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.

中文翻译：

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人工智能可以为科学现实主义做什么？

该论文提出了人类科学家和人工表征学习模型之间的综合，作为增强现实主义理论对抗各种反现实主义企图的认知保证的一种方式。为此，这篇论文充实了未设想的替代方案，不是作为对科学现实主义的批判，而是作为一种强化，因为它拒绝对科学进步的回顾性解释，这首先带来了替代方案的问题。通过利用对抗性机器学习，综合探索了未设想替代方案的可用证据的可能性空间，提供其中可能发生的模态知识。因此，随着可用证据的不确定性减少，综合实在论的认知保证应该得到支持。