Auto Added by WPeMatico

From Computational Creativity to Creative AI and Back Again


I compare and contrast the AI research field of Computational Creativity and the Creative AI technological movement, both of which are contributing to progress in the arts. I raise the sceptre of a looming crisis wherein public opinion moves on from the spectacle of software being creative to viewing the lack of authenticity in creative AI systems as being a major drawback. I propose a roadmap from Creative AI systems to Computationally Creative systems which address this lack of authenticity via the software expressing aspects of its computational life experiences in the art, music, games and literature that it produces. I posit that only by harnessing Creative AI technologies and Computational Creativity philosophies in the pursuit of truly creative software able to express the machine condition, will we gain maximum societal benefit in further understanding the human condition.


  1. Introduction

This year, we passed a milestone in my field, as the 10th annual International Conference on Computational Creativity (ICCC) was held in the USA. The conference brings together AI researchers who test the idea of software being independently creative, describing projects with goals ranging from enhancing human creativity to advancing our philosophical understanding of creativity and producing fully autonomous creative machines. The conference series was built on roughly ten years of preceding workshops [1], with interest in the idea of machine creativity going back to the birth of modern computing. For instance, in their 1958 paper [2], AI luminary Alan Newell and Nobel Prize winner Herbert Simon hypothesised that: “Within ten years, a digital computer will discover and prove an important mathematical theorem”. In [3], we proposed the following working definition of Computational Creativity research as:

“the philosophy, science and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative.”

In the last few years, we have seen unprecedented interest across society in generative AI systems able to create culturally interesting artefacts such as pictures, musical compositions, texts and games. Indeed, it’s difficult to read a newspaper or magazine these days without stumbling across a story about a new project to generate poems, or a symphony orchestra playing AI-generated music or an art exhibition in which AI systems are purported to be artists.

This wave of interest has been fuelled by a step change in the quality of computer-generated cultural artefacts, brought on largely by advances in machine learning technologies, and in particular the deep learning of artificial neural networks. Such techniques are able to generate new material by learning from data about the structure of existing material – such as a database of images, a corpus of texts or a collection of songs – and determining a way to create more of the same. An umbrella term for this groundswell of interest and activity in generative art/music/literature/games is “Creative AI”, and people from arts and sciences, within and outwith academia are actively engaged in producing art using AI techniques. We surveyed different communities engaged in generative arts – including Creative AI practitioners – in a recent ICCC paper [4].

While we might have expected the Creative AI community to have grown from the field of Computational Creativity, this is not the case. Indeed, somewhat of a schism has developed where the two communities have different aims and ambitions. Both communities have a main interest in the development of generative technologies for societal good. The Creative AI movement has an emphasis on quality of output and developing apps to commercial level for mass consumption. There is also a tendency to disavow the idea that software itself could/should be independently creative, in favour of a strong commitment to producing software purely for people to use to enhance their own creativity. In contrast, Computational Creativity researchers tend to be interested in the bigger picture of Artificial Intelligence, philosophical discourse around notions of human and machine creativity, novel ways to automate creative processes, and the idea that software, itself, could one day be deemed to be creative.

To highlight the schism: I personally find it difficult to think of any computational system as being “a Creative AI” if it cannot communicate details about a single decision it has taken, which is generally the case for approaches popular in Creative AI circles, such as Generative Adversarial Networks (GANs) [5]. I prefer therefore to describe Creative AI projects as “AI for creative people”, because the most literal reading of the phrase “Creative AI” is currently inaccurate for the majority of the projects under that banner. I often go further to point out that many Creative AI applications should be categorised as graphics (or audio, etc) projects which happen to employ techniques such as GANs that were originally developed by AI researchers.

As another example, I’ve argued in talks and papers many times that the end result of having more computer creativity in society is likely to be an increased understanding and celebration of human creativity, in much the same way that hand-made craft artefacts, like furniture or food, are usually preferred over machine-produced ones. I point out that I’ve met dozens of artists, musicians, poets and game designers, none of whom have expressed any concern about creative software, because they understand the value of humanity in creative practice. On the other hand, I’ve also spoken to Creative AI practitioners who remain convinced that truly creative software will lead to job losses, demoralisation and devaluation in the creative industries.


  1. Product versus Process

The Creative AI movement has helped to swing the global effort in engineering creative software systems firmly towards human-centric projects where AI techniques are used purely as tools for human use, with ease of use and quality of output disproportionately more important than any other considerations. I’ve been trying recently to put together arguments and thought experiments to help explain why I believe this is a retrograde step, and I’ve been trying to articulate ways in which the wealth of knowledge accrued through decades of Computational Creativity projects could be of use to Creative AI practitioners. Almost every project ever presented within Computational Creativity circles started with building a generative system with similar aims to Creative AI projects. Hence I feel we are well placed to consider the role that AI systems could have in creative practice, and to encourage Creative AI researchers and practitioners to consider some of the ideas we’ve developed over the years.

Imagine a generative music system created by a large technology company, which is able to generate 10,000 fully orchestrated symphonies in just 1 hour. Let’s say that each symphony would be lauded by experts as a beautiful work of genius had it been produced by a human composer like Beethoven; and each one sounds uniquely different to the others. If we accept the reality of an AI system (AlphaGo Zero) able to train itself from scratch to play Go, Chess and Shogi at superhuman levels [6], then we should entertain the idea that superhuman symphony writing is possible in our lifetimes. If we only concentrate on the quality of output and ease of which software can generate outputs as complex as a symphony, then the above scenario is presumably a suitable end point for generative music and would be a cause for celebration – it would certainly tick the box of huge technical achievement, as the AlphaGo project did. However, one has to wonder what the benefits of having these symphonies (and the ability to generate them so easily) are for society.

I would predict that the classical music world would find very few practical applications for a database of 10,000 high-quality symphonies, and it would likewise find little value in generating more such material. I would also predict that there would be little, if any, devaluation of symphonic music as a whole, and no devaluation of the work of gifted composers able to hand-produce symphonies. Superhuman chess playing by computers has been around since the time of Deep Blue, and has likely increased rather than decreased the popularity of the game. The chess world has responded to computer chess by being clearer about the human-centric struggle at the heart of every game of chess, and “[a]mong the chess elite, the idea of challenging a computer has fallen into the realm of farce and retort” [7]. It is clear that computer chess has made the game of chess more human. Part of the attraction of the music from composers such as Mozart and Beethoven is that these were mere mortals with superhuman creative abilities in composition. Society celebrates such creative people, often by lauding the works they produce, but also by applauding their motivations, exploring their backgrounds, expressing awe about their process, and by taking inspiration for a fresh wave of creative activity. Creativity in society serves various purposes, only one of which is to bring into being artefacts of value.

While board games have hugely driven forward AI research, chess isn’t some mathematical Drosophilia for AI problem solving (as some researchers would have you believe). It is actually a game and pastime played by two people, which can be elevated to highly competitive levels. Likewise, a symphony isn’t just a collection of notes to guide musicians to produce sound waves, but is created by human endeavour for human entertainment, often condensing into abstract form aspects of human life experience and expression. I would predict that – in an age of superhuman symphony generation – a huge premium would be placed on compositions borne of human blood, sweat and tears, with the generation of music via statistical manipulation of data by computer remaining a second class process.


  1. Computational Authenticity

To hit home with the points above, I usually turn to poetry, due to the highly human-centric nature of the medium: poems are condensed humanity, written by people, for people, usually about people. The following poem provides a useful focal point to illustrate the humanity gap [8] in Computational Creativity.



by Maureen Q. Smith

The joy, the pain, the begin again. My boy.

Born of me, for me, through my tears, through my fears.


This short poem naturally invites interpretation, and we might think of the joy, pain, tears as fears as referring literally to the birth of a child, perhaps from the first-person perspective of the author, as possibly indicated by “My boy … Born of me”. We might also interpret the “begin again” as referring to the start of a baby’s life, but equally it might reflect a fresh start for the family.

Importantly, the poem was not actually written by Maureen Q. Smith. The author was in fact a man called Maurice Q. Smith. In this light, we might want to re-think our interpretation. The poem takes on a different flavour now, but we can still imagine the male author witnessing a childbirth, possibly with his own tears and fears, reflecting the joy and pain of a woman giving birth. However, I should reveal that Maurice Q. Smith was actually a convicted paedophile when he wrote this poem, and it was widely assumed to be about the act of grooming innocent children, which he referred to as “childbirth”. The poem now affords a rather sinister reading, with “tears” and “fears” perhaps reflecting the author’s concerns for his own freedom; and the phrases “Joy and pain” and “Born of me, for me” now taking on very dark tones.

Fortunately, as you may have guessed, the poem wasn’t written by a paedophile, but was instead generated by a computer program using a cut-up technique. Thankfully, we can now go back and project a different interpretation onto the poem. Looking at “Joy and pain”, perhaps the software was thinking about… Well, the part about “Born of me, for me” must have been written to convey… Hmmmm. We see fairly quickly that it is no longer possible to project feelings, background and experiences onto the author, and the poem has lost some of its value. If the words have been put together algorithmically with nothing resembling the human thought processes we might have expected, we may also think of the poem as having lost its authenticity and a lot, if not all, of its meaning. We could, of course, pretend that it was written by a person. In fact, it’s possible to imagine an entire anthology of computer generated poems that we are instructed to read as if written by various people. But then, why wouldn’t we prefer to read an anthology of poems written by actual people?

For full and final disclosure: I actually wrote the poem and found it remarkably easy to pen a piece for which a straightforward interpretation changes greatly as the nature of the author changes. I’ve been using this provocative poem to try to change the minds of researchers in Computational Creativity research for a few years, in particular to try and shift the focus away from an obsession with the quality of output judged as if it were produced by a person. I’ve argued that the nature of the generative processes [9], how software frames its creations [10], and where motivations for computational creativity come from [11] are more important for us to investigate than how to increase the quality or diversity of output. This led to a study of the notion of computational authenticity [12], which pays into the discussion below.

As with pretty much all things generative, the advent of deep learning has led to a step change in the quality of the output of poetry generators, which have a long history dating (at least) as far back as an anthology entitled: “The Policeman’s Beard is Half Constructed” [13]. On the whole, the scientists pushing forward these advances have barely thought of addressing the deficiencies with these poems, namely that they were made by an inauthentic process. It is not impossible to imagine a poem-shaped computer generated text that would have been classed as a masterpiece had it been written by a person, but is not accepted by anyone as even being a poem, because public opinion has swung against inauthentic generative processes. I have for many years advocated using the name “c-poem” for the poem-shaped texts produced by computers. Just as people know that they won’t be unwrapping a beautifully bound e-book for their birthday, they should know that their ability to project human beliefs, emotions and experiences onto the author of a c-poem will be very limited.


  1. Responses to the Rise of Creative AI

Returning to the observation that the quality of the artistic output of AI systems has much increased in recent years, we can consider some appropriate responses to this situation.

One response is to follow the lead from the Creative AI community, and disavow the idea that software should be developed to be fully creative, concentrating instead on using AI techniques to aid human creativity. This certainly simplifies the situation, with AI systems becoming just the latest tools for creative people. It is also a public-friendly response, as journalists, broadcasters and documentary makers (along with the occasional politician, member of the clergy, philosopher or royal) often publish missives about how AI software is going to take everyone’s job, strangle our cats and devalue our life. On the whole, I believe it would be very sad if this response dominates the discourse and drives the field, as it would certainly curtail the dream of Artificial General Intelligence, which brought many of us into AI, and it will limit the ways in which people interact with software, which has the potential to be much more than a mere muse or tool. Software systems we have developed in Computational Creativity projects can be seen as creative collaborators; motivating yet critical partners; and sometimes independent creative entities. We should not throw away the idea that software can itself be creative, as the world always needs more creativity, and truly creative AI systems could radically drive humanity forward.

A second response is to accept the point above that the processes and personality behind creative practice are indeed important in the cultural appreciation of output from generative AI systems. In this context, given that software won’t be particularly human-like anytime soon, we could say that it’s impossible to take an AI system seriously as an authentic creative voice. An extreme version of this argument is that machines will never be valuable in the arts because they are not human. I argue below that this is shortsighted and missing an opportunity to understand technology in-situ. A closely related opinion is that people should or could dislike computer generated material precisely because it has been made by computer. This point of view has certainly been simmering under the surface of many conversations I’ve had, leading people to talk of computers lacking a soul or a spark, and often employing other such obfuscating rhetoric. Perhaps surprisingly, I’ve argued on a number of occasions that such a view is not extreme, and is indeed perfectly natural: such a view would, in my opinion, be a suitable personal response to the childbirth poem above, if indeed it had been computer generated.

Well intentioned people would never dream of saying that they dislike something because it was produced by a particular minority (or majority) group of people. Hence it feels to those people that they are being prejudicial to say that a painting, poem or composition is inferior purely because it was computer generated. Moreover, the view that works such as paintings and novels should be evaluated in their own terms, i.e., independently from information about their author and the creative process, has been reinforced philosophically with movements such as the Death of the Author [14], and numerous artistic manifestos.

Software systems do not form a minority human group whose creative freedom has to be protected. Throughout the history of humanity, art has been celebrated as a particularly human endeavour, and the art world is utterly people-centric. Software is not human, but due to decades of anthropomorphic thinking on AI, it seems more acceptable to think of computers somehow as under-evolved or under-developed humans, perhaps like monkeys or toddlers, rather than non-humans with intelligence, albeit low. Disliking a work of art purely because of its computational origins is more akin to expressing a preference of one type of process over another, than it is to expressing preferences of one ethnicity, gender or religion over another. “I don’t like this painting because it is a pointillist piece” is not the same as: “I don’t like this painting because it was painted by a Brit”.

So, we could say that, while the output of the current/future wave of generative AI systems is remarkable, and could – under Turing-style conditions of anonymity – be taken for human works, there is a natural limiting factor in the non-humanity of computational systems which gives us a backstop against the devaluation of human artistic endeavour. This is a reasonable response and may lead to increased celebration of human creativity, which would be no bad thing. However, I believe that this response will also (eventually) be limiting and lead to missed opportunities, as I hope to explain below.

A third response, which I greatly favour, is to start from the truism that software is not human. In many research and industry circles, it often seems that creating human-like intelligence through nueroscience-inspired approaches such as deep learning, is the only goal and the only approach. Not every AI researcher wants to build a software version of the brain, but this fact is often lost, and helps to obfuscate the fact that software has different experiences to people. The Painting Fool is software that I’ve developed over nearly 20 years [15], and has met minor and major celebrities and painted their portraits in half a dozen different countries, often in front of large audiences in interesting venues ranging from science museums and art galleries to a pub in East London. I have, of course, anthropomorphised this experience and The Painting Fool didn’t experience it as I have portrayed. But it did have experiences, and those experiences were authentic in the sense that the software was present, did interact with people and created things independently of me which entertained and provoked people in equal measure.

We could therefore respond to the uptick in quality of output from Creative AI systems by agreeing to concentrate more on investigating plausible internal reasons for software to be creative, and developing ways in which it can impart its understanding of the world, through expressing aspects of its life experiences. Instead of challenging human creativity in terms of the quality of output, but failing due to lack of authenticity, Computational Creativity systems could be developed to explore aspects of creative independence such as intrinsic motivation, empowerment [10] and intentionality [8]. A side effect of this is that – if we get software to record and use its own experiences rather than pretending that it is a person having human experiences – we will gain a better understanding of computer processing, the impact of particular software systems and what it means for a machine to have a cultural existence in our human world. It may be that this communicative side effect actually becomes more important than having software be creative for the purpose of making things.

If software can express its experience of the world through artistic expression, surely this would add to our understanding of human culture in a digital age of tremendous, constant, technological change. While the non-human life experiences of software systems can seem other worldly, automation is very much a part of the human world, and our increasing interaction on a minute-by-minute basis with software means we should be constantly open to new ideas for understanding what it does. It’s not so strange to imagine building an automated painting system to add on to another piece of software so that it can express aspects of its experience. In fact, this would be a natural generalisation of projects such as DeepDream [16], where visualisations of deep-learned neural models were originally generated to enable people to better understand how the model processed image data. It turned out that the visualisations had artistic value as computational hallucinations, and were presented in artistic contexts, with this usage eventually dominating, fuelling a huge push in generative neural network research and development.


  1. A RoadMap from Creative AI to Computational Creativity

In a talk at a London Creative AI meetup event a while ago, I offered some advice for people in the Creative AI community who might be interested in pursuing the dream of making genuinely creative AI systems. At the time, there were already indications that Creative AI practitioners were beginning to see the limitations of mass generation of high-quality artefacts and were interested in handing over more creative responsibility to software. Some people were already testing the water using deep learning techniques in ways other than pastiche generation, for instance looking at style invention rather than just style transfer [17]. The advice I gave can be seen as a very rough roadmap, which reflects to some extent my own career arc in building creative AI systems, and provides one of many paths by which people can take their generative system into fascinating new territories.

While keeping much of the original, I will re-draw the roadmap below, from a fresh perspective of improving authenticity through expanding the recording and creative usage of life experiences that creative software might have. It is presented as a series of seven levels for Creative AI Systems to transition to via increased software engineering and cultural usage, with each level representing a different type of system that the software graduates to. Focused on generative visual art rather than poetry/music/games/etc., but intended to generalise over many domains, the roadmap offers direct advice to people who already have a generative system.

  • Generative Systems. So, you’ve designed a generative system and are having fun making pictures with it. You play around with input data and parameter settings, and realise that the output is not only high quality, but really varied. You write a little graphical user interface, which enables you to play around with the inputs/parameters, and this increases the fun and the variety. It becomes clear that the space of inputs/parameters is very You begin to suspect that the space of novel outputs is also vast. You’re at level one: you have an interesting generative system which is able to make stuff. 


  • Appreciative Systems. Generating images becomes addictive, and you gorge on the output. In your gluttony, you get a strong fear of missing out – what if I miss the parameters for a really interesting picture? You decide to systematically sample the space of outputs, but there are millions of images that can be produced. So, you encode your aesthetic preferences into a fitness function and get the software to rank/display its best results, according to the fitness function, perhaps tempered by a novelty measure to keep things fresh. You’re at level two: you have an appreciative system which is able to discern quality in output.


  • Artistic Systems. At some stage, some humility sinks in, and you begin to think that maybe… just maybe… your particular aesthetic preferences aren’t the only ones which could be used to mine images. You give the software the ability to invent its own aesthetic fitness functions and use them to filter and rank the images that it generates. You’re at level three, with an artistic system which has some potential to affect the world artistically.


  • Persuasive Systems. Some of the output is great – beautiful new images that you perhaps wouldn’t have found/made yourself. But some of the pictures are unpalatable and you can’t imagine why the software likes them. However, sometimes, an awful image grows in appeal to you, and you realise that your own aesthetic sensibilities are being changed by the software. This is weird, but fun. You want to give the software the ability to influence you more easily, so you add a module which produces a little essay as a commentary on the aesthetic generation, the artefact generation and the style that the software has invented. You’re at level four, with a persuasive system that can change your mind through explanations as well as high quality, surprising output.


  • Inventive Systems. You begin to realise that you enjoy the output partially because of what it looks like and partially because of the backstory to the generation of the output and the aesthetics being considered. You want to increase both aspects, by enabling the software to alter its own code, perhaps at process level, and by taking inspiration from outside sources like newspapers, twitter, art books, other artists, etc., so you have less control. And you add natural language generation to turn the commentary about the process/product into a little drama. You’re at level five, where what your inventive system does is as important, interesting and unpredictable as its output.


  • Authentic Systems. You’re loving the commentaries/essays/stories about how and why your software has made a particular picture/aesthetic/style/series or invented a new technique, and the software pretty much has an artistic persona. However, sometimes the persona doesn’t ring true and actually verges on being insulting, given how little the software knows about the world. You realise that you’re reading/viewing the output as if it were created by a person, which is a falsehood which has gotten very old and somewhat disturbing. You decide to give the software plausible and believable reasons to be creative, by implementing models of intrinsic motivation, reflection, self-improvement, self-determination, empowerment and maybe even consciousness. In particular, much of this depends on implementing techniques to record the life experiences that your software has, via: sensors detecting aspects of the environment the software operates in; improved in-situ and online HCI, wherein the software’s interactions with people are recorded and the software is able to probe people with questions; and methods which take life experiences and outside knowledge and operationalise them into opinions that can be reflected in generative processing and output. You then give the software the ability to use its recorded life experiences to influence its creative direction, in much the same way that twitter and newspaper sources were previously. You’re at level six, with an authentic system that is seen more as an autonomous AI individual than a pale reflection of a person.


  • Philosophical Systems. Ultimately, you find it thrilling to be in the presence of such an interesting creator as your software – it’s completely independent of you, and it teaches you new things, regularly inspiring you and others. You realise that for the software to be taken seriously as an artist, it needs to join the debate about what creativity means (as creativity is an essentially contested concept [18]) in practice and as a societal driving force. You implement methods for philosophical reasoning based on the software’s own creative endeavours, and you enable it to critique the thoughts of others. You add dialogue systems to propose, prove and disprove hypotheses about the nature of creativity, enabling your system to generally provoke discussion around the topic. You’re at level seven, where it’s difficult to argue that your philosophical system isn’t genuinely creative.


It is fair to say that no AI system gets close yet to levels 6 and 7 yet, but projects presented in Creative AI and Computational Creativity circles have tested the water up to and including level 5. If I were giving a talk about this roadmap, there would be much handwaving towards the end, as the road gets very blurry, with few signposts. This, of course, is the frontier of Computational Creativity research and reflects directions I will personally be taking software like The Painting Fool in. I’m particularly interested in exploring the notion of the machine condition and seeing how authentic we can make the processing and products from AI systems. That notwithstanding, I hope the roadmap offers some insight and inspiration to people from all backgrounds who are working with cool generative systems and want to take the project further.


  1. In Conclusion

More than a decade ago, I was dismayed to read in a graphics textbook the following statement:

“Simulating artistic techniques means also simulating human thinking and reasoning, especially creative thinking. This is impossible to do using algorithms or information processing systems. [19, p. 113]”

The topic of the textbook is Non-photorealistic Computer Graphics, part of which involves getting software to simulate paint/pencil/pastel strokes on-screen. Stating that computational creative thinking is impossible was short-sighted and presumably written to placate creative industry practitioners, who use software like the Adobe Creative Suite which employ such non-photorealistic graphics techniques. In the 17 years since the above statement was published, the argument seems to have moved on from whether software can be independently creative to whether it should be allowed to. It is my sincere hope that the argument will shift soon to the question of how best truly creative AI systems can enhance and inform the human world, and how we can use autonomous software creativity to help us understand how technology works.

Creative AI practitioners have emerged as much via scientists in the machine learning community embracing art practice as via tech-savvy artists picking up and applying tools such as Tensor Flow [20]. Speaking personally, and having witnessed numerous transitions, scientists tend to hold on too long to the idea that product is more important than process or personality in creative practice [21]. This is presumably due to scientific evaluation being objective, with scientific findings expected to be evaluated entirely independently of their origins.

It would be tempting to follow the lead of companies like DeepMind who often justify working on applications to the automated playing of board games and video games [22] by stating that this research pushes forward AI technologies in general, which ultimately leads to improvements in applications to other, more worthwhile, domains like protein structure prediction [23] and healthcare. Getting software to produce better poems, paintings, games, etc., will likely lead to improvements in AI techniques overall, so concentrating on improving quality of output is in some senses a good thing. However, this would serve to deflect from what I believe is a looming crisis in Creative AI, which is when the novelty of the computer generation gimmick wears off, and people begin to realise that authenticity of process, voice and life experience are more important than the so-called “quality” of computer generated artefacts.

The activities of playing games and predicting protein structures have the luxury of objective measures for success and thus progress (beating other players and nanoscale accuracy, respectively). This is not true in the arts, where there are only subjective – and highly debated – notions of the “best” painting, poem, game or musical composition. The humanity wrapped up in artefacts produced by creative people is absolutely critical in the evaluation of those artefacts, which is not true in scientific or (to a lesser extent) competitive scenarios.

It is similarly tempting to appeal to the creative outcomes of the AlphaGo match against Lee Sedol, which have been described beautifully by Cade Metz in [24]:

“In Game Two, the Google machine made a move that no human ever would. And it was beautiful. As the world looked on, the move so perfectly demonstrated the enormously powerful and rather mysterious talents of modern artificial intelligence.”

“But in Game Four, the human made a move that no machine would ever expect. And it was beautiful too. Indeed, it was just as beautiful as the move from the Google machine – no less and no more. It showed that although machines are now capable of moments of genius, humans have hardly lost the ability to generate their own transcendent moments. And it seems that in the years to come, as we humans work with these machines, our genius will only grow in tandem with our creations.”

In the thought experiment above, in the corpus of 10,000 new symphonies generated by computer, there would surely be many moments of inventive genius: a phrase, passage or flourish of orchestration found in the notes of the music produced. Humankind would learn from the software, and would in turn develop better generative approaches to music production. But would we necessarily learn anything about the human condition, as we generally hope to in the arts?

I posit that only if software is developed to record its life experiences and use them in the pursuit of creative practice will we learn anything about the human condition, through increased understanding of the machine condition. Developing better AI painters means engineering software with more interesting life experiences, not software with better technical abilities. While there might be advantages, there is no imperative for these life experiences to be particularly human-like, and society might be better served if we try and understand computational lives through art generation. We hear all the time that the workings of black box AI systems deep-learned over huge datasets are not understood even by the researchers in the project. While this difficulty is usually overstated, we are facing a situation of increased scenarios where AI-enhanced software makes decisions of real import for us, coupled with decreased understanding of how individual AI systems make those decisions.

Combining the best practices and understanding gained from both Computational Creativity as a research field and Creative AI as an artistic and technological movement, may be the best approach to bringing about a future enhanced by creative software expressing its life experiences artistically for our benefit. The diversity, enthusiasm and innovative thinking coming daily from the Creative AI community, guided by the philosophy of the Computational Creativity movement is a potent combination, and I’m optimistic that in my lifetime, we will reap the benefits of cross-discipline, cross-community collaborations. Creative AI practitioners may rail against interventions from people like myself: stuffy academic disciples of the Computational Creativity discipline. But it is worth mentioning that we were once the angry young men and women of a largely ostracised and ignored arm of AI, shouting into the void at an establishment who thought that notions of creativity in AI systems were too “wooly” to be taken seriously.

Who knows what history will record about the rise of creative machines in society. My sincere hope is that it will chart how Computational Creativity thinking evolved without the benefit of sophisticated technical implementations; this was massively influenced with a surge in the technical abilities of Creative AI Systems during the period of Deep Learning dominance; but then naturally turned back to the philosophical thinking of Computational Creativity in order to properly reap the benefits of truly creative technologies in society.



[1] Cardoso, A., Veale, T. and Wiggins, G. A. (2009). Converging on the divergent: The history (and future) of the international joint workshops in computational creativity. AI Magazine, 30(3), 15–22.

[2] Simon, H., and Newell, A. (1958). Heuristic problem solving: The next advance in operations research. Operations Research, 6(1), 1-10.

[3] Colton, S. and Wiggins, G. A. (2012). Computational Creativity: A Final Frontier? Proceedings of the European Conference on Artificial Intelligence, 2012.

[4] Cook, M. and Colton, S. (2018). Neighbouring Communities: Interaction, Lessons and Opportunities. Proceedings of the Ninth International Conference on Computational Creativity.

[5] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y. (2014). Generative Adversarial Networks. Proceedings of the International Conference on Neural Information Processing Systems.

[6] Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T. and Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature 550, 354-359.

[7] Max, D. T. (2011) The Prince’s Gambit: A chess star emerges for the post-computer age. New Yorker, March 14th 2011 edition.

[8] Colton, S., Cook, M., Hepworth, R. and Pease, A. (2014). On Acid Drops and Teardrops: Observer Issues in Computational Creativity. Proceedings of the AISB’50 Symposium on AI and Philosophy.

[9] Colton, S. (2008). Creativity versus the Perception of Creativity in Computational Systems.

Proceedings of the AAAI Spring Symposium on Creative Systems.

[10] Charnley, J., Pease, A. and Colton, S. (2012). On the Notion of Framing in Computational Creativity. Proceedings of the Third International Conference on Computational Creativity.

[11] Guckelsberger, C., Salge, C. and Colton, S. (2017). Addressing the “Why?” in Computational Creativity: A Non-Anthropocentric, Minimal Model of Intentional Creative Agency. Proceedings of the Eighth International Conference on Computational Creativity.

[12] Colton, S., Pease, A. and Saunders, R. (2018). Issues of Authenticity in Autonomously Creative Systems. Proceedings of the Ninth International Conference on Computational Creativity.

[13] Chamberlain, W. and Etter, T. (1984). The Policeman’s Beard is Half-Constructed: Computer Prose and Poetry. Warner Books.

[14] Barthes, R. (1967). The death of the author. Aspen 5-6.

[15] Colton, S. (2012) The Painting Fool: Stories from building an automated painter. In McCormack, J. and d’Inverno, M., eds., Computers and Creativity, 3–38. Springer.

[16] Mordvintsev, A., Olah, C. and Tyka, M. (2015). DeepDream – a code example for visualizing Neural Networks. Google AI Blog, July 1st 2015.

[17] Elgammal, A., Liu, B., Elhoseiny, M. and Mazzone, M. (2017). CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms. Proceedings of the Eighth International Conference on Computational Creativity.

[18] Gallie, W. (1956). Art as an essentially contested concept. The Philosophical Quarterly 6(23),97-114.

[19] Strothotte, H. and Schlechtweg, S. (2002). Non-Photorealistic Computer Graphics: Modelling, Rendering and Animation. Morgan Kaufmann.

[20] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A.,

Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jozefowicz, R.,  Jia, Y., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Schuster, M., Monga, R., Moore, S., Murray, D., Olah, F., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. and Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from

[21] Jordanous, A. (2016). Four PPPPerspectives on computational creativity in theory and in practice. Connection Science special issue on Computational Creativity, 28(2), 194-216.

[22] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A., Veness, J., Bellemare, M., Graves, A., Riedmiller, M., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S. and Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature 518, 529-533.

[23] Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Zidek, A., Nelson, A., Bridgland, A., Penedones, H., Petersen, S., Simonyan, K., Crossan, S., Jones, D., Silver, D., Kavukcuoglu, K., Hassabis, D. and Senior, A. (2018). De novo structure prediction with deep-learning based scoring. Proceedings of the Thirteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstracts).

[24] Metz, C. (2016). In Two Moves, AlphaGo and Lee Sedol Redefined the Future. Wired, 16th March 2016 edition.

The post From Computational Creativity to Creative AI and Back Again appeared first on Interalia Magazine.

Is consciousness a battle between your beliefs and perceptions?

Imagine you’re at a magic show, in which the performer suddenly vanishes. Of course, you ultimately know that the person is probably just hiding somewhere. Yet it continues to look as if the person has disappeared. We can’t reason away that appearance, no matter what logic dictates. Why are our conscious experiences so stubborn?

The fact that our perception of the world appears to be so intransigent, however much we might reflect on it, tells us something unique about how our brains are wired. Compare the magician scenario with how we usually process information. Say you have five friends who tell you it’s raining outside, and one weather website indicating that it isn’t. You’d probably just consider the website to be wrong and write it off. But when it comes to conscious perception, there seems to be something strangely persistent about what we see, hear and feel. Even when a perceptual experience is clearly ‘wrong’, we can’t just mute it.

Why is that so? Recent advances in artificial intelligence (AI) shed new light on this puzzle. In computer science, we know that neural networks for pattern-recognition – so-called deep learning models – can benefit from a process known as predictive coding. Instead of just taking in information passively, from the bottom up, networks can make top-down hypotheses about the world, to be tested against observations. They generally work better this way. When a neural network identifies a cat, for example, it first develops a model that allows it to predict or imagine what a cat looks like. It can then examine any incoming data that arrives to see whether or not it fits that expectation.

The trouble is, while these generative models can be super efficient once they’re up and running, they usually demand huge amounts of time and information to train. One solution is to use generative adversarial networks (GANs) – hailed as the ‘coolest idea in deep learning in the last 20 years’ by Facebook’s head of AI research Yann LeCun. In GANs, we might train one network (the generator) to create pictures of cats, mimicking real cats as closely as it can. And we train another network (the discriminator) to distinguish between the manufactured cat images and the real ones. We can then pit the two networks against each other, such that the discriminator is rewarded for catching fakes, while the generator is rewarded for getting away with them. When they are set up to compete, the networks grow together in prowess, not unlike an arch art-forger trying to outwit an art expert. This makes learning very efficient for each of them.

As well as a handy engineering trick, GANs are a potentially useful analogy for understanding the human brain. In mammalian brains, the neurons responsible for encoding perceptual information serve multiple purposes. For example, the neurons that fire when you see a cat also fire when you imagine or remember a cat; they can also activate more or less at random. So whenever there’s activity in our neural circuitry, the brain needs to be able to figure out the cause of the signals, whether internal or external.

We can call this exercise perceptual reality monitoring. John Locke, the 17th-century British philosopher, believed that we had some sort of inner organ that performed the job of sensory self-monitoring. But critics of Locke wondered why Mother Nature would take the trouble to grow a whole separate organ, on top of a system that’s already set up to detect the world via the senses. You have to be able to smell something before you can go about deciding whether or not the perception is real or fake; so why not just build in a check to the detecting mechanism itself?

In light of what we now know about GANs, though, Locke’s idea makes a certain amount of sense. Because our perceptual system takes up neural resources, parts of it get recycled for different uses. So imagining a cat draws on the same neuronal patterns as actually seeing one. But this overlap muddies the water regarding the meaning of the signals. Therefore, for the recycling scheme to work well, we need a discriminator to decide when we are seeing something versus when we’re merely thinking about it. This GAN-like inner sense organ – or something like it – needs to be there to act as an adversarial rival, to stimulate the growth of a well-honed predictive coding mechanism.

If this account is right, it’s fair to say that conscious experience is probably akin to a kind of logical inference. That is, if the perceptual signal from the generator says there is a cat, and the discriminator decides that this signal truthfully reflects the state of the world right now, we naturally see a cat. The same goes for raw feelings: pain can feel sharp, even when we know full well that nothing is poking at us, and patients can report feeling pain in limbs that have already been amputated. To the extent that the discriminator gets things right most of the time, we tend to trust it. No wonder that when there’s a conflict between subjective impressions and rational beliefs, it seems to make sense to believe what we consciously experience.

This perceptual stubbornness is not just a feature of humans. Some primates have it too, as shown by their capacity to be amazed and amused by magic tricks. That is, they seem to understand that there’s a tension between what they’re seeing and what they know to be true. Given what we understand about their brains – specifically, that their perceptual neurons are also ‘recyclable’ for top-down functioning – the GAN theory suggests that these nonhuman animals probably have conscious experiences not dissimilar to ours.

The future of AI is more challenging. If we built a robot with a very complex GAN-style architecture, would it be conscious? On the basis of our theory, it would probably be capable of predictive coding, exercising the same machinery for perception as it deploys for top-down prediction or imagination. Perhaps like some current generative networks, it could ‘dream’. Like us, it probably couldn’t reason away its pain – and it might even be able to appreciate stage magic.

Theorising about consciousness is notoriously hard, and we don’t yet know what it really consists in. So we wouldn’t be in a position to establish if our robot was truly conscious. Then again, we can’t do this with any certainty with respect to other animals either. At least by fleshing out some conjectures about the machinery of consciousness, we can begin
to test them against our intuitions – and, more importantly, in experiments. What we do know is that a model of the mind involving an inner mechanism of doubt – a nit-picking system that’s constantly on the lookout for fakes and forgeries in perception – is one of the most promising ideas we’ve come up with so far.

Hakwan Lau

This article was originally published at Aeon and has been republished under Creative Commons.

The post Is consciousness a battle between your beliefs and perceptions? appeared first on Interalia Magazine.

The Maths of Life and Death


Q & A with Kit Yates:

Maths is an unloved subject. It’s a commonplace view that maths is hard, that maths is abstract and removed from everyday concerns. Why do you think that is?

There’s no doubt that maths is perceived as polarising; despised by many and loved by just a few. As a mathematician interested in sharing the wonders of my subject, my biggest struggle is with this self-imposed false dichotomy: those who believe that they can do maths and those who think they can’t. There are far too many of the latter. But there is almost no-one who understands no maths at all, no-one who cannot count. At the other extreme, for hundreds of years there have been no mathematicians who understand all of known mathematics. We all sit somewhere on this spectrum; how far we travel to the left or to the right depends on how much we think this knowledge can be useful to us. Exposing the uses and importance of maths in everyday life is one way to shift people along the spectrum, to bring them into the middle ground.

This is exactly what I’ve tried to do in my book. It’s important to say upfront that The Maths of Life and Death is not a not a maths book. Nor is it a book for mathematicians. There isn’t a single equation in it. The point of the book is not to bring back memories of the school mathematics lessons you might have given up years ago. Quite the opposite. If you’ve ever been disenfranchised and made to feel that you can’t take part in mathematics or aren’t good at it, consider this book an emancipation.

I genuinely believe that maths is for everyone and that we can all appreciate the beautiful mathematics at the heart of the complicated phenomena we experience daily. If you’ve ever been made to feel that you can’t comprehend maths or aren’t good at it, I say this: you are experiencing it all the time, perhaps without even knowing it. Mathematics, at its most fundamental, is pattern. If you spot a motif in the fractal branches of a tree, or in the multi-fold symmetry of a snowflake, then you are seeing maths. When you tap your foot in time to a piece of music, or when your voice reverberates and resonates as you sing in the shower, you are hearing maths. If you bend a shot into the back of the net or catch a cricket ball on its parabolic trajectory, then you are doing maths. Part of the job I undertake in the book is to highlight the places where people are using maths, intuitively, perhaps without even realising it.

Unfortunately, all too often, mathematics is viewed as a sterile, abstract subject: at best an esoteric plaything for out-of-touch academics, and at worst a waste of school children’s time and taxpayers’ money. Few explanations of everyday mathematics filter through to non-specialists. Instead they are told that mathematics is inaccessible and inscrutable. Mathematics is often lauded for its beauty, its purity, its abstraction and otherworldliness; untainted by the messy details of reality. But for me, an applied mathematician, mathematics is first and foremost a practical tool to make sense of our complex world. Mathematical modelling can give us an advantage in everyday situations, and it doesn’t have to comprise hundreds of tedious equations or lines of computer code to do so. In fact, the simplest models are stories and analogies. For me, the stories that comprise this book – the most basic models – are the most useful of all. When viewed through the right lens we can tease out the hidden mathematical rules that underlie our common experiences.

Is this attitude to maths changing?

I think societal changes are slowly altering attitude towards the importance of maths. As our economies change, there is growing awareness that we need more mathematicians, engineers and scientists to fill the increasing numbers of jobs in the technology sector. To some degree this is reflected in maths’ rise to becoming the most popular A-level choice. This rise in popularity has also impacted on the number of students continuing to study mathematics in higher education. I always tell students who come to visit my department at open days, and who are trying to make up their mind about whether to study maths or not, that by studying maths they will only open doors for themselves and never close them. It’s so easy to jump out of mathematics and into another discipline, but much harder to go back the other way.

For example, I myself am a mathematical biologist. When I tell people this, the reaction I get is usually a polite nodding of the head accompanied by an awkward silence, as if I was about to test them on their recall of the quadratic formula or Pythagoras’ theorem. More than simply being daunted, people struggle to understand how a subject like maths, which they perceive as being abstract, pure and ethereal, can have anything to do with a subject like biology, which is typically thought of as being practical, messy and pragmatic.

I dropped biology at sixth-form and took A-levels in maths, further maths, physics and chemistry. When I went to university, I had to further streamline my subjects, and felt sad that I had to leave biology behind forever; a subject I thought had incredible power to change lives for the better. I was hugely excited about the opportunity to plunge myself into the world of mathematics, but I couldn’t help worrying that I was taking on a subject that seemed to have very few practical applications. I couldn’t have been more wrong.

Whilst I plodded through the pure maths we were taught at university I lived for the applied maths courses. I listened to lecturers as they demonstrated the maths that engineers use to build bridges so that they don’t resonate and collapse in the wind, or to design wings that ensure planes don’t fall out of the sky. I learned the quantum mechanics that physicists use to understand the strange goings-on at subatomic scales and the theory of special relativity that explores the strange consequences of the invariance of the speed of light. I took courses explaining the ways in which we use mathematics in chemistry, in finance and in economics. I read about how we use mathematics in sport to enhance the performance of our top athletes and how we use mathematics in the movies to create computer-generated images of scenes that couldn’t exist in reality. In short, I learned that mathematics can be used to describe almost everything.

I think as people start to see the way in which mathematics is increasingly pervading their everyday lives and to understand how even a little mathematical knowledge can be of benefit in real life, its importance will be increasingly realized. I also believe that when students see that there is a point to the maths they are being taught, rather than just rote learning to pass an exam, that maths can be transformed into something enjoyable.

This is what the Maths of Life and Death is all about. I try to convince the reader that maths is so much more than the esoteric subject they left behind at school. It is the false alarms that play on our minds and the false confidence that helps us sleep at night; the stories pushed at us on social media and the memes that spread through it. Maths is the loopholes in the law and the needle that closes them; the technology that saves lives and the mistakes that put them at risk; the outbreak of a deadly disease and the best way to control it. It is the best hope we have of answering the most fundamental questions about the enigmas of the cosmos and the mysteries of our own species. It leads us on the myriad paths of our lives and lies in wait, just beyond the veil, to stare back at us as we draw our final breaths.

A common everyday use of maths is in shopping – a trip to the greengrocer is one of the most cited examples in school maths teaching – but what are some other everyday, and more unusual uses of maths?

It’s funny you should mention shopping, because there’s actually so much more maths to shopping than just working out your change. For example, stores have traditionally over-represented price tags which end in .99, .95 or .90. In the UK .99 is the third most common price ending after .00 and .50. The marketing theory goes that because we read left to right we take account of the first digits on price tags, but ignore everything to the right of the decimal point. Unwittingly we are being tricked into thinking products are cheaper than they are because our brains are always subconsciously rounding down. In the book I also provide a nice rule of thumb called ‘the 37% rule’ which uses the maths of optimisation to help you join the shortest queue in the supermarket.

Of course there are so many more places where maths appears in everyday life. In the book, we explore the true stories of life-changing events in which the application (or misapplication) of mathematics has played a critical role: patients crippled by faulty genes and entrepreneurs bankrupt by faulty algorithms; innocent victims of miscarriages of justice and the unwitting victims of software glitches. I follow stories of investors who have lost fortunes and parents who have lost children, all because of mathematical misunderstanding. I wrestle with ethical dilemmas from screening to statistical subterfuge and examine pertinent societal issues such as political referenda, disease prevention, criminal justice and artificial intelligence. I show that mathematics has something profound or significant to say on all of these subjects, and more.

Rather than just pointing out the places in which maths might crop up, I also try to arm the reader with simple mathematical rules and tools which can help them in their everyday life: from getting the best seat on the train, to keeping one’s head when on the receiving end of an unexpected test result from the doctor. I suggest simple ways to avoid making numerical mistakes and get my hands dirty with newsprint when untangling the figures behind the headlines. I also get up close and personal with the maths behind consumer genetics and display maths in action as I highlight the steps we can all be taking to help halt the spread of deadly diseases.

What are some of the benefits of a better understanding of maths?

A little mathematical knowledge in our increasingly quantitative society can help us to harness the power of numbers for ourselves. Simple rules allow us to make the best choices and avoid the worst mistakes. Small alterations in the way we think about our rapidly evolving environments help us to ‘keep calm’ in the face of rapidly accelerating change, or adapt to our increasingly automated realities. Basic models of our actions, reactions and interactions can prepare us for the future before it arrives. The stories relating other people’s experiences are, in my view, the simplest and most powerful models of all. They allow us to learn from the mistakes of our predecessors so that, before we embark on any numerical expedition, we ensure we are all speaking the same language, have synchronised our watches, and checked we’ve got enough fuel in the tank.

Half the battle for mathematical empowerment is daring to question the perceived authority of those who wield the weapons – shattering the illusion of certainty. Appreciating absolute and relative risks, ratio biases, mismatched framing and bias gives us the power to be sceptical of the statistics screamed from newspaper headlines, the ‘studies’ pushed at us in adverts or the half-truths that come tumbling from the mouths of our politicians. Recognising mathematical sleights of hand allows us to disperse obfuscating smoke screens, making it harder to fool us with mathematical arguments, be they in the courtroom, the classroom or the clinic.

We must ensure that the person with the most shocking statistics doesn’t always win the argument, by demanding an explanation of the maths behind the figures. We shouldn’t let medical charlatans delay us from receiving potentially life-saving treatment when benefits their alternative therapies are just a mathematical anomaly. We mustn’t let anti-vaxxers make us doubt the efficacy of vaccinations, when mathematics demonstrates that they can save vulnerable lives and wipe out disease.

As I hope I show throughout the book, it is time for us to take the power back into our own hands, because sometimes maths really is a matter of life and death.



The post The Maths of Life and Death appeared first on Interalia Magazine.

In Praise of Form: Towards a New Post-Humanist Art

Today the litany of crises we face culturally and globally has become so familiar that it needs no further recitation. Indeed, so often are we reminded that the world has gone wrong that the word “crisis” has acquired a patina of banality. But this is to be an essay of hope, so let us move on. For protests to the contrary notwithstanding, there is good reason for it: across many strata of Western culture, there is a growing awareness, uneasy though it may be, that we have at last identified the problem. The problem is not out there, in some externalized other (would that it were so, so much more palatable would this be). Reluctantly, shamefully, but profoundly necessarily, we are finally meeting the enemy, and he is us: the human animal that placed itself in the center of the universe, the one that first severed itself from nature and then elevated itself above it, and the one that in imagining that this was really possible has dug its own grave. We can call this progress.

Daniel Hill, “Untitled 37,” 2012. Acrylic polymer emulsion on paper mounted to panel, 44″ x 60″ (diptych). Courtesy of ODETTA Gallery.

To be fair, the problem is more specific, and can be located in an idea. Although for most of us in the West the word “humanism” still conjures little but benevolence (“human values,” “human rights, “human dignity,” etc.), it harbors an implicit ideology that many are now challenging. This is none other than its premise of human exceptionalism: the assumption that the human being is the source of all meaning and, even further, the ultimate reality. In light of everything we’re witnessing in our ignoble Anthropocene, it is becoming increasingly clear that humanism has been as mistaken as the theism it sought to replace, for just as God’s omnipotence reduced us to servitude, so ours has done the same to the non-human world. The call for a post-humanist worldview grows ever more compelling. Can we achieve a new way of being that honors the nonhuman world, one that acknowledges its inherent richness and restores it to its rightful place in the cosmos? Spatially, chronologically, and in just about every other way, it does, after all, rather greatly exceed us.

William Holton, “Point of Convergence,” 2010. Oil and acrylic on canvas, 35″ x 36″. Courtesy of the artist.

But what does any of this have to do with art, you may be asking. And this is exactly the point. The answer is nothing – or very little, just yet. While the so-called non-human turn has inundated the humanities, leading even to the proposal a new “inhuman humanities,” visual art has undergone nothing of the kind. In fact, it could be argued that just the opposite has happened; with art’s preoccupation with social justice and an exhausted postmodernism, it’s easy for those of us in the field to forget anything beyond us exists. Adding to this our inherited assumptions about art being “self-expression” (and lest we be inclined to dismiss this as a pedestrian notion, what is our current “identity art” if not exactly this?), it becomes clear that visual art is mired in an obsolescent human centrism. Indeed, if “everything is a social construct,” as postmodernism tells us, the human being isn’t just the highest but the only reality.

But aside from the societal orientation of much visual art today, there is a deeper sense in which art has been complicit in perpetuating an old idea. It’s much more subtle than subject matter, and has to do with our very expectations for and valuations of art. For as art becomes ever more discursive, prioritizing issues and ideas over the forms in which they’re instantiated, it is reinforcing the implicit values of the humanist fallacy.

Werner Sun, “Double Vision 1B,” diptych, 2018. Archival inkjet prints and acrylic on board, 12″ x 25″ x 2″. Courtesy of the artist.

The problem is made evident when we consider prevailing attitudes toward form. “Empty formalism,” “mere formalism,” “shallow form devoid of content”: in a time when art is expected to address this or that issue, form has become a critical embarrassment, something insufficient in itself but useful for one purpose – namely, to serve as the delivery system for the real substance that is “content.” So pervasive is the disdain for “mere form” that today’s artist’s statements often read as hyper-intellectualized apologia – discursive treatises announcing in advance that there’s no “mere” happening here. And yet in the privacy of their studios, in the presence of that trust they have only with each other, many artists will confess that it is precisely form – the interplay of shapes, colors, textures, and materials, and the tensions and rhythms generated therein – that is not only captain but also navigator: the one with the first word, plenty in the middle, and certainly the last. A tacit understanding among those who make, discursive content is to many a mere maneuver of expediency.

David Mann, “YTB III,” 2016. Oil and alkyd on canvas stretched over board, 68″ x 72.”

Why the disavowal and disparagement of form? As our attitudes about art can’t be separated from the larger culture, we come back to humanism and its hierarchy of values. One of the most pernicious assumptions of the humanist worldview was its devaluation of the body and all that is associated with it. Carrying on the legacy of the great Cartesian cleavage, humanism had reason enthroned on high, casting off as inferior the emotions, the senses, all our autonomic functions – in short, anything rude enough to remind us that we are animals. And yet as today’s neuroscience has definitively shown, the body and the emotions are not separate from cognition; far from being “soft” and secondary faculties inferior to reason, they are in fact central to it, integral functions on which reason is entirely dependent. If form is something we apprehend with our senses and discursive content that which is grasped by the mind, the inferior status granted form is a tired recapitulation of the humanist error. But it is also more than this.  In denying form its rightful place in art, art is denying itself an exquisite opportunity. For if now is the time for us to move beyond ourselves, to reclaim our fleshly relations to earth, animal, and world, what better vehicle than the power of sensual form?

Debra Ramsay, “The Wind Turning in Circles Invents the Dance,” 2019. Acrylic on acrylic panel, 19″ x 18″. Courtesy of the artist.

In the spirit of the emerging ethos, then, can we imagine a new art for a post-humanist century? What would a post-humanist art look like, and how would it be experienced? First and foremost, a post-humanist art would be one that embraces form. It would be an art that considers form not as something that serves content, but rather as something that, like the body, possesses an intelligence of its own – an intelligence far deeper and more complex than conscious, discursive thought. In its address to the body and somatic experience, it would run directly counter to the prevailing emphasis on ideas, seeking not their propagation but exactly their cessation. For in order to gain access the beyond-human world, conscious thought, discursive thought, must first be extinguished. Rather than focusing on the contents of consciousness, then, post-humanist art would alight on its structure – all the subtle rhythms and patterns that constitute its movement. And not least, being decidedly oriented away from the self – away from personal identity, above all that of the artist – a post-humanist art would be one of transcendence. For with the thinker that thought itself into the center of the world silenced, we become living organisms again just like all others, participating in, and exquisitely sensitive to, the dynamic flux of the natural world.

Linda Francis, “Nostalgia for Messier #2,” 1994. Chalk on paper, 52″ x 39″. Courtesy of the artist.

With the affirmation of form as the powerful force that it is, the question becomes how, exactly, it delivers us to the non-human. We can begin by examining how form works on us, and why it moves us so deeply when indeed it does. Of all the arts, visual art is singular in a particularly significant way, and this is that it is physically embodied.[1] Its material presence being the first thing we apprehend, we confront in it not just it but ourselves: body to body, there is a certain carnal reciprocity absent in music and literature. Grasping the whole with an uncanny instantaneity, the eye moves in to probe the parts and their interrelations – this part to that, these to those over there, all of them in active tension with the overall organization.  Attraction and repulsion, assonance and dissonance, the ever-present tug of gravity that is the counterpoint to all visual form: whatever forces are enacted in the work’s particulars reverberate sympathetically on the instrument of our nervous system, causing subtle internal movements we cannot locate introspectively. Never fixing on any one area for too long, the eye is led by the forms in a rhythmic leaving and returning, ever expanding and contracting between the general and the particular. A kind of optical dance choreographed by the artist, the experience of viewing is far from the passive act of receiving information; rather, it is a profoundly active and participatory mode of engagement. When we say we are moved by a work of art, it is not just conceptual metaphor. In a very real sense, on every level of our organism we literally are moved. The experience of visual form is a distinct and particularly intense kind of electrochemical excitation.

But the real mystery of aesthetic form is not so much why it moves us but why it moves us so deeply. Why, when it does so, does it not merely delight? Why is it not just pleasant, the way the sound of a distant foghorn is pleasant, or the smell of fresh rain falling on stone, or the brush of a hand against the soft fur of an animal? Unlike these momentary pleasures, the experience of a great work of art seems in some way to change us, to rearrange the internal architecture on the deepest level of our being. And not only does it change us; it does so in a way that feels unusually significant. There is a profound rightness about it, a felt realignment, a re-membering of something unconsciously undone.  Indeed, so right is the feeling that is has, in the largest sense, the quality of coming home.

Ed Kerns, “Degree of Freedom in a Liquid Field; Not Overwhelmed,” 2018. Acrylic on canvas, 40″ x 30.” Courtesy of the artist.

Perhaps the experience of aesthetic form feels like coming home precisely because it is coming home. Home, that is, to the world that gave rise to us: the world of inanimate matter in all its myriad manifestations, and the whole kingdom of sentient creatures from whom we are descended. For what is the nature of this non-human world if not an endless cycle of dynamic patterns, from the rhythms of the tides to the sonic undulations of the animals to the expansions and contractions of the earth moved by forces to all manner – not least life and death – of arisings and evanescings? If the world out there is constituted of patterns of movement, it is in their deep visceral experience that we gain access to that world, moving from a consciousness of separation to one of participation. The experience of aesthetic form is an active engagement in the largest kind of communion.

It is also, and not insignificantly, an act of self-recognition. For in transcending the thinker and entering the greater world, we find not just the greater world but the greater parts of ourselves: the millions of years of evolution we carry in our bodies, and all that constitutes, unbeknownst to us, the richest reservoirs of our intelligence. We all know the feeling of being thus transported. Little else is as satisfying. The separatist ego will return, of course, to reassert its authority, but the experience of having left it lodges deep in the body, where, like a benevolent nuisance, it reminds us of something we only half want to remember – namely, that we live most of our lives locked in the smallest room in the house. Summoned on occasion by the exquisite rightness of a form, it comes back, and there we are again, and again we have to humbly concede that we really should get out more.

Yoshiaki Mochizuki, “Untitled, 6/6,” 2012. Gesso on board, clay, palladium leaf, and ink, 10.5″ x 10.5″. Courtesy of the artist and Marlborough, New York and London.

While it may not be our only means of participating in the Great Beyond, aesthetic form is surely one of the most powerful. If visual art continues to dismiss it, insisting on art’s identity as a discursive enterprise, it may end up on the losing side of our century’s catastrophe. For if the arrogance of reason is what brought us to where we are, it can hardly be expected to be the thing to get us out. What we need is reason reunited with the sensorium that sustains it and with the misconceived “other” that gave rise to it in the first place. And what is art if not an agent of integration, and what are artists if not those who know how to show us what that might look like? So let us reclaim form. Let us reclaim it as the transformative force it always was, and let us reclaim it in the name of something larger than ourselves – something beyond art, beyond culture, beyond even human history, something that, in returning us to our smallness, grants us full citizenship in the greatest largeness.

[1] Unless it is not. There is certainly much conceptual art that lacks any material component, but our focus here is on visual art that is visual – which is to say visual art that has sensual form.


The post In Praise of Form: Towards a New Post-Humanist Art appeared first on Interalia Magazine.

We talk about artistic inspiration all the time – but scientific inspiration is a thing too

We talk about artistic inspiration all the time – but scientific inspiration is a thing too

File 20190215 56226 u9o2q2.jpg?ixlib=rb 1.1
IR Stone/

Tom McLeish, University of York

I don’t know why it took so long to dawn on me – after 20 years of a scientific career – that what we call the “scientific method” really only refers the second half of any scientific story. It describes how we test and refine the ideas and hypotheses we have about nature through the engagement of experiment or observation and theoretical ideas and models.

But something must happen before this. All of this process rests upon the vital, essential, precious ability to conceive of those ideas in the first place. And, sadly, we talk very little about this creative core of science: the imagining of what the unseen structures in the world might be like.

We need to be more open about it. I have been repeatedly saddened by hearing from school students that they were put off science “because there seemed no room there for my own creativity”. What on earth have we done to leave this formulaic impression of how science works?

Science and poetry

The 20th century biologist Peter Medawar was one of the few recent writers to discuss the role of creativity in science at all. He claimed that we are quietly embarrassed about it, because the imaginative phase of science possesses no “method” at all. In his 1982 book Pluto’s Republic he points out:

The weakness of the hypothetico-deductive system, in so far as it might profess to cover a complete account of the scientific process, lies in its disclaiming any power to explain how hypotheses come into being.

Medawar is equally critical of glib comparisons of scientific creativity to the sources of artistic inspiration. Because whereas the sources of artistic inspiration are often communicated – they “travel” – scientific creativity is very much private. Scientists, he claims, unlike artists, do not share their tentative imaginings or inspired moments, but only the polished results of complete investigations.

The romantic poet William Wordsworth, on the other hand, two centuries ago, foresaw a future in which:

The remotest discoveries of the Chemist, the Botanist, or Mineralogist, will be as proper objects of the Poet’s art as any upon which it can be employed, if the time should ever come when these things shall be familiar to us.

Here is the need for ideas to “travel” again – which, if Medawar is correct, they have still failed to do. By and large poets still don’t write about science (with some notable exceptions such as R S Thomas). Nor is science “an object of contemplation”, as the historian Jacques Barzun put it. Yet the few scientists who have vocalised their experience of formulating new ideas are in no doubt about its contemplative and creative essence. Einstein, in his book with the physicist Leopold Infeld, The Evolution of Physics, wrote:

I am enough of an artist to draw freely upon my imagination. Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.

You don’t need to be a great scientist to know this. In my own experience I have seen mathematical solutions in dreams (one dream of a mathematical solution even coming to me and independently and identically to a collaborator on the same night), and imagined a specific structure of protein dynamics while sitting on a hillside.

Hillside or theoretical physics lab?
Tom McLeish

There is a large literature on “creativity” in science, but I have found nothing that really speaks to the lack of discussion of scientific inspiration today or to the pain of lingering experiences in education that set sciences and the arts and humanities in conflicting and opposed camps.

Stories of creativity

So I set off to ask scientists I knew to narrate, not just their research findings, but the pathways by which they got there. As a sort of “control experiment”, I did the same with poets, composers and artists.

I read past accounts of creation in mathematics (Poincaré is very good), novel-writing (Henry James wrote a book about it), art (from Picasso to my Yorkshire friend, the artist late Graeme Willson), and participated in a two day workshop in Cambridge on creativity with physicists and cosmologists. Philosophy, from medieval to 20th century phenomenology, has quite a lot to add.

Empyrean, an artwork inspired by the ancient geocentric model of the cosmos.
Alexandra Carr

From all these tales emerged a different way to think about what science achieves and where it lies in our long human story – as not only a route to knowledge, but also as a contemplative practice that meets a human need, in ways complementary to art or music. Above all I could not deny the extraordinary way that personal stories of creating the new mapped closely onto each other, whether these sprung from an attempt to create a series of mixed-media artworks reflecting the sufferings of war, or the desire to know what astronomical event had unleashed unprecedented X-ray and radio signals.

A common narrative contour of a glimpsed and desired end, a struggle to achieve it, the experience of constraint and dead-end, and even the mysterious “aha” moments that speak of hidden and sub-conscious processes of thought choosing their moments to communicate into our consciousness – all this is a story shared among scientists and artists alike.

In my resulting book – The Poetry and Music of Science – I try to make sense of why science’s imaginative and creative core is so hidden, and how to bring it into the light. It’s not the book I first imagined – it just wouldn’t permit a structure of separate accounts of scientific and artistic creativity. Their entanglements run too deep for that.

Instead there emerged three “modes” of imagination that both science and art engage: the visual, the textual and the abstract. We think in pictures, in words, and in the abstract forms that we call mathematics and music. It has become increasingly obvious to me that the “two cultures” division between the humanities and sciences is an artificial invention of the late 19th century. Perhaps the best way to address this is simply to ignore it, and start talking to one another more.The Conversation

Tom McLeish, Professor of Natural Philosophy in the Department of Physics, University of York

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post We talk about artistic inspiration all the time – but scientific inspiration is a thing too appeared first on Interalia Magazine.

The ‘real you’ is a myth – we constantly create false memories to achieve the identity we want

The ‘real you’ is a myth – we constantly create false memories to achieve the identity we want

File 20180917 158234 1ijbrhw.jpg?ixlib=rb 1.1
Vlasov Yevhenii/Shutterstock

Giuliana Mazzoni, University of Hull

We all want other people to “get us” and appreciate us for who we really are. In striving to achieve such relationships, we typically assume that there is a “real me”. But how do we actually know who we are? It may seem simple – we are a product of our life experiences, which we can be easily accessed through our memories of the past.

Indeed, substantial research has shown that memories shape a person’s identity. People with profound forms of amnesia typically also lose their identity – as beautifully described by the late writer and neurologist Oliver Sacks in his case study of 49-year-old Jimmy G, the “lost mariner”, who struggles to find meaning as he cannot remember anything that’s happened after his late adolescence.

But it turns out that identity is often not a truthful representation of who we are anyway – even if we have an intact memory. Research shows that we don’t actually access and use all available memories when creating personal narratives. It is becoming increasingly clear that, at any given moment, we unawarely tend to choose and pick what to remember.

When we create personal narratives, we rely on a psychological screening mechanism, dubbed the monitoring system, which labels certain mental concepts as memories, but not others. Concepts that are rather vivid and rich in detail and emotion – episodes we can re-experience – are more likely to be marked as memories. These then pass a “plausibility test” carried out by a similar monitoring system which tells whether the events fit within the general personal history. For example, if we remember flying unaided in vivid detail, we know straight away that it cannot be real.

But what is selected as a personal memory also needs to fit the current idea that we have of ourselves. Let’s suppose you have always been a very kind person, but after a very distressing experience you have developed a strong aggressive trait that now suits you. Not only has your behaviour changed, your personal narrative has too. If you are now asked to describe yourself, you might include past events previously omitted from your narrative – for example, instances in which you acted aggressively.

False memories

And this is only half of the story. The other half has to do with the truthfulness of the memories that each time are chosen and picked to become part of the personal narrative. Even when we correctly rely on our memories, they can be highly inaccurate or outright false: we often make up memories of events that never happened.

Remembering is not like playing a video from the past in your mind – it is a highly reconstructive process that depends on knowledge, self image, needs and goals. Indeed, brain imaging studies have shown that personal memory does not have just one location in the brain, it is based on an “autobiographical memory brain network” which comprises many separate areas.

Many parts of the brain are involved in creating personal memories.

A crucial area is the frontal lobes, which are in charge of integrating all the information received into an event that needs to be meaningful – both in the sense of lacking impossible, incongruent elements within it, but also in the sense of fitting the idea the individual remembering has of themselves. If not congruent or meaningful, the memory is either discarded or undergoes changes, with information added or deleted.

Memories are therefore very malleable, they can be distorted and changed easily, as many studies in our lab have shown. For example, we have found that suggestions and imagination can create memories that are very detailed and emotional while still completely false. Jean Piaget, a famous developmental psychologist, remembered all his life in vivid detail an event in which he was abducted with his nanny – she often told him about it. After many years, she confessed to having made the story up. At that point, Piaget stopped believing in the memory, but it nevertheless remained as vivid as it was before.

Memory manipulation

We have assessed the frequency and nature of these false and no-longer-believed memories in a series of studies. Examining a very large sample across several countries, we discovered that they are actually rather common. What’s more, as for Piaget, they all feel very much like real memories.

This remained true even when we successfully created false memories in the lab using doctored videos suggesting that participants had performed certain actions. We later told them that these memories never actually happened. At this point, the participants stopped believing in the memory but reported that the characteristics of it made them feel as if it were true.

A common source of false memories are photos from the past. In a new study, we have discovered that we are particularly likely to create false memories when we see an image of someone who is just about to perform an action. That’s because such scenes trigger our minds to imagine the action being carried out over time.

But is all this a bad thing? For a number of years, researchers have focused on the negatives of this process. For example, there are fears that therapy could create false memories of historical sexual abuse, leading to false accusations. There have also been heated discussions about how people who suffer from mental health problems – for example, depression – can be biased to remember very negative events. Some self-help books therefore make suggestions about how to obtain a more accurate sense of self. For example, we could reflect on our biases and get feedback from others. But it is important to remember that other people may have false memories about us, too.

Crucially, there are upsides to our malleable memory. Picking and choosing memories is actually the norm, guided by self-enhancing biases that lead us to rewrite our past so it resembles what we feel and believe now. Inaccurate memories and narratives are necessary, resulting from the need to maintain a positive, up-to-date sense of self.

My own personal narrative is that I am a person who has always loved science, who has lived in many countries and met many people. But I might have made it up, at least in part. My current enjoyment for my job, and frequent travels, might taint my memories. Ultimately, there may have been times when I didn’t love science and wanted to settle down permanently. But clearly it doesn’t matter, does it? What matters is that I am happy and know what I want now.The Conversation

Giuliana Mazzoni, Professor of Psychology, University of Hull

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post The ‘real you’ is a myth – we constantly create false memories to achieve the identity we want appeared first on Interalia Magazine.



The act of containing is always an act of restraining—holding something or someone in place.

Keeping proper control. Limiting expansion.

Preventing advancement.

When an infectious disease presents itself, we act to contain it.

An act that can be both liberating and traumatic.

Contained is an exhibition of installations that encompasses both these possibilities.

In 1944, my mother Louise Poulin, contracted Tuberculosis (TB) while caring for her stricken brother and sister-in-law. She was sent to a sanatorium in rural Quebec for treatments that were current at the time – rest therapy and artificial pneumothorax. These treatments involved almost constant bed rest, a healthy diet, hours and hours of fresh air (at times, covered in blankets on a porch in the middle of a Canadian winter), and collapsing of the lungs to cut off oxygen flow to the TB bacteria. For over two years, day-in day-out, she lay there wondering if she would survive while many around her succumbed to the disease. She often dreamed of escaping, flying past the surrounding farmlands, over the grand forests, and into the hopeful sky. My mother’s experience of contracting TB, and the fragility of all life, is the narrative that informs Contained.

Contained Installation view 2018 (Photo credit: Elaine Whittaker)


Contained Installation view 2018 (Photo credit: David Williams)

Illness as a young person, especially traumatic illness, embeds itself deep inside the psyche. It festers and often manifests itself as overwhelming fear. My mother was haunted by such fears and it shaped her identity in the world for the rest of her life. This was apparent in her reactions every time either I or my brothers became ill, and her adamant concerns for proper inoculations and medical tests. Her ever present fear that we might contract TB or another equally terrifying infectious disease made me aware that there was a world of invisible microbes with a potential to suddenly infect or even cause death. It was only later, when conducting research for my art practice, did I become aware that most microbes are not infectious, are more harmless than harmful, and our symbiotic relationship with them is part of our own well-being. This knowledge – of this necessity and danger – of microorganisms that form our natural and human ecology is a constant in my artwork.

Contained, first shown at the Red Head Gallery in Toronto in 2018, is composed of a series of mixed media installations, monoprints, drawings and sculptures. With these works I abstract and transform my mother’s experience of living in a TB sanatorium, to create a gallery atmosphere that is clinical, fantastical, immersive. Drawing on my ongoing artist residency at the Pelling Laboratory for Augmented Biology (University of Ottawa), I combine medical tools and scientific processes into a series of installations and sculptures encapsulating biomaterial, feathers, salt crystals, avian lungs and plant fibre containing human lung cells.

She Hungered for the Sky, 2018. Bed, bedside table, chair, crocheted shawl by Louise while in sanitorium, her books and music piano music sheets, personal items, table cloth, petri dishes with ink drawing and X-ray of lungs with TB (Photo credit: Elaine Whittaker).


She Hungered for the Sky (detail) (Photo credit: Elaine Whittaker)

The centrepiece installation, She Hungered for the Sky, recreates the atmosphere of the sanitorium – a white chair with the shawl she crocheted while living there; a bedside table with her books and personal items; and  an empty skeletal 1940s hospital bed with attached dangling petri dishes containing TB X-rays and drawings of lungs. In the centre of the bed frame, laid across the bare floor, lies her favourite crocheted table cloth, metaphorically emphasizing her fragility and confinement. Directly across from the bed is a wall installation entitled Fragile Forest. Representing the forest that captured my mother’s dreams and fantasies of escaping her illness and containment in the sanatorium, it is composed of white alveolar-like branches (waxed grape stems) that are adhered to cell culture plates. Above them, partially decellularized maple leaves, fragile and spotted like infected lungs, are precariously attached to the wall, fluttering from passing air currents. Decellularization means the plant cells have been dissolved leaving only a cellulose scaffold. This results in draining their colour, leaving them ghost-like and ephemeral. Lit from below, their silhouettes and that of the forest become even more heightened apparitions.

Fragile Forest (detail 1) 120”x 6”x 4”, 2017. Grape stems, wax, partially decellularized maple leaves, pipette tips, cell culture plates (Photo credit: David Williams)


Fragile Forest (detail 2) (Photo credit: Elaine Whittaker)

As one moves through the exhibition space, a series of framed monoprints and drawings of leaves and lungs, as well as small sculptures on pedestals, are encountered. These works continue to draw on the metaphors of forest and flight. They include test tubes inserted with partially decellularized maple keys (seeds) held in place by cell culture plates and stacked on synthetic maple leaves; sections of avian lung tissue displayed in tiny petri dishes; feathers in vials; miniature nests constructed from medical tubing; and a crow skull. All these objects are carefully placed and contained on clear acrylic bed-like trays.

Airborne 1, 12”x9” 2017. Ink monotype on paper (Photo credit: Elaine Whittaker)


At Rest: Dwelling, 6”x12”x3”, 2018. Plastic tubing, sparrow feathers, test tubes, acrylic trays (Photo credit: David Williams)


At Rest: Flight, 6”x12”x3”, 2018. Sparrow feathers, test tubes, Common Raven skull, acrylic trays (Photo credit: David Williams)


At Rest: Breath (detail) 6”x12”x3” 2018. Petri dishes, avian lung tissue encased in salt crystals, display & acrylic trays (Photo credit: Elaine Whittaker)


Quiescent Growth (detail) 36”x22”x6” 2018. Partially decellularized maple keys, test tubes, cell culture plates, synthetic leaves (Photo credit: Elaine Whittaker)


Vestigial 2 11”x14” 2017. Ink monotype on paper (Photo credit: Elaine Whittaker)

The notion of a confined bird on the edge of expiration and a fantastical forest that heals and provides hope is woven through the artworks. But the reality is that TB is still an infectious disease ravaging the world. A wall installation of over fifty eerie beautiful oxygen masks lined up with cascading tubes gives prominence to this continuing – even resurgent – plague. These empty ominous masks, entitled Fraught Air, starkly remind us that TB may be out of mind for many people but it is yet to be defeated, known too well by the marginalized in our communities and over the world.

Fraught Air (detail) 150”x 96”x 3”, 2018. Oxygen masks (Photo credit: Elaine Whittaker)


Lungs of the Earth 22”x 8”x 1”, 2018. Petri dishes, decellularized maple leaves with human lung epithelial cells (Photo credit: Elaine Whittaker)


Lungs of the Earth (detail) (Photo credit: Elaine Whittaker)

The final artwork in the exhibition is entitled Lungs of the Earth*. Three large petri dishes with four decellularized maple leaves are elegantly displayed in acrylic holders. Again the decellularization process of removing the leaves’ plant cells has left a ghostly cellulose scaffold, but this time the scaffold has been re-cultured with human epithelial lung cells. Merging human cells within a plant matrix, this artwork is a convergence of science and technology; a hybridization of human and plant; and a possibility that human and plant can merge. There is a core message of persistence, struggle and hope. Contained is an exhibit that finds hope when faced with a life curtailed by disease. With its blend of current scientific processes and past medical practices, it becomes, ultimately, a contemplation on past histories and possible futures.


* Lungs of the Earth was made possible through my artist-in-residence collaboration with Andrew Pelling and Ryan Hickey at the Pelling Laboratory for Augmented Biology at the University of Ottawa. It was shown in the 2019 exhibit La Fabrique du Vivant at the Centre Pompidou, curated by Marie-Ange Brayer and Olivier Zeitoun as part of the Mutations/Creations platform.


All images copyright and courtesy of Elaine Whittaker

The post Contained appeared first on Interalia Magazine.

How DNA ancestry testing can change our ideas of who we are

How DNA ancestry testing can change our ideas of who we are

File 20190328 139374 9u23bt.jpg?ixlib=rb 1.1
We’ve underestimated the extent of mixing between ancestral groups throughout human history.

Caitlin Curtis, The University of Queensland

Have you ever wondered who you are or where you come from?

I think it’s a fundamental human desire to want to know this.

One way we’re seeing this curiosity play out is in the rise of the at-home DNA ancestry business. You’ve probably seen the ads for tests like 23andme and Ancestry DNA: you spit in a tube, and then receive a report breaking you down into neat little slices in a pie chart telling you that you’re, say, 30% German and 70% English. As a population geneticist, I find this fascinating.

But how does our collective interest in ancestry testing interact with our ideas and conversations about race?

Read more:
A DNA test says you’ve got Indigenous Australian ancestry. Now what?

‘No borders within us’

Earlier this year, a Mexican airline, Aeromexico, ran a tongue-in-cheek ad campaign, called “DNA Discounts” with the slogan “there are no borders within us”. For the ad campaign they gathered a group of North Americans who were willing to take a DNA test and get their results on camera. This group contained some members with, let’s just say, a somewhat negative view of Mexico.

Do you want to go to Mexico?

In the ad, the airline offered rewards to these people based on their DNA results, in the form of a discounted airline ticket to Mexico. The size of the discount depended on the amount of Mexican ancestry. If their test showed 15% Mexican ancestry, that meant a 15% discount.

The footage of people getting their results on camera is pretty funny, and some of them seemed somewhat surprised, and maybe even upset about their reported ancestry. More than half of those tested appeared to have Mexican ancestry, even though they weren’t aware of it.

The slogan “there are no borders within us” has an element of political commentary related to Donald Trump’s border wall. But the ad also teaches us two important things.

It shows how DNA testing can challenge not just our ideas of race and identity, but our notion of being. Your genetic ancestry might be completely different from your cultural identity. Just ask the folks in the ad.

Beyond this, it also highlights how mainstream this kind of science has become, and how much DNA ancestry testing has entered into pop culture.

Read more:
Five things to consider before ordering an online DNA test

Recent, dark past

I think we humans have always been interested in our ancestry, but it hasn’t always been a healthy interest – sometimes it’s been much darker and more sinister. And we don’t even have to look too far into the past to see that.

The eugenics movement was part science and part social engineering, and based on the idea that certain things – such as being poor, lazy, “feeble-minded” or criminal – were actually traits that were inherited in families. These traits were often linked to certain ancestries or racial groups using biased methodology.

Eugenics was the idea that humanity could engineer a better future for itself by identifying and regulating these groups using science and technology.

Read more:
Boyer Lectures: the new eugenics is the same as the old, just in fancier clothes

In the United States in the early 20th century, eugenics became a recognised academic discipline at many prestigious universities – even Harvard. By 1928, almost 400 colleges and universities in America were teaching it.

In 1910 the Eugenics Record Office was set up to collect ancestry data, literally door to door. It then used this data to support racist agendas and influence things like the 1924 Immigration Act to curb immigration of southeastern Europeans, and ban most Asians and Arabs altogether.

Although we may think of eugenics as something linked with Nazi Germany in World War II, Hitler based some of his early ideas about eugenics on these academic programs in the US. There was a fear of “pollution” of the purebred genetic lineage, and that the “inferior” races would contaminate the “superior” race. Many Nazi defendants at the Nuremberg trials claimed there wasn’t much difference between the Nazi eugenics program and the ones in the US.

Racism with flawed science

The events of that time are still relevant now. More than seven decades have passed and we’re seeing the rise of far-right groups and ideologies – the world of Trump, and the return of restrictive immigration policies.

We’re seeing a mainstreaming of ideas about race that we rejected not long ago. We’re once again seeing the science of genetics being misappropriated to support racist agendas.

Read more:
Dramatic advances in forensics expose the need for genetic data legislation

Late last year, the New York Times reported on a trend among white supremacists to drink milk. Most people of northern European ancestry have a version of a certain gene, called a lactase gene, that means they can fully digest milk as adults. This is due to a genetic mutation several thousand years ago, around the time of the first cattle herders in Europe.

The article described how people from the far right have taken this scientific result and run with it – producing bizarre YouTube videos in which people chug milk from 2-litre containers, swigging it and throwing it around in celebration of their supposed “genetic superiority” – and urging people who cannot digest milk to “go back”. Comedian Stephen Colbert even picked up on this story (in his words: “lactose is their only form of tolerance”).

The white supremacists took this bit of science and twisted it to suit their needs. But what they have ignored is research showing that a similar version of this gene evolved among cattle breeders in East Africa too.

DNA does not define culture

It’s not just popular culture: DNA ancestry has also entered political culture.

The right-wing Australian nationalist One Nation recently called for DNA ancestry tests as a requirement to prove Aboriginal identity to access “benefits”. I don’t want to give this dangerous idea any more oxygen, and as a geneticist I can tell you it won’t work.

Cultural identity is much more than simply what is in our DNA. Aboriginal communities are the ones who determine who is and who is not Indigenous. I think this episode highlights a worrying trend for genetic tests to be seen as the ultimate decider of race and identity in public debates.

Read more:
Why DNA tests for Indigenous heritage mean different things in Australia and the US

So how does the marketing of the DNA companies themselves influence our thinking about ancestry?

These ancestry companies use the language of science in their marketing, and present their results as being highly scientific – which people interpret as meaning accurate and factual. The process of estimating ancestry from DNA is scientific, but people may not realise it can also be a bit of a blurry process, and actually more of an estimate.

When you look at your slice in the pie chart and it says 16% German, it is not a fact that you are 16% German. It’s an estimate, or an educated guess, of your ancestry based on statistical inference.

I think representation of our ancestries in pie charts is not helping our conversations.

Twins got different results

Recently, two identical twins put five DNA ancestry companies to the test, and this provides a really interesting look at how this process works.

The raw data for each twin was more than 99% identical, which shows that the way the companies produce the raw data is indeed quite accurate.

The shocking thing was that the companies provided each twin with noticeably different ancestry estimates.

From one company, the first twin got 25% Eastern European, and the second got 28%. Just to be clear, this shouldn’t happen with identical twins because they have the same DNA.

Read more:
Genetic ancestry tests don’t change your identity, but you might

Even more surprising, one company said the twins were 27-29% Italian, but another said they were 19-20% Greek. A lot of this difference would be based on the size of the databases that the companies use as references and who is in the databases, and – very importantly – who has been left out of the databases. These factors would be different between the different companies, and change through time.

So the results you get now could be different to the results you might get in, say, six months when the databases are updated.

Estimating our ancestry is hard, and the main reason it is hard is because our ancestry is much more mixed up than some people might have thought. It’s not really so clear-cut as a pie chart might suggest. The statistics are blurry because our populations are blurry.

The bigger picture that’s emerging from DNA ancestry testing is that we’ve underestimated the extent of mixing between ancestral groups throughout human history.

Looking at the pie chart might give you the impression that there are discrete borders within you and boundaries between your different ancestries, but as Aeromexico so eloquently put it, “there are no borders within us”.

This article is an edited version of a story presented on ABC’s Ockham’s Razor and delivered at the World Science Festival, Brisbane in March 2019.The Conversation

Caitlin Curtis, Research fellow, Centre for Policy Futures (Genomics), The University of Queensland

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The post How DNA ancestry testing can change our ideas of who we are appeared first on Interalia Magazine.

Notes on an Aphantasic Artist

There are aspects of experience that vary between individual humans, and that contribute to the way individuals think and behave differently to others – aspects that, in other words, make up personal identity.

One of these is the degree to which people experience mental imagery, or picturing with the ‘mind’s eye’. The strength or vividness of mental imagery differs across the population. This is known due to psychologists using a standardised questionnaire, called the Vividness of Visual Imagery Questionnaire (VVIQ), where participants are asked to visualise something then rate the vividness of what they picture, from ‘1’ for no image, to ‘5’ for an image ‘as vivid as real seeing’.

When this test is given to a group of people the results form a bell curve of normal distribution (leaning slightly towards higher scores, because of the social desirability of a ‘vivid imagination’). Most people’s scores fall around the middle of the curve, experiencing some degree of imagery, but at one edge of the curve are those report to lack visual mental imagery entirely, giving ‘1’ for every task in the VVIQ.

Fig. 1 Distribution of Vividness of Visual Imagery Questionnaire (VVIQ) scores in participants with aphantasia and control participants (VVIQ range extends from 16, lowest imagery score to 80, highest imagery score). [Zeman et al 2015, p2]

The phenomenon – that some people do not experience visual mental imagery – had been given little scientific attention until it was given a name in 2015 by the British neurologist Adam Zeman and colleagues: aphantasia.

This was publicised in the popular science press, and very soon, and for the ensuing years, thousands of people contacted Zeman to say that they had ‘aphantasia’. Among these, to the researchers’ surprise, were many artists – and designers, architects, and writers – who could not visualise. To investigate this phenomenon of creativity without visualisation the researchers, co-curating with artist Susan Aldworth, developed an exhibition of ‘aphantasic’ artwork.

While the resulting exhibition included 19 visual artists, I want to focus here on the work and procedural narrative of one in particular, which, I think, is particularly representative of the way that aphantasic artists tend to work. This is the British artist Michael Chance, who paints detailed figurative scenes. Rather than being a barrier to creativity, as one might expect, Chance views his aphantasia as a stimulus, because he cannot personally entertain images other than by creating them in paint:

The lack of ability to visualise images in my mind is a great motivation; I must physically work on a drawing or painting in order for my imagination to become visually manifest. I often start a picture with no intention and certainly no end goal; it materialises in an improvisatory way. This sense of stepping out into the unknown is thrilling and the subsequent discovery of latent imagery fascinating. Largely bypassing conscious decision making, the way images (usually figures) emerge from my subconscious is akin to dreaming, and the resulting work is often just as strange, surprising and revealing as that would suggest. However (yet, somewhat like dreams) these visions are informed by my everyday experience and observational drawing practice, and structured by my artistic understanding of illusionistic space, light, form and anatomy.’  (MacKisack & Aldworth, 2018, p35]

What is immediately noticeable is the way that Chance describes ‘physical’ work replacing mental work: painting takes the place of visualising or dreaming. This would be a good example of ‘extended cognition’ (Clark & Chalmers 1998): the brain delegating operations that it finds hard, or even impossible, to physical manipulations of external media. Also, by making the images the artist discovers something that they did not or could not ‘foresee’. This process of search, discovery and ‘materialisation’ is revealed by a time-lapse video Chance made of himself working.

Fig 2. Michael Chance, ‘Painting from Imagination (Bacchus Walk)’ 2016. Courtesy of the artist.

The completed painting shows how a reclining, prone, figure emerged – not, if Chance and our interpretation are correct, from the artist’s deliberations, but from the canvas itself and the two faces in profile, suggested by the negative space between them.

Michael Chance, Bacchus Walk, 2016 Oil on board, 92×122cm . Courtesy of the artist

What Chance and his work demonstrates is that the lack of conscious imagery has multiple implications for artistic practice – but none for the creativity of the artist. Imagery ability is not equal to ’imaginativeness’. It seems that aphantasia instead can have a more holistic’ affect on artistic identity, influencing the decisions one makes about how to work and what to do. For example, having no plan’ as such, you just start making marks and see where they lead – in Chance’s case, fed by his training and knowledge of anatomy, figures emerge. Of course, artists without aphantasia also do all these things. And one couldn’t know if a picture had been made by an artist with imagery or without. But that is what the phenomenon of aphantasic art forces us to realise: the diversity of the hidden routes to creation.


MacKisack, M. and Aldworth, S. (eds.) Extreme Imagination – Inside the Mind’s Eye. Exeter: The Eye’s Mind Press. (2018)

Zeman, A., Dewar, M., & Della Sala, S. (2015). Lives without imagery: Congenital aphantasia. Cortex, 73, 378e380.



The post Notes on an Aphantasic Artist appeared first on Interalia Magazine.