Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski, Jens Schröter, Alexander Waibel (eds.) Beyond Quantity

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Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski, Jens Schröter, Alexander Waibel (eds.)

# **Beyond Quantity**

Research with Subsymbolic AI

In cooperation with Julia Herbach, Liana Popa, and Jeffrey Röchling.

Funded by the Volkswagen Foundation

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#### **First published in 2023 by transcript Verlag, Bielefeld**

**© Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski, Jens Schröter, Alexander Waibel (eds.)**

Cover layout: Maria Arndt, Bielefeld Cover illustration: Julia Herbach, Cologne Printed by: Majuskel Medienproduktion GmbH, Wetzlar https://doi.org/10.14361/9783839467664 Print-ISBN: 978-3-8376-6766-0 PDF-ISBN: 978-3-8394-6766-4 EPUB-ISBN: 978-3-7328-6766-0 ISSN of series: 2698-7546 eISSN of series: 2703-0555

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# **Contents**

# **Acknowledgements**

First of all, we would like to thank our authors for the wonderful contributions and discussions as well as our student assistants very much for their support in the realization of this book: Julia Herbach for her assistance with the organization of the "Beyond Quantity" conference, the coordination and editing of the book's contributions, as well as the book's cover design; Liana Popa for the coordination, and especially the extensive linguistic editing of all contributions to this book; Jeffrey Röchling for his assistance during the review process and the editing of the book's contributions.We also very much appreciate the work of many of our colleagues for providing reviews and/or feedback for this collection. Further thanks go to our publisher transcript, in particular to Johanna Mittelgöker, as editor of this publication, to Dagmar Buchwald, as well as to Anna Tuschling and Bernhard Dotzler, the editors of the series "AI Critique".

This book publication is part of the research group "How is AI Changing Science? Research in the Era of Learning Algorithms"(HiAICS), which has been generously funded by the Volkswagen Foundation since 2022 (planning grant since 2019).

Finally, we would like to thank the Sorbonne Center of Artificial Intelligence, especially Gérard Biau, Xavier Fresquet and Nora Roger, for being such wonderful hosts to the opening conference of the HiAICS research group.

# **Introduction**

*Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski, Jens Schröter, Alexander Waibel*

For more than ten years now, we have witnessed an AI boom affecting basically all areas of culture and society,including the scientific field.This book explores the potentially profound transformation in academic research.

Such a focus is not only aiming at the question of AI's impact, be it as a technology, a component of a larger infrastructure, or a tool. It is also about exploring what AI as a concept actually means, which different techniques and approaches it addresses, to what extent it might be important to continue the long tradition of problematizing it, and last but not least, how a particular understanding of AI might be transformed by the practices and conditions of its scientific situatedness and application (Suchman 2006).

As a point of departure for the following considerations, we engage with the history of AI as a contest between two fundamental approaches: the symbolic and the subsymbolic (see also Dreyfus/Dreyfus 1988: 15–43). The former, also known as GOFAI (Good Old-Fashioned Artificial Intelligence), processes knowledge and tasks based on logical or rule-based procedures. Knowledge is explicitly represented, often hard-coded and manually entered into the system by experts. The latter is characterized by the fact that corresponding procedures seek to find patterns and correlations in data automatically. This approachinvolves statistical and neuralmodels to learn from data without relying on explicitly defined rules. Knowledge representation operates in an implicit manner. For example, knowledge can be implicitly encoded in the weights of a neural network. While this allows these systems to process sizable amounts of complex, unstructured data, it is also responsible for their black-box nature.

Subsymbolic AI and its scientific impact are the focus of this book. More specifically, the contributions from various fields shed light on artificial neural networks (ANNs) as the currently dominant and discourse-determining forms of AI, which are broadly inspired by the neuroinformatic model of the brain

(mostly related to humans, but also with regard to animals).1 In fact, prima facie it seems as if the subsymbolic approach of ANNs (mostly even only based on backpropagation) has become synonymous with AI as it has largely supplanted symbolic AI and even othermachine learningmethods,including symbolic learning, statistical learning, and Hidden Markov models, among others.

However, such a thesis must be differentiated more precisely in at least two respects: on the one hand with regards to the long tradition of hybrid connections of symbolic and subsymbolic methods; on the other hand that this distinction becomes blurred when traditionally symbolic problems(e.g.,language processing) are increasingly handled on a neural substrate as well (e.g., machine translation, parsing, large language models (LLMs)). The term "neurosymbolic AI" is pertinent in this context, as it refers to hybrid systems that integrate neural models with symbolic AI. In his Robert S. Engelmore Memorial Lecture at AAAI 2020, Henry Kautz (2022) provided a taxonomy of neurosymbolic AI systems. One of the categories he introduced was "symbolic Neuro symbolic" (ibid.: 118), which also directly applies to LLMs. Systems in this category have their inputs and outputs presented as a symbolic form and even natural language with its discrete tokens counts towards that. Although these systems are not widely regarded as neuro-symbolic, it does make the term more ambiguous.The "Neuro[Symbolic]"(ibid.: 119) category may be of greater interest and relevancy, as it embeds symbolic reasoning as part of the neural engine. A new development with contemporary LLMs, such as ChatGPT or Toolformer, is the ability to interact with plugins. One such plugin can be a symbolic reasoning engine like WolframAlpha.

However, the relations and connections between symbolic AI and the subsymbolic AI of ANNs are no impediment for focussing on the latter for the purposes of this book – on the contrary. Above all, given their specific history, the current relevance of ANNs is quite remarkable. Their technical foundations were already developed as early as the 1940s and 1950s (Sudmann 2018a; Sudmann 2018b), but more complex, foundational architectures emerged in the 1980s and 1990s as they enabled ANNs to operate on real-world problems that required context, shift invariance, or sequential processing (Waibel et al. 1987; Waibel et al. 1989; Hochreiter/Schmidhuber 1997; LeCun et al. 1998). Nevertheless, until the 2000s, ANNs were largely ignored by practitioners and struggled to find broad adoption: Because the computing of the day would

1 In addition to neural networks, other approaches or algorithms can also be subsumed as subsymbolic AI, for example k-nearest neighbors.

only permit training of small networks, simpler statistical methods could already deliver competitive performance. It would take 20 more years until computational resources and data had scaled sufficiently for ANNs to show their true potential: From networks with a dozen or hundred connections and a single hidden layer, we now see networks with 175 billion parameters (GPT-3) and dozens or hundreds of layers. And ANNs could now deliver (with the same or similar algorithms as in the 1980s) impressive performance advances over classical methods. In speech recognition, error rate reductions of 30% or more were observed on published benchmarks. In vision, significant improvements could be obtained over standardized object classification benchmarks (see ImageNet, Krizhevsky/Sutskever/Hinton 2012). And even in machine translation, performance leapt forward through the adoption of large recurrent neural encoder-decoder networks (Luong/Manning 2015). In many domains, e.g., speech (Nguyen/Stueker/Waibel 2020), vision, machine translation, performance now exceeds human capabilities over certain defined benchmarks.

Another decisive part was played by big tech. The AI renaissance was accelerated as soon as the information industry became aware of the economic potential of ANNs. This resulted in a concerted move to massively expand AI research activities, invest in computing resources, and to acquire and merge promising AI start-ups (like DeepMind, and others).

The widely broadcasted 2016 victory of the AlphaGo program over Go master Lee Sedol had a further reinforcing effect with regard to the perception of the lingering capabilities of AI. This media event significantly shaped public perception. Subsequently, experts in various scientific fields were alerted, and increasingly interested in AI and, ultimately, began to integrate the new technology into various methodological toolkits. Somewhat unexpectedly, the release of ChatGPT in 2022 proved to be another game-changer. AI could finally be experienced and utilized by a wider circle of users, an encounter that swept public perception, and made it impossible to overlook the ramifications of this new technology for the most basic practices of mainstream science, its quotation standards, and academic exams. Besides questions of authorship and reliability, one important provocation may lie with the political and moralistic overtones of these chatty AIs. Furthermore, as language models, they merely predict text based on massive amounts of past textual data und thus ethical standards or factual correctness can not be assured as of yet. Even if a majority-driven form of reinforcement learning from human feedback decides about the biases of such machines, a "mathematisation of ethics" and a quantitative

vote for majority morals is at hand (Simanowski 2023: 73). Still, large self-supervised models like LLMs can digest virtually all of humanity's textual data and thus generate predictions with surprising accuracy and relevance, resulting in a powerful illusion of human-like intelligence and clarity.

Nevertheless, it is necessary to unravel this rather event-centered and also person-centered historiography in more detail. For example, backpropagation as a central learning algorithm of ANNs was already developed in the 1970s and 1980s, some elements of it even as early as the 1960s.2 Accordingly, it is difficult to attribute the development of this algorithm to just one person or one group of people at a specific time. Moreover, it has been and continues to be the case that the development of AIinmany areasis based on close cooperation between industry and science, but also the military.Not least for this reason, AI research has always been, to a considerable extent, applied research.

To understand these transdisciplinary effects of the new technology, we must examine the level of data practices and scientific methodologies. Several recent publications have been addressing the impact of new AI technologies on scientific practices (Athey 2018; Fecher et al. 2023; Gethmann et al. 2022; Okerlund et al. 2022). At the same time, it seems evident that we are witnessing the effects of a much longer history of data, statistics, formalization, modeling, and simulation. Since the early days of AI, attempts were made to put 'intelligent' systems to use in various academic settings3 , but the corresponding reflections, if they had their place in the sciences at all, remained, in most cases, either necessarily speculative or their lasting contribution to the development of a research field ultimately proved to be extremely limited. There were, for example, early attempts to use AI systems for specified scientific tasks such as proving theorems (see Feigenbaum/Feldman 1963; Dick 2011), but corresponding implementations of the systems were typically very far from actually advancing research in the respective areas of knowledge.

With the successive establishment of so-called expert systems starting in the 1970s, the application-oriented perspective of AI finally gained some relevance, but this upswing ultimately did not last either. It is quite telling that Pamela McCorduck's relevant study on the history of AI – *Machines whoThink* –

2 For a technical history of backpropagation related to ANNs, see for example Schmidhuber (2022).

3 For discussing AI in the context of psychology, see for example Hunt (1968: 135–168); for organic chemistry, see Feigenbaum (1968: 23–27).

contains a separate chapter titled "Applied Artificial Intelligence", which introduces two of these early expert systems and their respective application contexts. But remarkably enough, this chapter begins by pointing out how AI is derided and mocked in terms of its supposed potential on a regular basis (Mc-Corduck 1979: 272f.).

The latter has not fundamentally changed today, even in light of the considerable achievements of large language models like ChatGPT. There continues to be a pronounced interest as well as a certain pleasure to expose the shortcomings of even the most advanced AI systems. Nevertheless, there is a significant shift in this respect: Currently, AI is no longer a speculative concept at its core; the relevant point of reference for (critical) reflections now is the concrete implementation of corresponding systems, not only with respect to areas of academic knowledge but all areas of culture and society.

There is little doubt about the fundamental importance of AI in all spheres of social life, given the prevailing assessments in public discourse. Furthermore, there seems to be no sign of an imminent end to today's AI boom. Following many booms and busts of previous AI excitement and promised revolutions, AI has now found its firm and sustainable footing.This is especially true for applications of AI in various fields of science, as countless research examples demonstrate (for an exemplary overview of AI research projects in Europe, see "How is Artificial Intelligence Changing Science?" 2023).

Unsupervised and self-supervised algorithms and the increasing use of simulations and data augmentation have advanced practical AI applications to astonishing performance levels and opened new applications. Sharing of open-source code, tools and large pretrained models now also accelerate progress by leapfrogging from one accomplishment to another at unprecedented speed. Google DeepMind, for example, has released a series of specialized models that aim to assist researchers in their respective fields, including AlphaFold (Jumper et al. 2021) which is able to predict 3D structures of proteins more accurately than previous models and, more importantly, is in many cases accurate enough to replace real-life experiments. AlphaFold is arguably the organization's biggest success so far and is now deeply ingrained as a tool in medicine and life sciences (Varadi/Velankar, 2022). More recently, Google DeepMind published AlphaTensor (Fawzi et al. 2022) and AlphaDev (Mankowitz et al. 2023), both of which have been used in the research area of computer science to optimize algorithms and low-level code such as matrix multiplication and sorting algorithms. In the case of AlphaTensor, the model was able to find an algorithm to reduce the number of multiplications necessary for certain types of matrix multiplications. On the related blog posting, Google DeepMind's headline "optimising the world's code one algorithm at a time" (Mankowitz/Michi 2023), aptly describes its current approach.

At present, the contributions of AI to scientific challenges are not always as spectacular as in the case of AlphaFold; often enough, standard AI technologies are used as elements of methods or in everyday applications (although usually at much better performance). However, it is remarkable how diversely and broadly AI is now being applied in various fields of research. In sports science, ML-based pattern recognition is increasingly used for the performance analysis of athletes, players and teams (Araújo et al. 2021). In art history, a computer vision system has been able to identify connections between artworks by analyzing poses of human subjects in paintings (Jeniczek & Chum, 2019). ML has also, for a long time, been used in particle physics, due to the enormous datasets analyzed in this field. In 2012, one of the important discoveries, the Higgs boson, owed much to the application of machine learning (Radovic et al. 2018; Bourilkov 2019).

Even though the general AI boom has been felt in many scientific fields for years now, one should note that the application of AI in many disciplines is still in its infancy. In our view, it is therefore even more important and timely to recognize, reflect on, and historically document this transformation of the sciences by AI in statu nascendi. To address this challenge, our transdisciplinary research group, encompassing the disciplines of media studies, computer science, and the history of science, has started its work in 2019, respectively 2022, to investigate the ways in which research is conducted not only on/about AI but *with* AI, in various fields from the natural and social sciences to the humanities. In particular, we are interested in exploring how AI interacts with the established practices and methods of science, whether they are complemented, modified, and/or potentially replaced.

Three disciplines or domains of research are at the center of our inquiry: environmental sciences/climatology, social sciences/sociology, and film studies. Three additional fields – literary studies, medicine and economics – are investigated to broaden the range of disciplines to be studied, partly in order to capture the heterogeneous range of uses of AI more accurately and to better generalize our results across scientific disciplines. In a first programmatic paper, the research group has already discussed some key challenges and perspectives, as well as some general considerations (Echterhölter/Schröter/ Sudmann 2021).

The question of the transformations of the sciences through AI requires description of precisely their different scales and dimensions, as well as the general heterogeneity of the aspects addressed by them. One way of illustrating the range of conceptualizations can be the marking of extreme positions and ways of thinking, thus allowing for a more nuanced perspective. For example, one might argue that as an advanced Artificial General Intelligence (AGI)4 system evolves, it would also be capable of handling any (new) scientific problem. Another option would be to develop a system, however specialized, that is used for more or less specific scientific tasks or only within a certain domain or discipline. Both concepts can be imagined as systems of a "superintelligence", to pick up Bostrom's (2014) term, insofar as the abilities and skills of human systems are (or can be) clearly surpassed in both scenarios.

In the emphatic sense, AI stands for the possibility of a computer being able to gain its own insights, formulate questions and hypotheses at some point, and thus also complete all other steps along this path more or less autonomously. AI systems used for scientific (research) purposes can be further differentiated according to how human-like they have been designed and oriented. Explainable AI requirements make it at least likely for machine communication to remain connectable to human understanding and control.5 This also applies to future machine-machine communication. AI processes can also be differentiated according to the extent to which they organize individual components/phases of scientific research processes autonomously or automatically, from formulating a research question to collecting data, analyzing and evaluating data, as well as presenting and disseminating research findings.

Furthermore, there is the fundamental question of which scientific problems seem a priori suitable to be addressed by AI at all. DeepMind (Hassabis 2022) has developed three criteria in this respect:

4 The notion denotes a hypothetical AI-system with cognitive, creative etc. capabilities comparable to or even exceeding those of humans. There are no realizations of such systems yet. Their development, if it will be possible at some point, is repeatedly discussed as a great danger.

5 In recent years, research on XAI systems has become increasingly important, and this applies in particular to scientific applications of AI. An overall very promising project in the German-speaking area was recently initiated with the Transregio "Constructing Explainability" of the Universities of Bielefeld and Paderborn.

#### 18 Beyond Quantity

As can be seen from these criteria, the use of ML must be carefully considered, especially with regard to the significant resources and costs involved.

A relatively recent phenomenon to reflect within our study is the fact that more and more explicitly AI-driven tools or apps are either directly intended for scientific work or can be indirectly used for it. A plethora of commercial applications like SciSpace Copilot or Elicit has been launched, promising to automate certain research workflows and help with literature research or understanding literature. Language models like ChatGPT are actively used by researchers as assistance in the writing process of scientific documents, prompting repositories and journals like arXiv to define a 'use of generative AI language tools' policy for authors. Towards the end of last year, the domainspecific language model Galactica (Taylor et al. 2022) caused a stir among the research community. It is exclusively trained on scientific data like research papers, chemical formulas, and DNA sequences. The generated text of the model sounded convincingly scientific but triggered concerns that it could easily spread inaccuracies. At the same time, there is a class of models, such as Minerva (Lewkowycz et al. 2022) and AI Descartes (Cornelio et al. 2021) that are used in research itself and are intended to automate reasoning processes.

Beyond such specific applications, it seems important to us to explore the general tool character and principal potential of current data-driven, statistical AI systems in methodological terms. A few years ago, computer scientist Pedro Domingos described ML as the "scientific method on steroids" (Domingos 2015: 13). Such a description strikes us as highly questionable as it conceptualizes ML per se as a scientific method. In addition, the metaphor "on steroids" suggests that ML allows an almost illegal and unhealthy form of performance enhancement in this respect. Nevertheless, it is obvious that the performance level of learning algorithms significantly increases when corresponding systems are trained with more and more data and computational power. The present publication, therefore, is also motivated by an interest in discussing AI through the lens of the ways in which learning algorithms potentially reconfigure the epistemic relationship of qualities and quantities. More specifically, we would like to shift the perspective on this relationship by highlighting epistemic aspects *beyond quantity* and thus also illuminate

perspectives beyond the dominant relation of AI and big data. Two aspects are particularly important to us in this respect:

Firstly, current approaches to AI, i.e., subsymbolic AI in the form of ANNs, are not merely capable of extracting information from large amounts of data and making it productive, but they can solve problems that can also be described as qualitative. They involve dealing with qualitative questions of content, aesthetics, style, e.g., in the field of natural language processing or computer vision, in ways that were unimaginable until recently.6

Secondly, current approaches in AI research are increasingly focused on reducing or avoiding dependence on large amounts of labeled data, e.g., through strategies of self-supervised learning, zero- or one-shot learning, transfer learning, or even the use of synthetic data or simulation data. Contemporary LLMs are, for example, the result of causal language modeling which is a type of self-supervised learning in the course of which the model is tasked with predicting the next token in a sequence while requiring no additional labels.

As a result of our project's opening conference hosted by the Sorbonne Center for Artificial Intelligence (SCAI), we present first explorations on the subject of AI in the natural sciences and humanities at a point in time where qualitative problems seem to come into reach to be handled by machines. At the same time, these discussions of European scientific applications tie in with concerns that lie beyond this subject area and concern general preconditions of digital humanities (DH) or also of STS. Certain problems of transformative processes in the scientific field, which are closely related to AI, emerge mutatis mutandis in other constellations as well.

It is important to keep in mind in this context that research on scientific practices in AI has been conducted in a wide variety of disciplines and analytical perspectives, such as science and technology studies, sociology, infrastructure studies, cultural anthropology, philosophy of science,and data science (Baurmann/Mans 1984; Carley 1996; Groß/Jordan 2023; Krämer 1994; Ligo et al. 2021; Manhart 1995), but also specifically in the disciplines involved in this project: media studies (MS), history of science (HS) and computer science (CS) itself.7

6 For the details, see our (the editor's) contribution to this book as well as the essay by Schröter and Sudmann, also published here.

7 For media studies, see for example Engemann/Sudmann 2018; Ernst et al. 2019; Mackenzie 2017; Mann/Matzner 2019; Pasquinelli 2017, 2023; Sudmann 2023. For the

Current investigations into AI research stem from various disciplines involved with the reflection of the sciences. The philosophy of science has deployed its specific expertise for problems of AI (about cognition, consciousness, etc.) and proliferates in the field of AI ethics in particular, a field in which, among other things, various critical perspectives on AI and its research are normatively negotiated (algorithmic biases, surveillance, opacity of technology, etc.; for an overview, see Coeckelbergh 2020; Dimock 2020; Mann/Matzner 2019). Moreover, AI clearly resonates with and functions as a catalyst for the research perspectives of the digital humanities (Jannidis 2013; Manovich 2017; Flückiger 2021).

To further enhance these discussions for the field of AI-based methods in the sciences, a more thorough investigation of scientific practices and infrastructures seems in order (Star 1999; Schabacher 2022). To keep track of current developments an integrated dialogue with computer science is of the essence. In addition to that, it seems highly desirable to observe, document and reflect the current shifts in scientific practices through AI-based methods. To capture these developments up close, a media ethnography of selected AI research projects is the most viable option and will be conducted as the research project unfolds (Dracklé 2014; Dippel 2017; Schüttpelz/Gießmann 2015; Bareither 2019). The integrated approach to scientific practices will further draw on the strengths of media archaeology to situate technically mediated knowledge production in larger frameworks. To this end, we emphasize the technological aspect as well as the social embeddedness of the emerging technology (Dotzler 2006; Schröter 2020; Ernst/Schröter 2020). Historical depth is provided for these findings on scientific practices by recent results from the history of data use in various disciplines (Aronova/von Oertzen/Sepkoski 2017; Schlicht/ Ledebur/Echterhölter 2021). In this newly developing field within the history of science, separate instances in data journeys are consulted (Leonelli/Tempini 2020), the emergence of specific algorithms are traced (Evans/Johns 2023) or models are investigated in and of themselves. One of the best researched cases may be weather models, which took a stunning trajectory from decentralized weather observers to dynamic climate models and eventually, their integration into the vastmachines of computer simulations(Coen 2018; Edwards 2000; Edwards 2010; Gramelsberger 2010).

history of science, see Seising 2021; Cave/Dihal/Dillon 2020; Evans/Johns 2023. For computer science, see Vaswani et al. 2017; Devlin et al. 2019; Brown et al. 2020; Rombach et al. 2022; Kirillov et al. 2023.

A new technical option for the sciences and humanities calls for a critical reflection of emerging forms (such as databases, algorithms, frameworks, interfaces, etc.) related to the production of knowledge. An engagement with possible transformations of scientific practices demands a methodological approach which refrains from creating prima facie distinctions between internal and external factors shaping these transformations, namely approaches from media ethnography, media archaeology, or the history of quantification. Establishing an account of what factors are important for the origin, the implementation (or non-implementation), and not least the retention of AI technology can possibly serve as a gateway for criticizing these very conditions in which the scientific endeavor takes place.

Various contemporary debates on AI technologies revolve around their social and cultural effects. Problems of algorithmic biases, data privacy, or opacity of infrastructures are commonly placed in the normative framework of AI ethics. Critical discussions of the high hopes invested in AI, as well as its present limitations, also continue to play a crucial role in ongoing debates (Broussard 2018). There is still little knowledge, however, about the relationship between the assumed problematic aspects of AI and the ways in which AI affects research practices, methodologies, and outcomes across different sciences. Adequate assessment of the impact of AI on science,including reference to its socio-political implications, is therefore a major research desideratum.

As has been pointed out here, research on the research of AI is confronted with significant challenges. The transdisciplinary view on the problems of AI in science requires distinctive expertise in very heterogeneous fields. However, there is no such thing as universal competence. Therefore, the research group hosting these discussions is all the more dependent on the dialogue and support of scholars from different disciplines and has benefited considerably from their civic engagement across the disciplines. The main focus of this publication is to explore different ways of thinking about the uses of AI in a broad set of scientific fields. At the same time, and in relation to selected disciplines, we want to exemplarily demonstrate the application of AI in specific academic contexts.

#### **List of contributions**

In their joint paper, the **members of the project "How is Artificial Intelligence Changing Science?"** discuss nine preliminary theses regarding the possible effects of the use of different AI technologies in the sciences. I) It is questioned if the widespread rhetoric of an "AI revolution" is helpful to describe the shifts that occur with the introduction of AI technologies in the sciences. II) It is emphasized that AI technologies can only be understood by understanding their embeddedness in infrastructures and social contexts. III) It is stated that AI systems can process fuzziness and uncertainty in a new way. In IV) the conflict between the big tech industry and academia in the development of AI is being highlighted. Thesis V) elaborates on how the fast introduction of AI technologies causes an expert crisis. In VI) it is discussed that many disciplines split into a computational and a non-computational branch. VII) points to the connection of AI technologies with data extraction and data colonialism. In VIII) the thesis is formulated that the introduction of AI will alter the labor landscape profoundly. IX) asks how the self-improvement and the self-evaluation of AI have to be conceptualized.

Mathematics struck gold when employing infinitesimal quantities to solve practical problems towards the end of the 17th century. In a further decolonial reflection on the inherent problems of AI and pattern recognition and discrimination, **Clemens Apprich** investigates this calculus in historical and present debates about incalculability. The calculus, which still performs reliable approximations within the schemes of artificial neural networks, should not be tamed into absolute congruence. On the contrary, it might be the imperfections and approximations which may help us to cultivate procedural and plural approaches. In this sense, the immanence residing at the end of all approximations (which would be tantamount with mastering the visual and compositional realms of quality by new AIs) does not appear fully desirable. Apprich acknowledges the insurmountable incongruity of the mathematical setup of AI and suggests strategic uses, such as Ramon Amaro's for possible "black totalities'".

**Matteo Pasquinelli**'s approach to AI is informed by the joint traditions of materialistic epistemology and media theory. He argues strictly against "folk AI", a perception of this new technology which all too readily accepts a new and contextless entity and its miraculous abilities. Instead, a much longer history of mediated thought and neoliberal entanglement of AI is in order. Without a shadow of a doubt automated and mechanical ways of reasoning have been part and parcel of the scientific endeavor long before artificial neural networks. The paper revisits Rosenblatt's 1957 strategy to facilitate pattern recognition via the modeling of the labor of perception and supervision. It integrates this historical analysis of AI with Peter Damerow's theory of mental representation,

the dialectics of tools and knowing, as well as neo-Gramscian approaches towards formalization, and the Hessen-Grossmann thesis of the labor dependency of all science. AI's advanced algorithms are not unique. They are the latest result in a long history of confluences and attempts at "epistemic scaffolding".

**Markus Ramsauer** offers a genealogy of the development of Early Warning Systems and the potential enhancement in the detection of danger via the use of AI. Taking the trope of birds as sentinels for future catastrophic developments as leitmotif, it is argued that the discovery of latent danger often depends on the use of non-human sensors or kinds of intelligence; be they animalistic or machinistic. This offers a lens for thinking about the concept of 'artificial' and 'non-artificial' intelligence beyond the question if machines can pass as human.

**Jean Gabriel-Ganascia**'s text discusses AI not only in terms of a tool for scientific practice but as a science itself. As such, the author claims,it evades classifications as 'theoretical science', 'science of nature' or 'science of culture'. The reason for this special status can be explained by the history of AI development of which the author provides a brief outline. As a second strand, the article explores the possibilities of 'epistemological ruptures' through the use of AI in the humanities as well as in the 'hard sciences'. Whereas for the former, these tools can assist in assessing individual cases, it contributes to an 'automation of induction' for the latter.

**Gabriele Schabacher** discusses in her essay the centrality of the notion of "pattern" for subsymbolic artificial intelligence. She asks what the power of patterns in contexts of cognition or application is, by distinguishing two ways of conceptualizing patterns, namely *template* and *correlation*. The reconstruction shows how these two forms are peculiarly blended in the horizon of AI technologies. The first example is the application domain of security research and how the blending of template and correlation works there. The focus will be on German pilot projects in Berlin and Mannheim that test the use of intelligent video analysis. Finally, Schabacher comments on the statistical creativity of AI image generators such as DALL-E, highlights four overarching aspects associated with the work of patterns of AI technologies, and describes their effects on scientific understanding, but also on culture and society in general.

The revolutionary potentials of AI in healthcare are covered in detail in the overview by**Urvi Sonawane** and **MatthieuKomorowski**.The usage of AI-based technologies is currently quite limited, the authors discover, despite its enormous potential. Responsible bottlenecks like technical, ethical, legal, and human aspects are examined and the need for a multidisciplinary approach involving regulatory bodies, clinicians, government, and patient committees is argued for.

In her position paper, **Isabelle Bloch** argues that a hybrid point of view of designing AI, considering both knowledge data representation and reasoning, offers opportunities towards explainability. This idea is illustrated on the example of medical image understanding, formulated as a spatial reasoning problem.

In his contribution, **Giaccomo Landeschi** shows how computer-based applications had a profound impact on the discipline of archaeology and how different methods and techniques, such as satellite remote sensing, geophysical prospections, and more recently, airborne laser scanning (LiDAR), have been employed for surveying purposes. Nowadays, artificial intelligence has also started to play an important role in the analysis of archaeological contexts. In the case of Sweden, approximately 70 per cent of its land comprises forests where a substantial number of archaeological sites remain hidden beneath the vegetation, undiscovered and unmapped. Landeschi explains how a team of scientists from Lund University recently undertook a project to showcase the potential of utilizing deep learning-based analysis and convolutional neural networks for automatically identifying a specific category of archaeological features called 'clearance cairns' in LiDAR-derived raster imagery.

**Sabine Wirth**'s paper sheds light on the ways how the concept of the interface matters for a critical understanding of AI technologies in use. From a media and culture studies perspective she discusses how research on machine learning techniques can profit from a critical perspective on interfaces. Drawing on the emerging field of critical interface studies, Wirth describes two examples of popular apps that rely on machine learning, and she outlines potential lines of inquiry and critical questions that address the central role of interfaces as mediators of AI within the field of popular media culture. Ultimately, this allows her to ask how critical interface studies can inform research on AI in science by providing an additional analytical layer.

In their contribution, **Andreas Sudmann** and **Jens Schröter** shed light on the role of media related to how AI is used in and potentially transforms different fields of academic research. Furthermore, they draw attention to some important problems of applied AI which thus require critical reflection, especially from a media studies perspective.

**Johannes Breuer** poses the question of how AI is changing scientific practice in the realm of the social sciences. His contribution "Putting AI into social science" highlights the importance of tools for different stages of the scientific endeavor. The author discusses a variety of AI-driven research tools which are suitable for the social sciences, emphasizing their potentially transformative potential as well as ethical challenges that go hand in hand with this transformation.The chapter concludes with aninvocation to focus on partnerships*with* AI, rather than on replacement *by* AI.

The paper by **Evangelos Pournaras** reviews the specific epistemological challenges and also the ethical and integrity risks related to generative AI and LLMs. In particular, Pournaras discusses emerging practices for research ethics, proposing ten recommendations that shape a response for a more responsible research conduct in the era of AI.

In his paper, **Fabian Retkowski** aims at concisely indicating the current state of the art in abstractive text summarization. The current paradigm shifts towards pre-trained encoder-decoder models and large autoregressive language models are outlined and the challenges of evaluating summarization systems and the potential of instruction-tuned models for zero-shot summarization are discussed in further detail. Additionally, the work gives a brief overview of how summarization systems are currently being integrated into commercial applications.

**Sabina Leonelli** maintains in her chapter that despite ever larger amounts of data and proclaimed bias-reducing algorithms, the employment of AI tools in scientific research is still heavily affected by the quality of the training data. The hardly traceable origin of data, combined with their often diverse nature and purpose, leads to what the author calls "in-practice opacity". Instead of focusing on quantitative modes of reproducibility as a panacea for making science transparent, the author calls for extended attention to questions about the quality and the funding of research data.

The use of data has been a key element of statistics, yet the dimensions of current data usages constitute a new situation. **Gérard Biau** is in a unique position to answer a set of questions about the changes affected by AI in this particular field of mathematics: He works at the Probability, Statistics, and Modeling Laboratory (LPSM), serves as director of the Sorbonne Center for Artificial Intelligence (SCAI), and was president of the *Société française de statistique*. Biau states that the impact of AI on mathematics is decisive. Some statistical tools, which have been stable for decades, are currently being revised. AIs start to make suggestions regarding results, or are instrumental in verifying the most advanced new proofs.

In his interview with **Sybille Krämer**, Jens Schröter poses nine questions which closely follow Krämer's writings over the decades. Her work has been, from the very beginning, revolving around questions that are of special relevance to understanding subsymbolic AI today.This starts with the question on the culturally shaped exteriority of the human mind, the relation of AI to the fundamental role of the analog and the digital, or the connection of AI to the field of digital humanities. Further fundamental points are discussed like the question if AI can be understood as a "cultural technique", especially when we observe the increasing role of computers in science. Finally, Krämer addresses questions of explainability and critique.

#### **List of references**

Cave, Stephen/Dihal, Kanta/Dillon, Sarah (eds.) (2020): AI Narratives: A History of Imaginative Thinking about Intelligent Machines, Oxford: Oxford University Press.

Coeckelbergh, Mark (2020): AI Ethics. Cambridge, MA: The MIT Press.

# **Research with Subsymbolic AI**

Preliminary Theses

*Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski, Jens Schröter*

The current developments within information technology not only challenge scientific disciplines to study new phenomena; they also potentially alter and enhance research methods, practices, and outcomes across the natural sciences, social sciences, and humanities. Researchers have to negotiate interdisciplinary conceptual frameworks and access to new data infrastructures in order to participate in and benefit from the ongoing AI boom. Prima facie, data-intensive AI approaches, especially artificial neural networks (ANNs) but also other approaches of machine learning (ML), increasingly enable and support the production of knowledge across all disciplines.

To conceptualize these shifts as mere effects of technology, however, arguably oversimplifies the interrelationship between technology and society. Since its learning capabilities rely on datafication, AI-based research is always connected to society from the start: AI uses its data to classify, categorize, and cluster society. What is more, as modern societies come to increasingly rely on scientific knowledge, any change in scientific practices and research methods brought about by AI technologies is bound to affect society at large (DFG 2020; Zhang et al. 2021). Research on the influence of AI on science is therefore of the utmost importance in order to comprehensively understand the effects of AI on present and future society. The term "science" here refers to natural sciences, social sciences, and humanities, and "scientific research" to those research methodologies within the sciences which use empirical and quantifiable data.

One of the big challenges is to understand and conceptualize the relationship between new technologies, specifically AI, and the epistemologies they enable: Are AI-based methods basically more efficient tools which continue non-AI methods, merely extending them in terms of velocity and scope? Or

do they allow research in new methodological ways, to ask and answer novel questions? Or both on different levels? Which socio-political implications does data-driven research with AI entail, compared to other long-standing databased research practices? Is AI-enabled data science still confined to numerical and quantifiable problems, or does it also give access to qualitative problems (e.g., problems of fuzziness; Seising, 2009)? How do AI-based methods try to reduce the dependency on (big) data, e.g., by making use of pre-trained models, or specific approaches like transfer learning or one-shot learning (Duan et al. 2017; Weiss/Khoshgoftaar/Wang 2016)?

To answer such questions, it is insufficient to merely look at AI models in a narrower sense (i.e., "learning algorithms") and deduce their impact. Since scientific research always occurs within specific *epistemic cultures*(Knorr-Cetina 1999; see also Fleck 1980 [1935]; Latour/Woolgar 1979), the impact of AI-based methods on scientific research is only determinable by closely observing the interplay of technology and research practices. Their alleged opacity is an obstacle here: While AI-based approaches make it possible to process data in novel ways (i.e., identifying, classifying, categorizing), they partially or completely disable researchers' abilities to comprehend and track these AI-based processes (Adadi/Berrada 2018; Sudmann 2019). In light of these observations, we follow the hypothesis that data-driven and AI-based methods enable new epistemologies precisely by transforming one of the most long-standing scientific practices of all: *AI changes the way researchers interact with and relate to data.* Hence, we believe it is important to compare the specific impact of ANNs and ML procedures with existing findings on digital methods in the sciences, most notably simulation, big data, and statistical probability (Ash/Kitchin/Leszczynski 2019; Gramelsberger 2011; Leonelli 2016; Krüger/Daston/Heidelberger 1987).

Therefore, we currently investigate how AI-based methods are situated in concrete and specific research environments which draw together technologies and practices. We carefully develop our findings from firsthand knowledge of outstanding current AI research projects operating within the novel conditions of data infrastructures. We also take into account the history of the specific methods in question, including their affordances, and contextualize these techno-practical configurations within an in-depth history of databased scientific practices. It is intended that our observations will eventually inform new research approaches, as our findings will be fed back into the development of an AI-based system that structures and comprehends scientific content from several modalities including text, speeches, and meetings. This

includes a multitude of components such as automatic speech recognition, segmentation to automatically divide the content into coherent chapters, and text summarization. With this system, we hope to support the answering of meta-research questions of AI as part of our project.

To this end, and given the prevalence of data-driven research across disciplines, we have set-up a *transdisciplinary research project* combining the expertise of three disciplines: thinking through the complex entanglements of technologies, culture, and practices is one of the core assets of *media studies*; providing an in-depth history of data and modeling practices in various methodological traditions is the key contribution of the *history of science*; developing a profound mathematical understanding of AI models and using computational methods to engineer a cutting-edge tool for using AI to study AI is the current task of our project in computer science. We combine the expertise of these three disciplines to study the socio-technical uses of AI in three carefully selected external research projects. From an original sample of close to 150 research projects working with AI-based methods in Europe ("How is Artificial Intelligence Changing Science?" 2023), we have chosen three projects from three different disciplines as the center of our investigation (film studies, sociology and climatology/Earth sciences). To capture the current changes brought about by AI in general and ML and ANNs in particular, our working groups in *media studies* (MS), *history of science* (HS), and *computer science* (CS) will combine the specific strengths of the most advanced methods from their respective fields. The project will allow a unique documentation and investigation in this pivotal decade. Otherwise, many of the traces of this historical shift which are obtainable now, will be irretrievably lost.

In the following, we have compiled a selection of theses that address some of the central aspects and considerations of our research group while also illustrating the range of different disciplinary perspectives on the various dimensions of AI in science. Each thesis is preceded by a quote, pointing to a larger topic to be further investigated in the course of the research project.

#### **Thesis I: AI revolution**

Major economies are on the 'cusp of an AI revolution' that could trigger job losses in skilled professions such as law, medicine and finance, according to an influential international organisation. (Milmo 2023)

One obvious rhetoric in the discourse of so-called "AI" is that we are dealing with a "revolution". This seems to imply that some fundamental change will occur with the advent of AI technologies. This is easier said than understood. Often the rhetoric of "revolution" is simply used for marketing – because a new product, be it an advanced toothbrush or a new type of AI software, sells better if it is claimed to be a brand-new breakthrough of some kind. In that case "revolution" is used as a synonym to the entrepreneurial buzzword "disruption". It is normally not meant that a fundamental societal upheaval is to be expected (and that is the idea connected with the word "revolution" in the twentieth century), but just that there is a new product that displaces other products on the market. Basically, this is also the meaning of the recent and somewhat disturbing announcements of AI-producing companies that their own productsmight put humanity in danger – and call for regulation. If they are in fact so dangerous, why don't they simply stop producing these programs? It is more likely that they want to direct attention to how powerful their brand-new products might be or that the established players want to impede competition.

Besides that and if you take the claim of "revolution"more seriously,it is often not very clear what exactly is meant by that. If we take the example above, mentioned in *The Guardian*, a "revolution" was indeed the case if the job losses triggered by AI would lead to the fundamental impossibility of our (capitalist) societies, based on wage labor, to reproduce itself (on the following, see Schröter 2019). This problem of "technological unemployment" is actually an effect that was predicted by certain strands of Marxian theory already long ago, and long before AI. For many authors, this means that capitalism has to be overcome, or at least that radical political solutions, like unconditional basic income, have to be sought for. But is this really meant by the headline of *The Guardian*? Even if *The Guardian* is left-leaning, it can be doubted that it really wants to say that the (often postponed) terminal crisis of capitalism is now really here – with AI. That would be a "revolution" indeed. It is more likely that the article wants to say that certain professions that seemed safe so far are now also under the threat of automation. Although this might be bad for the people involved, this is nothing new. Many technological transformations happened in the last 150 years, many people lost their jobs, but also many new professions appeared. In the current situation, one presumably new development is the destabilization of the position of 'knowledge workers' and creative workers, i.e., subjects whose tasks have up till now not been automatable to a satisfying degree. If this shift will evoke a fundamental change in the dynamics of the division of labor remains to be a point of investigation. However, drawing on

a wider perspective it can be said that certain rhetoric and discursive figures always return with new technologies (see Kümmel/Scholz/Schumacher 2004). New technologies are very often accompanied by utopian and dystopian ideas regarding their possible effects. To name only one example related to AI, that has also been the case with the internet (Schröter 2004). In the end, neither the worst fears came true nor the utopian paradise started. No (technologically driven) revolution happened, but given social structures were extended, accelerated and thereby transformed (but not in an abrupt "revolutionary" way). The internet is a very good example for that: Instead of leading immediately (dystopian) to a totalitarian hive-mind or(utopian) to a "frictionless capitalism" (a term coined by Bill Gates, see Schröter 2012) or even to a post-capitalist society, it became integrated into real existing capitalism (quite full of frictions), extending (to "friendship", into every moment and place of life etc.) and acceleratingit – step by step.Thereis no reason to expect that this will be fundamentally different with AI.Neither the dystopian (AI will take over the world and kill everyone, capitalism collapses and this leads to total social disaster, etc.), nor the utopian (AI will solve all problems, a wonderful post-capitalist society will be born, etc.) visions will come true – but as always some of the good and some of the bad prospects will be realized and a lot more things will happen which were not expected or predicted at all. But that they were not expected doesn't amount to a "revolution" – that's just what history is.

If we now turn to our project called "How is Artificial Intelligence Changing Science?" – can we say that there is a kind of "scientific revolution" caused by AI (that means today mainly machine learning)? Given the state of the research we have done,it is too early to give a clear-cut answer – but our preliminary research shows that it might make sense to be cautious here too. On the basis of our research on how machine learning (and computer simulation) is used in high-energy physics (Schröter 2021; Radovic et al. 2018), we were able to test a claim regarding an alleged "scientific revolution" in science – the case is formulated in Anderson's (2008) much discussed paper on the "end of theory". His argument does not address AI directly – but the role of detecting patterns in large amounts of data which is exactly the task of many AI-systems today. He argues that the classical procedure of the natural sciences is now obsolete: While it was, prior to "big data", necessary to formulate a theory which then has to be tested in experiments, now it is enough to observe patterns and correlations in data. Theory is not needed anymore – a "scientific revolution" indeed. But at least for the case studied, this argument turned out to be wrong. Theory, very complicated theory, is still needed in particle physics. It predicts

#### 38 Beyond Quantity

effects. Based on the theory, simulation models are generated that show how the patterns of the predicted effects would "look" like in the particle accelerator that is used to conduct experiments.The machine learning systems are trained with these simulated patterns – and then they filter out possible fitting patterns from the gigantic data stream produced by the accelerator. In this way, the predicted Higgs boson was found in 2012. Although the basic epistemology seems unchanged, in detail there are differences:

The traditional way to analyze, or generate simulated, data is to first develop algorithms based on domain knowledge, then implement them in software, and use the resulting programs to analyze or generate data. This process is labor intensive, and analyzing complex datasets with many input variables becomes increasingly difficult and sometimes intractable. Artificial intelligence (AI) and the subfield of machine learning (ML) attack these problems in a different way: instead of humans developing highly specialized algorithms, computers learn from data how to analyze complex data and produce the desired results. There is no need to explicitly program the computers.  These models can then be applied to predict the behavior of new, previously unseen data, to detect anomalies or to generate simulated data. (Bourilkov 2019: 1f.)

That means: The application of AI systems leads to continuities and discontinuities at the same time.Our thesis is also: "Revolution"is a too narrow concept to describe the coexistence of continuities and discontinuities in the process of the diffusion of AI. One needs more differentiated concepts from media historiography (Schröter/Schwering 2014) to describe the effects of AI, even when only focused on the use of AI in different fields of science.

It is, of course, not necessary to conclude that the application of machine learningin other scientific fields follows the same trajectory.Whilein physicsit seems to change nothing on a fundamental epistemic level (except for making use of far larger datasets as before), this might turn out to be different in other disciplines. This is essentially what our project tries to find out.

#### **Thesis II: AI embedded**

[I]n order to grasp […] unwillingness to comply with mechanical innovation, we need to widen our perspective beyond machine technology. (von Oertzen 2017: 131)

For research on the implementation or the non-implementation of new technologies in various (scientific) fields, close attention should be paid to the infrastructures in which these technologies like machine learning are embedded. This allows for an analysis which refrains from conceptualizing abstract "technological enhancements" as the sole driving force of history. In this regard, non-implementations of AI technologies in scientific fields should not a priori be regarded as motivated by irrational conservatism or technophobia but must be researched within their specific political economy.Three examples from different scientific fields will serve to underpin this argument:

In the article quoted above, it is illustrated how the 19th-century Prussian census system by relying on "manual concepts, technologies, and practices of data power" (von Oertzen 2017: 129) managed to reach a similar level of effectiveness compared to other states which had implemented the Hollerith machine. Even after the eventual switch to machine-readable punch cards in the late 19th century which "enabled statisticians to accomplish tasks that were impossible to perform manually […] they rested firmly on the concepts and paper tools developed for manual use." (ibid.: 132) This episode can serve as an incentive to investigate thoroughly the conditions of the possibility of implementing new technologies instead of focusing on tech companies' accelerating announcements of revolutions and breakthroughs in their technological products.

Another important aspect of institutions' rigidity or refusal to implement new technologies has to do with what STS scholars Sheila Jasanoff, Ulrike Felt and others have coined as "sociotechnical imaginaries", i.e., "collectively held, institutionally stabilized, and publicly performed visions of desirable futures, animated by shared understandings of forms of social life and social order attainable through, and supportive of, advances in science and technology." (Jasanoff 2015: 4). An episode from Western German computer history can serve as an example thereof: When Remington Rand delivered Europe's first large-scale computer system UNIVAC I to Frankfurt am Main in 1956, it caused considerable sensation about this "electronic brain". However,

the plan to rent the machine to local companies for computational work ultimately failed and the UNIVAC I was shut down. As computer historian Corinna Schlombs argues, this failure was mainly due to Remington Rand's ignorance of "local customs and traditions" (Schlombs 2010: 98). This concerned the different organizations and sizes of German companies (139), European labor law (97), different infrastructural conditions, like electrical plugs (140) as well as the – proclaimed by a company report – "German users tend[ing] to be somewhat skeptical of the large scale systems" (ibid.: 139). Only after adapting to European conditions were Remington Rand's products able to gain a foothold in the European market. The story of the UNIVAC I points to the important question of why a technology does not fit into an environment, its types of organization, its infrastructures, and its sociotechnical imaginaries and which adaptive measures are taken to enable an implementation.

For the case of AI technology, such a focus on the infrastructural conditions of possibility (or impossibility) can further be helpful to grasp the different transformative speeds of human (scientific) practice. Instead of standing in awe of the daily releases and presentations and new models, close attention should be paid to a possible gap between modeling and implementation as to refrain from writing a mere history of ideas. As Urvi Sonawane and Matthieu Komorowski show in their contribution to this volume for the field of medical intensive care, "there is an increasing number of AI prototypes and early models being developed and trialled"(Sonawane/Komorowski 2023: 161). At the same time, "there seems to be a disproportionate disparity when it comes to translating these AI models from production to clinical evaluation." (ibid.: 161). Although the number of AI models released for the use in intensive care has risen significantly, implementation remains scarce. According to the authors, this is because, "the successful algorithms are less suited to be rolled out on a large-scale healthcare service or even across a country"(ibid.: 164) as well as the fact that "AI systems are notoriously difficult to integrate within and between systems" (ibid.: 164). Again, here it is the "problem" of different organizational systems and infrastructures being grown over a considerable amount of time, which yet complicates the AI models' widespread use in intensive care. This is of course not to foreclose the possibility that these systems can be fundamentally altered by the advent of new technologies.However, close attention should be paid to these different speeds of practical transformation by (AI) technology which can be addressed by an approach as outlined above.

#### **Thesis III: Epistemological potentials**

Statistics is the study of uncertainty. (Lindley 2000: 301)

Machine learning is essentially a form of statistics, and AI applications clearly display a statistical anatomy (Alpaydin 2016: 27). Hence one might think that the main task of machine learning approaches of AI would be the study of uncertainty. Indeed, specifically, artificial neural networks (ANNs) allow a new technical level of dealing with problems of uncertainty, for example dealing with incomplete information or predicting future events. Nevertheless, it would be insufficient to simply describe the general capabilities of ANNs in the processing of uncertainty. As a predictive technology, ANNs are, of course, in some way always related to problems of uncertainty, yet this does not accurately describe their enormous epistemic-technical capability to deal with different forms of vagueness or fuzziness related to visual or acoustic challenges of pattern recognition.

Over the course of the 19th century, statistical methods and probabilistic approaches took a successful hold in sciences as diverse as psychology and paleontology, sociology and astronomy, evolutionary biology and economic reasoning about risk and crime, in insurance and gambling.This unanimous shift towards quantitative methods came at a price. At its core lay the acceptance of less precision – or the new form of evidence which was later deemed as probabilistic revolution (Krüger/Daston/Heidelberger 1987). Back then, questions of fuzziness and uncertainty were intensively discussed by scientists such as Gustav T. Fechner and Pierre-Simon Laplace, as a shift away from the ideal of determinism that still prevailed at the beginning of that century. Currently, the boom in statistical AI in the form of ANNs, among others, makes the discussion of questions of uncertainty and fuzziness seem particularly urgent.

Already Claude Shannon's mathematical theory of communication formulated as a theorem on what digital technology in the form of computers was soon to achieve, namely dealing with problems of uncertainty, be it in relation to communication and its encryption or decryption or in relation to the prediction of flying objects in the application of radar technology. What corresponding communications technology or, ultimately, a computer achieved in the one case as well as in the other, in information-theoretical terms, is to distinguish

between information and noise, and to make this possible as an exact calculation. And it was precisely for this purpose that the principle of binary circuitry proved to be particularly effective.

However, as became apparent in the course of the second half of the 20th century, certain more sophisticated problems of fuzziness were very difficult to solve for decades, e.g., enabling a computer to visually perceive its environment and objects in it. No matter which AI methods were used to approach such tasks, whether with so-called symbolic, rule-based AI or with subsymbolic AI in the form of ANNs or with approaches of so-called fuzzy sets, in the end, all these methods, despite selective progress, remained quite far away from what current AI implementations are capable of, until the 2000s.

It was only about 15 years ago that the situation changed significantly, as the important fields of AI work, computer vision or natural language processing, exemplify. Only then were computers able to cope with technically more demanding problems of fuzziness of various types much better.

We would like to briefly highlight this epistemic potential once again: Machine learning methods in the form of ANNs are in any case not only able to recognize patterns in complex data that are difficult for us humans to recognize due to their size and complexity and therefore present themselves to us as fuzzy, but which are themselves fuzzy and/or incomplete as statistical patterns. ANNs can produce usable output despite incomplete data or on the basis of fuzzy patterns, and they can do this by calculating not exactly, but approximately, i.e., quasi-fuzzy themselves. Thus, already the iterative, optimisationoriented training process of machine learning methods can ideally be understood as a process of successive reduction of the prediction error, thereby approximating the real data distribution.Moreover,in the context of ANNs, there is now a broad portfolio of methods for dealing with uncertainty problems including ensemblemethods, data augmentationmethods, dropout and transfer learning.

#### **Thesis IV: Big tech and academia**

One important feature of AI's modern R&D trajectory is that private companies native to the digital economy such as Google and Facebook are playing an increasingly important role in basic research activities that used to be the domain of academia. (Jurowetzki et al. 2021: 3)

As in many other fields, the conditions of possibility for applying AI processes in the sciences are shaped more than ever by the big tech industry.

First, there is an industry-wide tendency to provide universal, i.e., nondomain-specific, infrastructural support and tools to users. This includes systems such as ChatGPT.

Second, the big tech industry as well as leading AI companies such as Open AI or Antrophic are selectively engaged in solving fundamental problems of science in different domains for which a) technical-epistemic approaches of AI are particularly suitable and which b) should also redeem the claim to be socially responsible AI. In this respect, it cannot be surprising that some of the most important AI developments in this regard have taken place in the field of medicine.

Third, and this seems to us to be a particularly revealing area, the big tech industry is also the addressee for requests for support or funding of scientific projects that are developed by universities or private research institutions.

Fourth, it can be assumed that the big tech industry will drive the development of domain-specific tools and infrastructure offerings even more strongly in the future.Meta's scientific language model Galactica (Taylor et al. 2022) and Google's language model for medicine dubbed Med-PaLM (Singhal et al. 2023) are indicative of this trend.

The points listed here are certainly not specific to the big tech industry. On the contrary, it can be assumed that corresponding activities are generally driven by the tech industry, including start-ups. However, the more successful the respective activities and developments are, the greater the likelihood that either corresponding start-ups will be acquired or the big tech industry will develop similar tools or offerings, even if this potentially leads to legal conflicts.

From our perspective, at any rate, there is a serious transnational dependence of the sciences on industry, the precise conditions of which, in turn, urgently need to be researched on an interdisciplinary basis.

There are already signs that universities in Europe want to strengthen their independence from the big tech industry with regard to their research activities, but also in terms of teaching, while at the same time and to a certain extent paradoxically there is a university policy interest in promoting alliances between science and industry in the development of AI projects, whereby local and regional funding aspects may be of importance here.

One of the problems concerning the relationship between industry and science also includes the fact that, on the one hand, the industry generally has an interest in ensuring that universities are able to train sufficiently qualified scientists, while at the same time, it also has a considerable share in the fact that particularly qualified scientists leave universities for industry and, in part, industry is increasingly moving to promote the internal training of IT specialists.

The aspects mentioned above thus concern fundamental questions about the conditions of digital science in the present. It seems important to us, however, that all the industrial and infrastructural problems and challenges mentioned above are causally linked to the specific potentials of subsymbolic AI.

The dependencies indicated here must be critically questioned, not only as a questionable contrast between a big tech industry worthy of criticism and university research supposedly independent from the outset, but in general with a view to the possibility of sustainably protecting technology from misuse, whether in the context of the private sector or with a view to public/state structures. Democratic states may lose their democratic or progressive status, much as the policies of large corporations may change drastically, and with it the question of what purposes AI is used for in the first place. Based on our observations so far, having to seriously consider the long-term consequences of AI is a relatively new phenomenon. Until recently, it seemed important to free AI from speculative discourse, and rather address problems of AI's present, but in light of recent developments, it does indeed seem necessary to extrapolate current developments and their speed to what problems will arise not just now, but in 5 or 10 years.

#### **Thesis V: Expert crisis**

AI experts are in short supply. That's making the skills crisis worse. (Headline of an article on ZDnet, Hughes 2022)

One consequence of the fact that approaches of ANNs and other forms of machine learning could not really be used comprehensively or for advanced tasks, e.g., in many fields of science, is that a corresponding tradition of expertise was missing at the beginning of the current AI boom. Only a relatively small number of researchers in Europe and the US focused on or worked with such approaches in typical fields of AI research before 2016. Moreover, those who worked with ANNs in computer science in the 1990s and early 2000s, for example, had significant problems themselves at the time in being able to finance and publish their research at all. The establishment of the term 'deep learning' for ANNs from around 2006 onwards had a lot to do with the reputation of ANNs as ultimately being more or less a dead end in AI research, or as ultimately not being a target-oriented approach for many advanced problems in computer science. Accordingly, for a long time, it was an internationally very manageable community that continued unperturbed with ANN-based AI, in German-speaking countries, for example, research groups around Helge Ritter, Jürgen Schmidhuber and Alexander Waibel.

Against this background, the contrast to today's situation could hardly be more extreme. Especially from 2016 on, i.e., since the success of AlphaGo, a gold-rush mood has developed rapidly, which in turn not only affected the scientific field alone but more or less the society as a whole.1

From a disciplinary point of view, it is obvious that computer science in particular has benefited from the corresponding AI boom. In fact, it has not hurt the discipline in principle to have underestimated the epistemic potential of ANNs at the time.

Computer science is the big winner of the current AI boom in two respects: on the one hand, because of its historical core competence with respect to both the development and critical reflection of AI, and on the other hand, because of its now once again strengthened role as a collaborative or auxiliary discipline of other subjects. It may be that some disciplines, such as mathematics or physics, are not dependent on the external competencies of computer science to develop AI models for their purposes, but the humanities, cultural studies and social sciences are (even though in these areas knowledge on computer science has increased significantly in the recent past).

At the same time, the cultural sciences, social sciences, and humanities also benefit prima facie considerably from the sustained boom in AI. This applies, among other things, to philosophy, whose expertise has been called upon for some years now, especially for ethical issues in AI.

Finally, this concerns the interdisciplinary and transdisciplinary research field of digital humanities. Even if the corresponding orders of magnitude are difficult to estimate, one can certainly argue that AI, and especially forms of generative AI, have an important catalytic function in significantly expanding

1 A German platform currently lists 152 institutes and other institutions at German universities that conduct research with/on AI (Lernende Systeme 2022). Also see Huber/ Huth/Alsabah (2020), a Bitkom survey according to which there are about 220 AI professorships in Germany at the time of the survey. Finally, one could point to the 100 AI Professorships Initiative, initiated in 2018 (BMBF 2022).

the possible uses of computers and Big Data, in the humanities and cultural studies, whether in research, or also in other areas such as teaching.

However, on the basis of our studies so far, we can state that certain subjects or research fields, such as climatology, are only gradually incorporating AI approaches to their research questions, and to a rather limited extent, and in some cases, there is also a rather great skepticism, perhaps even a certain conservatism, about using corresponding technologies.

#### **Thesis VI: Sociological split seconds**

Computational social science is an interdisciplinary field that advances theories of human behavior by applying computational techniques to large datasets from social media sites, the Internet, or other digitized archives such as administrative records. Our definition forefronts sociological theory because we believe the future of the field within sociology depends not only on novel data sources and methods, but also on its capacity to produce new theories of human behavior or elaborate on existing explanations of the social world. (Edelmann et al. 2020)

A general phenomenon in the research landscape is the bifurcation of disciplines in a general and a computational branch. For instance, these twin disciplines have become a reality in many fields of the social sciences and are even traceable by citation analysis. Is computational sociology, which emerged towards the end of the 20th century, out of tune with the offline society or sociological theory? Will such disciplines eventually divide for good despite the integrative gestures the computational disciplines may provide? It is entirely conceivable that digital historians will seize to travel to the communities' main conferences (like the "Historikertag"). It is possible that computational Earth scientists find it easier to talk method with colleagues from the digital humanities than with colleagues returning from the field with samples and earth on their boots.

Technological advances provide new tools. Arguably, these provide new gravitational forces towards specific scientific methods and topics. The integration of new kinds of data practices across disciplines is all but new. Throughout the last centuries, the sciences, social sciences and humanities have benefited significantly from the availability of data. The empirical sciences of the 17th century, the social sciences of the 19th century and the digital humanities of the 21st century all profited from the influx of serialized and quantified types of information into their research methods. While it is probably the humanities which will be affected most by AI's conquest of the qualitative dimension, the social sciences present a puzzle.

For instance, the emergence of a separate field of computational sociology is a most interesting case, given the high affinity of sociology to data and empirical methods. This discipline can be said to have co-emerged with census taking and the intensified collection of data about the social from Adolphe Quetelet onwards. His "social physics" and his training with leading astronomers in Paris did not only lead him to stipulate neutral laws of the social, but let him develop moral statistics, a brand of criminology if not surveillance.

Given this high involvement of the discipline with social data, the division into separated disciplines of sociology may come as a surprise. This is not to say that sociologists do not embrace new data technologies. Attempts to map all articles in the Web of Science according to their level of AI-related methods show the social sciences almost as open to AI applications as the physical sciences or the life sciences (Gargiulo et al. 2022, fig. 1). Yet, this adaptability does not seem to appeal to all sociologists and at the moment several fields of knowledge split into computational versions of themselves. While this may prove to be a passing occurrence, it can also be an indication that AI-based methodologies are not perceived as empirical in the traditional sense.

#### **Thesis VII: Data colonialism**

[...] I wonder whether data colonialism goes far enough to prompt a decolonial shift in thinking, assuming again that we are in the realm of Quijano and the modernity/coloniality school. Because the concept is more concerned with datafication as resource extraction, and seems less concerned with the key decolonial insight that Europe convinced itself and others that it has a privileged objective position from which it may make universal assertions and claims. (Mumford 2022: 1512)

In their widely discussed studies on data colonialism Nick Couldry and Ulisses Mejias (2019a; 2019b) claim that a new regime of data extraction has emerged. They see a logic of colonial dispossession at work because most data are collected for free. After all, this annexation is happening on a global scale, and results in huge profits for a very small group of people. Most AI applications still hinge on the availability of vast datasets and are part and parcel of the said colonialism.The scope of this new raw material is lamented and the problematic uses of a global surveillance capitalism are evoked. Some emphasize the correlation of systems of data extraction with systems of value (Thatcher/ O'Sullivan/Mahmoudi 2016; Gray 2023). According to Denusa Mumford (2022), it seems questionable if the term colonization is already put to its best use in this discussion. The critiques mainly address capitalist strategies, especially the dynamics of primitive accumulation. While this dynamic is involved with the Global North as colonizer, the term points towards the fate of the Global South, but fails to show any specific engagement. Recent calls for epistemic decolonization remain unheard. A decolonial approach would entail efforts to decentralize one's position, to seek out "other worldings", to include specifics of the discussions from these regions, and to acknowledge the fundamental diversity of approaches.Thus, the diagnosis of data colonialism might not go far enough yet and would benefit from a deeper involvement with divergent perspectives from the outside, from the effects of data collection at the margins.

The history of data tries to make inroads and add to a fuller picture of the specific performative effects of data collection at various points of a data journey (Aronova/von Oertzen/Sepkoski 2017; Leonelli/Tempini 2020). The global data infrastructure is clearly built on an overexposure of marginalized and colonized bodies to various kinds of metrics (Hacking 1986; Lemov 2015; Radin 2017; TallBear 2013). Even these early colonial statistics and data collections already had radical effects. Indigenous communities could suddenly be shown to "go extinct" or dwindle under the curse of hunting parties of colonialists, hard physical labor and new contagious diseases (Rowse 2017; Malègue 2018; Renard 2021).The most widely discussed case of colonial statistics thoughis the performative effect and deepinfluences of the process of countingitself(Zimmerman 1999; Schlicht/Ledebur/Echterhölter 2021). The famous case in point is probably the Indian census, where the British used the categories of "caste" for the enumeration of all Hindus. Although castes existed in pre-colonial times, their statistical versions made them more rigid, scientifically defined, prominent, and publicly contested or lobbied for (Cohn 1987; Appadurai 1994; Dirks 2001). This shows that the stakes are high for any category, classificatory scheme, or label used on social data. The history of data classification is but one aspect of the decolonization of our rapidly growing data architectures.

During German colonialism, data collections and land surveys consistently relied on European legal notions and hence implemented foreign protocols. To reverse this and similar processes, several initiatives are trying to arrive at more fitting frameworks for data collection in non-industrialized societies today (Abdilla 2021). To involve communities and to encourage participation is but one strategy to improve a technology that is perceived as "White" (Cave/ Dihal 2020). Against this background, current initiatives for "indigenous data sovereignty" gain importance (Santos 2018; Kukutai/Taylor 2019; Lewis 2020), and fluid identities are being discussed as a blueprint to build better modeled and networked data infrastructures (Chun et al. 2019). Do indigenous perceptions of the non-human help to "breed" better algorithms? Who decides upon the categories used in clustering or classification in which region of the world? What would it mean, with Denusa Mumford, to arrive at decolonial data architectures?

#### **Thesis VIII: The labor landscape shift**

ChatGPT and the like do improvise, promising to destabilize a lot of whitecollar work, regardless of whether they eliminate jobs or not. (Lowrey 2023)

In stark contrast to our long-standing expectations and to great surprise, AI is not automating routine tasks or physical work as its first official act. Instead, generative AI impacts highly-skilled creative and knowledge workers by producing creative and knowledgeable output. In this respect, AI sets itself apart from previous technological developments.

This new realization is fueled by several contemporary developments in AI research. First and foremost, there has been remarkable progress in the area of language models leading to singular models capable of fulfilling diverse tasks such as creative content generation, summarization, translation, and code generation – to name a few of them.  Software engineers are fast to adopt new technical tools, such thatitis no surprise that GitHub Copilot, a coding assistant, already amassed over a million users and is "behind an average of 46% of a developers' code" (Zhao 2023).

There are similar applications for other areas such as SciSpace Copilot, aiming to help with scientific literature research, or Casetext, which assists with legal research. For now, these systems are meant to be assistant to workers rather than to act in full autonomy. However, AI research is in rapid development2 . The GitHub CEO, Thomas Dohmke, for example, claims that GitHub CoPilot will "sooner than later" (Scheffler 2023) write "80% of the code" (ibid.). It is an open question if this massive productivity gain will result in job losses but at the very least it reshapes the nature of some professions, adding oversight of and delegation to LLMs as a major bullet point to many job descriptions. Notably, Meta itself, as one of the leaders in AI research, announced to reduce hiring meaningfully going forward and focus instead on developer productivity, with AI toolings like code assistants and chatbots being a major part of that equation.

A relevant study in this context was conducted by Eloundou et al. (2023) in which the authors try to identify jobs that are exposed to LLM technology. While they neither provide a timeline nor make predictions on the impact on the labor market, it is found that higher-income occupations are more affected. Mathematicians, writers and authors, tax preparers, legal secretaries, or proofreaders are among a set of professions that are fully or close to fully exposed to LLMs; that means that LLMs take up to 100% of their occupational activities.3

At the same time, there is another area of AI research swiftly progressing and bringing completely different qualities to the debate. With the rise of recent diffusion models such as Adobe Firefly, Unity Muse, Midjourney, and Stable Diffusion, AI image synthesis is widely popularized, from the generation of digital art to photorealistic art. Point-E or Builder Bot are first approaches to 3D content generation while Imagen Video and Make-A-Video conceptualize video synthesis. Remarkably, the public outcry about this line of research has been much louder and more popular, leading to several copyright lawsuits and massive fears about potential job replacement. Presumably, that is, because visual output is more tangible and inaccuracies are not as crucial or obvious. Exemplary, Hollywood is one of several epicenters of this debate. While Disney just released the TV show *Secret Invasion* with an AI-generated opening, the actors guild SAG-AFTRA went on strike with one major contentious point being

2 Also see the following thesis, "AI's Self-Evolution".

3 The positive news for us in the research community is that professions related to science and critical thinking were found to have a low exposure to LLMs.

the digital replication of actors in films and shows. Again, the impacts on the labor market are hard to predict, however, in a *New York Times* (Roose 2022) article artists anecdotally describe the transformation of their work with the arrival of AI tools, while a report by *Rest of World* (Zhou 2023) already claims an effect of AI on the job market of game illustrators.

A recent McKinsey study (Chui et al. 2023) predicts a massive transformation of our economy and the labor market as a result of generative AI, encompassing both large language models and diffusion models. The report claims "that half of today's work activities could be automated" (ibid.). That is, however, on a largely uncertain timeline "between 2030 and 2060" (ibid.). In accordance with the study by Eloundou et al. and our own assumptions, the report eventually sees high-income workers as the most impacted group of this transformation. And while no one dares to make a definitive prediction on the impact of the labor market, "it's important to be honest that it's increasingly going to make some jobs not very relevant" (Altman 2022) as Sam Altman himself puts it in an article on his website. At the very least, AI is predestined to alter the labor landscape profoundly.

#### **Thesis IX: AI's self-evolution**

[A] Large Language Model (LLM) is capable of improving its performance [...] by training on its own generated labels. (Huang et al. 2022)

The landscape of AI is undergoing a transformative shift as we witness the emergence of a cycle of self-evaluating and self-improving systems. Large language models are at the forefront of this development, demonstrating the ability to assess the quality of and improve upon their own generations. At the same time, generated output can be used to leverage and train the next generation of models or distill knowledge into more efficient ones allowing to deploy models on a large scale. Several recent developments are indicative of this development.

To begin with, current LLMs are cheaper and already produce comparable output compared to human labelers on crowdsourcing platforms such as Mechanical Turk in many tasks (Gilardi/Alizadeh/Kubli 2023). At the same time, studies like the one by Veselovsky, Horta and West (2023) found that crowd

workersincreasingly utilize LLMs themselves to complete their tasks. It is foreseeable that this will supercharge data collection in many cases, increasing the quantity of data while reducing the time needed.

Separately, there are a number of developments in AI research itself that suggest an increasingly fast cycle of self-evaluation and improvement. For example, the popular Alpaca model from Stanford (Taori et al. 2023) uses GPT-3 to produce training data to fine-tune and align Meta's LLaMA language model (Touvron et al. 2023). All this happened within two weeks of the release of LLaMA, further indicating the rapid speed at which improvements are achieved. Taori et al. use a recent trend in AI called "Self-Instruct" (Wang et al. 2023) to automatically generate training data where a number of seed tasks is defined and an LLM generates new instructions and corresponding instruction-answer pairs for them.

In addition, there are other ways to utilize current LLMs. Traditionally, LLMs but also other AI systems are evaluated by certain automated metrics. However, these metrics are generally not perfect and only offer correlation with human judgment to a certain degree.This prompted several researchers to develop performance metrics based on the judgment of LLMs, such as GPTScore (Fu et al.2023) or GEMBA (Kocmi et al.2023). Itis also becomingmore common to evaluate LLMs only relative to each other using rating mechanisms like ELO (see Chatbot Arena by Zheng et al. 2023). One pressing question that may arise now, is whether it is possible to use these capabilities in the training process of an LLM. Presently, reinforcement learning from human feedback (RLHF; Christiano et al. 2017) is used by state-of-the-art models like GPT-3. As part of this, a reward model is trained based on human feedback. This reward model can then be used as a proxy of human feedback while fine-tuning the LLM. It is conceivable though to use a fine-tuned LLM in place of humans to create synthetic ranking data for the training of reward models.This technique is further called reinforcement learning from AI feedback (RLAIF) and can potentially be applied in an iterative fashion (Bai et al. 2022).

It also becomes more prevalent to use these self-evaluation capabilities to improve the performance of current LLMs at inference time. Even without further training or fine-tuning, the model can reflect or critique itself by passing the output *again* into the model and refine its output (Gou et al. 2023; Shinn/ Labash/Gopinath 2023; Xiao et al. 2023).

Now, with these trends reshaping AI, one could be tempted to speculate about a vicious self-improvement cycle leading to the "singularity". However, it is important to temper these advances with the recognition that current AI systems including LLMs remain narrow in nature and are far from achieving artificial general intelligence (AGI), thereby dispelling the notion of an imminent transformative "singularity" or emergence of "superintelligence" driven by existing AI paradigms. For this to happen, it might need a fundamentally different approach to AI as suggested by Yann LeCun (2022) and others.

#### **List of references**

# **When Achilles met the tortoise**

Towards the problem of infinitesimals in machine learning1

#### *Clemens Apprich*

I would like to begin with a little story, a story that you probably already know. It's the story of Achilles and the tortoise.

One day, the hero of the Iliad met a tortoise whose mind was quicker than its legs. She challenged Achilles to a race, but asked him for a head start. Achilles willingly – and rather arrogantly – agreed to do so. The turtle crawled away. Achilles took his time, laced his sandals and finally started to run. In no time he covered the distance that had separated him from the turtle. In the meantime, however, the tortoise had also crawled on, and, while Achilles was catching up, she had again made a little progress. To cut the story short: no matter how fast Achilles ran, the tortoise always stayed a little way ahead – and so the famous hero could never catch up with the animal.

The story was told in this or a similar way – there is no exact record – by the Eleatic philosopher Zeno (around 490 to 430 BC) in order to present one of his paradoxes. The dichotomy paradox goes as follows: Because the world is one, movement is impossible. Every distance that a moving object has to cover can be broken down into an infinite number of partial distances (e.g. by continuous bisection: 1 2 , 1 4 , 1 8 , and so on), with one distance always remaining. As a consequence, no movement can ever be carried out completely, because there is always a distance remaining, no matter how small it may be.2

1 Parts of the argumentation in this article were developed in context of Simon Denny's collaborative exhibition *Proof of Stakes: Technological Claims* (Denny et al. 2022).

2 With this paradox, which exists in different variations (e.g., in the form of an infinite regression), Zeno wanted to (at least if you follow common introductions into philosophy) defend the teachings of his mentor Parmenides of Elea (born around 515 BC). According to Plato (1997), Parmenides was accompanied by Zeno, when he met Socrates around 445 BC in Athens and confronted him with the astonishing claim that beings (reality) are a holistic, unchangeable and unified entity (i.e. ontological/ontic monism).

Of course, such an idea completely contradicts our everyday experience, as it declares the immediate perception to be an illusion. Nevertheless – or precisely because of this – Zeno's paradox, passed down via Plato's dialogue "Parmenides" (1997), would not let go of Western philosophy for the next two and a half thousand years (from Archimedes to Giovanni Benedetti, to Isaac Newton, David Hume, Gottfried Wilhelm Leibniz, to Georg Cantor, Alfred North Whitehead and Gilles Deleuze – and most recently Gregory Chaitin with his algorithmic information theory).What it introduced and has since then haunted the history of science, in particular mathematics, is the problem of infinitesimals – with infinitesimals being distances in space or time that denote a smallest possible unit. It is assumed that an infinitesimal quantity is so close to zero that it has no numerical effect; it simply eludes any attempt to measure it, like sand trickling through your fingers.

Infinitesimals were crucial for the development of differential and integral equations – also known as calculus. As is well established, Gottfried Wilhelm Leibniz (1646 – 1716) and Isaac Newton (1642 – 1726) developed the mathematical branch of infinitesimal calculus independently of each other (or so the story goes) in the late 17th century.3 Defining a systematic method for the calculation of surfaces and motion, it soon became a 'killer application' in modern mathematics as it geared to solve practical problems (e.g. ballistic calculations, motion of planets, the design of bridges).  The development of calculus marked a new era in mathematics and its uses within the sciences have continued to the present day.

Not surprisingly, calculus is also at the heart of today's machine learning processes. Understood as optimization problems, machine learning-algorithms, in particular in the field of artificial neural networks, draw on calculus and, as a consequence, entail some of the paradoxes that come with it. Hence, by addressing the "quality issues" brought up in this volume, I want to argue that a machine learning-model, precisely because it is built on an exhaustive approximation as part of its optimization process, can never fully converge, and as a consequence does not yield any final result. This is of relevance because it shows that – contrary to widespread belief – machine learning is deeply entangled with mathematics and logics. What's more, such a paradoxical take on machine learning, which can also be seen as yet another iteration

3 In fact, the question of who invented calculus first became the subject of a huge controversy, now known as the calculus controversy (cf. Hall 1980).

of the "halting problem" (Turing 1936), resonates with recent debates around the incomputability of reality (Parisi 2013; Fazi 2018; Galloway 2021) as well as speculative attempts to overcome modern computation altogether (Amaro 2022). The goal of these interventions and by consequence the following article, is to highlight the necessity of moving beyond the limited imagination of (statistical) probability with regard to machine learning models in order to search for new "politics of possibility" (Amoore 2013).

#### **1. Forever converging**

In the beginning of Google's Machine Learning Crash Course,4 Peter Norvig, Head of Google Research, makes the remarkable statement that – with machine learning – we are now moving from mathematics to natural science, from logics to statistics, and from coding to *growing* models:

Machine learning changes the way you think about a problem. Software engineers are trained to think logically and mathematically […]. With machine learning, the focus shifts from a mathematical science to a natural science: we're making observations about an uncertain world, running experiments, and using statistics, not logic, to analyze the results of the experiment. The ability to think like a scientist will expand your horizons and open up new areas that you couldn't explore without it. (Norvig 2020)

It is worthwhile to consider some of the deeper implications of Norvig's statement: What does it mean to move from mathematics to natural science? And, in the process, do we really leave logic behind? What the statement implies, is the fact that with machine learning, and respectively neural networks as the most recentimplementation ofmachine learning systems,we aremoving from deductive to inductive methods of data processing: the model learns a correlation pattern between input and output data in order to make predictions on unseen data. To do so, a loss function is calculated for each instance, which shows "how bad the model's prediction was on a single example"(Google Developers 2020).5 Similar to the hot and cold play, the iterative strategy constitutes

4 Google's MLCC is one of the most popular machine learning (online) course with tens of thousands of users (Rosenberg 2018).

5 In the context of machine learning, unsupervised learning is often spoken of. However, when training a model (e.g. a recommendation system) most commonly a supervised

#### 64 Beyond Quantity

the essential thing of this learning approach, which, of course, corresponds to the aforementioned optimization process.6

*Figure 1: Machine Learning Crash Course (screenshot from Google Developers 2020).*

Suppose we had the time and the computing power to calculate the loss function for all possible learning parameters: the result would be a convex curve in which the rate of loss moves towards zero, that is the limit value to which the model converges. However, since the calculation of the loss function

procedure – or at least a mix of supervised and unsupervised learning – is used; this means that a data set is used containing both features (e.g. age, gender, search history of the user, temporal or geographical features in the data) and labels (what we want to predict).

6 The search for the optimal parameters constitutes machine learning. As Adrian Mackenzie writes in *Machine Learners* (2017): "[O]ptimization techniques are the operational underpinning of machine learning. Without their iterative process, there is no machine in machine learning" (95).

for every instance of a training set would take too long, a statistical method is used to solve the optimization problem: the gradient descent.7

*Figure 2: Machine Learning Crash Course (screenshot from Google Developers 2020).*

As is the case with mountaineering, the trick is to choose a descent direction and a step length in order to reach the valley of the curve as quickly as possible or, in other words, to reduce the loss as quickly as possible. The starting point is set arbitrarily, because it usually has no effect on the end result. To find the next point along the loss function curve, the learning algorithm then multiplies the gradient by a scalar quantity called the *learning rate* (sometimes also step length, although this can be misleading because the length of the step changes relative to the scalar). For example: if the amount of the (negative) gradient is 5 and the learning rate is 0.1, then the algorithm selects the next point

7 Gradient descent is one of many examples of optimization problems within machine learning systems. Others include coordinate descent, coordinate ascent or convex optimization.

0.5 units from the starting point and the next but one point at 0.05 units from this point.8

The correct setting of the learning rate makes up a good part of machine training.However,in practice,it is not necessary or possible to find the 'perfect' (or near-perfect) learning rate for successful training.The goal simply is to find a learning rate large enough for the model to converge in a timely manner, but not so large that it overshoots the target.

*Figure 3: Machine Learning Crash Course (screenshot from Google Developers 2020).*

Hence, the idea behind gradient descent is to tweak the parameters iteratively until the algorithm converges to a minimum, that is to repeat the process "until the difference between the old value and the new value is very small" (Kansal 2020). Now, as you can already guess, behind the seemingly innocuous notion *very small* lurks the two and a half thousand-year-old paradox of Zeno. Because the learning steps gradually get smaller as the parameters approach

8 Another inspiration for this approach might be "fitness landscapes" (Wright 1932), a concept developed as part of evolutionary biology in the 1930s. I thank Claus Pias for this reference.

the minimum, each step can be divided into an infinite number of sections with the result that the model, at least in theory, never fully converges.

Peter Norvig argues that machine learning is no longer a logical problem, but an experimental one.That might be true if we follow the premise that with machine learning we are moving from a mathematical (i.e. deductive) to a natural (i.e. inductive) science. However, given the central role of mathematics in the natural sciences when converting observations into measurements (not to mention the creation ofmeasurementitself), the statement seems to be at odds with its own premise. What it does though, is play right into the hands of similar attempts to *biologize* AI and machine learning (i.e. to *naturalize* and thus *normalize* the labor processes, the material infrastructures, but also the data politics behind it).The apologists of the new machine learning paradigm want to make us believe that the world of data is simply a natural phenomenon that does away with logical, that is theoretical, explanations (cf. Anderson 2008).

What is not mentioned in Norvig's statement, but is definitely an issue in computer or the data sciences, is the fact that a machine learning algorithm "must embody some knowledge or assumptions beyond the data it is given in order to generalize beyond it" (Domingos 2012: 81). A machine learning algorithm cannot see, hear, or perceive input examples (images, text, audio files, etc.) directly. Instead, a representation of the data has to be created in order to allow the model to *see* it. In other words, for the model to train, features have to be selected (often even created) which, in the eyes of the still very human trainer, best represent the data.9

Now this basic insight contradicts the common idea that we, and the models respectively, simply have to look at the data to get the desired outcome. What gets omitted, if not to say oppressed, in this rather naïve view, is the fact that the desired outcome (together with its logics) is always already inscribed in the process.With each iteration, the model gets more and more tweaked towards *good* property values (also called identity values) in order to filter out the *right* information from the data set.10

9 A common practice in machine learning is actually called 'feature engineering.'

10 This is in particular true for 'reinforcement learning from human feedback' (RLHF), a technique to train a reward model from human feedback that is central to current generative AI-systems such as GPT.

#### **2. Never being**

With the alleged shift from deductive to inductive reasoning in machine learning, a new kind of *identity politics* has entered the field. The problem is that the hidden assumptions about the data, which directly inform the machine learning models, correspond in so many ways to the rather old, historically grown social categories (e.g. race, class or gender). Reintroduced as *natural* representations, these categories bring about the much-discussed issues of data bias and algorithmic discrimination (cf. Apprich et al. 2019). In this process, normalizing standards such as Whiteness in algorithmic filtering and face recognition, become the default setting of machine learning models (cf. Katz 2020: 172f.).

Due to the fact that models learn from past data in order to be able to make predictions about the future, machine learning turns into a self-fulfilling prophecy. In her new book *Discriminating Data* (2021), Wendy Hui Kyong Chun makes that point clear when she explains how

predictive algorithms […] are verified as correct if they predict the past correctly, for they are usually cross-validated using past data that are hidden during the training period or out of sample data, similarly drawn from the past. (ibid.: 46)

By becoming the *ground truth* of (inductive) machine learning, limited and biased data from the past foreclose, rather than enhance, the future, with the effect that existing (racial, social and sexual) discrimination is perpetuated.

The usual answer to this problem is a call for better data or better models. However, as Ramon Amaro (2022) has shown, those well-intended attempts do not break away from the epistemic violence of current machine learning models. Instead, they merely *optimize* discriminatory practices. He writes, "What we experience today as algorithmic prejudice is the materialization of an overriding logic of correlation and hierarchy hidden under the illusion of objectivity" (ibid.: 61). Given the eugenic and biometric roots of correlation techniques, the past truly *overrides* our present and future by propagating a natural (i.e. eternal) truth through machine learning. Yet, to insist on the fact that machine learning models never fully converge,implies that they do not determine an ultimate truth or identity (cf. Cheney-Lippold 2011).

*Beyond Quantity* then also means that there always remains a surplus that cannot be calculated, because it does not fit into the (normalizing) norm of machine learning models. It means to – as Amaro and Khan (2020) propose – deploy a "calculus of variations", able to explore the liminal space between algorithmic calculations, the gaps and cracks that might open up to other, in particular non-white, versions of reality. Hence, exposing the internal limits of machine learning systems by confronting them with indeterminacy, incompatibility, as well as a "Black totality, always already in the process of transformation" (Amaro 2022: 62), might provide a way to work through those systems and put them to different ends.

The goal is to come up with machinic logics that break the shackles of merely inductive, but also deductive, reasoning. Rather than confirming what was already there, a generative (abductive) approach might allow for infinite possibilities. Machine learning, in this perspective, exposes the limits of computability in a productive way: To the same extent that learning algorithms are contingent on infinitesimals, the models themselves are not fixed by any preset identities or categories. On the contrary, the indeterminacy, in particular its inclusion in the calculation process, is what constitutes the ability to learn (cf. Parisi 2018). Hence, if the goal of machine learning is to *generalize* a model based on data, then generalization, when understood as an ongoing, open process, is at the core of machine learning; this concerns the central idea that concepts are not merely some abstract content that can be learned, but actually develop through learning as a discursive (i.e. social) practice.11

For machine learning to transform (and not merely repeat) the world, it is thus necessary

to move from seeing an inert model as the machine learner to seeing the human researcher or developer – *along with*, and not separate from, his or her model and surrounding relations – as the machine learner. (Reigeluth/ Castelle 2021: 104)

Because humans and machines are part of the same symbolic realm, they are, as learners, contingent on the same "*regular, discrete framework*" (Galloway 2021: 123). Acknowledging the social (not merely mechanical or cognitive) aspect of machine learning can help us better understand its ambiguity and contingency – moving back and forth between the formalization of real-word

11 The idea of 'concept-learning' as a social practice goes back to the Soviet psychologist Lev Vygotsky (1986).

problems and the actual implementation of such models to process those problems.

## **3. The incomputable**

According do HartmutWinkler, processing as the thirdmedia function of computing(besides storing and transmitting)implies to recognize the double character of regularity or repetition and innovation:

Das Prozessieren – als Eingreifende Veränderung – scheint von vornherein auf die Seite des Neuen zu fallen, insofern es eben Eingriff und Veränderung, und mit Blick auf die Wiederholung die Verschiebung, betont. (Winkler 2015: 107)

The idea of an "interfering transformation" is crucial for machine learning as well. Precisely because it is characterized by variability and indeterminacy, it relies on repetitive steps. In this sense, machine learning, which is defined by the processing of data, also necessitates a formalization by means of programming (Python) and mathematics (Calculus).

In contradiction to Peter Norvig's statement, machine learning is deeply logical and heavily relying on mathematical science. To claim otherwise would be to promote a version of machine learning that is fetishized as a natural thing and, therefore, hides its inner workings (i.e. the processing steps) from its users. Consequently, Google TensorFlow as well as all the other machine learning-infrastructures, such as Amazon Web Services or Azure Machine Learning, depict themselves as mere *services*. 12 Similar to the Internet's client/server architecture (cf. Krajewski 2018), these hidden infrastructures are essential to how machine learning is presented to us and how these representations influence our understanding of it (cf. Luchs/Apprich/Broersma 2023).

Contrary to the common belief that machine learning algorithms simply process data until a final result is found, the actual process is rather messy. In fact, contingency, indetermination and uncertainty are at the center of modern mathematics and, therefore, computing. Luciana Parisi, by invoking

12 This becomes apparent in Google's MLCC itself, when the (Python) code to run the models is literally hidden in foldout boxes.

Gregory Chaitin's algorithmic information theory (Chaitin 2004),13 explains that "[s]ince there are infinities that cannot be compressed into simpler postulates, theories, truths, it follows that there are realities that are logically irreducible" (Parisi 2021: 82). Accordingly, there are realities that cannot be computed, because they cannot be captured by today's algorithms. What we experience with machine learning is not simply a shift from deduction to induction, from mathematics and logics to natural sciences, but rather the introduction of the incomputable (i.e. negativity) at the heart of computation.

With Turing's "halting problem" (Turing 1936), which basically says that no algorithm (i.e. a finite step-by-step procedure) exists, which can determine in advance whether a machine will finish running a program, a fundamental shift within the logic of calculation has occurred.The inherent limit of the discretestate machine opens it to dynamic forms of computation. Once more Parisi (2015): "the calculation of randomness or infinites has now turned what was defined as incomputables into a new form of probabilities, which are at once discrete and infinite." "In other words," she continues,

whereas algorithmic automation has been understood as being fundamentally Turing's discrete universal machine, the increasing volume of incomputable data (or randomness) within online, distributive, and interactive computation is now revealing that infinite, patternless data are rather central to computational processing. (ibid.: 131)14

Applied to machine learning, this means that we are dealing with both, patternless data being processed *and* symbolic learning systems feeding on trialand-error. Instead of a mere step-by-step procedure, those systems are adaptive, precisely because they have to deal with the contingency of messy data. Hence, the discrete framework of computation gets tainted by real-world applications with its infinite variations. Randomness, in this perspective, is not outside of computation or machine learning, but the very core of them.

13 With his Algorithmic information theory Chaitin wants to prove that there is no such thing as absolute certainty in mathematics. There are truths that cannot be proven, problems that are impossible to solve.

14 In a similar way, M. Beatrice Fazi (2018) argues for the incompleteness and, therefore, contingency of computation. Both see Kurt Gödel as a progenitor of the incompleteness problem and its productive application in mathematics and, consequently, computational thinking.

Rather than following claims about the end of logics and theoretical explanation,machine learningincludes realities that cannot be proven, but are yet to be discovered. The immanent logic of those machines, therefore, offers a radical break with the inductive explanation of natural sciences, without falling back into a deductive predictability of classic form of computation. Allowing for "a *computationalthought* thatis contingent,and yet does not break away from structure" (Fazi 2018: 210) could yield a machinic logic that actually might take us by surprise. A new mode of thinking about the machinic based on its learning capacities and not as a one-sided solution for or against inductive or deductive reasoning.

This brings us back to the beginning of this article. What if Zeno did not simply use his paradoxes to confirm the ontological monism (i.e. the static identity of all things) taught by his teacher Parmenides,15 but rather sought to defend the idea of motion by putting it to a test? In other words, what if he did not try to prove the one, but to problematize the many? That would bring him very close to the here discussed problematization of incomputability, in the sense that reality is less a question of true or false, but rather an affirmation ofits(infinite) possibilities.A paradox,after all,always containsmore than one perspective.

#### **List of references**

Amaro, Ramon (2022): The Black Technical Object. On Machine Learning and the Aspiration of Black Being, London: Sternberg Press.

Amaro, Ramon/Khan, Murad (2020): "Towards Black Individuation and a Calculus of Variations." In: e-flux 109 (https://www.e-flux.com/journal/109/33 0246/towards-black-individuation-and-a-calculus-of-variations).

15 In fact, Plato (1997) writes about the encounter with Socrates that Zeno felt misunderstood by him – "you do not fully apprehend the true motive of the composition."

Vygotsky, Lev (1986): Thought and Language, Cambridge, MA: The MIT Press.

# **From algorithmic thinking to thinking machines** Four theses on the position of artificial intelligence in the history of technoscience

*Matteo Pasquinelli*

## **1. AI and the historical epistemology of science and technology**

When analysing the impact of AI on science it would additionally be important to clarify the position of AIin the history of science and technology.Rather than seeing it as a recent phenomenon, this paper aims in fact to contextualise AI as part of the large history of technoscience. It further intends to shed light on the relation of AI to the making of modern science and, in particular, to the paradigms of mechanical, statistical and algorithmic thinking. Right here, at the beginning, we should add an observation that is obvious to historians of science and philosophy, but not as widely supported by computer scientists, namely that the definition of intelligence is always historical: a universal definition of intelligence does not exist and this should be the perspective in which AI should be regarded. For this reason, theintention of writing the history of AI very quickly also turns into the project of a *historical epistemology of intelligence*, in which AI is not only a technical artifact, but also a project based on and affecting the definition and formalisation of human intelligence and knowledge.

In fact this paper would like to suggest to the field of AI studies, the incorporation of the method of *historical epistemology of science and technology*, which has been propagated, in different ways, by Boris Hessen, Henryk Grossmann, George Canguilhem and Gaston Bachelard and more recently by the work of the Max Planck Institute for the History of Science in Berlin and other institutions.1 What is the approach of the historical epistemology of science and tech-

1 About the historical epistemology of AI, see Pasquinelli 2023; for a critique of social constructivism in technology studies, see Winner 1993; for an overview of historical and