Beyond Quantity : Research with Subsymbolic AI / Andreas Sudmann.

Acknowledgements -- Introduction -- List of contributions -- List of references -- Research with Subsymbolic AI -- Thesis I: AI revolution -- Thesis II: AI embedded -- Thesis III: Epistemological potentials -- Thesis IV: Big tech and academia -- Thesis V: Expert crisis -- Thesis VI: Sociological split seconds -- Thesis VII: Data colonialism -- Thesis VIII: The labor landscape shift -- Thesis IX: AI’s self‐evolution -- List of references -- When Achilles met the tortoise -- 1. Forever converging -- 2. Never being -- 3. The incomputable -- List of references -- From algorithmic thinking to thinking machines -- 1. AI and the historical epistemology of science and technology -- 2. AI as the denial of epistemology -- 3. AI as symbolic representation vs. modelling -- 4. AI as an experimental artefact -- 5. AI as an epistemic scaffolding and meta‐paradigm -- 6. Conclusion -- List of references -- A new canary in the coal mine? -- 1. An epistemology of Early Warning Systems -- 2. Quasi‐avian Early Warning Systems -- 3. EWS, AI, and Kinds of Intelligence -- List of references -- Cross‐interactions between AI and epistemology -- 1. Introduction -- 2. AI groundings -- 2.1 Prehistory of AI -- 2.2 Birth and epistemic assumptions of AI -- 2.3 Very brief history of AI -- 2.3.1 The time of the prophets -- 2.3.2 The dark years -- 2.3.3 Semantic artificial intelligence -- 2.3.4 Neo‐connectionism and machine learning -- 2.3.5 From artificial intelligence to ‘animistic informatics’... -- 2.3.6 The renaissance of artificial intelligence -- 2.4 Epistemology of AI -- 2.4.1 Logical‐mathematical approach -- 2.4.2 Semantic approaches -- 2.4.3 Learning theories and deep learning -- 2.4.4 Big Data -- 3. Impacts of AI on sciences -- 3.1 Impact on the natural sciences: In silico experimentations -- 3.2 Impacts on the humanities -- 4. Conclusion -- List of references -- AI and the work of patterns -- 1. Pattern formation -- 2. Pattern detection -- 3. Security in crowded settings -- 4. The work of patterns -- 5. Conclusion -- List of references -- Images -- Artificial Intelligence in medicine -- 1. Introduction -- 2. Survey of current AI applications in medicine -- 3. Limited deployment of AI tools -- 4. Challenges to validation and deployment of AI tools in medicine -- 5. Ethical considerations -- List of references -- Subsymbolic, hybrid and explainable AI -- 1. Introduction -- 2. Image understanding and spatial reasoning -- 3. Information and knowledge representation -- 4. Reasoning -- 5. Explanations -- 6. Discussion -- Acknowledgements -- List of references -- AI‑based approaches in Cultural Heritage -- 1. Computational methods in archaeology -- 2. Remote sensing -- 3. LiDAR -- 4. Artificial Intelligence and archaeology -- 5. Investigating archaeological features in a forestland -- 6. Methodology -- 7. Preliminary results -- 8. Ground truthing -- 9. Conclusion -- List of references -- Interfaces of AI -- 1. Introduction: Perspectives from critical interface studies -- 2. Interfaces as thresholds -- 3. Example A: Curating social media feeds -- 4. Example B: Editing images with AI‑based photo apps -- 5. From popular apps to AI in the sciences: Why interfaces matter -- List of references -- Media and the transformative potential of AI in the scientific field -- Thesis I -- Thesis II -- Thesis III -- Thesis IV -- Thesis V -- Thesis VI -- Thesis VII -- Thesis VIII -- Thesis IX -- List of references -- Putting the AI into social science -- 1. Introduction -- 2. Preliminaries -- 3. Practices -- 3.1 Discovery & idea generation -- 3.2 Study design & data collection -- 3.3 Data processing & analysis -- 3.4 Writing & dissemination -- 4. Potentials & promises -- 5. Pitfalls & perils -- 6. Conclusion -- List of references -- Science in the era of ChatGPT, large language models and generative AI -- 1. Introduction -- 2. The role of generative AI in research design -- 2.1 Generative AI as a research instrument -- 2.2 Generative AI as a research subject -- 3. Digital assistance by generative AI -- 3.1 AI‑assisted scientist -- 3.2 AI‑assisted participant -- 3.3 AI‑assisted reviewer -- 4. Research ethics review practices -- 5. Ten recommendations for research ethics committees -- 6. Conclusion and future work -- Acknowledgements -- List of references -- The current state of summarization -- 1. Introduction -- 2. Pre‐trained encoder‐decoder models -- 3. Large autoregressive language models -- 4. Instruction‐tuned models -- 5. Evaluation of large language models -- 6. Limitations and new frontiers -- 6.1 Long document summarization -- 6.2 Multi‐document summarization -- 6.3 Controllable summarization -- 6.4 Multi‐modal summarization -- 7. Commercialization -- 8. Conclusion -- List of references -- Opacity and reproducibility in data processing -- 1. Introduction -- 2. Investigating research data journeys -- 3. In‑practice opacity within data ecosystems -- 4. Reproducibility and the illusion of transparency -- 5. Cracks in the looking glass: AI and the data ecosystem -- List of references -- AI in mathematics -- List of references -- Artificial Intelligence as a cultural technique -- List of references -- List of contributors.

How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately?

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