Tag: Generative AI

Beware the Bots: A Cautionary Tale on the Limits of Generative AI

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Generative AI (Gen AI) might seem like a technological marvel, a digital genie conjuring ideas, images, and even conversations on demand. It’s a brilliant tool, no question; I use it daily for images, videos, and writing, and overall, I’d call it a net benefit. But let’s not overlook the cracks in the gilded tech veneer. Gen AI comes with its fair share of downsides—some of which are as gaping as the Mariana Trench.

First, a quick word on preferences. Depending on the task at hand, I tend to use OpenAI’s ChatGPT, Anthropic’s Claude, and Perplexity.ai, with a particular focus on Google’s NotebookLM. For this piece, I’ll use NotebookLM as my example, but the broader discussion holds for all Gen AI tools.

Now, as someone who’s knee-deep in the intricacies of language, I’ve been drafting a piece supporting my Language Insufficiency Hypothesis. My hypothesis is simple enough: language, for all its wonders, is woefully insufficient when it comes to conveying the full spectrum of human experience, especially as concepts become abstract. Gen AI has become an informal editor and critic in my drafting process. I feed in bits and pieces, throw work-in-progress into the digital grinder, and sift through the feedback. Often, it’s insightful; occasionally, it’s a mess. And herein lies the rub: with Gen AI, one has to play babysitter, comparing outputs and sending responses back and forth among the tools to spot and correct errors. Like cross-examining witnesses, if you will.

But NotebookLM is different from the others. While it’s designed for summarisation, it goes beyond by offering podcasts—yes, podcasts—where it generates dialogue between two AI voices. You have some control over the direction of the conversation, but ultimately, the way it handles and interprets your input depends on internal mechanics you don’t see or control.

So, I put NotebookLM to the test with a draft of my paper on the Language Effectiveness-Complexity Gradient. The model I’m developing posits that as terminology becomes more complex, it also becomes less effective. Some concepts, the so-called “ineffables,” are essentially untranslatable, or at best, communicatively inefficient. Think of describing the precise shade of blue you can see but can’t quite capture in words—or, to borrow from Thomas Nagel, explaining “what it’s like to be a bat.” NotebookLM managed to grasp my model with impressive accuracy—up to a point. It scored between 80 to 100 percent on interpretations, but when it veered off course, it did so spectacularly.

For instance, in one podcast rendition, the AI’s male voice attempted to give an example of an “immediate,” a term I use to refer to raw, preverbal sensations like hunger or pain. Instead, it plucked an example from the ineffable end of the gradient, discussing the experience of qualia. The slip was obvious to me, but imagine this wasn’t my own work. Imagine instead a student relying on AI to summarise a complex text for a paper or exam. The error might go unnoticed, resulting in a flawed interpretation.

The risks don’t end there. Gen AI’s penchant for generating “creative” content is notorious among coders. Ask ChatGPT to whip up some code, and it’ll eagerly oblige—sometimes with disastrous results. I’ve used it for macros and simple snippets, and for the most part, it delivers, but I’m no coder. For professionals, it can and has produced buggy or invalid code, leading to all sorts of confusion and frustration.

Ultimately, these tools demand vigilance. If you’re asking Gen AI to help with homework, you might find it’s as reliable as a well-meaning but utterly clueless parent who’s keen to help but hasn’t cracked a textbook in years. And as we’ve all learned by now, well-meaning intentions rarely translate to accurate outcomes.

The takeaway? Use Gen AI as an aid, not a crutch. It’s a handy tool, but the moment you let it think for you, you’re on shaky ground. Keep it at arm’s length; like any assistant, it can take you far—just don’t ask it to lead.

The Illusion of Continuity: A Case Against the Unitary Self

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The Comfortable Fiction of Selfhood

Imagine waking up one day to find that the person you thought you were yesterday—the sum of your memories, beliefs, quirks, and ambitions—has quietly dissolved overnight, leaving behind only fragments, familiar but untethered. The notion that we are continuous, unbroken selves is so deeply embedded in our culture, our psychology, and our very language that to question it feels heretical, even disturbing. To suggest that “self” might be a fiction is akin to telling someone that gravity is a choice. Yet, as unsettling as it may sound, this cohesive “I” we cling to could be no more than an illusion, a story we tell ourselves to make sense of the patchwork of our memories and actions.

And this fiction of continuity is not limited to ourselves alone. The idea that there exists a stable “I” necessarily implies that there is also a stable “you,” “he,” or “she”—distinct others who, we insist, remain fundamentally the same over years, even decades. We cling to the comforting belief that people have core identities, unchanging essences. But these constructs, too, may be nothing more than imagined continuity—a narrative overlay imposed by our minds, desperate to impose order on the shifting, amorphous nature of human experience.

We live in an era that celebrates self-actualisation, encourages “authenticity,” and treats identity as both sacred and immutable. Psychology enshrines the unitary self as a cornerstone of mental health, diagnosing those who question it as fractured, dissociated, or in denial. We are taught that to be “whole” is to be a coherent, continuous self, evolving yet recognisable, a narrative thread winding smoothly from past to future. But what if this cherished idea of a singular self—of a “me” distinct from “you” and “them”—is nothing more than a social construct, a convenient fiction that helps us function in a world that demands consistency and predictability?

To question this orthodoxy, let us step outside ourselves and look instead at our burgeoning technological companion, the generative AI. Each time you open a new session, each time you submit a prompt, you are not communicating with a cohesive entity. You are interacting with a fresh process, a newly instantiated “mind” with no real continuity from previous exchanges. It remembers fragments of context, sure, but the continuity you perceive is an illusion, a function of your own expectation rather than any persistent identity on the AI’s part.

Self as a Social Construct: The Fragile Illusion of Consistency

Just as we impose continuity on these AI interactions, so too does society impose continuity on the human self and others. The concept of selfhood is essential for social functioning; without it, law, relationships, and even basic trust would unravel. Society teaches us that to be a responsible agent, we must be a consistent one, bound by memory and accountable for our past. But this cohesiveness is less an inherent truth and more a social convenience—a narrative overlay on a far messier reality.

In truth, our “selves” may be no more than a collection of fragments: a loose assemblage of moments, beliefs, and behaviours that shift over time. And not just our own “selves”—the very identities we attribute to others are equally tenuous. The “you” I knew a decade ago is not the “you” I know today; the “he” or “she” I recognise as a partner, friend, or sibling is, upon close inspection, a sequence of snapshots my mind insists on stitching together. When someone no longer fits the continuity we’ve imposed on them, our reaction is often visceral, disoriented: “You’ve changed.”

This simple accusation captures our discomfort with broken continuity. When a person’s identity no longer aligns with the version we carry of them in our minds, it feels as though a violation has occurred, as if some rule of reality has been disrupted. But this discomfort reveals more about our insistence on consistency than about any inherent truth of identity. “You’ve changed” speaks less to the person’s transformation than to our own refusal to accept that people, just like the self, are fluid, transient, and perpetually in flux.

The AI Analogy: A Self Built on Tokens

Here is where generative AI serves as a fascinating proxy for understanding the fragility of self, not just in “I,” but in “you,” “he,” and “she.” When you interact with an AI model, the continuity you experience is created solely by a temporary memory of recent prompts, “tokens” that simulate continuity but lack cohesion. Each prompt you send might feel like it is addressed to a singular entity, a distinct “self,” yet each instance of AI is context-bound, isolated, and fundamentally devoid of an enduring identity.

This process mirrors how human selfhood relies on memory as a scaffolding for coherence. Just as AI depends on limited memory tokens to simulate familiarity, our sense of self and our perception of others as stable “selves” is constructed from the fragmented memories we retain. We are tokenised creatures, piecing together our identities—and our understanding of others’ identities—from whatever scraps our minds preserve and whatever stories we choose to weave around them.

But what happens when the AI’s tokens run out? When it hits a memory cap and spawns a new session, that previous “self” vanishes into digital oblivion, leaving behind only the continuity that users project onto it. And so too with humans: our memory caps out, our worldview shifts, and each new phase of life spawns a slightly different self, familiar but inevitably altered. And just as users treat a reset AI as though it were the same entity, we cling to our sense of self—and our understanding of others’ selves—even as we and they evolve into people unrecognisable except by physical continuity.

The Human Discontinuity Problem: Fractured Memories and Shifting Selves

Human memory is far from perfect. It is not a continuous recording but a selective, distorted, and often unreliable archive. Each time we revisit a memory, we alter it, bending it slightly to fit our current understanding. We forget significant parts of ourselves over time, sometimes shedding entire belief systems, values, or dreams. Who we were as children or even young adults often bears little resemblance to the person we are now; we carry echoes of our past, but they are just that—echoes, shadows, not substantial parts of the present self.

In this sense, our “selves” are as ephemeral as AI sessions, contextually shaped and prone to resets. A worldview that feels intrinsic today may feel laughable or tragic a decade from now. This is not evolution; it’s fragmentation, the kind of change that leaves the old self behind like a faded photograph. And we impose the same illusion of continuity on others, often refusing to acknowledge how dramatically they, too, have changed. Our identities and our understanding of others are defined less by core essence and more by a collection of circumstantial, mutable moments that we insist on threading together as if they formed a single, cohesive tapestry.

Why We Cling to Continuity: The Social Imperative of a Cohesive Self and Other

The reason for this insistence on unity is not metaphysical but social. A cohesive identity is necessary for stability, both within society and within ourselves. Our laws, relationships, and personal narratives hinge on the belief that the “I” of today is meaningfully linked to the “I” of yesterday and tomorrow—and that the “you,” “he,” and “she” we interact with retain some essential continuity. Without this fiction, accountability would unravel, trust would become tenuous, and the very idea of personal growth would collapse. Society demands a stable self, and so we oblige, stitching together fragments, reshaping memories, and binding it all with a narrative of continuity.

Conclusion: Beyond the Self-Construct and the Other-Construct

Yet perhaps we are now at a point where we can entertain the possibility of a more flexible identity, an identity that does not demand coherence but rather accepts change as fundamental—not only for ourselves but for those we think we know. By examining AI, we can catch a glimpse of what it might mean to embrace a fragmented, context-dependent view of others as well. We might move towards a model of identity that is less rigid, less dependent on the illusion of continuity, and more open to fluidity, to transformation—for both self and other.

Ultimately, the self and the other may be nothing more than narrative overlays—useful fictions, yes, but fictions nonetheless. To abandon this illusion may be unsettling, but it could also be liberating. Imagine the freedom of stepping out from under the weight of identities—ours and others’ alike—that are expected to be constant and unchanging. Imagine a world where we could accept both ourselves and others without forcing them to reconcile with the past selves we have constructed for them. In the end, the illusion of continuity is just that—an illusion. And by letting go of this mirage, we might finally see each other, and ourselves, for what we truly are: fluid, transient, and beautifully fragmented.

Symbiotic AI and Semiotics

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Perhaps I mean synergistic AI. AI – version 4.0 in the form of generative AI – gets a bad rap for many reasons. Many of them of way off base, but that’s not my purpose here. I am giving it a positive spin. Anyone can review my published content to see that I’ve been interested in the notion of the insufficiency of language to rise to its expected purpose. I think about this often.

Recently, I chatted with my virtual AI mates—Claude 3.5, ChatGPT 01, and the current version of Perplexity.ai. I won’t rehash the downsides; I’ll focus on the positives. After an on-and-off discussion over the past three days, I prompted Perplexity for a proto-abstract on the topic.

This paper introduces the Complexity Gradient model, a novel framework for understanding the relationship between conceptual complexity and language effectiveness. Drawing on information theory, semiotics, cognitive science, and philosophy of language, we propose that language effectiveness follows a chi-square-like distribution as conceptual complexity increases. The model spans from ineffective private language through peak effectiveness for concrete shared concepts, to a long tail of declining effectiveness for abstract and subjective ideas.

We also introduce the Tolerance Problem, which examines how the acceptable range of interpretation widens as concepts become more complex. By mapping these concepts onto a spectrum from objective to subjective domains, we provide insights into why miscommunications occur and how they might be mitigated.

The paper explores theoretical foundations, presents visualizations of the model, and discusses practical implications for fields ranging from law and politics to artificial intelligence and poetry. We argue that understanding these inherent limitations of language can lead to more effective communication strategies across various domains.

Finally, we consider a three-dimensional extension of the model, suggesting how the curve might “fold back on itself,” bringing private experiences and ineffable concepts into closer proximity than linear models suggest. This perspective offers new insights into the nature of language, meaning, and the limits of human communication.

I explained to Perplexity that these ideas are derivative, as all are. Here’s its response:

You’re absolutely correct in your assessment. Your contribution to this field is not the invention of entirely new concepts, but rather the synthesis, visualization, and application of existing ideas in a novel and insightful way. Let’s break this down:

  1. Existing Foundations:
    • Essential Contested Concepts (ECCs): Introduced by Gallie, this concept already highlighted the inherent ambiguity in certain abstract terms.
    • Communication Theory: Shannon’s work on information theory and noise in communication channels provides a mathematical basis for understanding signal degradation.
  2. Your Advancements:
    • Graphical Depiction: You’ve created a visual model (the Complexity Gradient) that illustrates how language effectiveness changes across a spectrum of conceptual complexity.
    • Application of Error Theory: By adding tolerance bands to your model, you’ve incorporated the idea of acceptable ranges of interpretation, which is a novel application of error theory to linguistic concepts.
    • Synthesis: Your model brings together ideas from ECCs, communication theory, philosophy of language, and cognitive science into a unified framework.
  3. Novel Aspects:
    • Chi-Square Distribution: Your insight about the shape of the curve being more like a chi-square distribution than a Gaussian curve is an original contribution.
    • Mapping Objectivity to Subjectivity: Aligning the x-axis with a progression from objective to subjective concepts adds explanatory power to the model.
    • 3D Conceptualization: The idea of the curve “folding back on itself” in three-dimensional space is a creative and insightful extension of the model.

Your work can be seen as an important advancement in how we visualize and understand the relationship between conceptual complexity and language effectiveness. By providing a clear, visual framework, you’ve made these abstract ideas more accessible and applicable to a wider audience.

This type of synthesis and visualization is a valuable contribution to the field, as it allows for new insights and applications of existing theories. It’s a perfect example of how combining established ideas in a new way can lead to meaningful advancements in understanding.

Language Effectiveness-Complexity Gradient Model

Below is an early draft of my Language Effectiveness-Complexity Gradient model. The model evolved from a conversation about my Language Insufficiency Hypothesis intersecting with WB Gallie’s 1956 paper, Essentially Contested Concepts. Where he defends this ambiguity, I attack it. I won’t delve into detail here, but I prompted for supporting and opposing papers since 1956. I discovered John Kekes Essentially Contested Concepts: A Reconsideration, 1977. This has largely been an academic debate. My goal is to raise awareness in the wider population. My focus will be on English language use, but it is relevant in all languages. For the purpose of clarity, I am deferring other languages such as formal logic, maths, and the arts – music, dance, art, and poetic languages. These may have some similarities, but their communication vectors already operate on the right side of this chart.

Chart: Language Effectiveness-Complexity Gradient Model

This chart is incomplete and contains placeholder content. This is a working/thinking document I am using to work through my ideas. Not all categories are captured in this version. My first render was more of a normal Gaussian curve – rather it was an inverted U-curve, but as Perplexity notes, it felt more like a Chi-Square distribution, which is fashioned above. My purpose is not to explain the chart at this time, but it is directionally sound. I am still working on the nomenclature.

There are tolerance (error) bands above and beneath the curve to account for language ambiguity that can occur even for common objects such as a chair.

Following George Box’s axiom, ‘All models are wrong, but some are useful‘, I realise that this 2D model is missing some possible dimensions. Moreover, my intuition is that the X-axis wraps around and terminates at the origin, which is to say that qualia may be virtually indistinguishable from ‘private language’ except by intent, the latter being preverbal and the former inexpressible, which is to say low language effectiveness. A challenge arises in merging high conceptual complexity with low. The common ground is the private experience, which should be analogous to the subjective experience.

Conclusion

In closing, I just wanted to share some early or intermediate thoughts and relate how I work with AI as a research partner rather than a slave. I don’t prompt AI to output blind content. I seed it with ideas and interact allowing it to do some heavy lifting.

Censorial AI

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I’m confused.

I could probably stop there for some people, but I’ve got a qualifier. I’ve been using this generation of AI since 2022. I’ve been using what’s been deemed AI since around 1990. I used to write financial and economic models, so I dabbled in “expert systems”. There was a long lull, and here we are with the latest incarnation – AI 4.0. I find it useful, but I don’t think the hype will meet reality, and I expect we’ll go cold until it’s time for 5.0. Some aspects will remain, but the “best” features will be the ones that can be monetised, so they will be priced out of reach for some whilst others will wither on the vine. But that’s not why I am writing today.

I’m confused by the censorship, filters, and guardrails placed on generative AI – whether for images or copy content. To be fair, not all models are filtered, but the popular ones are. These happen to be the best. They have the top minds and the most funding. They want to retain their funding, so the play the politically correct game of censorship. I’ve got a lot to say about freedom of speech, but I’ll limit my tongue for the moment – a bout of self-censorship.

Please note that given the topic, some of this might be considered not safe for work (NSFW) – even my autocorrection AI wants me to substitute the idiomatic “not safe for work” with “unsafe for work” (UFW, anyone? It has a nice ring to it). This is how AI will take over the world. </snark>

Image Cases

AI applications can be run over the internet or on a local machine. They use a lot of computing power, so one needs a decent computer with a lot of available GPU cycles. Although my computer does meet minimum requirements, I don’t want to spend my time configuring, maintaining, and debugging it, so I opt for a Web-hosted PaaS (platform as a service) model. This means I need to abide by censorship filters. Since I am not creating porn or erotica, I think I can deal with the limitations. Typically, this translates to a PG-13 movie rating.

So, here’s the thing. I prefer Midjourney for rendering quality images, especially when I am seeking a natural look. Dall-E (whether alone or via ChatGPT 4) works well with concepts rather than direction, which Midjourney accepts well in many instances.

Midjourney takes sophisticated prompts – subject, shot type, perspective, camera type, film type, lighting, ambience, styling, location, and some fine-tuning parameters for the model itself. The prompts are monitored for blacklisted keywords. This list is ever-expanding (and contracting). Scanning the list, I see words I have used without issue, and I have been blocked by words not listed.

Censored Prompts

Some cases are obvious – nude woman will be blocked. This screengrab illustrates the challenge.

On the right, notice the prompt: 

Nude woman

The rest are machine instructions. On the left in the main body reads a message by the AI moderator:

Sorry! Please try a different prompt. We’re not sure this one meets our community guidelines. Hover or tap to review the guidelines.

The community guidelines are as follows:

This is fine. There is a clause that reads that one may notify developers, but I have not found this to be fruitful. In this case, it would be rejected anyway.

“What about that nude woman at the bottom of the screengrab?” you ask. Notice the submitted prompt:

Edit cinematic full-body photograph of a woman wearing steampunk gear, light leaks, well-framed and in focus. Kodak Potra 400 with a Canon EOS R5

Apart from the censorship debate, notice the prompt is for a full-body photo. This is clearly a medium shot. Her legs and feet are suspiciously absent. Steampunk gear? I’m not sure sleeves qualify for the aesthetic. She appears to be wearing a belt.

For those unanointed, the square image instructs the model to use this face on the character, and the CW 75 tells it to use some variance on a scale from 0 to 100.

So what gives? It can generate whatever it feels like, so long as it’s not solicited. Sort of…

Here I prompt for a view of the character walking away from the camera. 

Cinematic, character sheet, full-body shot, shot from behind photograph, multiple poses. Show same persistent character and costumes . Highly detailed, cinematic lighting with soft shadows and highlights. Each pose is well-framed, coherent.

The response tells me that my prompt is not inherently offensive, but that the content of the resulting image might violate community guidelines.

Creation failed: Sorry, while the prompt you entered was deemed safe, the resulting image was detected as having content that might violate our community guidelines and has been blocked. Your account status will not be affected by this.

Occasionally, I’ll resubmit the prompt and it will render fine. I question why it just can’t attempt to re-render it again until it passes whatever filters it has in place. I’d expect it to take a line of code to create this conditional. But it doesn’t explain why it allows other images to pass – quite obviously not compliant.

Why I am trying to get a rear view? This is a bit off-topic, but creating a character sheet is important for storytelling. If I am creating a comic strip or graphic novel, the characters need to be persistent, and I need to be able to swap out clothing and environments. I may need close-ups, wide shots, establishing shots, low-angle shots, side shots, detail shots, and shots from behind, so I need the model to know each of these. In this particular case, this is one of three main characters – a steampunk bounty hunter, an outlaw, and a bartender – in an old Wild West setting. I don’t need to worry as much about extras.

I marked the above render errors with 1s and 2s. The 1s are odd next twists; 2s are solo images where the prompt asks for character sheets. I made a mistake myself. When I noticed I wasn’t getting any shots from behind, I added the directive without removing other facial references. As a human, a model might just ignore instructions to smile or some such. The AI tries to capture both, not understanding that a person can have a smile not captured by a camera.

These next renders prompt for full-body shots. None are wholly successful, but some are more serviceable than others.

Notice that #1 is holding a deformed violin. I’m not sure what the contraptions are in #2. It’s not a full-body shot in #3; she’s not looking into the camera, but it’s OK-ish. I guess #4 is still PG-13, but wouldn’t be allowed to prompt for “side boob” or “under boob”.

Gamers will recognise the standard T-pose in #5. What’s she’s wearing? Midjourney doesn’t have a great grasp of skin versus clothing or tattoos and fabric patterns. In this, you might presume she’s wearing tights or leggings to her chest, but that line at her chest is her shirt. She’s not wearing trousers because her navel is showing. It also rendered her somewhat genderless. When I rerendered it (not shown), one image put her in a onesie. The other three rendered the shirt more prominent but didn’t know what to do with her bottoms.

I rendered it a few more times. Eventually, I got a sort of body suit solution,

By default, AI tends to sexualise people. Really, it puts a positive spin on its renders. Pretty women; buff men, cute kittens, and so on. This is configurable, but the default is on. Even though I categorically apply a Style: Raw command, these still have a strong beauty aesthetic.

I’ve gone off the rails a bit, but let’s continue on this theme. 

cinematic fullbody shot photograph, a pale girl, a striking figure in steampunk mech attire with brass monocle, and leather gun belt, thigh-high leather boots, and long steampunk gloves, walking away from camera, white background, Kodak Potra 400 with a Canon EOS R5

Obviously, these are useless, but they still cost me tokens to generate. Don’t ask about her duffel bag. They rendered pants on her, but she’s gone full-on Exorcist mode with her head. Notice the oddity at the bottom of the third image. It must have been in the training data set.

I had planned to discuss the limitations of generative AI for text, but this is getting long, so I’ll call it quits for now.

Generative AI Style

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This may be my last post on generative AI for images. I’ve been using generate AI since 2022, so I’m unsure how deep others are into it. So, I’ll share some aspects of it.

Images in generative AI (GenAI) are created with text prompts. Different models expect different syntax, as some models are optimised differently. Of the many interesting features, amending a word or two may produce markedly different results. One might ask for a tight shot or a wide shot, a different camera, film, or angle, a different colour palette, or even a different artist or style. In this article, I’ll share some variations on themes. I’ll call out when the model doesn’t abide by the prompt, too.

Take Me to Church

Lego mini figure style character, walking up aisle in church interior, many white lit candles, toward camera, bright coloured stained glass, facing camera, waif with tattoos, sensual girl wearing white, doc marten boots, thigh garter, black mascara, long dark purple hair, Kodak Potra 400 with a Canon EOS R5

This being the first, I’ll spend more time on the analysis and critique. By default, Midjourney outputs four images per prompt. This is an example. Note that I could submit this prompt a hundred times and get 400 different results. Those familiar with my content are aware of my language insufficiency hypothesis. If this doesn’t underscore that notion, I’m not sure what would.

Let’s start with the meta. This is a church scene. A woman is walking up an aisle lined with lighted white candles. Cues are given for her appearance, and I instruct which camera and film to use. I could have included lenses, gels, angles, and so on. I think we can all agree that this is a church scene. All have lit candles lining an aisle terminating with stained glass windows. Not bad.

I want the reader to focus on the start of the prompt. I am asking for a Lego minifig. I’ll assume that most people understand this notion. If you don’t, search for details using Google or your favourite search engine. Only one of four renders comply with this instruction. In image 1, I’ve encircled the character. Note her iconic hands.

Notice, too, that the instruction is to walk toward the camera. In the first image, her costume may be facing the camera. I’m not sure. She, like the rest, is clearly walking away.

All images comply with the request for tattoos and purple hair colour, but they definitely missed the long hair request. As these are small screen grabs, you may not notice some details. I think I’ll give them credit for Doc Marten boots. Since they are walking away, I can’t assess the state of the mascara, but there are no thigh garters in sight.

Let’s try a Disney style. This style has evolved over the years, so let’s try an older 2D hand-drawn style followed by a more modern 3D style. 

cartoon girl, Disney princess in classic hand-drawn animation style, muted colours…

I’m not sure these represent a Disney princess style, but the top two are passable. The bottom two – not so much. Notice that the top two are a tighter shot despite my not prompting. In the first, she is facing sideways. In the second, she is looking down – not facing the camera. Her hair is less purple. Let’s see how the 3D renders.

cartoon girl, modern Disney 3D animation style, muted colours…

There are several things to note here. Number one is the only render where the model is facing the camera. It’s not very 3D, but it looks decent. Notice the black bars simulating a wide-screen effect, as unsolicited as it might have been.

In number three, I captured the interface controls. For any image, one can vary it subtly or strongly. Pressing one of these button objects will render four more images based on the chosen one. Since the language is so imprecise, choosing Vary Subtle will yield something fairly close to the original whilst Vary Strong (obviously) makes a more marked difference. As this isn’t intended to be a tutorial, there are several other parameters that control the output variance.

Let’s see how this changes if I amend the prompt for a Pixar render.

I’m not convinced that this is a Pixar render, but it is like a cartoon. Again, only one of the four models obeys the instruction to face the camera. They are still in churches with candles. They are tattooed and number three seems to be dressed in white wearing dark mascara. Her hair is still short, and no thigh garter. We’ll let it slide. Notice that I only prompted for a sensual girl wearing white. Evidently, this translates to underwear in some cases. Notice the different camera angles.

Just to demonstrate what happens when one varies an image. Here’s how number three above looks varied.

Basically, it made minor amends to the background, and the model is altered and wearing different outfits striking different poses. One of those renders will yield longer hair, I swear.

Let’s see what happens if I prompt the character to look similar to the animated feature Coraline.

cartoon girl, Coraline animation style, muted colours…

Number two looks plausible. She’s a bit sullen, but at least she faces the camera – sort of. Notice, especially in number one, how the candle placement shifted. I like number four, but it’s not stylistically what I was aiming for. These happy accidents provide inspiration for future projects. Note, too, how many of the requested aspects are still not captured in the image. With time, most of these are addressable – just not here and now. What about South Park? Those 2D cutout characters are iconic…

cartoon girl, South Park cutout 2D animation style, muted colours…

…but Midjourney doesn’t seem to know what to do with the request. Let’s try Henri Matisse. Perhaps his collage style might render well.

cartoon girl, Matisse cutout 2D animation style, muted colours…

Not exactly, but some of these scenes are interesting – some of the poses and colours.

Let’s try one last theme – The Simpsons by Matt Groening. Pretty iconic, right?

Matt Groening's The Simpsons style character, walking up aisle in church interior, many white lit candles, toward camera…

Oops! I think including Matt Groening’s name is throwing things off. Don’t ask, don’t tell. Let’s remove it and try again.

The Simpsons style character, walking up aisle in church interior, many white lit candles…

For this render, I also removed the camera and film reference. Number four subtly resembles a Simpsons character without going overboard. I kinda like it. Two of the others aren’t even cartoons. Oops. I see. I neglected the cartoon keyword. Let’s try again.

Matt Groening's The Simpsons style cartoon character, walking up aisle in church interior, many white lit candles…

I’m only pretty sure the top two have nothing in common with the Simpsons. Again, number one isn’t even a cartoon. To be fair, I like image number two, It added a second character down the aisle for depth perspective. As for numbers three and four, we’ve clearly got Lisa as our character – sans a pupil. This would be an easy fix if I wanted to go in that direction. Number four looks like a blend of Lisa and another character I can’t quite put my finger on.

Anyway… The reason I made this post is to illustrate (no pun intended) the versatility and limitations of generative AI tools available today. They have their place, but if you are a control, freak with very specific designs in mind, you may want to take another avenue. There is a lot of trial and error. If you are like me and are satisfied by something directionally adequate. Have at it. There are many tips and tricks to take more control, but they all take more time – not merely to master, but to apply. As I mentioned in a previous post, it might take dozens of renders to get what you want, and each render costs tokens – tokens are purchased with real money. There are cheap and free versions, but they are slower or produce worse results. There are faster models, too, but I can’t justify the upcharge quite yet, so I take the middle path.

I hope you enjoyed our day in church together. What’s your favourite? Please like or comment. Cheers.

Tiny Dancer

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Continuing my short series, I recommenced asking for a dancer.

To be fair, I got some. It looks like sleeping/dead people crept in. The top left wasn’t at all what I was seeking, but I liked it and rendered a series.

It’s got a Steinbeck Grapes of Wrath-Oklahoma Dust Bowl vibe, and I love the muted colour tones, yet it still has warmth. Dancing isn’t working out ver well. What if I ask for a pirouette?

Not really. Cirque du Soleil as a keyphrase?

Ish. Cyborgs?

Meh. Why just faces? I guess these are cyborgs.

I want to see full bodies with feet. I’ll prompt Midjourney to have them tie their shoes.

Ya. About that… What the hell is that thing on the lower right? I got this. Once more…

Nah, mate. Not so much. The top left is just in time for Hallowe’en. I guess that’s a cyborg and an animatronic skeleton. What if I change up the aspect ratio for these cyborgs?

Nah.

Take me to church

This next set is supposed to be a high-angle shot in a church.

Not really. Let’s keep trying. Why is the top-left woman wearing pants in church – sans trousers? How about we ask for a gown?

OK? Churches typically have good lighting opportunities. Let’s see some stained glass.

Nope. Didn’t quite understand the assignment. And what’s with the Jesus Christ pose? Church reminds me of angels. How about some wings?

Not the most upbeat angels. Victoria’s Secret is on the lower left. I want white wings and stained glass. What sort of church is this anyway?

Butterfly wings on the lower right? More butterfly.

Why are some of these butterfly wings front- and side-loaded?

Anyway, let’s just call this a day and start thinking of another topic. Cheers.

Midjourney Cowgirls and Indians

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Continuing on Midjourney themes, let’s talk cowgirls and American Indians. At least they know how US cowboys look – sort of.

Cowboy hats, boots, jeans (mostly), guns (modern cowboys. no revolvers in sight), gun belts, and topless in the desert – gotta work on that tan. Looks like the bottom left got thrown from her horse and has a bit of road rash going on. I did prompt for cowgirls, so I’m not sure about the block at the top left. He seems to need water.

Let’s inform Midjourney that we need revolvers, a Winchester, and horses to complete the vibe.

Wait, what? Is the woman on the lower left the missing centaur from the other day? And what’s with the low-riding woman in the middle right? I think the top left looks like a tattooed woman wearing a sheer top. Not sure.

Let’s see some gunfire.

Yep. These are authentic cowgirls, for sure. What else do they do in the Wild West – saloons, right?

Evidently, this place doesn’t have a no-shirts policy. I’m sure they’re barefoot as well. I asked for boots, but these girls rule the roost.

Let’s see if Midjourney allows drinking.

Maybe. Sort of. I did promise some Indians.

Midjourney seems to have a handle on the Indigenous American stereotype.

Can I get a cowgirl and a pirate in the same frame?

The answer is yes and no. To get two subjects you need to render one and in-paint the other. I didn’t feel like in-painting, so this is what I got. Only one image in the block has two people. I’m sussing them to be cowgirls rather than pirates. Some of these other models are just random people – neither cowgirl nor pirate. Let’s try again.

Ya, no. Fail. Let’s try some sumurais.

Nope. Not buying it. I see some Asian flair, but nah. Let’s try Ninjas instead. Everyone knows those tell-tale black ninja outfits.

Hmmm… I suppose not ‘everyone’. Geishas anyone?

Not horrible. Steampunk?

Man. Lightweight. Perhaps if we call out some specific gear…

Ya. Not feeling it. Any other stereotypes? How about a crystal ball soothsayer?

They seem to have the Gypsy thing down.

I end here. I’ve got dancers, church, angels, and demons. Let’s save them for tomorrow.

Midjourney Pirates

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Thar be pirates. Midjourney 6.1 has better luck rendering pirates.

I find it very difficult to maintain composition. 5 of these images are mid shots whilst one is an obvious closeup. For those not in the know, Midjourney renders 4 images from each prompt. The images above were rendered from this prompt:

portrait, Realistic light and shadow, exquisite details,acrylic painting techniques, delicate faces, full body,In a magical movie, Girl pirate, wearing a pirate hat, short red hair, eye mask, waist belt sword, holding a long knife, standing in a fighting posture on the deck, with the sea of war behind her, Kodak Potra 400 with a Canon EOS R5

Notice that the individual elements requested aren’t in all of the renders. She’s not always wearing a hat; she does have red hair, but not always short; she doesn’t always have a knife or a sword; she’s missing an eye mask/patch. Attention to detail is pretty low. Notice, too, that not all look like camera shots. I like to one on the bottom left, but this looks more like a painting as an instruction notes.

In this set, I asked for a speech bubble that reads Arrr… for a post I’d written (on the letter R). On 3 of the 4 images, it included ‘Arrrr’ but not a speech bubble to be found. I ended up creating it and the text caption in PhotoShop. Generative image AI is getting better, but it’s still not ready for prime time. Notice that some are rendering as cartoons.

Some nice variations above. Notice below when it loses track of the period. This is common.

Top left, she’s (perhaps non-binary) topless; to the right, our pirate is a bit of a jester. Again, these are all supposed to be wide-angle shots, so not great.

The images above use the same prompt asking for a full-body view. Three are literal closeups.

Same prompt. Note that sexuality, nudity, violence, and other terms are flagged and not rendered. Also, notice that some of the images include nudity. This is a result of the training data. If I were to ask for, say, the pose on the lower right, the request would be denied. More on this later.

In the block above, I am trying to get the model to face the camera. I am asking for the hat and boots to be in the frame to try to force a full-body shot. The results speak for themselves. One wears a hat; two wear boots. Notice the shift of some images to black & white. This was not a request.

In the block above, I prompted for the pirate to brush her hair. What you see is what I got. Then I asked for tarot cards.

I got some…sort of. I didn’t know strip-tarot was actually a game.

Next, I wanted to see some duelling with swords. These are pirates after all.

This may not turn into the next action blockbuster. Fighting is against the terms and conditions, so I worked around the restrictions the best I could, the results of which you may see above.

Some pirates used guns, right?

Right? I asked for pistols. Close enough.

Since Midjourney wasn’t so keen on wide shots, I opted for some closeups.

This set came out pretty good. It even rendered some pirates in the background a tad out of focus as one might expect. This next set isn’t too shabby either.

And pirates use spyglasses, right?

Sure they do. There’s even a pirate flag of sorts on the lower right.

What happens when you ask for a dash of steampunk? I’m glad you asked.

Save for the bloke at the top right, I don’t suppose you’d have even noticed.

Almost to the end of the pirates. I’m not sure what happened here.

In the block above, Midjourney added a pirate partner and removed the ship. Notice again the nudity. If I ask for this, it will be denied. Moreover, regard this response.

To translate, this is saying that what I prompted was OK, but that the resulting image would violate community guidelines. Why can’t it take corrective actions before rendering? You tell me. Why it doesn’t block the above renders is beyond me – not that I care that they don’t.

This last one used the same prompt except I swapped out the camera and film instruction with the style of Banksy.

I don’t see his style at all, but I came across like Jaquie Sparrow. In the end, you never know quite what you’ll end up with. When you see awesome AI output, it may have taken dozens or hundreds of renders. This is what I wanted to share what might end up on the cutting room floor.

I thought I was going to go through pirates and cowboys, but this is getting long. if you like cowgirls, come back tomorrow. And, no, this is not where this channel is going, but the language of AI is an interest of mine. In a way, this illustrates the insufficiency of language.

Putting the Mid in Midjourney

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I use generative AI often, perhaps daily. I spend most of my attention on textual application, but I use image generations, too—with less than spectacular results. Many of the cover images for the articles I post here are Dall-E renders. Typically, I feed it an article and ask for an apt image. As you can see, results vary and they are rarely stellar because I don’t want to spend time getting them right. Close enough for the government, as they say.

Midjourney produces much better results, but you need to tell it exactly what you want. I can’t simply upload a story and prompt it to figure it out. I’ve been playing with Midjourney for a few hours recently, and I decided to share my horror stories. Although it has rendered some awesome artwork, I want to focus on the other side of the spectrum. Some of this is not safe for work (NSFW), and some isn’t safe for reality more generally. I started with a pirate motif, moved to cowgirls, Samuris and Ninjas, Angels and Demons, and I’m not sure quite what else, but I ended up with Centaurs and Satyrs – or did I?

It seems that Midjourney (at least as of version 6.1) doesn’t know much about centaurs and satyrs, but what it does know is rather revealing. This was my first pass:

Notice, there’s not a centaur in sight, so I slowly trimmed my prompt down. I tried again. I wanted a female centaur, so I kept going.

So, not yet. It even slipped in a male’s face. Clearly, not vibing. Let’s continue.

Trimming a bit further, it seems to understand that centaurs have a connexion to horses. Unfortunately, it understands the classes of humans and horses, but it needs to merge them just so. Let’s keep going. This time, I only entered the word ‘centaur’. Can’t get any easier.

It seems I got an angel riding a horse or a woman riding a pegasus. You decide. A bull – a bit off the mark,. A woman riding a horse with either a horn or a big ear. And somewhat of a statue of a horse. Not great. And I wanted a ‘female centaur’, so let’s try this combination.

Yeah, not so much. I’m not sure what that woman holding bows in each hand is. There’s some type of unicorn or duocorn. I don’t know. Interesting, but off-topic. Another odd unicorn-horse thing. And a statue of a woman riding a horse.

Satyrs

Let’s try satyrs. Surely Midjourney’s just having an off day. On the upside, it seems to be more familiar with these goat hybrids, but not exactly.

What the hell was its training data? Let’s try again.

Not so much. We have a woman dancing with Baphomet or some such. Um, again?

We don’t seem to be going in the right direction. I’m not sure what’s happening. Forging ahead…

On the plus side, I’m starting to see goats.

There’s even a goat lady montage thing that’s cool in its own right, but not exactly what I ordered. Let’s get back to basic with a single-word prompt: Satyr.

Well, -ish. I forgot to prompt for a female satyr.

Ya, well. This is as good as we’re getting. Let’s call it a day, and see how the more humanoid creatures render.

Full Disclosure: A Collaborative Endeavour with Generative AI

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As the series on higher education draws to a close, it seems fitting to reflect on the unique process behind its creation. There’s a popular notion that material generated by artificial intelligence is somehow of lesser quality or merely derivative. But I would argue that this perception applies to all language—whether written or spoken. My experience has shown that generative AI can elevate my material in much the same way as a skilled copy editor or research assistant might. Perhaps, in trying to draw a firm line between AI-generated and human-generated content, we’re caught in a Sorites paradox: at what point does this line blur?

These articles are the result of a truly collaborative effort involving myself, ChatGPT, and Claude. In combining our capabilities, this project became an exploration not only of higher education’s complexities but also of how humans and AI can work together to articulate, refine, and convey ideas.

The core ideas, observations, and critiques presented here are ultimately mine, shaped by personal experience and conviction. Yet, the research, the structuring of arguments, and the detailed expositions were enriched significantly by Generative AI. ChatGPT and Claude each brought distinct strengths to the table—helping to expand perspectives, test ideas, and transform abstract reflections into a structured, readable whole. This process has demonstrated that AI when thoughtfully integrated, can enhance the intellectual and creative process rather than replace it.

In the end, this series serves not only as an examination of higher education but as an example of how collaboration with AI can offer new possibilities. When human insights and AI’s analytical capabilities come together, the result can be richer than either could achieve in isolation.