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...

What is “Consciousness?“ Is the question unanswerable?

“Two Cheers for Uncertainty?“ “Untying the knot of self.”

Is science fully up to the coming challenges?

New issue of Science up online.

 

And, of course, as is my Jones, I head straight for the book reviews.

Bergson is all but forgotten today, a situation Emily Herring hopes to redress with her new biography, Herald of a Restless World. Herring points out that Bergson’s ideas, which included subjective experience, nuance, and open-endedness, appealed to a populace frightened by the increasing mechanization quickly transforming everyday life...

Indeed.

...A few years after his fateful decision to study philosophy, Bergson started his first teaching job. He was asked to give a speech before an assembly of students and teachers during the traditional end-of-term awards ceremony. Barely out of school himself, the young man invited the students to reflect on the “severe disadvantages of what we call ‘specialisation.’” He argued that great men of science of the past, such as the illustrious Frenchmen Blaise Pascal, René Descartes, and Louis Pasteur, had made sure to consider problems from all sorts of different angles and perspectives, using methods from a variety of disciplines. But as the nineteenth century ended, this became more and more difficult to do. The accumulation of knowledge seemed to have reached a tipping point that fragmented the sciences into increasingly narrow fields and subfields and drove a wedge between science and philosophy. In his speech, Bergson warned that this fragmentation, this loss of big-picture, synthetic thinking, impoverished human knowledge as a whole. Bergson conceded that the impulse towards specialisation was a natural one, prompted by the “miserable discovery that the universe is greater than our mind; that life is short, education time-consuming and the truth infinite.” But he urged the students to resist this impulse, to put off committing to one specialised subject for as long as possible, and instead to broaden their minds as much as they could.

The young Bergson’s aversion to specialisation had started at some point in the late 1870s, when he discovered that, unlike other academic disciplines, philosophy was not limited to a specific object but opened up an infinity of theoretical avenues. It represented an opportunity to encompass all areas of knowledge, to look at the biggest, most important problems, to embrace every aspect of reality in one sweeping gesture. By choosing philosophy, he would not have to abandon any of his interests but could keep them all under investigation. Conceivably, Bergson had also realised in that moment that mathematical problems, though fascinating, were too narrow for his intellectual ambitions. By specialising as a mathematician, he would be willingly cutting himself off from whole areas of human knowledge, whereas, as a philosopher, the entirety of human knowledge would be his subject matter.

A “BAD” SCIENTIST
Desboves was devastated when he found out about Bergson’s decision. His young prodigy, the teenager who had bested his hero Pascal, was squandering his incredible mathematical gift, and for what? To pursue his interest in an inferior subject. The teacher wrote to the boy’s parents, stating in no uncertain terms that their son was committing an irreparable folly. But Bergson did not budge, and his parents stood by his decision. The next time Desboves caught sight of Henri, he grumbled: “You could have been a mathematician; you will be a mere philosopher.” Of course, the teacher could not have foreseen that his student would in fact grow up to be anything but a “mere” philosopher.

Desboves’s comment nevertheless ended up haunting Bergson. Throughout his career, Bergson would find himself repeatedly accused of being a philosopher who rejected science because he misunderstood it. As the American journalist Walter Lippmann wrote: “Though his thinking has been about biology, mathematics, and psychology, people call Bergson an artist.” Such misconceptions about him would stick. In a scathing article published in the Monist in 1912, the British philosopher and mathematician Bertrand Russell would paint Bergson as mathematically illiterate and accuse him of promoting “anti-intellectual philosophy” that led to the absurd view that “incapacity for mathematics is therefore a sign of grace.” This view, Russell added sarcastically, was “fortunately a very common one.” In 1922, Albert Einstein dismissed Bergson’s interpretation of relativity, claiming that the philosopher did not have a sufficient grasp of the physics at play. The following year the evolutionary biologist Julian Huxley wrote that Bergson was a “good poet, but a bad scientist.”

Of all the misconceptions about his philosophy, the idea that Bergson was promoting an anti-science agenda was the one that exasperated him the most. Although he was critical of certain aspects of scientific thought, he did not reject science through and through. Just because he found limitations in the methods of science did not mean that his understanding of these methods was limited.

Bergson viewed science and metaphysics as two different but complementary forms of knowledge, each limited in its own way. The perspective on reality offered by science would always be relative to its own symbols. Metaphysics, on the other hand, could aspire to absolute knowledge but would never produce the practical results of science. Yet, if both forms of knowledge came together in a way that recognised their fundamental differences, they could progress by pushing each other forward.

This had not, however, always been Bergson’s belief. In 1878, when he became a student at the prestigious École normale supérieure, he leaned towards the side of those who placed absolute faith in the power of science, thanks in large part to the English philosopher Herbert Spencer…

Herring, Emily. Herald of a Restless World: How Henri Bergson Brought Philosophy to the People (pp. 25-27). Basic Books. Kindle Edition. 

Just getting fully underway. In Chapter 4 now.

Love it.

BLUESKY UPDATE

Also in the Science Magazine issue.

Very cool.

I continue to build my bsky.social footprint.

 

BACK TO MUSING ON SCIENCE & PHILOSOPHY...

 

 

 

OK, is it "provably unprovable" (Scientific American article) that "perception is an illusion?" (via "Big Think")

 

Lordy Mercy. Recall the tiresome subjectivist bane of undergrad Phil101—"there is no such thing as objective truth."

More shortly...

_________

  

nullius in verba;

nonetheless, strive hard to maintain an attitude of curiosity and humility—what Zen Buddhism refers to as a “beginner’s mind.”

  

...We are not just helpless victims of fate but are the agents in charge of our own narrative, for better or worse, victorious or defeatist. This forceful shaping of our attitudes to events beyond our control has profound consequences for well-being and sickness…

How experience comes into the world has been an abiding mystery since the earliest days of recorded thought. Aristotle warned his readers more than two thousand years ago that “to attain any assured knowledge about the soul is one of the most difficult things in the world.” Mind is radically different from the stuff that makes up the brain and everything else. Quantum mechanics and general relativity, the periodic table of chemical elements, the endless strings of ATGC nucleotides that make up our genes—these appear to describe the physical, not the mental (I write “appear to” as quantum mechanics demonstrates that there are no observer-independent events, opening the door for consciousness to enter, at the ground level of reality). Yet we awaken every day to our subjective world of experiences.

The intellectual position that has garnered the most respect in contemporary Anglo-American philosophy departments is the ever more strident denigration or even outright denial of subjectivity. What is real is people talking obsessively about their experiences and acting on them; there is nothing above and beyond these speech acts and other intended or actual behaviors. The feeling part of consciousness, called phenomenal consciousness, is a big illusion. Philosophers in the know dispense with the “awful painfulness of my toothache” in the manner that Ebenezer Scrooge dealt with Christmas: “Bah! Humbug!” Furthermore, free will, our ability to deliberate about an upcoming fork in the road and to decide which path to take, is also thrown under this “illusion” bus. This rejection of the reality of lived experience constitutes a mind-boggling repudiation of what is immediately and indubitably given to us. It is also profoundly antihumanist, depriving us of those attributes that make us different from machines—indeed, equating us with machines.

It’s an absurd adjuration, akin to Cotard’s delusion, a rare psychiatric disorder in which able-bodied patients, often severely depressed, vehemently insist that some of their limbs are missing, that their bodies are rotting from the inside, or even that they are dead. When confronted with the fact that they are having a conversation, right now, with their doctor, they do admit that the situation is a bit baffling, but the fact is that they are dead, and that’s all there is to it. So it is with some contemporary thinkers who insist, against the evidence of their own senses, that experiences don’t exist. Truly astounding—gaslighting all of us into believing that our experiences are fake!

Fortunately, consciousness can’t be cancelled forever. The mental, having refused to yield, is returning with a vengeance. Indeed, the wheel is turning back to much more ancient understandings of experience, including idealism, the proposition that ultimately even matter and energy are mental manifestations, and panpsychism, the school of thought that all creatures, and perhaps even matter itself, are ensouled, that it feels-like-something to be anything, not just a human or even a bat. Modern science is supporting aspects of this remarkable turn of events…

What about nonhuman, artificial minds, rivaling or even exceeding ours? This topic is treated last. Sentient machines have been a recurring theme in science fiction. In 2022, this topic burst into public view with the startling claim by a Google software engineer that the company’s “large language model” was sentient and had to be considered a person with associated legal rights. The linguistic skills and knowledge of these models and their competitors, most famously ChatGPT and GPT-4 by OpenAI, trained on a vast trove of books and online documents far beyond what any human can read in a lifetime, are astonishing by the standards of even a few of years ago. They write summaries, emails, jokes, (bad) poetry, computer code, letters of recommendation, and dialogue indistinguishable from human-generated material, including plausible-sounding fabrications. They are evolving at an astounding pace and will transform society in fundamental ways.

These chatbots seemingly constitute living proof of the dominant narrative of liquid modernity: the mind is software that can be as readily embodied within silicon wafers as it is within flesh, echoing a pernicious Cartesian dualism. Smart money in Silicon Valley thinks so, most engineers and many philosophers think so, and popular movies and TV shows reinforce this belief.

Against the grain, integrated information theory radically disagrees with this functionalist view. It argues from first principles that digital computers can (in principle) do everything that humans can do, eventually even faster and better. But they can never be what humans are. Intelligence is computable, but consciousness is not. This is not because the brain possesses any supernatural properties. The critical difference between brains and digital computers is at the hardware level, where the rubber meets the road—that is, where action potentials are relayed to tens of thousands of recipient neurons versus packets of electrons shuttled back and forth among a handful of transistors. As we’ll see, the integrated information of digital computers is negligible. And that makes all the difference.

It means that these machines will never be sentient, no matter how intelligent they become. Furthermore, that they will never possess what we have: the ability to deliberate over an upcoming choice and freely decide.

The brain is the most complex piece of self-organized, active matter in the known universe. By no coincidence, it is also the organ of consciousness. Unlike scientific advances in genomics or astrophysics, progress in understanding the brain and the mind directly relates to who we are, our strengths and infirmities, how we can live a contented life, and whether we partake of some larger, ultimate reality. Humanity is not condemned to walk around forever in an epistemological fog—we can know, and we will know.

Koch, Christof. Then I Am Myself the World (pp. 14-21). Basic Books. Kindle Edition. 

BLURB

—Bernardo Kastrup, Executive Director, Essentia Foundation

"ADVERSARIAL COLLABORATION?" 

 

 Click link, read on. Christof Koch is involved with this Foundation.

 

Interesting.

 

UPDATE

 

Christof's book is a gold mine of illuminating quotes.

 

 Coheres wonderfully in many ways with Brian Klaas's Flukes.

Also apropos, "Sentience," anyone?

I can see that Dr. Christof's book themes may require several posts to do all of the implications justice. Toward that end see also
  

UnlikelyCollaborators.com

TinyBlueDotFoundation.org

BigThink.com

FreeThink.com 

"THE PERCEPTION BOX?"

 

It's a metaphor. Who is Elizabeth Koch?

 

You buyin' this?
  

OK. Unequivocal declarative sentence "truth claim" (assertion of fact). Perception is an Illusion.

Well, what of the sensory inputs and outputs converging and culminating in that claim? Bit of a quibble perhaps wafts up.

Whatever. Also relevant in line with factors adverse to clear, logical thinking: Claude Steiner's "Script Theory."

 

All of this stuff goes to my chronic Jones going to so-called "Deliberation Science."

 

Also, I am reminded of my episodic David J. Linden riffs. 

   

A DIGRESSION (SORT OF)

   

Didn't see this coming. But, oddly, it resonates broadly with the current topic.

 

 

What might have been and what has been

Point to one end, which is always present.

Footfalls echo in the memory

Down the passage which we did not take

Towards the door we never opened.

—T. S. Eliot, Burnt Norton

 

I ran across this disarming mind-bender "SciFi" miniseries on Apple TV+. Hmmm... Perception Box, Quantum Superposition Cube? Stay with me here...

 

Another book comes to mindm re: "Dark Matter."

   

Click

An ordinary family man, geologist, and Mormon, Soren Johansson has always believed he’ll be reunited with his loved ones after death in an eternal hereafter. Then, he dies. Soren wakes to find himself cast by a God he has never heard of into a Hell whose dimensions he can barely grasp: a vast library he can only escape from by finding the book that contains the story of his life...

A fun read.
 

UPDATE: TAKE IT BACK TO THE TOP

"...We are not just helpless victims of fate but are the agents in charge of our own narrative, for better or worse, victorious or defeatist. This forceful shaping of our attitudes to events beyond our control has profound consequences for well-being and sickness…"

Agents in charge? What would Sapolsky say? 

 

 

"Two Cheers for Uncertainty?"

 

More to come...

_________

   

Hermeneutics?

 

This book had not been on my radar. Then I read a lengthy article in Harper's by the book's author, Dr. Jason Blakely. Fortunate to have run across these. Both the article and the book are outstanding.

I'd been familiar with the term "hermeneutics," but after reading the book I had to admit we'd given the topic significantly short shrift in grad school, sufficing to note its application in "interpretations" of theological writings. Our Bad.

...Indeed, a culture of scientism helps produce a culture that also rejects genuine scientific authority. The scientism studied in these pages, by falsely trading on an authority it does not wield, helps to sow a wider skepticism and cynicism about the “elite” voices of scientists as such. A disturbing increase in science denial (e.g., conspiracy theorists, anti-vaxxers, climate change deniers) is in a mutually supporting dialectic with the absolute scientism of a Pinker or a Dawkins. Although they have not yet realized it, figures like Pinker and Dawkins, far from defending science, undermine it by overpromising and exaggerating its authority. Ultra-Darwinists and biblical literalists are dance partners.

The only way out of this dilemma that does not involve the dual irrationalisms of rejecting science and inflating the authority of science beyond reasonable bounds involves recovering other ways of knowing the world. One of the chief resources in this regard is the humanities. The humanities insist that there is an art to interpreting human behavior that is never reducible to a strict or exact science. Although it is not scientific, this art is not subjective or arbitrary, either. Rather, it is an art practiced by many historians, literary scholars, cultural theorists, and even some rogue social scientists. Only the art of interpretation can begin to restore our culture to a clearer form of self-understanding that escapes the current delusions and disappointments of our reigning scientism. Only this will help correct the frightening tendency in our present hour to reject the rightful authority of natural science (e.g., ecology, vaccines) while at the same time submitting uncritically to the scientism of popular social theories (e.g., broken windows, Homo economicus). In the past of the humanities and interpretive disciplines lies a new future. But a key question remains: Where are the new humanists?

Blakely, Jason. We Built Reality (pp. 135-136). Oxford University Press. Kindle Edition.

I've long tended to bristle at the word "scientism." I'm now pretty much over it. Pretty much.

 

INTERVIEW WITH THE AUTHOR

Nice. Very nice. And I'm not even a "religious" person (fully recovered Episcopalian, subsequent dilettante UU, Zen-leaning dude).

 

 

Goes to my recurrent focus on "Deliberation Science."

 

 

THE HERMENEUTICS OF "BLADE RUNNER?" WHAT?

 

 

Stsy with me here...

In this regard, a far better proposal for evaluating AI than the Turing test is suggested by the science fiction classic Blade Runner. This film—based on a novel by Philip K. Dick—opens with a scene depicting an interview in which a human is testing for the presence of AI. The test requires determining whether an android (known in the movie as a “replicant”) is capable of empathy. Empathy is a state that involves an awareness of how another person is experiencing a situation: what matters to him or her and what the emotional significance of a set of circumstances might be. This is closer to the criterion for human intelligence that Searle calls “semantics” and Taylor the “significance” factor. A reworked Turing test would need to be able to determine if an agent were experiencing significance or meanings. Such a test would be an interpretive or hermeneutic threshold for intelligence.

Blade Runner also serves as a powerful interpretive fable for the anxieties surrounding technological society. Taking place in a future version of Los Angeles, the plot follows a man named Deckard, whose profession is “blade running,” or hunting and destroying rogue replicants. Yet Deckard finds himself increasingly disturbed and alienated not only by his own severe loss of empathy for those around him but also by the atomized social relations of an impersonal, consumer society dominated by distant corporations. In this setting an awakening of empathy comes from a strange place: Deckard falls in love with one of the replicants he has been hired to kill.

At the center of this story is a deeper cultural fear that is the actual, repressed object of anxiety in the contemporary AI debate between doomsayers and boosters. This is a repressed fear of ourselves and what we might become if we go further down the road of the form of selfhood presented by Homo machina. That is to say, fear of robots is fear of ourselves without humanity, without empathy. Or perhaps more accurately, fear of AI is fear not of technology but of a new constellation of meanings opened up by technological society. The machine-self is one possible form of identity that humans embody in a culture of scientism.

This in turn might be linked to the distinctively modern cultures of violence—as scientifically planned by military experts and technocrats—so common in societies across the ideological spectrum. Consider in this light Joseph Stalin’s conviction that social science had revealed society could be explained “in accordance with the laws of movement of matter.” This machine view of society was the prologue to treating people like basic parts, to be replaced with other purportedly better parts. Stalinism was only one extreme version of the propensity of modern societies to conduct “scientific” mass killings. This is the kind of killing carried out remotely and planned by scientific experts. A dark dream that began in the French Revolution with the guillotine has reached an apotheosis with the invention of the concentration camp-laboratory, where violence is perfectly justified because it is perfectly rational. There is no “I” behind the system of violence in the camp-laboratory; neither is there a “you” on the receiving end. In the last analysis, there is only the impersonal mechanics of a machine grinding humanity into cinder and fire.

In Blade Runner we are offered a capitalist version of this mechanistic culture of violence and antihumanism. The humans who populate a future, dystopian Los Angeles have become radically more robotic in this way; they are no longer attuned to the experiences of their neighbors and are willing to treat them like mute objects. The streets of this Los Angeles are filled with a babble of tongues, homeless people dig through the trash, and crowds rush through the sidewalks distracted by their own individual market activity. No one speaks to one another, while neon advertisements shout platitudes about enjoying soft drinks or starting a new life on an “off-world” colony in outer space. Deckard at one point remarks that his ex-wife used to call him a “cold fish,” but the audience is relentlessly confronted with an entire society of cold fishes. What distinguishes Deckard is that he struggles mightily throughout the film to overcome his hardened willingness to assassinate others as simply part of his job, a mere market transaction. The entire plot is thus absorbed in the problem of the loss of human empathy and its replacement with a roboticized self that sees all relationships—even those of violence—as mechanical and rational. In all these ways, the city and inhabitants depicted in Blade Runner are not a portrait of the future at all but a dramatic picture of the present: the world as built by Homo machina. [Blakely, pp. 66-69].

Yeah. That's one of my all-time favorite flicks.

Buy Jason's book. You're welcome in advance.

 

More to come...

__________

 

"Ethics" prior post follow-up.

"Every year I ask my Stanford students to send me their top seven principles (e.g., below, 2019). Some choose personal traits, like honesty and curiosity; others select priorities, such as education and family. I generally recommend that individuals and organizations consider somewhere in the range of five to eight principles." (Dr. Susan Liautaud, The Power of Ethics, p. 26 et seq.)

1. Honesty
2. Integrity
3. Kindness
4. Compassion
5. Loyalty
6. Empathy
7. Authenticity

Much discussion ensues regarding the practical importance of honesty, as least as expressed by the academic elites of places such as Stanford.

per "elites"—Oops...

NY Times.

When behavioral-science researchers are accused of misbehavior, the allegations have a funny way of being a little on the nose. The former Harvard psychologist Marc Hauser, author of Moral Minds: The Nature of Right and Wrong, was found to have fabricated data and manipulated results. The University of Michigan psychologist Lawrence Sanna, who studied judgment and decision making, resigned after facing similar allegations. Diederik Stapel, a Dutch social psychologist whose work touched on such topics as selfishness and morality, fabricated data at least 50 times, making him “perhaps the biggest con man in academic science.” And last month, Francesca Gino, a Harvard Business School professor who studies dishonesty—and who wrote a book titled Rebel Talent: Why It Pays to Break the Rules at Work and in Life—was accused of falsifying data in at least four papers, three of which are on their way to being retracted. Her accusers now suggest that Gino, who has been placed on administrative leave from Harvard, may have faked data in dozens of her other published papers.

…The obvious irony of Gino’s situation makes for a punchy headline—“Dishonesty Researcher Accused of Dishonesty”—but it also speaks to a vexing paradox of human behavior, one that Gino has herself returned to again and again in her academic work. “Researchers across disciplines have become increasingly interested,” she wrote in a 2014 paper, “in understanding why even people who care about morality predictably cross ethical boundaries.” Let’s assume, for the sake of argument, that she is such a person—someone who cares about doing right but, at some point, for some reason, started doing wrong…

Hmmm...

SOME COOL STUFF FROM TX McCOMBS SCHOOL OF BUSINESS: CONCEPTS UNWRAPPED

36 short videos well worth your time and attention. Excellent job, folks.

All is Not Relative
Appropriation & Attribution
Being Your Best Self, Part 1: Moral Awareness
Being Your Best Self, Part 2: Moral Decision Making
Being Your Best Self, Part 3: Moral Intent
Being Your Best Self, Part 4: Moral Action
Bounded Ethicality
Causing Harm
Cognitive Dissonance
Conflict of Interest
Conformity Bias
Ethical Fading
Ethical Leadership, Part 1: Perilous at the Top
Ethical Leadership, Part 2: Best Practices
Framing
Fundamental Attribution Error
Fundamental Moral Unit
Implicit Bias
Incentive Gaming
Incrementalism
Intro to Behavioral Ethics
Legal Rights & Ethical Responsibilities
Loss Aversion
Moral Agent & Subject of Moral Worth
Moral Emotions
Moral Equilibrium
Moral Imagination
Moral Muteness
Moral Myopia
Obedience to Authority
Overconfidence Bias
Representation
Role Morality
Self-serving Bias
Systematic Moral Analysis
Tangible & Abstract

NOTE: Their complete glossary comprises 58 video items.

TUES UPDATE: I've just finished reviewing all 58..

A COUPLE OF RELEVANT READS

 

 

JUSTIN GREGG'S BOOK

Humans have been designed by evolution to be liars. Liars that are, strangely enough, susceptible to lies. This is a problem unique to our species. We are not an exceptional species because we can deceive; as we’ve seen, other species—from insects to cuttlefish—produce communicative signals that contain false information. And a few of them even intend to deceive others. But our species has weaved the intention to deceive—to lie by manipulating the beliefs of others—into the very fabric of our brand of social cognition. At best, we can educate our children to be sensitive to the proliferation of false information, and to want to reduce the harm it causes. But we cannot remove the human capacity to both produce and believe lies any more than we can remove our capacity for walking upright. It is who we are.

Gregg, Justin. If Nietzsche Were a Narwhal (p. 76).

More to come...

__________

 

The Patio Doors of Perception

 

(w/apologies to Aldous Huxley) OK, I continue to read a ton of books, as I've done for decades. These days, while my interests remain eclectic, I am particularly keen on anything that helps factually and logically clarify my thinking and postings on so-called “deliberation science” on this blog. 

I get most of my book recommendations from Science Magazine, followed by The Atlantic, The New Yorker, NY Times Review of Books, Scientific American, various random online resources (e.g., The Neurologica Blog, Science-Based Medicine, WIRED)—and, increasingly, pitches coming in from Amazon's "We thought you'd like this" emails (apropos of the above title). Amazon’s AI “algorithms” think they know me. Well enough to be profitable for them in the aggregate, I suppose. But…

 

Charitably, permit the author:

…[This] book is resolutely a work in philosophy of science—specifically philosophy of psychology. Perceptual psychology, centrally the psychophysics of vision, has become a mature science in the last fifty years. It gives philosophy an opportunity to understand important features of psychological capacities at a level of depth, rigor, and empirical groundedness that has never before been attainable. Philosophy should leap at the opportunity to make use of such a powerful and rapidly advancing science, as a basis for philosophical understanding. Some philosophy of perception makes no use at all of perceptual psychology. Much philosophy of perception makes at best decorative use. I think that it is no longer intellectually responsible to philosophize about perception without knowing and seriously engaging with that science. I believe that the practice of centering philosophical reflection about perception on phenomenology, or on analysis of ordinary talk about perception, without closely connecting the reflection with what is known from science (a centering that is a residue of the early empiricist model of perception), and the practice of allowing epistemology to guide reflection on what perception must be like, will all soon become museum pieces of past, misdirected philosophy.

Most of [this] book’s claims are, of course, supported only empirically, by interpreting the empirical results of the science. Some of the claims are, however, supported apriori. One should not confuse apriority with innateness, certainty, obviousness, infallibility, dogmatism, unrevisability, or immunity from revision based on empirical considerations. To be apriori supported, or apriori warranted, is to have support or warrant that does not depend for its force on perception or on sensing. Most apriori warranted judgments in this book are warranted by reflection that yields understanding of key concepts or principles used or presupposed in the science. All the relevant apriori judgments are synthetic, certainly in the sense of being non-vacuous and the sense of not being truths of logic. I think that the judgments are also synthetic in the sense of not being the products of analysis of conceptual complexes into concepts contained in the complexes. I think that most concepts that are central to our discussion are not complexes. They are simple. They are, however, necessarily and apriori embedded in networks with other concepts. Reasoning through such networks sometimes yields synthetic apriori understanding of foundations of mind.

Apriori supported judgments can be further supported empirically, by the science. But insofar as they are apriori warranted, they have sufficient warrant to support belief; and the warrant derives from reasoning or understanding, independently of support from perception, perceptual experience, or sensory registration. An example of an apriori warranted judgment, I think, is that perceptual states can be accurate or inaccurate. Another example is that perceptual states have a representational function—to accurately pick out and characterize particulars via causal relations to them: perceptual states fail in some way (representationally) if they are not accurate. I doubt that one can know apriori that any individual has perceptual capacities. Our empirical knowledge that we do have such capacities is, however, firm. It is more certain than some things that we know apriori about perception. As noted, being apriori does not imply some super-strong type of support. Apriori warrant for belief in simple arithmetical truths is super-strong. But much apriori support is not stronger, often less strong, than strong empirical support.

Our firm empirical knowledge that individuals have perceptual states does not require a detailed, reflective, philosophical understanding of what perception is. Knowing that individuals have perceptual states requires only a minimal understanding. One must be able to distinguish perception, at least by some cases, from just any sensing. And one must be able to recognize various examples of perception. Detailed philosophical understanding requires reflection, articulation, and elaboration of a minimal understanding of the concept perception and of relations between perception and other matters—semantical, functional, biological, causal, and so on. Elaboration is mainly empirical, but partly apriori. Given an elaborated understanding of what perception is, it is possible to draw, apriori, some further conclusions about the form, semantics, and functions of perceptual states. Such conclusions are abstract and limited. They are important in being basic to understanding.

Again, most of the book’s claims are empirical. For example, the accounts of how perceptual and perceptual-motor systems work in Parts III and IV, and the accounts of what these systems are in Part IV, are warranted partly by appeal to explanations in the science. Those accounts and those explanations are certainly empirically, not apriori, warranted.

I became interested in perception partly because it promises insight into basic types of representation of the world, and partly because it is a key factor that must be understood if one is to understand empirical knowledge. This book shows some fruits of the first motivation. In investigating the structure and semantics of perceptual representation, one investigates primitive and basic types of reference and attribution. My interest in the role of perception in empirical knowledge remains. But I take understanding perception to owe almost nothing to epistemology, whereas understanding epistemology absolutely requires understanding perception. Epistemology investigates epistemic norms for capacities that can contribute to obtaining knowledge. One cannot understand the norms without understanding the capacities. One understands perceptual capacities by reflecting on empirical science and its basic commitments, not by reflecting on epistemology. Understanding perception is the task of this book. Epistemic use of an understanding of perception is posterior. For epistemic work in this direction, see my ‘Perceptual Entitlement’, Philosophy and Phenomenological Research 67 (2003), 503–548; and ‘Entitlement: The Basis for Empirical Warrant’, in P. Graham and N. Pedersen eds., Epistemic Entitlement (Oxford: Oxford University Press, 2020).

I have some slight hope, even in this specialized world, that this book will interest not only philosophers, but at least some scientists in perceptual psychology and other areas of psychology. The best science is informed by a breadth and depth of perspective that is philosophical. This point is particularly relevant to perceptual psychology. A central, often stated, aim of the science is to understand conditions in which accurate perception occurs, and conditions under which illusions occur. (See Chapter 1, note 25.) Accuracy is a semantical concept.

So the science is committed at its very core to there being a semantics for perception—a systematic account of relations between perceptual representation and its subject matters. The account must explain what it is for perception to be accurate or inaccurate. Of course, the science is mainly concerned with causal patterns and mechanisms. Much of it, indeed probably most of it to date, focuses on pre-representational, pre-perceptual states that register the proximal stimulus. But the point of this scientific work is partly to build toward understanding perception of the physical environment. Part of understanding perception scientifically is to understand not only the causal patterns that lead to accurate and inaccurate perception, but also to understand the form and content of perceptual states, and what it is for them to be accurate or inaccurate.

Yet the science has paid no serious attention to these issues—specifically to semantics. It has not developed a vocabulary or set of principles that enable it to discuss accuracy and inaccuracy of perception with the precision and clarity of its accounts of causal, formational aspects of psychological states and processes. It provides no answers to questions like ‘What is it for a perceptual state to be accurate or inaccurate?’, ‘What sorts of representational competencies are involved in forming a state that is accurate or inaccurate?’; ‘In what ways can a perception be partly accurate and partly inaccurate?’; ‘What is the representational form or structure of perceptual states?’. Such questions are addressed in Parts I and II of the book.

Scientific understanding of perception is incomplete if it does not incorporate a systematic semantical understanding of perceptual states into its understanding of principles according to which perceptual states are causally generated. Semantical understanding is understanding of the representational contents, their forms, and their accuracy conditions—the conditions for representational success. Perceptual psychology would benefit from mastering the vocabulary necessary to think systematically about the semantics of perception.

Philosophy is the source of modern work in semantics—first the semantics of mathematics and logic, later the semantics of natural language. The basic semantical concepts, in something like their modern form, come from Gottlob Frege, about 130 years ago. In the last section of Chapter 1, I explain some of Frege’s basic concepts. I think that these concepts, with some modification, are valuable in understanding perception, even though they were first developed for understanding much higher-level representation—representation in mathematics.

I think that parts of the science need not only a deeper grip on semantics, but a much more rigorous terminology. Uses of terms like ‘representation’, ‘knowledge’, ‘cognition’, ‘recognition’, ‘judgment’, ‘belief’, ‘concept’, ‘prediction’, ‘intention’, ‘voluntary’ are far from reflective, much less standardized, in the science. Assimilating the whys and wherefores of terminology, is often the beginning of better, more fruitful empirical inquiry. Centrally, in Chapter 19, the section Uses and Misuses of the Term ‘Cognition’, but also throughout the book, there is a concerted effort to emphasize sharper uses of key mentalistic terms so as to respect basic differences in representational level. Such differences correspond to important differences in representational kinds—that is, representational capacities.

This is a long, complex book. Understanding anything well requires effort and patience. Genuine philosophical and scientific understanding cannot be grabbed off the shelf. The time and effort required to understand this book will be considerable. One cannot get there in a few sittings. The key point is to read and reread carefully and slowly, noting and reflecting on nuances and qualifications, mastering terminology, reading in context, connecting different contexts together, reading the footnotes, going back to earlier passages—all the while, reflecting. Few readers outside philosophy ever read this way. Most philosophers have, I think, lost the art. Iris Murdoch, in harmony with the marvelous quote that heads Chapter 1, wrote: ‘In philosophy, the race is to the slow’. Too many race at high speeds. The psychological and sociological pressures to form opinions and publish them quickly, and often, are very strong. Academic pressures and computer fluency have yielded much more writing, with no more time to master the increasingly complex topics written about. Careless reading, misdirected criticism, uninformed opinions, simplistic proposals abound. Perhaps it was always so. However, as knowledge grows—and grows more complex—lack of patience in pursuing understanding is an increasingly debilitating vice. Given that philosophical understanding of this book’s topics has become harder—because more is known and what is known is more complex—patience is more required than ever…

Burge, Tyler (2022-05-12T23:58:59.000). Perception: First Form of Mind  OUP Oxford. Kindle Edition.

OK, then. "Understanding anything well requires effort and patience. Genuine philosophical and scientific understanding cannot be grabbed off the shelf." No pick with that.

 

I typically first surf through some Amazon reviews (always on the lookout for sensible naysayers to help mitigate false positives, especially pricey ones). This rant was a doozy.

The death knell of armchair philosophy
Reviewed in the United States on December 13, 2022 Akiko Yano
Verified Purchase

A watershed moment in the history of philosophy. Here is one of the field's most pedantic authors, a Phil Review regular no less, a man who often cannot resist piling four adjectives on top of a single noun, basically vindicating Fodor's prophecy, from the 1960s onward, that philosophy - especially epistemology - would necessarily turn into psychology, due to the advent of the cognitive revolution, and Chomsky's work in particular. Even that old grey-beard Frege - who was massively influential precisely in separating epistemology from psychology - is brought down to earth and reinterpreted as elucidating perceptual psychology, where unconscious computational 'referential applications' (Bedeutungen) and 'characterizing attributives' (Sinne) abound. Burge's simply Herculean knowledge of the scientific literature is so overwhelming that any philosopher trying to hold on to epistemic-norm talk divorced from information processing - or 'what perception-must-be-like' independent of empirical research - is bound to feel very cowed indeed. And although Burge does not seem to realize his differences with Fodor are largely terminological - in particular, it was Fodor in 1983 who argued against MARR no less that the perceptual system has to be able to recognize poodle-shape attributives (a subject Burge lingers on here) and that such shape-based attributives ('concepts' of the perceptual module) must be systematically distinguished from concepts which figure in propositional knowledge, a point Burge likes to make ad nauseam - Burge's views here are unique enough to merit seminars devoted to Perception alone. The reason why this won't happen anytime soon is, budding philosophers would then have to ask, for whom do these bells toll? But of course, these bells toll for thee, O norm-loving philosophers.

Stay tuned. Just getting underway. Havin' some WTAF? Moments with this stuff at first blush...

UPDATE

"Most of [this] book’s claims are, of course, supported only empirically, by interpreting the empirical results of the science. Some of the claims are, however, supported apriori. One should not confuse apriority with innateness, certainty, obviousness, infallibility, dogmatism, unrevisability, or immunity from revision based on empirical considerations. To be apriori supported, or apriori warranted, is to have support or warrant that does not depend for its force on perception or on sensing. Most apriori warranted judgments in this book are warranted by reflection that yields understanding of key concepts or principles used or presupposed in the science..."

"apriori?" Really just IMO a hifalutin' synonym for "assuming to be" (though some might more narrowly construe it to infer "deductive"). Beyond that nitpick, my reading comprehension skills are fairly sharp and I can sling $50 words with the best of them, but the above call-out (not to mention the entire foregoing longer excerpt) leaves me shaking my aching head. As my early 90s industrial engineering boss would say “if I have to read something more than once, I don’t like it.“

“What does a priori mean? A priori is a term applied to knowledge considered to be true without being based on previous experience or observation. In this sense, a priori describes knowledge that requires no evidence. A priori comes from Latin and literally translates as “from the previous” or “from the one before.” 

i.e., “Assuming.”

This book runs to nearly 900 pages. To Amazon's credit, the downloadable Kindle sample notes "6 hours, 34 minutes remaining" at the outset (ending in the 5th of its 20 chapters). Kindle edition price is $38.00. I will likely read more of this comp download, though I'm dubious at this point that I'd get my 38 bucks worth out of the entire volume in light of the "scholarly" obtuseness evident thus far. And, Amazon reviewer "Akiko Yano" (a nom de guerre?) ain't helping much.

______

 

FOUR ADDITIONAL NEW READS

 

Got onto the new Alan Lightman book via Science Magazine. I bought "The Liars of Nature..." to compare to my prior engagement with "If Nietzsche Were a Narwhal."

 

UPDATE

Alan Lightman is kickin’ my butt. His PBS stuff is riveting. Well worth your time.

 

____

 

____

 

__________