ABSTRACT
In 1987 Christopher Langton, a postdoctoral fellow from Los Alamos National Laboratories, New Mexico, hosted a conference in which he officially named into being both a new scientific discipline and a provocative new conceptual term which would capture the imagination of scientists, writers, and the general public alike: Artificial Life was ‘born’ (Helmreich, 1998, p. 9). If Artificial Intelligence (AI) was originally conceived of as a replication and extension of the human mind, then Artificial Life (ALife) occupies the other half of the Cartesian binary and was originally focused on replicating the activities of living bodies. The ‘artificiality’ of both AI and ALife stems from the digital ‘reproduction’ of existing ‘natural’ structures as digital information; form, in both disciplines, is always more important than matter. However, if the ability to convincingly communicate information as evinced through the Turing Test remains the (unachievable) benchmark for ‘real’ AI, then for ALife the test for ‘real’ life is evolution (Hayles, 1999, p. 224). ALife as a discipline started from the idea that successful computer-generated biological models which ‘accurately’ exhibited evolutionary traits would illuminate ‘real’ life simply in an artificial (digital) context. Originally, ALife was focused on ‘replicating’ life-as-we-know-it, but quickly edged into the more speculative modelling of life-as-it-could-be. Feminist critic Alison Adam has characterised this transition as a move from ‘weak ALife’, which has the potential to reveal ‘interesting things about the way life has evolved’, to ‘strong ALife’ which views the ALife simulations as really alive in their own right (Adam, 1998, p. 151). However, many analyses and critiques have already argued that weak ALife models of life-as-we-know-it are as subjectively constructed by the scientists involved, as the strong ALife creations of life-as-it-could-be (Helmreich, 1998, p. 224). As Stefan Helmreich argues in his anthropological study of Artificial Life practitioners, Silicon Second Nature:
Artificial Life scientists’ computational models of ‘possible biologies’ are powerfully inflected by their cultural conceptions and lived understandings of gender, kinship, sexuality, race, economy, and cosmology and by the social and political contexts in which these understandings take shape. Ideas and experiences of gender and kinship circulating in the heterosexual culture in which most researchers participate, for example, inform theories about ‘reproduction,’ ‘sex,’ ‘relatedness,’ and ‘sexual selection’ in artificial worlds, and notions of competition and market economies in the capitalist West shape the construction of ‘artificial ecologies’ in which populations of programs vie to ‘survive’ and ‘reproduce.’
(1998, p. 11) Just as any application of evolutionary theory must make assumptions about what evolution really entails, so too must the modelling of Artificial Life rely upon cultural assumptions about what it means to be ‘alive’. Also implicit is the big stumbling block for artificial evolution: evolutionary theory requires life to mutate and change itself without the need for any form of guiding creator or God; for artificial evolution to occur, the scientists and computer programmers must act as God to build the digital contextual conditions and original forms of life in order to even begin simulating evolution (Kember, 2003, p. 57). Charles Darwin’s evolutionary theory may have ‘killed God’, but ALife seems to have resurrected ‘Him’. 1