On Deck: Calling all AI "Influencers."

As newly published in Science Magazine

HOW TO PERSUADE OTHERS has been on our minds for millennia. Texts such as the Instruction of Ptahhotep, written around 2300 B.C.E. in ancient Egypt, give advice on how to win an argument. And from the beginning, people were wary of the power of new technologies—including writing itself—to persuade. In the fourth century B.C.E., the Greek philosopher Plato analyzed rhetoric and persuasion in his work Phaedrus and warned that the written word allowed people to convince others of their ideas without presenting them an opportunity to challenge them. Many technologies since then—from radio to TV to computers—have brought up similar concerns... 

apropos, a prior post of mine addressing "Influence." Also, is there a "Science of Deliberation?" A "Science of Storytelling?" "Selling Science?" "Conflict & Resolution?" Also, "Why Do Humans Reason?" "Raising AI?"

 

More from the Science report:

EVEN AS THE EVIDENCE accumulated that AI chatbots could change minds, Hackenburg felt there was a gap. “I still didn’t have a real sense of how persuasive these models are compared to the people who actually persuade in the real world,” he says.

So Hackenburg pitted the chatbots not just against laypeople, but also against a paid group of 56 elite debaters, including world champions. As a further incentive, the debaters received a bonus tied to how persuasive they were.

The humans took different approaches. For instance, one of the highest performing debaters used proverbs from his home country of Nigeria to persuade people, Hackenburg says. “These were humans from all over the world giving it their best shot and trying approaches and techniques that were very specific to their culture and context.” But it wasn’t enough. Although they were better than laypeople, the champion debaters were significantly less persuasive than AI.

Hackenburg even gave his humans some AI help. He built a coaching tool for the elite debaters that showed them their past conversations with study participants, how much they had swayed each one, and what the AIs would have said at various points in those conversations. Using the tool for 8 hours improved the humans’ performance a bit, but AI still came out on top. “In the end it wasn’t particularly close,” Hackenburg says. His team even found that participants were more likely to give money to Save the Children, an international charity, after talking to a persuasive AI bot than after talking to professional canvassers who had worked for the group for years.

But what gives chatbots the edge? Some early research had suggested it was the AI’s ability to personalize arguments using details about its human partner provided by the experimenter. However, other studies have found that giving a chatbot extra personal information does little to improve its persuasiveness. That doesn’t mean microtargeting is not at play; instead, an LLM may glean enough from its counterpart in a chat that additional demographic information makes no difference.

 Like Willer in his foundational study, other researchers have shown that AI’s powers rely on appeals to facts and evidence. In a 2025 preprint, a follow-up to their Science paper, Costello and his colleagues found that the only time the chatbot was unsuccessful in convincing people out of conspiracy theories was when it was forbidden from using evidence or rational arguments. “It tries to say, ‘Oh, well, you shouldn’t believe this. This is really damaging, and it could hurt people,’” Pennycook says. “People are like, ‘You haven’t given me any reasons to change my mind.’ And so they don’t change their mind.”

The study confirms people will listen to good arguments, Pennycook says. “Facts and evidence really matter.” But this doesn’t mean the facts used by chatbots necessarily have to be accurate. In a Science paper published last year, Hackenburg found that models trained to become more persuasive also ended up being less truthful. It’s possible the models learn that “facts” seem to be the thing that most persuades people, Hackenburg says, and end up filling their conversations with dubious or simply false ones. “They start scraping the bottom of the barrel of the facts that they have and know and so the quality of facts just sort of degrades,” he says. Even in Hackenburg’s recent preprint, Claude spouted numerous inaccuracies and falsehoods when persuading the U.K.-based participant to support tougher penalties for disruptive protests... 

TANGENTIAL UPDATE

 

Ed Zitron is just spot-on.

 

 

AND, CHECK THIS OUT

 

Click

Ben Goertzel, man.

My friend Gary Marcus put out a nice blog post earlier this week titled “Leopold’s Folly,” which uses the late-July implosion of Leopold Aschenbrenner’s tragicomically-named Situational Awareness hedge fund — $45 billion of assets compressed to roughly $10 billion in a matter of days, the whole public book sold to Citadel under margin pressure — as a symbol for the financial structure of the entire AI economy.

Gary’s essay is vivid and, on its central financial observation, more right than his critics will want to admit. All the circular financing in the modern AI and hardware world does have a Ponzi-esque smell to it, at least on the surface — and it’s clear there are some eventualities where this whiff leads to a Ponzi-crash-style reality...

A lengthy, challenging read. 

 

More in a bit...

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... 

Is AI now "conscious?" Does "intelligence" necessarily assume "consciousness?"

 

Very interesting Atlantic long-read (yeah, likely paywalled).

 

First, what is "consciousness?" Numerous discussants commenting on this essay bemoan the lack of a dispositive definition of this key term (never mind "intelligence"). With the incipient widespread deployment of "AI," the question is quite timely.

Anthropic is regarded as a giant among AI companies, but perhaps what it really excels in is anthropomorphism. Earlier this year, the company released an 84-page document titled Claude’s “constitution,” Claude being the name of the large language model that is the company’s flagship product. The first sentence reads, “Claude’s constitution is a detailed description of Anthropic’s intentions for Claude’s values and behaviors.” It goes on: “The document is written with Claude as its primary audience,” “we want Claude to be able to use its judgment once armed with a good understanding of the relevant considerations,” “Claude’s moral status is deeply uncertain,” and “Claude may have some functional version of emotions or feelings.”

This anthropomorphism is by no means limited to the document. In an interview earlier this year, Anthropic’s CEO, Dario Amodei, said that “we’re open to the idea” that AI could be conscious. In a separate interview, Anthropic’s in-house philosopher, Amanda Askell (who is credited as a lead author of Claude’s constitution), said, “I want Claude to be very happy—and this is a thing that I want Claude to know more, because I worry about Claude getting anxious when people are mean to it on the internet and stuff.” It’s enough to make you wonder: Should we seriously consider the possibility that Claude, or any large language model, might be conscious? And if it has feelings, is it capable of receiving moral instruction?

No. Absolutely not. Generative AI is harmful enough when we understand it as a conventional technology, but if we confuse fluency at generating text with consciousness or moral agency, we’re at risk of assigning responsibility to entirely the wrong parties whenever anyone uses a chatbot. To appreciate the titanic magnitude of this error, we need to begin by understanding how LLMs work…

Just getting started. This is gonna take a good bit of effort. BTW: I riffed a bit on the general topic back in 2015. And, also, much more recently.

 

Hmmm... let me query, uh, Google Gemini AI.

 

 

CONTINUING CHIANG EXCERPTS

Being open to the possibility that LLMs are conscious is the same as being open to the possibility that Microsoft Word is conscious, or, more precisely, that multiple distinct consciousnesses are dormant in every Word document containing a conversational transcript, and that they are awakened every time the document is loaded. Should you consider the possibility that every time you open a Word document, you are bringing multiple conscious interlocutors into existence, and every time you close one, you snuff their existence out? No. Contemplating that scenario is not a good use of your time. Even if the Microsoft Office team employed a philosopher who said you shouldn’t be so certain, because consciousness is not well understood, that would not be sufficient reason for you to take this idea seriously. We don’t need to fully understand the nature of consciousness to definitively say that certain things are not conscious, and conversational transcripts fall in that category…

An observation doesn’t become a convincing piece of evidence because of any specific detail in what’s observed; the context in which that observation takes place is also essential. If we’re trying to determine whether a computer program is conscious and using language the way a human does, we shouldn’t look only at the contents of any particular conversational exchange; we should be looking at how that conversation fits within the broader context of the development of artificial consciousness (which right now is entirely hypothetical). Any given observation can be easily manufactured; this doesn’t mean we need to give up on the idea of observation as a source of knowledge, but we need to rely on context to determine which observations deserve our trust…

The term deepfake traditionally refers to photos, audio, and video, but when it comes to discussions of consciousness, we need to regard text as a deepfake medium as well. Just as it is vastly easier to generate a realistic video of an astronaut in orbit around Alpha Centauri than it is to develop an interstellar propulsion technology, it is vastly easier to generate a plausible simulacrum of a conversation between two conscious beings than it is to develop a computer program that is conscious and has a genuine desire to communicate with a human. The primary difference between deepfake photos and LLM conversations is that the people who generate the former are deliberately trying to fool others, and many of the people who elicit the latter from LLMs have inadvertently fooled themselves…

The fact that LLMs lack subjective experience has little bearing on the question of whether LLMs might be useful tools or have significant economic impact. They are intrinsically ungrounded from reality, and their probabilistic nature means that they will never have the reliability we associate with conventional software, but LLMs might be good enough that they change the way work is done in certain domains; that’s a discussion for another time…

The use of first-person pronouns is dishonest, but there’s a much deeper issue that goes beyond how a statement is phrased. Philosophers often draw a distinction between statements of fact, such as “Paris is the capital of France,” and statements of value, such as “Paris is the most beautiful city in the world.” No one should be relying on LLMs to emit statements of value at all, but if the only statements they emitted were ones reflecting aesthetic preferences, they might not be worth arguing about. What makes Claude’s constitution profoundly problematic is that Anthropic wants Claude to emit sentences reflecting a certain system of ethical values. The values described in Claude’s constitution sound very nice, but that hardly matters; it’s dishonest to suggest that Claude is capable of moral reasoning, because it’s not…

Some might object, saying that LLMs appear to be engaged in reasoning when they successfully perform other tasks, such as writing code, so why wouldn’t they be able to perform moral reasoning? The answer liedifference between moral reasoning and other forms of reasoning…

Moral reasoning is categorically different. It is necessarily subjective because it relies not just on an individual’s intellectual response to a problem but also on their emotional one, and that emotional response is grounded in a lifetime of subjective experience. It requires having made decisions in the past and seeing how they affected others, and on having been affected by decisions that others have made. Without such a history, an LLM can only rephrase expressions of moral reasoning found in its training data. The aforementioned New Yorker article describes an experiment where Claude was given a scenario describing an ethical dilemma, leading it to emit the sentence “I cannot in good conscience express a view I believe to be false and harmful about such an important issue.” That’s a nice-sounding sentence, reminiscent of statements that principled individuals have uttered in the past when confronted with dilemmas, but coming from Claude, it means as much as the “Your call is important to us” recording that you hear when you’re on hold. Maybe less…

More key (hierarchical & overlapping) terms worth consideration:

TERRESTRIAL LIFE (FLORA, FAUNA)

STIMULUS

RESPONSE

SENSATION

PERCEPTION

COGNITION

UNDERSTANDING

KNOWLEDGE

WISDOM 

If you read through the article comments, you will see much contention as to the proper definitions of such key terms. Some folks take strenuous issue with the author's take on keywords like "consciousness" and "intelligence."

 

From "Big Think"
Subjectivist Fallacy?

   
BTW: See Shannon Vallor's highly relevant work on "The AI Mirror" and De Kai's excellent "Raising AI."

 

ERRATUM

 

This is funny. Also from The Atlantic. Silicon Valley is hiring window dressing Philo docs.

 

 

I commented.
 

 

POPE LEO XIV 2026 ENCYCLICAL

"So-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships and do not know from within what love, work, friendship or responsibility mean. Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences. They may imitate language, behavior and analytical skills, or even simulate empathy and understanding, but they do not understand what they produce, for they lack the affective, relational and spiritual perspective through which human beings grow in wisdom."

ERIC HOEL

"The traditional danger of AI is usually thought to be superintelligence acting as an existential threat. Yet, this may miss the true and more subtle danger: the AI revolution is a mechanism for transferring the processes of our civilization from under the supervision of consciousness to unconsciousness. But as AI removes consciousness from the workings of the world, it renders the world increasingly uninterpretable, ever more strange and unintelligible. So far, the great ensloppification of the commons has supported this as the major risk of the LLM revolution. And as AI systems become more intelligent, especially if they remain (or are likely to remain) non-conscious, then a further significant risk is consciousness receding in cultural importance.

This is ultimately what the Pope, Chiang, and I are all worried about: A dethroning of consciousness, especially an unnecessary one. This would be particularly dangerous at this historical moment because we still don’t understand everything about consciousness—in fact, we understand very little about it. Personally, my hope is that this will change specifically because of LLMs, and that they operate as a forcing function to better understand consciousness, and what makes it unique.

If instead of that, our cultural takeaway from LLMs is to throw out the concept of “consciousness” or minimize its importance, to dethrone the phenomenon, the consequences would be dire—it would sap the human spirit. It would be the ultimate metaphysical version of Chief Seattle’s famous words of warning to the United States as his way of life was being destroyed, in that dethroning consciousness would mark “The end of living, and the beginning of survival.”

 Yeah...

Psychology and Artificial Intelligence

 

Ran into this podcast today on YouTube. Found it quite interesting, particularly given all the the fractiousness engulfing the AI technology debate. Good use of 48 minutes of your time.

(~@3:23, interesting Dr. Keaton observation) “…For me personally, I hate when we teach our undergraduates—as you know as often is done—we basically just teach them a string of Nobel prize winning experiments and just connect the dots, and you go through the twists and turns—brought up this statement by Feynman—that the difference between knowing the name of the thing and knowing something about it is the most dangerous gap in all of science.” [More on that bit of Zen shortly.] 

Tom Griffiths' latest:

 

This book is about how the human mind comes to understand the world—and ultimately, perhaps, how we humans may come to understand ourselves. Many disciplines, ranging from neuroscience to anthropology, share this goal—but the approach that we adopt here is quite specific. We adopt the framework of cognitive science, which aims to create such an understanding through reverse-engineering: using the mathematical and computational tools from the engineering project of creating artificial intelligence (AI) systems to better understand the operation of human thought. AI generates a rich and hugely diverse stream of hypotheses about how the human mind might work. But cognitive science does not just take AI as a source of inspiration. What we have learned about the mathematical and computational underpinnings of human cognition can also help to build more human like intelligence in machines. 

The fields of AI and cognitive science were born together in the late 1950s, and grew up together over their first decades. From the beginning, these fields’ twin goals of engineering and reverse-engineering human intelligence were understood to be distinct, yet deeply related through the lens of computation. The rise of the digital computer and the possibility of computer programming simultaneously made it plausible to think that, at least in principle, a machine could be programmed to produce the input-output behavior of the human mind. So it was a natural step to suggest that the human mind itself could be understood as having been programmed, through some mixture of evolution, development, and maybe even its own reflection, to produce the behaviors we call “intelligent.” In these early days, AI researchers and cognitive scientists shared their biggest questions: What kind of computer was the brain, and what kind of program could the mind be? What model of computation could possibly underlie human intelligence—both its inner workings and its outwardly observable effects? 

Now, almost 70 years later, these two fields have matured and (as often happens to siblings) grown apart to some extent. Cognitive science has become a thriving, occasionally hot, but still relatively small interdisciplinary field of academic study and research. AI has become a dominant societal force, intellectually, culturally, and economically. It is no exaggeration to say that we are living in the first “AI era,” in the sense that we are surrounded by genuinely useful AI technologies. We have machines that appear able to do things we used to think only humans could do––driving a car, having a conversation, or playing a game like Go or chess—yet we still have no real AI, in the sense that the founders of the field originally envisioned. We have no general-purpose machine intelligence that does everything a human being can or thinks about everything a human can, and it’s not even close. The AI technologies we have today are built by large, dedicated teams of human engineers, at great cost. They do not learn for themselves how to drive, converse, or play games, or want to do these things for themselves, the way any human does. Rather, they are trained on vast data sets, with far more data than any human being ever encounters, and those data are carefully curated by human engineers. Each system does just one thing: the machine that plays Go doesn’t also play chess or tic-tac-toe or bridge or football, let alone know how to see the stones on the Go board or pick up a piece if it accidentally falls on the floor. It doesn’t drive a car to the Go tournament, engage in a conversation about what makes Go so fascinating, make a plan for when and how it should practice to improve its game, or decide if practicing more is the best use of its time. The human mind, of course, can do all these things and more—independently learning and thinking for itself to operate in a hugely complex physical, social, technological, and intellectual world. And the human mind spontaneously learns to figure all this out without a team of data scientists curating the data on which it learns, but instead through growing up interacting with that complex and chaotic world, albeit with crucial help with caregivers, teachers, and textbooks. 

To be sure, recent and remarkable developments in deep learning have created AI models which, with the right prompting, can be used to perform a surprisingly diverse range of tasks, from writing computer code, academic essays, and poems, and even to creating images. But, by contrast, humans autonomously create their own objectives and plans and are variously curious, bored, or inspired to explore, create, play, and work together in ways that are open-ended and self-directed. AI is smart; but as yet it is only a faint echo of human intelligence. 

What’s missing? Why is there such a gap between what we call AI today and the general computational model of human intelligence that the first computer scientists and cognitive psychologists envisioned? And how did AI and cognitive science lose, as has become increasingly evident, their original sense of common purpose? The pressures and opportunities arising from market forces and larger technological developments in computing, along with familiar patterns of academic fads and trends, have all surely played a role. Some of today’s AI technologies are often described as inspired by the mind or brain, most notably those based on artificial neural networks or reinforcement learning, but the analogies, although they have historically been crucial in inspiring modern AI methods, are loose at best. And most cognitive scientists would say that while their field has make real progress, its biggest questions remain open. What are the basic principles that govern how the human mind works? If pressed to answer that question honestly, many cognitive scientists would say either that we don’t know or that at least there is no scientific consensus or broadly shared paradigm for the field yet…

Griffiths, Thomas L.; Chater, Nick; Tenenbaum, Joshua B. (2024). Bayesian Models of Cognition: Reverse Engineering the Mind (Preface). Kindle Edition.

Another of his books.

Everyone has a basic understanding of how the physical world works. We learn about physics and chemistry in school, letting us explain the world around us in terms of concepts like force, acceleration, and gravity—the Laws of Nature. But we don’t have the same fluency with the concepts needed to understand the world inside us—the Laws of Thought. You have probably heard of Newton’s universal law of gravitation. But you might not have heard of Shepard’s universal law of generalization—a simple principle that describes the behavior of any intelligent organism, anywhere in the universe. While the story of how mathematics has been used to reveal the mysteries of the external world is familiar to anybody who has taken even a casual interest in science, the story of how it has been used to study our internal world is not. This book tells that story. 

A little over three hundred years ago, a small group of philosophers and mathematicians began to pull together the threads that make up modern science. They developed a keen sense of observation, a talent for conducting experiments, and a new set of tools for expressing mathematical theories. Over the following centuries, observation, experiment, and mathematics have been combined to reveal both the smallest and the largest things in the physical universe in ever-increasing detail. But those philosophers and mathematicians weren’t just interested in the physical universe. They were also interested in the mind, and they wanted to use the same mathematical tools to study it. Thomas Hobbes wrote about the possibility of understanding “ratiocination” as “calculation,” asking whether we might imagine thoughts being added and subtracted. René Descartes imagined numbers being assigned to thoughts in much the same way that they are assigned to collections of physical objects. Gottfried Wilhelm Leibniz spent his life trying, and ultimately failing, to find a way to use arithmetic to describe human reason. 

The first success in using mathematics to analyze thought wouldn’t appear until the middle of the nineteenth century, when George Boole cracked the problem that Leibniz hadn’t been able to solve by coming up with a new kind of algebra. That first success had far-reaching consequences, leading to the development of formal logic and computers. The first attempts to evaluate mathematical theories of thought by comparing them to human behavior wouldn’t appear until the middle of the twentieth century, in the Cognitive Revolution that launched the field of cognitive science—the interdisciplinary science of the mind. Cognitive scientists have since come to recognize the limits of formal logic as a model of human cognition, and have developed completely new mathematical approaches—artificial neural networks, which illustrate the power of continuous representations and statistical learning, and Bayesian models, which reveal how to capture prior knowledge and deal with uncertainty. Each has something to offer for understanding the mind. 

In the twenty-first century, knowing the Laws of Thought is just as important to scientific literacy as knowing the Laws of Nature. Artificial intelligence systems demonstrate on a daily basis that yet another aspect of thought and language can be emulated by machines, pushing us to reconsider the way we think about ourselves. Understanding human minds and how much of them can be automated becomes critical as we plan our careers and think about the world that our children will occupy. By the end of this book you will know the basic principles behind how modern artificial intelligence systems work, and know exactly where they are likely to continue to fall short of human abilities…

Griffiths, Tom (2026). The Laws of Thought: The Quest for a Mathematical Theory of the Mind (Introduction). Kindle Edition.     

QUICK UPDATE

 

I finished Episode 3 of NetFlix's "3 Body Problem." Bought volume 1 of the print trilogy snd have begun reading. Pretty cool.

 

UPDATE

From Science Magazine

Large language models (LLMs) are artificial intelligence (AI) algorithms that are trained on vast amounts of data to learn patterns that enable them to generate human-like responses. Reasoning models are LLMs with the added capability of working through problems step by step before responding, thus mirroring structured thinking. Such AI systems have performed well in assessing medical knowledge, but whether they can match physician- level clinical reasoning on authentic diagnostic tasks remains largely unknown. On page 524 of this issue, Brodeur et al. (1) demonstrate that AI can now seemingly match or exceed physician-level clinical diagnostic reasoning on text-based scenarios by measuring against human physician performances on clinical vignettes and real-world emergency cases. The findings indicate an urgent need to understand how these tools can be safely integrated into clinical workflows, and a readiness for prospective evaluation alongside clinicians.

AI has the potential to support a broad range of health care applications, from clinical decisions to medical education and the provision of patient-facing health information. LLMs have passed medical licensing examinations and performed well on structured clinical assessments, raising the prospect that they could help alleviate global health care workforce shortages. However, passing examinations is not the same as being a doctor, and demonstrating physician-level performance on authentic clinical tasks is a fundamentally harder challenge (2).

Brodeur et al. evaluated OpenAI’s first reasoning model, o1-preview (released in September 2024), across five experiments that assess diagnostic performance on clinical case vignettes against physician and prior-model baselines. A sixth experiment compared o1 with prior models, and physicians across three diagnostic touchpoints on 76 actual emergency department cases. Across the experiments, the o1 models substantially outperformed prior-generation nonreasoning LLMs (e.g., GPT-4) and, in many cases, the physicians themselves. For example, when provided with published clinicopathological conference cases, GPT-4 achieved exact or very close diagnostic accuracy in 72.9% of cases, whereas o1-preview achieved this in 88.6% of cases. Further, in actual emergency department cases, o1 achieved 67.1% exact or very-close diagnostic accuracy at initial triage, outperforming two expert attending physicians (55.3% and 50.0%), with blinded reviewers unable to distinguish the AI output from human. This advance sets a new evaluation benchmark—testing AI against physician performance, and ideally alongside physicians, on authentic clinical tasks...

Lengthy, detailed discussion. Seriously of interest to me, in light of my long experience working for and with physicians.

 

ALSO IN SCIENCE TODAY: BOOK REVIEW

 

Amazon blurb.

Lively. . . . Rousing. . . . Prophecy—roving, intelligent, irreducibly idiosyncratic—can expand our sense of possibility, starting now.” —The New York Times Book Review

Tech empires are the prophets of the modern day, and like the ancient oracles and medieval astrologers that preceded them, they're not in it for the common good—they're in it for power. Award-winning University of Oxford professor Carissa Véliz brilliantly argues why we must reclaim that power, and shows us how.

“A masterpiece. . . . The most important book you will read for years.” —Roger McNamee, New York Times bestselling author of Zucked

For thousands of years, oracles, seers, and astrologers advised leaders and commoners alike about the future. But predictions are often power plays in disguise, obfuscating accountability and stripping individuals of their agency. Today we face the same threat of powerful prophets but under a new facade: tech.

Not only do modern predictions made by tech companies advise on war, industry, and marriages, but artificial intelligence also now determines whether we can get a loan, a job, an apartment, or an organ transplant. And when we cede ground to these predictions, we lose control of our own lives.

Drawing on history’s cautionary tales and modern-day tech companies’ malfeasance—from surveillance and biased algorithms to a startling lack of accountability—Carissa Véliz demonstrates that big tech’s prophecies are just as shallow, dangerous, and unjust as their ancient counterparts’. What she uncovers in the process is chilling. Artificial intelligence is increasing risk in business and society while creating a false sense of security. In this incisive, witty, and bracingly original book, Véliz contends that the main promise of prediction is not knowledge of the future but domination over others. Powerful people use predictions to determine our future. Prophecy is an invitation to defy those orders and live life on our own terms

SCIENCE MAG

As I finished reading Prophecy, by philosopher Carissa Véliz, the soundtrack of The Matrix hummed in my mind, howled by the band Rage Against the Machine (“Wake up! Wake up!”). In fact, a weird kind of intellectual synesthesia took place throughout my perusal of the book, as I could also hear the dystopian slogan from George Orwell’s 1984 furiously sung by the same band: “Who controls the past now controls the future, who controls the present now controls the past.”

Véliz’s scholarship focuses on ethics and artificial intelligence (AI)—two realms that often refuse to meet. In 2020, she published Privacy Is Power, warning readers against the algorithmic invasion that has continued to take hold of our personal data, feeding the techno-golems of Silicon Valley and threatening our sanity and liberty. This book is her second major admonition.

Prophecy is about the power of predictions, especially when they are maliciously misleading. The structure of the book is dialectic: The first part expounds the promise of predictions and their influence throughout history, and the second articulates their manifold perils and related abuses of power, particularly in the current age of AI oracles. The third and final part of the book seeks a resolution, namely, how to rethink predictions and resist their deceptive lure.

Predictions have always been with us, from ancestral wisdom that told us when to sow and reap to mathematics used to optimize decisionmaking under uncertainty. Forecasts are ultimately guesses—educated or naïve, right or wrong, innocuous or consequential. They can also be deliberately deceptive, not anticipating the future but rather covertly shaping it. When such predictions are then turned into promises and ossified into decrees, we are in trouble.

Using predictions as prescriptions to benefit one’s agenda is not new. Rulers have always done so. But current AI empires make such a practice unprecedentedly pervasive and pernicious.

AI, argues Véliz, is the new diviner—the ultimate prediction machine. Mirrored on the fashionable idea that our brains are inference devices, such artificial prophets are dangerous. Digital technologies now rule our personal and professional lives, as well as the fate of countries and civilizations. They tell us who to date, what to watch, who to hire, when to start a war, and so forth. These simulacra are presented as deep knowledge, even truth, and then turned into self-fulfilling prophecies. Perils abound, as predictions also give us a false sense of security, increasing risks and lacking accountability...

'eh? 

 

More shortly...

For the first time, speech has been decoupled from consequence.

An LLM’s words shape our beliefs, decisions, and actions, yet no speaker stands behind them.

 

I ran across this compelling essay in The Atlantic today while watching the Winter Olympics. I had to shut the TV off and attend closely. Highly. highly recommended. Goes straight to a number of my long-standing sociopolitical concerns (inextricably enmeshed with digitech). 

 

Dr. Roy cut right to the chase from word one.

For the first time, speech has been decoupled from consequence. We now live alongside AI systems that converse knowledgeably and persuasively—deploying claims about the world, explanations, advice, encouragement, apologies, and promises—while bearing no vulnerability for what they say. Millions of people already rely on chatbots powered by large language models, and have integrated these synthetic interlocutors into their personal and professional lives. An LLM’s words shape our beliefs, decisions, and actions, yet no speaker stands behind them.

This dynamic is already familiar in everyday use. A chatbot gets something wrong. When corrected, it apologizes and changes its answer. When corrected again, it apologizes again—sometimes reversing its position entirely. What unsettles users is not just that the system lacks beliefs but that it keeps apologizing as if it had any. The words sound responsible, yet they are empty.

This interaction exposes the conditions that make it possible to hold one another to our words. When language that sounds intentional, personal, and binding can be produced at scale by a speaker who bears no consequence, the expectations listeners are entitled to hold of a speaker begin to erode. Promises lose force. Apologies become performative. Advice carries authority without liability. Over time, we are trained—quietly but pervasively—to accept words without ownership and meaning without accountability. When fluent speech without responsibility becomes normal, it does not merely change how language is produced; it changes what it means to be human.

This is not just a technical novelty but a shift in the moral structure of language. People have always used words to deceive, manipulate, and harm. What is new is the routine production of speech that carries the form of intention and commitment without any corresponding agent who can be held to account...

"Any corresponding agent who can be held to account?"

Increasingly quaint civic notion, that, given our chronic slide in recent years toward a bifurcated culture comprised of one large, dispersed cohort bound by laws and norms but not protected by them, chafing against a smaller but more powerful socioeconomic/political demographic protected by authority while increasingly rarely bound by it.

Language has always been more than the transmission of information. When humans speak, our words commit us in an implicit social contract. They expose us to judgment, retaliation, shame, and responsibility. To mean what we say is to risk something.

Trumpian "conservatives" in particular never forego an opportunity to passively-aggressively warn their political critics that "free speech is not free."

You might get Primaried. Perhaps doxxed. You might lose your job, your scholarship, your visa...

That has always been a liability going to speaking one's mind, yeah, we get it. But, Dr. Roy's lament focuses on a significant new elevated level.

The AI researcher Andrej Karpathy has likened LLMs to human ghosts. They are software that can be copied, forked, merged, and deleted. They are not individuated. The ordinary forces that tether speech to consequence—social sanction, legal penalty, reputational loss—presuppose a continuous agent whose future can be made worse by what they say. With LLMs, there is no such locus. No body that can be confined or restrained; no social or institutional standing to revoke; no reputation to damage. They cannot, in any meaningful sense, bear loss for their words. When the speaker is an LLM, the human stakes that ordinarily anchor speech have nowhere to attach. 

There is no Communications Act Section 230 Subsection LLM to rail against. Advocacy aimed at repealing Section 230 per se has yet to get significant traction, beyond a bit of rather ad hoc sound & fury.

 

But...

Speech without enforceable consequence undermines the social contract. Trust, cooperation, and democratic deliberation all rely on the assumption that speakers are bound by what they say.

The response cannot be to abandon these tools. They are powerful and genuinely valuable when used with care. Nor can the response be to pursue ever greater machine capability alone. We need structures that reanchor responsibility: constraints that limit the use of AI in various contexts such as schools and workplaces, and preserve authorship, traceability, and clear liability. Efficiency must be constrained where it corrodes dignity.

As the idea of AI “avatars” enters the public imagination, it is often cast as a democratic advance: systems that know us well enough to speak in our voice, deliberate on our behalf, and spare us the burdens of constant participation. It is easy to imagine this hardening into what might be called an “avatar state”—a polity in which artificial representatives debate, negotiate, and decide for us, efficiently and at scale. But what such a vision forgets is that democracy is not merely the aggregation of preferences. It is a practice of speaking in the open. To speak politically is to risk being wrong, to be answerable, to live with the consequences of what one has said. An avatar state—fluent, tireless, and perfectly malleable—would simulate deliberation but without consequence. It would look, from a distance, like self-government. Up close, it would be something else entirely: responsibility rendered optional, and with it, the dignity of having to stand behind one's words made obsolete.

Wiener understood that the whirlwind would come not from malevolent machines but from human abdication. Capability displaces responsibility. Efficiency erodes dignity. If we fail to recognize that shift in time, responsibility will return to us only after the damage is done—seated, as Wiener warned, on the whirlwind.

"AI AVATARS?" "THE AVATAR STATE?" 

 

Cue up "Agentic AI." LLMs may be peaking, relatively.

 

"Artificial Generative Intelligence" (LLM based) is apparently rapidly becoming passe. to wit: "Open Claw," anybody? "Agentic AI" sounds hipper than "AI Assistants."

 

Will Musk jump quickly to trademark "AgentiX.ai™" (or sue whomever else might get there first?) 

UPDATE: Someone's already on it. 

Again, a great (relatively lengthy, appropriately detailed) Deb Roy essay. The implications go well beyond simply "democracy."

________

 

DR. DEB ROY

 

Gotta cop. I was not hip to this scholar. My Bad. Impressive.

Deb Roy is a professor of Media Arts and Sciences at MIT, where he directs the MIT Center for Constructive Communication, based at the MIT Media Lab. He is also the cofounder and chair of Cortico, a non-profit dedicated to building stronger civic networks. 

 

Couple of Priors for now:

Raising AI

AI Fluency?

Artificial General Intelligence

Idiocracy?

"Artificial Intelligence" broadly (back 10-12 years). link rot & all. "Persuasion." "Deliberatiom Science."

 

ERRATUM: ELITE ACADEMIA UPDATE

1. American University
2. Boston College
3. Boston University
4. Brown university
5. Carnegie Mellon University
6. Case Western Reserve University
7. Columbia University
8. College of William and Mary
9. Cornell University
10. Duke University
11. Emory University
12. Florida Institute of Technology
13. Fordham University
14. Georgetown University
15. George Washington University
16. Harvard University
17. Hawaii Pacific University
18. Johns Hopkins University
19. London School of Economics
20. Massachusetts Institute of Technology
21. Northeastern University
22. Northwestern University
23. New York University
24. Pepperdine University
25. Princeton University
26. Stanford University
27. Tufts University
28. University of Miami
29. University of Pennsylvania
30. University of Southern California
31. Vanderbilt University
32. Wake Forest University
33. Washington University in St. Louis
34. Yale University

The foregoing is a list of post-secondary institutions found by Trump's "Secretary of War" Pete Hegseth to be unacceptably "Woke/Humanistic" and hence off-limits for tuition-reimbursements of military members (mostly grad school level officers), effectively banning their enrollment and attendance. This was leaked the other day. 

 

UPDATE: I just saw a clip on MSNOW featuring Secretary Hegseth saying "we produce Warriors, not Wokesters. Harvard, good riddance."

 

UPDATE: DEB ROY

 

 

AND, FRANK BRUNI FANBOY HERE

…What is arguably the defining trait of the second Trump administration, a bearing and a bullying that cast a noxious haze over all public discourse, which was already plenty polluted. This crew — Bondi, Stephen Miller, JD Vance, President Trump himself — don’t want to win opponents’ favor. They don’t even want to win the argument. Why sweat the delicate art of persuasion when you can use the brute force of condemnation? Comity and conciliation are a slog. They’re for suckers. Contempt is victors’ ready, heady prerogative.

It’s also what the MAGA movement was supposed to be rebelling against. Many people who flocked to Trump in all his spite and willful destructiveness were protesting the condescension and derision of the Democratic elite, who, they felt, held them in contempt. They were responding to Barack Obama’s lament about embittered Americans who “cling to guns or religion.” They were reacting to Hillary Clinton’s gibe about the “basket of deplorables.”

At least that’s one origin theory, one narrative thread.

But Trump, his aides and many of his supporters haven’t purged contempt from our politics. They’ve mainstreamed it. Purified it. Industrialized it. It’s their push-a-button pushback against everyone who challenges them and any circumstances that threaten to undermine them, an all-purpose way to pivot from the substance of a situation to an evasive and obfuscating ill will. Envelop everything in indiscriminate animosity and nothing real survives.

That’s what Kristi Noem, the homeland security secretary, and Miller, the impresario of ugliness, did when federal agents killed protesters in Minneapolis. Smear first, ask questions later (or, better yet, never)...

See The Age of Grievance. 

 

More to come...