resources

Digital histories and futures of intuition

1. Intuition, speculation, and affect
2. Training, discovery, and agency
3. The rise of artificial intuition
4. Abductive reasoning and machine learning
5. More-than-human intuitive agencies
6. The computational politics of intuition
7. Prediction, chaos, and the counterfactual

Project resources

1. Archival research
2. Bibliography of scholarly sources

Intuition, speculation, and affect

Colloquially, we might associate intuition with direct sensing or fast-thinking that bypasses rational deliberation. We might also describe intuition as an affective premonition, an embodied hunch, or a gut feeling based on experience.

Yet intuition has a long intellectual history. Within Western philosophical traditions, it dates back at least as far as Plato, who understood intuition as intellectual perception, distinct from sensory perception, which corresponds to ‘the eternal’.¹

The publication of the French philosopher Henri Bergson’s An Introduction to Metaphysics in 1903, however, offered something quite different. For Bergson, intuition is a form of immersive engagement with the world premised on the experience of duration, process, and change.

Bergsonian intuition brings together ‘experience and experiment’ to produce speculative knowledge about new and specific problems as they unfold in time.²

Bergson’s interest in movement, temporality, and speculation resonates with contemporary affect theories. As the cultural studies scholar Gregory J. Seigworth has argued, the Welsh cultural theorist Raymond Williams’s influential account of ‘structures of feeling’³ aligns with Bergsonian intuition.

Bergson and Williams are each interested in how we encounter “pre-emergent” social and material forces – in how we become affectively attuned to that which hovers ‘at the very edge of semantic availability’.⁴

Both thinkers, then, explore how we might sense change as it is happening⁵ – an imperative brought to life by the more recent affect scholarship of Kathleen Stewart, Erin Manning, and Lauren Berlant, in their varying modes of intuitively inhabiting the unfolding sensations of everyday life.⁶

Training, discovery, and agency

In Cruel Optimism, the cultural theorist Lauren Berlant describes intuition as a ‘process of dynamic sensual data gathering’ though which ‘we make reliable sense of life’. From this perspective, intuition is shaped recursively through lived experience and ‘visceral response is a trained thing’.⁷

In this particular way, Berlant’s vision intersects with cognitive psychologies and philosophies which understand intuition as a trained mode of action-perception.⁸

Think, for instance, of how, as the psychologist David G. Meyers puts it, ‘thanks to a repository of experience, a tennis player automatically – and intelligently – knows just where to run to intercept the ball, with just the right racquet angle … a near-perfect intuitive physics’.⁹

Or consider how, as a classic 1973 study by the computer science pioneers Herbert Simon and William Chase demonstrated, expert chess players can reproduce the chess board layout after a mere five-second glance.¹⁰

Yet, if mainstream cognitive psychologists, philosophers, and behavioural economists assume a bounded individual and pay scant attention to the politics of intuition, Berlant is more interested in collective practices of anticipation in which ‘affect meets history, in all its chaos, normative ideology, and embodied practices of discipline and invention’.⁷

Across these theories and philosophies, intuition involves the ongoing interplay of conscious and non-conscious modes of thought. Throughout his writing, Henri Bergson suggests that it is, in part, the less-than-conscious aspects of behaviour that enable ingenuity, creativity, and discovery.¹¹ Bergson’s framework overlaps, in this respect, with mathematical intuition.

For the British mathematician Alan Turing, intuition is a mathematical faculty that ‘consists of making spontaneous judgements that are not the result of conscious trains of reasoning’.¹² Or, as the nineteenth-century French mathematician Henri Poincaré puts it: ‘It is by logic that we prove. It is by intuition that we discover’.¹³

One key implication of these accounts is an imperative to relinquish our persistent attachment to human-centric notions of will, agency, and intentionality – to move away more decisively, as the philosopher Erin Manning puts it, from ‘the notion that it is the human agent, the intentional, volitional subject who determines what comes to be’.¹⁴

The rise of artificial intuition

In theorising intuition beyond “the human”, these perspectives resonate with digital media scholarship which observes the range of computational processes and systems that now entangle human and non-human modes of sensing, perception, and cognition.

The computer scientist and literary scholar N. Katherine Hayles, for instance, conceptualises ‘a planetary cognitive ecology’ in which cognition is engaged in by ‘technical systems as well biological life-forms’ and agency is therefore more-than-human, distributed, and ‘punctuated’.¹⁵

Within computer science, research on ‘artificial intuition’ in decision-making enabled by deep learning defines it as an automatic process ‘which does not search for rational alternatives, jumping to useful responses in a short period of time’.¹⁶

Drawing on network representations of past knowledge and experience, artificial intuition ‘combine[s] logic and randomness’ to assess problem contexts characterised by ‘partial information’ and select effective courses of action.¹⁷

This work has been enabled by the consolidation of machine learning technologies over the past four decades. From the 1980s, high-level collaboration between mathematics, economics, and neuroscience led to the integration of probability and decision theory into AI—including the development of Bayesian networks.¹⁸

Developing insights from the eighteenth-century mathematician Thomas Bayes, who offered ‘a novel way to reason about the probability of events’, Bayesian networks proved a powerful tool in machine learning technologies; often combining with neural network algorithms to allow ‘AI to learn adequately despite imperfect data’.¹⁹

For the political geographer Louise Amoore, the re-making of eighteenth-century rules of chance via Bayesian inference models, alongside the development, from the 1990s, of advanced data mining techniques, signalled the infiltration of ‘the intuitive and the speculative within the calculation of probability’.²⁰

In conjunction with the design of advanced evolutionary algorithms, such developments have been crucial to the post-millennial rise of artificial intuition.

Abductive reasoning and machine learning

Across AI, computer science, and the technology press, artificial intuition is understood to be abductive rather than deductive. Unlike ‘deductive reasoning by hypothesis testing’, advanced machine learning systems ‘deploy abductive reasoning so that what one will ask of the data is a product of patterns and clusters derived from the data’.²¹

Artificial intuition may thus be less relatable metaphorically to Bergsonian intuition than it is to the abductive reasoning associated with the twentieth-century American mathematician and pragmatist philosopher Charles Sanders Peirce.

Variously linked throughout Peirce’s oeuvre to ‘hypothetical thinking, imagination, intuition and guessing’,²² abduction consolidates for him in the 1890s as a kind of inference involving the ‘generation and evaluation of explanatory hypotheses’.²³

In this context, artificial intuition is understood to be fundamentally experimental and generative; using advanced forms of pattern recognition it discovers previously unknown associations. Often working with raw and unlabelled data streams, deep neural networks map the structures and patterns of their input data and rapidly identify hidden correlations.²⁴

Hayles suggests, in this vein, that generative AI acquires ‘a kind of intuitive knowledge’ derived from ‘the intricate and extensive connections that it builds up from the references it makes from its training dataset’.²⁵ It produces a kind of ‘tacit knowledge’ developed from ‘countless indexical correlations, embodied in indirect and direct ways’.²⁶ 

More-than-human intuitive agencies

The digital computing pioneers Alan Newell and Herbert Simon announced in 1958 that ‘intuition, insight, and learning are no longer the exclusive possessions of human beings and any large high-speed computer can be programmed to exhibit them also.’²⁷

Yet various intuitive human gestures and capacities have proven stubbornly difficult to replicate with machine intelligence, given the contextual embodied awareness they seem to require.²⁸

Some media scholars suggest that current algorithmic modes of “thinking”, “sensing” and “speculating” offer less an augmentation of “the human” than they do radically different modes of operation which are not subject to comparison to, or comprehension by, anthropocentric processes and capacities.

As the digital media theorist Luciana Parisi puts it, ‘soft(ware) thought’ involves the automated prehension of infinite data that cannot be fully compressed, comprehended, or sensed by totalities such as “the mind”, “the machine” or “the body”.²⁹

If Alan Turing’s imitation game inaugurated a ‘simulative paradigm’³⁰ for AI in which biological and mechanistic processes of cognition came to be figured comparatively or analogically, and Norbert Weiner’s cybernetics proposed a capacity for recursive feedback as what links humans and machines with ‘sense organs’, a new techno-social paradigm is now consolidating.

In this emergent landscape what constitutes intuition or speculation in machine learning is not expressible in human terms, and algorithmic systems have become too immense, complex, and unwieldy to control via feedback in the way first order cybernetics imagined.

What is vital to post-cybernetic logic is, as the media theorist and sociologist Patricia Clough articulates, not ‘the reliable relationship between input and output’³¹ (2018: 104), but rather the speculative capacity to generate value through leveraging computational uncertainty itself.

The computational politics of intuition

Critical computational literatures examine the workings (and risks) of abductive reasoning within algorithmic governance, decision-making, and capitalisation – in which machine learning increasingly acts ‘to control the flow of actions and future events’,³² and human experience is claimed ‘as free raw material for hidden commercial practices of extraction, prediction, and sales’.³³

Significantly, these everyday computational systems work unevenly, (re)producing hierarchical modes of (non)humanity through their biopolitical and geopolitical logics. As such, they compel attention to the regulation, exclusion, and violence which algorithmically-mediated intuition may entail.³⁴

Data-driven “hunches” frequently reproduce a recursive loop of dominant cultural associations³⁵ or, as the digital media scholar Wendy Hui Kyong Chun has shown, make probabilistic speculations on the basis of iterative biases and prejudices projected into the future.³⁶

What is at stake in the consolidation of artificial intuition, then, is not only to the ability of corporations and governing bodies to nudge, shape, and control the future, but also to recursively constitute ‘the very conditions of the intelligible and the sensible’.³⁷

From this angle, we might argue that digitally mediated intuition today seeks to create all-encompassing computational ecologies which train more-than-human modes of thinking and feeling that serve dominant political, economic, and ideological interests.

And yet, an analysis of artificial intuition that engages contextual nuance and the messiness of lived experience must recognise, in the spirit of Berlant’s account, that visceral response is immanently trained in multiple ways with diverse, and often contradictory, affective, material, and socio-political effects.

A vital ongoing challenge is how to approach complex political and ethical quandaries wherein the primary unit of investigation is neither ‘ir(responsible) human’ nor ‘errant machine’ but instead emergent ‘human-algorithm composite’ – within affective atmospheres in which intuition ‘never meaningfully belonged to a unified “I” who thinks’.³⁸

Prediction, chaos, and the counterfactual

Salient questions emerge concerning what kind of uncertainty, complexity, or chaos artificial intuition is equipped to address – and what happens when unruly or unknowable dynamics are flattened by the statistical tools of machine learning.

Statistical climate models, for example, encounter difficulties in predicting ecological futures because they rely on past data and many atmospheric changes currently afoot are unprecedented and chaotic.

Although machine learning systems are said to specialise in abductive discovery, what is being claimed by their advocates is arguably a mode of abduction that is both highly self-referential and ex post facto.

As Louise Amoore suggests, deep learning operates via a retroactive logic ‘of beginning with an end target and abductively working back to adjust the parameters of the model in order to converge on the target’. The potential for different visions of the future to emerge from such models is therefore ‘radically foreclosed’.³⁹

Amid far-right disinformation and growing allegiances between political authoritarianism and Big Tech, an everyday reality in which truth ‘is by nature retroactive’⁴⁰ is amplified as conditional AI models come to inform a wider socio-political sensibility and environmental mode of operation.

To work effectively with ‘inconsistency, subjectivity, and generally noisy data’, counterfactual reasoning within machine learning systems may draw ‘rough conclusions’ based on analogies, similarities, and tendencies rather than on an ‘assumption of absolute truth’.⁴¹

What have been called AI “hallucinations”, in turn, refer to how the predictions of generative AI systems such as large language models produce outputs that are ‘factually wrong yet linguistically fluent and seemingly coherent’.⁴²

Further issues arise from the use of synthetic data, and the production of new outputs immanently fed back into generative AI systems, which can amplify bias, error, and misinformation at scale as such systems become increasingly self-referential.⁴²

A significant risk, from this perspective, is that emergent forms of artificial intuition mobilise noise, error, and the counterfactual towards immanent optimisation – but in ways that restrict the worldly problematisations and possibilities that can materialise.

In the balance at present, then, is how narrow, self-referential, retroactive, and conditional forms of prediction come to stand in for all prediction which, in turn, may colonise and corrupt what it can mean to intuit, anticipate, speculate, and navigate atmospheric and socio-political dynamics.

Archival research

In addition to library-based scholarly texts, this project draws on a range of archival resources. So far, the following collections have been consulted:

• The Alan Turing Papers, Archive Centre, King’s College, Cambridge University
• The Turing Digital Archive
• The Electronic Computer Project Papers, Shelby White and Leon Levy Archives Center, Institute for Advanced Study, Princeton
• The Julian Bigelow Papers, Shelby White and Leon Levy Archives Center, Institute for Advanced Study, Princeton
• The Norbert Wiener Papers, MIT Libraries Distinctive Collections
• The Wellcome Collection, London

Project bibliography

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Amoore, L. 2023. ‘Machine learning political orders,’ Review of International Studies, 49(1): 20–36.

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