Can Artificial Intelligence Make Art?

Mikalonytė, Elzė Sigutė; Kneer, Markus

doi:10.48550/arxiv.2104.07598

Cited by 1 publication

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“…Beyond concrete, not (yet) fully successful attempts to automate hermeneutic procedures using machines, there are many findings in the literature hinting at the possibility that machines can indeed be capable of extracting meaning from data (or at least appear to do so): Take, for instance, artificial intelligence applications that are able to write poems and prose (e.g., He et al, 2012;Hopkins & Kiela, 2017), music (Bickerman et al, 2010), and summaries of texts (Dang et al, 2022). Further examples of machines simulating intuitive, creative, and context-aware cognitive processes resembling hermeneutic inference are the much-discussed artificial intelligence agents creating pictures of human faces that look realistic and even more trustworthy to onlookers than real faces (Nightingale & Farid, 2022), creating visual art (Mikalonytė & Kneer, 2021), creating movie trailers (Smith et al, 2017), simulating anthropomorphic dreams (Merchán & Molina, 2020), or understanding the meaning of words in context and this way, for instance, generating text summaries (Joublin et al, 2023).…”

Section: Hermeneutic Interpretation Versus Measuring Describing Predi...mentioning

confidence: 99%

Inference in the Data Science Era –Do we Need a New Epistemological Debate in the Social Sciences?

Moeller,

Schmidt

2023

Preprint

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Where does trustworthy knowledge come from? This article suggests that the answer to this epistemological question has fundamentally changed due to the recent bloom of machine learning (ML) techniques, artificial intelligence (AI), and data science (DS). Where previously the quantitative social sciences were committed to a hypothetico-deductive research logic, machine learning, artificial intelligence, and data science frequently use abductive and inductive inferences and integrate them with deductive and abductive approaches.This article therefore proposes that Social Science needs a new epistemological debate to clarify what role abduction, induction, and deduction can and shall play in scientific inference.To give starting points to this debate, we first present an example of paradigmatic epistemological changes that machine learning and artificial intelligence bring to Social Sciences: We show how the traditional separation between quantitative and qualitative approaches becomes increasingly blurry and integrative due to innovations enabled by ML, AI, and data science.Following that, we present a new framework for integrating data-driven and theory-driven approaches in research with ML and AI.We conclude by discussing whether and how machines can make trustworthy inferences. Following that, we discuss under which circumstances machines can make trustworthy inferences, to provide a starting point for the new epistemological debate in the Social Sciences that we call for.

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