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.

What's Next for AIart? (Dystopian Surprise!)

Lately, I've been a bit out of the loop with the rapid developments in generative AIart. Life threw a curveball with some eye issues and a corneal graft, which severely limited my computer time – even with NightCafe's regular emails urging me to claim my daily free credits.

My vision is improving, and I decided to jump back in by following one of NightCafe's invitations. This time, the free credits led to another invite to join one of their weekly challenges; they even pre-filled a prompt for me! I only caught the first couple of lines but decided to see what might happen. I'd previously experimented with prompts about AI/robots making art, so I wondered if that had "seeded" my new prompt, or perhaps another AI had "scanned" my earlier work on NightCafe (nothing impressive, just me playing around with the generative AI tech, by the way).

The image generated quickly, and I was genuinely taken aback by how dystopian and depressing it felt. It struck a chord, echoing my own concerns about the "look-at-me" culture of celebrity-seeking masses, which often feels like a desperate race to the bottom. Are we, as real artists, being replaced by soulless images churned out by "pseudo-intelligent" applications, all designed to grab attention so that advertisers pay more? Was I becoming just another cog in a process, simply there to click a link or hit enter?

"A melancholic robot with glowing eyes, standing in an abandoned art studio, surrounded by discarded paintbrushes and canvases, with a single tear of oil streaming down its metallic face, in the style of surrealism, with melting clocks and distorted perspectives, reminiscent of Salvador Dali, with a matte background and a somber, introspective mood."

Upon looking at the full prompt (above) and selected Flux model settings, I realised this prompt was likely just a refinement of my last work from a month or so ago (when I was effectively blind in one eye), rather than a new AI "spoon-feeding" me. The original prompt was: 

"a blind artist being assisted by an AI robot to create a large abstract painting surrealism Salvador Dali matte background melting oil on canvas."

I had used the Dreamscape XL Lightning model, a diffusion style model I was starting to like. However, the results weren't very Dalí-esque. A couple of the generated options had reasonable likenesses of Dalí, but nothing suggested he was blind or that a robot was assisting him. he just looked sad. Plus, the backgrounds were sharp and detailed, not melting, surreal or painterly. Was I to blame?, "Does my prompt need refining?" Yeah-Nah I just lost motivation and stopped.

Perhaps this is the true moral of the story for the artistic and creative communities: Keep an eye on what's going on, but don't worry too much. There's a lot of real life happening away from your computer or phone screen, and it's okay to let the over hyped generative AIart 'race to the bottom.' A bit sad really, if we want to benefit from the technology.

P.S. I even used Gemini Ai (Gemini 2.7 Flash) to help fix up my dictated jipperish for this post!

Is AI getting “good enough”?

In two minds about using AI for art works

This is an AI image I created with the prompt above, using google Deep Mind’s latest incarnation of its generative app Imogen-3. it really looks better than most other generative AI. Less overcrowding with intricate but less relevant detail, with consistent lighting and better capturing the emotional direction of my prompt. It was also my only creation with this prompt.

Google claims 

"We’ve significantly improved Imagen 3’s ability to understand prompts, which helps the models generate a wide range of visual styles and capture small details from longer prompts.

To be even more useful, Imagen 3 will be available in multiple versions, each optimized for different types of tasks, from generating quick sketches to high-resolution images."

Ok that’s nice wording google but what does it mean and why do I see such a difference to other prompt generated images

Well perhaps there is a hint right at the end of their hype.

"Imagen 3 was built with our latest safety and responsibility innovations, from data and model development to production.

We used extensive filtering and data labeling to minimize harmful content in datasets and reduced the likelihood of harmful outputs. We also conducted red teaming and evaluations on topics including fairness, bias and content safety.”

Recent developments in AI technology have raised some intriguing questions about data handling, promoting fake news and bias. It appears that at least google is implementing input checking mechanisms on the information they use to train their models. This likely extends to assessing image quality as well, ensuring that the data fed into these systems meets certain standards. Looks “good enough”.

However, this observation leads to a more pressing personal concern: 

Does responsible AI development truly encompass ethical practices across the board? The issue of data collection methods remains a significant point of contention. Are these companies indiscriminately scraping data from various sources without proper consent or consideration?

This brings me to a personal worry many of us share: the privacy of our own data, particularly our photos. With the prevalence of cloud-based photo storage services like Google Photos, and the vast number of images captured and uploaded from mobile devices daily, it's natural to wonder about the security and usage of this data. I worry some companies are "hoovering up" these personal images en masse? If so, what are the implications for our privacy and the control we have over our own digital footprint? 

As consumers and digital citizens, it's important that we stay informed about these practices and advocate for ethical standards in AI development. The balance between technological advancement and personal privacy is delicate, and it's a conversation we need to keep having as AI continues to evolve and integrate into our daily lives.

Luminar Neo's GenAI Tools Need More Time in the Oven

Luminar Neo recently added three new generative AI tools called GenErase, GenExpand, and GenSwap, all supposedly based on generative AI technology. I’d seen a bit of hype about them and they just turned up for a 30 day trial, so I just need to play and try these new features. I have to say they clearly needed more time in development before being released to the public.

The tools are only available through Neo's subscription service presumably because they utilize cloud computing power. This means they will likely never be available to run locally on a desktop.

I was most interested in testing out GenExpand, which is supposed to let you extend the edges of an image. I do like Neo’s Panorama stitching extension but I often get bulbous, untrimmed images when stitching together handheld panoramas in Neo, so I thought GenExpand could help with that. Unfortunately, my first attempt to expand a massive 588MB panorama got stuck taking forever and then just produced blackness over the area I’d selected. Oh well, back to the drawing board.

 A smaller test image did successfully expand, but the new edge addition was blurry and grayscale. On closer inspection the horizon matched byt clouds and waves didn’t matchup well to the original.

Hoping for better luck, I tried GenSwap to insert a kangaroo into a photo. The AI clearly wasn't trained on enough Aussie animals, its a bizarre creature but “thats not a real kangaroo”. At this point, my enthusiasm was waning.

Finally, I tested GenErase to remove objects from photos. It performed decently but didn't seem much better than the standard erase tool already in Luminar Neo. Trying to erase a larger object again resulted in the tool freezing up.

In the end, while the ideas behind these new GenAI tools are intriguing, I feel they simply aren't ready for practical use. Too many bugs, glitches, and failures to finish make them more frustrating than functional. Luminar Neo would have been better served by traditional beta testing before releasing them. For now, I don't trust these tools, or for that matter many other developers' generative AI tools to deliver satisfactory results, or are my expectations too high? Maybe someday the technology will mature into something more reliable, but for now GenAI feels more like a breakable flashy toy and “trying to keep up with the Jones”.

Where Do We Go From Here?

Testing Out New AI Tools for Writing - The Good 👍and The Bad 👎

I've been experimenting with some of the new large language AI models like ChatGPT Bard and Claude to help summarize and clean up my dyslexic writing. At first, it seemed amazing - I could just dictate my random thoughts and the AI would turn it into clear, readable text. I even had it generate content like blog posts, YouTube scripts, and Instagram captions.

However, I started noticing some issues:

• The AI can be overenthusiastic, especially when mentioning product names. It reads like advertising copy. I have had to rewrite these sections to keep them factual.

• Outrageous claims and incorrect facts. About 40% of the time, the AI includes claims or "facts" that are just plain wrong. I end up removing entire paragraphs.

• About 30% of the time, the content is good as is. The other 30% needs some reworking to tone down the language.

Clearly there's an issue here with misinformation. My current theory is that these large language models are trained on in-discriminant internet data containing conspiracy theories, misinformation, and bias. Garbage in, garbage out. 

I'm finding Anthropic's Claude model more reliable with fewer glaring errors. I have used it on this post, but I still have to carefully review any AI-generated text before publishing. 

As AI becomes more ubiquitous, it's crucial that we understand how these models are trained and what biases they may contain. We have to establish checks and balances, verifying information and not blindly trusting AI outputs.

I'll keep experimenting with AI writing assistants, AI in photography and digital Graphics (generative AI images such as the one abouve), but always maintain oversight. Stay tuned for more on responsible use of generative AI.

Asking AI for help writing this post

I had set up this topic to do a little test of AI chatbots from Open AI's (chatGPT) and Google’s (Bard), using the simple prompt “write a short blog post on why an old TV makes a great monitor for an artist's studio”. Well, it was a no contest, ChatGPT took old TV to mean a cathode ray screen and its text was overly enthusiastic and provided very dubious reasons. Bard did provide clearly correct information and a couple of items I’d overlook. Neither appealed for direct use as the "content" in this blog.

So then I tried Generative AI (Text-to-Image) comparing Stable Diffusion versus Dall.E (using nightcafe studio). I altered the prompt a little “Artist viewing a wall-mounted LCD TV to copy a reference photo and paint at easel”. Again pretty unusable perhaps except for the large one shown below. It’s superficially ok has some artistic merit and just might be partly on topic.

Worryingly he appears to be considering painting on the wall-mounted picture, which isn't a TV!
Using his right hand attached to his left arm!!

Maybe I’m getting a bit picky now. I’m a little spooked now that Google might be downgrading access to your blog if it’s using chatGPT.