, classification, question answering, and generation.

Under the hood, when you strip away all the fancy words and all the hype about AI, these models are nothing more than prediction engines. Yes, with extremely large datasets, they exhibit interesting emergent behaviors like some degree of mathematical reasoning and other tests of logic, but these behaviors are simply the results of very large probability computations. When you type a prompt into ChatGPT or Midjourney, you are fundamentally just asking the model to predict the next thing you want it to do – the next word in the sequence, the next pixel in the image.

Generative AI models, therefore, perform two fundamental types of operations, comparative and generative. Generative is when we ask for the next thing – the next word in a sentence, the image from a description, etc. Every time you ask one of these models to make something net new, you are doing generation. Comparative tasks are when we give the model a word and ask it to compare it to what it predicts, or to take a series of words, look them up in its probability tables, and then highlight only the most important probabilities. In image work, it’s when we ask a model to do inpainting, or to recolor something, or remove an object from the image.

Why do models do better with comparative efforts than generative efforts? Because there’s fundamentally less predicting. When you do comparisons, you’re providing most, if not all of the data. If I ask a model to summarize this issue of the newsletter, I’m providing all the materials, and all it has to do is score each word, compare it to its internal probabilities database, and return only a certain number of those probabilities. It doesn’t have to make anything up.

In human terms, this is the difference between writing and editing. Which is easier for you – to get out a red pen and light a document on fire with it, or to stare at the gaping abyss of a blank page and a blinking cursor? Many folks find editing easier, at least to get started, because there’s already something to work with, even if it’s not very good.

Machines are better at editing tasks – summarization, extraction, rewriting, and classification – than they are at generating. That’s just the way the models work. Prompts for editing – “summarize this article in 50 words or less” – can be much, much shorter than prompts for writing, because the machine doesn’t need to predict anything new. It just needs to compare what’s already there with what it knows.

So, that’s the second test. If you’re facing a task that’s editing, AI is usually a great choice. If you’re facing a task that’s creating, AI might still be a good choice, but it’s going to be more effort to get a good result from it – better prompts, more iterations, etc.

Commodity Versus Premium

The last test we have to ask is whether or not what we want to create is commodity content or premium content. Commodity content is content that isn’t particularly special. It should communicate what we want to communicate, but the value it provides isn’t in the way it’s crafted. Premium content is content that is special, that is valuable, that requires something like subject matter expertise or substantial skill to produce, and that premium has value.

Again, because machines are fundamentally just predicting off known probabilities, what they create is mathematically an average of what they’ve been trained on. As a result, they will always produce content that is inherently average. How good the content is depends on how specific the prompt is; the more specific and detailed your prompt, the more creative your work will be because it’s an average of a smaller amount of data.

So, what’s the difference between commodity content and premium content? Commodity content is exactly what it sounds like: content that’s a commodity, that’s common, that’s nothing special. Here’s an example:

Sailboats on the Charles River

Suppose I told you that this is a photo I took in my hotel room of a painting on the wall. Is that believable? Of course. Hotel rooms are filled with images like this sailboat, or this pitcher of flowers:

Pitcher of flowers

It’s tasteful, inoffensive art that may or may not move you, but it does the job of breaking up the vast emptiness of a hotel room wall.

Is it valuable? Is it impactful? Does it move you? If you saw this painting in your hotel room and you knew you wouldn’t get caught, would you steal it for your own home?

Probably not. It’s not terrible, but it’s not amazing.

And you wouldn’t know – or care – whether it was made by a person or a machine. To be clear, both of these are machine-generated – and you probably couldn’t tell the difference if I put them in a hotel room.

This is the essence of commodity content. It’s content that’s just okay. It’s content that doesn’t require a lot of care per se. We generate commodity content all day long, when we write emails to each other, when we post a memo in the office about not microwaving fish in the common area microwave, when we sit down and assemble our grocery list.

Premium content, on the other hand, is content that requires serious effort, serious thought, serious expertise. It’s content that we know has value, has significance, has meaning to us. It’s content that is uniquely ours and has to communicate very specific details in the way that only we can do.

I asked ChatGPT, using the GPT-4 model, to write up the same points of view that I’ve just written above. Here’s what it had to say about commodity versus premium content:

Lastly, but perhaps most importantly, is the value of the content. AI can generate volumes of content quickly, but quantity does not always equate to quality. For high-stakes content, such as keynote speeches, brand positioning statements, or crisis communication, the subtle nuances and deep understanding of human emotion that a skilled copywriter brings to the table are irreplaceable.

This is factually correct, but it lacks… well, me. It lacks my voice, the unique way I communicate, and presumably at least part of the reason you read this newsletter in the first place.

So this is the third test for when to use AI: the closer you are to premium content, the less you should use AI. Can it help you brainstorm or critique what you’ve created? Sure. Should you have it write for you? With anything that requires deep skill or knowledge, probably not, at least not with today’s models.

Apply the Three Tests

So, that’s the three questions I’d ask before using generative AI for any content task. How important is it that the result be copyrightable? How much of the task is comparative versus generative? And how premium is the resulting content?

Having AI craft diplomatic replies to random inbox pitches? Great use of AI. It’s totally a commodity task, copyright isn’t an issue, and even though it’s generative, quality doesn’t matter after a certain point. Once it’s factually correct, grammatically sound, and inoffensive, it’s good enough.

Having AI write your wedding vows? Maybe not.

Having AI paint the decor for your hotel rooms? It depends on how important that artwork is to the customer experience. If you’re like every other hotel I’ve stayed at, AI is probably the way to go. But if you want to use art as a differentiator for your customer experience, then probably not.

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See you next week,

Christopher S. Penn

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Christopher S. Penn is one of the world’s leading experts on AI in marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers: An Introduction and Primer. Christopher Penn is an AI keynote speaker around the world.


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