Abstract
The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling.
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Notes
It’s also perhaps a bit unfortunate that contemporary reviews of automated discovery adhere to somewhat dated typologies of artificial intelligence applications within science (f.e. Giza 2017). Reflecting for the most part earlier results entirely omits recent successes delivered by Deep Learning, while underappreciating the influence of artificial representation learning on science in general (cf. ibid.).
As Goodfellow notes, since the players are neural networks, and their parameters acquired by back-propagation of error, heuristically, to secure a non-vanishing gradient it is better to consider the generator as maximising the probability of the discriminator being mistaken (Goodfellow et al. 2014). This slightly changes the nature of the game, since it can no longer be described in terms of a single value function (ibid.). Although this represents a shift from describing the scenario in terms of a minimax game, it doesn’t lessen the game’s relevance towards the theoretical analysis of adversarial artificial representation learning.
The model can get also stranded in an overfitted state, arising from what is usually described as memorisation of the training data (observational evidence), likewise hampering its capability to generalise beyond the evidence. However, as it is underfitting which mostly imperils the current generative adversarial models, and the subject at hand comprises mainly underdetermination, a discussion of overfitting would diverge from the goal of the paper.
We are indebted to John Symons for pointing out this limit of the human–machine learning synthetisation of (realist) scientific theories.
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Acknowledgements
We are grateful to our reviewers for the helpful feedback as well as to the editors at Axiomathes. Especially, we would like to thank John Symons for his comments and support. This research was supported by Charles University Research Centre of Excellence UNCE/HUM/037 ‘The Human–Machine Nexus and International Order’.
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Spelda, P., Stritecky, V. What Can Artificial Intelligence Do for Scientific Realism?. Axiomathes 31, 85–104 (2021). https://doi.org/10.1007/s10516-020-09480-0
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DOI: https://doi.org/10.1007/s10516-020-09480-0