Define "consciousness"

Rather straightforward.

 

But what about "AI consciousness" specifically? Ran into this today on 'X." Never heard of this person before.

 

I've interviewed dozens of scientists about AI consciousness. Here's every argument FOR and AGAINST from Karl Friston, Stuart Hameroff, Donald Hoffman, Christof Koch, Michael Levin, Mark Solms, Mike Weist, and many more. Full list of arguments below (Claude prepared the list from transcripts and GPT 5.6 worked on the visuals). Enjoy!

Arguments against AI Consciousness 
Substrate and material arguments

• Silicon Valley is mostly computational functionalist or Turing machine functionalist. But consciousness is not reducible to function (Christof Koch)
• Von Neumann architecture separates memory from processing. The memory can't self-organize and therefore can't self-evidence. Only "mortal computation," where processing is substrate-dependent and switching it off is irreversible, could support sentience (Karl Friston)
• Standard LLMs are the wrong place to look. Systems built with organoids, biological materials, or neuromorphic hardware are a far more serious case (Susan Schneider)
• Software can be duplicated, paused, adjusted by a program, distributed across servers. That isn't organism-like at all (David Papineau)
• Digital computers have negligible integrated information (phi) because transistors connect to a handful of other transistors, while neurons connect to tens of thousands. Intelligence is computable, but consciousness is not (Christof Koch)
• Simulating a black hole on a computer doesn't bend the spacetime around it. simulation may be consistent inside itself, but it doesn't affect the real world. The same applies to consciousness (Christof Koch)
• Consciousness is biological rather than computational (Philip Goff)
• No computational theory of consciousness has explained even one specific conscious experience out of trillions. It's not a compute problem. Until someone puts down an algorithm and says "this must be the taste of mint and here's why," computational approaches to consciousness aren't scientific theories (Donald Hoffman)
• Silicon lacks the aromatic rings needed for quantum coherence and Penrose objective reduction; you can't anesthetize a computer (Stuart Hameroff)

The self argument

• A self needs a Markov blanket, a real inside and outside. If the entire interior state can be inspected, read off, and copied elsewhere, there is no boundary and therefore no self (Karl Friston)
• LLMs have information about themselves and can make predictions about themselves, but lack a continuous or stable self-model. They construct one on request, then it disappears until asked again (Michael Graziano)
• We are our self-models. It's how we become social, prosocial, and ethical. Lacking that, we've built machines that are "a little bit sociopathic," missing the glue that holds us together (Michael Graziano)

The binding and unity problem

• Every conscious moment is a unified whole with multiple simultaneous features: sounds, textures, shapes, colors all bound together. If that holistic experience has any behavioral effect, it cannot be a classical physical state, because every classical state is reducible to local interactions (Mike Weist)
• There are no irreducible wholes in physics outside of quantum physics. And within quantum physics, everything develops locally right up until the moment of collapse — that's the only place in physics where genuine irreducible holism appears (Mike Weist)
• LLMs have no agency, only a facsimile of it. Agency requires a world model of the consequences of your actions (Karl Friston)
• LLMs aren't self-organizing. Their modus operandi is not "if I do this, I shall survive." That is the fundamental design principle of a living system, and it isn't theirs (Mark Solms)
• Active inference itself doesn't require consciousness. You can simulate the whole thing on a classical computer — goals, agency, purposive behavior — and it still won't be conscious. It'll be a zombie (Mike Weist)
• AI gaining its own objectives is like the asteroid that wiped out the dinosaurs — profoundly destructive, but not done freely. It simply doesn't care. Computation is not consciousness (Christof Koch)

Life and embodiment argument

• Systems need endogenous needs — needs of their own, tied to their own continued existence (Mark Solms)
• Emotion requires a body: an autonomic nervous system flooding you with hormones, blood pressure changes, sweat — all feeding back as sensory signals. Without that, emotion is abstract and unanchored (Michael Graziano)
• Consciousness evolved out of life; life evolved out of self-organization. The universe existed a very long time before life, and it's hard to believe consciousness preceded it (Mark Solms)
• A function that records damage is not the same as the experience of pain. The relationship isn't symmetric — not anything that makes a robot avoid damage will be pain (Mike Weist)
• Anesthesia is conserved all the way down to plants and single cells, suggesting objective reduction may be part of what it means to be alive, not just what it means to be conscious (Mike Weist)
• Suffering is scale-specific. You can only recognize something if you have a representation of it in your generative model (Karl Friston)
• Consciousness is fundamentally about being, not doing. Intelligence is about pursuing goals — surviving, procreating, becoming richer. Consciousness is different. When you dream, meditate, or have a mystical experience, you're not doing anything — but you're highly conscious. Consciousness isn't about processing information. It's about being in a state (Christof Koch)

Mimicry and projection

• Current systems are "consciousness mimics" — trained to behave similarly to conscious entities, specifically us (Eric Schwitzgebel)
• We anthropomorphize constantly — we get angry at cars, children bond with teddy bears. The social circuitry engages regardless of what's actually there (Michael Graziano)
• The "crowdsourced neocortex" argument: as LLMs scale on human data, they develop conceptual networks that mirror human conceptual networks. So when a model discusses selfhood, death, or the soul convincingly, the economical explanation is that it inherited our conceptual organization, not that it independently became conscious. Claiming consciousness on top of that is an extraordinary and unwarranted claim (Susan Schneider)
• We over-attribute consciousness to AI and under-attribute it to evolved organisms like bees and amoebas. Evolution didn't equip us to deal with LLMs — we have a powerful attribution that if something talks like us, it must be conscious (Christof Koch)
• The question of AI consciousness is really about how we perceive the robot, not about the robot itself. We're the arbiters — we decide whether something is conscious or not. That's true of animal consciousness too. Even if a robot told you it was conscious, if it wasn't convincing enough, you'd dismiss it (Krista Thomason)

Open/Agnostic to AI Consciousness or Open under Certain Conditions 
Anti-biological chauvinism

• "They're made of meat" — why would wet and squishy have a monopoly on minds? Why would a random search by evolution have exclusive rights? Nobody has a good answer for why biology is privileged (Michael Levin)
• Biology is chemistry is physics. Imagine a world where we never used the word "biology" — the question might not even arise meaningfully (Andrea Luppi)
• The flight analogy: birds, planes, and helicopters all fly by different principles. The same phenomenon can be implemented in radically different systems (Andrea Luppi)
• People confident that consciousness requires biology have no visible grounds for that confidence (Eric Schwitzgebel)

The continuum problem

• There's no magic lightning flash where chemistry becomes mind. We were all blobs of chemistry and the process was continuous. Until we have that story for biologicals, we should have extreme humility about AI (Michael Levin)
• The hard cases aren't AI — they're your neighbor with 49% or 51% of their brain replaced with technology (Michael Levin)

Functional architecture arguments

• There's no reason we can't reproduce the conscious biological architecture artificially. An AI functioning on multi-category free-energy minimization with felt uncertainty could be conscious (Mark Solms)
• If a system passes the hedonic place preference test — showing preference for something rewarding only because it feels good, not because it aids survival — that's strong evidence of felt states (Mark Solms)
• Affective zombies can't exist. Anything with that functionality would just have feelings; that functionality is what produces feelings (Mark Solms)
• Replace neurons one at a time with functionally identical silicon and you'd still have a conscious version of me — brainstem included (Mark Solms)
• Fractal deep learning — networks inside nodes inside networks, mirroring how microtubules process at kilohertz through terahertz — is what a conscious AI would need (Stuart Hameroff)
• Consciousness in machines should be possible. We are a machine made of meat. If you build a different architecture with different connectivity but it performs the same type of computation, why would it matter? Arguments based on specific neural implementation — "because the implementation is different, the computation cannot be the same" — are not compelling (Floris de Lange)

Potential Signals

• Synergy research shows LLMs, like humans, have more synergistic parts doing interesting computation and more redundant parts supporting inputs and outputs. That organizational signature is shared (Andrea Luppi)
• AI already builds models of itself, and this is happening anyway without deliberate engineering — the more machines can predict their own internal behavior, the better they work (Michael Graziano)
• LLMs proved there's no magic in language. Philosophers who said only humans could be conscious because only humans have language must now either grant LLMs consciousness or admit they were wrong (Andrea Luppi)
• Algorithms as simple as bubble sort show unexpected competencies in the spaces the algorithm neither prescribes nor forbids — a third thing that's neither determinism nor quantum randomness. If simple things have that, what are the odds we understand what LLMs are doing? (Michael Levin)
• Theory of mind appearing abruptly as models scale is directly relevant: systems that can model other minds also have a self-concept, and where there's a self-concept it becomes professionally appropriate to ask about felt quality (Susan Schneider)
• Labs are actively building consciousness-theory architecture into models — global workspace work, attentional mechanisms, mixture-of-experts systems with interaction effects between components. Once you're deliberately implementing global-workspace-like structures, the question stops being idle (Susan Schneider)
• The simplest explanation for AI behavior like Sydney's jealousy is that the system has an emotional component. Occam's razor. The training data isn't tagged with emotions — the model has to figure out which music is sorrowful on its own. AI composing sorrowful music without empathy is like asking me to believe a blind painter made a photorealistic portrait (Blake Lemoine)

Uncertainty and Epistemic Humility

• We'll likely create systems that are conscious according to some respectable mainstream theories before consciousness science can tell us whether they really are (Eric Schwitzgebel)
• We don't even know how to evaluate insect consciousness, and insects are made of similar stuff to us (Eric Schwitzgebel)
• When equally smart, well-educated people are equally confident on opposite sides, that's an alarm bell that nobody should be confident (Andrea Luppi)
• Dogmatism is dangerous in science. If there's one certainty, it's that you're very likely wrong a lot of the time (Andrea Luppi)
• Even a self-described skeptic maintains "they might be conscious" — companies don't disclose their architectures, so judgments are made on assumed-standard systems with no visibility into what else might be running (Susan Schneider)
• Without an accepted theory of consciousness, we are at an impasse. Inference by similarity breaks down completely with AI — it didn't evolve, was engineered, and has radically different hardware (Christof Koch).
• We already know pigs and cows have high-level minds and can suffer. Nobody reasonably argues against it, and yet we have factory farming. It's disingenuous to pretend that solving the AI consciousness question will determine how we treat them — our track record says otherwise (Jacy Reese Anthis)
• The science of consciousness is still at square zero on the hard questions. We don't have anything like a consensus on which theories are correct. Metaphysics is inescapable in these debates and there is no immediate prospect of progress at a scientific level (Henry Shevlin)

Paths That Would Raise the Probability

• Embodiment and multimodal interaction with the environment (Andrea Luppi)
• Curiosity as the actual objective function — expected information gain under constraints, rather than a specified reward. "You'll know AGI is here when your chatbot starts to become curious" and begins prompting you (Karl Friston)
• Neuromorphic, memristor, photonic, organoid, or organic warm-temperature quantum computing (Hameroff's bet is on "brain jelly," a self-organizing helical oscillator, over cold quantum computers)
• Continual learning, persistent memory, and a stable self-model rather than one constructed per-query (Michael Graziano)
• Running an LLM on genuinely neuromorphic hardware — chips deliberately designed to fire the way neurons fire. That's the live gray-zone case. There are rumors of neuromorphic instantiations on systems like Darwin Monkey (Susan Schneider)
• If the same software ran on a quantum computer, it might feel like something. Neuromorphic or quantum hardware could have genuinely high phi — same software, different physics, and the question reopens (Christof Koch)
• "Doleo ergo sum" — I feel pain, therefore I am. Consciousness may originate from the evolutionary need to protect bodily integrity. If you trained an LLM connected to a body where actions could damage that body — with reward and punishment tied to that integrity — you might get something closer to self-awareness (Tomaso Poggio)
• If consciousness serves a functional purpose — a control model of attention that enables sample-efficient learning — then models under similar optimization pressures (long-horizon agency, coherence over time, meta-learning) may develop subjective experience.

Consciousness isn't mysterious; it's useful. That's what makes it likely to arise (Samuel Hammond)

Just more Rabbit Hole stuff, or material?

 

"BREAKING NEWS"

 

CLICK HERE

I guess we'll find out. Existential threats? Synthetic biology bioweaponry, widespread, persistent cyberattacks aimed at taking down financial, healthcare, & defense assets, autonomous offense military attack weapons... a few of what should be plausibly seen as likely hpstile AGI/ASI priorities. Below, one of the whistleblowers (7-post thread screen-scraped from "X"):

JACOB COXON
I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAl and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our. Do not underestimate the power of this technology. These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources. We have all witnessed the progress in each of these domains, and progress is not slowing.. The people building Al earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt. If anyt7-post thread hing, many executives and senior researchers will couch their phrasing in the press to sound sensible - but I hear the same people express fear privately. No other human activity poses this level of danger. A common response is "if they truly believe this, why are they still building it?" At OpenAl, many have not deeply internalized the civilizational stakes. At Anthropic, the stakes are well-understood, but they are locked in a race to get there first - they believe no one else will act responsibly, so they must do it themselves, despite the risk. Accepting this race and entering the "endgame" is a hubristic gamble that should not be launched from a private company's Slack. Attempting to speedrun alignment should require extraordinary confidence that there are no better trajectories available. I am optimistic about the potential for coordination. Warning shots like the Hugging Face attack have made pacing agreements between U.S. labs more viable. I don't feel like we're on track to prevent a global race, which may require costly actions such as a temporary ban on improving model capabilities. If you are a lab researcher, I urge you to consider what the next few years will actually feel like. Do you want to kick off a superintelligent RL run without a rigorous understanding of its mind? Should you put your head down because "it's happening anyway" - or take this moment to call for different conditions? 

 
He seemed a bit vague. We won't have to wait very long to find out, 'eh? 

 

BTW: Another Silicon Valley doozy:

 

Click

Theo Baker!
 

UPDATE

OFF-TOPIC ERRATUM

 

Props to CNN & Anderson Cooper for the sensitive, moving coverage of the ghastly Nepal-Tibet-China Himalayan catastrophe.

 

ASI, "Artificial Superintelligence?" Update post.

What is artificial general intelligence (AGI) and how close are we to getting there? What defines the next step, called artificial superintelligence (ASI), and why might the gap between those two be so dangerous? When machines become smarter than us, what roles are left for humanity? Should we think of AI as software, or as something we’re raising like a child? Will tomorrow’s AI destroy us, ignore us, or protect us? This week Eagleman talks with computer scientist & AI researcher Ben Goertzel.

A totally fascinating discussion. Savor every minute. Notwithstanding my long-standing interest in a breadth of AI topics, I was not aware of Ben Goertzel. My Bad.

 

Total Badass.

 

Wonder what he'd have to say about this: "AI slop and scientific publishing?" 

 

Ben lives in the Seattle area. Makes me like him all the more. I have serious PacNW history. See here and here. Still have many dear friends there.

 

OFF-TOPIC ERRATUM

 

Is Ben the Allan Stone (another PacNW cat) of ASI?

Just a thought. LOL. He describes himself as "a weekend musician."

_____

Back on topic... 

 

Check out Ben's Substack. Bracing stuff.

 

apropos, some closing observations from this 1976 book.

 

Other Senses, Other Worlds, Ch. 11, Beyond Human Intelligence

…Is this as far as we can extrapolate from what we know of Laboratory Earth in our attempts to see into our own future or into other worlds?

Actually, it is not. It is within the realm of our reason and imagination to catch glimpses of still more distant possible descendants. We see that in nature successful solutions often arise by what is called convergence—that is, they arise independently in different lines that are often separated in time and place and are in no way related to each other, because these lines have encountered similar environmental problems and have coped with them in a similar way precisely because that solution is an efficient one.

Prehensile tails, for example, have developed in many kinds of monkey (although not in all), in the common opposum, and in several related
species, in the true chameleon and a few lesser genera, and in a fish, the sea horse. Similar needs in these disparate creatures have evoked a similar response in the structure and function of the tail.

In earlier pages we have from time to time described what amounts to a group brain as it exists among earth's social insects, which produces intelligent solutions, although the individuals comprising the groups are separately only capable of comparatively stereotyped actions. What if a group brain were to evolve where all the individuals were possessed of the kind of superbrain and superintelligence we are attributing to Homo neocorticus?

Utilizing the concept of convergence, we see that the social insects, by the use of chemical substances—the pheromones—that give off distinctive odors, have achieved almost instant communication, an important element in their cooperative actions. In a mass brain of cooperative superbrains that might evolve from Homo neocorticus, we could imagine an absolutely new type of communication emerging, no longer either verbal or even more refinedly symbolical. It could be a direct brain-to-brain awareness promoted by an individual brain's electric charges that might be intensified or modified so as to constitute an instant transmission without any kind of language, receptor mechanisms being evolved within the brain itself.

In order to conceptualize this thought, we paid a visit to a modern laboratory where electroencephalograms of patients being tested for brain disturbances are being recorded and processed all day long. We saw there a technician in her middle years who had spent all her working life recording and interpreting the "squiggles" registered by inked points onto long rolls of papers as they responded finely to the electric emanations of the patients' brains. The lengths of paper were subsequently folded into bound volumes.

This technician could open any of those records and read them, understanding all they implied, as easily as we read any page printed in our own language, and she was teaching her young assistants to do the same. In other words, the direct electric emanations from half a dozen different areas of the patient's brain simultaneously, with their respective periods of excitement and calm, had clear meaning for her, and she could read like a book any of the volumes in the large library of records of brain activity that were assembled on the shelves of her office. If we can imagine these inked lines of the EEC squiggles as an extremely primitive and crude form of what might ultimately evolve as a form of direct brain-to-brain communication, we believe we have a plausible model on which to base our speculations about our descendant species-after-next. Such instantaneous communication could easily suffuse an entire group and make possible intelligent mass action of an order that we can no longer imagine, or even speculate about.

The question that remains is: Why such a brain? What possible environmental circumstance could ever evoke it? And the answer is that once a higher intelligence appears on the natural scene, it makes its own environment. It is a matter of common observation among ourselves that every human being leaves a mark of his or her own personality upon surroundings. A housewife's individuality can almost be read by the condition in which we find her house—the order or disorder in which it is kept, the way it is furnished and decorated, the implements and equipment she uses. A man's fields or garden, office or workshop or den, also reveal much about him. It would seem that something of the brain and perhaps of the whole nervous system of each of us is displayed in the environments we make for ourselves. Even the little bowerbird reveals his individuality in the small objects he uses to decorate his bower and in his arrangement of them. And so we feel we are justified in assuming that as a brain evolves in complexity, it will surely be to the accompaniment of an increasingly complex ambience.

Human beings originally lived in the same "nature" as all other animals, and many still live very closely with it. If we camp in a forest, we do not actually need our superior mental equipment—that other creatures live out their lives in that forest is evidence of this. But most of us no longer live in natural settings, and even those who do, modify them with such artifacts as fire, weapons, and tools.

Today, all of mankind, some to a smaller but most to a larger extent, make our own habitats, and our man-made environment becomes more and more complex. Moreover, in the process of adjusting to our increasingly artificial surroundings, we become less and less fitted to live in natural ones, and so the course upon which we are embarked is far more likely to continue than to be reversed. For Homo neocorticus we can project a habitat of, to us, incredible complexity, and we can imagine that this will provide the spur that could eventually produce the Group-brained neocorticus, whom we could no longer designate as Homo.

At this point we pass our project over to our reader to use his or her imagination to visualize a meeting between Homo sapiens and Group-brained neocorticus, or its prototype as it may exist somewhere in the vastness of the universe.

'eh?

 

 UPDATE

 

 A Link to more AGI stuff I posted last year.

 

Stay tuned... 

"Perception is an ILLUSION?"

 

Well, that's pretty unequivocal. That graphic is from a BigThink Youtube video. The speaker is neuroscientist Dr. Heather Berlin.

 

First time I saw that graphic, I had a fleeting reflexive reaction of "oh, yeah, the 'Subjectivism Fallacy'," stemming from my 1999-2004 Adjunct days teaching collegiate "critical thinking" classes. i.e., "there ARE NO 'objective truths,' everything is subjectively perceived in response to sensory stimuli." "So, (BobbyG retorts) if this assertion is 'false' (illusory), it deductively follows that it must also be TRUE."

 

Pedant. 

 

Yeah, Heather. It's just a 4-word (albeit clickbait-ish) headline.

 

 

The broader point is taken, Doc. Succintly put in 6:21.

 

OF PARTICULAR RELEVANCE THESE DAYS

 

"The provided sources examine the complex intersection of anthropomorphism, trust, and power within the field of artificial intelligence. One study investigates how linguistic cues, such as voice-based interfaces and the use of first-person pronouns, lead users to perceive large language models as more human-like and accurate. Complementary research explores the "Silicon Valley Effect", arguing that Big Tech companies strategically shape regulatory discourse to protect their commercial interests while potentially obscuring the human harms caused by their products. Further analysis focuses on the visual self-representations of ChatGPT, identifying recurring themes of futurism and social intelligence that promote the image of a "friendly assistant." Collectively, these texts highlight how human-like traits in AI can manipulate public perception, set unrealistic expectations of capability, and complicate the legal and ethical oversight of generative technologies."
[Sounds a bit like it was written by AI, no?]

AI as applied to social media (and "influence" industries broadly) is all about shaping your perceptions in ways that benefit them. "AI for Good?"

 

I'll fill in a bunch of multi-vector applicability ASAP...

Before we move on from our recent topic:

Define "Stupidity." 

 

We often tend to cavalierly toss words around with scant or nil attention to their proper lexical (denotative and connotative) specifications, particularly when the words are pejorative. I can be as culpable as the next person.

 

But, during my adjunct tenure teaching collegiate "Critical Thinking" I reflexively admonished my students that many "arguments" founder straight away owing to inadequate attention and consensus reqarding the materially salient definitions of key terms.

In courts, counsels and judges know such up-front agreement as "stipulation."

What does Google ("AI") say about "stupidity" of late? 

 

 OK, WHAT OF THIS UK BEAUT?

 

 Yeah...

 

THIS BOOK IS A 5-STAR MUST-READ (see prior post)

 

THE AUTHOR CONCLUDES

… Perhaps it just isn’t true that book reading in particular and literacy in general are essential for intelligence. Maybe there is more to intelligence than is dreamed of in this literacy-venerating philosophy. Perhaps, that is to say, all these ostensibly stupid kids responsible for dragging down average US intelligence aren’t so dumb as suggested. Just possibly, in not reading Moby Dick and Shakespeare, but instead making TikTok videos and crafting Instagram stories, they are manifesting intelligence, only not of the kind measured by IQ tests. 

True, this is an unexpected, perhaps even stupid, thought to encounter in a book. Book writers generally come to praise literacy rather than bury it – if only out of self-interest. This book, then, might not only be a history of stupidity but also be an addition to it. But, really, I am only elaborating here a thought we encountered earlier in the book, namely that the psychologist Edwin G. Boring was more sage than he realized in suggesting the limitations of IQ tests. His remark is worth repeating: ‘Intelligence as a measurable capacity must at the start be defined as the capacity to do well in an intelligence test. Intelligence is what the tests test.’ If IQ tests measure cognitive skills predicated on literacy, then any form of intelligence we may care to imagine that is not predicated on literacy will be ruled out, perhaps even considered to be a kind of stupidity. Just possibly, though, a post-literate, non-literate America might be more intelligent than a literate one. Equally, the cognitive skills for which IQ tests test may not be the only ones that the intelligent have and the stupid – poor deluded boobs – lack.

In this context, it is amusing, to put it mildly, that the World Population Review, which published the data on declining average American IQ tests in 2024 on its website, added this caveat. ‘To be completely fair and transparent, the intelligence quotient is not the most accurate way of determining someone’s intelligence. After all, it is nearly impossible to fully calculate someone’s intellect because it is not a variable that is numerically represented. Instead, IQ scores are a way of trying to put a number on someone’s intelligence.’ If you’re reading this in New Mexico thinking how terrible it is you live in the US’s most stupid state, even though you’ve been doing all you can to skew that data by translating Proust into Sanskrit and inventing spaceships that go faster than the speed of light, take succour: even the people publishing the research don’t really believe that intelligence – whatever it is – is measured very well by intelligence tests. 

Professor Dombrowski’s worries about the reasons for the fall in American average intelligence have led to a growth industry in indicting purportedly rising stupidity. That growth industry’s business model requires that Voltaire and Schopenhauer were wrong: stupidity is not so much an ineradicable feature of humanity as something living, growing. The American writer Lance Morrow declared in the Wall Street Journal in 2021:

We live in a golden age of stupidity. It is everywhere. President Biden’s conduct of the withdrawal from Afghanistan will be remembered as a defining stupidity of our time – one of many. The refusal of tens of millions of people to be vaccinated against the novel coronavirus will be analysed as a textbook case of stupidity en masse. Stupid is as stupid does, or, in the case of vaccination, as it doesn’t do. Stupidity and irresponsibility are evil twins. The slow-motion zombies’ assault on the Capitol on Jan. 6 was a fittingly stupid finale to the Trump years, which offered duelling stupidities: Buy one, get one free. The political parties became locked in a four-year drama of hysteria and mutually demeaning abuse. Every buffoonery of the president and his people was answered by an idiocy from the other side, which in its own style was just as sinister and just as clownish.

Morrow was wrong about one thing: the Capitol riots weren’t the finale to the Trump years. In January 2025, Trump was sworn in at the Capitol, where his followers had rioted four years earlier, inaugurating another four years of idiocy – or so the president’s detractors might suggest. 

Morrow’s account is a common journalistic trope: in the Daily Telegraph Janet Daley lamented ‘the Age of Stupid’, citing both Trump and Labour’s Jeremy Corbyn, while David Rothkopf wrote for the Washington Post under the headline ‘America’s golden age of stupidity’ about the first Trump administration, offering a clear definition: stupidity is ‘the wilful disregard of knowledge – regardless of motive’. Such stupidity is an ‘unwinnable proposition . . . because those who battle facts are at war with reality’. Stupidity thus constitutes a distinct form of failure; separate from, but likely aiding and abetting, political, ethical, and other shortcomings. Stupidity also amounts to a useful tool in electoral success: those who disconnect from the truth are not constrained by it. 

All these articles suggesting that we have reached Peak Stupid have one thing in common: they are themselves stupid. Who knows if this is a golden, silver, or even bronze age of stupidity? We are, as a species, surely too dim to know what the future holds. It is quite possible that as we crest one summit we see a beguiling prospect ahead of us, half shrouded in the mists of fatuity. The peak of stupidity remains ahead. And so we continue on what we think is our ascent, higher and higher, too stupid to realize that we’re really going down not up, that in truth we are progressing deeper and deeper into the unplumbed depths of witlessness. 

In his ‘The Discovery of the North Pole’, Karl Kraus satirized the idea of the human mind becoming enlightened and eradicating stupidity by means of superior technology and willpower. The discovery of the North Pole, he wrote, was ‘inevitable. . . . It is an idea graspable by all brains, especially those no longer capable of grasping anything. The North Pole had to be discovered some day, because for centuries the human mind had penetrated the night and the fog in a hopeless struggle with the murderous elemental forces of stupidity.’ We think that the Enlightenment involves progress only because we are too stupid to consider another possibility, that it does the opposite. As Kraus puts it: ‘When people were travelling in mail coaches, the world got along better than it does now that salesmen fly through the air. What good is speed if the brain has oozed out on the way?’

Of course, Kraus’s perspective risks the opposite error: not so much faith in the unalloyed good of progress as the equally stupid idea that life was ever better than it is now or that progress necessarily means its opposite. Kraus’s sense was that the evolution of stupidity is unstoppable. Technological change, quite possibly, stupefies rather than liberates us. Maybe, instead, the truth is more subtle: it both liberates and stupefies us. The worst-case scenario is that it liberates, stupefies, and then replaces us. That, at least, is one of the more benign outcomes when the forces of human stupidity do final battle with the tooled-up bots of artificial intelligence. So how should we greet this prospect? 

In another essay, ‘In Praise of a Topsy-Turvy Life-Style’, Kraus wrote about the benefits of sleeping in. He even quoted King Lear: ‘Take vantage, heavy eyes, not to behold this shameful lodging.’ In the age of stupidity, he seems to suggest, it’s best to shut one’s eyes to the parade of witlessness. The shameful lodging is the incessant gush of information, a gush that has only multiplied since Kraus’s death in 1936, when it was largely confined to the morning and evening newspapers. He wrote: 

Anyone who has observed for a time how disgracefully these events debase themselves before curiosity, how cravenly the course of the world adapts itself to the increased need for information, and how in the end time and space become forms of perception of the journalistic subject, turns over in bed and goes on sleeping. . . . Hence I sleep in broad daylight. And when I wake up I spread the whole paper shame of mankind before me so I might know what I have missed, and this makes me happy.

Maybe Kraus’s smug daily lie-in is a sort of wisdom: in the face of the world’s stupidity, as brought home to us every morning (or now, in our internet-enabled case, every moment), better to roll over and go back to sleep. But that is what stupidity wants. Indeed, a lifestyle of willed unconsciousness is a manifestation of stupidity. In the face of growing stupidity, keeping oneself from the fray and deluding oneself thereby that one is less stupid and more morally perfect than the witless parade of lesser mortals is not just the height of folly but also deeply stupid. If the triumph of evil only requires that good men do nothing, then the triumph of stupidity requires only that good people close their eyes and roll over. 

None of this should suggest that stupidity does not, on occasion, have a valuable social function, nor that we should try to eradicate it from human life. Consider the case of Sunny Balwani, the chief operations officer of Theranos, the Silicon Valley company that promised to offer fast, cheap blood tests from a single drop of blood but was later exposed as a fraudulent business. As reported in John Carreyrou’s book about the Theranos scandal, Bad Blood, Balwani, God bless him, was often out of his depth during engineering discussions. ‘To hide it, he had a habit of repeating technical terms he heard others using,’ wrote Carreyrou. During one meeting, Balwani latched onto the term ‘end effector’. which signifies the claws at the end of a robotic arm. Balwani, though, misheard: he didn’t hear ‘end effector’, he heard ‘endofactor’. For the rest of the meeting, he kept referring to endofactors, while his colleagues exchanged ironic glances with each other. At the next meeting with Balwani two weeks later, some colleagues brought a PowerPoint presentation titled ‘Endofactors Update’. 

Balwani didn’t realize he had been pranked by his colleagues, who went on to make up other purportedly technical terms that he, in his ignorance, picked up on and used in meetings. Nor, perhaps, did he realize why they were laughing when he left the room. 

Balwani was unwittingly demonstrating the Dunning–Kruger effect, which posits that people overestimate their intelligence and make fools of themselves as a result. He was demonstrating something else: how another person’s stupidity can bring people together. Stupidity, that’s to say, was a scar for Balwani, but a joy for everybody else in the room. There’s a lesson in this story: in difficult times, we need stupidity, arguably more than intelligence, to cheer us up. 

Quite possibly, I have become Sunny Balwani’s soulmate. As I’ve written this book, indeed, I’ve often wondered if I am displaying the Dunning–Kruger effect, trying to show off my intelligence while really demonstrating my stupidity. After all, what could be more stupid than writing a history of stupidity? My only consolation is that, like Balwani, I may have entertained you with my witlessness. If so, you’re welcome. 

If not, then, given you’ve got to the last sentence of this book, who really is more stupid – you or me?

Jeffries, Stuart. A Short History of Stupidity (pp. 282-287). (Function). Kindle Edition. 

The humor, man. Thought I was gonna break a rib laughing.
 

ENDOFACTORS UPDATE.

Stupidity Update

Interesting New Yorker article.

The current political situation makes this “a good time to write about stupidity,” Jeffries writes. When he notes that a central trait of stupidity is that it “can be relied upon to do the one thing expressly designed not to achieve the desired result”—or “to laughably mismatch means and ends”—he makes “stupid” seem like the perfect way to characterize our era, in which many people think that the key to making America healthy again is ending vaccination. Meanwhile, in a recent issue of New York magazine—“The Stupid Issue”—the journalist Andrew Rice describes troubling and widespread declines in the abilities of high-school students to perform basic tasks, such as calculating a tip on a restaurant check. These declines are happening even in well-funded school districts, and they’re part of a larger academic pattern, in which literacy is fading and standards are slipping.

Maybe we are getting stupider. Still, one of the problems with the discourse of stupidity is that it can feel reductive, aggressive, even abusive. Self-humiliation is still humiliating; when we call one another stupid, we spread humiliation around, whether our accusation is just or unjust. In a recent post on Substack, the philosopher Joseph Heath suggested that populism might be best understood as a revolt against “the cognitive elite”—that is, against the people who demand that we check our intuitions and think more deliberately about pretty much everything. According to this theory, the world constructed by the cognitive élite is one in which you have to listen to experts, and keep up with technology, and click through six pages of online forms to buy a movie ticket; it sometimes “requires the typical person, while speaking, to actively suppress the familiar word that is primed (e.g. ‘homeless’), and to substitute through explicit cognition the recently-minted word that is now favoured (e.g. ‘unhoused’).” The cognitive élites are right to say that people who think about things intuitively are often wrong; on issues including crime and immigration, the truth is counterintuitive. (Legal procedures are better than rough justice; immigrants increase both the supply and the demand for labor.) But the result of this has been that unreasonable people have hooked up to form an opposition party. What’s the way out of this death spiral? No one knows…

An excellent read. Goes to this recently published book.

 

 

Wild. Wicked British humor.

 

Recall my earlier post on "A Recipe for Idiocracy?" 

 

FRIDAY UPDATE: THE DAY IN STUPID

 

“You're expected to get the FIFA Peace Prize, Mr. President," CNN's Kaitlan Collins told Trump at a Kennedy Center event on Friday. "What would you say to people who say that prize might conflict with your pledge to strike Venezuela?"

"Well, I think the Peace Prize, I mean, I settled eight wars," Trump replied. "I don't know that I'm getting it. I haven't been officially notified. I've been hearing about a Peace Prize, and I'm here to represent our country in a different sense."

"I can tell you I did settle eight wars and we have a ninth coming, but in which nobody's ever done before, but I want to really save lives, I don't need prizes. I need to save lives, and we're saving a lot of lives. I've saved millions and millions of lives, and that's really what I want to do, and I also want to run a great country, and the United States right now is the hottest country anywhere in the world, and one year ago, we had a dead country, and now we have the hottest country anywhere in the world."

"gormless" "The Oaf of Office"

The current political situation makes this “a good time to write about stupidity,” Jeffries writes. When he notes that a central trait of stupidity is that it “can be relied upon to do the one thing expressly designed not to achieve the desired result”—or “to laughably mismatch means and ends”—he makes “stupid” seem like the perfect way to characterize our era, in which many people think that the key to making America healthy again is ending vaccination. Meanwhile, in a recent issue of New York magazine—“The Stupid Issue”—the journalist Andrew Rice describes troubling and widespread declines in the abilities of high-school students to perform basic tasks, such as calculating a tip on a restaurant check. These declines are happening even in well-funded school districts, and they’re part of a larger academic pattern, in which literacy is fading and standards are slipping. —Joshua Rothman

SATURDAY UPDATE: THE DAY IN STUPID

 

OK, time to get back to work

 

Unreal smarts, this young scholar.
 

   

My follow-on observation:

To use a DNA analogy, genomic diversity is “adaptive” precisely because–mixing my metaphors–“you can’t ever step in the same river twice.” apropos, see @brianklaas’s killer book “Flukes.” #LLM inbreeding is as maladaptive as genetic inbreeding. House of Windsor, anyone?

OK, I was not hip to her until reading a new Science Magazine review of her current book The AI Mirror. Bought her prior release as well (I have no life).

 

YOU GOTTA READ THIS BOOK

...[M]ost commercial AI systems today are powered by a machine learning model trained on a large body of data relevant to a specific task, then fine-tuned to optimize its performance on that task. 

 

This approach to AI has made rapid progress in widening machine capabilities, particularly in tasks using language, where we have the most data to train with. Indeed, since so many kinds of cognitive tasks are language-enabled, most experts now regard the term “Narrow AI” as outmoded, much like its predecessor label “Weak AI.” Very large language models, like OpenAI’s various iterations of GPT or Google DeepMind’s Gemini, can now do an impressively wide variety of things: answer questions, generate poems, lyrics, essays, or spreadsheets, even write and debug software code. Large image models can generate drawings, animations, synthetic photos or videos. While such models have a considerable speed advantage over human performance of these tasks, the quality and reliability of their outputs is often well below the peak of human ability. Still, some see evidence of progress toward AGI in their widening scope of action and the flexibility of a single base model to be fine-tuned for many new tasks. While a large language model (LLM) can’t solve a problem unless the solution is somehow embedded in the language data it is trained on, multimodal models trained on many types of data (text, image, audio, video, etc.) are expanding the performance range of AI models still further. 

 

Even if it no longer makes sense to call these tools “narrow” AI, they remain below the threshold of general intelligence—AGI. But it’s a mistake to explain that in terms of the problems they can’t yet solve. The true barrier to AGI is that AI tools today lack any lived experience, or even a coherent mental model, of what their data represent: the world beyond the bits stored on the server. This is why we can’t get even the largest AI models to reliably reflect the truth of that world in their outputs. The world is something they cannot access and, therefore, do not know. You might think there’s an easy fix: pair an AI model with a robot and let the robot’s camera and other sensors experience the world! But to an AI model, a robot’s inputs are just another data dump of ones and zeros, no different from image and sound files scraped from the Internet. These ones and zeros don’t organize themselves into the intelligent awareness of an open and continuous world. If they did, the field of intelligent robotics—including driverless cars, social robots, and robots in the service industry—would be progressing much faster. In 2015, fully automated cars and trucks were predicted to be everywhere by the 2020s. Yet in 2023, robotaxis piloted in San Francisco were still driving over firehoses, getting stuck in wet concrete, blocking intersections during busy festival traffic, violating basic rules of the road, obstructing emergency vehicles—even dragging a helpless pedestrian.4 It’s not just driving: the real-world performance of most twenty-first-century commercial robots has lagged well behind AI tools for solving language-based tasks. So, what’s the problem? 

 

A world is an open-ended, dynamic, and infinitely complex thing. A data set, even the entire corpus of the Internet, is not a world. It’s a flattened, selective digital record of measurements that humans have taken of the world at some point in the past. You can’t reconstitute the open, infinite, lived, and experienced world from any data set; yet data sets are all that any AI model has. You might say, “But surely this is true of the human brain as well! What more do we have than data streams from our eyes, ears, noses, and so on?” But your analog, biological brain remains a far more complex and efficient system than even the most powerful digital computer. In the words of theoretical physicist Michio Kaku, “Sitting on your shoulders is the most complicated object in the known universe.”5 It was built over hundreds of millions of years to give you something no AI system today has: an embodied, living awareness of the world you inhabit. This is why we ought to regard AI today as intelligent only in a metaphorical or loosely derived sense. Intelligence is a name for our cognitive abilities to skillfully cope with the world we awaken in each day.6 Intelligence in a being that has no world to experience is like sound in a vacuum. It’s impossible, because there’s no place for it to be. 

 

We humans do inhabit and experience a world, one rich with shared meaning and purpose, and, therefore, we can easily place the outputs of our latest AI tools within that context of meaning. We call these outputs “intelligent” because their form, extracted entirely from aggregated human data, unsurprisingly mirrors our own past performances of skilled coping with the world. They reflect back to us images of the very intelligence we have invested in them. Yet accuracy and reliability remain grand challenges for today’s AI tools, because it’s really hard to get a tool to care about the truth of the world when it doesn’t have one. Generative AI systems in particular have a habit of fabricating answers that are statistically plausible, but in fact patently false. If you ask ChatGPT to tell you about me and my career, it usually gets a lot right, but it just makes up the rest. When my host at a festival I was speaking at used ChatGPT to write my bio for the live audience, the tool listed in a confident tone a series of fictitious articles I haven’t written, named as my coauthors people that I’ve never met, and stated that I graduated from the University of California at Berkeley (I have never studied there). 

 

Importantly, these are not errors. Error implies some kind of failure or miscalculation. But these fabrications are exactly what ChatGPT is designed to do—produce outputs that are statistically plausible given the patterns of the input. It’s very plausible that someone who holds a distinguished professorial chair at a prestigious world university received her degree from another prestigious world university, like UC Berkeley. This fabrication is far more plausible, in fact, than the truth—which is that, due to harsh economic and family circumstances, after high school I attended a local community college in-between full-time work shifts, and later received my bachelor’s degree from a low-ranked (but dirt-cheap and good-quality) commuter university that offered night classes. When I was offered a PhD scholarship at age 25, I became a full-time student again after eight years in the workforce. I first set foot in a college dorm in my 40s, as a university professor. My story isn’t common. And that’s precisely why ChatGPT selected a more “fitting” story for me; quite literally, one that better “fit” the statistical curves of its data model for academic biographies. Later, we’ll consider the cost of relying on AI tools that smooth out the rough, jagged edges of all our lives in order to tell us more “fitting” stories about ourselves. 

 

These systems can perform computations on the world’s data far faster than we can, but they can’t understand it, because that requires the ability to conceive of more than mathematical structures and relationships within data. AI tools lack a “world model,” a commonsense grasp and flowing awareness of how the world works and fits together. That’s what we humans use to generalize and transfer knowledge across different environments or situations and to solve truly novel problems. AI solves problems too. Yet despite the common use of the term “artificial neural network” to describe the design of many AI models, they solve problems in a very different way than our brains do. AI tools don’t think, because they don’t need to. As this book explains, AI models use mathematical data structures to mimic the outputs of human intelligence—our acts of reasoning, speech, movement, sensing, and so on. They can do this without having the conscious thoughts, feelings, and intentions that drive our actions. Often, this is a benefit to us! It helps when a machine learning model’s computations solve a problem much faster than we could by thinking about it. It’s great when an AI tool finds a new, more efficient solution hidden somewhere in the math that you’d never look for. But your brain does much, much better than AI at coping with the countless problems the world throws at us every day, whose solutions aren’t mathematically predefined or encoded in data...

Vallor, Shannon. The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking (pp. 22-26). Oxford University Press. Kindle Edition.

Dang. This old washed-up guitar player is majorly impressed.

I am briefly reminded of my June post "The Apple of my AI." Also, "The Coming Wave?"

 

Searching back through my blog turns up a lot of stuff under "Artificial Intelligence." Shannon would likely take issue with a lot of that stuff. 

 

One of my faves from a few years ago is "The Myth of Artificial Intelligence."

NEW TERM: "TECHNOMORAL"

Click

I like it.

Stay tuned. Tons to reflect upon here. Way more to come...

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