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Why is it art?

a wall painting at lascaux cave

Since the cave paintings at Lascaux and similar locations were painted before concepts of composition were even thought of, are they art?

Some time ago, in a Facebook group, someone asked this good question.

My initial answer was as below.

I doubt if the people making them saw them as ‘art’. That’s imposing our view of the world on the makers. From our perspective, though, yes they are art. Plus, places like Lascaux were painted by numerous hands, perhaps over centuries. They are not single compositions. What they show though is considered mark making towards an idea.

I suspect the principles they were following were more in the line of magical thinking than aesthetics. They may have thought, “if I paint this animal being killed, we will be successful on our next hunt.” Another possibility is that it was an offering to the spirit world, giving thanks for a successful hunt. Both these have been found in modern times by anthropologists. Whatever it was, they seem to have had something guiding them. They were not just throwing pigment around at random.

Sadly, the discussion didn’t progress much. The rest of this post is based on the argument I was trying to make. It includes a few extra points that didn’t occur to me at the time.

I now think the original question was based on a false premise. We don’t know if they had ideas about composition. We do know that they are not random daubs on a wall. They represent a significant human achievement, only possible because of a great deal of effort and time. They meant something to their makers.

We can never know the motives of the makers or the guiding principles they were working under. We can only speculate. Our speculations cannot fail to be coloured by our own world view, as was the original questioner.

The question of what was in the minds of the original makers of these paintings is a different question to whether they are ‘art.’ We don’t even know if the makers had a concept of art as an endeavour in its own right. There seems to have been an urge to decorate, shown in other finds from many similar cultures, but we still don’t know why it was done.

If we shift our viewpoint from looking at cave paintings to looking at scientific illustrations, it is perhaps clearer. Hooke made incredibly detailed and brilliantly executed drawings of what he saw in his microscope. These are hugely valuable in terms of their scientific intent. To see them as art means looking at them from the perspective of a separate set of values to those of the original maker. The one doesn’t negate the other. Both reference frameworks can apply simultaneously.

In the end, I suspect defining art is like defining a game. After all, what links tennis, golf, poker and Resident Evil? All games, but we would find it hard to describe the common characteristic. So in my view art includes Lascaux cave paintings, Neolithic rock carving, Medieval illuminated manuscripts, Rembrandt, Monet, Malevich, Hockney, Basquiat, and David Bailey.

Hooke is close enough in time for us to have some idea of his thought processes. I think we can feel reasonably confident that he had a sense of his work as having an aesthetic value beyond being a ‘just’ a scientific illustration. We can’t know whether the creators of cave paintings had ideas or concepts of composition, or what they were thinking. We can be sure, though, that they were thinking…

This post is related to several others about meaning in art.

Neolithic art has also provided inspiration for many of my own prints.

Mixed media print - monotype and pastel
Rocking in Rhythm #1 mixed media pastel over monoprint
Collagraph print inspired by neolithic rock carvings
Hammer marks on Weardale
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AI, Art and AI Art – Part 4

ai generated portrait of a woman in green with a green, broad-brimmed hat, with flowers

This is the last of four posts dealing with AI and AI Art. It takes a different form to the previous three. In this post, I look only at the output from AI Art apps, without regard to how it works or what issues its use might raise. The post concludes with an overall assessment of AI art and my reactions.

In part 3, I briefly described how I tested the app, and mentioned the problems experienced with the output. This post was intended to expand on that, giving example text prompts and the resulting image. However, the theory and the practice have proved very different.

I’m sure I will be returning to the topic. I will try and come here to add new inks.

Is AI just a party trick?

Testing the AI art app

To recap, my initial aim in testing the AI art app was to push it as far as possible. I was not necessarily trying to generate useable images. The prompts I wrote:

  • brought together named people, who could never have met in real life, and put them in unlikely situations.
  • were sometimes deliberately vague.
  • were written with male and female versions.
  • used a variety of ethnicities and ages.
  • used single characters, and multiple characters interacting with each other.
  • used characters in combination with props and/or animals
  • used a range of different settings.

I realised I also needed to test the capacity of the AI to generate an image to a precise brief. This is, I believe, the area where AI art is likely to have the most impact. Doing this proved much harder than I expected.

In essence, generating an attractive image with a single character does not require a complex prompt. I suspect this is already being used by self-publishers on sites like Amazon.

Creating more complex images, at least with Imagine AI, is much more difficult. There are ways around the problem, but these require use of special syntax. This takes the writing of the prompt into a form of coding for which documentation is minimal.

Talking to the AI art app

This problem of human-AI communication is not something I’ve given any real thought to, beyond fiddling with the text prompt. This paper addresses one aspect of it. From this, it became clear that the text prompt used in AI art apps, or the query used by the likes of ChatGPT are not used in their original form. The initial text (in what is termed Natural Language or NL) has to be translated into logical form first. Only then can it be turned into actions by the AI, namely the image generation, although that glosses over a huge area of other complex programming.

This is a continuously evolving area of research. As things stand, the models used have difficulty in capturing the meaning of prompt modifiers. This mirrors my own difficulties. The paper is part of the effort to allow the use of Natural Language without the need to employ special syntax or terms.

Research into HCI

The research, described in this paper, points towards six different types of prompt modifiers use in the text-to-image art community. These are:

  • subject terms,
  • image prompts,
  • style modifiers,
  • quality boosters,
  • repeating terms, and
  • magic terms.

This taxonomy reflects the community’s practical experience of prompt modifiers.

The paper’s author made extensive use of what he calls ‘grey literature’. Grey literature is materials and research produced by organizations outside of the traditional commercial or academic publishing and distribution channels. In the case of AI art, much is available from the companies developing the apps. This from Stable Diffusion and this, deal with prompt writing.

Both of them take a similar approach to preparing the text prompt. They suggest organising the content into categories, which could be mapped onto the list of prompt modifiers referred to above.

The text-to-image community

As with any sphere of interest, there seems to be a strong online community. Given the nature of this particular activity, they post their images on social media. Some of this, being tactful, is best described as ‘specialist’. Actual porn is generally locked down by the AI companies. That doesn’t stop people pushing the boundaries, of course. If you decide to explore the online world of AI art, expect lots of anime in the shape of big chested young women in flimsy clothing. From the few images I’ve seen which made it past the barrier, the files used for training the app must have included NSFW (Not Safe For Work) images. What we get is not quite Rule 34, but skates close…

What else?

It’s not all improbable girls, though. The Discord server for the Imagine AI art app has a wide range of channels. These include nature, animals, architecture, food, and fashion design as well as the usual SF, horror etc. The range of work posted is quite remarkable in isolation, but in the end quite samey. Posters tend not to share the prompt alongside the image. It isn’t clear therefore if this is a shortcoming in the AI, or a reflection of the comparatively narrow interests of those using the app.

Judging by the public response to AI, it seems unlikely that many artists in other media are using it with serious intent. That too will bias users to a particular mind set. Reading between the lines of the posts on Discord, my guess is that they tend to be young and male. Again, this limited user base will affect the nature of images made.

The output from the AI app

The problems I described above have prevented me from the sort of systematic evaluation, I planned. A step by step description of the process isn’t practical. It takes too long. The highest quality model on Imagine is restricted to 100 image generations in a day, for example. I hit that barrier while testing one prompt, still without succeeding.

In addition, I did a lot of this work before I decided to write about it, so only have broad details of the prompts I. I posted many of those images on Instagram in an account I created specifically for this purpose.

Generic Prompts

I began with some generic situations, adding variations as shown in brackets at the end of each prompt. In some cases, I inserted named people into the scenario. An example:

  • A figure walking down a street (M/F and various ages, physique, ethnicities, hair style/colour, style of dress)

Capturing a likeness

I wanted to see how well the app caught the likeness of well known people. By putting them in impossible, or at least unlikely situations, this would push the app even further. An example:

  • Marilyn Monroe dancing the tango with Patrick Stewart. I also tried Humphrey Bogart, Donald Trump and Winston Churchill.

I discovered a way to blend the likenesses of two people. This enables me to create a composite which can be carried through into several images. Without that, the AI would generate a new face each time. The numbers in the example are the respective weights given to the two people in making the image. If one is much better known than the other, the results may not be predictable, but should still be consistent:

  • (Person A: 0.4) (Person B: 0.6) sitting at a cafe table.

Practical applications

I also wanted to test the possibility of using the app for illustrations such as book covers, magazine graphics etc. Examples:

  • Man in his 50s with close-cropped black hair and a beard, wearing a yellow three-piece suit, standing at a crowded bar
  • Woman in her 50s with dark hair, cut in a bob, wearing a green sweater, sitting alone at a table in a bar.
  • Building, inspired by nautilus shell, art nouveau, Gaudi, Mucha, Sagrada Familia

To really push things, I wrote prompts drawn from texts intended for other purposes. Examples:

  • Lyrics to Dylan’s Visions of Johanna
  • Extracts from the Mars Trilogy by Kim Stanley Robinson
  • T S Eliot’s The Waste Land

I tried using random phrases, drawn from the news and whatever else was around, and finally random lists of unrelated words.

Worked example

This post would become too long if I included examples of everything from the list above, which is already shortened. Instead, I will show examples from a single prompt and some of those as I develop it. The prompt is designed to create the base image for a book cover. The story relates to three young people who become emotionally entangled as a consequence of an SF event. (A novel I’m currently writing)

Initial prompt:

Young man in his 20s, white, cropped brown hair, young woman, in her 20s, mixed race, afro, young woman in her 20s, white, curly red hair

This didn’t work, the output never showed three characters, often only one. If I wasn’t trying to get a specific image, they would be fine as generic illustrations.

Shifting away from photo realism, this one might have been nice, ethnicities apart, but for one significant flaw…

Next version

In order to get three characters, I obviously needed to be more precise. So I held off on the physical details in an attempt to get the basic composition right. After lots of fiddling and tweaking, I ended up with this

(((Young white man))), 26, facing towards the camera, standing behind (((two young women))), both about 24, facing each other

The brackets are a way to add priority to those elements with strength from 1 (*) to 3 (((*))).

The image I got, wasn’t perfect, but workable and certainly the closest so far.

Refining the prompt

My next step was refining the appearance, which proved equally problematic

(((young white man))), cropped brown hair, in his 20s, facing towards the camera, standing behind (((a young black woman))), ((afro hair)), in her 20s, facing (((a young white woman, curly red hair))), (((the two women are holding hands)))

I got nowhere with this. I usually got images where the man was standing between two black girls. In one a girl was wearing a bikini for some reason. In another she was wearing strange trousers, with one leg shorts, the other full length. I also got one with the composition I wanted, but with three women.

More attempts and tweaks failed. The closest to a usable image was this, using what is called by the app a pop art style. I eventually gave up. If there is a way to generate an image with three distinct figures in it, I have yet to find it.

This section is simply a slideshow of other images generated by the AI in testing. They are in no particular order, but show some of the possibilities, in terms of image quality. If only the image could be better matched to the prompt…

Consumer interest

I relaunched my Etsy shop, to test the market, so far without success. I haven’t put a lot of effort into this, so probably not a fair test. References to sales from the shop are to the previous version. At the time of writing, no AI output has sold. This is the URL:

I also noticed on Etsy, and in adverts on social media, what looks like a developing market in prompts, with offerings of up to 100 in a variety of genre. These are usually offered at a very low price. The differing syntax used by the different apps may be an issue, but I haven’t bought anything to check. I saw, too, a number offering prompts to generate NSFW images. I’m not sure how they bypass the AI companies restrictions. Imagine, at least, seems to vet both the prompt and the output.

Overall Conclusions

It’s art, Jim, but not as we know it

In Part 3, I asked ‘Is AI Art, Art?’ It’s clear that many of those in the AI art community, consider the answer to be yes. They even raise similar concerns to ‘traditional’ artists about posting on social media, the risk of theft etc. The more I look, the more I think they have a point. The art is not I believe in the image alone, but in the entire process. It is not digital painting, it is, in effect, a new art form.

Making the images, getting them to a satisfactory state, is a sort of negotiation with the AI. It requires skill and creative talent. It requires more than simple language fluency, but an analytic approach to the language which allows the components of the description to be disaggregated in a specific way. Making AI art also requires an artistic eye to evaluate and assess the images generated and to evaluate what is needed to refine that image, both in terms of the prompt and the image itself.

The State of the art

As things stand, AI art is far from being a click and go product. Paradoxically, it is that imperfection which triggers the creativity. It means users develop an addictive, game-like mind set, puzzling away at finding just the right form of words. In Part 3, I referred to Wittgenstein and his definition of games. This seemed a way into looking at the many forms taken by art. A later definition, by Bernard Suits, is “the voluntary attempt to overcome unnecessary obstacles.” This could be applied to poetry, for example, with poetic forms like the sonnet.

Writing the prompt is very similar, it needs to fit a prescribed format, with specific patterns of syntax. In this post, I wrote about breaking the creative block by working within self-imposed, arbitrary rules. The imperfect text-to-image system, as it currently stands, is, in effect, the unnecessary obstacle that triggers creative effort.

The future

It seems inevitable that the problems of Human-AI communication will be resolved. AIs will then be able to understand natural language. I don’t know if we will ever get a fully autonomous AI art program. It certainly wouldn’t be high on my To-Do list. We don’t need it. A better AI, able to understand natural language and generate art, without the effort it currently takes, would be a mixed blessing. It would, however minimally, offer an opportunity for creativity to people who, for whatever reason, don’t believe themselves to be creative. On the other hand, too many jobs and occupations have already had the creativity stripped from the by automation and computerisation. Stronger AIs are going to accelerate that process.

It’s easy to say, ‘new jobs will be created’, but those jobs usually go to a different set of people. Development of better, but still weak, AIs will be disruptive. With genuine strong AI, all bets are off. We cannot predict what will happen. It is possible that so many jobs will be engineered away by strong AI that we will be grateful for the entertainment value, alone, of deliberately weak AI art apps and games.

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Art, AI and AI Art – Part 1

Alien lizards roller skating in a Pride march with Pride flags

Introduction to Part 1

This post began as a limited review of what has become known as AI Art. In order to do that, I had to delve deeper into AI in general. Consequently, the post has grown significantly and is now in three parts. This post, Part 1, looks at AI in general. Part 2 will look at more specific issues raised by AI art, while Part 3 will look at AI art from the perspective of an artist. Part 4 will be a gallery of AI-generated images.

Links to each post will be here, once the series is complete.

What is AI?

It has felt almost impossible to avoid the topic of AI in recent weeks. Hardly a day passed, it seems, without some pronouncement, whether promoting the next big thing or predicting the end of the world. One thing seems clear though. Whatever concerns exist, the push to develop this thing called ‘AI’ is now probably unstoppable.

Looking deeper into the topic, I soon hit problems. AI seems to mean anything, depending on the motivation of the person using it. As a descriptive term, it is almost useless without a qualifier. This isn’t unusual in public discourse. I’m not going to consider all the bad-faith uses of the term, though. I will limit myself to trying to get some clarity.

What is now called AI is different to the former idea of ‘expert systems’. Instead of depending on humans trying to input rules to be followed, AI uses the raw data to find rules and patterns. This has only become possible with the advent of so-called ‘big data’. The term refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. This is why research AIs are being used in, for example, drug research or cancer screening, using data from health systems like the NHS.

Weak and Strong AI

Essentially, there are two sorts of AI, ‘weak’ and ‘strong’. For a summary of the difference, I turned to AI. Here’s what the free version of ChatGPT said:

The terms “strong AI” and “weak AI” are used to describe different levels of artificial intelligence capabilities. They refer to the extent to which a machine or computer system can simulate human-like cognitive functions and intelligence.

Weak AI (Narrow AI)

Weak AI refers to artificial intelligence systems designed and trained for a specific or narrow range of tasks. These systems excel at performing well-defined tasks but lack general cognitive abilities and consciousness. Weak AI systems are prevalent in our daily lives. They are commonly found in applications like virtual personal assistants (e.g., Siri, Alexa), recommendation systems, image recognition, and natural language processing. They can appear intelligent within their specific domain, but do not possess real understanding or awareness.

Strong AI (General AI or AGI – Artificial General Intelligence)

Strong AI refers to artificial intelligence systems with the ability to understand, learn, and apply knowledge across a wide range of tasks and domains, just like a human being. A strong AI system would have cognitive abilities that match or exceed human intelligence. This includes not only performing specific tasks but also understanding context, learning from experience, and reasoning about various subjects. Strong AI would have a level of consciousness, self-awareness, and general problem-solving capabilities similar to humans.

Despite the claims of Google engineer, Blake Lemoine, it seems clear that we are nowhere near achieving Strong AI (AGI). All apps and programs currently in use, including ChatGPT, are examples of Weak AI. They are ‘designed and trained for a specific task or a narrow range of tasks.’ In other words, they are tools, just as much as the power looms of the late 18th century.

That does not mean that the introduction of even Weak AI is without risk. Like all disruptive technologies, it will create stress as older ways of working are replaced. Jobs will be lost, new jobs will be created, almost certainly not for the same people. If steam transformed the 19th century, and computers the 20th, Weak AI is likely to trigger a wave of change that will be an order of magnitude greater. We are beginning to see some of the potential in health care. It is already aiding in diagnostics, drug discovery, personalized treatment plans, and more.

Concerns raised in AI ‘training’

The ultimate goal in training an AI is to enable it to find patterns in data. Those patterns are then used to make accurate predictions or decisions on new, formerly unseen data. This process requires huge computational resources and careful design. The vast amounts of data involved, whether in training or in day-to-day use, raises significant ethical concerns.

Ethical concerns

Examples include:

  • Biases present in training data are likely to lead to biased outcomes. There is a need for transparency and accountability in training of AI systems, but the sheer volume of data involved makes this difficult. AI can help but…
  • Privacy concerns also arise in the use of training data. Improper handling of data or breaches in AI systems could lead to identity theft, unauthorized access to sensitive information, and other security risks. AI could also be used aggressively, to identify weak points in data protection, to break in and generally to create disruption.
  • Facial recognition systems could increase security by identifying potentially suspicious behaviour but at the risk of loss of privacy. Bias in the training data could lead to inequality of treatment in dealings with some ethnicities.
  • Adoption of AI in medical contexts potentially creates risk for patients. Avoiding risk requires rigorous testing, validation, and consideration of patient safety and privacy.
  • AI-generated content, such as art, music, and writing, raises questions about creativity and originality and challenges traditional notions of copyright or even ownership of the content.
  • Self-driving vehicles may enhance convenience and safety, but ethical issues arise in, for example, resolving safety issues affecting the vehicle occupant vs pedestrians or other road users.
  • AI can be used to optimize energy consumption, monitor pollution, and improve resource management. On the other hand, the energy demands of training AI models and running large-scale AI systems could contribute to increased energy consumption if not managed properly.

Fears about AI

Expressed fears of what AI might lead to range from the relatively mundane to the apocalyptic. The more dramatic risks generally relate to AGI, not narrow AI, although the distinction is often fudged. There are serious risks, of course, even with ‘Weak’ AI.

Impact on employment and skills

Widespread use of AI could lead to mass unemployment, (although similar fears were expressed in the early years of the Industrial Revolution). This is one of the concerns of the Screenwriters Guild in their present strike. Writers wanted artificial intelligence such as ChatGPT to be used only as a tool that can help with research or facilitate script ideas, and not as a tool to replace them. The Screen Actors Guild has also expressed concern over the use of AI in their strike.

To a limited extent, they are too late. Forrest Gump, Zelig, Avatar, The Matrix, and Toy Story, all in different ways, used elements of what will need to be in any workable film AI. There has been talk for many years about using computer-generated avatars of deceased actors in live-action filming.

Military AI

Military use of AI could lead to life-and-death decisions being made by machines with no human involvement. Associated risks include the possible use of AI being used by bad actors in cyber warfare. ‘Deep Fakes’, such as those of Volodymyr Zelenskyy and Barack Obama, are only the start.

Text-based AI

I have had only limited experience with text-based AI. I have used ChatGPT to support my research into AI, but I don’t know how it would cope with longer pieces like this blog post. None of the other programs I have tried have produced anything beyond banal superficialities. Too often the output is junk. There have been reports of the AI simply making things up, which paradoxically is probably the closest it gets to originality. With the current state of development, I would not believe any publicly available AI which claimed to generate original text.

Issues for education

Even so, packages like ChatGPT seem already to be causing problems in education. Unsurprisingly, plagiarism checkers are also being deployed, although they probably use AI too! So far as I can tell, these text generators don’t provide academic references as normally used, or any link to their original sources. If developers can crack that, then there will be a real problem, not just for schools and universities.

I asked ChatGPT to list some further reading on issues around the use of AI. It gave me what looks to be a good, varied list, probably as good as anything I might find by a Google search. I asked three times, getting differently formatted results each time. Version one gave me 2 or 3 titles under a range of headings, version two gave me a list of ten titles, while version three organised the list by books, articles and online resources. There is a significant overlap, but the content varies between the three versions. None of them are properly formatted to provide references, although enough information is given to find them.

I asked again, for a list in Harvard style, and got a list of 10 books and journal articles. When asked. I got details of the requirements for Harvard Style references. A further request gave me a list of other referencing systems in use in academic circles.


Strong AI has been a common fear for centuries. From Pygmalion or Pinocchio to the films 2001 or AI (in itself a partial retelling of Pinocchio), similar tropes arise. I covered the same theme in my short stories, here and here.

Weak AI

The use of even weak AI raises many complex moral and philosophical questions. Some of these, such as the Trolley Problem, had been interesting ways to explore ethical issues, but now, faced with self-driving cars, they have become real. Using AI in any form of decision-making process will raise similar problems.

There is still a long way to go to get ‘intelligence.’ If it happens, I suspect it will be an accident. Eventually, though, I believe even ‘weak’ AI will be able to provide a convincing facsimile. Whether dependence on AI for critical systems is desirable is another question.

AI as a research tool

In the more limited area of text generation, ChatGPT, used as a research tool, appears to work as well as Google, without the need to trawl through pages of results. On the other hand, it is probably the later pages of a Google search which provide the equivalent experience to the chance find on the library shelves. Without careful choice of words, it seems probable that AI search tools will ignore the outliers and contrary opinions, reinforcing pre-existing biases. But then, the researcher has to actually look at that chance find on the shelf.

I asked ChatGPT to list some further reading on issues around the use of AI and to include some contrary views. This it duly did, giving 2 or 3 references under a range of sub-topics with an identified contrary view for each. As a research tool, I can see that AI will be very useful. Its use will require care, but no more care than is required in selecting and using reference material found in any other manner.

On the other hand, it seems likely that, whether we like it or not, within a few years, AI packages will be able to generate a good facsimile of original text. A good proportion of journalism is already generated in response to press releases. How long before we get AI-generated news reports created in response to AI-generated press releases about AI-developed products? All untouched by human hands…

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Painters and printmakers

Prints made by painters seem to have a distinctive quality to them. It seems to me this is a matter of perspective. Painters seem to be focused first on the effect they want, rather than thinking about any specific print technique.

Emily Mason

My first example is new to me. Emily Mason (1927-2019) was an American abstract painter and printmaker. I first came across her work in a video on YouTube by Albert van der Zwart, in his Channel ‘Imperfect paintings‘ (well worth following) Having watched it, though, the memory slipped away until I rewatched Albert’s video, after which I did some more online research. This was when I discovered she was also a printmaker.

From there I was led to a short documentary about her, on Vimeo, showing her at work and talking about what she was trying to achieve. Although talking about her paintings I still found it illuminating. I always find I get more from watching an artist making their own work than telling me how to do mine, however well-intentioned.

Richard Diebenkorn

You can see a good example of the interplay between painter and printmakers at work in this video of Richard Diebenkorn at work in the studios of Crown Point Press in 1986. I very much like his work and really regret missing the major exhibition in London because of illness. His paintings, especially those he made in New Mexico, seem to have strong affinities with those of Mason, although I don’t know if they ever met.

The sound quality isn’t brilliant, but you can see at the beginning he knows broadly what he wants and is trying to duplicate the improvisational quality of his paintings in prints and is more or less reliant on the printmakers to tell him how to get a given effect.

Howard Hodgkin

Howard Hodgkin’s work as a printmaker was instrumental in getting me into printmaking. I visited a printmaking workshop where work was in progress on one of his prints. I touch on this on the About page. From what I understand, his relationship with the printmakers was much more hands-off than Diebenkorn’s. His work is characterised by strong, bold colours.

Gillian Ayres

I saw this print, along with several others, at the Alan Cristea Gallery in Cork Street in London. They, like Hodgkin, were characterised by strong colours, but with more organic shapes. The retrospective exhibition of her work at Cardiff Museum and Art Gallery was stunning.

Albert Irvin

Albert Irvin was the subject of an exhibition of his work at the Royal West of England Academy in 2019. I’ve adapted the approach to screen printing shown in this video to the making of my own gel monotype prints.

Martyn Brewster

I haven’t managed to see Martyn Brewster’s work in the flesh yet. His work was recommended to me by a gallery owner I was talking too. I’ve now got a couple of catalogues from former shows and looked at a lot of work online. I certainly want to see the real thing.

For all these artists, their work making prints complements their painting. Each practice supports the other.

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Wiltshire Print Creatives

Last year I joined with a group of other artist printmakers who share the same workshop space to form Wiltshire Print Creatives, an informal collective. We had our first group show in November at 44AD Artspace in Bath. We were very pleased with the reception we got and sold quite well, so we are now firming things up to create a more coherent presence with a website and social media. You can already find us on Twitter and get an informal peak behind the scenes but we intend to ramp this up over the next few months and probably add other social media platforms like Instagram.

We also have a couple of further shows lined up – more later – but are still looking for new opportunities and venues, especially where we might be able to establish a more permanent presence.

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Some Mixed Media and Collage pieces

Avebury Neolithic circle

Because I couldn’t stand at the press for quite a while (see the previous post for why), I also made some small collage pieces. These have no particular theme, they are just playing with the medium.

All are available in my Etsy shop here (£25.00 each, matted to fit a 10″ x 8″ frame – see the listing for full details):

This link to an Etsy search on ‘mixed media collage’ also brings up some other items including a number from my ‘Made to Music’ series (£15 for unmatted, £30 matted – see the listing for full details – and making me realise that I’ve worked this way on several occasions.

I have another series called ‘Around Avebury’, which has yet to be added to the Etsy shop. These are all in handmade oak frames. You can find these on my Portfolio Pages.

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A new print series

I’ve been unable to do any printing for quite a while. I have been plagued with problems with my foot, putting me on crutches since mid-November. I used my down time looking at a lot of pictures, but also thinking about subjects and themes. I’ve been interested in neolithic rock art for some time now and living near Avebury and Stonehenge it’s impossible to avoid standing stones. Scattered also across the landscape of Wessex are hundreds, if not thousands of barrows and burial mounds and of course the White Horses like Pewsey, Cherhill, Westbury etc in Wiltshire or Uffington in Oxfordshire. In addition we have the famous Cerne Abbas Giant, the Long Man of Wilmington and assorted other hill figures, many now lost completely.

It isn’t just Britain with these features. The stone alignments of Carnac in Brittany are well known, but there are a multitude of standing stone circles, alignments and dolmens across France and Ireland and much further afield.

Putting all this together made me wonder about the sort of landscape we might see if more had survived. Out of that has evolved the print series I’m putting together. This will take landscapes, more or less stylised and incorporate into them other figures. I will draw on a range of sources. I’m researching Celtic and Saxon myths, cave paintings as well as the sort of abstract shapes found in rock art.

Technically, these prints will incorporate collagraph and dry point plus perhaps solar etching and ultimately hand embellishments. I also expect to use monoprinting or hand painting as the ground on which the prints will be made. I’m also going to try and incorporate some of the techniques used by Australian artist, Kim Westcott. (, although the site did not load properly for me.) She reuses old plates in combination with new, mixing in shadow prints and rotation of the plates to create her drypoints.

I have no prints as yet, but here are some rough sketches and photos of some plates in preparation.

With luck, I’ll have more over the next few weeks.


I’m not the only one finding inspiration in these themes. See the web pages for Irish artist Tommy Barr.

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Group Show

A bit late in the day for a promo, but I’m taking part in a group show at 44AD Gallery in Bath as a part of Wiltshire Print Creatives. We are an informal collective of printmakers, united only by the use of the same workshop space at Wiltshire College in Trowbridge.

We hung the show on Monday morning. Coming back for the evening Preview and seeing the show as a whole for the first time it was obvious that sharing the same workspace has allowed for some sort of artistic osmosis. Everywhere I looked I could see commonalities in vision and expression across all the work. I was very proud to see what we had achieved. Despite the fact that this is the work of friends I can genuinely say that the work on show is to a high standard both technically and creatively and well worth a visit if you can. My thanks to the other 11 for their support over the years and for their work in putting this together and making it happen.

The Wiltshire Print Creatives are…

  • Tonia Gunstone
  • Caroline Morriss
  • Kerrie McNeil
  • Martin Covington
  • Bella Bee
  • Judy Brett
  • Ian Bertram
  • Hayley Cove
  • Flora Jayne Camacho
  • Alex Nash
  • Claire Camacho
  • Jane Temperley
Setting up
Final Hang
Private View