Raising AI

“It takes a village?”

 Ran into a new Science Magazine book review. Another one jumps the rope line...

Can AI Help Us Conquer Fear? 

In the era of AI media, what is most crucial to remember is that the enemy of fear, divisiveness, polarization, and hatred is empathy. Being able to see things from another’s frame of mind, to feel how they feel—that is empathy, and it’s what changes a dehumanized object from your out-groups into a human in your in-group. 

AI needs to be helping us humans to develop empathy. 

     Because empathy is hard: it’s expensive; it’s far easier when safety and security are plentiful; and it’s affordable only to those with sufficient means. 

We’re not talking just about sympathy. Sympathy is when you react to someone’s feelings and thoughts from your own perspective—for example, showing pity or offering soothing words or mannerisms. Empathy, in contrast, is when you share someone’s feelings and thoughts from their perspective. 

Nor are we talking about knee-jerk unconscious reflexive affective empathy, like when you feel sad when someone cries. Or when you wince if you see someone trip and fall. Or when your heart swells up watching a sweet kid being happy to receive an award. (This kind of emotional empathy has been theorized to be related to what’s called mirror neurons.) 

Rather, we’re talking about conscious cognitive empathy, where you truly take yourself out of your own in-group’s tribal mindset and instead put yourself in the head and heartspace of a culturally different out-group, of another tribe. 

Conscious empathy requires carrying a heavy cognitive load, heavier than those who are struggling to feed themselves and their families can typically afford to do take on. 

But just as with other cognitively expensive and difficult tasks—like the maps and contact books on my phone—AI can help us. 

We can no longer afford the “us and them” mindset. “From me to we” is a cliché we need to take much more seriously. We need AI to help us to make the cognitively challenging shift toward empathy so that it becomes far more broadly accessible. 

Human culture is heavily based on linguistic constructs: language shapes how we frame ideas, aspirations, concerns in ways that invoke either fear or trust or joy or anticipation or some other response and promote mindsets such as “abundance versus scarcity.” 

My research pioneered global-scale online language translators, which spawned AIs such as Google and Microsoft and Yahoo Translate. But today our research program has been making an even more ambitious paradigm shift to advance from just language translation to cultural translation because in the AI era it is crucial that we develop AI to help humans with the cognitively difficult task of better understanding and relating to how out-group others frame things. 

We need AI to be democratizing empathy rather than WMDs. We must stop AI-powered fearmongering from driving our civilizations headlong into mutually assured destruction. Even if all our many cultures don’t agree on everything, we need AI to help us with listening to each other, suspending our fear. 

And we all need to be a part of this cultural shift. It takes a village.

Kai, De. Raising AI: An Essential Guide to Parenting Our Future (pp. xiii-xv). (Function). Kindle Edition.

This is a totally riveting read. 

 

UPDATE

 

Some topically related prior posting.

"Artificial General Intelligence?" Human existential risks?

AI 2027  

More shortly...

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...

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...

"There is no precision medicine without AI"

A reasonable assertion, I guess. But, need we still be mindful of "AI vs IA" (Artificial Intelligence vs. Intelligence Augmentation)? Or, is the latter essentially morphing increasingly into the former?

Comes a recent paper by the young wizard author of this book I've cited before:

The role of artificial intelligence in precision medicine
Bertalan Mesko
The Medical Futurist Institute; Department of Behavioral Sciences, Semmelweis University, Budapest, Hungary
Accepted 13 September 2017 (pdf link in title)

1. Introduction
The essence of practicing medicine has been obtaining as much data about the patient’s health or disease as possible and making decisions based on that. Physicians have had to rely on their experience, judgement, and problem-solving skills while using rudimentary tools and limited resources.

With the cultural transformation called digital health, disruptive technologies have started to make advanced methods available not only to medical professionals but also to their patients. These technologies such as genomics, biotechnology, wearable sensors, or artificial intelligence (AI) are gradually leading to three major directions. They have been (1) making patients the point-of-care; (2) created a vast amount of data that require advanced analytics; and (3) made the foundation of precision medicine. 

Instead of developing treatments for populations and making the same medical decisions based on a few similar physical characteristics among patients, medicine has shifted toward prevention, personalization, and precision. 

In this shift and cultural transformation, AI is the key technology that can bring this opportunity to everyday practice...

2. The dawn of practicing medicine
In previous centuries, healthcare has focused on working out generalized solutions that can treat the largest number of patients with similar symptoms. If cough syrup was good for the majority of the coughing masses and only a few people had a rash as an allergic reaction to it, there was no question about treating sore throat with cough syrup. Obtaining experience and empirical evidence on a generalized basis was the working method of the medical community since Hippocrates until around the beginning of the twentieth century. 

With the refinement of diagnostic tools, the detection of viruses or bacteria, the development of new pharmaceuticals and medical methods, healthcare has been going through sweep- ing changes since the start of the last century. The experience- based and somewhat ‘trial-and-error’ approach of medicine made place for evidence-based medicine. As a consequence, physicians not only prescribed treatments because their ancestors also used the same methods, but they proved the efficacy of treatments and diagnostic methods in scientific papers and clinical studies…

3. There is no precision medicine without AI
As the National Institutes of Health described it, precision medicine is ‘an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person.’[3] This approach allows doctors and researchers to predict more accurately which treatment and prevention strategies for a particular disease will work in which groups of people. 

It requires significant computing power (supercomputers); algorithms that can learn by themselves at an unprecedented rate (deep learning); and generally, an approach that uses the cognitive capabilities of physicians on a new scale (AI)…

4. AI in clinical practice
A major application of AI in healthcare is collecting, storing, normalizing, and tracing data. The AI research branch of the search giant, Google, launched its DeepMind Health project, which is used to mine the data of medical records in order to provide better and faster health services. In 2016, they launched a cooperative project with the Moorfields Eye Hospital NHS Foundation Trust to improve eye treatment [9]. To investigate how technology could help to analyze eye scans, Moorfields shared a set of one million anonymized eye scans with DeepMind and some related anonymous information about eye condition and disease management…

5. Is precision medicine the end of the human touch?
With advantages will also come ethical considerations and legal issues. Who is to blame if an AI system makes a false decision or prediction? Who will build in safety features? How will the economy respond to the appearance of AI when it starts making certain jobs useless? With driverless cars, there is a global debate about what decisions the algorithms would make in tricky situations. When it comes to health, this becomes a vastly bigger ethical challenge. There are more unanswered questions today than we can deal with and hopefully, with public discussions worldwide, this will clear up as AI is becoming a reality.

AI also has serious limitations in healthcare. Forecasting and prediction are mediated based on precedence in the case of machine learning, but algorithms can be underperforming in novel cases of drug side effects or treatment resistance where there is no prior example to build on. Hence, AI may not replace tacit knowledge that cannot be codified easily…

…Through the cultural transformation called digital health, the hierarchy of traditional medicine is transforming into an equal-level partnership between patients and caregivers. Besides many disruptive technologies, AI has the biggest potential to support this transition by analyzing the vast amounts of data patients and healthcare institutions record in every moment. By taking away the repetitive parts of a physician’s job, it might lead to being able to spend more precious time with their patients, improving the human touch. However, AI can only fulfill its mission if it remains a safe, efficient, and proven aid in treating patients and improving healthcare.

"By taking away the repetitive parts of a physician’s job, it might lead to being able to spend more precious time with their patients, improving the human touch. However, AI can only fulfill its mission if it remains a safe, efficient, and proven aid in treating patients and improving healthcare."

Well, yeah, you can't argue with that closing sentiment. But, for one thing, revisit my prior post "Artificial Intelligence and Ethics." See also my post on remediating "Clinician Burnout."

And, recall, we gotta "Fix the EHRs."

Bertalan's paper is well worth a close read. This stuff is coming, for better and/or worse.

apropos...

I've run across (via my new issue of Harper's) a breathtakingly marvelous young writer of riveting eloquence and broad perceptivity, Meghan O'Gieblyn: 

There are two kinds of technology critics. On one side are the determinists, who see the history of technology as one of inexorable progress, advancing according to its own Darwinian logic—the wheel, the steam engine, the autonomous car—while humans remain its hapless passengers. It is a fatalistic vision, one even the Luddite can find bewitching. “We do not ride upon the railroad,” Thoreau said, watching the locomotive barrel through his forest retreat. “It rides upon us.” On the opposite side of the tracks lie the social constructivists. They want to know where the train came from, and also, why a train? Why not something else? Constructivists insist that the development of technology is an open process, capable of different outcomes; they are curious about the social and economic forces that shape each invention.

Nowhere is this debate more urgent than on the question of artificial intelligence. Determinists believe all roads lead to the Singularity, a glorious merger between man and machine. Constructivists aren’t so sure: it depends on who’s writing the code. In some sense, the debate about intelligent machines has become a hologram of mortal outcomes—a utopia from one perspective, an apocalypse from another. Conversations about technology are almost always conversations about history. What’s at stake is the trajectory of modernity. Is it marching upward, plunging downward, or bending back on itself? Three new books reckon with this question through the lens of emerging technologies. Taken collectively, they offer a medley of the recurring, and often conflicting, narratives about technology and progress...

I could not recommend Meghan's eclectic writing more enthusiastically.

UPDATE

Erratum, from THCB:

Twitter-Based Medicine: How Social Media is Changing the Public’s View of Medicine
By BENJAMIN MAZER, MD

Doctors can be two-faced. This isn’t necessarily a negative attribute. Doctors have distinct personas for our patients and our colleagues. With patients, doctors strive for a compassionate but authoritative role. However, with each other, doctors often reveal a different demeanor: thoughtful and collaborative, but also opinionated and even sometimes petty. These conflicts are often the result of our struggle with evidence-based medicine. The modern practice of evidence-based medicine is more than the scientific studies we read in journals. Medicine doesn’t just change in rational, data-driven increments. Evidence-based medicine is a dialectic, a conversation. Doctors are being continually challenged to reconcile personal experience, professional judgment, and scientific data. Conflict can naturally result.

This struggle has been ongoing since the rise of evidence-based medicine decades ago. There are factions in medicine who are skeptical of clinical trials as the answer to all of medicine’s important questions, while other factions are wary of authority and consensus-driven medicine. These battles have traditionally been confined to the doctor’s lounge, both literal and in the figurative “safe spaces” of academic journals and conferences. But now the doctor’s lounge is going public. Social media is enabling doctors to rapidly communicate with each other. The heated public arguments that often result are in turn raising new questions about the effect of public discourse on the medical profession and the patients we serve.

I think the social media platform that’s doing the most to influence public debate about medicine is Twitter. Twitter, with its character limits, bandwagons, and trolls may seem inhospitable to nuanced medical debate, but the power of Twitter to broadcast physicians’ instinctive and abbreviated thoughts is underappreciated…

Interesting post.

Then there's the biz sided of things. From the NY Times:

Is the doctor in?

In this new medical age of urgent care centers and retail clinics, that’s not a simple question. Nor does it have a simple answer, as primary care doctors become increasingly scarce.

“You call the doctor’s office to book an appointment,” said Matt Feit, a 45-year-old screenwriter in Los Angeles who visited an urgent care center eight times last year. “They’re only open Monday through Friday from these hours to those hours, and, generally, they’re not the hours I’m free or I have to take time off from my job.

“I can go just about anytime to urgent care,” he continued, “and my co-pay is exactly the same as if I went to my primary doctor.”

That’s one reason big players like CVS Health, the drugstore chain, and most recently Walmart, the giant retailer, are eyeing deals with Aetna and Humana, respectively, to use their stores to deliver medical care.

People are flocking to retail clinics and urgent care centers in strip malls or shopping centers, where simple health needs can usually be tended to by health professionals like nurse practitioners or physician assistants much more cheaply than in a doctor’s office. Some 12,000 are already scattered across the country, according to Merchant Medicine, a consulting firm.

On the other side, office visits to primary care doctors declined 18 percent from 2012 to 2016, even as visits to specialists increased, insurance data analyzed by the Health Care Cost Institute shows.

There’s little doubt that the front line of medicine — the traditional family or primary care doctor — has been under siege for years. Long hours and low pay have transformed pediatric or family practices into unattractive options for many aspiring physicians.

And the relationship between patients and doctors has radically changed. Apart from true emergency situations, patients’ expectations now reflect the larger 24/7 insta-culture of wanting everything now…

Lots of moving parts, many of them still moving at cross-purposes. Lots of "disruption."

JUST IN...

Announcing the RWJF AI & The Healthcare Consumer Challenge!

…With the advent of advanced and robust AI platforms in marketing, law, and other sectors, we’re observing the vast opportunities for AI solutions in healthcare decision making. As medicine becomes more specialized, talent bottlenecks are developing and leading to increased professional strain on healthcare providers and consumers. To bring clarity and personalization to the healthcare industry, the Robert Wood Johnson Foundation is teaming up with Catalyst @ Health 2.0 to foster change in this space.

The RWJF AI & The Healthcare Consumer Challenge is calling all innovators to create AI enabled tools that support well-informed health decisions. By accelerating image recognition, data analysis, and pattern detection, we can start to remove harmful elements of human error from our systems. With predictive analysis, deep learning, and other AI enabled tools, innovators have the opportunity to help healthcare consumers make more informed and accurate decisions about the best health pathways to explore. With $100,000 in total challenge prizes, the most innovative solutions can bring attention to the benefits of AI in the consumer domain, as well as gain funding to continue tech development. Applicants can submit solutions such as tools to help find the ideal physician, estimate the cost of a health plan, or chatbots that track daily health decisions; it is up to YOU to do your part in solving a multi-billion dollar problem that affects all Americans…

_____________

More to come...

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? 

 

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