Rice fields and rain


Humans primarily live in a world of beings, each of which has meaning. Meaningful beings appear to us interconnected, referencing practices and other beings in a referential totality. Buttons suggest pushing, chairs suggest sitting, a tractor suggests farming. A (Japanese) rice paddy may suggest the heavy labour that goes into the rice harvest each year, the tools and equipment that go with it, as well as the gradual depopulation of the village, since the young ones prefer a different line of work elsewhere. It may be part of the site and locus of an entire set of concerns and an outlook on life.

The world of beings is the one that is most immediate to us, and a world of molecules, atoms, energy or recorded data, useful as it may be, is something much further away. In each case it must be derived and renewed from the use of a growing and complex apparatus of equipment, practices and body of concepts, such as the traditions of physics or mathematics. Yet nobody would dispute that these worlds – the world of beings and the calculated world – are interrelated. In some cases they are even deeply intertwined.

But how can we reconcile the calculated world with the world of beings? How exactly do they influence each other? And if the calculated world is expanding aggressively, thanks to the spread of computational machinery and its servants, is the world of beings being pushed back? Receding? Are we abandoning it, since it is no longer good enough for us? Refusing to touch it, other than with thick gloves?

The calculated world concerns itself with propositions, true facts, formal models, records. A conceptual basis is needed to codify and engage with it. A record is formed when an observation is made, and the observer writes down what was observed. Initially, it retains an intimate connection with the world (of beings). The record is interpreted in light of the world and allowed to have its interplay with other beings. The observation “it rained heavily this week” is allowed to mean something in the context of farming, in the context of a possible worry about floods, or as a comment on an underwhelming holiday. Depending on who the reader is and what their concerns are, all these meanings can be grasped. The record may thus alter the reader’s outlook in a way similar to what direct experience of the rainfall would do.

At this level, the only facts we may record are that it rained or did not rain, and whether the rain was heavy or light. But given that we have some notion of space or time, as human beings do, repetition becomes possible. Scales for measuring time and space can be constructed, The rainfall can now be 27 or 45 mm. We are now further away from the world of farming, floods and holidays – “45 mm” of rain needs to be interpreted in order to be assigned any meaning. It has been stripped of most the world where it originated. The number 45 references only calculable repetition of an act of measurement. Enabled by the notions of space and time, already it tries to soar above any specific place or time to become something composable, calculable, nonspecific. Abstraction spreads its wings and flaps them gently to see if they will hold.

So on all the way up to probability distributions, financial securities, 27 “likes” in a day on social media and particle physics. At each level of the hierarchy, even when we purport to move “downward” into the “fundamentals” of things, layers of meaning are shed and a pyramid of proverbial ivory soars to the sky.

Spatial and temporal observations depend on measurement on linear scales, such as a stopwatch or a ruler. Such scales are first constructed through repeated alignment of some object with another object. Such repeated alignment depends on counting, which in turn depends on the most basic and most impoverished judgment: whether something is true or false, does or does not accord. Thus something can have the length of 5 feet or the duration of 3 hourglasses: it accords with the basic unit a certain number of times. This accordance is the heavily filtered projection of a being through another. The side of a plot of land is measured, in the most basic case, by viewing the land through a human foot – how many steps or feet suffice to get from one side to the other? Even though the foot is actually able to reveal many particularities of the land being measured – its firmness, its dampness, its warmth – the only record that this attitude cares to make is whether or not spatial distance accords, and how many times in succession it will accord. All kinds of measurement devices, all quantitative record making, follows this basic principle. Thus, the calculable facts are obtained by a severe discarding of a wealth of impressions. This severity is obvious to those who are being trained to judge quantitatively for the first time, but soon internalised and accepted as a necessity. Today, these are precisely the facts we are accustomed to calling scientific and objective.

But the accordance of beings with distance or time is, of course, very far from the only things we can perceive about them. The being emits particular shapes, configurations, spectra that make impressions on us and on other beings. Thus it is that we may perceive any kind of similarity – for example the notion that two faces resemble each other, that a dog resembles its owner, or that a constellation of stars looks like a warrior. We delight in this particularity, which in a way is the superfluous or excess substance of beings – it is not necessary for their perception but it forms and adds to it. Thus the stranger I met is the stranger with a yellow shirt and not merely the stranger. He can also be the stranger with a yellow shirt and unkempt hair, or the stranger with a yellow shirt and unkempt hair and a confident smile, and so on – any number of details may be recorded, any number of concepts may be brought into the description. These details are not synthetic or arbitrary. But they are also not independent of the one who observes. They would depend both on a richness that is of the being under observation, and on the observer’s ability to form judgments and concepts, to see metaphorically, creatively and truthfully.

Such impressions, which carry a different and perhaps more immediate kind of truth than the truth that we derive from calculations and records, may now have become second class citizens in the calculated world that grows all around us.

Reading shelf, September 2016

Currently reading:

C.G. Jung: Nietzsche’s Zarathustra (vol. 2) (seminar notes)

J.G. Ballard: Empire of the Sun (fiction)

Eric Hobsbawm: Age of Extremes (nonfiction)


Just finished:

J.G. Ballard: Extreme Metaphors (interviews)

Alain de Botton: The Course of Love (semi-fiction)

Ursula K. LeGuin: The Earthsea Quartet (fiction)



AI and the politics of perception

Elon Musk, entrepreneur of some renown, believes that the sudden eruption of a very powerful artificial intelligence is one of the greatest threats facing mankind. “Control of a super powerful AI by a small number of humans is the most proximate concern”, he tweets. He’s not alone among silicon valley personalities to have this concern. To reduce the risks, he has funded the OpenAI initiative, which aims to develop AI technologies in such a way that they can be distributed more evenly in society. Musk is very capable, but is he right in this case?

The idea is closely related to the notion of a technological singularity, as promoted by for example Kurzweil. In some forms, the idea of a singularity resembles a God complex. In C G Jung’s view, as soon the idea of God is expelled (for example by saying that God is dead), God appears as a projection somewhere. This because the archetype or idea of God is a basic feature of the (western, at least) psyche that is not so easily dispensed with. Jung directs this criticism at Nietzsche in his Zarathustra seminar. (Musk’s fear is somewhat more realistic and, yes, proximate, than Kurzweil’s idea, since what is feared is a constellation of humans and technology, something we already have.)

But if Kurzweil’s singularity is a God complex, then the idea of the imminent dominance of uncontrollable AI, about to creep up on us out of some dark corner, more closely resembles a demon myth.

Such a demon myth may not be useful in itself for understanding and solving social problems, but its existence may point to a real problem. Perhaps what it points to is the gradual embedding of algorithms deeply into our culture, down to our basic forms of perception and interaction. We have in effect already merged with machines. Google and Facebook are becoming standard tools for information finding, socialising, getting answers to questions, communicating, navigating. The super-AI is already here, and it has taken the form of human cognition filtered and modulated by algorithms.

It seems fair to be somewhat suspicious — as many are — of fiat currency, on the grounds that a small number of people control the money supply, and thus, control the value of everybody’s savings. On similar grounds, we do need to debate the hidden algorithms, controlled by a small number of people (generally not available for perusal, even on request, since they would be trade secrets), and pre-digested information that we now use to interface with the world around us almost daily. Has it ever been so easy to change so many people’s perception at once?

Here again, as often is the case, nothing is truly new. Maybe we are simply seeing a tendency that started with the printing press and the monotheistic church, taken to its ultimate conclusion. In any case I would paraphrase Musk’s worry as follows: control of collective perception by a small number of humans is the most proximate concern. How we should address this concern is not immediately obvious.

The minimal genome of Craig Venter’s Syn3.0

The J Craig Venter Institute has published a paper detailing the genome of their new Syn3.0 synthetic organism. The major accomplishment was to construct a viable cell with a synthetic, extremely small genome: only 473 genes and about 500 kbp.

Even though it is considered to be fully “synthetic”, this genome is not built from scratch. Instead, the starting point is the Mycoplasma genitalium bacterium, from which genes and regions are deleted to produce something that is much smaller, but still viable. This means that even this fully synthetic genome still contains regions and functionalities that are not fully understood. M. genitalium was also the basis for JCVI’s Syn1.0, which was produced in 2008, but the genome of Syn3.0 is the smallest so far – “smaller than that of any autonomously replicating cell found in nature”. Syn3.0 should be a very valuable starting point for developing an explicit understanding of the basic gene frameworks needed by any cell for its survival – the “operating system of the cell” in the words of the authors.

Since so many genes are still basically not understood, the authors could not rely entirely on logic and common sense when choosing what genes to remove. They used an approach that introduced random mutations into the starting organism, and then checked which mutations where viable and which were not. This allowed them to classify genes as essential, inessential or quasi-essential (!). The deletion of essential genes would cause the cell to simply die. The deletion of quasi-essential genes would not kill it, but would dramatically slow its replication rate, severely crippling it. The final Syn3.0 organism has a doubling time of about 3 hours.

Some of the points I took away from this readable and interesting paper were:

Synthetic biology methods are starting to resemble software development methods. The authors describe a design-build-test (DBT) cycle that involve several nontrivial methods, such as in silico design, oligonucleotide synthesis, yeast cloning, insertion into the bacteria, testing, and then (perhaps) sequencing to go back to computers and figure out what went wrong or what went well. Thus, a feedback loop between the cells and the in silico design space is set up.

A very small genome needs a very tightly controlled environment to survive. The medium (nutrient solution) that Syn3.0 lives in apparently contains almost all the nutrients and raw materials it could possibly need from its environment. This means that many genes that would normally be useful for overcoming adverse conditions, perhaps for synthesising nutrients that are not available from the environment, are now redundant and can be removed. So when thinking about genome design, it seems we really have to think about how everything relates to a specific environment.

The mechanics of getting a synthetic genome into a living cell are still complex. A huge amount of wet-lab (and, presumably, dry-lab) processes are still needed to get the genome from the computer into something viable in a cell culture. However, things are going much faster than in 2008, and it’s interesting to think about where this field might be in 2021.


Method and object. Horizons for technological biology

(This post is an attempt at elaborating the ideas I outlined in my talk at Bio-pitch in February.)

The academic and investigative relationship to biology – our discourse about biology – is becoming increasingly technological. In fields such as bioinformatics and computational biology, the technological/instrumental relationship to nature is always at work, constructing deterministic models of phenomena. By using these models, we may repeatedly extract predictable results from nature. An example would be a cause-effect relationship like: exposing a cell to heat causes “heat shock proteins” to be transcribed and translated.

The implicit understanding in all of these cases is that nature can be turned into engineering. Total success, in this understanding, would amount to one or both of the following:

  1. Replacement/imitation as success. If we can replace the phenomena under study by its model (concretely, a machine or a simulation), we have achieved success.
  2. Control as success. If we can consistently place the phenomena under study in verifiable, fully defined states, we have achieved success. (Note that this ideal implies that we also possess perfect powers of observation, down to a hypothetical “lowest level”).

These implicitly held ideals are not problematic as long as we acknowledge that they are mere ideals. They are very well suited as horizons for these fields to work under, since they stimulate the further development of scientific results. But if we forget that they are ideals and begin to think that they really can become realities, or if we prematurely think that biology really must be like engineering, we might be in trouble. Such a belief conflates the object of study with our relatedness to that object. It misunderstands the role of the equipment-based relationship. The model – and associated machines, software, formulae. et cetera – is equipment that constitutes our relatedness to the phenomena. It cannot be the phenomena themselves.

Closely related to the ideals of replacement and control is the widespread application of abstraction and equality in engineering-like fields (and their application to new fields that are presently being clad in the trappings of engineering, such as biology). Abstraction and equality – – the notion that two entities, instances, moments, etc., are in some way the same – allow us to introduce an algebra, to reason in the general and not in specifics. And this is of course what computers do. It also means that two sequences of actions (laboratory protocols for example), although they are different sequences, or the same sequence but at different instances in time, can lead to the same result. Just as 3+1 and 2+2 both “equal” 4. In other words, history becomes irrelevant, the specific path taken no longer means very much. But it is not clear that this can ever truly be the case outside of an algebra, and that is what risks being forgotten.

We might call all this the emergence of technological biology, or technological nature, the conquest of biology by λόγος, et cetera. The principal danger seems to be the conflation of method with object, of abstraction with the specific. And here we see clearly how something apparently simple – studying RNA expression levels in the software package R, for example – opens up the deepest metaphysical abysses. One of the most important tasks right now, then, would be the development of a scientific and technological culture that keeps the benefits of the technological attitude without losing sight of a more basic non-technological relatedness. The path lies open…