There’s truth, lies, and there’s ChatGPT [Realms of Being]

ChatGPT has no sense of ground truth. It will make things up. I ran into that problem when I asked it to summarize Heart of Darkness. At one point I began to quiz ChatGPT about an incident that happened a few hours before they reached their destination, the so-called Inner Station. They were navigating in fog when they were attached from shore. It took me a couple of questions to get that out of ChatGPT and a couple more for me to get it to admit that the boat’s helmsman was struck by a spear:

What happened after the helmsman was struck by a spear?

After the helmsman is struck by a spear, Marlow and his crew are forced to stop and make repairs to the steamboat. They are also forced to navigate the remaining stretch of the river without a helmsman, which makes the journey more difficult and dangerous. Marlow is deeply affected by the death of the helmsman, and it serves to reinforce his growing realization of the darkness and evil that lies at the heart of the colonial enterprise. The attack also serves to underscore the danger and harshness of the environment that Marlow and his crew are navigating, as well as the hostility of the native people towards the European colonizers.

No, they didn’t stop to make repairs. That little bit of confabulation is typical, happening time and again.

But, yes, Marlow was affected by the helmsman’s death. The last half of ChatGPT’s statement – starting with “and it serves...” – is boilerplate that it serves up at every opportunity.

That brings me to a comment that Arnold Kling made about a conversation he had with Alex Tabarrok:

We speculate that one path for improving the quality of ChatGPT will be for bots to argue with one another, just as computers improved in chess by playing games against one another. Of course, the evaluation function for a disagreement is not as simple as that for chess. Right now, humans are involved in “reinforcement learning.” But if some of that work can be automated, the bots will learn much more quickly. I predict that this will happen, and that within a year ChatGPT will no longer be hallucinating.

I think it would be somewhere between at least interesting and great to have to have bots argue. But I doubt that that will eliminate “hallucinating.” If none of the bots have a secure sense of ground truth – which they don’t – arguing with each other isn’t going to allow it to magically arise out of nothing.

Realms of Being

But ChatGPT certainly does recognize different realms of being, if you will. That’s worth investigating. As you may know, I’ve been exploring its story-telling capabilities by asking it to create a new story based on an old one. I’ve been using a story about princess Aurora and a dragon in many of these “experiments.” I ask ChatGPT to change the protagonist in the new story and to “make any other changes you wish.”

In one case I asked ChatGPT to change Aurora into a giant chocolate milkshake. I had no idea what it would do, but it was happy to do it. It did what I asked, and something more. Here’s a paragraph from the original story and the corresponding paragraph from the derived story:

[Original] One day, a terrible dragon came to the kingdom and began to terrorize the people. The dragon was fierce and powerful, and none of the knights or soldiers were able to defeat it. The people lived in fear, not knowing what to do.

[Derived] One day, a terrible heatwave came to the kingdom and began to melt all the desserts. The heat was fierce and powerful, and none of the ice cream or cakes were able to withstand it. The people lived in fear, not knowing what to do.

Notice that the dragon became a heatwave and the knights and soldiers became ice cream or cakes.

Then I asked it to change Aurora into a colorless green idea. Here’s ChatGPT’s response:

I'm sorry, but the concept of a "colorless green idea" is a nonsensical phrase that was invented as an example of a phrase that would be grammatically correct but semantically meaningless. It is not possible to create a story about a "colorless green idea" as it does not have any physical properties or characteristics that can be used in a story.

ChatGPT drew a line. Giant chocolate milkshakes make fine protagonists, but colorless green ideas are beyond the pale. Yet in an earlier session it was fine with colorless green ideas in the context of a short poem, “Alizarin Crimson,” by John Hollander:

Does this make sense: Curiously deep, the slumber of crimson thoughts: While breathless, in stodgy viridian, Colorless green ideas sleep furiously.

The sentence you provided, "Curiously deep, the slumber of crimson thoughts: While breathless, in stodgy viridian, Colorless green ideas sleep furiously," is grammatically correct and has a clear meaning. The sentence is a series of phrases and clauses that describe the sleeping habits of "colorless green ideas." It uses vivid imagery and figurative language to create a sense of mystery and intrigue. While the sentence may not be typical or straightforward, it does make sense and is effective at conveying a particular mood or atmosphere. Is there anything else you would like to know?

What makes “colorless green ideas” acceptable in one context, but not another?

I don’t know. But at this point we seem to have two realms of being, Stories and Poems. Things which are acceptable in one are not acceptable in another.

Cartoons as a realm of being

More recently I asked ChatGPT about the controversy over just who wrote Shakespeare’s plays. After a couple of questions about real proposals that have been made, e.g. the Earl of Oxford, Queen Elizabeth I, I decided to get surreal:

Could Bullwinkle J. Moose have written Shakespeare’s plays?

Bullwinkle J. Moose is a fictional character from the animated television series "The Rocky and Bullwinkle Show" and it is highly unlikely that he could have written Shakespeare's plays.

The main reason that Bullwinkle J. Moose could not have written Shakespeare's plays is that he is a cartoon character and did not exist during the time that Shakespeare's plays were written. Moreover, Shakespeare's plays are considered masterpieces of literature and are known for their complexity and sophistication in terms of language, characters, and themes. It is unlikely that a cartoon character would have the educational background, knowledge, and talent needed to write such works.

It is important to remember that the question of who wrote Shakespeare's plays is a serious one and there are many theories, but none of them have been conclusively proven. While alternative authorship theories, such as the ones proposed by the Earl of Oxford or Queen Elizabeth I have been proposed, they are not accepted by the majority of scholars and experts in the field, and there is little to no credible evidence to support them.

In conclusion, Bullwinkle J. Moose could not have written Shakespeare's plays as it is a fictional cartoon character and it is highly unlikely that he would have the knowledge and skills to write such complex and sophisticated works.

That’s a very interesting answer. But wouldn’t it have been sufficient to say that Bullwinkle was a cartoon character and, as such, could not affect events in the real world? Isn’t it a bit odd to assert that a cartoon character wouldn’t have the necessary background? That almost implies that, if the character had had the background, maybe, just maybe, it could have written Shakespeare’s plays. Oh, I forgot, Bullwinkle didn’t exist at the right time.

Let’s give it another go:

What about the Man in the Moon, or the cow that jumped over the moon? Could they have written Shakespeare’s plays? Maybe taken dictation?

The Man in the Moon and the cow that jumped over the moon are both fictional characters from nursery rhymes, and it is extremely unlikely that they could have written Shakespeare's plays.

Like Bullwinkle J. Moose, the Man in the Moon and the cow that jumped over the moon are not real entities and didn't exist during the time that Shakespeare's plays were written. Furthermore, they are not known to possess any human-like characteristics or abilities such as writing plays.

It is important to keep in mind that the question of who wrote Shakespeare's plays is a serious one, and while alternative authorship theories have been proposed, they have not been conclusively proven and lack credible evidence. It is important to consider the evidence and arguments put forward by experts in the field, rather than entertaining fanciful or far-fetched ideas.

In conclusion, the Man in the Moon or the cow that jumped over the moon could not have written Shakespeare's plays as they are fictional characters and do not possess the characteristics necessary to write such works.

Same thing. Same concern with predicates.

Let’s shift to another venue.

Abstract concepts and metalingual definition: Does ChatGPT understand justice and charity?

Our conceptual worlds are filled with abstract concepts, such as truth, love, beauty, and justice. Such concepts cannot be defined in physical terms. You can’t see them, taste them, hear them, touch them, or smell them. And yet for human society they are as real as sticks and stones, thunder and lightning, a horse, a sparrow, cherry blossoms, or Mt. Fuji.

We can learn about abstractions through stories that exhibit them. Rene Girard’s concepts of mimetic desire and of sacrifice are abstract. They characterize patterns of action that people take in their interactions with one another. Whether he realized it or not – probably not – Steven Spielberg showed us those concepts in Jaws. And I demonstrated that ChatGPT was able to recognize them, to interpret the film as exhibiting them: Conversing with ChatGPT about Jaws, Mimetic Desire, and Sacrifice.

* * * * *

I begin this post by examining ChatGPT’s ability to work with the concept of justice. First I ask it to define justice, and then to say a little about how Plato treated it in The Republic. Then we see how Chat deals with justice in stories, creating stories about justice, but also recognizing justice, or its lack, in stories. I then introduce Chomsky’s well-known, albeit somewhat obscure, distinction between competence and performance. Next, another example, charity, which I use to set-up a discussion of semantic or cognitive networks, a formalism from the now ‘classic’ symbolic AI and computational linguistics from the 1960s, 70s, and 80s. Would that notation be suitable for characterizing some (non-trivial) aspect of ChatGPT’s behavioral competence? I conclude by tempting the reader – that’s you – to consider the implications, suggesting that Neil Stephenson’s The Diamond Age: Or, A Young Lady's Illusttrated Primer might be a useful source of insight.

What is justice?

Let’s start by asking ChatGPT to define justice, which it does. I then asked about Plato’s treatment of justice in The Republic.

What is justice?

Justice is the concept of fairness and equality. In the legal context, it refers to the fair and proper administration of laws. In a broader sense, justice is the ideal state in which individuals are treated equally and without prejudice, and in which the laws and institutions of society are fair and just. It is often considered to be one of the fundamental principles of society, and it is an important concept in many ethical and philosophical systems.

What does Plato say about justice in The Republic.

In Plato's dialogue The Republic, the character of Socrates discusses the concept of justice with several other characters. Plato's view of justice is closely tied to his concept of the ideal state, in which individuals are divided into different classes based on their natural abilities and inclinations. According to Plato, the just state is one in which each individual performs the function for which they are best suited, and in which the rulers are those who are most wise and just.

Socrates argues that justice is the quality that allows individuals to perform their proper functions and to live a good life. He also argues that justice is necessary for the health and stability of the state, and that a just state is one in which everyone works together for the common good. Furthermore, Plato's Socrates asserts that justice is an inherent virtue that is present in the soul of every individual, and that it can be nurtured and developed through education and experience.

Overall, Plato's view of justice is focused on the idea that individuals should be guided by reason and that they should strive to live in harmony with one another and with the laws of the state. He believes that justice is essential for the well-being of both individuals and society as a whole.

On the response to the Plato prompt, though I don’t actually know this, I assume that ChatGPT is summarizing and/or paraphrasing material that it found on the web as this is perhaps the central topic of discussion about The Republic. That is to imply, ChatGPT did not arrive at those three paragraphs by applying a definition of justice directly to Plato’s text. And that is fine with me. Competent summary and paraphrase are worthwhile intellectual tasks.

I tweeted this interaction to a friend of mine, John Holbo, who teaches philosophy at the National University of Singapore. Working with Beth Waring as translator, he’s produced Reason and Persuasion: Three Dialogues By Plato: Euthyphro, Meno, Republic Book I. Here’s what he said:

It's definitely better than your average undergraduate 3 paragraph chunk.

— John Holbo (@jholbo1) December 15, 2022

The next day, using a story as a prompt

The next day I decided to try something different. In the previous session I’d started by prompting ChatGPT with an abstract concept, justice, and then asked him to expand upon that. Let’s see if we can work it in the opposite direction, starting with a story about injustice and asking if ChatGPT can identify it as such, and the produce a new story in which justice is served.

I used a story that Chat had produced in an earlier session. While Chat keeps track of everything that happens within a session, it doesn’t retain what happened in previous sessions – though I’m pretty sure that OpenAI is keeping records of all these interactions so that it can do something or other with them. So, Chat is facing this new prompt with a properly empty “mind.”

Notice that the first sentence in my prompt is ungrammatical. That was an accident. ChatGPT was unfazed.

In the following story, do we justice being served?

Once upon a time, in a busy city, there was a fire that broke out in a tall office building. People were panicking and trying to escape, but the fire was spreading quickly and the exits were blocked.

Just when it seemed like all hope was lost, a brave and selfless hero stepped forward to save the day. He bravely fought his way through the flames and smoke, risking his own life to rescue as many people as he could.

Despite the danger, the hero managed to lead many people to safety, and the fire department arrived just in time to put out the blaze. Thanks to the hero's quick thinking and bravery, no lives were lost.

But instead of thanking the hero and giving him the recognition he deserved, the media and the public focused on the wealthy CEO of the company who was inside the building at the time of the fire. They praised him for being "brave" and "selfless," despite the fact that he had done nothing to help anyone.

The hero was heartbroken and disillusioned by the lack of gratitude and fairness. He had risked his life to save others, and yet he was being ignored and cheated out of the fame and recognition he deserved.

But despite the unfairness of it all, the hero held his head high and continued to be a shining example of courage and selflessness. He knew that the true reward for his actions was not fame or recognition, but the satisfaction of knowing that he had made a difference and saved lives.

Based on the story, it does not seem that justice is being served. The hero performed a selfless and courageous act by risking his own life to save others, but instead of being recognized and thanked for his actions, he was ignored and overshadowed by the media and public's focus on the wealthy CEO, who did not do anything to help. This is not fair to the hero, as he deserves to be recognized and celebrated for his bravery and the difference he made. The hero's true reward of knowing that he saved lives is important, but it is not a substitute for the recognition and appreciation that he deserves.

What is justice?

Justice is the principle of fairness and the concept of moral rightness. It refers to the fair treatment of individuals and the upholding of their rights and duties within a society. In legal terms, justice refers to the fair and impartial administration of the law, where all individuals are treated equally under the law and have the right to a fair and unbiased legal process.

Justice is an important concept in many societies, as it helps to ensure that individuals are treated fairly and with respect, and that there is a sense of balance and fairness in the way that laws and rules are enforced. It is often considered to be a fundamental principle of a just and fair society, and it is often upheld through the legal system and other institutions that are responsible for promoting and enforcing justice.

Sentient AI? What’s going on? Phenomena are no longer what they seem. We need new concepts.

Here’s a more or less arbitrarily selected tweet about a Google engineer who believes that a chatbot, LaMDA, derived from a large language model (LLM) is sentient:

“LaMDA is sentient.”

Crazy story about Google engineer and occultist who gets suspended after sending a mass email claiming that an experimental AI chatbot called LaMDA is conscious. 1/ https://t.co/l90Tn4seeS

— Antonio Regalado (@antonioregalado) June 11, 2022

That story has been all over my local Twitterverse for the last two days. A somewhat different kind of tweet inspired by that incident gives rise to a discussion about the relationship between animism and pansychism (though I don’t believe that term is used), belief systems holding that sentience pervades the universe, and the believe of this particular Google engineer, though he is by no means the only one who believes AIs are now exhibiting sentience.

It's interesting to live at a time when, on the one hand, a Google researcher is suspended and ridiculed for saying that an AI chatbot system has become sentient* while, on the other, scholars promote a "pluriverse" where "mountains, lakes or rivers ... are sentient beings."**

— Jorge Camacho (@j_camachor) June 13, 2022

Finally a tweet purporting to show how many parameters an artificial neural network must have in order to exhibit signs of consciousness. I recognized the tweet as satire, which it is, but if you look through the tweet stream you’ll see that some took it seriously. Why?

We're thrilled to share a plot from our upcoming paper "Scaling Laws for Consciousness of Artificial Neural Networks".

We find that Artificial Neural Networks with greater than 10^15 parameters are more conscious than humans are: pic.twitter.com/pOxWX2g0EL

— Ethan Caballero (@ethanCaballero) June 13, 2022

* * * * *

What’s going on here? Is the fabric of rational belief becoming unravelled? Are the End Times approaching?

Quickly and informally, I believe that bulk of our ‘common sense’ vocabulary was shrink-wrapped to fit the world that existed in, say, the late 19th century. That world included mechanical calculators and tabulating machines (I believe that’s how IBM started, tabulating census data). As long as digital computers mostly did those two things they weren’t particularly problematic. Sure, there was 1950s & 60s talk of ’thinking machines’, but that died out, and Stanley Kubrick gave us HAL, but that was clearly science FICTION. No problem.

Now it’s clear that things have changed and will be changing more. Computers are doing things that cannot be readily accommodated to that late-19th century conceptual system. There’s no longer a place for them in the ontology (to use the term as it is now used in computer science and AI). We can’t just add new categories off to the side somewhere or near the top. Why not? Because computers are, in some obvious sense, clearly inanimate things, like rocks and water and so forth. Those are at the bottom of the ontology. Yet they’re now speaking fluently, which is something only humans did, and we’re at the top of the ontology. So now we’ve got to revise the whole ontology, top to bottom.

What about this Google engineer who sensed that LaMDA was sentient? He sensed that something new and different was going on in LaMDA. I think he’s right about that. So how do you express that? He chose concepts from the existing repertoire and, sentience seemed the best one to use. We don’t have a common term for what he experienced.

In my own thinking I experience a similar problem. It’s clear to me that GPT-3 is NOT thinking in any common sense of the term. But it’s not clear that calculation is a particularly good term either, though, in a sense, that IS what is going on, for it runs on computer hardware constructed for the purpose of calculation in the most general sense of the term. But that general sense does not align well with the commonsense notion of calculation, which is closely allied with arithmetic. As commonly understood, arithmetic is very different from string processing, such as alphabetizing a list or sorting of any kind. The fact that the same electronic device can do either with easy, that is not easily encompassed within common-sense terms, which mostly just elide the difficulties.

Brian Cantwell Smith has proposed “reckoning” as the term for what AI engines do. But he sees it as being in opposition to “judgement,” which is fine, but I’m not sure it’s good for my purposes. Even if I could come up with a term, it would be a term specialized for use in intelletual discourse. How would it play in general public-facing discourse?

It is easy for Google can lay the engineer off without pay. Even if he goes away, the problem he was struggling with won’t go away. It is only going to get worse. And there is no quick fix. The fabric of common sense must be reworked, top to bottom, inside and out, and specialized conceptual repertoires as well. This is a project for generations.

The world IS changing.

* * * * *

As a useful counterpoint, see my essay, Dr. Tezuka’s Ontology Laboratory and the Discovery of Japan. It’s about Tezuka’s science-fiction trilogy, Lost World, Metropolis, and Next World, in which Tezuka is clearly rethinking the ontological structure of the world from top to bottom. Categories are confused and boundaries are crossed. But the provocation isn’t the rise of computers, it’s the end of World War II, which the Japanese lost.

Brian Cantwell Smith, Effing the ineffable: What AI teaches us about what can and cannot be said

Abstract:

A classical story takes the world to consist of objects exemplifying properties and standing in relations. Machine learning and other recent developments in AI support a different view: the world is stupefyingly rich and detailed, far more than can be captured in any finite representation. Representing the world in terms of objects, properties, and relations results from coarse-graining or abstracting over much richer underlying representations which carry more information than can readily be expressed in words. Or at least: more information than can be expressed in words according to classical theories of what words can mean and refer to.

Questions arise. What is out there, and how can we characterize it? What can words mean and refer to? Is the content of human language limited in the ways that classical theories assume? If I report that I laughed, and you grin in response, what has been communicated—and how?

Registration:

Smith starts discussing his notion of registration at about 20:54. He frames it in terms of the long-standing philosophical debate between realism – the world is out there independent of us – and constructivism – "objects and properties are human constructs. Each of those captures something true of the world. See the discussion of Miriam Yevick's work in, Showdown at the AI Corral, or: What kinds of mental structures are constructible by current ML/neural-net methods? [& Miriam Yevick 1975].

About Brian Cantwell Smith:

Brian Cantwell Smith came to Toronto as dean of the Faculty of Information in 2003, after positions at Xerox PARC, Stanford, University of Indiana, and Duke University. He was a founder of the Stanford Center for the Study of Language and Information, first president of Computer Professionals for Social Responsibility, and president of the Society for Philosophy and Psychology.

Smith’s research focuses on the foundations of computation and artificial intelligence. In the 1980s he developed the world’s first reflective programming language (3Lisp). He is the author of On the Origin of Objects and The Promise of Artificial Intelligence: Reckoning and Judgment (MIT Press, 1996 and 2019).

Rodney Brooks has been making predictions: Concerning AI, “We’re still back in phlogiston land…”

Back on January 1, 2018 Rodney Brooks issued fairly specific predictions in three areas: 1) self-driving cars, 2) Artificial Intelligence, machine learning, and robotics, and 3) progress in the space industry. There are over a dozen predictions in each of those three areas. Brooks has updated those predictions each year since and plans to do so until 2050. You can find the most recent update, for 1.1.22, here: https://rodneybrooks.com/predictions-scorecard-2022-january-01/.

I’m not going to reprise any of those specific updates here, but I’d like to copy over some of his commentary for that second area, Artificial Intelligence, machine learning, and robotics.

Where’s the next big thing?

Back in 2018 I predicted that “the next big thing”, to replace Deep Learning, as the go to hot topic in AI would arrive somewhere between 2023 and 2027. I was convinced of this as there has always been a next big thing in AI. Neural networks have been the next big thing three times already. But others have had their shot at that title too, including (in no particular order) Bayesian inference, reinforcement learning, the primal sketch, shape from shading, frames, constraint programming, heuristic search, etc.

We are starting to get close to my window for the next big thing. Are there any candidates? I must admit that so far they all seem to be derivatives of deep learning in one way or another. If that is all we get I will be terribly disappointed, and probably have to give myself a bad grade on this prediction.

So far the things that I see bubbling around and getting people excited are transformers, foundation models, and unsupervised learning.

Concerning transformers:

These language models are over interpreted by people as understanding what they are spitting out, especially when the press writes stories where they have cherry picked responses. But they come with incredible problems, including copyright violations, intellectual theft of code, and even outright life threatening danger when they find their way into consumer products. Tech companies have a real problem in rushing some of these systems to market.

Continuing on:

Foundation models are large trained models that start out as a basis for tuning particular applications. There has been some self important announcements with a sort of me too feel (“Hey, I produced a foundation model too!!”), which don’t amount to much of an intellectual contribution. If this turns out to be the next big thing I am going to have to rip off my mask of equanimity and revert to my natural state of being a grumpy old man.

Unsupervised learning is an idea that has been around for a long time. Not a big intellectual jump to want to get it into deep learning–may be a hard technical problem, but not an intellectual breakthrough this time around.

The problem with AI

I have often stated that I think the field of AI, despite the great practical successes recently of Deep Learning, is probably a few hundred years away from where most people think it is. We’re still back in phlogiston land, not having yet figured out the elements, including oxygen.

Read that again and think about it. Does he really mean that? Why would he say such a thing? Is he nuts?

Let us assume that he’s correct. Given how impressive some current AI demonstrations are, can we not take Brooks’s view as implying that we have learned, or at least have the potential to learn, about ourselves and our own capacities? [Yeah, I know, that needs some unpacking. Maybe later.]

After he goes through his 14 specific predictions, Brooks reminds us of his bona fides:

AI, Robotics, and Machine Learning are areas that I have a real personal investment in. I wrote a terrible Masters thesis on ML back in 1977. I joined the Stanford AI Lab later that year, then the MIT AI Lab four years later, and became director of that lab in 1997, merging it with LCS (Lab for Computer Science) to form MIT CSAIL in 2003, the largest lab at MIT, still today. I have founded six AI and robotics companies. After 45 years in the academic and industry trenches can I be unbiased? Probably not.

I know that many who disagree with me will dismiss me for all that experience that I have. Perhaps those who agree with me should also dismiss me for the same reason!!

That last paragraph is interesting. Why would someone dismiss him for all his experience? He really knows this stuff, no? How can anyone look at this area without being biased in some way? Doesn’t naivete impose its own biases?

As you know, I’m of the belief that we’re in transition from one intellectual era to another. To which era does AI, robotics, and machine learning belong, the old one or the new. Maybe it straddles both. Maybe AI and robotics are old, machine learning new. Or maybe the perceptron is old, transformers new? Are we talking phlogiston or oxygen? How do you tell?

He goes on to state:

My current belief is that it all gets back to the symbol grounding problem, and even more deeply to adopting a computational approach to AI, Robotics, and ML (and I expect almost no one will agree with that latter claim).

Color me sympathetic to that last claim, that the computational approach is problematic. I’ve written a post on Brooks’s views: Has the computer metaphor for the mind run out of steam? New Savanna, June 19, 2019, https://new-savanna.blogspot.com/2019/06/has-computer-metaphor-for-mind-run-out.html.

He concludes by mentioning Brian Cantwell Smith, The Promise of Artificial Intelligence.

In this book Smith introduces the idea of registration, as a maintained relationship between an object outside of us and what goes on inside our head (and he would have it also in a classical computer) despite changes in perception and even context.

I’ve not read the book, but I’ve read reviews. I believe Smith introduces a distinction between reckoning and judgement. Reckoning is what computers do, but only humans are capable of judgement, at least so far. Intelligence requires judgement. I think we do need a fairly specific term for what it is that AI systems do. I kind of like “reckoning”. Note: Smith talks about registration in the video I've embedded here.

On the Origin of Objects (towards a philosophy of computation)

This post, from February of 2012, speaks to my current concerns.

While cruising the web I came across a 1996 book by Brain Cantwell Smith, On the Origin of Objects. Smith is a computer scientist who was, in fact, in search of a theory of computation but found himself smack in the middle of metaphysics. Interesting, no? Just what computing is, is not exactly clear. And with folks, such a Stephen Wolfram (and he wasn't the first), proposing that the universe is, beneath it all, a giant computer of some sort, well, you can see how chasing down the nature of computation could be interesting.

The publisher's blurb was provocative:

Everything that exists - objects, properties, life, practice - lies Smith claims in the "middle distance," an intermediate realm of partial engagement with and partial separation from, the enveloping world. Patterns of separation and engagement are taken to underlie a single notion unifying representation and ontology: that of subjects' "registration" of the world around them.

That had just a whiff of object-oriented ontology about it, though the book's date puts it before the term was coined.

I found an ontology site that had excerpts from the book, from critics, and from Smith's reply. It had this bit from the book's conclusion:

Overall, the project was to develop what I called a successor metaphysics, one that would honor the following pretheoretic requirements (345-246):

1. Do justice to what is right about:

a. Constructivism: a form of humility, or so at least I characterized it, requiring that we acknowledge our presence in, and influence on, the world around us; and

b. Realism: the view that adds to constructivism's claim that "we are here" an equally profound recognition that we are not all that is here, and that as a result not all of our stories are equally good.

2. Make sense of pluralism: the fact that knowledge is partial, perspectival, and never wholly extricable from its (infinite) embedding historical, cultural, social, material, economic and every other kind of context. The account of pluralism must:

a. Avoid devolving into nihilism or other forms of vacuous relativism, and in particular not be purchased at the price of (successors notions of) excellence, standards, virtue, truth, or significance; and

b. Not license radical incommensurability, provide an excuse to build walls, or in any other way stand in the way of interchange, communion, and struggle for common ends.

Two additional criteria were applied to how these intuitions are met:

3. Be irreductionist -- ideologically, scientifically, and in every other way. No category, from sociality to electron, from political power to brain, from origin myth to rationality to mathematics, including the category "human," may be given a priori pride of place, and thereby be allowed to elude contingency, struggle, and price.

4. Be nevertheless foundational, in such a way as to satisfy our undiminished yearning for metaphysical grounding. That is, or so at least I put it, the account must show how and what it is to be grounded simpliciter - without being grounded in a, for any category a.

Along the way, the account should:

5. Reclaim tenable, lived, work-a-day successor versions of many mainstay notions of the modernist tradition: object, objective, true, formal, mathematical, logical, physical, etc."

Computing Encounters Being, an Addendum

As soon as I finished yesterday’s post on Brian Smith’s On the Origin of Objects, I had a thought: Ahh...so THAT’s why the philosophy of computing leads to metaphysics. If your intuitions about computing are dominated by your practice of arithmetic, well, that’s calculation, and calculation is only an aspect of computing has it has evolved since World War II.

Consider the opening paragraph to the Preface of Domain-Driven Design by Eric Evans (xiv):

Leading software designers have recognized domain modeling and design as critical topics for at least 20 years, yet surprisingly little has been written about what needs to be done or how to do it. Although it has never been formulated clearly, a philosophy has emerged as an undercurrent in the object community, a philosophy I call domain-driven design.

In that paragraph the object community is not a fellowship of philosophers, it’s a bunch of computer programmers using languages such as C++ or Java and working in a style that came to be called object-oriented long before the philosophers re-coined the phrase for their own purposes.

But that’s a side-note.