This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...) argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us. (shrink)

This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence (AI) technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture tackle the (...) possibility to ‘re-present’ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability (originally developed in the philosophy of science) to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic ‘thinking’ operations. (shrink)

This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays considers different (...) dimensions of algorithmic thought, engaging with a diverse set of epistemological questions and issues. (shrink)

The outputs of generative artificial intelligence (generative AI) are often called “synthetic” to imply that they are not natural but artificial. Against that use of the term, this article focuses on a different denotation of synthesis, stressing the unifying and compositional aspects of anything synthetic. The case of large language models (LLMs) is used as an example to address synthesis philosophically alongside notions of representation in contemporary computational systems. It is argued that synthesis in generative AI should be understood as (...) a search for unity that is fundamental to the making of a representational reality. This representational reality, internal to computation, is a stable (if imperfect) whole, a togetherness of distributed representations. The article thus demonstrates that developing the philosophical concept of synthesis to investigate today’s generative AI involves examining how structuring occurs in LLMs and studying the kinds of forms that structuring results in. (shrink)

In this article, M. Beatrice Fazi takes up Media Theory’s invitation to engage with Alan Díaz Alva’s analysis of her philosophical work on contingency in computation. The central argument of Fazi’s Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics is that computation can be productive of ontological novelty. This piece revisits that argument in the light of the technological developments that have occurred since 2018, when the book was published. Focusing on generative artificial intelligence (generative AI), the article considers (...) how the concepts of generativity, aesthetics and contingency are tackled in the book and in present-day debates about generative AI systems. (shrink)

In 1931, Kurt Gödel determined the incompleteness of formal axiomatic systems by demonstrating that there are propositions that cannot be proved or disproved within the system in question. In 1936, Alan Turing showed that some functions cannot be computed, and thereby described the limits of computing machines before any such machine was built. In this essay I will turn to these logical discoveries in order to argue that incompleteness and incomputability can be employed as conceptual tools to re-engage with the (...) axiomatic character of computation. This re-engagement with formal axiomatic structures, I will claim, has important consequences for the aesthetic investigation of computation. Computational aesthetics is understood here as an enquiry into the relation between abstraction and experience in computation. In this respect, the concepts of the incomplete and the incomputable will be mobilised philosophically, beyond the technical scope of Gödel’s and Turing's work, to argue for the autonomy and reality of computational abstraction. Gödel’s and Turing’s discoveries preclude the possibility that axiomatic formulation could be the method through which the metacomputation of the intelligible and the sensible is accomplished. These discoveries in fact prove that the computational axiomatic system is not transcendentally closed to contingency, as metacomputational approaches geared towards the formulisation of reality would have it, but is instead immanently open to its own eventuality. I will thus argue that there is a contingent ontology of computation that is to be found within computation’s formalisms. This contingent ontology is not predicated upon the empirical or the phenomenal, but is inherently formal and computational. For the philosophical investigation of the aesthetics of computation, such an ontology of the contingent computational structure opens up the possibility of thinking computational forms beyond the limits of formulae, together with that of engaging with formalism beyond the idealisation of beautiful and truthful determinisms. (shrink)

In Contingent Computation, M. Beatrice Fazi offers a new theoretical perspective through which we can engage philosophically with computing. The book proves that aesthetics is a viable mode of investigating contemporary computational systems. It does so by advancing an original conception of computational aesthetics that does not just concern art made by or with computers, but rather the modes of being and becoming of computational processes. Contingent Computation mobilises the philosophies of Gilles Deleuze and Alfred North Whitehead in order to (...) address aesthetics as an ontological study of the generative potential of reality. Through a novel philosophical reading of Gödel’s incompleteness theorems and of Turing’s notion of incomputability, Fazi finds this potential at the formal heart of computational systems, and argues that computation is a process of determining indeterminacy. This indeterminacy, which is central to computational systems, does not contradict their functionality. Instead, it drives their very operation, albeit in a manner that might not always fit with the instrumental, representational and cognitivist purposes that we have assigned to computing. (shrink)

It is often argued (and feared) that the human capacity to pay attention is being transformed by computational technologies. Are computing machines distraction machines? This article takes this question as its starting point in order to address concerns about attention deficits vis-à-vis questions and issues about the mechanisation of cognitive procedures. I will claim that, when approaching the attention ecology of the twenty-first century, it is necessary to differentiate between augmentation and automation. While augmentation implies the extension of predefined forms (...) or modes of behaviour, contemporary developments in computational automation ask us instead to consider the possibility of moving beyond phenomenological analogies. The article will thus discuss how transformations in the capacity to pay attention in a computational age need to be analysed in relation to the emergence of quasi-autonomous artificial cognitive agents driven by AI technologies, such as those known as machine learning. I will argue that these artificial cognitive agents can no longer be described in terms of technological add-ons to pre-existing human cognitive capacities. Today, we think alongside machines that are, is a sense, already thinking. Similarly, we pay attention alongside machines that are, in a sense, already paying attention. The challenge for philosophy and cultural theory is that of moving beyond ‘projectionist’ conceptions of such technological agency. This challenge, however, also involves overcoming the anthropomorphism that is implicit in expression such as ‘thinking machines’. In a century where robot-to-robot communications have outpaced and outnumbered human-machine interactions, these artificial cognitive agents are not just reframing the human capacity to pay attention: they are also re-structuring the conditions for such capacity. Addressing the conditions for attention beyond augmentation and vis-à-vis computational automation involves considering the role and scope of both human and algorithmic decision-making, and engaging with the ways in which the humanities can intervene upon contemporary complex cognitive scenarios. (shrink)

This interview with M. Beatrice Fazi explores in detail her work on computation. Focusing in particular upon her recent publications, it covers the themes of contingency and indeterminacy. The questions explore Fazi’s perspectives on computational aesthetics, abstraction and experience. Through an interrogation of the conceptual insights that Fazi’s recent work offers, the interview outlines an agenda for future work in the philosophy of computation and sets forward a series of conceptual policies for seeing the digital, software and data in a (...) fresh light. (shrink)

This paper contends that soft computing can help us investigate the aesthetics of digital computation. Employing broader conceptions of aesthetics and perception, and whilst drawing upon the ontology of Alfred N. Whitehead, it uses soft computing to address the 'prehensive' dimension of the quantitative procedures of computation, and explores the interrelationship between the factuality and formality of computational structures.