Almost Timely News: What ChatGPT is Really Good At, Measurement Strategies for Agencies Course (2023-01-22)
Almost Timely News: What ChatGPT is Really Good At, Measurement Strategies for Agencies Course (2023-01-22) ::?View in Browser
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I’m mildly excited to announce that we’ve got a new mini-course, and this one’s free. It’s called Measurement Strategies for Agencies. You’ll learn the 5 things agencies do most wrong when it comes to developing effective measurement strategies for clients - and how to fix it. It’s just about an hour long, it’s free, and it’s for two groups of people:
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What’s On My Mind: AI Creation Vs. Refinement
Let’s take a minute to talk about originality, AI, and content marketing. A lot of folks, myself included, have had a lot to say about generative AI, about how AI is ushering in a new age of generated content. Machines that write almost as well as we do on average, machines that can crank out incredible artwork.
Yet, that’s not what these machines excel at.
When it comes to the use of artificial intelligence, in particular, large language models like GPT-3 and the ChatGPT interface, what these models are good at is transforming inputs. Large language models, in general, are nothing more than massive statistical probability matrices. There was a great quote from the “This Week in Machine Learning and AI” podcast that goes something like “a word is told by the company it keeps.”
This means that these tools and models understand language only to the extent of the statistical distributions of the words, phrases, sentences, and paragraphs that they appear in. That’s why they can replicate grammar very well because grammar is nothing more than statistical distributions of words. They’re autocomplete on steroids.
For example, what’s the next word in these sentences?
“For all you do, this Bud’s for _____”
“That’s not a knife, this is a _____”
“God save the _____”
We know what these various sentences are because in their respective cultures, they’re so frequently used that we are accustomed to the word distributions, though the last one is now changing once again because the people it’s about have changed.
What this means is that from a generation capacity, these tools can generate text very capably, but that generation is going to be mathematically an average of the text that most commonly surrounds those keywords. That’s why your prompts to generate new stuff have to be so incredibly detailed, so that the tools can understand the increased sense of probabilities for the words you’re asking them to generate.
Telling a large language model to write a blog post about social media marketing is going to generate extremely bland, average content. Telling it to generate social media content about the engagement rates on TikTok with regard to time of day and gender is going to give you more specific content because the large language model itself can understand based on the additional words you’ve provided, more of the context. It is drawing from additional statistical probabilities from those words - a word is known by the company it keeps.
However, what these tools produce is still a statistical average of what they’ve been trained on. They’re not going to produce anything original because they can’t by definition. Certainly, they’ll produce original orderings of words to some degree, but they can’t produce new concepts that aren’t in the original model. That’s why it’s such a big deal when new versions of models - bigger models especially - get released, because the models have more original ideas in them to work with.
So some marketers are going to create an avalanche of average, a swamp of sameness as they dramatically accelerate the quantity of their content production but not the quality. Their use of AI will be to scale quantity in the hopes that wins them the game, or at the very least frees up their time to do other things. And for some companies, that will be a win, and that’s okay. If your company blog is atrocious now, a completely machine-generated blog of mediocrity will be a GIANT upgrade for your company.
But what if you don’t want average? What if you aspire to more than mediocrity? What role do these tools play? Here’s the part everyone is overlooking: these tools are better at refining than creating, and that’s the secret we need to understand to unlock their power.
Because these models - their technical name really is transformers - are adept taking in inputs and transforming them into outputs, they are actually BETTER at refining text than they are creating it. About a third of this article, the first third, was written with the help of ChatGPT. But it’s not what you think - it took my words and just cleaned them up. Here’s how - I did a voice recording while I was waiting to pick my kid up from art class, and fed it to?Otter:
then took that transcript and fed it to ChatGPT:
Is what you’re reading my words? Yes. It’s my words, but changed from one medium to another and cleaned up. My words were transformed by the GPT model - which stands for generative pretrained transformer - into text that’s almost exactly what I said, minus some things that weren’t helpful.
This is what these tools excel at - taking data and transforming it, rearranging it, making it more useful. This preserves our originality, our ideas, our language, while improving the quality - and that’s what they’re best at. Because they’re not relying on a gigantic average of all the content they’ve ingested, because they’re using our own words and just cleaning up or rephrasing, they perform great AND keep the spirit of what we’re trying to say.?There was a great story on Buzzfeed about an AI app made for a contractor who is dyslexic, helping refine the inputs into better quality outputs.
We can even use multiple, different voices to create something useful from original inputs. My martial arts teacher, Mark Davis, has said it’s challenging sometimes to create social media copy that resonates with audiences. What better way to create ads than to use the voice of the customer itself? I wrote this prompt for GPT-3, using real customer reviews from the school’s Google Business profile:
What will happen? The large language model will digest not just my directions, but also the language of what customers had to say in their reviews of the school, then generate social media copy based on that. It’ll preserve the main ideas, the original ideas it was provided rather than resort to dipping into the pool of average commentary about martial arts schools.
And what was the outcome?
Those are some good suggestions to get started with social media content. They’re clear, they’re specific, and they’re appealing.
This is the power of transformer-based large language models. You can have them create something average from scratch, or provide them with the raw materials and they’ll create refined products - but keep your originality and spark in the final product.
Now you know the secret!
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See you next week,
Christopher S. Penn
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1 年That “alternative” use case is genius. (Also +1 for your crystal clear audio while walking during You Ask I Answer.)