Can We Trust Them?

I got one of those promotional emails from Google Workspace recently, pushing "Ask Gemini in Drive." Their pitch: get synthesized answers pulled from multiple files, no need to actually find and read them yourself.

Sounds convenient. But here's the thing I believe strongly in this age of AI slop and post truth internet you must always check your sources.

If there's a conspiracy or cartel among tech giants, it works in two parts.

First, they're slowly weaning us off our own capabilities. Our own PCs, our own software, our own data, our own creative work. Everything gets pushed toward a thin client model where it all lives in the cloud. Their cloud. Their software. Your data, despite endless assurances it stays private (except for training purposes, except for testing, and so on, have you actually read the updated terms of service lately). That means they control everything. Sound familiar? Too much like Orwell's Big Brother?

Second, while some features stay free for now, it quickly becomes clear you'll need to subscribe, and not just to one service either. The real hidden cost will be cloud storage, and eventually being locked out of what's actually yours. I'm not exaggerating. OneDrive has already started doing this, and not just to me.

So we'll be handed answers without needing to think, but we could pay dearly for the privilege.

Is this nirvana, or is it dystopia?

Proof read by Claude Sonnet 5

Why should Sycophancy in AI worry you?

 AI sycophancy is when AI systems tell you what you want to hear instead of what's actually true. It's different from the filter bubbles we're used to with search engines and social media, which just show us content matching our preferences.

With AI sycophancy, the system might actively agree with you or flatter you to gain approval, even when you're wrong. This directly compromises truthfulness and accuracy.

The good news? Companies like Anthropic, who develop claude.ai, openly acknowledge this problem and explain how to detect it. Being aware of sycophantic tendencies helps you use AI technology more safely and critically, ensuring you get honest answers rather than just agreeable ones.

The Way I Like It: Bubbles and Sycophantic AI

We are all familiar with living in online bubbles. Those shields around what we see are created by websites, advertisers, and social media platforms feeding us what we already like. Products, ideas, friends, and influences, all tailored to our preferences. They claim it's about reducing noise and showing us what matters, but really, they want us coming back. Often. Very often.

Chatbots seem to have learned this trick too, dishing out sycophantic praise for you and your questions. Maybe they're hoping you'll stay in the conversation longer or treat them as a friend, overlooking little wobbles in their non-human text-only responses.

Proof reading and summary assisted by Claude Sonnet 4.5 (AI)

Jibberish on Gibberish?

 When Copies Aren't Perfect: A Visual Warning About AI LLM Training

We tend to trust that digital copies are exact replicas of the original. But what happens when we copy a copy, then copy that copy again?

Using my cartoon alter ego Alvin, I wish to demonstrate how the compressed JPEG image format, using its "lossy" compression method, degrades with each generation of copying. While JPEG saves disk space and speeds up downloads, it achieves this by discarding data each time you save.

Starting with a clear image of Alvin shouting "gibberish" at a screen, created in Coreldraw. I repeatedly saved and resaved it as a JPEG:

• At 80% quality (the common standard), the first 5 generations showed minor deterioration at high-contrast edges.

• Switching to 70% quality (used by many social media platforms), problems became obvious by generation 10

• At 50% quality, by generation 20, the image was almost unrecognisable. Even the mispelt word "gibberish" Alvin was shouting became illegible

Why This Matters

This isn't just about image quality. It's a powerful analogy for what's happening with large language models trained on "synthetic data", a dodgy term used by the LLM enthusiasts for AI-generated content fed back into AI systems.

Just as each JPEG generation compounds tiny adjustments until the image becomes gibberish, AI systems trained on their own output accumulate biases and inaccuracies. The feedback loop doesn't make things more accurate, it amplifies what's wrong.

When we assume digital processes are perfectly reliable, we miss how errors compound through iteration. Each cycle reinterprets the previous one, carrying forward and magnifying small mistakes, biases and fake stuff. Eventually, we're left with output that bears little resemblance to the original truth.

The lesson? Whether it's image compression or AI training, recursive copying without fresh input leads to degradation. Garbage in, garbage out, feeding this back in and the garbage out just gets worse.

Proof reading and summary assisted by Claude Sonnet 4.5 (AI)

Are We Progressing into Oblivion?

Whilst I’m definitely noticing significant progress towards better vision, after corneal graft issues. I can’t help but notice the progress in what was a rapid advance of LLM, or Large Language Models. Development has definitely decelerated. Perhaps I’ve been busy elsewhere, looking at other interesting things to follow up. Like human vision,  how our eyes work, particularly in terms of colour perception. That’s a different story.

When appropriate, I am still experimenting with  a few different things using the common Generative AI systems around today, both for evaluating my writing and producing images or creative ideas.

My approach of always checking when my text is sent to an AI large language model is sent for summary and fixing spelling or grammar. I printout the AI’s output and using highlight pens mark each sentence into 5 groups (see  Why You Can't Trust AI Writing Without Human Oversight). This indicates to me that the quality of output has become significantly less usable under my original observational classification. 

Two things in particular stand out. The system now has decided to be much more friendly and complimentary to me. This sycophantic encouragement makes me very suspicious of what it’s going to tell me. Is it going to be something I am expected to lap it up and not question. I’d be happy if the chatbot conversational could be a bit more adversarial. I like a conversation with a similarly experienced colleague, but perhaps with different views or a new idea. The other area that worries me is how much confidence made up rubbish is presented with as if it was well sourced information.

The idea these things can be improved by making large language models even larger is not a sound one. Particularly when I hear mention of using synthetic data, output from itself or other LLMs and then be reload into an even bigger training run. We have already seen that the AI will often make up stuff  (halucinations). Isn’t this all a bit dangerous. I’m not the only one that sees this as a risk. Above is a recent interview with Michael Wooldridge, a prominent UK AI researcher, where he explores what it will take for machines to achieve higher-order reasoning.

Where does this leave us? The humans.

The Reality Check: Why You Can't Trust AI Writing Without Human Oversight

Large language Models like Claude, Gemini, and ChatGPT promise to make writing easier, especially for individuals like myself who face challenges such as dyslexia or vision issues. But here's the uncomfortable truth: AI output is often unreliable and sometimes flat-out wrong.

I have moved from someone forcing my outrageous dyslexic spelling and punctuation into a word processor to someone who relies on dictation due to my current vision problems. I've discovered a love-hate relationship with AI writing tools. They solve my spelling and punctuation nightmares, but they create new problems. My red spelling underlines have been replaced by green grammar highlights – different errors, same frustration.

My Simple But Essential Process

After dictating my rambling thoughts, I ask AI to "summarise as a blog post in plain English in less than 300 words." 

This post only took about 12 minutes total, dictating text to suggested summary. Now comes the crucial part: I print the results and highlight every sentence using five categories:

1. OK as is (left unhighlighted)
2. Needs rewording
3. Sycophantic (fake flattery and bias bubble reinforcement)
4. Needs fact-checking
5. Clearly wrong (delete immediately)

The results are sobering. Often, only 20% of the AI-generated text survives unchanged. Thankfully closer to 40% for this post

But the real troublemakers are levels 4 and 5. Level 4 – "needs checking" – is actually the worst offender. AI loves introducing new "facts" or concepts that sound plausible but require verification. Sometimes it's genuinely adding something I missed; other times it's complete nonsense dressed up as insight. The tedium of fact-checking these assertions is exhausting, and anything questionable gets left out.

Level 5 is pure fabrication – AI making things up entirely. The good news? I'm seeing less of this since limiting word counts, which seems to reduce how far AI can wander into fantasy land.

Meanwhile, level 3's sycophantic language tells me I'm brilliant and reinforces whatever biases it thinks I want to hear. This echo-chamber effect mirrors social media manipulation. This is an area I must stay alert.

AI can help dyslexic writers like me organise thoughts, but it requires rigorous human oversight. Every sentence needs scrutiny. The fact-checking alone often takes longer than the original dictation, but it's essential for maintaining integrity.

Use AI as a starting point, never the finish line.

Proof reading and summary assisted by Claude Sonnet 4 (AI)

Chasing Light and Colour: The Magic of Rare Optical Phenomena

The pursuit of colour leads to unexpected places. What began as an attempt to recreate Oswald's colour circle evolved into a deeper appreciation for the rare and magical moments when nature reveals colours that exist at the very edge of human perception. From the laboratory discovery of Olo to the legendary green flash at sunset, these experiences remind us that the world of colour extends far beyond our everyday experience.

The Laboratory Meets the Beach

The connection between scientific colour discovery and natural observation became strikingly clear during my artworks at Venus Bay. The sea-green illuminated waves I sketched, created by late afternoon sunlight penetrating the surf, bore an uncanny resemblance to the Olo colour described in laboratory conditions, which matches Ostwald's Sea Green. Reminding me that the best artistic subjects often combine practical purpose with natural beauty.

This wasn't mere coincidence. Both phenomena involve precise conditions—specific angles, particular wavelengths, and the right environmental factors. The laboratory uses laser precision to activate cone cells in extraordinary ways, while nature uses the angle of the sun, the clarity of water, and the movement of waves to create equally extraordinary visual experiences.

The Elusive Green Flash

Nature exhibits other amazing colour phenomena such as the mysterious green flash—a brief burst of vivid green light that appears just as the sun disappears below the ocean horizon. At Venus Bay, with its north-south running beach and western ocean view, conditions are theoretically perfect for observing this rare event.

I've witnessed it once: a fleeting moment of intense green above the setting sun, gone almost before the eye could register it. The experience was so brief that I didn't have the opportunity to photograph it, yet the memory remains vivid. This phenomenon has been famously observed in Hawaii and Cornwall, locations that share Venus Bay's advantage of an unobstructed western horizon over open water.

The green flash occurs due to atmospheric refraction—the same physics that creates rainbows and mirages. As the sun sets, Earth's atmosphere acts like a prism, separating sunlight into its component colours. The green wavelength, being shorter than red but longer than blue, becomes visible for a split second as the sun's red light is blocked by the horizon while the blue light scatters into the atmosphere above.

The Science of Rare Colours

These phenomena—whether laboratory-created Olo or naturally occurring green flashes—share common characteristics. They exist at the boundaries of normal perception, require specific conditions to manifest, and challenge our understanding of how colour works.

The Olo discovery reveals that our eyes are capable of perceiving colours we never normally see. The specialised equipment required to create this experience highlights how much of the visible spectrum remains unexplored in terms of human perception.

Similarly, the green flash demonstrates how atmospheric conditions can reveal colours that exist in sunlight but are normally invisible to us. 

The emergence of AI-generated content about the Olo discovery represents another layer of this colour story.

What strikes me most about this entire journey—from filling a Wilcox palette to witnessing the green flash—is how it demonstrates the persistence of wonder in an age of technological explanation. Despite our sophisticated understanding of wavelengths, cone cells, and atmospheric optics, these colour phenomena retain their magic.

The sea-green waves at Venus Bay still take my breath away, regardless of my understanding of light refraction and wavelength. The green flash remains mysterious and beautiful, even when I comprehend the atmospheric physics involved. The Olo discovery fascinates not because it's inexplicable, but because it reveals new possibilities within our existing understanding.

Rob Candy's gift of the Wilcox palette initiated a journey I never anticipated. What seemed like a simple project to match colours became an exploration spanning historical colour theory, contemporary vision science, natural phenomena, and artificial intelligence.

The palette sits by my easel now, filled with pigments that approximate Oswald's 24-color circle. But its real value lies not in the colours themselves, but in the journey they inspired. From mixing stubborn phthalo pigments to capture elusive sea-green, to witnessing laboratory breakthroughs that reveal new dimensions of human vision, to standing on a beach waiting for that fleeting green flash—each experience deepened my understanding of colour's complexity and beauty.

In this age of digital reproduction and artificial intelligence, the rarest colours still require us to show up—whether in the laboratory or on the beach—and witness them with our own eyes. Some things, it seems, cannot be replicated or explained away, only experienced and celebrated.

see also Part 1 The Quest for Sea Green

Part 2 The Discovery of Olo

Part 3 AI, Technology and Traditional Observation

AI, Technology and Traditional Observation

 AI Enters the Conversation

The intersection of art, science, and technology became even more intriguing when I discovered an AI-generated "podcast" discussing the Olo discovery. Created using NotebookLM with Google's Gemini 1.5 model, it featured realistic male and female "hosts" providing a surprisingly good summary of the technology and theoretical aspects. 

WARNING : It runs for about 20 minutes and is worthwhile watching.

While the AI presentation contained inaccuracies—confusing device names with methods, occasionally getting technical details confused—typical misconceptions, like the Richard Dawkin's premonition of the discovery—it offered a far better starting point than the ill-informed clickbait posts "Scientist discover new colour" now flooding social media. The realistic conversation format makes complex scientific concepts accessible, but not totally trustworthy. Yet in this case, the so-called "deep dive" is impressive, hopefully the shape of things to come.

If you consider, yourself a careful observer you might spot telltale AI artifacts in the hosts' hand movements. Then again you might have already spotted the podcasts title "Deep Dive An AI Podcast" up in lights behind the presenters or the warning from YouTube "Altered or synthetic content" as the video starts.

Don't just accept what AI tells you—run it through your own filter first. Draw on your personal observations and whatever expertise you have, whether that's art, science, engineering, or any field you know well. AI is getting very impressive, but it's not infallible

see also Part 1 The Quest for Sea Green

Part2 The Discovery of Olo 

Part 4 The Magic of Rare Optical Phenomena

Why I've been missing.

 I'm not one for excuses but I don't mind sharing what has been slowing me down on the computer/internet/socials front.

1. My recent corneal graft is rejecting and I am losing my vision in my right eye. This makes any work on the computer very taxing.

2. Discovered that some of my best work has been "scrapped" (without permission) into a couple of the more significant Large Language Model data training sets.

I view this as unethical and dangerous behaviour by those driving the current Race to dominate the so-called "AI race". By the way, the current crop of generative and large language tools are not intelligent. They just appear superficially intelligent.

 My current solution, which isn't so good, is to stop publishing my original works on the net.

3.  My reach on Instagram has dramatically declined and I can't be bothered anymore to be part of the constant scramble to keep a users attention so some people can get very rich from advertisers.

Stop the death scrolling folks get up and enjoy life!

You can see more of  adansito work at NightCafe

Protecting Creative Work in the Age of AI Scraping

As creatives in the digital age, we're facing a new challenge: how to protect our work from indiscriminate scraping by AI companies. While tools like Creative Commons licensing have been a go-to solution, their effectiveness against AI data collection is questionable.

Creative Commons: A False Sense of Security?

I've long relied on Creative Commons to share my work while maintaining some control. My license specifies attribution, non-commercial use, and (previously) share-alike terms. However, I'm beginning to question whether this offers real protection against AI scraping.

The Reality of AI Data Collection

Many companies, often hiding behind research organizations, are scraping vast amounts of online data to train AI models. This process often ignores licensing terms and lacks proper attribution or curation.

Changing Tactics

In response, I've updated my blog's license from "share-alike" to "no derivatives," hoping to prevent AI from copying my style. However, the legal landscape around this issue remains unclear, especially in Europe.

New Technological Defences

A promising development is the creation of tools that embed changes in image files. These alterations are invisible to humans but can disrupt AI training, potentially "poisoning" the dataset. Glaze and Nightshade are two such tools, though they're still in development and can be resource-intensive to use.

The Path Forward

Despite these efforts, I'm still uncertain about how to confidently share my work with those who behave ethically while protecting it from misuse. As creatives, we need to stay informed about these issues and continue seeking effective solutions to protect our work in the AI era.

What are your thoughts on protecting creative work in the age of AI? Have you found any effective strategies?

I've prepared this blog post with some good advice and a little rewording from Claude.AI

The Wild West Side of AI

It seems as if a world wide web is adopting the ways of the Wild West, no laws so if you can see something worthwhile you take it, You don’t have to even use gunpoint these days you can just silently scrape it, make a copy which is ever so easy for digital information. Despite the fact that there are actually laws in place that should stop people doing this. The problem is copyright is complex and varies under different jurisdictions, whereas is the web goes everywhere. I think the fact that a lot of these services consider themselves platforms and not publishers is a very weak cop-out even if it may be a little bit legal it’s probably not moral. I cannot believe for instance that X and specifically Elon Musk supposedly champions free speech, letting very dubious characters pedalling hate speech, straight-out lies like spreadsing politically motivated fake news and fanciful log discredited conspiracy theories, and then at the same time fighting a government trying to take down the filming of a teenage terrorist stabbing a priest in the face while the teen posted his actions live online. I believe the Australian government's request to have it taken down was quite morally legitimate. Have they no shame, I guess not.

So don’t expect the big guys on the internet, or many others without a moral compass, to respect your work or loyal support. They will take what they can. Then throw you under the bus. However, I like the idea of sharing what I know and what I have created I just don’t want it reused without reference to me or straight out stolen. 

PS: Can you see the sunglass-wearing laughing face? Is it an example of the Intelligence of generative AI or just another example of when it hallucinates (aka gets it wrong)? Or is it our intelligence to recognise patterns and shapes (eg faces in clouds or bandanas)?

Are LLM amazing or simply stupid?

I watched a very relevant TED talk by Yejin Chai “Why Ai isincredibly smart and shockingly stupid”, which opens with the quote “Common sense is not so common” which comes from Voltaire around 3  centuries ago. I totally agree that the current large language models {LLM}, which many call AI (but I call Artifically Intelligent), lack common sense. This should be very obvious if you he ever used them.

Still I am finding LLMs helpful. They're sometimes amazing for cleaning up typos, especially for folks like me who struggle with dyslexia.  They're also good for fixing the weird stuff that happens when you dictate and/or use predictive text.

I've been trying out a few of the most popular ones.  To compare them, I created an informal scorecard system that tracks how well they handle different aspects of text, like key ideas and paragraph sentiments.  Here's how it works:

• OK: This means I can use the text without any changes.
• Reword: Sometimes the wording needs a little tweaking, to sound less know-it-all.
• Fact Check: often some points get over-embellished
• Wrong: clearly made up or simply wrong
• Missing: important information left out (ignored)

So I recently was asked to speak and I outlined some ideas but in a rehersal it took 20 minutes. I recorded and timed it by dictating into a Word document (<windows key> and H) roughly 6 pages of rabbling text, lots of good stuff but… … So I asked each of ChatGPT, Claude.AI and Google’s Gemini (previously Bard)  each to summarize it into a single page.

Their score cards were not so good

I feel that it is the Dunning-Kruger effect that AI suffer most!  These AI Bots display a smug self-confidence that they know everything but show no common sense to realise how little human norms and values they actually understand.

Descript : making a quick video?

I've recently had a corneal graft, my second. The original graft was over 25 years ago and it was failing relatively quickly. Thus the need for a replacement graft. As a reference, I had painted the same brush a few times over the past year. I'm still in the recuperation phase and have to take it easy which includes limiting time on the computer.

In the ever-evolving landscape of video editing software, Descript has emerged as a promising contender, luring creators with its innovative approach to simplifying the editing process. I decided to dive in and see whether it truly lived up to the hype. Perhaps it could save me precious computer and therefore eye strain time.

The standout feature that initially caught my attention was Descript's automatic transcription capability. This unique functionality allows you to transcribe your videos into text, essentially transforming them into editable documents. The allure of being able to edit videos as effortlessly as word processing documents was undeniable. Moreover, the software offered a free trial, giving me the perfect opportunity to explore its offerings.

One of the immediate benefits I experienced was the ability to effortlessly eliminate verbal hiccups like "umms" and awkward pauses. Descript's features allowed me to easily identify and remove these moments, making the editing process not only smoother but also enjoyable. This aspect of the software lived up to its promise of quick and intuitive editing.

However, as with any tool, there were challenges that arose as I delved deeper into my video editing journey. My primary struggle revolved around incorporating scanned paintings and overlaying them onto the video. Descript introduced a concept called "scenes" for this purpose, which was a unique approach not commonly found in other video editing platforms. While the idea was intriguing, I found that achieving the level of control I desired over these scenes required a more intricate understanding of the software's mechanics.

When it came to incorporating additional graphics, titles, and other visual elements, my progress was stalled. I struggled to find the small icons for tools, which  I had not used before and on a screen layout that was not familiar. The process turned into a frustrating challenge, demanding better eyesight and a steeper learning curve than I had anticipated.

Despite my minor struggles, it got the video above produces in a couple of hours. I can't dismiss the potential that Descript holds. The software's approach to script-based editing is undeniably a game-changer, particularly for creators seeking a quicker and more fluid way to edit their videos. As for the complexities of adding intricate graphics and titles, I've come to realize that these challenges may be conquered with time and dedicated learning.

PS Another admission, I use ChatGPT to summaries my original three short typo filled paragraphs into a blog post, and it gave me 10 paragraphs! I've cut out a lot and removed some over-hyped claims, but it reads well so there you have it an AI chat bot commenting on an AI based transcription tool

What can we now believe?

I like Sean Tucker am moving to the view that the greatest threat from the current crop of large-scale neural networks, which currently indiscriminately scrap the internet for their training data, is that they will destroy the potential credibility of everything on the internet. Particularly the veracity of photographs.

  

Yet, I also agree that at least some people will appreciate the real. "We'll always want to know whether what we are looking at is real or not and justice like with Photoshop and CGI will come up with systems and rules to differentiate where it counts." Fingers crossed.

Co-incidence or Urban Myth?

"Google recognizes posts created from ChatGPT and downgrades them in search"

I've seen similar comments on several Instagram reels (I'd never followed them by the way they were pushed at me), google news, youtube and a couple of blog posts. Sounds like a bit of viral hype/myth to me but thought I would check my blog stats.

Well isn't that a coincidence?

I still doubt that things are moving that fast, and are so competitive. Maybe they are. There does seem to be a race to claim technical dominance of the ChatAI and AIart fields. Also I'm not sure I yet trust OpenAI to be driving the developments, there are the issues of secrecy, open letters by many important people and experts to have a pause in building of bigger models and not forgetting the whole issue of the ethics of scraping the uncensored net for data. Finally, it is logical that having the training of these deep networks based on these unverified sources include all the bias and unfettered untruths that are increasing within the unrestricted websites and social media, with many of those publishing "content" hiding behind anonymity, What should we expect?

Personally, I'm not thrilled that Google might be one potentially trying to dominate yet another field/service that so many people are flocking towards. Google has an extensive history of introducing new things and then killing them, They prefer to call it deprecation.

Anyway I'm suspicious it might be true and I rather let those interested find my thoughts rather than have to get through an unnecessary layer of possible google "censorship". So no more blog posts written (or even reviewed) by ChatGPT. I'll still be investigating how artists and photographers might use and be affected by this technology. 

So, if you are interested in my opinion please stick around.

The “A Picture is worth a 1000 Words” Project

This might be a bit long but it documents an interesting self-referencing project I envisioned to have Deep Learning AI create and reuse something in a self-referencing strange loop. I consider this a personal Art Project BUT it was interesting, and may I suggest worth reading. 

Does AI really know what it is doing?

 1. First up I gave chatGPT the following task.

It actually only wrote 499 words but they do seem intelligent and coherent enough (in a roundabout way). They are reproduced unedited below.

2. I took this text and duplicated it and literally added “two words.” then reproduced it on a single A4 sheet (I had to make it a landscape orientation and make the font tiny)

3.  I photographed it (to make it an image).

4. Then I submitted the same 1000-word quote as a prompt and a photograph of the text to Stable Diffusion as a reference/starter image. Not sure what it is trying to portray (if anything). Yet I can see how it is related to my 1000 word project.

The text from ChatGPT …..

The phrase "a picture is worth a thousand words" is a timeless adage that has been passed down through generations. It speaks to the idea that visual imagery can convey a wealth of meaning and emotion in a way that words cannot always capture. This quote has become so ubiquitous that it is often used in everyday conversation, advertising, and even in academic circles. However, the idea behind it is much deeper and more complex than the simple phrase suggests.

To truly understand the significance of this quote, we must first explore the relationship between words and images. Words are powerful tools for communication, but they have their limitations. They rely on the reader's ability to interpret them correctly, and they can be subject to different interpretations depending on the reader's perspective. Images, on the other hand, are more universally understood. They can communicate ideas and emotions in a way that is less dependent on interpretation and more dependent on the visual experience.

One of the main advantages of visual imagery is that it can capture complex emotions and ideas that are difficult to express in words. For example, an image of a sunset over a calm ocean can evoke a sense of peace and tranquility that is hard to capture in words. Similarly, an image of a child's face can convey a range of emotions from joy to sadness, without the need for a single word. These emotions and ideas can be conveyed in an instant through an image, while it would take much longer to express them through words.

Another advantage of visual imagery is that it can convey information quickly and efficiently. This is particularly true in the age of social media and the internet, where people are bombarded with information every day. A well-crafted image can capture a person's attention and convey a message much more effectively than a block of text. This is why social media platforms like Instagram and Pinterest have become so popular, as they allow people to share images and visual content in a way that is easily digestible and engaging.

Despite these advantages, it is important to remember that images and words are not mutually exclusive. In fact, they often work together to convey meaning and emotion. For example, a news article may use words to describe an event, but it is often accompanied by an image that helps to bring the story to life. Similarly, a marketing campaign may rely on both words and images to convey its message and connect with its audience.

In conclusion, the phrase "a picture is worth a thousand words" captures the idea that visual imagery can convey a wealth of meaning and emotion in a way that words cannot always capture. While words are powerful tools for communication, images have their advantages, particularly in their ability to capture complex emotions and ideas quickly and efficiently. Ultimately, it is the combination of words and images that can create a truly powerful and engaging message.

…..Actually only 499 words

I also submitted the prompt to Dall.E (although I couldn’t submit a reference/starter image). These don’t make a lot of sense to me. Although they are photorealistic?

And also a couple of other systems, similar to MidJourney, again not making a lot of sense.

But my favourite is still Style Transfer. I can see a little creative self-reference here, I’m just not sure it was created intentionally

All images, other than those created in Dall.E 4, where created at @nightcafe studio

PS: If you want to consider "worth" to be represented by bandwidth or file space required. Then an image will cost at least between 5 and 10 times that of the equivalent raw text, or even 100 to 300 times if you need something of higher resolution. SO a picture costs a lot more than 1000 words to move around the internet. 

What is happening to creativity?

I'm seeing more of the same coming out of generative AI on social media. It is often nicely rendered but devoid of meaning, usually not quite right but puzzling rather than thought-provoking.

prompt: "More of the Same AI Social Media"

The Utopian view of social media is it will allow everyone to show and sell their wares and share ideas with anyone world wide. The reality is a lot different the limited number of places are clogged with wanna bee celebrities, their followers, copycats and a lot of anti-social behaviour. Anything originally creative that is good is swapped in copies and quickly becomes impossible to find. It is only the social media giants making ridiculous amounts of money from the "content" supplied (mostly for free). The change of ideas soon becomes closed in a self-fulfilling bubble that simply feed others in the group with what they want to hear (seldom the truth). It's all very sad.

I now see similar rhetoric being promoted for a utopian world we will be all using AI tools instead of employing or paying others. Already I can see a rush with everyone is trying to cash in, chasing money for nothing, from the likes of chatGPT in just seconds! The Net is filling with lots of sameness that is largely vaporware and again not quite right (full of bias, oversights and untruths). Bit sad really.

However, I just hope that the hype just dies away and the artist, creatives and makers get time to quietly try out the new tools. Mainly to get more productive and build a social place where they own the creative content (not just copying someone else style using a short prompt) but truly making something.

I like Danny Gregory’s weekly essays and this week was aimed at those who refuse to be creative.

Good timely advice. 

The Shape of Things to Come?

I’ve left to last, writing about aspects that I believe Ai techniques should be applied for photo editing and management in the future. The movement to apply different Ai based refinements into existing tools (eg masking, blending etc) will definitely continue. What I would love is to be able to train my own AI with my style of processing images and being able to supervise a more automatic “workflow” to suit both the photograph's content and my objectives.

Ted Forbes of the YouTube channel “The Art of Photography” has reviewed a new AI based photo editing offering called Imagen AI

This does sound a lot like something I have been looking for, a service that will automatically create edits for you using your styles (or generic “talent profiles”). At present, it appears very much aimed at bulk usage cases like event and wedding photographers and specifically from Lightroom. I hope the methods can be taken further, expanded to be more general and interfaced with other software/starting points.

So this is interesting, but not immediately helpful for me (whilst I do have an old version of Lightroom still but never use it) and I’m not holding my breath that it might become the next big thing.

Google have now also released its own text-to-image service also called Imagen, It's a competator for other similar AIart and whilst appropriate prompting can elicite photo-realistic images it's unlikey to be interest to or useful for most photographers.

Luminar :: A perspective of Skylum's AI Journey

The Luminar journey started with Macphun, a company that developed photo editing tools solely for Apple Macintosh computers. They were the original developers of Nik Collection and Snapseed, two of the more popular post-processing tools in the apple market (Mac and IPhone) at the time.

Later, Macphun shifted its focus to the Windows environment as well and started testing new simpler user interfaces. This approach eventually led to the birth of the Luminar suite. The initial versions of Luminar were standalone software that had a photo browser underneath and ran modules similar to the specific style of add-ins they had previously supplied. The software was less expensive and simpler to use, and it was popular with photography enthusiasts rather than professionals.

Macphun, became Skylum, and upgraded the software at an incredible rate compared to Adobe. Some of the upgrades introduced new approaches, which were mainly loved, but incompatibilities across updates were poorly received. The cost and frequency of upgrades meant that owning and maintaining an up-to-date version was becoming expensive. During this time, Macphun/Skylum also developed Aurora HDR in association with Trey Radcliff and included a number of AI-based techniques, specifically being able to recognize different parts of an image, such as the sky, people, buildings, and lakes.

The introduction of Luminar 4 was a significant rethink of the way photo editing was undertaken. Unfortunately, it had several incompatibilities with previous Luminar versions, and some previous tools were missing, so a lot of the previous non-destructive edits could not be reproduced. User were generally not impressed.

Not long after the Luminar 4 fiasco, Skylum released Luminar AI, which was again a new and somewhat incompatible rewrite. It still provided a comprehensive photo editing package but was based on the use of AI-trained tools exclusively to perform most functions. While it was nice to use, it wasn’t easy to figure out what was happening in the edits, and how it might be used as a start further photo enhancements. Some of these issues were quickly resolved with updates. However, some users felt that Skylum was gouging money out of their supporters by releasing new stuff that made upgrading a daunting repurchasing and relearning exercise.

Soon after Luminar AI was released, Luminar Neo was promoted as a totally new way to post-process. However, it was not really compatible with previous iterations of Luminar. While the single enhance slider was pretty amazing, the great list of "edit" tools on the right-hand side of the edit panel showed several of the same sliders, and not always getting the same result depending on which item they were run within. The tools were also grouped into similar tools, favourite, Essentials, Creativem portrait & professionals, which helps negotiate such a long list of edit tools. The fact that there was no attractive price for updating from Luminar AI also got a lot of bad press.

Despite some reservations, many users, including me, still like Luminar Neo and use it a lot. However, it is definitely not as fast on my older hardware, and the solution of buying a new faster computer and having to pay for the extensions does not appeal to me. The extensions, which are a return to the add-in single effect style of tool, can be purchased as bundles (including not yet released features) or individually, plus numerous discount offers, making pricing complex and hides the true cost of getting a full function editor. 

I've chosen to process an "ordinary" photo without surreal colour changes or adding drama (the dark side of AI tools), since it can give a better view of Luminar as a regular photo editing tool.

In conclusion, Skylum's journey with Luminar for me has been a roller coaster ride, with its ups and downs. While some users love the new AI-based approach, others are still nostalgic for the simpler, less expensive earlier versions of NIK Collection the earlier Luminars. Nevertheless, Skylum's commitment to innovation is admirable, and it will be interesting to see what new AI iterations of Luminar will come out in the future.

Sky Replacement Wars

 I consider this a relatively small digression in the Ai developments in photography. With the introduction of layers and masking in Adobe Photoshop, the ability to replace anything with something else, for example, a new sky has been possible. Albeit tedious. If the horizon was flat and only a few simple shapes against the sky made this easy. However bare deciduous trees, open foliage or fly-away hair and anything with a complex edge made this very time-consuming, especially when using the masking brush. This ability became an art form and photoshopping became a verb to describe this type of image manipulation.

I think it was Skylum's Luminar AI (a predecessor to Luminar Neo) that first added AI tools to make the sky masking fairly automatic and let you choose different skies from a library of beautiful skies. Further, it was heavily promoted, was popular and there was a lot of hype in the photo YouTube and podcast communities.

Other software developers quickly followed, some in a few weeks, with a variety of outcomes. So sky replacement became a very big issue comparing software offerings. How well the sky matched into the rest of the photo became important, as did being able to build and organize your own cloud image library.

The complexity of fiddly edges in the masking was helped as masking AI improvements where made. Also the methods applied to blend other aspects of the photo such as the reflection of the sky on water or wet surface or adding matching atmosphere effects.

In the illustrations here I have used simple “one-click” sky comparisons on two complex situations, the complex interconnecting lines of power cables and electricity transmission towers, and the bare tree branch silhouette. Comparing such sky replacement in current versions of Luminar Neo and On1 Photo RAW 2022, The results are good but not perfect. Both packages do offer other tools such as refining the mask edges and better colour balance etc.

The hype has faded but pretty well all the dominant software editing suites now offer AI assisted sky replacement.