PODCAST

What is philosophy? — Simon Critchley

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From what human need does philosophy emerge? And where can it lead us?

Simon Critchley is Hans Jonas professor of Philosophy at the New School in New York, and a scholar of Heidegger, Pessoa, Football (Liverpool FC), humour. He crosses over between analytic and continental traditions and freely draws on quotes from Hume and British pop bands.

Simon argues that philosophy begins in disappointment, not wonder. But it does not end there, its goals can be wisdom, knowledge, enlightenment, and freedom. Its concerns can be yet more varied: it can work as a tool for developing scientific theories, for exposing ideology, or for tracing the underpinnings of language and experience. Anywhere where other fields fear to tread, that’s where philosophers step in.

Since recording with Simon, one question has kept turning over in my head: how important is context to the understanding of philosophical ideas? An issue we discussed towards the end of the interview and did not quite have sufficient time to loop around as many times as I would have liked.

On the one hand, I believe in such things as facts and truth. I believe that science and some philosophical reasoning can deliver us these — although I recognize that belief cannot be justified beyond doubt. It is an act of faith. However, certain types of things, for example, facts concerning the laws of physics, I take to be impervious to our attitudes and human concerns. Why we believe them, and why we even look for them, are valid sociological and phenomenological questions. Yet the evidence for these facts can be produced usefully without any reference to historical context and the facts themselves do not depend on human factors.

In this way, to the question of whether the sun existed before humans — the subject of a heated discussion between Maurice Merleau-Ponty, Georges Bataille and A J Ayer in a Parisian bar — my answer is yes.

And yet.

There are questions that philosophy asks about issues that have no basis in fact. Impossible objects.

I recently asked ChatGPT to produce some predictions about the future for a project I am working on (What Year is Now? A Hallucinatory Horological Mapping). There are some obvious errors with the below — the tercentenary of the first interdimensional travel falling in 2307 would imply we first made that leap back in 2007. I suppose that’s possible if the dimension in question is time.

More interesting is what is predicted for 2306:

2306: Interdimensional exploration reveals the nature of existence itself

ChatGPT

I do not think that will happen. I don’t think it can. Determining the nature of existence is a question we cannot solve, even though there is value in turning it over, the value is not to get to an answer. It’s a means justify the ends (and btw there are no ends) sort of question.

For questions like this, and more particularly questions about the meaning of existence, context is important. Consider also questions in ethics and meta-ethics. There are no truths to be had here, and what is right for society — perhaps even the right way of thinking for society — will depend on that society. For example, in times of high uncertainty, where predictions are costly and inaccurate (perhaps this moment?) we might have reasons to consider virtue ethics over consequentialist frameworks.

Does it matter that I write this on Sunday morning, in 2023, in a stony city, that the sun — prehistoric or not — is hidden by several layers of cloud? It’s not for me to say.

Some links

Moments

(00:00) Intro
(3:00) Beginning of conversation: disappointment as the start of the journey
(7:55) Punk & Philosophy
(11:20) Trauma and tabula rasa
(12:30) Not making it in a band, becoming a philosopher
(19:30) Wittgenstein as a bridge between analytic and continental philosophy
(21:50) Mill and the origin of the label “continental philosophy”
(24:30) Philosophy has a duty to be part of culture
(28:00) The difficulty with philosophy being an academic tradition
(29:30) The Stone
(32:30) Football as a phenomenon for study that invites people in to philosophy
(35:00) Philosophy as pre-theoretic & Pessoa’s Ultimatum
(39:00) Will analytic philosophy run out for road and be subsumed into science?
(41:00) Two lines of human imagination
(42:00) Should philosophy ever be a single honours subject, or should it always aid other realms of thought?
(43:00) Philosophy as pre-science
(44:30) Phenomenology as reflection on the lived world
(47:00) Alberto Caeiro (Pessoa) and anti-poetry
(48:50) The saying of ordinary things to fascinate angels
(54:00) Impossible objects will keep philosophers busy
(57:00) The task of philosophy as deflationary, as not making progress
(1:00:00) Should philosophy of physics be part of physics?
(1:04:30) Context: What can’t I read Descartes like I’m talking to your right now?
(1:06:00) Is context colour or is it inseparable from ideas?
(1:15:30) Rorty: Continental philosophy as proper names vs problems in analytic philsophy
(1:19:20) Trying to walk the line between two traditions of philosophy
(1:20:00) Obscurantism vs scientism
(1:23:00) Permission to think on their own, to expose ideology
(1:26:00) The internet has been good for philosophy
(1:26:30) Audio as a new platform or agora for philosophy

ChatGPT as a Glider — James Intriligator

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Large language models, such as ChatGPT are poised to change the way we develop, research, and perhaps even think (see The Offshoring of Thought and Memory). But how do we best understand LLMs to get the most from our prompting?

Thinking of LLMs as deep neural networks, while correct, is not very useful in practical terms. It doesn’t help us interact with them, rather as thinking of human behavior as nothing more than the result of neurons firing won’t make you many friends. However, thinking of LLMs as search engines is also faulty — they are notoriously unreliable for facts.

Some other models have been proposed:

  • LLMs are “stochastic parrots” as Bender, Gebru, McMillan-Major, and (Sh)Mitchell argue
  • ChatGPT is “A fuzzy JPEG of the web” according to Ted Chiang

These both capture something of how they work, but they do not provide any direction on how to create prompts.

Our guest this week is James Intriligator. James trained as a cognitive neuroscientist at Harvard, but then gravitated towards design and is currently Professor of the Practice in Human Factors Engineering and Director of Strategic Innovation at Tufts University. 

James proposes viewing ChatGPT not as a search engine, parrot, or JPEG, but as a “glider” that journeys through knowledge. By guiding it through diverse domains, it learns your interests and customizes better answers. Dimensional prompts activate specific areas like medicine or economics. 

I believe we’ll need to have various mental models to understand how best to interact with LLMs. This is one for the toolbox.

Links:

Phylogeny & The Canterbury Tales — Peter Robinson

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The physical solidity of books encourages notions of “the text” or “the canonical edition”. The challenges to this view from post-modernist thought are well known. But there are other ways in which this model of a static text may fail. Our guest this week is Peter Robinson (my dad!) who takes us through his work … Read more

MV#11 — AI, risk, fairness & responsibility — John Zerilli

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AI is already changing the world. It’s tempting to assume that AI will be so transformative that we’ll inevitably fail to harness it correctly, succumbing to its Promethean flames. While caution is due, it’s instructive to note that in many respects AI does not create entirely new challenges but rather exacerbates or uncovers existing ones. … Read more

Plants, Roots, Spirals and Palaeobotany — Sandy Hetherington

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Plants have transformed the surface of the earth and the contents of our atmosphere. To do this they’ve developed elaborate systems of roots and branches which (sometimes) follow uncanny mathematical patterns such as the Fibonacci sequence. Sandy Hetherington leads Edinburgh’s Molecular Palaeobotany and Evolution Group, they take a no-holds-barred approach to understanding plant development by … Read more

MV#9 — The Hunt for H2 — Rūta Karolytė

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For many years scientific consensus has averred that hydrogen does not occur naturally in significant quantities without being bound to other atoms (such as in H20, water, or CH4, methane). To obtain the gas — whether as a fuel or for use in fertilizers — we need to strip it from those molecules — typically by electrolysis and steam reformation. But our understanding may be ripe for change.

Website of National Renewable Energy Laboratory from February 2023, following the publication of a New York Times article on natural H2, the text was changed to “Because hydrogen typically does not exist freely in nature” (emphasis mine)

Rūta Karolytė is at the vanguard of prospectors looking for large, naturally occurring reservoirs of hydrogen. She’s a researcher from Oxford specializing in the geochemistry of the Earth and she enlightens us to the mechanisms that are likely to be producing hydrogen in the crust: radiolysis and serpentinization. 

In reviewing the evidence for naturally occurring hydrogen we pass through exotic terrain: a Soviet-era theory of hydrocarbon production, fairy rings, hydrothermal vents and chemosynthetic life. These organisms, remarkably, do not depend on the sun, plants, or any other life forms for their energy. Instead, they draw directly from the power stored in inorganic compounds. Their existence is testimony to the natural occurrence of hydrogen.

This does not guarantee that hydrogen is present in large quantities, but modeling of the processes that produce it — particularly serpentinization — suggests it is. Serpentinization is a kind of rusting whereby rocks are oxidized and hydrogen is freed from water molecules, wherever water and the right kinds of rocks are present and the pressure and temperature are right, hydrogen will be produced. What is more this process could be sped up by the introduction of more water underground.

If Rūta and her fellow prospectors are correct, the tapping of natural hydrogen could have transformative consequences for the “Hydrogen economy” — such as cutting out the substantial fossil fuel emissions associated with deriving fertilizers from methane or creating a cheap basis for building synthetic fuels.

In the first half of the show, we also delve into carbon sequestration — another cool climate topic. But I’ve got so excited writing up the first half, that I’ll leave it here. 

References

Thought Experiments, Mach, Galileo & Phenomenology — Harald Wiltsche

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Thought experiments have played a starring role in physics. They seem, sometimes, to pluck knowledge out of thin air. This is the starting point for my discussion this week with the philosopher Harald Wiltsche: what are thought experiments?

How do they function — are they platonic laboratories with no moorings in observations or a way of supercharging our reasoning about phenomena?

What do they deliver? Much emphasis has been put on the paradigm-shattering insights of Einstein where thought experiments appear like midwives in the production of new theories. But they can also function in explaining those theories, in ensuring they are understood.

This leads to the question: what is understanding? Harald argues that it’s the ability to manipulate or deploy knowledge backed up by a mental model. He reasons that thought experiments can help us to make sense of the abstraction of mathematical models.

We discuss many topics: the mathematization of science with Galileo, where thought experiments go wrong, transcendental arguments, the links between Husserl and Mach — it was the latter’s emphasis on grounding physics in phenomenona that inspired the phenonomenological tradition in philosophy.

While there are many notes we touch on, the theme that we dance to is this: there are lots of forms of reasoning that can work well within physics and we need equal pluralism in our philosophy of science to understand the its startling, uncanny success in modeling nature.

Notes

How well does ChatGPT do with thought experiments?

Pretty well.

This question came up when discussing understanding. Large language models are certainly adept at manipulating symbols in ways that sound convincingly human, but how well do they in reasoning (or appearing to reason) about physics?

I go through two sets of prompts here, the second — where I prompt it with a thougth experiment that gets increasingly bizarre — is the more intriguing. It’s possible in all of these cases that a similar experiment has somewhere been discussed and ChatGPT is esstially parroting someone else’s understanding, however, the setup is rather complex: a helicopter magnetically levitating a butterfly in a plastic plane — has that really been dicussed before? I think one is forced to concede that it appears to have a good grasp of the physics.

Asking ChatGPT (GPT4) to think up a thought experiment

User Can you construct a thought experiment to explore the issue of whether space is absolute or relative, the ingredients in the experiment are two brass balls, scissors and a ribbon


ChatGPT
Absolutely, let’s consider a thought experiment, that could potentially show how to understand the difference between the concepts of absolute and relative space.

First, let’s define these terms for clarity:

Absolute Space: This refers to the Newtonian concept of space, which exists independently and objectively, as an unchangeable and infinite background where all physical events occur.

Relative Space: This refers to the Einsteinian concept of space, where space doesn’t exist independently of the things within it. Instead, space and time are interwoven into a four-dimensional fabric known as spacetime, and the presence of mass and energy can distort this spacetime fabric. This is the basis of Einstein’s theory of General Relativity.

Now, let’s proceed to the thought experiment:

Imagine you’re in a spaceship with no windows, floating in deep space, far away from any celestial bodies. In this spaceship, you have two brass balls, a ribbon, and a pair of scissors.

You tie the two balls together with the ribbon and hang them in the middle of your spaceship. Since you’re in deep space with no other forces acting on your spaceship, the balls just float there, not moving relative to the spaceship.

Experiment 1: Now, you give your spaceship a slight spin. After a while, you notice that the balls are now rotating around the center of the spaceship, the ribbon taut, maintaining a fixed distance from each other due to the centrifugal force caused by the rotation of the spaceship. In this case, from the perspective of Newton’s absolute space, you’ve imparted a rotation on the spaceship, and the balls are simply responding to this by moving in a circle.

Experiment 2: Now, imagine an alternate scenario where your spaceship remains stationary, but all the universe around it starts to spin instead. According to the principle of relativity, you wouldn’t be able to tell the difference between these two situations from inside the spaceship. The balls would behave in exactly the same way, spinning around the center due to what appears to be the centrifugal force. However, this time, according to the relative space concept of Einstein’s General Relativity, the mass-energy of the universe spinning around the spaceship would distort the spacetime around it, creating a frame-dragging effect which leads to the balls’ circular movement.

This experiment illustrates that whether space is absolute or relative can dramatically change our interpretation of the same phenomena. Although this is just a thought experiment and not a practical one (we can’t spin the entire universe), it highlights the difference between the Newtonian concept of absolute space and the Einsteinian concept of relative space.

User
If a butterfly is inside a plane, but flying — so not on the seats — does the plane need to generate more thrust to maintain altitude than if the butterfly were absent?

Asking ChatGPT (GPT4) to reason through a thought experiment


ChatGPT
The plane doesn’t need to generate any additional thrust to maintain altitude whether a butterfly is flying or resting within the airplane cabin. This is due to the principle of conservation of momentum.

In the closed system of the airplane, the mass and the momentum are conserved. This means that the butterfly’s presence, flight, or rest doesn’t change the total momentum of the plane. When the butterfly is flying, it pushes air downwards to generate lift. The air pushes back on the butterfly, but it also pushes (very slightly) on the plane. The sum of these forces and reactions remains constant, so no extra thrust from the plane’s engines is necessary to maintain altitude.

In other words, when the butterfly takes off from a perch inside the airplane, it imparts a slight force to the airplane in the opposite direction. But as it flies around, all the movements it makes, the flapping of its wings, the changes in direction, etc., just redistribute the mass and momentum within the plane. The net effect on the plane is zero.

The situation would be the same whether the object in question is a butterfly, a bird, or a passenger walking down the aisle. As long as the action takes place within the closed system of the plane, it won’t affect the thrust needed to maintain the plane’s altitude or direction.

User
But if the butterfly were absent, wouldn’t the plane weigh less and therefore less thrust would be required?

ChatGPT
You are correct that if the butterfly were not present at all, the total weight of the airplane would be marginally less by the weight of the butterfly. The plane would therefore need a tiny amount less lift to maintain altitude, and consequently a tiny amount less thrust to maintain a particular speed.

However, once the butterfly is on board the plane, whether it is flying within the cabin or resting doesn’t affect the thrust required to maintain the plane’s altitude. This is because the weight of the plane system (including the butterfly, passengers, seats, etc.) is the same whether the butterfly is in flight or at rest.

In simpler terms, the energy required to lift the butterfly against gravity is still accounted for within the closed system of the plane, whether the butterfly is using its own wings to stay aloft or if it’s sitting on a seat.

So, to summarize, the plane does weigh less if the butterfly isn’t there at all, which would mean slightly less thrust is required. However, if the butterfly is present in the plane, whether it’s flying or not doesn’t make a difference to the thrust required.

User
What happens if the inside of the plane were a vacuum and the butterfly could levitate (perhaps magnetically) inside the plane, would it contribute to the weight of the plane’s contents then?


ChatGPT
Your scenario is a fascinating one. If we’re assuming the butterfly can somehow levitate magnetically, in a vacuum, inside the plane, then the answer to your question is yes, the butterfly still contributes to the total weight of the plane.

In this case, even if the butterfly is levitating, it doesn’t escape the pull of gravity. The butterfly still has mass, and the Earth’s gravity still acts on it, exerting a force (its weight). That gravitational force doesn’t disappear when it levitates; instead, it’s just counterbalanced by another force (in this case, a magnetic one).

This means that the butterfly’s weight still contributes to the overall weight of the plane. So if the butterfly weren’t present at all, the plane would be marginally lighter by the weight of the butterfly, regardless of whether it’s levitating or not.

The important point here is that gravity acts on all mass, regardless of its state or position. A levitating object in a gravitational field is still subject to gravity and still contributes to the total weight of the system it’s part of.

User
What if the plane were made of plastic, and the magnetic force holding the butterfly in place was one due to a helicopter flying above the plane, would it still contribute to the weight of the plane and thus lead the plane to require a tiny amount more thrust than if the butterfly were absent?

ChatGPT
If the magnetic force attracting the butterfly is not coming from the plane itself, but from an external source like a helicopter above it, then the situation changes.

In this case, the force that’s supporting the levitating butterfly is not being applied by the plane, but by the helicopter. So, the weight of the butterfly (which is the force of gravity acting on its mass) is essentially being “transferred” to the helicopter, not the plane.

Therefore, in this specific scenario, the butterfly’s weight would not contribute to the total weight of the airplane while it’s levitating. As a result, the plane would not require more thrust to maintain altitude than if the butterfly were completely absent.

However, remember that this situation is based on quite a few assumptions that would be very difficult to realize in the real world, and it doesn’t take into account other potential factors that could affect the plane’s flight.


Genomics, PRS, genetic ancestry, race & ethics — Anna Lewis

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Over the past two decades, the cost of sequencing a whole human genome has fallen ten million-fold. However, the advances in sequencing have not equally progressed our understanding of the genetic code. Alongside the scientific struggle to interpret the Book of Life, we must also confront the ethical and social issues brought up by ongoing research.

Anna Lewis, a researcher at the Edmond & Lily Safra Center for Ethics at Harvard, guides us through some of the most complex issues in this field in this week’s episode. She discusses the use of polygenic risk scores, and how a poor choice of scientific framework can hinder medical advances, or even worse, lend support to racist ideologies.

In 2003, the Human Genome Project announced it had sequenced the 3.2 billion base pairs that constitute the human genetic code, more precisely the genetic code of a few humans, on just one-half of each chromosome. This project cost around $3 billion and took thirteen years. In 2023, it takes less than 24 hours and under $300 to sequence a whole genome. The learning rates for renewable technology and computing (Moore’s law) pale in comparison, making genomics a leading candidate for the technology that has seen the steepest price declines in history.

Proteomics and gene editing have also witnessed revolutionary advances in recent years:

  • Protein folding: Google’s DeepMind unveiled AlphaFold in 2021, a deep learning model that can predict the shapes formed by proteins with speed and accuracy, thus enhancing our ability to understand their functions.
  • CRISPR: a technology that adopts the mechanism by which bacteria splice in DNA from their predators (phages) to remember them. CRISPR allows for low-cost copy-paste editing of DNA.

Despite these breakthroughs, Anna points out that many problems remain partially understood. One of the deeper reasons for this is the highly complex interactions of genes. Prior to the sequencing of the human genome, scientists anticipated they would find 80,000 – 140,000 genes. Instead, depending on our understanding of the term ‘gene’, they found 20,000 – 30,000. This initial overestimate reflected an under-appreciation of the intricacies of how genes coordinate their functions.

Pathologies such as Huntington’s disease and hemophilia can be traced very clearly to single genes. In many cases, health outcomes depend on multiple genes; or single genes that lead to disease may not always be expressed (“switched on”), furthermore, non-human DNA can play a role in health. Epigenetics and hologenomics are two fast-developing fields that study, respectively, the mechanisms driving the expression of human genes and the importance to the health of non-human cells such as those in the gut microbiome.

We have transcribed The Book of Life, but we do not yet know how to read it. Not fully.

As a consequence, progress in medical technology has not kept pace with the improvements in gene sequencing. It should be conceded, however, that this lag is also due to the essential regulatory hurdles involved in bringing therapies safely to market. Undoubtedly, a similarly rigorous framework for AI would have prevented Language Learning Models (LLMs) such as ChatGPT from emerging as early as they did.

Nonetheless, there is cause for optimism in the treatments of monogenic diseases such as Huntington’s, hemophilia, and Duchenne’s muscular dystrophy. But for the many human traits, whether pathogenic or not, that are polygenic — and thus depend on the expression of multiple genes — progress is slower. Considering that 12,000 genetic variants influence height we can appreciate the difficulties of linking traits back to the genome.

One of the tools developed to cut through this complexity is the Polygenic Risk Score (PRS). By examining a population, researchers apply statistical methods to understand how the frequency of occurrence of a trait (for example, type 2 diabetes) correlates with the genes observed. This is used to predict the probability of a trait based on an individual’s genome, with varying results. One of the key factors in the efficacy of such scores is that the genome for which risk is being calculated is similar to those sampled in the population. But what does that mean?

Population, Anna argues, is a term with a dangerously confusing array of meanings. The population of New York is a number, but it also represents a group of people who do not necessarily share anything meaningful for the purposes of genomics. In the past, PRSs have relied on concepts of race for creating a sample set and understanding the portability of results from that set to other individuals. However, while race remains an important and valid variable for studies in sociology and economics—for example, in understanding and correcting for the historical and ongoing consequences of discrimination—it lacks a scientific basis. Race is a social construct subject to shifts and changes; who is considered “white,” for instance, has more to do with power structures than phenotype. A century ago, Southern European immigrants to the USA were not counted as such.

Recently, there has been a move away from race to the notion of genetic ancestry. However, Anna and her colleagues have observed that unless properly defined, this risks being no more than race renamed. In particular, if genetic ancestry is understood as simple continental groupings—East Asian origin, and so forth—it neither serves science nor society. On one hand, it fails because these groupings do not accurately represent genomically salient features. For example, a continental African grouping contains as much diversity as all other groups combined. On the other hand, by suggesting it has scientific meaning, the use of these groupings may inadvertently propagate racist ideologies.

It is crucial to correctly develop Polygenic Risk Scores. If done correctly, medical advice and interventions can be more effectively targeted. Anna and her colleagues argue that the most appropriate way to understand genetic ancestry is through the Ancestral Recombination Graph (ARG). Unlike continental ancestry groupings, which are flat in time and wide in extent, the ARG looks at how an individual’s DNA has been traced back from various different points and places in time. It traces how an individual’s genome branches out back in time among ancestors.

The ARG is much more specific than continental groupings. However, because genes tend to be passed down in chunks rather than individually, it offers sufficient coarse-graining to highlight where variations are significant and apply metrics for grouping people such that highly predictive PRSs can be calculated. The ARG is a precisely defined and scientifically meaningful object, lacking the connotations of race. As Anna and her colleagues put it in their recent paper, this gets genetic ancestry right for science and society. So far the research community is yet to combine ARGs and PRSs, Anna and her colleagues’ paper is a rallying call to do so.

To make the most of this window of opportunity to move away from race as a biological variable,
we would urge the adoption of a multidimensional and continuous conceptualization of ancestry,
free wherever possible of population categories, and not relying on continental labels that bear
striking resemblance to prior racist groups.

Anna Lewis et al, Getting genetic ancestry right for science and society. Science. 2022 Apr 15

Anna and I studied together at Oxford, starting our degrees in 2003, just as the human genome was first sequenced. It has been a pleasure to follow her career since then, through a PhD in systems biology, medtech startups, and back into academia. More technological and medical breakthroughs are on the horizon—perhaps polygenic CRISPR edits will allow us to influence intelligence, strength, and tendency towards violence. The work that Anna and others are doing in the ELSI field (Ethical, Legal & Social Implications) is crucial to the choices we will make as a species.

 We are learning how we can use our tools, we need to be mindful of how we should use them.

Notes

Poetry, Constraints, DNA & The Xenotext — Christian Bök

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Poetry is a game that can be played in many ways. Perhaps the most traditional and popular is “emotion recollected in tranquility” as Wordsworth termed it, whereby the poet’s feelings are carefully expressed. Christian Bök is a bestselling poet who plays a very different game. To use his turn of phrase, he fights with icosahedra, not swords.

Multiverses.xyz with Christian Bök — the four games of poetry

Above: the four games of poetry that Christian outlines. The two dimensions are self-expression (seeming to say something) and self-consciousness (meaning to say something).

Christian imposes tight sets of constraints on his writing. His bestseller Eunoia, for example, uses univocalic lipograms — each chapter can only make use of a single vowel. This produces such gems as “Writing is inhibiting. Sighing, I sit, scribbling in ink this pidgin script.” But that’s not all. That would be too easy:

Eunoia abides by many subsidiary rules. All chapters
must allude to the art of writing. All chapters must de-
scribe a culinary banquet, a prurient debauch, a pas-
toral tableau and a nautical voyage. All sentences must
accent internal rhyme through the use of syntactical
parallelism. The text must exhaust the lexicon for each
vowel, citing at least 98% of the available repertoire
(although a few words do go unused, despite efforts
to include them: parallax, belvedere, gingivitis, mono-
chord and tumulus). The text must minimize repeti-
tion of substantive vocabulary (so that, ideally, no word
appears more than once). The letter Y is suppressed.

— Eunoia, Christian Bök

For the past two decades, Christian has pursued an even more ambitious project, The Xenotext. This project involves enciphering an “alien text” within the DNA of a resilient bacterium, Deinococcus radiodurans. One goal of The Xenotext is to create a text that could outlast human civilization. To add to the genomic challenge Christian has set a remarkable rule: the symbols of the text should be interpretable in two different ways, resulting in two poems that are encoded within the same string.

Christian combines scientific techniques, trial and error, and computer programming to construct his poems, adhering to the rules he has established within his own poetic universe. Furthermore, he transforms art back into science by employing gene-editing to inscribe his poetic creation into the “book of life,” the DNA of a living organism.

Instead of looking back and inwards (the ideal of “emotion recollected in tranquility”, Christian looks outwards and to the future, fusing science and art to produce uncanny and unforgettable (and perhaps ineradicable) verse.

References