A/V Seminar by Isaac Won Kim
Transcription
The laboratory is the seat of myopia and the starting line of global misanthropy, a factory of meaning which we call upon in times of distress to remind us of our higher callings. In its best moments it contains all the beauty of ecstatic and life-affirming art, and in its lowest it dredges up nothing but the cruelest and basest impulses, cast in dismal artificial light and remorseless bureaucratic pessimism.
Academic biology–especially its largest sector, biomedical research–has a proclivity for hyper-specialization and fixations on the smallest possible aspects of a biological phenomena. There is a tendency towards thinking about problems in the smallest possible mechanical terms and ignoring high level considerations.
This atomization of the biological sciences is parallel to the profound and widespread atomization in almost every aspect of postmodern society.
While the superficial details of a biologist’s work may differ from their colleagues, the overarching social functions and underlying views on core conceptual frameworks have never been more universal.
Today’s society is no longer Foucault’s disciplinary world of hospitals, madhouses, prisons, barracks, and factories. It has long been replaced by another regime, namely a society of fitness studios, office towers, banks, airports, shopping malls, and genetic laboratories. Twenty-first-century society is no longer a disciplinary society, but rather an achievement society. Also, its inhabitants are no longer “obedience-subjects” but “achievement-subjects.” They are entrepreneurs of themselves.
If scientists believe themselves capable of foresight, and demand serious consideration for their ongoing contributions to society, it is not enough for them to simply continue scientific production.
Contemporary approaches to biology and the ontological systems therein operate with two central concepts: function and differentiation. If a scientist were to talk of a novel protein-to-protein interaction, they would most likely be speaking to its function, or its regulation of functions. In categorizing the microbiota of a soil sample using 16s ribosomal sequencing, a scientist is characterizing a differentiation, an evolutionary relationship between separate bacterial species.
From this newly defined system of difference, the concept of function arises as a way to describe the relations of these different organisms over time.
Within this new modern epoch, biological function and differentiation are empirical concepts as opposed to transcendental ones. When a biologist says something about the function of a bird’s beak, it is a neo-Darwinian statement of its relation to its environment, its food, and how beak shape allows for certain interactions with said food. Beyond that, a biologist may elaborate how a beak is encoded in the bird’s ‘genetic code’. This is essentially an attribution of said function to a discrete genetic location.
Wholly empirical methods cannot describe what life is as a system, only what it looks like at a particular time, in a particular place. It can describe the ways in which these processes are observed.
The principal organizational factor of biological research in the 21st century is biomedical and bioscience industries, both private and public. Public interest in biological science exists as a multi-dimensional collision of international competition, interests in medical advancement, and significant contributions to the economic stability of the host country. In the private domain capital holdings dominate the logic of healthcare, and accounts for its rapid expansion towards a significant portion of global spending, but considerations of the bioindustry’s capture of biological ontology must be considered at a more robust level than financial relations.
This is a biology focused on drugs, therapies, and the isolation of biomechanical points of failure. Each highly lucrative discovery or major medical achievement socially affirms itself. A granular breakthrough in disease treatment becomes a confirmation of the ontology’s success, driving further investment from government bodies, elevating the careers of bottom-up biologists, and solidifying the working bureaucracy of the biological sciences into a monolith.
Everywhere, the Critique of political economy and its correlate, the critique of alienated society, are used in one way or another as aids in programming the system.
The very function of political critique as an assertion of an alternative political subject loses all meaning through the integration of all political information into a hegemonic political object. When all human political capacities are subsumed into a cybernetic worldview, there is no way to engage in political critique from the point of view of a subject, as a political body which exists outside this worldview. It can only be engaged from the point of view of an object totally within a cybernetic society, as information reflexively feeds back into the process.
The systemic principles of the issue–private ownership, agrobusiness decline, colonial displacement, speculative development, and mass-media viewership–dissolve behind the mask of intricate information systems which prescribe not negation of the culprit systems, but tweaking and redirection of the processes.
Contemporary biological sciences orient their investigations around an ever-expanding roster of illnesses, diseases, complexities, concerns, and maladies, while its growing scale crushes investigators with over-specialized meaning. Mountains of data, connections, protein isoforms, phenotypic data, and expertise suffocate any capacity to create meaningful portraits of systems.
Since the 1990’s, the technological and analytical complexity of modern biological research has ballooned exponentially. The advent and adoption of next-generation sequencing technologies, mass spectrometry for organic macromolecules, and a rapidly expanding set of data procurement and prediction methods have promoted a veritable arms race in data scale within the field. Within this increasing scale of data complexity, the approaches to deriving meaning from that data remain disappointingly limited.
A cybernetic biology is one of highly-specified information and results, tabulations and compendiums. A systems biology is opaque and uncharacterized, it finds biologists investigating questions which are general and nonfunctional by their core conceits. Within such a space biologists and pseudo-biologists can generate predictions and integrate them to the needs and vectors of the potential that they operate in. This is a biomedical field where research directs itself towards concepts and can be pulled into local fields and local concerns instead of a global bureaucratic functional churn. It is a field where the novel and unpredictable demands of the living world can be met as they are presented, not through a long-delayed and exhaustive measure of reductive repetition.
A body begins when its specifics cannot be constrained.
Artificial Intelligence is likely to dramatically change the field in coming years–more so than it already has in the past decade–and we biologists are obligated to be biology futurists, capable of speculating and wrangling with the long term implications of current work. Even when such speculations fall outside the biological realm of expertise, their position as analytical tools must be conceptually grasped to begin making sense of their uses.
In the common portrayal of neural networks they are the ultimate submission to bottom-up ontologies, the abdication of responsibility for knowledge itself to black box modeling. In many real instances this is actually how neural networks are applied: they are summations of existing knowledge, fed into a system of hidden layers, the output of which is received by the user like magic.
Despite training LLMs in millions of parameters, and by virtue of their inherently obfuscating structure, it is hard for an observer to grasp exactly what is being learned. This is a major factor in the recurrent 'hallucination' problem in LLMs.
Applied neural networks can behave like any other pseudo-systematic combination of existing knowledge.
As with any other approach, the potential of neural networks is vast, but actual uses are limited by the constraints of socially-enforced ontology. Whether they will be emancipated from their brutish enforced stupidity remains to be seen, but there are historical precedents to support the idea that neural networks could develop away from bottom-up ontology. The speed of integration of these models into capital and the arrival of proprietary and private AI model architecture however, bodes poorly for those hopes.
It is often at the theoretical borders-the places which markets and capital have not yet accounted for–that alternative ontological projects sprout and metastasize. Marketization and capital capture eventually collapse that ontological complexity. New growth begins its process of ossification. Can lasting opacity be generated before the fall of an ontological wall?
©2026, Berlin/Nicosia
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