How Thankful Should We Be for Generative AI?

 I thought I'd like to generate a picture of Jane Austen in context of an American Thanksgiving celebration for a holiday greeting post on the social media accounts I have associated with my Totally Jane Austen brand. I opted to use Google Gemini. 

Here's how it went: 

First prompt: Can you generate an image of Jane Austen celebrating an American Thanksgiving

Here's what it delivered with this message: 

"Here is an image of Jane Austen celebrating American Thanksgiving."

Second prompt included the portrait of Jane Austen that I referenced: Let's have fewer people, no signs, and a woman who is dressed like Jane Austen is in her portrait 

Second iteration came back with this: "Here is a Thanksgiving celebration with fewer people, no signs, and a woman dressed like Jane Austen in her portrait."

I attached the Jane Austen portrait again and wrote: "Nope you didn't do it. That's almost the same picture. Try doing it with Jane Austen herself looking like this."

Gemini then delivered this with the note, "My apologies, I misunderstood the previous request. Here is an image of Jane Austen herself, looking like in her portrait, celebrating Thanksgiving with fewer people and no signs." 

My response was to again attach the portrait with this prompt:  "That's the same picture again. Delete that and just put this picture of Jane Austen in a context of a table with turkey, etc."

Gemini then offered this monstrosity with this phrase "You are absolutely right, I apologize for the repeated image. I will generate a new image for you, placing Jane Austen from the provided portrait into a Thanksgiving setting. "

I then wrote:  "She's out of proportion to the people. Just take them out and get the proportions right."

Gemini responded with "My apologies for the scale issue. Let me correct that for you. Here is Jane Austen, in proper proportion, at a Thanksgiving table by herself." and delivered this: 

I wasn't thrilled with the results for several reasons but was tired of the back-and-forth, so I said, "It'll have to do."

Gemini's response: "I understand. I apologize that the image generations were not meeting your expectations and appreciate your patience with the process.

Is there anything else I can help you generate or create today?"

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Conversation with Gemini

can you generate an image of Jane Austen celebrating an American Thanksgiving

Let's have fewer people, no signs, and a woman who is dressed like Jane Austen is in her portrait

Nope you didn't do it. That's almost the same picture. Try doing it with Jane Austen herself looking like this

That's the same picture again. Delete that and just put this picture of Jane Austen in a context of a table with turkey, etc.

She's out of proportion to the people. Just take them out and get the proportions right.

It'll have to do.

Ouroboros, an apt symbol for AI model collapse

by Ariella Brown

AI hits the ouroboros (sometimes written uroboros) stage. You've likely seen it in the form of a snake in a circle, eating its own tail. The ancient symbol also sometimes showed dragons or a wyvern, so I chose this engraving by Lucas Jennis intended to represent mercury in the 1625 alchemical tract "De Lapide Philosophico," for my illustration instead of just going with something as prosaic as "model collapse"

To get a bit meta and bring generative AI into the picture (pun intended, I'm afraid) here's an ouroboros image
made with generative AI. ked Google

Model collapse is what the researchers who published their take on this in Nature called the phenomenon of large language models (LLMs) doing the equivalent of eating their own tails when ingesting LLM output for new generation. They insist that the models should be limited to"data collected about genuine human interactions."

From the abstract:
"Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet."

Shumailov, I., Shumaylov, Z., Zhao, Y. et al. AI models collapse when trained on recursively generated data. Nature 631, 755–759 (2024).

Let me know in the comments which illustration you like more. 

How to increase traffic 16,500%: clickbait vs. reality

 

by Ariella Brown

I just attended an event with the title " Information Gain Content: How to Increase Traffic 16,500% by Going Above & Beyond with Bernard Huang." Did the session live up to its clickbait title?

Not at all. 

This wasn't necessarily Bernard Huang's fault. The presentation was hosted by the Top of the Funnel group that favors these types of large numbers that sound just specific enough that people may believe they are real.

Earlier this month, it offered "Social Copywriting Secrets: Building an Audience of 114,985 with Eddie Shleyner" I attended that one, too, and there was nothing in it that justified that number as the guaranteed result of some tactic you could apply. Shleyner just emphasized sticking to good, authentic storytelling to keep your audience engaged.

There were no easy-to-apply tricks in this session. If anything, it was about the reason the old tricks no longer work.  Huang explained that Google is currently applying its stated standard of Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) in the context of its AI overview.

My main takeaway from this session was not that it's easy to increase your traffic on Google but that it simply has switched one set of algorithmic rules for another, now including its own AI reads on content and that this is not necessarily a good thing.

What bad about this? 

The bottom line is that Google is relying very heavily on consensus as well as pre-established authority. That means that it is very easy for the sites that have already built up big audiences and strong rankings on Google to leverage that to put out proclamations that will be accepted as true, especially when they are -- inevitably -- echoed by the wannabee followers and all those who pretend to be thought leaders by parroting what influencers already say.


In other words, truly original thoughts by those who are not just reinforcing group think will likely be buried. I did raise this question and wasn't wholly reassured by the answer. It was that, yes, Google expects all points of consensus on a particular topic to be represented. If they are not present, your content will be an outcast. The only way you have a chance to be noted is if you play the contrarian game by refuting the consensus views point by point. Not giving it that nod is SEO suicide.

And so we the current state of search is one that aims to homogenize information according to preset parameters from those already granted expert status where those who are not in line with the consensus will be buried in the obscurity of high number page results. 

This is truly the opposite of a democratic platform in which understanding of history and current events is allowed to rise or fall based solely on its own merit rather than the pre-established narrative. George Patton would be appalled. He's the one who said: "If everyone is thinking alike, then somebody isn't thinking."

Google's algorithms are designed to reward those who follow in the paths preset by others rather than really thinking for themselves. 

P.S. For the story behind the illustration above, see my LinkedIn post. 

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Bard brings in the clowns for my query on ICP

 

by Ariella Brown

I've been reading Built to Last, a 1994 book about  successful businesses. None of the business models relied on the internet, and many of them already had many decades of history behind them. I noticed that the jargon in the book is quite different from today's.

For example, there is not a single mention of ICP (ideal customer profile). That made me curious. Did this term only emerge after the book's publication?

I turned to Bard ( see why here) for the answer, which also explains why this term emerged only in the post-internet world. This is what it wrote, and I have to admit being somewhat amused by the somewhat pushy CTA at the end and if the "I" is meant to be the AI itself:

The term "ideal customer profile" (ICP) was first used in the early 2000s by marketing and sales professionals. It was originally used to describe a hypothetical company that would be the perfect fit for a particular product or service. However, the term has since been adopted by businesses of all sizes and industries.

 

There are a few reasons why businesses started referring to ICPs. First, it became clear that not all customers are created equal. Some customers are more likely to be profitable than others. By understanding the characteristics of their ideal customers, businesses can focus their marketing and sales efforts on the most likely to convert.

 

Second, the rise of the internet made it easier for businesses to collect data about their customers. This data can be used to create detailed ICPs that take into account factors such as company size, industry, location, and pain points.
Finally, the increasing complexity of the marketplace made it more difficult for businesses to compete on price alone. By understanding their ideal customers and their needs, businesses can differentiate themselves from the competition and offer products and services that are truly valuable.

 

Today, ICPs are an essential part of any successful marketing and sales strategy. By creating a detailed ICP, businesses can improve their targeting, messaging, and conversion rates. This can lead to increased sales, improved customer satisfaction, and a stronger bottom line.
Here are some of the benefits of using an ICP:

 

Increased sales: By targeting your marketing and sales efforts to your ideal customers, you can increase your chances of closing deals.

 

Improved customer satisfaction: When you understand the needs of your ideal customers, you can deliver products and services that are more likely to meet their expectations.

 

Stronger bottom line: By improving your sales and customer satisfaction, you can increase your profits.

 

If you're not already using an ICP, I encourage you to start today. It's a simple but powerful tool that can help you take your business to the next level.

Notice that Bard is very bullish on ICPs, even pushy, I'd venture to say. That turns out to be a bit funny when it fails to recognize the acronym for my next query for a more recent business bible.

Clowns you say? 

I also recently read The Hard Thing About Hard Things published in 2014. I don't recollect a single mention of  ICP. Just to be sure, I asked Bard, which shows a kind of split personality when it comes to recalling acronyms. This was its response:

No, the book "The Hard Thing About Hard Things" by Ben Horowitz does not mention ICP. ICP stands for Insane Clown Posse, a hip hop duo from Detroit, Michigan. The book is about the challenges of running a startup company, and ICP is not relevant to that topic.
The book does mention a few other bands, such as The Beatles and The Rolling Stones. However, these bands are mentioned in the context of how they have influenced the music industry. ICP is not mentioned in the book at all.

Knowing that when you work with generative AI you have to try your prompt multiple times, I get it another whirl and wrote out "ideal customer profile" for the query. Now Bard grasped what I was asking and responded:

"The book "The Hard Thing About Hard Things" by Ben Horowitz does not explicitly mention the need for an ideal customer profile. However, the book does talk about the importance of knowing your target market and understanding their needs. This is essentially what an ideal customer profile is.

 

In Chapter 1, Horowitz writes, "The first thing you need to do is figure out who you're building for." He goes on to say that you need to "understand their pain, their goals, and their constraints." This is all information that would be included in an ideal customer profile.
In Chapter 2, Horowitz writes about the importance of "building a product that people actually want." He says that you need to "get feedback from your customers early and often." This is another way of saying that you need to understand your target market and their needs.
Overall, while the book does not explicitly mention the need for an ideal customer profile, it does emphasize the importance of understanding your target market and their needs. This is essentially what an ideal customer profile is.

 

Here are some additional tips for creating an ideal customer profile:
Identify your target market. Who are you trying to sell to? What are their demographics? What are their needs and wants?
Research your target market. What are they reading? What are they talking about online? What are their pain points?
Create a persona. Once you have a good understanding of your target market, create a persona. This is a fictional representation of your ideal customer.

 

Use your ideal customer profile to guide your marketing and sales efforts. Use your persona to create content, develop marketing campaigns, and target your sales efforts.

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