On Hybrid Creativity

arts Article On Hybrid Creativity Andy Lomas ID Department of Computing, Goldsmiths College, University of London, London SE14 6NW, UK; [email protected] Received: 2 May 2018; Accepted: 5 July 2018; Published: 9 July 2018 Abstract: This article reviews the development of the author’s computational art practice, where the computer is used both as a device that provides the medium for generation of art (‘computer as art’) as well as acting actively as an assistant in the process of creating art (‘computer as artist’s assistant’), helping explore the space of possibilities afforded by generative systems. Drawing analogies with Kasparov’s Advanced Chess and the deliberate development of unstable aircraft using fly-by-wire technology, the article argues for a collaborative relationship with the computer that can free the artist to more fearlessly engage with the challenges of working with emergent systems that exhibit complex unpredictable behavior. The article also describes ‘Species Explorer’, the system the author has created in response to these challenges to assist exploration of the possibilities afforded by parametrically driven generative systems. This system provides a framework to allow the user to use a number of different techniques to explore new parameter combinations, including genetic algorithms, and machine learning methods. As the system learns the artist’s preferences the relationship with the computer can be considered to change from one of assistance to collaboration. Keywords: art; computer; evolutionary design; machine learning; computationally assisted design 1. Introduction How are we to work creatively with generative systems that computationally create results? In particular, how should we work with systems deliberately designed to encourage emergence: complex systems where results are intrinsically difficult to predict? There is a strong analogy with plant breeding, where we are working with a medium that is naturally rich. Through experimentation and experience we can develop insights into what is possible and how to influence plants to develop in ways that give desired properties. We need to discover the potentialities of the system we are working with, as well as the limits of its capabilities. Which features can be independently influenced, and which are co-dependent? Whether art, design or architecture, this involves changing our relationship with the computer. Traditional top-down design methods are no longer appropriate. We need to be open to a process of exploration. Participating in a search for interesting behavior: selecting and influencing rather than dictating results. Generative systems are typically based on algorithmic processes that are controlled by a number of parameters. Given a set of parameter values the process is run to create an output. Classic examples include Conway’s Game of Life (Conway 1970) and reaction diffusion equations (Turing 1952). Generative systems have been used by a number of artists, from pioneering early work by Algorists such as Manfred Mohr (Mohr and Rosen 2014), Frieder Nake (Nake 2005), Ernest Edmonds (Franco 2017), and Paul Brown (Digital Art Museum 2009a) to more recent work by artists such as William Latham (Todd and Latham 1992), Yoichiro Kawaguchi (Digital Art Museum 2009b), Casey Reas (Reas 2018), and Ryoji Ikeda (Ikeda 2018). The most interesting systems are generally those that create emergent results: genuinely unexpectedly rich behavior that cannot be simply predicted from the constituent parts. For these Arts 2018, 7, 25; doi:10.3390/arts7030025 www.mdpi.com/journal/arts Arts 2018, 7, x FOR PEER REVIEW 2 of 10 Arts 2018, 7, 25 2 of 10 The most interesting systems are generally those that create emergent results: genuinely unexpectedly rich behavior that cannot be simply predicted from the constituent parts. For these systems the relationship between the input parametersparameters and the output is generally complex and non-linear, with effects such as sensitive dependencedependence on initial conditions. This makes working with such systems particularly challenging: both fascinating and potentially frustrating. With a small number of dimensions,dimensions, such as up to three parameters, the space of results can be relatively easily explored by simply varying individual parameter values and plotting the effects of different combinations.combinations. One One common common technique technique is to is create to create charts charts where where all the parametersall the parameters are sampled are sampledindependently independently at regularly at regularly spaced values spaced and values results an ared results plotted are to plotted show the to results.show the What results. scientists What wouldscientists call would a phase call space a phase plot, space or in plot, the animationor in the animation and visual and effects visual industry effects industry is commonly is commonly called a wedgecalled a sheet. wedge This sheet. method This ofmethod parameter of parameter exploration exploration can be effective, can be andeffective, was used and bywas the used author by the for authorearlier workfor earlier such work as for such his ‘Aggregation’ as for his ‘Aggregation’ (Lomas 2005 (Lomas) and ‘Flow’2005) and (Lomas ‘Flow’ 2007 (Lomas) series. 2007) series. Figure 11 showsshows oneone ofof thethe chartscharts thethe authorauthor createdcreated whenwhen workingworking onon hishis ‘Aggregation’‘Aggregation’ series,series, exploring the effect that two differentdifferent parametersparameters hadhad onon thethe formsforms created.created. In this example the author was takingtaking 88 samplessamples inin eacheach dimension. dimension. With With two two parameters parameters only only 64 64 samples samples were were needed needed to tocomplete complete this this chart. chart. However, However, as the as number the number of parameters of parameters goes up goes the number up the ofnumber samples of needed samples to neededexplore differentto explore sets different of combinations sets of usingcombinatio this methodns using increases this method rapidly. increases Three parameters rapidly. wouldThree parametersrequire 512 samples.would require Four parameters 512 samples. would Four need para 4096meters samples. would If we need had a4096 system samples. with 10 If parameters we had a systemthen just with over 10 a billionparameters samples then would just over be needed. a billion This samples problem would is commonly be needed. called This the problem ‘Curse ofis commonlyDimensionality’ called( Bellmanthe ‘Curse 1961 of )(Dimensionality’Donoho 2000), (Bellman where the 1961) number (Donoho of samples 2000), thatwhere need the tonumber be taken of samplesincreases that exponentially need to be withtaken the increases number exponentially of parameters. with Even the if number enough of samples parameters. can be Even taken, if enough how to visualizesamples can and be understand taken, how the to spacevisualize becomes and unders a significantlytand the difficultspace becomes problem a andsignificantly concepts difficult such as problemfinding the and nearest concepts neighbors such as to finding any point the in neares the parametert neighbors space to any become point increasingly in the parameter meaningless space (becomeMarimont increasingly and Shapiro meaningl 1979).ess (Marimont and Shapiro 1979). Figure 1. Phase space plot from the Aggregation series (Lomas 2005). Figure 1. Phase space plot from the Aggregation series (Lomas 2005). One approach is to simply limit the number of parameters, but this can be at the expense of overlyOne limiting approach the type is to of simply system limit that the we number are willing of parameters, to work with. but If this we canare beworking at the with expense richly of emergentoverly limiting system the these type problems of system are that often we furthe are willingr compounded. to work with. A direct If we consequence are working of withemergence richly emergent system these problems are often further compounded. A direct consequence of emergence Arts 2018, 7, 25 3 of 10 is that the parameters often work in difficult to comprehend, unintuitive ways. Effects are typically non-linear, often with sudden tipping points as the system goes from one type of behavior to another. Indeed, the most interesting results, such as those in the right hand columns of Figure1 above, are often near regions of instability or at transitions between behaviors. In particular, in many systems the most interesting emergent behavior occurs close to the boundary between regularity and chaos (Kauffman 1996). The shape of this type of boundary can be extremely complex, a classic example being the ornately fractal shaped boundary between regularity and chaos in the Mandelbrot set (Douady and Hubbard 1984). This raises the idea of working with the machine not merely as the medium but as an active collaborator in the process of exploration and discovery. Can computational methods be used to allow exploration of generative systems like these in ways that would not be otherwise possible? The computer becomes an active part of the process of discovery, not just as the medium used to create artefacts. One analogy worth exploring is that of Advanced Chess: a form of the game introduced by Garry Kasparov where each human player can use a computer to assist them to explore possible moves (Kasparov 2017). In particular, computer chess programs are generally very good at quickly detecting whether a proposed move will have catastrophic results.

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