[This post was written using GPT-3. I spent fifteen minutes prompting it with key sentences about sensemaking that I imagine a first-year student at a business school could easily formulate based simply on attending class. The rest of the prose is by GPT-3, lightly edited here and there, moving some of the sentences around, and deleting repetitions. There are about 1200 words, produced in bursts of about 200. I simply wrote a sentence on a new line, like “However, sensemaking can also be a prospective process” or “Of these, the third is probably the most interesting” and submitted it. At the end, I wrote “References” and GPT-3 created the reference list.

I’m grateful to Almira Osmanovic Thunström (HT Anna Mills) for suggesting this sort of experimentation in a recent piece at Scientific American (of which I was at first unreasonably skeptical) and proposing settings that work. See her experiment here; my presets are here. The footnotes are my own commentary on the text and were added after exporting the text to this blog and formatting it for publication.

Your comments are more than than welcome. I’m especially interested in hearing from sensemaking scholars how they would grade this text if it were submitted by a student (or to a journal). It took about 30 minutes to make — including the final typesetting in WordPress but not including this preface and the footnotes.]  

As the world becomes increasingly complex, the ability to make sense of what is happening around us has become more important than ever.¹ While there is no single agreed-upon definition of sensemaking, the term generally refers to a retrospective process of trying to understand what has happened and why.² The concept was first introduced by Karl E. Weick in his 1995 book, “Making Sense Of The Organization.” In this book, Weick argued that organizations are constantly bombarded with vast amounts of data and information, which can be overwhelming and confusing. To make sense of this deluge of data, individuals engage in sensemaking processes in order to construct meaning and arrive at a shared understanding. Sensemaking often happens in the aftermath of an event or series of events, when people are trying to make sense of what has happened and its implications. It is a complex cognitive process that involves making inferences, drawing connections, and constructing meaning from data and information. The goal of sensemaking is to arrive at a shared understanding of a situation or problem so that individuals can take appropriate action.³

However, sensemaking can also be a prospective process. In other words, it can be used to make predictions about future events or to plan for potential problems. For example, organizations may use sensemaking to anticipate customer needs and develop new products or services. Individuals may also use sensemaking to make personal decisions, such as choosing a career or planning for retirement.

The sensemaking process is often likened to puzzle-solving.⁴ Individuals must first gather all of the pieces of information (data) and then try to fit them together in a way that makes sense. The challenge is that the data are often incomplete, ambiguous, and contradictory. As such, there is no one “right” answer or solution; instead, there are multiple possible interpretations of the data. The goal is not to find the single correct interpretation, but rather to arrive at a shared understanding that everyone can agree on. This shared understanding forms the basis for taking action.

There are many different approaches to sensemaking, but all share a common set of steps:⁵

1) Data collection: Collecting data from various sources (e.g., observations, interviews, documents).
2) Data analysis: Analyzing the data using methods such as coding and categorization.
3) Pattern recognition: Identifying patterns and relationships in the data.
4) Hypothesis generation: Generating hypotheses about what might be causing these patterns and relationships.
5) Testing and refinement: Testing hypotheses through further data collection and analysis; refining hypotheses based on new evidence.

Of these, the third is probably the most interesting.⁶ Pattern recognition is a key part of sensemaking, but it is also a notoriously difficult cognitive task. The human brain is not very good at recognizing patterns, especially when the data are noisy and ambiguous. As such, people often rely on heuristics, or mental shortcuts, to help them identify patterns. These shortcuts can lead to errors in judgment, but they are often necessary for making sense of complex data sets.

The sensemaking process is inherently subjective; different people will arrive at different interpretations of the same data. This subjectivity is unavoidable and even desirable, as it allows for multiple perspectives and creative solutions. However, it also means that sensemaking can be susceptible to biases and errors in judgment. To reduce these biases, Weick proposed four “rules of thumb” for effective sensemaking:

1) Be humble: Don’t assume that you know more than you do. Acknowledge the limits of your knowledge and understanding.
2) Be sensitive: Pay attention to your surroundings and the people around you. Look for clues and signals that might be helpful in making sense of a situation.
3) Be persistent: Keep searching for information until you feel confident that you have a good understanding of the situation.
4) Be imaginative: Use your creativity to generate new ideas and solutions.

But Weick has been criticized by a number of scholars in recent years.⁷ In particular, his focus on individual cognition has been critiqued as being too narrow and ignoring the social and organizational context in which sensemaking takes place. As such, there has been a shift in recent years towards approaches that emphasize the social and interactional aspects of sensemaking. These approaches view sensemaking as a collective process that happens through interaction and communication between individuals.

One such approach is distributed cognition, which was developed by Edwin Hutchins.⁸ This approach views cognition as something that is distributed across people and artifacts (e.g., tools, documents, technologies). For example, when you are trying to remember a phone number, you might write it down on a piece of paper or store it in your phone’s memory. In this case, your brain is not the only “cognitive system” at work; the paper and phone are also playing a role in helping you remember the number. Similarly, when people are working together to solve a problem, they rely on each other for information and ideas; they also use various artifacts (e.g., computers, whiteboards) to help them think through the problem. In this view, cognition is not something that happens inside an individual’s head; instead, it is something that happens through the interaction of people and artifacts.

Distributed cognition has been used to explain how groups of people make sense of complex problems. For example, Hutchins et al. (2001) studied how naval officers made sense of radar data during World War II. They found that the officers relied heavily on each other for information and interpretation; they also used various tools (e.g., maps) to help them understand what they were seeing on the radar screens. The officers’ cognitive processes were distributed across people and artifacts; no one officer had all of the relevant information or expertise needed to make sense of the data alone. Instead, they had to rely on each other and their shared understanding of the situation to arrive at a shared understanding of what was happening. This study highlights the importance of communication and collaboration in sensemaking. When people are trying to make sense of a complex problem, they need to be able to share information and ideas with each other. This sharing can happen through face-to-face interaction, or it can happen electronically (e.g., via email, instant messaging, social media). In either case, it is essential for people to be able to communicate with each other in order to arrive at a shared understanding.⁹

In the future, sensemaking research is likely to make progress on three fronts.¹⁰ First, there is a need for more studies that focus on the social and interactional aspects of sensemaking. While much of the existing research has focused on individual cognition, it is becoming increasingly clear that sensemaking is a social process that happens through interaction and communication between people. Second, there is a need for more studies that use experimental methods. Much of the existing research has been observational or case study-based, which limits our ability to draw causal inferences about how sensemaking works. Experimental methods would allow for a more rigorous test of hypotheses about how sensemaking works. Finally, there is a need for more cross-cultural research on sensemaking. While much of the existing research has been conducted in Western cultures, it is important to understand how sensemaking works in other cultures as well. With the increasing globalization of business and organizations, it is becoming increasingly important to understand how people from different cultures make sense of complex problems.

References¹¹

Weick, K. E. (1995). Making sense of the organization. Malden, MA: Blackwell.
Hutchins, E., Hollan, J., & Norman, D. A. (2001). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 8(2), 174-196.

Notes (as of 15.07.22. I may add more later.)

1. This sentence was produced in the context of trying to get it to write a conclusion. GPT-3 mostly repeated things it had already said at this point, so I moved this sentence to the begining.

2. This is the only sentence I rewrote a little for style.

3. This paragraph is the most edited of all the ones in this post. It was assembled from sentences GPT-3 offered in a different order. The prompt was simply “Sensemaking is a retrospective process.” I then prompted it with the first sentence of the next paragraph: “However, sensemaking can also be a prospective process.” The result is what you see.

4. This is not a key sentence I came up with. It was generated by GPT-3 based on what had come before. I’m quite impressed with it.

5. GPT-3 came up with this itself. It does not reflect any specific prompting by me. (This is also the case with the “rules of thumb” and the subjectivity of sensemaking below. This was not prompted by me.)

6. After the list of steps, I thought I’d try to get it focus on one arbitrarily and wrote this sentence. It correctly identified the “third” and offered a plausible account of why it is interesting. I’ve left it entirely as is.

7. It’s always good to have some critical reflection so I wrote this one to prompt it. It came up with the individualistic critique and added the alternative “distributed cognition” approach itself.

8. Hutchins is indeed the right reference for distributed cognition.

9. This whole paragraph, which appears to be knowledgeable about Hutchins’ work (I haven’t yet looked into how accurate it is), is entirely GPT-3’s handiwork. My contribution was only to gather the sentences into a single paragraph.

10. I figured this was a good way to head towards a conclusion. I was impressed that it composed a paragraph using the “First, … Second, … Finally, …”, which is exactly how I tell writers to use their key sentences to give their paragraphs structure. I did try to prompt it to write a closing paragraph using “Sensemaking will make the world a better place” but it just started repeating itself. Probably a function of the length of the text. (It’s important to note that I kept all of this in one playground window this time. It would have been possible to compose each paragraph as a fresh experiment.)

11. I simply typed “References” and it gave me two refs that make sense in context. Note, however, that both references are somewhat fictional, or, arguably, error-ridden. A little Googling would easily fix them.

 questi è Nembrotto per lo cui mal coto
 pur un linguaggio nel mondo non s'usa.
	 	              
 This is Nimrod, because of whose vile plan
 the world no longer speaks a single tongue.

 Dante, Inferno, XXXI

et me make an attempt at allegory. Suppose someone said that the Burj Khalifa is an improvement on the Tower of Babel, a step toward accomplishing the goal of that ill-fated project. The claim, to be clear, is not just that, as the Encyclopedia Britannica suggests, “the Tower of Babel was the world’s first skyscraper” and the Burj is the state of the art; rather, imagine someone arguing that the Burj Khalifa should give us hope that one day we will build a tower all the way to heaven. I think we can all agree that this would be silly. The Tower of Babel wasn’t just a project ahead of its time that “fell short” of its objective, waiting for reinforced concrete and the buttressed core to be invented.

In his De Vulgare Eloquentia, Dante described the Tower of Babel as a “work of evil” (L. opus iniquitatis). The “infamous device” of my title is Mark Musa’s translation of “mal coto” in Canto XXXI of the Inferno, which is rendered as “vile plan” in the epigraph and “evil” or “wicked thought” by others. Mary Jo Bang calls the Tower of Babel simply a “bad idea”, which made me chuckle. What they all have in common is the imputation that the project was ill-conceived.

A recent “big ideas” piece in the Guardian by Regina Rini, a philosopher at York University, and some exchanges on Twitter, reminded me of all this. In it, she compares Google’s most advanced “Language Model for Dialog Applications” (LaMDA) to the children’s toy See ‘n Say, just as I have compared it to a Magic 8 Ball. Like me, she found that LaMDA is just a “very fancy” input-output device, i.e., a machine that mindlessly responds to prompts from the user with utterances that are meaningful to that user. Like me, she rejects Blake Lemoine’s claim that LaMDA is conscious (and may even have a soul). But then she says something odd:

One day, perhaps very far in the future, there probably will be a sentient AI. How do I know that? Because it is demonstrably possible for mind to emerge from matter, as it did first in our ancestors’ brains. Unless you insist human consciousness resides in an immaterial soul, you ought to concede it is possible for physical stuff to give life to mind. There seems to be no fundamental barrier to a sufficiently complex artificial system making the same leap. While I am confident that LaMDA (or any other currently existing AI system) falls short at the moment, I am also nearly as confident that one day, it will happen.

LaMDA, she tells us, merely “falls short” of sentience. “One day” it could happen and LaMDA is well on its way.

Of course, if that’s far off in the future, probably beyond our lifetimes, some may question why should we think about it now. The answer is that we are currently shaping how future human generations will think about AI, and we should want them to turn out caring. There will be strong pressure from the other direction. By the time AI finally does become sentient, it will already be deeply woven into human economics. Our descendants will depend on it for much of their comfort. Think of what you rely on Alexa or Siri to do today, but much, much more. Once AI is working as an all-purpose butler, our descendants will abhor the inconvenience of admitting it might have thoughts and feelings.

There’s a lot going on here. In fact, it should remind us of another take on the Tower of Babel, namely, Kafka’s “City Coat of Arms”. He also began with the premise that the project would take many generations to complete.

The essential thing in the whole business is the idea of building a tower that will reach to heaven. In comparison with that idea everything else is secondary. The idea, once seized in its magnitude, can never vanish again; so long as there are men on the earth there will be also the irresistible desire to complete the building. That being so, however, one need have no anxiety about the future; on the contrary, human knowledge is increasing, the art of building has made progress and will make further progress, a piece of work which takes us a year may perhaps be done in half the time in another hundred years, and better done, too, more enduringly. So why exert oneself to the extreme limit of one’s present powers? There would be some sense in doing that only if it were likely that the tower could be completed in one generation. But that is beyond all hope. It is far more likely that the next generation with their perfected knowledge will find the work of their predecessors bad, and tear down what has been built so as to begin anew. Such thoughts paralyzed people’s powers, and so they troubled less about the tower than the construction of a city for the workmen.

By the time we actually reach heaven, he might have said, the tower will “already be deeply woven into human economics”. A great city will lie at its base, having grown up around it throughout the construction process to provide for the needs of the builders. Of course, this city will be no less real, no less bustling, no less rife with human conflict and ethical dilemmas, at every stage of the project before it reaches heaven. Indeed, the city will be what it is regardless of whether the Tower is ever completed.

And this brings us back to Dante. In De Vulgare Eloquentia, he offered an almost sociological explanation for the “confusion” that the name of Babel has come to stand for. The story of the Tower of Babel becomes a sort of allegory of the division of labor and the fragmentation of the disciplines.

Almost the whole of the human race had collaborated in this work of evil. Some gave orders, some drew up designs; some built walls, some measured them with plumb-lines, some smeared mortar on them with trowels; some were intent on breaking stones, some on carrying them by sea, some by land; and other groups still were engaged in other activities – until they were all struck by a great blow from heaven. Previously all of them had spoken one and the same language while carrying out their tasks; but now they were forced to leave off their labours, never to return to the same occupation, because they had been split up into groups speaking different languages. Only among those who were engaged in a particular activity did their language remain unchanged; so, for instance, there was one for all the architects, one for all the carriers of stones, one for all the stone-breakers, and so on for all the different operations. As many as were the types of work involved in the enterprise, so many were the languages by which the human race was fragmented; and the more skill required for the type of work, the more rudimentary and barbaric the language they now spoke.

Perhaps Rini’s “big idea” is our version of Nimrod’s “vile plan”? Artificial intelligence is presented to us as a glorious project that we should care deeply about and devote both our technical and philosophical energies to even if it cannot be completed in our lifetimes. But will our descendants speak of it with the same contempt with which Dante speaks of the Tower of Babel? Will it be known as the “infamous device [because of which] the world no longer speaks a common language,” indeed, perhaps, because of which the world no longer speaks (or writes) at all, having left language, and the age-old business of concealing our thoughts (or the fact that we have no thoughts) to the machines? Kafka’s closing words are apt:

All the legends and songs that came to birth in that city are filled with longing for a prophesied day when the city would be destroyed by five successive blows from a gigantic fist. It is for that reason too that the city has a closed fist on its coat of arms.

Are not “all our legends and songs” (in the alternate universe of our science fiction) full of longing for the day when the machines take over and rid this Earth of the scourge to the planet we have made ourselves?

___________

If you like this post, you might also like a post I wrote a five years ago on my other blog applying the same imagery to the Search for Extraterrestrial Intelligence, a project that, unfortunately (because who doesn’t love it?), may be as incoherent as the quest for AI.

Image credit: German Late Medieval (c. 1370s) depiction of the construction of the tower, Meister der Weltenchronik – The Yorck Project (2002) 10.000 Meisterwerke der Malerei, via Wikipedia.

“One great use of words,” said Voltaire, “is to hide our thoughts.” In his famous treatise The Concept of Anxiety, Kierkegaard picked up on this idea, via Young and Talleyrand, and put a distinctively mischievous spin on it. We do, indeed, use language to hide our thoughts, he said, “namely, the fact that we don’t have any .” This is a great way to get into the core of my anxieties about artificial intelligence in general, and large language models like GPT-3 and LaMDA specifically. After all, I’m entirely certain that they have no conscious thoughts, but at least one person who is very close to the action, Blake Lemoine at Google, has been persuaded by their facility with language that they do. For my part, I’m concerned that the presumption that people generally use language to say what they think is being undermined by the apparent ability of unthinking machines to talk.

Now, my concern is mainly with academic or scholarly writing, i.e., writing done by students and faculty in universities. My working definition of this kind of writing has always been that it is the art of writing down what you know for the purpose of discussing it with other knowledgeable people. But this definition is of course a rather earnest one (some would say it is outright quaint) when compared with more cynical definitions that are, I should add, sometimes put forward without a hint of irony. Academic writing, it is said, is the art of putting words together in way that meets the expectations of your teachers; scholarly writing is the art of getting something past your reviewers so that it will be published and impress your tenure committee. On this view, that is, we use language at university, not to tell each other what we know, but to hide what we don’t know from each other, or, as Kierkegaard might suggest, the fact that we don’t really know anything at all. This is not a pleasant thing to think about for a writing instructor.

Two recent pieces in the Economist provide me with a good way of framing my concerns. “Neural language models aren’t long programs,” Blaise Agüera y Arcas tells us; “you could scroll through the code in a few seconds. They consist mainly of instructions to add and multiply enormous tables of numbers together.” Basically, these programs just convert some text into numbers, look up some other numbers in a database, carry out some calculations, the results of which are used to update the database, and are then also converted into a string of text. That’s all. What is confusing is that Agüera y Arcas then goes on to say that “since social interaction requires us to model one another, effectively predicting (and producing) human dialogue forces LaMDA to learn how to model people too.” His description of the program clearly says that it doesn’t “model people” at all. We might say that it uses language to hide the fact that it doesn’t have a model of people.

“There are no concepts behind the GPT-3 scenes,” Douglas Hofstadter explains; “rather, there’s just an unimaginably huge amount of absorbed text upon which it draws to produce answers.” But he, too, ends up being “strangely” optimistic about where this could go if we just turn up the computing power.

This is not to say that a combination of neural-net architectures that involve visual and auditory perception, physical actions in the world, language and so forth, might not eventually be able to formulate genuinely flexible concepts and recognise absurd inputs for what they are. But that still wouldn’t amount to consciousness. For consciousness to emerge would require that the system come to know itself, in the sense of being very familiar with its own behaviour, its own predilections, its own strengths, its own weaknesses and more. It would require the system to know itself as well as you or I know ourselves. That’s what I’ve called a “strange loop” in the past, and it’s still a long way off.

How far off? I don’t know. My record for predicting the future isn’t particularly impressive, so I wouldn’t care to go out on a limb. We’re at least decades away from such a stage, perhaps more. But please don’t hold me to this, since the world is changing faster than I ever expected it to. 

I feel at something of a disadvantage with people like this because they understand how the technology works better than I do and seem to see a potential in it that I don’t. That is, after trying to understand how they tell me it works, I conclude that intelligent language models aren’t just “a long way off” but are simply impossible to imagine. But then they tell me that they think these are all possibilities that we can expect to see even within a few decades. Some promoters of this technology even tell me that the systems already “model”, “reason”, “perceive”, “respond” intelligently. But looking at the technical details (within my limited ability to understand them) I simply don’t see them modeling anything — no more than a paper bag can add, as I like to put it, just because if you put two apples in there, and then another two, there are four.

My view is that we haven’t taken a step towards artificial intelligence since we invented the statue and the abacus. We have always been able to make things that look like people and other things that help us do things with our minds. The fantasy (or horror) of making something with a mind like ours is also nothing new. In other words, my worry is not that the machines will become conscious, but that we will one day be persuaded that unconscious machines are thinking.

At a deeper level, my worry is that machines will become impressive enough in their mindless output to suggest to students and scholars that their efforts to actually have thoughts of their own are wasted, that the idea of thinking something through, understanding it, knowing what you’re talking about, etc. will be seen as a quaint throwback to a bygone era when getting your writing done actually demanded the inconvenience of making up your mind about something. Since their task is only to “produce a text” (for grading or publication) and since a machine can do that simply by predicting what a good answer to a prompt might be, they might think it entirely unnecessary to learn, believe, or know anything at all to succeed.

That is, I worry that artificial intelligence will give scope to Kierkegaard’s anxiety. Perhaps, guided by ever more sophisticated language models, academic discourse will become merely a game of probabilities. What is the sequence of words that is most likely to get me the grade I want or the publication I need?

On Substack, Gary Marcus recently called the claim that LaMDA, or any other language model (like GPT-3), is sentient “nonsense on stilts.” Mark Coeckelbergh agreed, but with a twist. It is nonsense, he argued, not because of what we know about artificial intelligence, but because of what we don’t know about sentience. “The inconvenient truth,” he tells us at Medium, “is that we do not really know [whether LaMDA is sentient]. We do not really know because we do not know what sentience or consciousness is.” As he put it on Twitter in response to me, “we know how the language model works but we still don’t have a satisfactory definition of consciousness.” This strikes me as a rather strange philosophy.

Consider the Magic 8 Ball. Ask it a yes/no question and it will randomly give you one of twenty answers: 10 affirmative, 5 negative, 5 undecided. These answers are presented using familiar phrases like, “Without a doubt,” “Don’t count on it,” or “Cannot predict now.” Suppose someone asked us whether this device is sentient. Would we say, “The inconvenient truth is that we don’t know. We still don’t have a satisfactory definition of sentience”? (Presumably, we could run the same argument for the Magic 8 Ball’s alleged “clairvoyance”, which is surely not better defined than “sentience”.) Obviously not. Knowing how the device works is a sufficient basis for rejecting the claim that the device has an inner life to speak of, regardless of the fact that its output consists of recognizable linguistic tokens.

In his contribution to the debate in the Atlantic, Stephen Marche points out that the trouble begins with the language we use to describe our devices. To explain how the Magic 8 Ball “works”, I said that we “ask it” a question and that “it gives” us an answer. Likewise, Marche notes, the developers of language models tell us that they exhibit “impressive natural language understanding.” He warns against this kind of talk, citing a Google exec.

“I find our language is not good at expressing these things,” Zoubin Ghahramani, the vice president  of research at Google, told me. “We have words for mapping meaning between sentences and objects, and the words that we use are words like understanding. The problem is that, in a narrow sense, you could say these systems understand just like a calculator understands addition, and in a deeper sense they don’t understand. We have to take these words with a grain of salt.”

STEPHEN MARCHE, “Google’s AI Is Something Even Stranger Than Conscious,” The ATLANTIC, june 19, 2022

If you read that just a little too quickly you might miss another example of the way language misleads us about technology. “You could say that these systems understand just like a calculator understands addition,” Ghahramani says. But calculators don’t understand addition at all! Consider a series of examples I offered on Twitter:

Would we say that an abacus “understands” addition? What about a paper bag? You put two apples in it. Then you put another two apples in it. Then you have a look and there are four apples in the bag. The paper bag knows how to add? I don’t think so. If you want something that uses symbols, consider a spring scale. You calibrate it with standard weights such that 1 unit on the scale is one unit of weight. You have increasing weights labeled 1, 2, 3, 4 etc. On the tray there’s even a plus sign; you put two weights on it labelled “2” and the dial says “4”. Can the scale add? Of course not. A computer, likewise, is just a physical system that turns meaningless inputs into meaningless outputs. We understand the inputs and outputs. We imbue the output with meaning as the answer to a question.

Justin E.H. Smith wrote a thoughtful (as ever) piece about the incident on Substack. “Much of this speculation,” he suggests, “could be mercifully suspended if those involved in it just thought a little bit harder about what our own consciousness is actually like, and in particular how much it is conditioned by our embodiment and our emotion.” Note that this is basically the opposite of Coeckelbergh’s suggestion. Smith is telling us to remember what we know about sentience and consciousness from our own experience rather than get lost in the philosophy of consciousness and its lack of a “satisfactory definition” of its object. We know LaMDA is not conscious because we know it’s not sentient, and we know it’s not sentient because we know what sentience is and that it requires a body. And we know LaMDA doesn’t have one.

I note that Spike Jonze’s Her is now streaming on Netflix. When I first saw it, it occurred to me that it was actually just a story about love and loss told from inside a very clever satire of the absurdity of artificial intelligence. Descartes once said that he could imagine that he had no body. I’ve never believed him; I think he was was pretending. His “I” was literally no one ever … on philosophical stilts.

The universe (which others call the Library) …

Jorge luis Borges

Borges’s famous “Library of Babel” contains every possible 410-page book, 40 lines to the page, 80 characters to the line, 25 characters to choose from. William Goldbloom Bloch has written a fascinating study of its “unimaginable mathematics” in which we are told, among many other things, that it contains 25^1,312,000 books. To put this in perspective (if we can call it that), Goldbloom also informs us that stuffing the known universe with nothing but books would require only 10⁸⁴ books. Perhaps we can put that into further perspective by considering that Queneau’s hundred thousand billion (10¹⁴) poems would fill the pages of 2.4 x 10¹¹ 410-page books. That, at least, is a possible arrangement of some of the 3.28 x 10⁸⁰ particles that our real universe consists of.

Borges’s library, by contrast, is impossibly large. I agree with Goldbloom that it is in some sense “unimaginable” and that the wonder is that it is nonetheless quantifiable. We can put numbers on it but we simply cannot make sense of it. We can’t get our minds around it. While the library contains all the great works of literature that ever have been and ever will be written, it also contains a version of each with every imaginable combination of misprints. There are books with pages and pages of mostly As and others with mostly Bs. Borges tells us that there is no discernable order to the way the books have been arranged, which means that the odds of picking a random book off the shelf that contains the text of, say, Hamlet, are astronomically low. The vast majority of the books in this library will contain nonsense. In that sense, the library, which Borges calls “the universe” is absurd.

In his “intermittently philosophical dictionary,” Quine has proposed a simple way to understand this absurdity, a way to get our minds around its unthinkability, a way to see that Borges’s universe is not, properly speaking, a library at all and that what it contains are not, properly speaking, books. (To anticipate a later post, let’s say that they could not, properly speaking, be written.) He begins by reminding us what we’re dealing with:

The collection is finite. The entire and ultimate truth about everything is printed in full in that library, after all, insofar as it can be put in words at all. The limited size of each volume is no restriction, for there is always another volume that takes up the tale — any tale, true or false where any other volume leaves off. In seeking the truth we have no way of knowing which volume to pick up nor which to follow it with, but it is all right there.

Quine (1989), p. 224

The fact that the size of each volume is both arbitrary and unimportant suggests a way of reducing the amount of books. Instead of using every combination of 25 characters we could write all the books in Morse code, i.e., in sequences of dots and dashes. We now have 2^1,312,000 rather than 25^1,312,000 books. This will give us less information per page and therefore less information in each book. But, as Quine reminds us, “since for each cliff-hanging volume there is still every conceivable sequel on some shelf or other,” the library would still contain everything ever written by human hands (along with much, much more nonsense never seen by human eyes). We can go further.

There will be a great many books whose first or last halves are identical. So, if we split all the books in half, and discard all but one of the now identical ones, and then allow ourselves to serialize them when necessary to produce 410-page (and longer) works, no information is lost. And it is just as easy (i.e., it is impossible) to find what you’re looking for in this much smaller library (2^656,000 books.)

Let us press on: the library could of course simply contain all possible pages of 3200 characters of Morse code (there are just 2³²⁰⁰ such possible pages). But we can do better. Remembering Queneau’s sonnets, where each line is printed on a separate slip of paper, we can also imagine a library of all possible lines of 80 characters (only 2⁸⁰ lines), or even, as Quine now suggests, strips of seventeen characters. That gives us a mere 2¹⁷ or 131,072 strips. By combining them any which way we can produce everything that Borges’s library contained. And, still, it will be as easy to produce Hamlet by these random combinations as it would be to find a reasonably legible copy of it in the chaos of the universal library.

Quine now puts a button on the thought experiment:

The ultimate absurdity is now staring us in the face: a universal library of two volumes, one containing a single dot and the other a dash. Persistent repetition and alternation of the two is sufficient, we well know, for spelling out any and every truth. The miracle of the finite but universal library is a mere inflation of the miracle of binary notation: everything worth saying, and everything else as well, can be said with two characters. It is a letdown befitting the Wizard of Oz, but it has been a boon to computers.

Quine (1989), p. 225

Perhaps you can see where this is going? Perhaps you briefly saw a Library of Tokens flash before your eyes? We’ll get there. For now, I merely want to point out how truly artificial the Library is. It cannot occur in nature. It is what happens when you put no natural constraints on a model and the let the possibilities multiply, if not endlessly, then at least perfectly, imagining the instantiation of every arbitrary combination of already arbitrary signs. It is not a natural language model and its books are not displays of intelligence.

See also: “Robot Writes” and “A Hundred Thousand Billion Bots”