Anna Ridler Artist – CAS AI Image Art talk 2023 transcript

ANNA RIDLER – Artist

It was really interesting hearing both of your talks [Geoff and Luba], and especially it’s always a pleasure to speak after Luba because, as she mentioned, we have been working together quite closely for the past five, six, seven years has it been? And it’s been really interesting to see how the space has evolved in that time, especially now with the recent advancements in these diffusion models and the text to image models. I’m most well known for my Tulip projects, which I will talk about. But I did also want to which I made using Gans. I want to touch on how the field has changed, because now when you talk about AI art, it does now have, I think, like if you go on Twitter or if you go on Discord, it very much is being used to refer to a very particular type of work, which is this text to image work. What I’m showing now is just some very quick examples of Dall-E Tulips that I made. I was actually very lucky, and I was one of the first people to get access to Dall-E back when it was released.

And I found it actually really difficult to make work with. And it’s taken me a long time to get my hands into things like stable diffusion and Daly and mid journey to make work with because I find that the way that it’s structured and the way that it’s designed, particularly with Daly, I found it really you have no access to the code base. You have no access to the data set. There’s no way that you can tinker with it. And you’re very reliant on an API and everything is closed off. So as an artist who’s very interested in the tools and the means of production, I found it very difficult to get into and work with. I mean, that is changing. I still think there are conceptually interesting things that you can do with it. There’s really interesting research that is coming out around how it relates to memory and how it relates to and you can do some interesting things around language and ontology, but because for the most part, it’s locked away. And even when you’re working with stable diffusion, I find that you can’t look through all of the data and you can fine tune it, but you’re always going to be working off the base of the like the massive lion data set.

But for me, it’s been a long time to get to a place where I think I can do something with it. And that being said, there’s so much being produced every day with it. It does feel like magic when you play with it. I remember the first time I typed something in and got these images out. It did feel so incredible to get it. But it does raise this real question about kind of, I think, what art is, because not every image is necessarily art. And I think that’s a debate that is now going on because so much is being produced and there is this question about where does the art sit? And I think for me, the art is very much like how it’s then displayed or the message that it contains and the experience that someone has through interacting with it. So, as Liber mentioned, I’m most well known for my data sets, the work that I do with data sets. And this isn’t something that is just explicitly linked with machine learning. It’s something that I’ve worked with for a much longer period of time.

I’ve always been interested in archives and libraries and data and information because for me, like every piece of data, every piece of information is a trace of something that once existed. In many ways, I feel like reconstructing that data or that data set is like a very human thing to do work almost like a detective building up these bits of data to produce an idea or a story or to use it in my project. I think there are lots of interesting parallels between encyclopaedias and dictionaries and the history of those with some of the issues around machine learning and data sets that I’ve explored. This project that I showed in the previous slide, and it’s playing now, which was commissioned by the Photographers Gallery, where I essentially created my own ImageNet using Victorian and Edwardian encyclopaedias. I think one of the things that’s also really important for me and part of my practice is showcasing the labor that goes into these projects and the way of working so it’s not just for me like the final output.

It’s also how I got to that output. And so a lot of the time, I will document the process of making, and that documentation will be equally as important part of the final project as the artefact. That’s something that I come to you again and again in my work project that Luba commissioned me to make back in 2017. It was a project that really took off for me, actually, which is about Chileps, where I created a piece using a GAN. It was a Singan back then, the first one, which I trained on 10,000 images of tulips that I took myself. I didn’t make the tulips myself. I was in the Netherlands at the time working. And one of the reasons why I was really interested in tulips was I was making a comparison between Chulip Mania this was the first no speculative bubble and bitcoin and also the bubble that was going on around AI at the time. So in the gan piece shall show in a bit. The gan is controlled, the price of bitcoin. And it was a really important project for me to do.

And I spent a lot of time building this data set. And it’s you have a very different relationship to the data when you’re when you’re working with it very physically. Carrying all of these tulips was very heavy. Stripping them was very heavy. And one of the reasons why I stopped at 10,000 tulips wasn’t because it’s a very nice round number, although it is it’s because tulip season ended. So even though this was a very digital project, it was driven by the rhythms of nature. After I took these, if I can go to the next slide. After I took all of these photographs, I really wanted to display the data as an artwork in and of itself, which led to a separate piece called Myriad, where I’ve taken the photographs and showed them with some of the labels that I attached to them handwritten underneath. And for me, it was part of the way to draw the attention to all of the human decision making that sits somewhere in the chain of AI. Because at the time that this was being shown back in 2018, there wasn’t yet that discussion and bias around how human, Eric can creep into these systems.

This piece, when it was shown, and you can see on some of them that you can see my handwriting where I’ve crossed things out. And the piece, when it’s shown, is huge, and it’s around 50 m². It’s only been shown its entirety twice because you need quite a large space to put it up in. And I think it also gives people a real sense of the scale of data, because 10,000, when you scroll through on a thumb drive, you don’t really understand what that means in the way that you do when your body is physically reacting to it. So it takes a long time to walk by all of these photographs, and you get a sense of how long the process of putting it all together was and the labor, effort, energy and all of those things that sits behind creating a data set. Another reference point for this project was very much the Dutch still life, like the Golden Age Dutch painter, very heavily referenced in how I composed my data set, which is also another reason why I really like doing things myself.

You can’t well, I suppose you can now with like tools like stable diffusion and things. But at the time, you can create a data set of you can Google and ask for 10,000 images of Tulips against a black background. And one of the things that I really liked about the further comparison that I liked about like these Dutch steel lifes and the way that Gans work is that these paintings, the flowers in them can all exist at once. They’re flowers from spring and summer and autumn and winter that are combined. So they’re botanical impossibilities, these bouquets. But they’re combined using all of the fragments that the artist has got of the flowers that he’s seen, sketches and memories and things like that. So rather than copying from nature, paintings are drawing in from the experience of the painter. And for me, that’s a really nice parallel to how gowns work. They’re not merely copying images from the data set and collaging them together, but creating an imagined botanical possibility through the knowledge that it’s gained through the data set.

So I think there’s, like, that nice parallel that exists there. The other reference that I like to bring out with this project is the history of floral data sets that sit inside machine learning. This is the Iris data set, which is inside psychic learn. So every time that you’re importing that into a piece of code, you’re also importing the Iris data set, which was something that it was a data set created by Ronald Fisher, which has all this different data about Irises. So there is this history, this hidden history inside machine learning of Laurel data sets, which is also something that I quite like in this project. The final piece. This is a later made two versions of it. This is the 2019 edition that I made after StyleGAN was released. It’s three screen installation. And as I mentioned, the tulips are controlled by the price of Bitcoin becoming more stripy and open as the price goes up. The title references the disease that gives Tulips their stripes, which was also made them the most valuable at the height of Tulip mania.

And it’s asking questions around value and around notions of value and like speculation and collapsing these two different moments in history. And what I also really enjoyed about this project, because it is a very complicated project, was that through working with the data set and with the Gan, I was able to explore very different things in each part of it. So the data set piece was much more explicitly about machine learning and about the issues and ethics that maybe sit inside of it. Whereas the Gan piece was much more talking about something not really related to the technology and about like wider questions around value and notions of like speculation. I said, it’s a work that still gets exhibited quite regularly in various different variety of different institutions and cultural spaces everywhere. From like public it was on like buses in a town in Germany, just as a very pretty, moving image piece through to critical overviews as to where photography is going in different museums. And then because I know we don’t have masses amount of time, I just wanted to end with something that I often end talks that I do where I often am asked about where I see like AI art sitting.

And I can only answer for myself because I’m not a curator. But I find that I look back into history to see where it might go and I find it hard to connect to some of the early algorithmic artists. But I find it very easy to see the parallels between my practice and land and environmental artists. And I often look to them for inspiration, because land and environmental art is so much about planning. It’s so much about thinking through like all the various different possibilities and then allowing something that you can predict but you can never control, to then act on that planning. And for me, that’s the same as spending all of this time building my data set. All of this time like constructing like a model and then like pressing go and then allowing something to come out of it. And then also like there is this question about where the art sets. And in London Environmental Artists, a lot of it is in the documentation. I think for AI artists it’s also the documentation.

It’s not necessarily like the model as it runs or like the insides of what’s going on it’s the images, it’s the sound, it’s the performance that comes out of it. And so, yeah, that’s like where I wanted to end it and how even like now, I’m still amazed at the possibilities that this technology can offer and how inspirational I find out on a daily basis.

13 June 20239 May 2025

Luba Elliott curator – AI Art History 2015-2023 – CAS AI Image Art talk 2023 transcript

LUBA ELLIOTT – AI Creative Researcher, Curator and Historian

Visit Luba Elliott’s Creative AI Research website.

I’m looking forward to giving a quick overview of AI art from 2015 to the present day. These are the years I’ve been active in the field and part of the recent generation that includes Anna Ridler, Jake Elwes, and Mario Klingemann.

To start off, I’ll mention a couple of projects to explain the perspective I’m coming from. I began by running a meetup in London that connected the technical research community and artists working with these tools. That led to a number of projects: curating a media art festival, organizing exhibitions at business conferences, and launching the NeurIPS Creativity Workshop, which is probably one of the projects I’m best known for. This was a workshop on creative AI at a major academic AI conference. Alongside that, I also curated an art gallery that still exists online from 2017 to 2020. The workshop now continues, currently run by Tom White, an artist from New Zealand.

If you’re interested, you can still submit work to it. I also curate the Art and AI festival in Leicester, where we exhibit work in public spaces around the city. I’ve done some work with NFTs, including exhibitions at Faro File and at Unit Gallery.

Now I’ll start the presentation, and I usually begin with Deep Dream. This was a technology that came out of Google in 2015. You’d input an image, and the algorithm would enhance certain features, producing vivid colors and strange shapes. It was one of the first developments that excited the mainstream about AI. It’s still one of my favorite projects because it’s quite creative and aesthetically interesting. Few artists continued working with it. Daniel Ambrosi is one who has, often creating landscape artworks that retain the Deep Dream aesthetic while preserving the subject matter. That’s important because many artists let the aesthetic overshadow the image itself. Ambrosi has also experimented with cubist influences to refresh his approach.

Then came style transfer, where you could take an image and apply the style of Monet or Van Gogh. This excited many AI researchers and software engineers, who saw it as a representation of art similar to what you’d find in museums. In contrast, many contemporary artists and art historians found it unappealing because today’s artists aim to create something new, whether aesthetically or conceptually. Interesting work in this area often requires broadening the definition of style beyond just artistic style. Gene Kogan, a key figure in the field, created variations of the Mona Lisa using styles like Google Maps, calligraphy, and astronomy.

Next came GANs, which gained popularity around 2014 and evolved rapidly over the next few years. By 2018 or 2019, they were producing photorealistic images. Some of my favorite works come from the earlier GAN period. Mario Klingemann created striking images exploring the human form that drew comparisons to Francis Bacon. These early works often contained visual glitches—misplaced facial features, oddly angled limbs—which became integral to the artistic expression. As GANs improved, artists had to move beyond relying on those glitches.

Scott Eaton is one example. He deeply studies human anatomy and uses GANs to combine realistic textures with slightly distorted forms familiar to those tracking GAN development. Mario Klingemann also continued experimenting. At a show I curated for Unit Gallery, we displayed two of his works: one from his 2018 “Neural Glitch” project, and another made using Stable Diffusion based on the earlier image. The newer image is more realistic, illustrating how much the technology has advanced.

Ivona Tau explores machine learning itself. One of her projects involved machine forgetting, where image quality deteriorated over time, challenging the usual goal of improvement in machine learning. Entangled Others—Sofia Crespo and Feileacan McCormick—have done fantastic work inspired by the natural world. Their recent projects combined generations of GAN images to create creatures with traits from multiple species.

Other artists have focused on how to display their work or how to engage with ecosystems. Jake Elwes, whose work is currently on show at Gazelli Art House in London, trained an AI on images of marsh birds and installed a screen in the Essex marshes. Real birds interacted with this digital bird, creating a fascinating encounter between two species.

In sculpture, Ben Snell created a project called “Dio,” where he trained an AI on sculptures from antiquity to modern times, then destroyed the computer that created the designs and used its remains to make the sculpture. Conceptually, this is far more developed than many other AI art pieces and recalls 20th-century artists who destroyed their own work.

Roman Lipski is an artist who considers datasets deeply. Though he primarily paints landscapes, he experimented with AI by photographing a scene, painting nine versions, training an AI on those, and responding to its output. His style evolved through this interaction, becoming more abstract and cooler in tone. Despite using digital tools, he continued working in physical media like paint and engraving.

Helena Sarin is known for using her own datasets and developing a distinct aesthetic. She often combines media—flowers, newspapers, photography—with GANs to create highly original work.

Normally, I talk about Anna Ridler’s tulip project, which I commissioned for the 2018 Impact Festival. Since she will be discussing it later, I’ll just mention that she made a conscious effort to highlight the human labor behind AI art. Her exhibitions often paired generated tulips with walls of hand-drawn flowers, drawing attention to the dataset—a rare approach at the time.

In more recent years, with the rise of DALL·E and CLIP, attention has shifted to text-to-image generators. These tools create images from written prompts and have changed the focus of AI art. Earlier AI artists often explored the underlying technology or its ethical implications. In contrast, much current text-to-image work is more focused on aesthetics.

Some projects still stand out. Botto, by Mario Klingemann, operates as a DAO. A community votes on which image to sell, and during the NFT boom, some pieces fetched over a million euros. Vadim Epstein has worked deeply with CLIP, developing a personal aesthetic and narrative video works. Maneki Neko, whom I curated in an NFT exhibition, creates intricate, detailed images that feel distinct from typical Stable Diffusion outputs, likely combining multiple images and heavy post-processing.

Ganbrood has found success with fantasy-themed images. Artists like Varvara and Mar use text-to-image generation to design sculptures. Controversies have emerged too. Jason Allen won first prize at a US art fair with an image made using Midjourney and Stable Diffusion. Critics questioned whether he clearly disclosed the AI’s role. He argued that refining the prompt was itself artistic labor.

At the Sony Photo Awards, Boris Eldagsen submitted a more ambiguous image that won praise from judges. He later withdrew it, aiming to spark discussion about AI’s place in such contests.

Jake Elwes’ “Closed Loop,” made in 2017, involved two neural networks: one generated images from text, the other generated text from images, creating an ongoing dialogue. It demonstrates both how far the technology has come and how conceptually rich earlier works were.

To close, I want to highlight a project by South Korean artists Shin Seung Back and Kim Yong Hun. They used facial recognition in a fine art context, asking portrait painters to disrupt the algorithm’s ability to detect faces. The results varied—some still looked like portraits, others did not. One portrait took me a long time to recognize because the face was tilted 90 degrees. It’s a brilliant example of using AI tools outside their original purpose to produce meaningful art.

That’s the end of my presentation. You can find out more about my work on my website or email me with any questions. Now I’ll pass over to Geoff for the next speaker.

13 June 202326 June 2023

AI & Image Art CAS Talk 1 June 2023 – video & transcripts online

AI & Image Art CAS Talk 1st June 2023

The talk included Geoff Davis (host and Introduction), Luba Elliot (curator) with a history of AI Art, and the artists Anna Ridler, Mark Webster and Patrick Lichty.

Transcripts are below the video. With thanks to CAS and Sean Clark.

AI and Text talk is also online, please see the AI & Text Transcript page which has the video link, or visit the Computer Arts Society Talks page

The AI & Image Art CAS talk video:

TRANSCRIPTS

Geoff Davis – Introduction – AI Researcher at UAL CCI London, Artist

Luba Elliott – Curator, Creative AI Researcher, Historian

Anna Ridler – Artist

Mark Webster – Artist

Patrick Lichty – Artist, Writer

31 March 202331 March 2023

AI News summary

Now my new book AI Creative Writing Anthology (Goodreads link) is out, I will add any interesting news in blog posts.

AI Creative Writing Anthology Geoff Davis 2023 GPT-4 ChatGPT — AI Creative Writing Anthology Geoff Davis 2023

BBC News – Friend or foe: Can computer coders trust ChatGPT?
https://www.bbc.co.uk/news/business-65086798

OpenAI may have to halt ChatGPT releases following FTC complaint

A nonprofit claims OpenAI is breaking the law with a ‘biased, deceptive’ AI model.

https://www.engadget.com/openai-may-have-to-halt-chatgpt-releases-following-ftc-complaint-172824646.html

2 December 202010 August 2023

Tower of Doom part 2: The Bloody Tale

Episode 2 of the Tower of Doom.

This story has been retired.

1 December 202017 May 2021

A Mountain of Pent Up Tears – children’s stories generated texts

Children’s stories – this is part of the classic literature series on this blog, experiments in hybrid fiction.

I turned out so fast, I barely looked back. (I turned to look, only to find the figure had gone.) What has gone before was the dark room.

In the far left corner lay a pile of books. The ones on the page, all full of life’s lessons; the black ones, I thought of the colorless, the dark books, my soul had never seen red in my life. These weren’t books of old stories. These were children’s stories told by old schoolboys, and children’s books have been around ever since:

This was written in the 1840s when young, red dwarfs lived in the forest. They wore their clothes brown and brown and purple. At the close of the story, the author makes the child a dwarf, the woods were too far for them to go, so they are made brown. His book is told like many children’s stories.

“This is where the forest is.”

“The forest, where do I go then?”

“In the woods. In the woods, as you said.”

“I, too, am brown.”

“Yes, I am. I am in the forest. It is the way of the forest.”

“And me?”

“Yes, too, me too.”

“And you too?”

“Yes, too.”

The forest is the only place in which these children can know what things are true. They see the forest as their only home. If they go away, then they see them as an enemy who must be conquered. But these children have all travelled over, and gone past. They’ve seen it all over again.

They know what the forest is; how it works, how things go. They can see everything. And for some reason they know that they are all in the same boat, that they can never cross over to other paths. So they know, even in that boat, what the forest means, and why it is there.

What’s behind the green, silver-tinted door. Where’s the white door? That’s not the forest, is it? The white door has never been there. What was behind that door, the door to the land?

They do not know why they were born into the woods. They think that the forest is God’s gift for them. But what’s there to be gifted for? They are the only ones, after all.

When asked how the sun comes out, and when asked why the sun comes out.

Answer: There is no one here who knows.

And they need not have seen the sun.

GD: the first 68 words of the generated text (above) was next used as the prompt to generate the text (below). The 68 words are shown in italics.

I turned out so fast, I barely looked back. (I turned to look, only to find the figure had gone.) What has gone before was the dark room.

In the far left corner lay a pile of books. The ones on the page, all full of life’s lessons; the black ones, I thought of the colorless, the dark books, my soul had never seen red in my life.

There, I thought, was the door leading to what was yet to come. But in the darkness, an unseen figure, one I couldn’t yet recognize, looked from the left to the right, slowly. As it followed, my eyes scanned the walls, but I couldn’t find the same room. I reached out to touch the door, but the figure stayed, a blank shadow across the room, it never left the door. It seemed to vanish into nothingness.

Then came an accelerating sound, as the figure dropped, and it moved slowly back into the room.

“What have you done?” I cried, the words begins to rise up like a mountain of pent up tears. There, I found, was the door. It turned, and my body seemed to lift it, before the figure itself left to my side.

A smile appeared on my face as I looked at the room, the books filled a giant library with life and lessons, the black ones I had hoped would be my own in my own time, the colorless ones, the ones I’ve already read, the ones I’ve been looking for. They were to tell me what I would need to know, how to survive this night, to know the unknown. They were a path, a map.

In the darkest hall, the room I saw the figure enter, a pale shadow in the light. The place was dark, it was cold. He took me in, and I felt he was going to kill me, he was taking me inside. The sound woke me, and I opened my eyes to find another shadow, a figure standing behind me. I didn’t know what he was, but I was in my own home.

That man walked towards me, eyes glow green, his eyes were gloomy, the healers he wore, his hair was matted with grey, it was grey that faded in between them. He was big, at least, if a little tall. At least I had grown the right size for this thing. I don’t think his arms were big, so that is probably just his arms, or perhaps his face was too short for his eyes. His fingers were lank, the seated fingers were black, and in the dimmest corner of his head was a hood, with no lights.

It was very dark, there was an open door beyond it. He held my hand like a…well, for I was small. I could tell the size of his hand. He had long grey hair in his hands. He held my hand, and I saw red, it was black, as if it was a red colour.

I thought, and in that moment, my skin was soft and shiny. In between my knees. So, I moved over and looked, in my hands, and there was a dark green hand, with red hands, I knew this was a shadow, but that doesn’t mean anything to him.

The first hand, the first hand was big, its face was black, it was large, for I was small. The black was green, its color was darker than red. I touched it, and it was cold.

The second hand was big, but the hands were dark, and had big fingers, the first hand was long and dark green, it was like a purse, I didn’t know how it was, but it was cold, in that moment, I froze.

I could only watch.

You can guess the original story from the image (below).

Photo credit https://www.publicdomainpictures.net/

1 December 202027 December 2020

Sentiment Analysis of Caption, News and Fiction text generation experiments

From: text generation editor research (UAL London 2020 see credits).
What happens when writers use a computer text generator to write articles, giving them only an image prompt.

Go to Index of AI research

Sentiment Analysis

The Study had three text generation and editing tasks, to make a Caption, a News article, and a Fiction story, using the same image prompt of a dog and man.

Summary

This shows differences in Positive sentiment affect between the three different experiments. Most positive at the start, by the end, a balanced neutral view had become evident.

Fear was shown in the first Caption experiment, but was lost in the second News and third Fiction, showing acclimatisation by experience. Generally Negative scores were low.

By Fiction experiment, the third and final, only Sadness was left.

Comment

This shows a learning process during the three experiments.

All results are cautioned by the strong ‘Tentative’ score 0.91 and lack of any ‘Confidence’ scores over 0.5.

Details – Method

For overall sentiment of responses, all text feedback was summed into a total text field per respondent.

Text comment fields were:

Caption, News and Fiction Experiments (3 fields);

Questions 1 (2 fields), 2-6a (5 fields), 6b Fake news (1 field).

This gives 11 feedback text samples per respondent (not all were filled). These are summed vertically in Excel to give the overall text block per column/field.

Texts were also summed per respondent, horizontally in Excel.

NLP and IBM Tone Analyser

NLP (natural language processing) allows computer analysis of text blocks. For this volume of text, the online IBM Tone Analyser (See References at bottom) was used. Writing a custom analyser was outside the scope of the study. Human grading was not possible due to the size of texts when totalled, however human analysis is used for the summary of texts.

IBM Tone Analysis gives a rating for:

Anger Fear Sadness Joy Analytical Confident Tentative

(Coded on data as Ang, A, F, S, J, A, C, T.)

The “most prevalent tones that are detected for each utterance” are shown at a document level, and sentence level.

The document level analysis has scores, and the sentence level (which shows lower occurrences) was added in brackets. This gives a results for example, where the detected tones have a numeric score over 0.5. Scores: <.5 None; =>.5 – .75 Mid; >.75 Strong

At document level, each tone if found, has a score .5-.1.0

Lower graded tones (placed in brackets in data) are placed at the 0.25 level

Examples

J,S,A,T(C,F) – Joy, Sadness, Analytic and Tentative have scores > .5 (and C Confidence, F Fear have lower occurrences only, between >0 and < 0.5)

Ang,F,S() – Ang is Anger, Fear and Sadness have scores > .5 (no others over 0)

F(S) –Fear scores > .5 (Sadness between >0 and < 0.5)

In my data display, a ‘lower occurrence’ (bracketed) is scored at 0.25

Confidence and Tentative are general attitudes shown in the text.

Results

All respondents summed

Caption experiment – all feedback comments

Graph - IMB TA Caprion.png

With motivated stakeholders as respondents, there are high scores for ‘Analytical’ and ‘Tentative’. ‘Confidence’ did not appear at all as a document level tone in all 82 people, and occasionally as a sentence level tone.

Using the highest score amongst ‘Anger’, ‘Fear’ and ‘Sadness’ as Negative, and using ‘Joy’ as Positive, shows a higher degree of positive response.

Fear is evident, but at a low level. Sadness is the strongest of the negative reactions.

Positive is about 20% more than Negative. (Significance.)

News experiment – all feedback comments

Graph - IMB TA News.png

More overall positive result than Caption experiment.

Fear has gone, low levels for Anger and Sadness Negative tones. Positive is about 42% more than Negative.

Fiction experiment – all feedback comments

Graph - IMB TA Fiction.png

Sadness at its highest level. Not Positive or Negative. Anger and Fear do not appear.

Summary

This shows differences in Positive sentiment affect between the three different experiments. Most positive at the start, by the end, a balanced neutral view had become evident.

Fear was shown in the first Caption experiment, but was lost in the second News and third Fiction, showing acclimatisation by experience. Generally Negative scores were low.

By Fiction experiment, the third and final, only Sadness was left.

Comment

This shows a learning process during the three experiments.

All results are cautioned by the strong ‘Tentative’ score 0.91 and lack of any ‘Confidence’ scores over 0.5.

References

IBM Tone Analyser

Please see full Report for further statistics (tba).

27 November 20203 December 2020

All the Rubbish of a Great City – classic literature vs text generation

From the series of classic literature vs the future…

All The Rubbish of a Great City

Part 1: No Sun Ever Since That Day

Dear Farewell

You may be glad to see your letter of 6th April last from me. You are still in the good humour of the last time, and I believe that the people will be kind and kind to you in your letters.

I hope you will send me the following on the 15th of June: 1st: to my brother, a messenger, and my wife; 2nd: my daughter; and to my sister; then my brother; and my sister to my husband; and my brother to my son; and my father to my son; and my daughter to my husband and sons; and to my daughter’s father.

It is impossible for me to perceive a trace of the abominable scenes which I have experienced. There are but a few houses destroyed, and most of them, that is, very pretty ones, and all are occupied with families, and a few shops: one street is covered with all the rubbish of a great city; its streets are of a grey nature; and the streets are all closed off by some heavy iron gate.

I take a seat in a corner of the square; and after taking food from a little table and taking a seat beside a shopkeeper, or at least an old man with a hat on, I look about me. Nothing remarkable is seen or seen; there is no ruin or any signs of destruction; you might as well go near the ruins of the pyramids. The women seem to be quite contented, and are making the little fires that are burning. We are very anxious, we are very sad, to see them.

As I was about to arrive at the coast, I was suddenly knocked out by the wind; my companions, who were not surprised, rushed to me. I lay in their arms, and told them I could not recover myself, and said it was time for my expedition to proceed.

I begged them to give me some of the best part of the sea to make my recovery. They assented, and then threw out with me a large quantity of salt that I could not bring home to myself, and I fell dead.

They gave me some of their gold as I lay dying.

I never heard of such an occurrence; I should have been ashamed to have done so; but I had too much hope in the fortune of the sea; for if I were lost by such misfortunes, one does not have what has been given him; for if the fortune be bad, man do have a right to hope.

They put me into the boat with their captain, Sir Richard, and took me back to the ship where we were sitting. I lay there several days; I thought I had done well, but they told me the fortune was the same; the sun set over the mountains that night, and gave no sun ever since that day.

My mind was so troubled about my condition, that I could not bear the noise of the ship, so I cried, and fell into a terrible state of sleep, and then lay, my head, and neck, and legs, down upon a bed.

[These words, which do not make any impression on the ear, should appear to prove the correctness of the saying.]

Original

Frankenstein or The Modern Prometheus

Mary Wollstonecraft (Godwin) Shelley 1818

Letter 1

To Mrs. Saville, England.

St. Petersburgh, Dec. 11th, 17—.

You will rejoice to hear that no disaster has accompanied the commencement of an enterprise which you have regarded with such evil forebodings. I arrived here yesterday, and my first task is to assure my dear sister of my welfare and increasing confidence in the success of my undertaking.

I am already far north of London, and as I walk in the streets of Petersburgh, I feel a cold northern breeze play upon my cheeks, which braces my nerves and fills me with delight. Do you understand this feeling? This breeze, which has travelled from the regions towards which I am advancing, gives me a foretaste of those icy climes. Inspirited by this wind of promise, my daydreams become more fervent and vivid. I try in vain to be persuaded that the pole is the seat of frost and desolation; it ever presents itself to my imagination as the region of beauty and delight.

There, Margaret, the sun is for ever visible, its broad disk just skirting the horizon and diffusing a perpetual splendour. There—for with your leave, my sister, I will put some trust in preceding navigators—there snow and frost are banished; and, sailing over a calm sea, we may be wafted to a land surpassing in wonders and in beauty every region hitherto discovered on the habitable globe. Its productions and features may be without example, as the phenomena of the heavenly bodies undoubtedly are in those undiscovered solitudes. What may not be expected in a country of eternal light?

I may there discover the wondrous power which attracts the needle and may regulate a thousand celestial observations that require only this voyage to render their seeming eccentricities consistent for ever. I shall satiate my ardent curiosity with the sight of a part of the world never before visited, and may tread a land never before imprinted by the foot of man.

These are my enticements, and they are sufficient to conquer all fear of danger or death and to induce me to commence this laborious voyage with the joy a child feels when he embarks in a little boat, with his holiday mates, on an expedition of discovery up his native river. But supposing all these conjectures to be false, you cannot contest the inestimable benefit which I shall confer on all mankind, to the last generation, by discovering a passage near the pole to those countries, to reach which at present so many months are requisite; or by ascertaining the secret of the magnet, which, if at all possible, can only be effected by an undertaking such as mine.

…Etc…

Your affectionate brother,
R. Walton

27 November 202017 February 2021

Why Don’t Cannibals Eat Clowns?

Part of the text generation series – see blog index.

Why don’t cannibals eat clowns?

Because cannibalism doesn’t produce the same health benefits as killing clowns, you will have only about 60% of your body weight of body fat left, leaving you with less body fat and no protection against the other 50%.

By choosing a suitable size clown, you can take advantage of that. If I am in a small town and in need of a good pair of shoes, I’ll eat an ogre and I’ll have about 100% body fat and 70% of my body weight in a pair of shoes.

What the hell am I supposed to do, I am going to starve?!

In that case I would just have to get one of those, because those are probably my only options.

Also, by choosing a size bigger or smaller than I am currently supposed to be, you can take advantage of that as well. I’m going to give up on these shoes and use the boots instead. I mean that literally.

If I were in a small town where there is limited supplies, and the only food available is a variety of meats and fruit that I’m supposed to eat (such as a spider or something), I would eat some squirrel.

But if there’s too much food and no other choice, then I will instead go for the more dangerous monster I can get.

Now the issue of choice is just that these are all options. You don’t know what your favourite will be unless you decide to look into it.

For example, while you can choose a size small enough to be your “smallest” and go for the larger you’re going to be the bigger your body fat will be, it just won’t work like that. The bigger you get, the bigger your body fat will be so it’s not going to be as easy to “do the right thing.”

What you want is for your body fat to be close to your own body weight, which is probably going to be under 0 (the ideal is probably between 10 and 12).

Also what you want is to weigh on average only about 70-75% of your body weight (about 70lbs for one person, or roughly a fat person for another).

That’s what makes you a normal person.

Why don’t cannibals like clowns?

If they’re not being eaten by the clown, it’s because they’re so fat they don’t have a stomach, so they get crushed by the horse. In the real world, people’s appetite changes over time as well. A hungry hippo’s hunger will rise over time so an old horse gets eaten by a zombie, and so on.

In fact, a lot of people have to eat a lot in order to keep the hunger in check. And there are also the many social cues that cause our appetite to rise and fall over time. Food is good for us, because it tastes good and is a good source of calories. And when it’s too much, we have a panic attack. When it’s just right, we can just be happy to just eat a bag of popcorn. And once we’re just right, we can eat the popcorn and forget about it.

Why aren’t hippos getting eaten by horses? Because they have a digestive system that has to move in to the horse’s stomach first, or there will be too much food in the horse’s stomach. The idea that this food goes in straight through the horse’s digestive system is just silly.

The only way I’ve seen this food in the wild is that it was consumed by some people who were infected with a fungal life-form and got it into their digestive system and their appetite was just too great.

The best part about all this is that it’s simple enough to understand. The only thing that we can do is stop eating, to do that you just wait till your health gets better. If you could just get rid of all these stupid foods for a week or so and let your appetite return to the normal state, even better. It’ll be like the beginning of autumn in the wild, there’s just not enough sun to cook food like it used to be and it’ll feel like no one’s eating.

As you have experienced, I’ve never.

This from a joke in Geoff Davis’s new novel The Circular River which will be serialised on this website next week

Photo credit – the Solomon Islanders, feasting trough.

27 November 202016 February 2021

Guess the original classics – Born Into Slavery

Born into Slavery

This is a game to see if you can identify the original story author in the generated text below. It has a new title: Born Into Slavery. The generated text is followed by the classic story which was used as the prompt for the computer generation. For technical details see end.

Born Into Slavery

What would occur when the daughter came for a visit? Her daughter could not wish to come here but if she had to she was certain to be disappointed.

When her eldest daughter was in her early thirties, Mr. Smith thought it might be advisable to take her away with him to her own country. The time had been long since passed; what had gone before was now forgotten. She was quite happy at home, though she knew the consequences of her leaving the family.

Mrs. Smith, who lived on her own farm, was the more interested of the two, as she had a son she loved. She would rather she had one of her own daughters than, with him, a son. As it was she was obliged to take him along, though with a slight advantage in health and strength. The child, however, was well behaved, and was very fond of her.

The old couple would have had a child when Mrs. Smith was dead; the latter would not have died till Mr. Smith, as they thought, had been ninety years old, and with her, after him. The child, as they said, would live till old Mr. Smith had died; if that should prove impossible, they would have had an heir to one of the first families to the west of the river.

Mrs. Smith was still an old woman, she remembered; her eyes had become dim, her hands and feet were weak, her feet were dull and bent, her hands wrinkled and loose. A change from her old condition, she thought, would have been too much for her now. She had always been happy in her old condition, though always in the present mode.

This story made her do something she thought necessary for the little girl, and she went into the hall and sat down in the great old chair opposite to Mrs. Smith

A woman’s strength, she thought, was her knowledge of her father’s affairs. She knew that as long as her children could walk with her, they could not be married.

Her husband knew that too; he knew where he was going, what he was going to do, and was never the same afterwards. There was a very large number of children, her daughters, living abroad, in the most irregular manners. He had grown somewhat deaf, with a peculiar difficulty about their voices.

And Mrs. Smith never liked it when she was in any degree unhappy; she could not bear to see her men, much less her children, so miserable. Every time they came she wanted to have a chat with them. She was a woman of very little self-control, and she would have felt an extraordinary strain in talking to men in whom she was so anxious to be connected. She had grown quite aware of the dangers of these visits. She felt that she had the moral to avoid them. It was the same with her husband.

One of the dangers she had to observe was whether they should try to give her good advice on an important subject; then she could say nothing but good things about them. And she did not give you any reason why.

And so he took her into another house, where she found him dead. Here is the explanation of one of our own writers, that was born into slavery, and who has since been made a public writer.

In the first place, we say that his wife knew, but she did not have sufficient excuse for going back to him. For she had been married to his eldest son, whom he kept with her, but she had never had a son, not that she was less prone to be the subject of any kind of cruelty than any other woman.

She was a woman well grounded, and could be trusted not to become a mistress or to marry a knave. Now, the reason why Master Smith was the property of Mr. Smith was, that there were several houses there, called for the use of this one Mr. Smith, and he was not there, but was there at a different time; so he did not know him or know of his own family.

But of course this also became a reason for the destruction of his father’s property; for those other houses were to be used by Mr. Smith at a later time, and hence there was no excuse to lose them. And he knew of the condition of his father’s property, and that he had been made a public writer.

Now he said to Master Smith,
“What is the reason of your going to me at this time, that I do not go to him myself?”

“Well, I have said to him before, I do not go to him, because my father was in debt to you.”

“How did I know it?”

“He had a contract of sale of my house, for you had a lease for the premises. You and your father made a bargain, and did not pay; but he does me to make good my debt. He gave me this lease, but left the property to me.”

“What can I do for it?”

“I will not go with you.”

And Mr. Smith went away in his ship; and while he was doing this, he heard the voice of his father’s son, who was living in the ship, saying to him,

“Your father has made you a public writer, at a time when I should have told you everything that you ought to know about me; and he is coming to me to sell my property for your property, and that we must not meet there till the day after the feast.”

As it happened, at the end of his talk with Mr. Smith, who would then have come to him, he heard of the destruction of Mr. Smith’s house, and of the damage done, for a great many months afterwards, to the houses belonging to his son, because of that man’s writing; and Mr. Smith went away in his ship, that there might be no necessity of his telling Mr. Brown that he might not receive anything from him from him without reason.

And it was, at that time, a great pleasure to him to have the ship and his house in the same box, and he said nothing of it.

Original

Pride and Prejudice

Jane Austen 1813

It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.

However little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters.

“My dear Mr. Smith,” said his lady to him one day, “have you heard that Manorfield Park is let at last?”

Mr. Smith replied that he had not.

“But it is,” returned she; “for Mrs. Long has just been here, and she told me all about it.”

Mr. Smith made no answer.

“Do you not want to know who has taken it?” cried his wife impatiently.

“You want to tell me, and I have no objection to hearing it.”

This was invitation enough.

“Why, my dear, you must know, Mrs. Long says that Manorfield is taken by a young man of large fortune from the north of England; that he came down on Monday in a chaise and four to see the place, and was so much delighted with it, that he agreed with Mr. Morris immediately; that he is to take possession before Michaelmas, and some of his servants are to be in the house by the end of next week.”

“What is his name?”

“Bingley.”

“Is he married or single?”

“Oh! Single, my dear, to be sure! A single man of large fortune; four or five thousand a year. What a fine thing for our girls!”

“How so? How can it affect them?”

“My dear Mr. Smith,” replied his wife, “how can you be so tiresome! You must know that I am thinking of his marrying one of them.”

“Is that his design in settling here?”

“Design! Nonsense, how can you talk so! But it is very likely that he may fall in love with one of them, and therefore you must visit him as soon as he comes.”

“I see no occasion for that. You and the girls may go, or you may send them by themselves, which perhaps will be still better, for as you are as handsome as any of them, Mr. Bingley may like you the best of the party.”

“My dear, you flatter me. I certainly have had my share of beauty, but I do not pretend to be anything extraordinary now. When a woman has five grown-up daughters, she ought to give over thinking of her own beauty.”

“In such cases, a woman has not often much beauty to think of.”

“But, my dear, you must indeed go and see Mr. Bingley when he comes into the neighbourhood.”

“It is more than I engage for, I assure you.”

“But consider your daughters. Only think what an establishment it would be for one of them. Sir William and Lady Lucas are determined to go, merely on that account, for in general, you know, they visit no newcomers. Indeed you must go, for it will be impossible for us to visit him if you do not.”

“You are over-scrupulous, surely. I dare say Mr. Bingley will be very glad to see you; and I will send a few lines by you to assure him of my hearty consent to his marrying whichever he chooses of the girls; though I must throw in a good word for my little Lizzy.”

“I desire you will do no such thing. Lizzy is not a bit better than the others; and I am sure she is not half so handsome as Jane, nor half so good-humoured as Lydia. But you are always giving her the preference.”

“They have none of them much to recommend them,” replied he; “they are all silly and ignorant like other girls; but Lizzy has something more of quickness than her sisters.”

“Mr. Smith, how can you abuse your own children in such a way? You take delight in vexing me. You have no compassion for my poor nerves.”

“You mistake me, my dear. I have a high respect for your nerves. They are my old friends. I have heard you mention them with consideration these last twenty years at least.”

“Ah, you do not know what I suffer.”

“But I hope you will get over it, and live to see many young men of four thousand a year come into the neighbourhood.”

“It will be no use to us, if twenty such should come, since you will not visit them.”

“Depend upon it, my dear, that when there are twenty, I will visit them all.”

Mr. Smith was so odd a mixture of quick parts, sarcastic humour, reserve, and caprice, that the experience of three-and-twenty years had been insufficient to make his wife understand his character. Her mind was less difficult to develop. She was a woman of mean understanding, little information, and uncertain temper. When she was discontented, she fancied herself nervous. The business of her life was to get her daughters married; its solace was visiting and news.

Notes

Prompt was 10-20 words from the original. GPT-2 system by Fabrice Bellard – see the site below for credits etc.
Made on my creativity app Story Live – please visit.

Text from Gutenburg free classic ebooks

A certain amount of cherry-picking of the most grammatical and interesting generated parts, but no actual editing (moving around of words or new words). Identifying names are changed.

More of these will be posted, see the index.