Art+ificiality: Machine Creativity & Its Critics (Part 1)

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§. In Sean D. Kelly’s, A philosopher argues that an AI can’t be an artist, the author, at the outset, declares:

“Creativity is, and always will be, a human endeavour.” (S. D. Kelly)

A bold claim, one which can hardly be rendered sensible without first defining ‘creativity,’ as the author well realizes, writing:

“Creativity is among the most mysterious and impressive achievements of human existence. But what is it?” (Kelly)

The author attempts to answer his selfsame query with the following two paragraphs.

“Creativity is not just novelty. A toddler at the piano may hit a novel sequence of notes, but they’re not, in any meaningful sense, creative. Also, creativity is bounded by history: what counts as creative inspiration in one period or place might be disregarded as ridiculous, stupid, or crazy in another. A community has to accept ideas as good for them to count as creative.


As in Schoenberg’s case, or that of any number of other modern artists, that acceptance need not be universal. It might, indeed, not come for years—sometimes creativity is mistakenly dismissed for generations. But unless an innovation is eventually accepted by some community of practice, it makes little sense to speak of it as creative.” (Kelly)

§. Through Kelly, we have the definition-via-negation that ‘creativity is not just novelty,’ that it is not random, that it is a practice, bounded by history, and that it must be communally accepted. This is a extremely vague definition of creativity; akin to describing transhumanism as, “a non-random, sociohistorically bounded practice” which is also “not nordicism, arianism or scientology.” While such a description is accurate (as transhumanism is not constituted through or by the three aforementioned ideologies) it doesn’t tell one much about what transhumanism is, as such a description could describe any philosophical system which is not nordicism, arianism or scientology, just as Kelly’s definition does not tell one much about what creativity is. If one takes the time to define ones terms, one swiftly realizes that, in contradistinction to the proclamation of the article, creativity is most decidedly not unique to humans (ie. dolphins, monkeys and octopi, for example, exhibit creative behaviors). One may rightly say that human creativity has been and remains (as of this writing) unique to humans, but not creativity-as-such, and that is a crucial linguistic (and thus conceptual) distinction; especially since the central argument that Kelly is making is that a machine cannot be an artist (he is not making the claim that a machine cannot be creative, per-se) thus, a non-negative description of creativity is necessary. To quote The Analects, “If language is not correct, then what is said is not what is meant; if what is said is not what is meant, then what must be done remains undone; if this remains undone, morals and art will deteriorate; if justice goes astray, people will stand about in helpless confusion. Hence there must be no arbitrariness in what is said. This matters above everything” (Arthur Waley, The Analects of Confucius, New York: Alfred A. Knopf, 2000, p. 161).

§. A more rigorous definition of ‘creativity’ may be gleaned from Allison B. Kaufman, Allen E. Butt, James C. Kaufman and Erin C. Colbert-White’s Towards A Neurobiology of Creativity in Nonhuman Animals, wherein they lay out a syncretic definition based upon the findings of 90 scientific research papers on human creativity.

Creativity in humans is defined in a variety of ways. The most prevalent definition (and the one used here) is that a creative act represents something that is different or new and also appropriate to the task at hand (Plucker, Beghetto, & Dow, 2004; Sternberg, 1999; Sternberg, Kaufman, & Pretz, 2002). […]


“Creativity is the interaction among aptitude, process, and environment by which an individual or group produces a perceptible product that is both novel and useful as defined within a social context” (Plucker et al., 2004, p. 90). [Kaufman et al., 2011, Journal of Comparative Psychology, Vol. 125, No. 3, p.255]

§. This definition is both broadly applicable and congruent with Kelly’s own injunction that creativity is not a mere product of a bundle of novelty-associated behaviors (novelty seeking/recognition), which is true, however, novelty IS fundamental to any creative process (human or otherwise). To put it more succinctly: Creativity is a novel-incorporative, task-specific, multi-variant neurological function. Thus, Argumentum a fortiori, creativity (broadly and generally speaking), just as any other neurological function, can be replicated (or independently actualized in some unknown way). Kelly rightly notes that (human) creativity is socially bounded, again, this is (largely) true, however, whether or not a creative function is accepted as such at a later time is irrelevant to the objective structures which allow such behaviors to arise. That is to say that it does not matter whether or not one is considered ‘creative’ in any particular way, but rather, that one understands how the nervous system generates certain creative behaviors (however, it would matter as pertains to considerations of ‘artistry’ given that the material conditions necessary for artistry to arise require a audience and thus, the minimum sociality to instantiate it). I want to make clear that my specific interest here lies not in laying out a case for artificial general intelligence (AGI), of sapient-comparability (or some other), nor even, in contesting Kelly’s central claim that a machine intelligence could not become a artist, but rather, in making the case that creativity-as-a-function can be generated without an agent. Creativity is a biomorphic sub-function of intelligence; intelligence is a particular material configuration, thus, when a computer exceeds human capacity in mathematics, it is not self-aware (insofar as we are aware) of its actions (that it is doing math or how), but it is doing math all the same, that is to say, it is functioning intelligently but not ‘acting.’ In the same vein, it should be possible for sufficiently complex systems to function creatively, regardless of whether such systems are aware of the fact. [the Open Worm Project is a compelling example of bio-functionality operating without either prior programming or cognizance]

“Advances in artificial intelligence have led many to speculate that human beings will soon be replaced by machines in every domain, including that of creativity. Ray Kurzweil, a futurist, predicts that by 2029 we will have produced an AI that can pass for an average educated human being. Nick Bostrom, an Oxford philosopher, is more circumspect. He does not give a date but suggests that philosophers and mathematicians defer work on fundamental questions to ‘superintelligent’ successors, which he defines as having ‘intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.’


Both believe that once human-level intelligence is produced in machines, there will be a burst of progress—what Kurzweil calls the ‘singularity’ and Bostrom an ‘intelligence explosion’—in which machines will very quickly supersede us by massive measures in every domain. This will occur, they argue, because superhuman achievement is the same as ordinary human achievement except that all the relevant computations are performed much more quickly, in what Bostrom dubs ‘speed superintelligence.’


So what about the highest level of human achievement—creative innovation? Are our most creative artists and thinkers about to be massively surpassed by machines?




Human creative achievement, because of the way it is socially embedded, will not succumb to advances in artificial intelligence. To say otherwise is to misunderstand both what human beings are and what our creativity amounts to.


This claim is not absolute: it depends on the norms that we allow to govern our culture and our expectations of technology. Human beings have, in the past, attributed great power and genius even to lifeless totems. It is entirely possible that we will come to treat artificially intelligent machines as so vastly superior to us that we will naturally attribute creativity to them. Should that happen, it will not be because machines have outstripped us. It will be because we will have denigrated ourselves.” (Kelly)

§. For Kelly, then, the concern is not that machines will surpass human creative potential, but that we will think that they have after fetishizing them and turning them into sacral objects; deifying them through anthropomorphization and turning them into sites of worship. This is a salient concern, however, the way to obviate such a eventuality (if that is one’s goal) is to understand not just the architecture of the machine but the architecture of creativity itself.

“Also, I am primarily talking about machine advances of the sort seen recently with the current deep-­learning paradigm, as well as its computational successors. Other paradigms have governed AI research in the past. These have already failed to realize their promise. Still other paradigms may come in the future, but if we speculate that some notional future AI whose features we cannot meaningfully describe will accomplish wondrous things, that is mythmaking, not reasoned argument about the possibilities of technology.


Creative achievement operates differently in different domains. I cannot offer a complete taxonomy of the different kinds of creativity here, so to make the point I will sketch an argument involving three quite different examples: music, games, and mathematics.


Can we imagine a machine of such superhuman creative ability that it brings about changes in what we understand music to be, as Schoenberg did?


That’s what I claim a machine cannot do. Let’s see why.


Computer music composition systems have existed for quite some time. In 1965, at the age of 17, Kurzweil himself, using a precursor of the pattern recognition systems that characterize deep-learning algorithms today, programmed a computer to compose recognizable music. Variants of this technique are used today. Deep-learning algorithms have been able to take as input a bunch of Bach chorales, for instance, and compose music so characteristic of Bach’s style that it fools even experts into thinking it is original. This is mimicry. It is what an artist does as an apprentice: copy and perfect the style of others instead of working in an authentic, original voice. It is not the kind of musical creativity that we associate with Bach, never mind with Schoenberg’s radical innovation.


So what do we say? Could there be a machine that, like Schoenberg, invents a whole new way of making music? Of course we can imagine, and even make, such a machine. Given an algorithm that modifies its own compositional rules, we could easily produce a machine that makes music as different from what we now consider good music as Schoenberg did then.


But this is where it gets complicated.


We count Schoenberg as a creative innovator not just because he managed to create a new way of composing music but because people could see in it a vision of what the world should be. Schoenberg’s vision involved the spare, clean, efficient minimalism of modernity. His innovation was not just to find a new algorithm for composing music; it was to find a way of thinking about what music is that allows it to speak to what is needed now.


Some might argue that I have raised the bar too high. Am I arguing, they will ask, that a machine needs some mystic, unmeasurable sense of what is socially necessary in order to count as creative? I am not—for two reasons.


First, remember that in proposing a new, mathematical technique for musical composition, Schoenberg changed our understanding of what music is. It is only creativity of this tradition-defying sort that requires some kind of social sensitivity. Had listeners not experienced his technique as capturing the anti-­traditionalism at the heart of the radical modernity emerging in early-­20th-century Vienna, they might not have heard it as something of aesthetic worth. The point here is that radical creativity is not an “accelerated” version of quotidian creativity. Schoenberg’s achievement is not a faster or better version of the type of creativity demonstrated by Oscar Straus or some other average composer: it’s fundamentally different in kind.” (Kelly)

§. Arnold Schoenberg (1874–1951) was a Austrian-American composer who became well known for his atonal musical stylings. Kelly positions Schoenberg as a exemplar of ‘radical creativity’ and notes that Shoenberg’s achievement is not a faster or better version of the type of creativity demonstrated by the Viennese composer Oscar Straus (1870–1954) or, ‘some other average composer: it’s a fundamentally different kind.’ This is true. There are different kinds of creativity (as it is a obviously multi-faceted behavioural domain); thus, a general schema of the principal types of creativity is required. In humans, creative action may be “combinational, exploratory, or transformational” (Boden, 2004, chapters 3-4), where combinational creativity (the most easily recognized) involves a uncommon fusion of common ideas. Visual collages are a very common example of combinational creativity; verbal analogy, another. Both exploratory and transformational creativity, however, differ from combinational creativity in that they are conceptually bounded in some socially pre-defined space (whereas, with combinational creativity the conceptual bounding theoretically extends to all possible knowledge domains and, though it almost always is, need not be extended to the interpersonal). Exploratory creativity involves utilizing preexisting strictures (conventions) to generate novel structures (such as a new sentence, which, whilst novel, will have been constructed within a preexisting structure; ie. the language in which it is generated). Transformational creativity, in contrast, involves the modulation or creation of new bounding structures which fundamentally change the possibility of exploratory creativity (ie. creating a new language and then constructing a new sentence in that language wherein the new language allows for concepts that were impossible within the constraints of the former language). Transformational creativity is the most culturally salient of the three, that is to say, it is the kind which is most likely to be discussed, precisely because the externalization of transformational creativity (in human societies) mandates the reshaping, decimation or obviation of some cultural convention (hence, ‘transformational’). Schoenberg’s acts of musical innovation (such as the creation of the twelve-tone technique) are examples of transformational creativity, whereas his twelve-tone compositions after concocting his new musical technique are examples of exploratory and combinational creativity (ie. laying out a new set of sounds; exploring the sounds; combining and recombining them). In this regard, Kelly is correct; Schoenberg’s musical development is indeed a different kind of creativity than that exhibited by ‘some average composer’ as a average composer would not initiate a paradigm shift in the way music was done. That being said, this says nothing about whether a machine would be able to enact such shifts itself. One of the central arguments which Kelly leverages against transformational machine creativity (potential for an AI to be an artist) is that intelligent machines presently operate along the lines of computational formalism, writing,

“Second, my argument is not that the creator’s responsiveness to social necessity must be conscious for the work to meet the standards of genius. I am arguing instead that we must be able to interpret the work as responding that way. It would be a mistake to interpret a machine’s composition as part of such a vision of the world. The argument for this is simple.

Claims like Kurzweil’s that machines can reach human-level intelligence assume that to have a human mind is just to have a human brain that follows some set of computational algorithms—a view called computationalism. But though algorithms can have moral implications, they are not themselves moral agents. We can’t count the monkey at a typewriter who accidentally types out Othello as a great creative playwright. If there is greatness in the product, it is only an accident. We may be able to see a machine’s product as great, but if we know that the output is merely the result of some arbitrary act or algorithmic formalism, we cannot accept it as the expression of a vision for human good.

For this reason, it seems to me, nothing but another human being can properly be understood as a genuinely creative artist. Perhaps AI will someday proceed beyond its computationalist formalism, but that would require a leap that is unimaginable at the moment. We wouldn’t just be looking for new algorithms or procedures that simulate human activity; we would be looking for new materials that are the basis of being human.” (Kelly)

§. It is noteworthy that Kelly’s perspective does not factor in the possibility that task-agnostic, self-modeling machines (see the work of Robert Kwiatkowski and Hod Lipson) could network such that they develop social capabilities. Such creative machine sociality answers the question of social embeddedness proposed by Kelly as a roadblock. Whilst such an arrangement might not appear to us as ‘creativity’ or ‘artistry,’ it would be pertinent to investigate how these hypothetical future machines ‘self’ perceive their interactions. It may be that future self-imaging thinking machines will look towards our creative endeavours the same way Kelly views the present prospects of their own.


  1. Allison B. Kaufman et al. (2011) Towards a neurobiology of creativity in nonhuman animals. Journal of Comparative Psychology.
  2. Brenden M. Lake et al. (2016) Building machines that learn and think like people. Cornell University. [v.3]
  3. Oshin Vartanian et al. (2013) Neuroscience of Creativity. The MIT Press.
  4. Peter Marbach & John N. Tsitsklis. (2001) Simulation-based optimization of markov reward processes. IEEE Transactions on Automatic Control.
  5. R. Kwiatkowski & H. Lipson. (2019) Task-agnostic self-modeling machines. Science Robotics, 4(26).
  6. Samer Sabri & Vishal Maini. (2017) Machine Learning For Humans.
  7. Sean Dorrance Kelly. (2019) A philosopher argues that AI can’t be an artist. MIT Technology Review.
  8. S. R. Constantin. (2017) Strong AI Isn’t Here Yet. Otium.
  9. Thomas Hornigold. (2018) The first novel written by AI is here—and its as weird as you’d expect it to Be. Singularity Hub.

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