Are we the cows of the future?

One of the questions posed by Yuval Harari in his writing on our possible futures is "What are we to do with all these humans who are, except for a small technocratic elite, no longer required as the means of production?" Esther Leslie, a professor of political aesthetics at Birkbeck College, University of London, does an essay on this issue, pointing out that our potential futures in the pastures of digital dictatorship — crowded conditions, mass surveillance, virtual reality — are already here. You should read her essay, and I passon just a few striking clips of text:

...Cows’ bodies have historically served as test subjects — laboratories of future bio-intervention and all sorts of reproductive technologies. Today cows crowd together in megafarms, overseen by digital systems, including facial- and hide-recognition systems. These new factories are air-conditioned sheds where digital machinery monitors and logs the herd’s every move, emission and production. Every mouthful of milk can be traced to its source.

And it goes beyond monitoring. In 2019 on the RusMoloko research farm near Moscow, virtual reality headsets were strapped onto cattle. The cows were led, through the digital animation that played before their eyes, to imagine they were wandering in bright summer fields, not bleak wintry ones. The innovation, which was apparently successful, is designed to ward off stress: The calmer the cow, the higher the milk yield.

A cow sporting VR goggles is comedic as much as it is tragic. There’s horror, too, in that it may foretell our own alienated futures. After all, how different is our experience? We submit to emotion trackers. We log into biofeedback machines. We sign up for tracking and tracing. We let advertisers’ eyes watch us constantly and mappers store our coordinates.

Could we, like cows, be played by the machinery, our emotions swayed under ever-sunny skies, without us even knowing that we are inside the matrix? Will the rejected, unemployed and redundant be deluded into thinking that the world is beautiful, a land of milk and honey, as they interact minimally in stripped-back care homes? We may soon graze in the new pastures of digital dictatorship, frolicking while bound.

Leslie then describes the ideas of German philosopher and social critic Theodor Adorno:

Against the insistence that nature should not be ravished by technology, he argues that perhaps technology could enable nature to get what “it wants” on this sad earth. And we are included in that “it.”...Nature, in truth, is not just something external on which we work, but also within us. We too are nature.

For someone associated with the abstruseness of avant-garde music and critical theory, Adorno was surprisingly sentimental when it came to animals — for which he felt a powerful affinity. It is with them that he finds something worthy of the name Utopia. He imagines a properly human existence of doing nothing, like a beast, resting, cloud gazing, mindlessly and placidly chewing cud.

To dream, as so many Utopians do, of boundless production of goods, of busy activity in the ideal society reflects, Adorno claimed, an ingrained mentality of production as an end in itself. To detach from our historical form adapted solely to production, to work against work itself, to do nothing in a true society in which we embrace nature and ourselves as natural might deliver us to freedom.

Rejecting the notion of nature as something that would protect us, give us solace, reveals us to be inextricably within and of nature. From there, we might begin to save ourselves — along with everything else.

(The above is a repost of MindBlog's 1/7/21 post)

An expanded view of human minds and their reality.

I want to pass on this recent essay by Venkatesh Rao in its entirety, because it has changed my mind on agreeing with Daniel Dennett that the “Hard Problem” of consciousness is a fabrication that doesn’t actually exist. There are so many interesting ideas in this essay that I will be returning to it frequently in the future.  

We Are All Dennettians Now

An homage riff on AI+mind+evolution in honor of Daniel Dennett

The philosopher Daniel Dennett (1942-2024) died last week. Dennett’s contributions to the 1981 book he co-edited with Douglas Hofstadter, The Mind’s I,¹ which I read in 1996 (rather appropriately while doing an undergrad internship at the Center for AI and Robotics in Bangalore), helped shape a lot of my early philosophical development. A few years later (around 1999 I think), I closely read his trollishly titled 1991 magnum opus, Consciousness Explained (alongside Steven Pinker’s similar volume How the Mind Works), and that ended up shaping a lot of my development as an engineer. Consciousness Explained is effectively a detailed neuro-realistic speculative engineering model of the architecture of the brain in a pseudo-code like idiom. I stopped following his work closely at that point, since my tastes took me in other directions, but I did take care to keep him on my radar loosely.

So in his honor, I’d like to (rather chaotically) riff on the interplay of the three big topics that form the through-lines of his life and work: AI, the philosophy of mind, and Darwinism. Long before we all turned into philosophers of AI overnight with the launch of ChatGPT, he defined what that even means.

When I say Dennett’s views shaped mine, I don’t mean I necessarily agreed with them. Arguably, your early philosophical development is not shaped by discovering thinkers you agree with. That’s for later-life refinements (Hannah Arendt, whom I first read only a few years ago, is probably the most influential agree-with philosopher for me). Your early development is shaped by discovering philosophers you disagree with.

But any old disagreement will not shape your thinking. I read Ayn Rand too (if you want to generously call her a philosopher) around the same time I discovered Dennett, and while I disagreed with her too, she basically had no effect on my thinking. I found her work to be too puerile to argue with. But Dennett — disagreeing with him forced me to grow, because it took serious work over years to decades — some of it still ongoing — to figure out how and why I disagreed. It was philosophical weight training. The work of disagreeing with Dennett led me to other contemporary philosophers of mind like David Chalmers and Ned Block, and various other more esoteric bunnytrails. This was all a quarter century ago, but by the time I exited what I think of as the path-dependent phase of my philosophical development circa 2003, my thinking bore indelible imprints of Dennett’s influence.

I think Dennett was right about nearly all the details of everything he touched, and also right (and more crucially, tasteful) in his choices of details to focus on as being illuminating and significant. This is why he was able to provide elegant philosophical accounts of various kinds of phenomenology that elevated the corresponding discourses in AI, psychology, neuroscience, and biology. His work made him a sort of patron philosopher of a variety of youngish scientific disciplines that lacked robust philosophical traditions of their own. It also made him a vastly more relevant philosopher than most of his peers in the philosophy world, who tend, through some mix of insecurity, lack of courage, and illiteracy, to stay away from the dirty details of technological modernity in their philosophizing (and therefore cut rather sorry figures when they attempt to weigh in on philosophy-of-technology issues with cartoon thought experiments about trolleys or drowning children). Even the few who came close, like John Searle, could rarely match Dennett’s mastery of vast oceans of modern techno-phenomenological detail, even if they tended to do better with clever thought experiments. As far as I am aware, Dennett has no clever but misleading Chinese Rooms or Trolley Problems to his credit, which to my mind makes him a superior rather than inferior philosopher.

I suspect he paid a cost for his wide-ranging, ecumenical curiosities in his home discipline. Academic philosophers like to speak in a precise code about the simplest possible things, to say what they believe to be the most robust things they can. Dennett on the other hand talked in common language about the most complex things the human mind has ever attempted to grasp. The fact that he got his hands (and mind!) dirty with vast amounts of technical detail, and dealt in facts with short half-lives from fast-evolving fields, and wrote in a style accessible to any intelligent reader willing to pay attention, made him barely recognizable as a philosopher at all. But despite the cosmetic similarities, it would be a serious mistake to class him with science popularizers or TED/television scientists with a flair for spectacle at the expense of substance.

Though he had a habit of being uncannily right about a lot of the details, I believe Dennett was almost certainly wrong about a few critical fundamental things. We’ll get to what and why later, but the big point to acknowledge is that if he was indeed wrong (and to his credit, I am not yet 100% sure he was), he was wrong in ways that forced even his opponents to elevate their games. He was as much a patron philosopher (or troll or bugbear) to his philosophical rivals as to the scientists of the fields he adopted. You could not even be an opponent of Dennett except in Dennettian ways. To disagree with the premises of Strong AI or Dennett’s theory of mind is to disagree in Dennettian ways.

If I were to caricature how I fit in the Dennettian universe, I suspect I’d be closest to what he called a “mysterian” (though I don’t think the term originated with him). Despite mysterian being something of a dismissive slur, it does point squarely at the core of why his opponents disagree with him, and the parts of their philosophies they must work to harden and make rigorous, to withstand the acid forces of the peculiarly Dennetian mode of scrutiny I want to talk about here.

So to adapt the line used by Milton Friedman to describe Keynes: We are all Dennettians now.

Let’s try and unpack what that means.

Mysterianism

As I said, in Dennettian terms, I am a “mysterian.” At a big commitments level, mysterianism is the polar opposite of the position Dennett consistently argued across his work, a version of what we generally call a “Strong AI” position. But at the detailed level, there are no serious disagreements. Mysterians and Strong AI people agree about most of the details of how the mind works. They just put the overall picture together differently because mysterians want to accommodate certain currently mysterious things that Strong AI people typically reject as either meaningless noise or shallow confusions/illusions.

Dennett’s version of Strong AI was both more robustly constructed than the sophomoric versions one typically encounters, and more broadly applied: beyond AI to human brains and seemingly intelligent processes like evolution. Most importantly, it was actually interesting. Reading his accounts of minds and computers, you do not come away with the vague suspicion of a non-neurotypical succumbing to the typical-mind fallacy and describing the inner life of a robot or philosophical zombie as “truth.” From his writing, it sounds like he had a fairly typical inner-life experience, so why did he seem to deny the apparent ineffable essence of it? Why didn’t he try to eff that essence the way Chalmers, for instance, does? Why did he seemingly dismiss it as irrelevant, unreal, or both?

To be a mysterian in Dennettian terms is to take ineffable, vitalist essences seriously. With AIs and minds, it means taking the hard problem of consciousness seriously. With evolution, it means believing that Darwinism is not the whole story. Dennett tended to use the term as a dismissive slur, but many, (re)claim it as a term of approbation, and I count myself among them.

To be a rigorous mysterian, as opposed to one of the sillier sorts Dennett liked to stoop to conquer (naive dualists, intelligent-designers, theological literalists, overconfident mystics…), you have to take vitalist essences “seriously but not literally.” My version of doing that is to treat my vitalist inclinations as placeholder pointers to things that lurk in the dank, ungrokked margins of the thinkable, just beyond the reach of my conceptualizing mind. Things I suspect exist by the vague shapes of the barely sensed holes they leave in my ideas. In pursuit of such things, I happily traffic in literary probing of Labatutian/Lovecraftian/Ballardian varieties, self-consciously magical thinking, junk from various pre-paradigmatic alchemical thought spaces, constructs that uncannily resemble astrology, and so on. I suppose it’s a sort of intuitive-ironic cognitive kayfabe for the most part, but it’s not entirely so.

So for example, when I talk of elan vital, as I frequently do in this newsletter, I don’t mean to imply I believe in some sort of magical fluid flowing through living things or a Gaian planetary consciousness. Nor do I mean the sort of overwrought continental metaphysics of time and subjectivity associated with Henri Bergson (which made him the darling of modernist literary types and an object of contempt to Einstein). I simply mean I suspect there are invisible things going on in the experience and phenomenology of life that are currently beyond my ability to see, model, and talk about using recognizably rational concepts, and I’d rather talk about them as best I can with irrational concepts than pretend they don’t exist.

Or to take another example, when I say that “Darwin is not the whole story,” I don’t mean I believe in intelligent design or a creator god (I’m at least as strong an atheist as Dennett was). I mean that Darwinian principles of evolution constrain but do not determine the nature of nature, and we don’t yet fully grok what completes the picture except perhaps in hand-wavy magical-thinking ways. To fully determine what happens, you need to add more elements. For example, you can add ideas like those of Stuart Kauffman and other complexity theorists. You could add elements of what Maturana and Varela called autopoiesis. Or it might be none of these candidate hole-filling ideas, but something to be dreamt up years in the future. Or never. But just because there are only unsatisfactory candidate ways for talking about stuff doesn’t mean you should conclude the stuff doesn’t exist.

In all such cases, there are more things present in phenomenology I can access than I can talk about using terms of reference that would be considered legitimate by everybody. This is neither known-unknowns (which are holes with shapes defined by concepts that seem rational), nor unknown-unknown (which have not yet appeared in your senses, and therefore, to apply a Gilbert Ryle principle, cannot be in your mind).

These are things that we might call magically known. Like chemistry was magically known through alchemy. For phenomenology to be worth magically knowing, the way-of-knowing must offer interesting agency, even if it doesn’t hang together conceptually.

Dennett seemed to mostly fiercely resist and reject such impulses. He genuinely seemed to think that belief in (say) the hard problem of consciousness was some sort of semantic confusion. Unlike say B. F. Skinner, whom critics accused of only pretending to not believe in inner processes, Dennett seemed to actually disbelieve in them.

Dennett seemed to disregard a cousin to the principle that absence of evidence is not evidence of absence: Presence of magical conceptualizations does not mean absence of phenomenology. A bad pointer does not disprove the existence of what it points to. This sort of error is easy to avoid in most cases. Lightning is obviously real even if some people seem to account for it in terms of Indra wielding his vajra. But when we try to talk of things that are on the phenomenological margins, barely within the grasp of sensory awareness, or worse, potentially exist as incommunicable but universal subjective phenomenology (such as the experience of the color “blue”), things get tricky.

Dennett was a successor of sorts to philosophers like Gilbert Ryle, and psychologists like B. F. Skinner. In evolutionary philosophy, his thinking aligned with people like Richard Dawkins and Steven Pinker, and against Noam Chomsky (often classified as a mysterian, though I think the unreasonable effectiveness of LLMs kinda vindicates to a degree Chomsky’s notions of an ineffable more-than-Darwin essence around universal grammars that we don’t yet understand).

I personally find it interesting to poke at why Dennett took the positions he took, given that he was contemplating the same phenomenological data and low-to-mid-level conceptual categories as the rest of us (indeed, he supplied much of it for the rest of us). One way to get at it is to ask: Was Dennett a phenomenologist? Are the limits of his ideas are the limits of phenomenology?

I think the answers are yes and yes, but he wasn’t a traditional sort of phenomenologist, and he didn’t encounter the more familiar sorts of limits.

The Limits of Phenomenology

Let’s talk regular phenomenology first, before tackling what I think was Dennett’s version.

I think of phenomenology, as a working philosophical method, to be something like a conceited form of empiricism that aims to get away from any kind of conceptually mediated seeing.

When you begin to inquire into a complex question with any sort of fundamentally empirical approach, your philosophy can only be as good as a) the things you know now through your (potentially technologically augmented) senses and b) the ways in which you know those things.

The conceit of phenomenology begins with trying to “unknow” what is known to be known, and contemplate the resulting presumed “pure” experiences “directly.” There are various flavors of this: Husserlian bracketing in the Western tradition, Zen-like “beginner mind” practices, Vipassana style recursive examination of mental experiences, and so on. Some flavors apply only to sense-observations of external phenomena, others apply only to subjective introspection, and some apply to both. Given the current somewhat faddish uptick in Eastern-flavored disciplines of interiority, it is important to take note of the fact that the phenomenological attitude is not necessarily inward-oriented. For example, the 19th century quest to measure a tenth of a second, and factor out the “human equation” in astronomical observations, was a massive project in Western phenomenology. The abstract thought experiments with notional clocks in the theory of relativity began with the phenomenology of real clocks.

In “doing” phenomenology, you are assuming that you know what you know relatively completely (or can come to know it), and have a reliable procedure for either unknowing it, or systematically alloying it with skeptical doubt, to destabilize unreliable perceptions it might be contributing to. Such destabilizability of your default, familiar way of knowing, in pursuit of a more-perfect unknowing, is in many ways the essence of rationality and objectivity. It is the (usually undeclared) starting posture for doing “science,” among other things.

Crucially, for our purposes in this essay, you do not make a careful distinction between things you know in a rational way and things you know in a magical or mysterian way, but effectively assume that only the former matter; that the latter can be trivially brushed aside as noise signifying nothing that needs unknowing. I think the reverse is true. It is harder, to the point of near impossible, to root out magical ideas from your perceptions, and they signify the most important things you know. More to the point, it is not clear that trying to unknow things, especially magical things, is in fact a good idea, or that unknowing is clarifying rather than blinding. But phenomenology is committed to trying. This has consequences for “phenomenological projects” of any sort, be they Husserlian or Theravadan in spirit.

A relatively crude example: “life” becomes much less ineffable (and depending on your standards, possibly entirely drained of mystery) once you view it through the lens of DNA. Not only do you see new things through new tools, you see phenomenology you could already see, such as Mendelian inheritance, in a fundamentally different way that feels phenomenologically “deeper” when in fact it relies on more conceptual scaffolding, more things that are invisible to most of us, and requires instruments with elaborate theories attached to even render intelligible. You do not see “ATCG” sequences when contemplating a pea flower. You could retreat up the toolchain and turn your attention to how instruments construct the “idea of DNA” but to me that feels like a usually futile yak shave. The better thing to do is ask why a more indirect way of knowing somehow seems to perceive more clearly than more direct ways.

It is obviously hard to “unsee” knowledge of DNA today when contemplating the nature of life. But it would have been even harder to recognize that something “DNA shaped” was missing in say 1850, regardless of your phenomenological skills, by unknowing things you knew then. In fact, clearing away magical ways of knowing might have swept away critical clues.

To become aware, as Mendel did, that there was a hidden order to inheritance in pea flowers, takes a leap of imagination that cannot be purely phenomenological. To suspect in 1943, as Schrodinger did, the existence of “aperiodic covalent bonded crystals” at the root of life, and point the way to DNA, takes a blend of seeing and knowing that is greater than either. Magical knowing is pathfinder-knowing that connects what we know and can see to what we could know and see. It is the bootstrapping process of the mind.

Mendel and Schrodinger “saw” DNA before it was discovered, in terms of reference that would have been considered “rational” in their own time, but this has not always been the case. Newton, famously, had a lot of magical thinking going on in his successful quest for a theory of gravity. Kepler was a numerologist. Leibniz was ball of mad ideas. One of Newton’s successful bits of thinking, the idea of “particles” of light, which faced off against Huygens’ “waves,” has still not exited the magical realm. The jury is still out in our time about whether quantized fields are phenomenologically “real” or merely a convenient mnemonic-metaphoric motif for some unexpected structure in some unreasonably effective math.

Arguably, none of these thinkers was a phenomenologist, though all had a disciplined empirical streak in their thinking. The history of their ideas suggests that phenomenology is no panacea for philosophical troubles with unruly conceptual universes that refuse to be meekly and rationally “bracketed” away. There is no systematic and magic-free way to march from current truths to better ones via phenomenological disciplines.

The fatal conceit of naive phenomenology (which Paul Feyerabend spotted) is the idea that there is privileged reliable (or meta-reliable) “technique” of relating to your sense experiences, independent of the concepts you hold, whether that “technique” is Husserlian bracketing or vipassana. Understood this way, theories of reality are not that different from physical instruments that extend our senses. Experiment and theory don’t always expose each other’s weaknesses. Sometimes they mutually reinforce them.

In fact, I would go so far as to suggest—and I suspect Dennett would have agreed—that there is no such thing as phenomenology per se. All we ever see is the most invisible of our theories (rational and magical), projected via our senses and instruments (which shape, and are shaped by, those theories), onto the seemingly underdetermined aspects of the universe. There are only incomplete ways of knowing and seeing within which ideas and experiences are inextricably intertwined. No phenomenological method can consistently outperform methodological anarchy.

To deny this is to be a traditional phenomenologist, striving to procedurally separate the realm of ideas and concepts from the realm of putatively unfactored and “directly perceived” (a favorite phrase of meditators) “real” experiences.

Husserlian bracketing — “suspending trust in the objectivity of the world” — is fine in theory, but not so easy in practice. How do you know that you’re setting aside preconceived notions, judgments, and biases and attending to a phenomenon as it truly is? How do you set aside the unconscious “theory” that the Sun revolves around the Earth, and open your mind to the possibility that it’s the other way around? How do you “see” DNA-shaped holes in current ways of seeing, especially if they currently manifest as strange demons that you might sweep away in a spasm of over-eager epistemic hygiene? How do you relate, as a phenomenologist, to intrinsically conceptual things like electrons and positrons that only exist behind layers of mathematics describing experimental data processed through layers of instrumentation conceived by existing theories? If you can’t check the math yourself, how can you trust that the light bulb turning on is powered by those “electrons” tracing arcs through cloud chambers?

In practice, we know how such shifts actually came about. Not because philosophers meditated dispassionately on the “phenomenology” with free minds seeing reality as it “truly is,” but because astronomers and biologists with heads full of weird magical notions looked through telescopes and microscopes, maintained careful notes of detailed measurements, informed by those weird magical theories, and tried to account for discrepancies. Tycho Brahe, for instance, who provided the data that dethroned Ptolemy, believed in some sort of Frankenstein helio-geo-centric Ptolemy++ theory. Instead of explaining the discrepancies, as Kepler did later, Brahe attempted to explain them away using terms of reference he was attached to. He failed to resolve the tension. But he paved the way to Kepler resolving that particular tension (who of course introduced new ones, while lost in his own magical thinking about platonic solids). Formally phenomenological postures were not just absent from the story, but would have arguably retarded it by being too methodologically conservative.

Phenomenology, in other words, is something of a procedural conceit. An uncritical trust in self-certifying ways of seeing based entirely on how compelling they seem to the seer. The self-certification follows some sort of seemingly rational procedure (which might be mystical but still rational in the sense of being coherent and disciplined and internally consistent) but ultimately derives its authority from the intuitive certainties and suspicions of the perceiving subject. Phenomenological procedures are a kind of rule-by-law for governing sense experience in a laissez-faire way, rather than the “objective” rule-of-law they are often presented as. Phenomenology is to empiricism as “socialism with Chinese characteristics” is to liberal democracy.

This is not to say phenomenology is hopelessly unreliable or useless. All methodologies have their conceits, which manifest as blindspots. With phenomenology, the blindspot manifests as an insistence on non-magicality. The phenomenologist fiercely rejects the Cartesian theater and the varied ghosts-in-machines that dance there. The meditator insists he is “directly perceiving” reality in a reproducible way, no magic necessary. I do not doubt that these convictions are utterly compelling to those who hold them; as compelling as the incommunicable reality of perceiving “blue” is to everybody. I have no particular argument with such insistence. What I actually have a problem with is the delegitimization of magical thinking in the process, which I suspect to be essential for progress.

My own solution is to simply add magical thinking back into the picture for my own use, without attempting to defend that choice, and accepting the consequences.

For example, I take Myers-Briggs and the Enneagram seriously (but not literally!). I believe in the hard problem of consciousness, and therefore think “upload” and “simulationism” ideas are not-even-wrong. I don’t believe in Gods or AGIs, and therefore don’t see the point of Pascal’s wager type contortions to avoid heaven/hell or future-simulated-torture scenarios. In each case my commitments rely on chains of thought that are at least partly magical thinking, and decidedly non-phenomenological, which has various social consequences in various places. I don’t attempt to justify any of it because I think all schemes of justification, whether they are labeled “science” or something else, rest on traditional phenomenology and its limits.

Does this mean solipsism is the best we can hope for? This is where we get back to Dennett.

Dennett, to his credit, I don’t think he was a traditional phenomenologist, and he mostly avoided all the traps I’ve pointed out, including the trap of solipsism. Nor was he what one might call a “phenomenologist of language” like most modern analytical philosophers in the West. He was much too interested in technological modernity (and the limits of thought it has been exposing for a century) to be content with such a shrinking, traditionalist philosophical range.

But he was a phenomenologist in the broader sense of rejecting the possible reality of things that currently lack coherent non-magical modes of apprehension.

So how did he operate if not in traditional phenomenological ways?

Demiurge Phenomenology

I believe Dennett was what we might call a demiurge phenomenologist, which is a sort of late modernist version of traditional phenomenology. It will take a bit of work to explain what I mean by that.

I can’t recall if he ever said something like this (I’m definitely not a completist with his work and have only read a fraction of his voluminous output), but I suspect Dennett believed that the human experience of “mind” is itself subject to evolutionary processes (think Jaynes and bicameral mind theories for example — I seem to recall him saying something approving about that in an interview somewhere). He sought to construct philosophy in ways that did not derive authority from an absolute notion of the experience of mind. He tried to do relativity theory for minds, but without descending into solipsism.

It is easiest to appreciate this point by starting with body experience. For example, we are evolved from creatures with tails, but we do not currently possess tails. We possess vestigial “tail bones” and presumably bits of DNA relevant to tails, but we cannot know what it is like to have a tail (or in the spirit of mysterian philosopher Thomas Nagel’s What is it Like to Be a Bat provocation, which I first read in The Mind’s I, what it is like for a tailed creature to have a tail).

We do catch tantalizing Lovecraftian-Ballardian glimpses of our genetic heritage though. For example, the gasping reflex and shot of alertness that accompanies being dunked in water (the mammalian dive reflex) is a remnant of a more aquatic evolutionary past that far predates our primate mode of existence. Now apply that to the experience of “mind.”

Why does Jaynes’ bicameral mind theory sound so fundamentally crackpot to modern minds? It could be that the notion is actually crackpot, but you cannot easily dismiss the idea that it’s actually a well-posed notion that only appears crackpot because we are not currently possessed of bicameral mind-experiences (modulo cognitive marginalia like tulpas and internal family systems — one of my attention/taste biases is to index strongly on typical rather than rare mental experiences; I believe the significance of the latter is highly overstated due to the personal significance they acquire in individual lives).

I hope it is obvious why the possibility that the experience of mind is subject to evolution is fatal to traditional phenomenology. If despite all the sophistication of your cognitive toolchain (bracketing, jhanas, ketamine, whatever), it turns out that you’re only exploring the limits of the evolutionarily transient and arbitrary “variety of mind” that we happen to experience, what does that say about the reliability of the resulting supposedly objective or “direct” perceptions of reality itself that such a mind mediates?

This, by the way, is a problem that evolutionary terms of reference make elegantly obvious, but you can get here in other ways. Darwinian evolution is convenient scaffolding to get there (and the one I think Dennett used), but ultimately dispensable. But however you get there, the possibility that experiences of mind are relative to contingent and arbitrary evolutionary circumstances is fatal to the conceits of traditional phenomenology. It reduces traditional phenomenology in status to any old sort of Cartesian or Platonic philosophizing with made-up bullshit schemas. You might as well make 2x2s all day like I sometimes do.

The Eastern response to this quandary has traditionally been rather defeatist — abandoning the project of trying to know reality entirely. Buddhist and Advaita philosophies in particular, tend to dispense with “objective reality” as an ontologically meaningful characterization of anything. There is only nothing. Or only the perceiving subject. Everything else is maya-moh, a sentimental attachment to the ephemeral unreal. Snap out of it.


I suspect Western philosophy was starting to head that way in the 16th century (through the Spinoza-vs-Leibniz shadowboxing years), but was luckily steered down a less defeatist path to a somewhat uneasy detente between a sort of “probationary reality” accessed through technologically augmented senses, and a subjectivity resolutely bound to that probationary reality via the conceits of traditional phenomenology. This is a long-winded way of saying “science happened” to Western philosophy.

I think that detente is breaking down. One sign is the growing popularity of the relatively pedestrian metaphysics of physicists like Donald Hoffman (leading to a certain amount of unseemly glee among partisans of Eastern philosophies — “omigod you think quantum mechanics shows reality is an illusion? Welcome to advaita lol”).

But despite these marginally interesting conversations, and whether you get there via Husserl, Hoffman, or vipassana, we’re no closer to resolving what we might call the fundamental paradox of phenomenology. If our experience of mind is contingent, how can any notion of justifiable absolute knowledge be sustained? We are essentially stopped clocks trying to tell the time.

Dennett, I think favored one sort of answer: That the experience of mind was too untrustworthy and transient to build on, but that mind’s experience of mathematics was both trustworthy and absolute. Bicameral or monocameral, dolphin-brain or primate-brain, AI-brain or Hoffman-optimal ontological apparatus, one thing that is certain is that a prime number is a prime number in all ways that reality (probationary or not, illusory or not) collides with minds (typical or atypical, bursting with exotic qualia or full of trash qualia). Even the 17 and 13 year cicadas agree. Prime numbers constitute a fixed point in all the ways mind-like things have experience-like things in relation to reality-like things, regardless of whether minds, experiences, and reality are real. Prime numbers are like a motif that shows up in multiple unreliable dreams. If you’re going to build up a philosophy of being, you should only use things like prime numbers.

This is not just the most charitable interpretation of Dennett’s philosophy, but the most interesting and powerful one. It’s not that he thought of the mysterian weakness for ineffable experiences as being particularly “illusory”. As far as he was concerned, you could dismiss the “experience of mind” in its entirety as irrelevant philosophically. Even the idea that it has an epiphenomenal reality need not be seriously entertained because the thing that wants to entertain that idea is not to be trusted.

You see signs of this approach in a lot of his writing. In his collaborative enquires with Hofstadter, in his fundamentally algorithmic-mathematical account of evolution, in his seemingly perverse stances in debates both with reputable philosophers of mind and disreputable intelligent designers. As far as he was concerned, anyone who chose to build any theory of anything on the basis of anything other than mathematical constancy was trusting the experience of mind to an unjustifiable degree.

Again, I don’t know if he ever said as much explicitly (he probably did), but I suspect he had a basic metaphysics similar to that of another simpatico thinker on such matters, Roger Penrose: as a triad of physical/mental/platonic-mathematical worlds projecting on to each other in a strange loop. But unlike Penrose, who took the three realms to be equally real (or unreal) and entangled in an eternal dance of paradox, he chose to build almost entirely on the Platonic-mathematical vertex, with guarded phenomenological forays to the physical world, and strict avoidance of the mental world as a matter of epistemological hygiene.

The guarded phenomenological forays, unlike those of traditional phenomenologists, were governed by an allow list rather than a block list. Instead of trying to “block out” suspect conceptual commitments with bracketing or meditative discipline, he made sure to only work with allowable concepts and percepts that seemed to have some sort of mathematical bones to them. So Turing machines, algorithms, information theory, and the like, all made it into his thinking in load-bearing ways. Everything else was at best narrative flavor or useful communication metaphors. People who took anything else seriously were guilty of deep procedural illusions rather than shallow intellectual confusions.

If you think about it, his accounts of AI, evolution, and the human mind make a lot more sense if you see them as outcomes of philosophical construction processes governed by one very simple rule: Only use a building block if it looks mathematically real.

Regardless of what you believe about the reality of things other than mathematically underwritten ones, this is an intellectually powerful move. It is a kind of computational constructionism applied to philosophical inquiry, similar to what Wolfram does with physics on automata or hypergraphs, or what Grothendieck did with mathematics.

It is also far harder to do, because philosophy aims and claims to speak more broadly and deeply than either physics or mathematics.

I think Dennett landed where he did, philosophically, because he was essentially trying to rebuild the universe out of a very narrow admissible subset of the phenomenological experience of it. Mysterian musings didn’t make it in because they could not ride allowable percepts and concepts into the set of allowable construction materials.

In other words, he practiced demiurge phenomenology. Natural philosophy as an elaborate construction practice based on self-given rules of construction.

In adopting such an approach he was ahead of his time. We’re on the cusp of being able to literally do what he tried to do with words — build phenomenologically immersive virtual realities out of computational matter that seem to be defined by nothing more than mathematical absolutes, and have almost no connection even to physical reality, thanks to the seeming buffering universality of Turing-equivalent computation.

In that almost, I think, lies the key to my fundamental disagreement with Dennett, and my willingness to wander in magical realms of thought where mathematically sure-footed angels fear to tread. There are… phenomenological gaps between mathematical reconstructions of reality by energetic demiurges (whether they work with powerful arguments or VR headsets) and reality itself.

The biggest one, in my opinion, is the experience of time, which seems to oddly resist computational mathematization (though Stephen Wolfram claims to have one… but then he claims to have a lot of things). In an indirect way, disagreeing with Dennett at age 20 led me to my lifelong fascination with the philosophy of time.

Where to Next?

It is something of a cliche that over the last century or two, philosophy has gradually and reluctantly retreated from an increasing number of the domains it once claimed as its own, as scientific and technological advances rendered ungrounded philosophical ideas somewhere between moot and ridiculous. Bergson retreating in the face of the Einsteinian assault, ceding the question of the nature of time to physics, is probably as good a historical marker of the culmination of the process as any.

I would characterize Dennett as a late modernist philosopher in relation to this cliche. Unlike many philosophers, who simply gave up on trying to provide useful accounts of things that science and technology were beginning to describe in inexorably more powerful ways, he brought enormous energy to the task of simply keeping up. His methods were traditional, but his aim was radical: Instead of trying to provide accounts of things, he tried to provide constructions of things, aiming to arrive at a sense of the real through philosophical construction with admissible materials. He was something like Brouwer in mathematics, trying to do away with suspect building blocks to get to desirable places only using approved methods.

This actually worked very well, as far as it went. For example, I think his philosophy of mind was almost entirely correct as far as the mechanics of cognition go, and the findings of modern AI vindicate a lot of the specifics. For example, his idea of a “multiple drafts” model of cognition (where one part of the brain generates a lot of behavioral options in a bottom-up, anarchic way, and another part chooses a behavior from among them) is basically broadly correct, not just as a description of how the brain works, but of how things like LLMs work. But unlike many other so-called philosophers of AI he disagreed with, like Nick Bostrom, Dennett’s views managed to be provocative without being simplistic, opinionated without being dogmatic. He appeared to have a Strong AI stance similar to many people I disagree with, but unlike most of those people, I found his views worth understanding with some care, and hard to dismiss as wrong, let alone not-even-wrong.

I like to think he died believing his philosophies — of mind, AI, and Darwinism — to be on the cusp of a triumphant redemption. There are worse ways to go than believing your ideas have been thoroughly vindicated. And indeed, there was a lot Dennett got right. RIP.

Where do we go next with Dennettian questions about AI, minds, and evolution?

Oddly enough, I think Dennett himself pointed the way: demiurge phenomenology is the way. We just need to get more creative with it, and admit magical thinking into the process.

Dennett, I think, approached his questions the way some mathematicians originally approached Euclid’s fifth postulate: Discard it and try to either do without, or derive it from the other postulates. That led him to certain sorts of demiurgical constructions of AI, mind, and evolution.

There is another, equally valid way. Just as other mathematicians replaced the fifth postulates with alternatives and ended up with consistent non-Euclidean geometries, I think we could entertain different mysterian postulates and end up with a consistent non-Dennettian metaphysics of AI, mind, and evolution. You’d proceed by trying to do your own demiurgical constructing of a reality. An alternate reality.

For instance, what happens if you simply assume that there is human “mind stuff” that ends with death and cannot be uploaded or transferred to other matter, and that can never emerge in silico. You don’t have to try accounting for it (no need to mess with speculations about the pineal gland like Descartes, or worry about microtubules and sub-Planck-length phenomena like Penrose). You could just assume that consciousness is a thing like space or time, and run with the idea and see where you land and what sort of consistent metaphysical geometries are possible. This is in fact what certain philosophers of mind like Ned Block do.

The procedure can be extended to other questions as well. For instance, if you think Darwin is not the whole story with evolution, you could simply assume there are additional mathematical selection factors having to do with fractals or prime numbers, and go look for them, as the Santa Fe biologists have done. Start simple and stupid, for example, by applying a rule that “evolution avoids rectangles” or “evolution cannot get to wheels made entirely of grown organic body parts” and see where you land (for the latter, note that the example in Dark Materials trilogy cheats — that’s an assembled wheel, not an evolved one).

But all these procedures follow the basic Dennettian approach of demiurgical constructionist phenomenology. Start with your experiences. Let in an allow-list of percepts as concepts. Add an arbitrarily constructed magical suspicion or two. Let your computer build out the entailments of those starter conditions. See what sort of realities you can conjure into being. Maybe one of them will be more real than your current experience of reality. That would be progress. Perhaps progress only you can experience, but still, progress.

Would such near-solipsistic activities constitute a collective philosophical search for truth? I don’t know. But then, I don’t know if we have ever been on a coherent collective philosophical search for truth. All we’ve ever had is more or less satisfying descriptions of the primal mystery of our own individual existence.

Why is there something, rather than nothing, it is like, to be me?

Ultimately, Dennett did not seem to find that question to be either interesting or serious. But he pointed the way for me to start figuring out why I do. And that’s why I too am a Dennettian.


footnote  1
I found the book in my uncle’s library, and the only reason I picked it up was because I recognized Hofstadter’s name because Godel, Escher, Bach had recently been recommended to me. I think it’s one of the happy accidents of my life that I read The Mind’s I before I read Hofstadter’s Godel, Escher, Bach. I think that accident of path-dependence may have made me a truly philosophical engineer as opposed to just an engineer with a side interest in philosophy. Hofstadter is of course much better known and familiar in the engineering world, and reading him is something of a rite of passage in the education of the more sentimental sorts of engineers. But Hofstadter’s ideas were mostly entertaining and informative for me, in the mode of popular science, rather than impactful. Dennett on the other hand, was impactful.

New protocols for uncertain times.

I want to point to a project launched by Venkatest Rao and others last year: “The Summer of Protocols.”  Some background for this project can be found in his essay “In Search of Hardness”.  Also,  “The Unreasonable Sufficiency of Protocols”  essay by Rao et al. is an excellent presentation of what protocols are about.  I strongly recommend that you read it if nothing else. 

Here is a description of the project: 

Over 18 weeks in Summer 2023, 33 researchers from diverse fields including architecture, law, game design, technology, media, art, and workplace safety engaged in collaborative speculation, discovery, design, invention, and creative production to explore protocols, boadly construed, from various angles.

Their findings, catalogued here in six modules, comprise a variety of textual and non-textual artifacts (including art works, game designs, and software), organized around a set of research themes: built environments, danger and safety, dense hypermedia, technical standards, web content addressability, authorship, swarms, protocol death, and (artificial) memory.

I have read through through Module One for 2003, and it is solid interesting deep dive stuff.  Module 2 is also available. Modules 3-6 are said to be 'coming soon’  (as of 4/4/24, four months into a year that has Summer of Protocols program 2024 already underway, with the deadline for proposals 4/12/24.)

Here is one clip from the “In Search of Hardness” essay:

…it’s only in the last 50 years or so, with the rise of communications technologies, especially the internet and container shipping, and the emergence of unprecedented planet-scale coordination problems like climate action, that protocols truly came into focus as first-class phenomena in our world; the sine qua non of modernity. The word itself is less than a couple of centuries old.

And it wasn’t until the invention of blockchains in 2009 that they truly came into their own as phenomena with their own unique technological and social characteristics, distinct from other things like machines, institutions, processes, or even algorithms.

Protocols are engineered hardness, and in that, they’re similar to other hard, enduring things, ranging from diamonds and monuments to high-inertia institutions and constitutions.

But modern protocols are more than that. They’re not just engineered hardness, they are programmable, intangible hardness. They are dynamic and evolvable. And we hope they are systematically ossifiable for durability. They are the built environment of digital modernity.”

Our seduction by AI’s believable human voice.

 I want to point to an excellent New Yorker article by Patrick House titled  “The Lifelike Illusion of A.I.”  The article strikes home for me, for when a Chat Bot responds to one of my prompts using the pronoun “I”  I unconsciously attribute personhood to the machine, forgetting that this is a cheap trick used by programmers of large language model to increase the plausibility of responses.

House starts off his article by describing the attachments people formed with the Furby, an animatronic toy resembling a small owl, and Pleo, an animatronic toy dinosaur. Both use a simple set of rules to make the toys appear to be alive. Furby’s eyes move up and down in a way meant to imitate an infant’s eye movements while scanning a parent’s face. Pleo mimes different emotional behaviors when touched differently.

For readers who hit the New Yorker paywall when they click the above link, here are a few clips from the article that I think get across the main points:

“A Furby possessed a pre-programmed set of around two hundred words across English and “Furbish,” a made-up language. It started by speaking Furbish; as people interacted with it, the Furby switched between its language dictionaries, creating the impression that it was learning English. The toy was “one motor—a pile of plastic,” Caleb Chung, a Furby engineer, told me. “But we’re so species-centric. That’s our big blind spot. That’s why it’s so easy to hack humans.” People who used the Furby simply assumed that it must be learning.”

Chung considers Furby and Pleo to be early, limited examples of artificial intelligence—the “single cell” form of a more advanced technology. When I asked him about the newest developments in A.I.—especially the large language models that power systems like ChatGPT—he compared the intentional design of Furby’s eye movements to the chatbots’ use of the word “I.” Both tactics are cheap, simple ways to increase believability. In this view, when ChatGPT uses the word “I,” it’s just blinking its plastic eyes, trying to convince you that it’s a living thing.

We know that, in principle, inanimate ejecta from the big bang can be converted into thinking, living matter. Is that process really happening in miniature at server farms maintained by Google, Meta, and Microsoft? One major obstacle to settling debates about the ontology of our computers is that we are biased to perceive traces of mind and intention even where there are none. In a famous 1944 study, two psychologists, Marianne Simmel and Fritz Heider, had participants watch a simple animation of two triangles and a circle moving around one another. They then asked some viewers what kind of “person” each of the shapes was. People described the shapes using words like “aggressive,” “quarrelsome,” “valiant,” “defiant,” “timid,” and “meek,” even though they knew that they’d been watching lifeless lines on a screen.

…chatbots are designed by teams of programmers, executives, and engineers working under corporate and social pressures to make a convincing product. “All these writers and physicists they’re hiring—that’s game design,” he said. “They’re basically making levels.” (In August of last year, OpenAI acquired an open-world-video-game studio, for an undisclosed amount.) Like a game, a chatbot requires user input to get going, and relies on continued interaction. Its guardrails can even be broken using certain prompts that act like cheat codes, letting players roam otherwise inaccessible areas. Blackley likened all the human tinkering involved in chatbot training to the set design required for “The Truman Show,” the TV program within the eponymous film. Without knowing it, Truman has lived his whole life surrounded not by real people but by actors playing roles—wife, friend, milkman. There’s a fantasy that “we’ve taken our great grand theories of intelligence and baked them into this model, and then we turned it on and suddenly it was exactly like this,” Blackley went on. “It’s much more like Truman’s show, in that they tweak it until it seems really cool.”

A modern chatbot isn’t a Furby. It’s not a motor and a pile of plastic. It’s an analytic behemoth trained on data containing an extraordinary quantity of human ingenuity. It’s one of the most complicated, surprising, and transformative advances in the history of computation. A Furby is knowable: its vocabulary is limited, its circuits fixed. A large language model generates ideas, words, and contexts never before known. It is also—when it takes on the form of a chatbot—a digital metamorph, a character-based shape-shifter, fluid in identity, persona, and design. To perceive its output as anything like life, or like human thinking, is to succumb to its role play.

How communication technology has enabled the corruption of our communication and culture .

I pass on two striking examples from today’s New York Times, with few clips of text from each:

A.I.-Generated Garbage Is Polluting Our Culture:

(You really should read the whole article...I've given up on trying to assemble clips of text that get across the whole message, and pass on these bits towards the end of the article:)

....we find ourselves enacting a tragedy of the commons: short-term economic self-interest encourages using cheap A.I. content to maximize clicks and views, which in turn pollutes our culture and even weakens our grasp on reality. And so far, major A.I. companies are refusing to pursue advanced ways to identify A.I.’s handiwork — which they could do by adding subtle statistical patterns hidden in word use or in the pixels of images.

To deal with this corporate refusal to act we need the equivalent of a Clean Air Act: a Clean Internet Act. Perhaps the simplest solution would be to legislatively force advanced watermarking intrinsic to generated outputs, like patterns not easily removable. Just as the 20th century required extensive interventions to protect the shared environment, the 21st century is going to require extensive interventions to protect a different, but equally critical, common resource, one we haven’t noticed up until now since it was never under threat: our shared human culture.

Is Threads the Good Place?:

Once upon a time on social media, the nicest app of them all, Instagram, home to animal bloopers and filtered selfies, established a land called Threads, a hospitable alternative to the cursed X,..Threads would provide a new refuge. It would be Twitter But Nice, a Good Place where X’s liberal exiles could gather around for a free exchange of ideas and maybe even a bit of that 2012 Twitter magic — the goofy memes, the insider riffing, the meeting of new online friends

...And now, after a mere 10 months, we can see exactly what we built: a full-on bizarro-world X, handcrafted for the left end of the political spectrum, complete with what one user astutely labeled “a cult type vibe.” If progressives and liberals were provoked by Trumpers and Breitbart types on Twitter, on Threads they have the opportunity to be wounded by their own kind...Threads’ algorithm seems precision-tweaked to confront the user with posts devoted to whichever progressive position is slightly lefter-than-thou....There’s some kind of algorithm that’s dusting up the same kind of outrage that Twitter had.Threads feels like it’s splintering the left.

The fragmentation of social media may have been as inevitable as the fragmentation of broadcast media. Perhaps also inevitable, any social media app aiming to succeed financially must capitalize on the worst aspects of social behavior. And it may be that Hobbes, history’s cheery optimist, was right: “The condition of man is a condition of war of every one against every one.” Threads, it turns out, is just another battlefield.

 

The magic of spurious correlations...

 I have to pass on this link, sent to me by my techie son, Jonathan:  

Fundamentally changing the nature of war.

I generally try to keep a distance from 'the real world' and apocalyptic visions of what AI might do, but I decided to pass on some clips from this technology essay in The Wall Street Journal that makes some very plausible predictions about the future of armed conflicts between political entities:

The future of warfare won’t be decided by weapons systems but by systems of weapons, and those systems will cost less. Many of them already exist, whether they’re the Shahed drones attacking shipping in the Gulf of Aden or the Switchblade drones destroying Russian tanks in the Donbas or smart seaborne mines around Taiwan. What doesn’t yet exist are the AI-directed systems that will allow a nation to take unmanned warfare to scale. But they’re coming.

At its core, AI is a technology based on pattern recognition. In military theory, the interplay between pattern recognition and decision-making is known as the OODA loop— observe, orient, decide, act. The OODA loop theory, developed in the 1950s by Air Force fighter pilot John Boyd, contends that the side in a conflict that can move through its OODA loop fastest will possess a decisive battlefield advantage.

For example, of the more than 150 drone attacks on U.S. forces since the Oct. 7 attacks, in all but one case the OODA loop used by our forces was sufficient to subvert the attack. Our warships and bases were able to observe the incoming drones, orient against the threat, decide to launch countermeasures and then act. Deployed in AI-directed swarms, however, the same drones could overwhelm any human-directed OODA loop. It’s impossible to launch thousands of autonomous drones piloted by individuals, but the computational capacity of AI makes such swarms a possibility.

This will transform warfare. The race won’t be for the best platforms but for the best AI directing those platforms. It’s a war of OODA loops, swarm versus swarm. The winning side will be the one that’s developed the AI-based decision-making that can outpace their adversary. Warfare is headed toward a brain-on-brain conflict.

The Department of Defense is already researching a “brain-computer interface,” which is a direct communications pathway between the brain and an AI. A recent study by the RAND Corporation examining how such an interface could “support human- machine decision-making” raised the myriad ethical concerns that exist when humans become the weakest link in the wartime decision-making chain. To avoid a nightmare future with battlefields populated by fully autonomous killer robots, the U.S. has insisted that a human decision maker must always remain in the loop before any AI-based system might conduct a lethal strike.

But will our adversaries show similar restraint? Or would they be willing to remove the human to gain an edge on the battlefield? The first battles in this new age of warfare are only now being fought. It’s easy to imagine a future, however, where navies will cease to operate as fleets and will become schools of unmanned surface and submersible vessels, where air forces will stand down their squadrons and stand up their swarms, and where a conquering army will appear less like Alexander’s soldiers and more like a robotic infestation.

Much like the nuclear arms race of the last century, the AI arms race will define this current one. Whoever wins will possess a profound military advantage. Make no mistake, if placed in authoritarian hands, AI dominance will become a tool of conquest, just as Alexander expanded his empire with the new weapons and tactics of his age. The ancient historian Plutarch reminds us how that campaign ended: “When Alexander saw the breadth of his domain, he wept, for there were no more worlds to conquer.”

Elliot Ackerman and James Stavridis are the authors of “2054,” a novel that speculates about the role of AI in future conflicts, just published by Penguin Press. Ackerman, a Marine veteran, is the author of numerous books and a senior fellow at Yale’s Jackson School of Global Affairs. Admiral Stavridis, U.S. Navy (ret.), was the 16th Supreme Allied Commander of NATO and is a partner at the Carlyle Group.

 

An inexpensive Helium Mobile 5G cellphone plan that pays you to use it?

This is a followup to the previous post describing my setting up a G5 hotspot on Helium’s decentralized 5G infrastructure that earns MOBILE tokens. The cash value of the MOBILE tokens earned since July 2022 is  ~7X the cost of the equipment needed to generate them.

Now I want to put down further facts I want to document for my future self and MindBlog’s techie readers.

Recently Helium has introduced Helium Mobile, a cell phone plan using using this new 5G infrastructure which costs $20/month - much less expensive than other cellular providers like Verizon and ATT.  It has partnered with T-Mobile to fill in coverage areas its own 5G network hasn’t reached.

Nine days ago I downloaded the Helium Mobile app onto my iPhone 12 and set up an account with an eSIM and a new phone number, alongside my phone number of many years now in a Verizon account using a physical SIM card.  

My iPhone has been earning MOBILE tokens by sharing its location to allow better mapping of the Helium G5 network.  As I am writing this, the app has earned 3,346 Mobile tokens that could be sold and converted to $14.32 at this moment (the price of MOBILE, like other cryptocurrencies, is very volatile).

If this earning rate continues (a big ‘if’), the cellular account I am paying $20/month for will be generating MOBILE tokens each month worth ~$45. The $20 monthly cell phone plan charge can be paid with MOBILE tokens, leaving $15/month passive income from my subscribing to Helium Mobile and allowing anonymous tracking of my phone as I move about.  (Apple sends a message every three days asking if I am sure I want to be allowing continuous tracking by this one App.)

So there you have it.  Any cautionary notes from techie readers about the cybersecurity implications of what I am doing would be welcome.  
 

MindBlog becomes a 5G cellular hotspot in the the low-priced ‘People’s Cell Phone Network’ - Helium Mobile

I am writing this post, as is frequently the case, for myself to be able to look up in the future, as well as for MindBlog techie readers who might stumble across it. It describes my setup of a G5 hotspot in the new Helium G5 Mobile network. A post following this one will describe my becoming a user of this new cell phone network by putting the Helium Mobile App on my iPhone using an eSIM.

This becomes my third post describing my involvement in the part of the crypto movement seeking to 'return power to the people.' It attempts to bypass the large corporations that are the current gate keepers and regulators of commerce and communications, and who are able to assert controls that are more in their own self interests and profits more than the public good. 

The two previous posts (here and here) describe my being seduced into crypto-world  by my son's having made a six hundred-fold return on investment by virtue of being one of the first cohort (during the "genesis" period) to put little black boxes and antennas on their window sills earning HNT (Helium blockchain tokens) using  LoRa 868 MHz antennas transmitting and receiving in the 'Internet of Things." I was a latecomer, and in the 22 months since June of 2022 have earned ~$200 on an investment of ~$500 of equipment. 

Helium next came up with the idea of setting up its own 5G cell phone network, called Helium Mobile. Individual Helium 5G Hotspots (small cell phone antennas) use Citizens Broadband Radio Service (CBRS) Radios to provide cellular coverage like that provided by telecom companies' more expensive networks of towers (CBRS is a wide broadcast 3.5Ghz band in the United States that does not require a spectrum license for use.)

In July of 2022, I decided to set up the Helium G5 hot spot equipment shown in the picture below to be in the genesis period for the establishment of this new Helium G5 cellular network.  I made my Abyssinian cat named Martin shown in front of the Bobber 500 miner the system administrator. The G5 antenna seen on the sill in the middle of window views ~170 degree of the southern sky. 

This system cost ~$2,500 and by early March 2024 has earned ~4.3 Million MOBILE tokens worth ~$18,000. As in a Ponzi scheme, most of the rewards are from the Genesis period, March 2024 earnings are ~ $45/week.  If this rate of earnings persists, this represents an annual ROI (return on investment)l of ~ 100%

The writing above is mine, and I decided just for the heck of it to recruit the assistance of several GPT engines (Google Gemini, Open AI ChatGPT-4, Anthropic Claude, and Microsoft Bing)  with the following prompt: 

Could you please act as Deric Bownds writing a post for his Blog, Deric’s MindBlog at http://mindblog.dericbownds.net. Describe my setting up of a Helium G5 hot spot using the Bobber 500 miner with FreedomFi CBRS and an internal antenna during the genesis period for the establishment of the new Helium G5 cellular network. 

The responses of Gemini and Bing were excited, poetic, lyrical, overwrought hyperbole, very much in the spirit of the cyber-world pitches one sees online. ChatGPT-4 (the best response) and Claude did toss up some interesting paragraphs, and below I pass on some of ChatGPT-4's slightly edited and redundant text. I would be more sanguine about all of hype over 'democratic participation (or at least my participation)' if I had not been completely flummoxed by the cacophony of  the Helium Discord Discord Server, YouTube videos, and the complexity of the HIP (Helium Improvement Protocol) votes that ratify new changes and improvements to the system.  

The Helium Network, known for pioneering decentralized wireless infrastructure, has embarked on an ambitious journey to extend its reach into 5G, leveraging the power of blockchain technology to create a community-powered network. The initiative is ambitious, aiming to democratize cellular connectivity, making it more accessible and affordable. This endeavor is not just about technology; it's about reimagining the fabric of our digital society.

This genesis period of the Helium G5 network represented a fascinating intersection of technology, psychology, and community. The network's decentralized nature challenges traditional models of infrastructure ownership and control, proposing a more democratic and participatory framework. It prompts us to reflect on our relationship with technology: Are we mere consumers, or can we also be contributors and custodians of the digital landscapes we inhabit?

As more individuals and communities join this initiative, setting up their hotspots and expanding the network, a unique form of collective intelligence emerges. This network, powered by the people, for the people, embodies a new kind of digital ecosystem—one that values collaboration over competition, and shared success over isolated gains.

The implications for our cognitive landscapes are profound. By democratizing access to 5G connectivity, we are not just enhancing our ability to communicate; we are potentially altering the way we think, learn, and engage with the world around us. The Helium G5 network invites us to imagine a future where connectivity is as natural and essential as the air we breathe, a future where our digital and physical realities are seamlessly intertwined.

The technology we embrace today shapes the world we live in tomorrow. Through initiatives like the Helium G5 network, we have the opportunity to craft a digital environment that reflects our highest aspirations for society—one that is open, inclusive, and fundamentally human.

In this journey of connectivity, we are all contributors, shaping the digital ethos of our era. The genesis of the Helium G5 network is not just a technical milestone; it is a call to action, inviting us to rethink our role in the digital age and to contribute to a network that embodies our collective values and aspirations.

The next post describes my becoming a customer of the new Helium Mobile cellular service plan costing $20/month.

 

How AI’s GPT engines work - Lanier’s forest and trees metaphor.

Jaron Lanier does a piece in The New Yorker titled "How to Picture A.I." (if you hit the paywall by clicking the link, try opening an 'empty tab" on your browser, then copy and paste in the URL that got you the paywall). I tried to do my usual sampling of small chunks of text to give the message, but found that very difficult, and so I pass several early paragraphs and urge you to read the whole article. Lanier's metaphors give me a better sense of what is going on in a GPT engine, but I'm still largely mystified. Anyway, here's some text:

In this piece, I hope to explain how such A.I. works in a way that floats above the often mystifying technical details and instead emphasizes how the technology modifies—and depends on—human input.

Let’s try thinking, in a fanciful way, about distinguishing a picture of a cat from one of a dog. Digital images are made of pixels, and we need to do something to get beyond just a list of them. One approach is to lay a grid over the picture that measures something a little more than mere color. For example, we could start by measuring the degree to which colors change in each grid square—now we have a number in each square that might represent the prominence of sharp edges in that patch of the image. A single layer of such measurements still won’t distinguish cats from dogs. But we can lay down a second grid over the first, measuring something about the first grid, and then another, and another. We can build a tower of layers, the bottommost measuring patches of the image, and each subsequent layer measuring the layer beneath it. This basic idea has been around for half a century, but only recently have we found the right tweaks to get it to work well. No one really knows whether there might be a better way still.

Here I will make our cartoon almost like an illustration in a children’s book. You can think of a tall structure of these grids as a great tree trunk growing out of the image. (The trunk is probably rectangular instead of round, since most pictures are rectangular.) Inside the tree, each little square on each grid is adorned with a number. Picture yourself climbing the tree and looking inside with an X-ray as you ascend: numbers that you find at the highest reaches depend on numbers lower down.

Alas, what we have so far still won’t be able to tell cats from dogs. But now we can start “training” our tree. (As you know, I dislike the anthropomorphic term “training,” but we’ll let it go.) Imagine that the bottom of our tree is flat, and that you can slide pictures under it. Now take a collection of cat and dog pictures that are clearly and correctly labelled “cat” and “dog,” and slide them, one by one, beneath its lowest layer. Measurements will cascade upward toward the top layer of the tree—the canopy layer, if you like, which might be seen by people in helicopters. At first, the results displayed by the canopy won’t be coherent. But we can dive into the tree—with a magic laser, let’s say—to adjust the numbers in its various layers to get a better result. We can boost the numbers that turn out to be most helpful in distinguishing cats from dogs. The process is not straightforward, since changing a number on one layer might cause a ripple of changes on other layers. Eventually, if we succeed, the numbers on the leaves of the canopy will all be ones when there’s a dog in the photo, and they will all be twos when there’s a cat.

Now, amazingly, we have created a tool—a trained tree—that distinguishes cats from dogs. Computer scientists call the grid elements found at each level “neurons,” in order to suggest a connection with biological brains, but the similarity is limited. While biological neurons are sometimes organized in “layers,” such as in the cortex, they are not always; in fact, there are fewer layers in the cortex than in an artificial neural network. With A.I., however, it’s turned out that adding a lot of layers vastly improves performance, which is why you see the term “deep” so often, as in “deep learning”—it means a lot of layers.

AI makes our humanity matter more than ever.

I want to pass on this link to an NYTimes Opinion Guest essay by Aneesh Raman, a work force expert at LinkedIn,  and Maria Flynn, president of Jobs for the Future,  For readers blocked by the NYTimes paywall, I paste in the conclusion of the article:

Minouche Shafik, who is now the president of Columbia University, said: “In the past, jobs were about muscles. Now they’re about brains, but in the future, they’ll be about the heart.”

The knowledge economy that we have lived in for decades emerged out of a goods economy that we lived in for millenniums, fueled by agriculture and manufacturing. Today the knowledge economy is giving way to a relationship economy, in which people skills and social abilities are going to become even more core to success than ever before. That possibility is not just cause for new thinking when it comes to work force training. It is also cause for greater imagination when it comes to what is possible for us as humans not simply as individuals and organizations but as a species.

 

 

 

Comparing how generative AI and living organisms generate meaning suggests future direction for AI development

I want to pass on this open source opinion article in Trends in Cognitive Sciences by Karl Friston, Andy Clark, and other prominent figures who study generative models of sentient behavior in living organisms.  (They suggest a future direction for AI development that is very similar to the vision described in the previous MindBlog post, which described a recent article by Venkatesh Rao.) Here are the highlights and abstract of the article.

Highlights

• Generative artificial intelligence (AI) systems, such as large language models (LLMs), have achieved remarkable performance in various tasks such as text and image generation.
• We discuss the foundations of generative AI systems by comparing them with our current understanding of living organisms, when seen as active inference systems.
• Both generative AI and active inference are based on generative models, but they acquire and use them in fundamentally different ways.
• Living organisms and active inference agents learn their generative models by engaging in purposive interactions with the environment and by predicting these interactions. This provides them with a core understanding and a sense of mattering, upon which their subsequent knowledge is grounded.
• Future generative AI systems might follow the same (biomimetic) approach – and learn the affordances implicit in embodied engagement with the world before – or instead of – being trained passively.

Abstract

Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with the control of purposive, life-sustaining sensorimotor interactions, the generative models of living organisms are inextricably anchored to the body and world. Unlike the passive models learned by generative AI systems, they must capture and control the sensory consequences of action. This allows embodied agents to intervene upon their worlds in ways that constantly put their best models to the test, thus providing a solid bedrock that is – we argue – essential to the development of genuine understanding. We review the resulting implications and consider future directions for generative AI.

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

I pass on the PDF of an article from the Gemini Team at Google. Here's the abstract describing a "working memory" system vastly greater than our own, that can hold 10 million tokens  (a 'token' is roughly 0.75 words):

In this report, we present the latest model of the Gemini family, Gemini 1.5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. Gemini 1.5 Pro achieves near-perfect recall on long-context retrieval tasks across modalities, improves the state-of-the-art in long-document QA, long-video QA and long-context ASR, and matches or surpasses Gemini 1.0 Ultra’s state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5 Pro’s long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 2.1 (200k) and GPT-4 Turbo (128k). Finally, we highlight surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

An agent-based vision for scaling modern AI - Why current efforts are misguided.

I pass on my edited clips from Venkatesh Rao’s most recent newsletter - substantially shortening its length and inserting a few definitions of techo-nerd-speak acronyms he uses in brackets [  ].  He suggests interesting analogies between the future evolution of Ai and the evolutionary course taken by biological organisms:

…specific understandings of embodiment, boundary intelligence, temporality, and personhood, and their engineering implications, taken together, point to an agent-based vision of how to scale AI that I’ve started calling Massed Muddler Intelligence or MMI, that doesn’t look much like anything I’ve heard discussed.


…right now there’s only one option: monolithic scaling. Larger and larger models trained on larger and larger piles of compute and data…monolithic scaling is doomed. It is headed towards technical failure at a certain scale we are fast approaching


What sort of AI, in an engineering sense, should we attempt to build, in the same sense as one might ask, how should we attempt to build 2,500 foot skyscrapers? With brick and mortar or reinforced concrete? The answer is clearly reinforced concrete. Brick and mortar construction simply does not scale to those heights


…If we build AI datacenters that are 10x or 100x the scale of todays and train GPT-style models on them …problems of data movement and memory management at scale that are already cripplingly hard will become insurmountable…current monolithic approaches to scaling AI are the equivalent of brick-and-mortar construction and fundamentally doomed…We need the equivalent of a reinforced concrete beam for AI…A distributed agent-based vision of modern AI is the scaling solution we need.

Scaling Precedents from Biology

There’s a precedent here in biology. Biological intelligence scales better with more agent-like organisms. For example: humans build organizations that are smarter than any individual, if you measure by complexity of outcomes, and also smarter than the scaling achieved by less agentic eusocial organisms…ants, bees, and sheep cannot build complex planet-scale civilizations. It takes much more sophisticated agent-like units to do that.

Agents are AIs that can make up independent intentions and pursue them in the real world, in real time, in a society of similarly capable agents (ie in a condition of mutualism), without being prompted. They don’t sit around outside of time, reacting to “prompts” with oracular authority…as in sociobiology, sustainably scalable AI agents will necessarily have the ability to govern and influence other agents (human or AI) in turn, through the same symmetric mechanisms that are used to govern and influence them…If you want to scale AI sustainably, governance and influence cannot be one way street from some privileged agents (humans) to other less privileged agents (AIs)….

If you want complexity and scaling, you cannot govern and influence a sophisticated agent without opening yourself up to being governed and influenced back. The reasoning here is similar to why liberal democracies generally scale human intelligence far better than autocracies. The MMI vision I’m going to outline could be considered “liberal democracy for mixed human-AI agent systems.” Rather than the autocratic idea of “alignment” associated with “AGI,” MMIs will call for something like the emergent mutualist harmony that characterizes functional liberal democracies. You don’t need an “alignment” theory. You need social contract theory.

The Road to Muddledom

Agents, and the distributed multiagent systems (MAS) that represent the corresponding scaling model, obviously aren’t a new idea in AI…MAS were often built as light architectural extensions of early object-oriented non-AI systems… none of this machinery works or is even particularly relevant for the problem of scaling modern AI, where the core source of computational intelligence is a large-X-model with fundamentally inscrutable input-output behavior. This is a new, oozy kind of intelligence we are building with for the first time. ..We’re in new regimes, dealing with fundamentally new building materials and aiming for new scales (orders of magnitude larger than anything imagined in the 1990s).

Muddling Doctrines

How do you build muddler agents? I don’t have a blueprint obviously, but here are four loose architectural doctrines, based on the four heterodoxies I noted at the start of this essay (see links there): embodiment, boundary intelligence, temporality, and personhood.

Embodiment matters: The physical form factor AI takes is highly relevant to to its nature, behavior, and scaling potential.

Boundary intelligence matters. Past a threshold, intelligence is a function of the management of boundaries across which data flows, not the sophistication of the interiors where it is processed.

Temporality matters: The kind of time experienced by an AI matters for how it can scale sustainably.

Personhood matters: The attributes of an AI that enable humans and AIs to relate to each other as persons (I-you), rather than things (I-it), are necessary elements to being able to construct coherent scalably composable agents at all.


The first three principles require that AI computation involve real atoms, live in real time, and deal with the second law of thermodynamics

The fourth heterodoxy turns personhood …into a load-bearing architectural element in getting to scaled AI via muddler agents. You cannot have scaled AI without agency, and you cannot have a scalable sort of agency without personhood.

As we go up the scale of biological complexity, we get much programmable and flexible forms of communication and coordination. … we can start to distinguish individuals by their stable “personalities” (informationally, the identifiable signature of personhood). We go from army ants marching in death spirals to murmurations of starlings to formations of geese to wolf packs maneuvering tactically in pincer movements… to humans whose most sophisticated coordination patterns are so complex merely deciphering them stresses our intelligence to the limit.

Biology doesn’t scale to larger animals by making very large unicellular creatures. Instead it shifts to a multi-cellular strategy. Then it goes further: from simple reproduction of “mass produced” cells to specialized cells forming differentiated structures (tissues) via ontogeny (and later, in some mammals, through neoteny). Agents that scale well have to be complex and variegated agents internally, to achieve highly expressive and varied behaviors externally. But they must also present simplified facades — personas — to each other to enable the scaling and coordination.

Setting aside questions of philosophy (identity, consciousness),  personhood is a scaling strategy. Personhood is the behavioral equivalent of a cell. “Persons” are stable behavioral units that can compose in “multicellular” ways because they communicate differently than simpler agents with weak or non-existent personal boundaries, and low-agency organisms like plants and insects.

When we form and perform “personas,” we offer a harder interface around our squishy interior psyches that composes well with the interfaces of other persons for scaling purposes. A personhood performance is something like a composability API [application programmers interface] for intelligence scaling.

Beyond Training Determinism

…Right now AIs experience most of their “time” during training, and then effectively enter a kind of stasis. …They requiring versioned “updates” to get caught up again…GPT4 can’t simply grow or evolve its way to GPT5 by living life and learning from it. It needs to go through the human-assisted birth/death (or regeneration perhaps) singularity of a whole new training effort. And it’s not obvious how to automate this bottleneck in either a Darwinian or Lamarckian way.

…For all their power, modern AIs are still not able to live in real time and keep up with reality without human assistance outside of extremely controlled and stable environments…As far as temporality is concerned, we are in a “training determinism” regime that is very un-agentic and corresponds to genetic determinism in biology.What makes agents agents is that they live in real time, in a feedback loop with external reality unfolding at its actual pace of evolution.

Muddling Through vs. Godding Through

Lindblom’s paper identifies two patterns of agentic behavior, “root” (or rational-comprehensive) and “branch” (or successive limited comparisons), and argues that in complicated messy circumstances requiring coordinated action at scale, the way actually effective humans operate is the branch method, which looks like “muddling through” but gradually gets there, where the root method fails entirely. Complex here is things humans typically do in larger groups, like designing and implementing complex governance policies or undertaking complex engineering projects. The threshold for “complex” is roughly where explicit coordination protocols become necessary scaffolding. This often coincides with the threshold where reality gets too big to hold in one human head.

The root method attempts to fight limitations with brute, monolithic force. It aims to absorb all the relevant information regarding the circumstances a priori (analogous to training determinism), and discover the globally optimal solution through “rational” and “comprehensive” thinking. If the branch method is “muddling through,” we might say that the root, or rational-comprehensive approach, is an attempt to “god through.”…Lindblom’s thesis is basically that muddling through eats godding through for lunch.

To put it much more bluntly: Godding through doesn’t work at all beyond small scales and it’s not because the brains are too small. Reasoning backwards from complex goals in the context of an existing complex system evolving in real time doesn’t work. You have to discover forwards (not reason forwards) by muddling.

..in thinking about humans, it is obvious that Lindblom was right…Even where godding through apparently prevails through brute force up to some scale, the costs are very high, and often those who pay the costs don’t survive to complain…Fear of Big Blundering Gods is the essential worry of traditional AI safety theology, but as I’ve been arguing since 2012 (see Hacking the Non-Disposable Planet), this is not an issue because these BBGs will collapse under their own weight long before they get big enough for such collapses to be exceptionally, existentially dangerous.

This worry is similar to the worry that a 2,500 foot brick-and-mortar building might collapse and kill everybody in the city…It’s not a problem because you can’t build a brick-and-mortar building to that height. You need reinforced concrete. And that gets you into entirely different sorts of safety concerns.

Protocols for Massed Muddling

How do you go from individual agents (AI or human) muddling through to masses of them muddling through together? What are the protocols of massed muddling? These are also the protocols of AI scaling towards MMIs (Massed Muddler Intelligences)

When you put a lot of them together using a mix of hard coordination protocols (including virtual-economic ones) and softer cultural protocols, you get a massed muddler intelligence, or MMI. Market economies and liberal democracies are loose, low-bandwidth examples of MMIs that use humans and mostly non-AI computers to scale muddler intelligence. The challenge now is to build far denser, higher bandwidth ones using modern AI agents.

I suspect at the scales we are talking about, we will have something that looks more like a market economy than like the internal command-economy structure of the human body. Both feature a lot of hierarchical structure and differentiation, but the former is much less planned, and more a result of emergent patterns of agglomeration around environmental circumstances (think how the large metros that anchor the global economy form around the natural geography of the planet, rather than how major organ systems of the human body are put together).

While I suspect MMIs will partly emerge via choreographed ontogenic roadmaps from a clump of “stem cells” (is that perhaps what LxMs [large language models] are??), the way market economies emerge from nationalist industrial policies, overall the emergent intelligences will be masses of muddling rather than coherent artificial leviathans. Scaling “plans” will help launch, but not determine the nature of MMIs or their internal operating protocols at scale. Just like tax breaks and tariffs might help launch a market economy but not determine the sophistication of the economy that emerges or the transactional patterns that coordinate it. This also answers the regulation question: Regulating modern AI MMIs will look like economic regulation, not technology regulation.

How the agentic nature of the individual muddler agent building block is preserved and protected is the critical piece of the puzzle, just as individual economic rights (such as property rights, contracting regimes) are the critical piece in the design of “free” markets.

Muddling produces a shell of behavioral uncertainty around what a muddler agent will do, and how it will react to new information, that creates an outward pressure on the compressive forces created by the dense aggregation required for scaling. This is something like the electron degeneracy pressure that resists the collapse of stars under their own gravity. Or how the individualist streak in even the most dedicated communist human resists the collapse of even the most powerful cults into pure hive minds. Or how exit/voice dynamics resist the compression forces of unaccountable organizational management.

…the fundamental intentional tendency of individual agents, on which all other tendencies, autonomous or not, socially influencable or not, rest…[is]  body envelope integrity.

…This is a familiar concern for biological organisms. Defending against your body being violently penetrated is probably the foundation of our entire personality. It’s the foundation of our personal safety priorities — don’t get stabbed, shot, bitten, clawed or raped. All politics and economics is an extension of envelope integrity preservation instincts. For example, strictures against theft (especially identity theft) are about protecting the body envelope integrity of your economic body. Habeas corpus is the bedrock of modern political systems for a reason. Your physical body is your political body…if you don’t have body envelope integrity you have nothing.

This is easiest to appreciate in one very visceral and vivid form of MMIs: distributed robot systems. Robots, like biological organisms, have an actual physical body envelope (though unlike biological organisms they can have high-bandwidth near-field telepathy). They must preserve the integrity of that envelope as a first order of business … But robot MMIs are not the only possible form factor. We can think of purely software agents that live in an AI datacenter, and maintain boundaries and personhood envelopes that are primarily informational rather than physical. The same fundamental drive applies. The integrity of the (virtual) body envelope is the first concern.

This is why embodiment is an axiomatic concern. The nature of the integrity problem depends on the nature of the embodiment. A robot can run away from danger. A software muddler agent in a shared memory space within a large datacenter must rely on memory protection, encryption, and other non-spatial affordances of computing environments.

Personhood is the emergent result of successfully solving the body-envelope-integrity problem over time, allowing an agent to present a coherent and hard mask model to other agents even in unpredictable environments. This is not about putting a smiley-faced RLHF [Reinforcement Learning from Human Feedback]. mask on a shoggoth interior to superficially “align” it. This is about offering a predictable API for other agents to reliably interface with, so scaled structures in time and social space don’t collapse.  [They have] hardness - the property or quality that allows agents with soft and squishy interiors to offer hard and unyielding interfaces to other agents, allowing for coordination at scale.

…We can go back to the analogy to reinforced concrete. MMIs are fundamentally built out of composite materials that combine the constituent simple materials in very deliberate ways to achieve particular properties. Reinforced concrete achieves this by combining rebar and cement in particular geometries. The result is a flexible language of differentiated forms (not just cuboidal beams) with a defined grammar.

MMIs will achieve this by combining embodiment, boundary management, temporality, and personhood elements in very deliberate ways, to create a similar language of differentiated forms that interact with a defined grammar.

And then we can have a whole new culture war about whether that’s a good thing.