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, we know from the science of linguistics and the philosophy of knowledge that they differ profoundly from how humans reason and use language. These differences place significant limitations on what these programs can do, encoding them with ineradicable defects.

It is at once comic and tragic, as Borges might have noted, that so much money and attention should be concentrated on so little a thing — something so trivial when contrasted with the human mind, which by dint of language, in the words of Wilhelm von Humboldt, can make “infinite use of finite means,” creating ideas and theories with universal reach.

The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question

the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations

such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case

Those are the ingredients of explanation, the mark of true intelligence.

Here’s an example. Suppose you are holding an apple in your hand. Now you let the apple go. You observe the result and say, “The apple falls.” That is a description. A prediction might have been the statement “The apple will fall if I open my hand.”

an explanation is something more: It includes not only descriptions and predictions but also counterfactual conjectures like “Any such object would fall,” plus the additional clause “because of the force of gravity” or “because of the curvature of space-time” or whatever. That is a causal explanation: “The apple would not have fallen but for the force of gravity.” That is thinking.

The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws

any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered.

ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible.

Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.

For this reason, the predictions of machine learning systems will always be superficial and dubious.

some machine learning enthusiasts seem to be proud that their creations can generate correct “scientific” predictions (say, about the motion of physical bodies) without making use of explanations (involving, say, Newton’s laws of motion and universal gravitation). But this kind of prediction, even when successful, is pseudoscienc

While scientists certainly seek theories that have a high degree of empirical corroboration, as the philosopher Karl Popper noted, “we do not seek highly probable theories but explanations; that is to say, powerful and highly improbable theories.”

The theory that apples fall to earth because mass bends space-time (Einstein’s view) is highly improbable, but it actually tells you why they fall. True intelligence is demonstrated in the ability to think and express improbable but insightful things.

This means constraining the otherwise limitless creativity of our minds with a set of ethical principles that determines what ought and ought not to be (and of course subjecting those principles themselves to creative criticism)

True intelligence is also capable of moral thinking

To be useful, ChatGPT must be empowered to generate novel-looking output; to be acceptable to most of its users, it must steer clear of morally objectionable content

In 2016, for example, Microsoft’s Tay chatbot (a precursor to ChatGPT) flooded the internet with misogynistic and racist content, having been polluted by online trolls who filled it with offensive training data. How to solve the problem in the future? In the absence of a capacity to reason from moral principles, ChatGPT was crudely restricted by its programmers from contributing anything novel to controversial — that is, important — discussions. It sacrificed creativity for a kind of amorality.

Here, ChatGPT exhibits something like the banality of evil: plagiarism and apathy and obviation. It summarizes the standard arguments in the literature by a kind of super-autocomplete, refuses to take a stand on anything, pleads not merely ignorance but lack of intelligence and ultimately offers a “just following orders” defense, shifting responsibility to its creators.

In short, ChatGPT and its brethren are constitutionally unable to balance creativity with constraint. They either overgenerate (producing both truths and falsehoods, endorsing ethical and unethical decisions alike) or undergenerate (exhibiting noncommitment to any decisions and indifference to consequences). Given the amorality, faux science and linguistic incompetence of these systems, we can only laugh or cry at their popularity.

“It depends on what you mean by artificial intelligence.”

Computers are flexible enough to model the strange evolved convolutions of our thought, and yet responsive only to precise instructions. So if the endeavor succeeds, it will be a double victory: we will finally come to know the exact mechanics of our selves—and we’ll have made intelligent machines.

Ever since he was about 14, when he found out that his youngest sister, Molly, couldn’t understand language, because she “had something deeply wrong with her brain” (her neurological condition probably dated from birth, and was never diagnosed), he had been quietly obsessed by the relation of mind to matter.

How could consciousness be physical? How could a few pounds of gray gelatin give rise to our very thoughts and selves?

Consciousness, Hofstadter wanted to say, emerged via just the same kind of “level-crossing feedback loop.”

In 1931, the Austrian-born logician Kurt Gödel had famously shown how a mathematical system could make statements not just about numbers but about the system itself.

But then AI changed, and Hofstadter didn’t change with it, and for that he all but disappeared.

By the early 1980s, the pressure was great enough that AI, which had begun as an endeavor to answer yes to Alan Turing’s famous question, “Can machines think?,” started to mature—or mutate, depending on your point of view—into a subfield of software engineering, driven by applications.

Take Deep Blue, the IBM supercomputer that bested the chess grandmaster Garry Kasparov. Deep Blue won by brute force.

Hofstadter wanted to ask: Why conquer a task if there’s no insight to be had from the victory? “Okay,” he says, “Deep Blue plays very good chess—so what? Does that tell you something about how we play chess? No. Does it tell you about how Kasparov envisions, understands a chessboard?”

AI started working when it ditched humans as a model, because it ditched them. That’s the thrust of the analogy: Airplanes don’t flap their wings; why should computers think?

It’s a compelling point. But it loses some bite when you consider what we want: a Google that knows, in the way a human would know, what you really mean when you search for something

Cognition is recognition,” he likes to say. He describes “seeing as” as the essential cognitive act: you see some lines a

How do you make a search engine that understands if you don’t know how you understand?

s “an A,” you see a hunk of wood as “a table,” you see a meeting as “an emperor-has-no-clothes situation” and a friend’s pouting as “sour grapes”

That’s what it means to understand. But how does understanding work?

analogy is “the fuel and fire of thinking,” the bread and butter of our daily mental lives.

there’s an analogy, a mental leap so stunningly complex that it’s a computational miracle: somehow your brain is able to strip any remark of the irrelevant surface details and extract its gist, its “skeletal essence,” and retrieve, from your own repertoire of ideas and experiences, the story or remark that best relates.

in Hofstadter’s telling, the story goes like this: when everybody else in AI started building products, he and his team, as his friend, the philosopher Daniel Dennett, wrote, “patiently, systematically, brilliantly,” way out of the light of day, chipped away at the real problem. “Very few people are interested in how human intelligence works,”

For more than 30 years, Hofstadter has worked as a professor at Indiana University at Bloomington

The quick unconscious chaos of a mind can be slowed down on the computer, or rewound, paused, even edited

project out of IBM called Candide. The idea behind Candide, a machine-translation system, was to start by admitting that the rules-based approach requires too deep an understanding of how language is produced; how semantics, syntax, and morphology work; and how words commingle in sentences and combine into paragraphs—to say nothing of understanding the ideas for which those words are merely conduits.

, Hofstadter directs the Fluid Analogies Research Group, affectionately known as FARG.

Parts of a program can be selectively isolated to see how it functions without them; parameters can be changed to see how performance improves or degrades. When the computer surprises you—whether by being especially creative or especially dim-witted—you can see exactly why.

When you read Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, which describes in detail this architecture and the logic and mechanics of the programs that use it, you wonder whether maybe Hofstadter got famous for the wrong book.

ut very few people, even admirers of GEB, know about the book or the programs it describes. And maybe that’s because FARG’s programs are almost ostentatiously impractical. Because they operate in tiny, seemingly childish “microdomains.” Because there is no task they perform better than a human.

“The entire effort of artificial intelligence is essentially a fight against computers’ rigidity.”

“Nobody is a very reliable guide concerning activities in their mind that are, by definition, subconscious,” he once wrote. “This is what makes vast collections of errors so important. In an isolated error, the mechanisms involved yield only slight traces of themselves; however, in a large collection, vast numbers of such slight traces exist, collectively adding up to strong evidence for (and against) particular mechanisms.

So IBM threw that approach out the window. What the developers did instead was brilliant, but so straightforward,

The technique is called “machine learning.” The goal is to make a device that takes an English sentence as input and spits out a French sentence

What you do is feed the machine English sentences whose French translations you already know. (Candide, for example, used 2.2 million pairs of sentences, mostly from the bilingual proceedings of Canadian parliamentary debates.)

By repeating this process with millions of pairs of sentences, you will gradually calibrate your machine, to the point where you’ll be able to enter a sentence whose translation you don’t know and get a reasonable resul

Google Translate team can be made up of people who don’t speak most of the languages their application translates. “It’s a bang-for-your-buck argument,” Estelle says. “You probably want to hire more engineers instead” of native speakers.

But the need to serve 1 billion customers has a way of forcing the company to trade understanding for expediency. You don’t have to push Google Translate very far to see the compromises its developers have made for coverage, and speed, and ease of engineering. Although Google Translate captures, in its way, the products of human intelligence, it isn’t intelligent itself.

“Did we sit down when we built Watson and try to model human cognition?” Dave Ferrucci, who led the Watson team at IBM, pauses for emphasis. “Absolutely not. We just tried to create a machine that could win at Jeopardy.”

For Ferrucci, the definition of intelligence is simple: it’s what a program can do. Deep Blue was intelligent because it could beat Garry Kasparov at chess. Watson was intelligent because it could beat Ken Jennings at Jeopardy.

“There’s a limited number of things you can do as an individual, and I think when you dedicate your life to something, you’ve got to ask yourself the question: To what end? And I think at some point I asked myself that question, and what it came out to was, I’m fascinated by how the human mind works, it would be fantastic to understand cognition, I love to read books on it, I love to get a grip on it”—he called Hofstadter’s work inspiring—“but where am I going to go with it? Really what I want to do is build computer systems that do something.

Peter Norvig, one of Google’s directors of research, echoes Ferrucci almost exactly. “I thought he was tackling a really hard problem,” he told me about Hofstadter’s work. “And I guess I wanted to do an easier problem.”

Of course, the folly of being above the fray is that you’re also not a part of it

As our machines get faster and ingest more data, we allow ourselves to be dumber. Instead of wrestling with our hardest problems in earnest, we can just plug in billions of examples of them.

Hofstadter hasn’t been to an artificial-intelligence conference in 30 years. “There’s no communication between me and these people,” he says of his AI peers. “None. Zero. I don’t want to talk to colleagues that I find very, very intransigent and hard to convince of anything

Everything from plate tectonics to evolution—all those ideas, someone had to fight for them, because people didn’t agree with those ideas.

Academia is not an environment where you just sit in your bath and have ideas and expect everyone to run around getting excited. It’s possible that in 50 years’ time we’ll say, ‘We really should have listened more to Doug Hofstadter.’ But it’s incumbent on every scientist to at least think about what is needed to get people to understand the ideas.”

We were led to wonder whether a broken robot can break a child.

Kids are central to the sociable-robot project, because its agenda is to make people more comfortable with robots in roles normally reserved for humans, and robotics companies know that children are vulnerable consumers who can bring the whole family along.

In October, Mattel scrapped plans for Aristotle — a kind of Alexa for the nursery, designed to accompany children as they progress from lullabies and bedtime stories through high school homework — after lawmakers and child advocacy groups argued that the data the device collected about children could be misused by Mattel, marketers, hackers and other third parties. I was part of that campaign: There is something deeply unsettling about encouraging children to confide in machines that are in turn sharing their conversations with countless others.

Recently, I opened my MIT mail and found a “call for subjects” for a study involving sociable robots that will engage children in conversation to “elicit empathy.” What will these children be empathizing with, exactly? Empathy is a capacity that allows us to put ourselves in the place of others, to know what they are feeling. Robots, however, have no emotions to share

What they can do is push our buttons. When they make eye contact and gesture toward us, they predispose us to view them as thinking and caring. They are designed to be cute, to provoke a nurturing response. And when it comes to sociable AI, nurturance is the killer app: We nurture what we love, and we love what we nurture. If a computational object or robot asks for our help, asks us to teach it or tend to it, we attach. That is our human vulnerability.

digital companions don’t understand our emotional lives. They present themselves as empathy machines, but they are missing the essential equipment: They have not known the arc of a life. They have not been born; they don’t know pain, or mortality, or fear. Simulated thinking may be thinking, but simulated feeling is never feeling, and simulated love is never love.

Breazeal’s position is this: People have relationships with many classes of things. They have relationships with children and with adults, with animals and with machines. People, even very little people, are good at this. Now, we are going to add robots to the list of things with which we can have relationships. More powerful than with pets. Less powerful than with people. We’ll figure it out.

The nature of the attachments to dolls and sociable machines is different. When children play with dolls, they project thoughts and emotions onto them. A girl who has broken her mother’s crystal will put her Barbies into detention and use them to work on her feelings of guilt. The dolls take the role she needs them to take.

Sociable machines, by contrast, have their own agenda. Playing with robots is not about the psychology of projection but the psychology of engagement. Children try to meet the robot’s needs, to understand the robot’s unique nature and wants. There is an attempt to build a mutual relationship.

Some people might consider that a good thing: encouraging children to think beyond their own needs and goals. Except the whole commercial program is an exercise in emotional deception.

when we offer these robots as pretend friends to our children, it’s not so clear they can wink with us. We embark on an experiment in which our children are the human subjects.

it is hard to imagine what those “right types” of ties might be. These robots can’t be in a two-way relationship with a child. They are machines whose art is to put children in a position of pretend empathy. And if we put our children in that position, we shouldn’t expect them to understand what empathy is. If we give them pretend relationships, we shouldn’t expect them to learn how real relationships — messy relationships — work. On the contrary. They will learn something superficial and inauthentic, but mistake it for real connection.

In the process, we can forget what is most central to our humanity: truly understanding each other.

For so long, we dreamed of artificial intelligence offering us not only instrumental help but the simple salvations of conversation and care. But now that our fantasy is becoming reality, it is time to confront the emotional downside of living with the robots of our dreams.

What none of these parents understand is that their children’s and teens’ destructive obsession with technology is the predictable consequence of a virtually unrecognized merger between the tech industry and psychology.

Dr. B.J. Fogg, is a psychologist and the father of persuasive technology, a discipline in which digital machines and apps — including smartphones, social media, and video games — are configured to alter human thoughts and behaviors. As the lab’s website boldly proclaims: “Machines designed to change humans.”

These parents have no idea that lurking behind their kids’ screens and phones are a multitude of psychologists, neuroscientists, and social science experts who use their knowledge of psychological vulnerabilities to devise products that capture kids’ attention for the sake of industry profit.

psychology — a discipline that we associate with healing — is now being used as a weapon against children.

This alliance pairs the consumer tech industry’s immense wealth with the most sophisticated psychological research, making it possible to develop social media, video games, and phones with drug-like power to seduce young users.

Likewise, social media companies use persuasive design to prey on the age-appropriate desire for preteen and teen kids, especially girls, to be socially successful. This drive is built into our DNA, since real-world relational skills have fostered human evolution.

Called “the millionaire maker,” Fogg has groomed former students who have used his methods to develop technologies that now consume kids’ lives. As he recently touted on his personal website, “My students often do groundbreaking projects, and they continue having impact in the real world after they leave Stanford… For example, Instagram has influenced the behavior of over 800 million people. The co-founder was a student of mine.”

Persuasive technology (also called persuasive design) works by deliberately creating digital environments that users feel fulfill their basic human drives — to be social or obtain goals — better than real-world alternatives.

Kids spend countless hours in social media and video game environments in pursuit of likes, “friends,” game points, and levels — because it’s stimulating, they believe that this makes them happy and successful, and they find it easier than doing the difficult but developmentally important activities of childhood.

While persuasion techniques work well on adults, they are particularly effective at influencing the still-maturing child and teen brain.

“Video games, better than anything else in our culture, deliver rewards to people, especially teenage boys,” says Fogg. “Teenage boys are wired to seek competency. To master our world and get better at stuff. Video games, in dishing out rewards, can convey to people that their competency is growing, you can get better at something second by second.”

it’s persuasive design that’s helped convince this generation of boys they are gaining “competency” by spending countless hours on game sites, when the sad reality is they are locked away in their rooms gaming, ignoring school, and not developing the real-world competencies that colleges and employers demand.

Persuasive technologies work because of their apparent triggering of the release of dopamine, a powerful neurotransmitter involved in reward, attention, and addiction.

As she says, “If you don’t get 100 ‘likes,’ you make other people share it so you get 100…. Or else you just get upset. Everyone wants to get the most ‘likes.’ It’s like a popularity contest.”

there are costs to Casey’s phone obsession, noting that the “girl’s phone, be it Facebook, Instagram or iMessage, is constantly pulling her away from her homework, sleep, or conversations with her family.

Casey says she wishes she could put her phone down. But she can’t. “I’ll wake up in the morning and go on Facebook just… because,” she says. “It’s not like I want to or I don’t. I just go on it. I’m, like, forced to. I don’t know why. I need to. Facebook takes up my whole life.”

B.J. Fogg may not be a household name, but Fortune Magazine calls him a “New Guru You Should Know,” and his research is driving a worldwide legion of user experience (UX) designers who utilize and expand upon his models of persuasive design.

“No one has perhaps been as influential on the current generation of user experience (UX) designers as Stanford researcher B.J. Fogg.”

the core of UX research is about using psychology to take advantage of our human vulnerabilities.

As Fogg is quoted in Kosner’s Forbes article, “Facebook, Twitter, Google, you name it, these companies have been using computers to influence our behavior.” However, the driving force behind behavior change isn’t computers. “The missing link isn’t the technology, it’s psychology,” says Fogg.

UX researchers not only follow Fogg’s design model, but also his apparent tendency to overlook the broader implications of persuasive design. They focus on the task at hand, building digital machines and apps that better demand users’ attention, compel users to return again and again, and grow businesses’ bottom line.

the “Fogg Behavior Model” is a well-tested method to change behavior and, in its simplified form, involves three primary factors: motivation, ability, and triggers.

“We can now create machines that can change what people think and what people do, and the machines can do that autonomously.”

Regarding ability, Fogg suggests that digital products should be made so that users don’t have to “think hard.” Hence, social networks are designed for ease of use

Finally, Fogg says that potential users need to be triggered to use a site. This is accomplished by a myriad of digital tricks, including the sending of incessant notifications

moral questions about the impact of turning persuasive techniques on children and teens are not being asked. For example, should the fear of social rejection be used to compel kids to compulsively use social media? Is it okay to lure kids away from school tasks that demand a strong mental effort so they can spend their lives on social networks or playing video games that don’t make them think much at all?

Describing how his formula is effective at getting people to use a social network, the psychologist says in an academic paper that a key motivator is users’ desire for “social acceptance,” although he says an even more powerful motivator is the desire “to avoid being socially rejected.”

the startup Dopamine Labs boasts about its use of persuasive techniques to increase profits: “Connect your app to our Persuasive AI [Artificial Intelligence] and lift your engagement and revenue up to 30% by giving your users our perfect bursts of dopamine,” and “A burst of Dopamine doesn’t just feel good: it’s proven to re-wire user behavior and habits.”

Ramsay Brown, the founder of Dopamine Labs, says in a KQED Science article, “We have now developed a rigorous technology of the human mind, and that is both exciting and terrifying. We have the ability to twiddle some knobs in a machine learning dashboard we build, and around the world hundreds of thousands of people are going to quietly change their behavior in ways that, unbeknownst to them, feel second-nature but are really by design.”

Programmers call this “brain hacking,” as it compels users to spend more time on sites even though they mistakenly believe it’s strictly due to their own conscious choices.

Banks of computers employ AI to “learn” which of a countless number of persuasive design elements will keep users hooked

A persuasion profile of a particular user’s unique vulnerabilities is developed in real time and exploited to keep users on the site and make them return again and again for longer periods of time. This drives up profits for consumer internet companies whose revenue is based on how much their products are used.

“The leaders of Internet companies face an interesting, if also morally questionable, imperative: either they hijack neuroscience to gain market share and make large profits, or they let competitors do that and run away with the market.”

Social media and video game companies believe they are compelled to use persuasive technology in the arms race for attention, profits, and survival.

Children’s well-being is not part of the decision calculus.

one breakthrough occurred in 2017 when Facebook documents were leaked to The Australian. The internal report crafted by Facebook executives showed the social network boasting to advertisers that by monitoring posts, interactions, and photos in real time, the network is able to track when teens feel “insecure,” “worthless,” “stressed,” “useless” and a “failure.”

The report also bragged about Facebook’s ability to micro-target ads down to “moments when young people need a confidence boost.”

These design techniques provide tech corporations a window into kids’ hearts and minds to measure their particular vulnerabilities, which can then be used to control their behavior as consumers. This isn’t some strange future… this is now.

The official tech industry line is that persuasive technologies are used to make products more engaging and enjoyable. But the revelations of industry insiders can reveal darker motives.

Revealing the hard science behind persuasive technology, Hopson says, “This is not to say that players are the same as rats, but that there are general rules of learning which apply equally to both.”

After penning the paper, Hopson was hired by Microsoft, where he helped lead the development of the Xbox Live, Microsoft’s online gaming system

“If game designers are going to pull a person away from every other voluntary social activity or hobby or pastime, they’re going to have to engage that person at a very deep level in every possible way they can.”

This is the dominant effect of persuasive design today: building video games and social media products so compelling that they pull users away from the real world to spend their lives in for-profit domains.

Persuasive technologies are reshaping childhood, luring kids away from family and schoolwork to spend more and more of their lives sitting before screens and phones.

“Since we’ve figured to some extent how these pieces of the brain that handle addiction are working, people have figured out how to juice them further and how to bake that information into apps.”

Today, persuasive design is likely distracting adults from driving safely, productive work, and engaging with their own children — all matters which need urgent attention

Still, because the child and adolescent brain is more easily controlled than the adult mind, the use of persuasive design is having a much more hurtful impact on kids.

But to engage in a pursuit at the expense of important real-world activities is a core element of addiction.

younger U.S. children now spend 5 ½ hours each day with entertainment technologies, including video games, social media, and online videos.

Even more, the average teen now spends an incredible 8 hours each day playing with screens and phones

U.S. kids only spend 16 minutes each day using the computer at home for school.

Quietly, using screens and phones for entertainment has become the dominant activity of childhood.

Younger kids spend more time engaging with entertainment screens than they do in school

teens spend even more time playing with screens and phones than they do sleeping

kids are so taken with their phones and other devices that they have turned their backs to the world around them.

many children are missing out on real-life engagement with family and school — the two cornerstones of childhood that lead them to grow up happy and successful

persuasive technologies are pulling kids into often toxic digital environments

A too frequent experience for many is being cyberbullied, which increases their risk of skipping school and considering suicide.

And there is growing recognition of the negative impact of FOMO, or the fear of missing out, as kids spend their social media lives watching a parade of peers who look to be having a great time without them, feeding their feelings of loneliness and being less than.

The combined effects of the displacement of vital childhood activities and exposure to unhealthy online environments is wrecking a generation.

as the typical age when kids get their first smartphone has fallen to 10, it’s no surprise to see serious psychiatric problems — once the domain of teens — now enveloping young kids

Self-inflicted injuries, such as cutting, that are serious enough to require treatment in an emergency room, have increased dramatically in 10- to 14-year-old girls, up 19% per year since 2009.

While girls are pulled onto smartphones and social media, boys are more likely to be seduced into the world of video gaming, often at the expense of a focus on school

it’s no surprise to see this generation of boys struggling to make it to college: a full 57% of college admissions are granted to young women compared with only 43% to young men.

Economists working with the National Bureau of Economic Research recently demonstrated how many young U.S. men are choosing to play video games rather than join the workforce.

The destructive forces of psychology deployed by the tech industry are making a greater impact on kids than the positive uses of psychology by mental health providers and child advocates. Put plainly, the science of psychology is hurting kids more than helping them.

Hope for this wired generation has seemed dim until recently, when a surprising group has come forward to criticize the tech industry’s use of psychological manipulation: tech executives

Tristan Harris, formerly a design ethicist at Google, has led the way by unmasking the industry’s use of persuasive design. Interviewed in The Economist’s 1843 magazine, he says, “The job of these companies is to hook people, and they do that by hijacking our psychological vulnerabilities.”

Marc Benioff, CEO of the cloud computing company Salesforce, is one of the voices calling for the regulation of social media companies because of their potential to addict children. He says that just as the cigarette industry has been regulated, so too should social media companies. “I think that, for sure, technology has addictive qualities that we have to address, and that product designers are working to make those products more addictive, and we need to rein that back as much as possible,”

“If there’s an unfair advantage or things that are out there that are not understood by parents, then the government’s got to come forward and illuminate that.”

Since millions of parents, for example the parents of my patient Kelly, have absolutely no idea that devices are used to hijack their children’s minds and lives, regulation of such practices is the right thing to do.

Another improbable group to speak out on behalf of children is tech investors.

How has the consumer tech industry responded to these calls for change? By going even lower.

Facebook recently launched Messenger Kids, a social media app that will reach kids as young as five years old. Suggestive that harmful persuasive design is now honing in on very young children is the declaration of Messenger Kids Art Director, Shiu Pei Luu, “We want to help foster communication [on Facebook] and make that the most exciting thing you want to be doing.”

the American Psychological Association (APA) — which is tasked with protecting children and families from harmful psychological practices — has been essentially silent on the matter

APA Ethical Standards require the profession to make efforts to correct the “misuse” of the work of psychologists, which would include the application of B.J. Fogg’s persuasive technologies to influence children against their best interests

Manipulating children for profit without their own or parents’ consent, and driving kids to spend more time on devices that contribute to emotional and academic problems is the embodiment of unethical psychological practice.

“Never before in history have basically 50 mostly men, mostly 20–35, mostly white engineer designer types within 50 miles of where we are right now [Silicon Valley], had control of what a billion people think and do.”

Some may argue that it’s the parents’ responsibility to protect their children from tech industry deception. However, parents have no idea of the powerful forces aligned against them, nor do they know how technologies are developed with drug-like effects to capture kids’ minds

Others will claim that nothing should be done because the intention behind persuasive design is to build better products, not manipulate kids

similar circumstances exist in the cigarette industry, as tobacco companies have as their intention profiting from the sale of their product, not hurting children. Nonetheless, because cigarettes and persuasive design predictably harm children, actions should be taken to protect kids from their effects.

in a 1998 academic paper, Fogg describes what should happen if things go wrong, saying, if persuasive technologies are “deemed harmful or questionable in some regard, a researcher should then either take social action or advocate that others do so.”

I suggest turning to President John F. Kennedy’s prescient guidance: He said that technology “has no conscience of its own. Whether it will become a force for good or ill depends on man.”

The APA should begin by demanding that the tech industry’s behavioral manipulation techniques be brought out of the shadows and exposed to the light of public awareness

Changes should be made in the APA’s Ethics Code to specifically prevent psychologists from manipulating children using digital machines, especially if such influence is known to pose risks to their well-being.

Moreover, the APA should follow its Ethical Standards by making strong efforts to correct the misuse of psychological persuasion by the tech industry and by user experience designers outside the field of psychology.

It should join with tech executives who are demanding that persuasive design in kids’ tech products be regulated

The APA also should make its powerful voice heard amongst the growing chorus calling out tech companies that intentionally exploit children’s vulnerabilities.

Most A.I. researchers agree that models trained using R.L.H.F. are better behaved than models without it. But some argue that fine-tuning a language model this way doesn’t actually make the underlying model less weird and inscrutable. In their view, it’s just a flimsy, friendly mask that obscures the mysterious beast underneath.

In a nutshell, the joke was that in order to prevent A.I. language models from behaving in scary and dangerous ways, A.I. companies have had to train them to act polite and harmless. One popular way to do this is called “reinforcement learning from human feedback,” or R.L.H.F., a process that involves asking humans to score chatbot responses, and feeding those scores back into the A.I. model.

Shoggoths are fictional creatures, introduced by the science fiction author H.P. Lovecraft in his 1936 novella “At the Mountains of Madness.” In Lovecraft’s telling, Shoggoths were massive, blob-like monsters made out of iridescent black goo, covered in tentacles and eyes.

@TetraspaceWest said, wasn’t necessarily implying that it was evil or sentient, just that its true nature might be unknowable.

And it reinforces the notion that what’s happening in A.I. today feels, to some of its participants, more like an act of summoning than a software development process. They are creating the blobby, alien Shoggoths, making them bigger and more powerful, and hoping that there are enough smiley faces to cover the scary parts.

“I was also thinking about how Lovecraft’s most powerful entities are dangerous — not because they don’t like humans, but because they’re indifferent and their priorities are totally alien to us and don’t involve humans, which is what I think will be true about possible future powerful A.I.”

when Bing’s chatbot became unhinged and tried to break up my marriage, an A.I. researcher I know congratulated me on “glimpsing the Shoggoth.” A fellow A.I. journalist joked that when it came to fine-tuning Bing, Microsoft had forgotten to put on its smiley-face mask.

@TetraspaceWest, the meme’s creator, told me in a Twitter message that the Shoggoth “represents something that thinks in a way that humans don’t understand and that’s totally different from the way that humans think.”

In any case, the Shoggoth is a potent metaphor that encapsulates one of the most bizarre facts about the A.I. world, which is that many of the people working on this technology are somewhat mystified by their own creations. They don’t fully understand the inner workings of A.I. language models, how they acquire new capabilities or why they behave unpredictably at times. They aren’t totally sure if A.I. is going to be net-good or net-bad for the world.

That some A.I. insiders refer to their creations as Lovecraftian horrors, even as a joke, is unusual by historical standards. (Put it this way: Fifteen years ago, Mark Zuckerberg wasn’t going around comparing Facebook to Cthulhu.)

If it’s an A.I. safety researcher talking about the Shoggoth, maybe that person is passionate about preventing A.I. systems from displaying their true, Shoggoth-like nature.

A great many people are dismissive of suggestions that any of these systems are “really” thinking, because they’re “just” doing something banal (like making statistical predictions about the next word in a sentence). What they fail to appreciate is that there is every reason to suspect that human cognition is “just” doing those exact same things. It matters not that birds flap their wings but airliners don’t. Both fly. And these machines think. And, just as airliners fly faster and higher and farther than birds while carrying far more weight, these machines are already outthinking the majority of humans at the majority of tasks. Further, that machines aren’t perfect thinkers is about as relevant as the fact that air travel isn’t instantaneous. Now consider: we’re well past the Wright flyer level of thinking machine, past the early biplanes, somewhere about the first commercial airline level. Not quite the DC-10, I think. Can you imagine what the AI equivalent of a 777 will be like? Fasten your seatbelts.

@thomas h. You make my point perfectly. You’re observing that the way a plane flies — by using a turbine to generate thrust from combusting kerosene, for example — is nothing like the way that a bird flies, which is by using the energy from eating plant seeds to contract the muscles in its wings to make them flap. You are absolutely correct in that observation, but it’s also almost utterly irrelevant. And it ignores that, to a first approximation, there’s no difference in the physics you would use to describe a hawk riding a thermal and an airliner gliding (essentially) unpowered in its final descent to the runway. Further, you do yourself a grave disservice in being dismissive of the abilities of thinking machines, in exactly the same way that early skeptics have been dismissive of every new technology in all of human history. Writing would make people dumb; automobiles lacked the intelligence of horses; no computer could possibly beat a chess grandmaster because it can’t comprehend strategy; and on and on and on. Humans aren’t nearly as special as we fool ourselves into believing. If you want to have any hope of acting responsibly in the age of intelligent machines, you’ll have to accept that, like it or not, and whether or not it fits with your preconceived notions of what thinking is and how it is or should be done … machines can and do think, many of them better than you in a great many ways. b&

@BLA. You are incorrect. Everything has nature. Its nature is manifested in making humans react. Sure, no humans, no nature, but here we are. The writer and various sources are not attributing nature to AI so much as admitting that they don’t know what this nature might be, and there are reasons to be scared of it. More concerning to me is the idea that this field is resorting to geek culture reference points to explain and comprehend itself. It’s not so much the algorithm has no soul, but that the souls of the humans making it possible are stupendously and tragically underdeveloped.

When even tech companies are saying AI is moving too fast, and the articles land on page 1 of the NYT (there's an old reference), I think the greedy will not think twice about exploiting this technology, with no ethical considerations, at all.

@nome sane? The problem is it isn't data as we understand it. We know what the datasets are -- they were used to train the AI's. But once trained, the AI is thinking for itself, with results that have surprised everybody.

The unique feature of a shoggoth is it can become whatever is needed for a particular job. There's no actual shape so it's not a bad metaphor, if an imperfect image. Shoghoths also turned upon and destroyed their creators, so the cautionary metaphor is in there, too. A shame more Asimov wasn't baked into AI. But then the conflict about how to handle AI in relation to people was key to those stories, too.

The crash, which killed all 49 people on board as well as one person on the ground, should never have happened.

aptain’s response to the stall warning, the investigators reported, “should have been automatic, but his improper flight control inputs were inconsistent with his training” and instead revealed “startle and confusion.

Automation has become so sophisticated that on a typical passenger flight, a human pilot holds the controls for a grand total of just three minutes.

We humans have been handing off chores, both physical and mental, to tools since the invention of the lever, the wheel, and the counting bead.

And that, many aviation and automation experts have concluded, is a problem. Overuse of automation erodes pilots’ expertise and dulls their reflexes,

No one doubts that autopilot has contributed to improvements in flight safety over the years. It reduces pilot fatigue and provides advance warnings of problems, and it can keep a plane airborne should the crew become disabled. But the steady overall decline in plane crashes masks the recent arrival of “a spectacularly new type of accident,”

“We’re forgetting how to fly.”

The experience of airlines should give us pause. It reveals that automation, for all its benefits, can take a toll on the performance and talents of those who rely on it. The implications go well beyond safety. Because automation alters how we act, how we learn, and what we know, it has an ethical dimension. The choices we make, or fail to make, about which tasks we hand off to machines shape our lives and the place we make for ourselves in the world.

What pilots spend a lot of time doing is monitoring screens and keying in data. They’ve become, it’s not much of an exaggeration to say, computer operators.

Automation is different now. Computers can be programmed to perform complex activities in which a succession of tightly coordinated tasks is carried out through an evaluation of many variables. Many software programs take on intellectual work—observing and sensing, analyzing and judging, even making decisions—that until recently was considered the preserve of humans.

That may leave the person operating the computer to play the role of a high-tech clerk—entering data, monitoring outputs, and watching for failures. Rather than opening new frontiers of thought and action, software ends up narrowing our focus.

A labor-saving device doesn’t just provide a substitute for some isolated component of a job or other activity. It alters the character of the entire task, including the roles, attitudes, and skills of the people taking part.

when we work with computers, we often fall victim to two cognitive ailments—complacency and bias—that can undercut our performance and lead to mistakes. Automation complacency occurs when a computer lulls us into a false sense of security. Confident that the machine will work flawlessly and handle any problem that crops up, we allow our attention to drift.

Automation bias occurs when we place too much faith in the accuracy of the information coming through our monitors. Our trust in the software becomes so strong that we ignore or discount other information sources, including our own eyes and ears

Examples of complacency and bias have been well documented in high-risk situations—on flight decks and battlefields, in factory control rooms—but recent studies suggest that the problems can bedevil anyone working with a computer

Automation turns us from actors into observers. Instead of manipulating the yoke, we watch the screen. That shift may make our lives easier, but it can also inhibit the development of expertise.

Since the late 1970s, psychologists have been documenting a phenomenon called the “generation effect.” It was first observed in studies of vocabulary, which revealed that people remember words much better when they actively call them to mind—when they generate them—than when they simply read them.

When you engage actively in a task, you set off intricate mental processes that allow you to retain more knowledge. You learn more and remember more. When you repeat the same task over a long period, your brain constructs specialized neural circuits dedicated to the activit

What looks like instinct is hard-won skill, skill that requires exactly the kind of struggle that modern software seeks to alleviate.

In many businesses, managers and other professionals have come to depend on decision-support systems to analyze information and suggest courses of action. Accountants, for example, use the systems in corporate audits. The applications speed the work, but some signs suggest that as the software becomes more capable, the accountants become less so.

What’s most astonishing, and unsettling, about computer automation is that it’s still in its early stages.

Experts used to assume that there were limits to the ability of programmers to automate complicated tasks, particularly those involving sensory perception, pattern recognition, and conceptual knowledge

Who needs humans, anyway? That question, in one rhetorical form or another, comes up frequently in discussions of automation. If computers’ abilities are expanding so quickly and if people, by comparison, seem slow, clumsy, and error-prone, why not build immaculately self-contained systems that perform flawlessly without any human oversight or intervention? Why not take the human factor out of the equation?

The cure for imperfect automation is total automation.

That idea is seductive, but no machine is infallible. Sooner or later, even the most advanced technology will break down, misfire, or, in the case of a computerized system, encounter circumstances that its designers never anticipated. As automation technologies become more complex, relying on interdependencies among algorithms, databases, sensors, and mechanical parts, the potential sources of failure multiply. They also become harder to detect.

conundrum of computer automation.

Because many system designers assume that human operators are “unreliable and inefficient,” at least when compared with a computer, they strive to give the operators as small a role as possible.

People end up functioning as mere monitors, passive watchers of screens. That’s a job that humans, with our notoriously wandering minds, are especially bad at

people have trouble maintaining their attention on a stable display of information for more than half an hour. “This means,” Bainbridge observed, “that it is humanly impossible to carry out the basic function of monitoring for unlikely abnormalities.”

a person’s skills “deteriorate when they are not used,” even an experienced operator will eventually begin to act like an inexperienced one if restricted to just watching.

You can program software to shift control back to human operators at frequent but irregular intervals; knowing that they may need to take command at any moment keeps people engaged, promoting situational awareness and learning.

You can put limits on the scope of automation, making sure that people working with computers perform challenging tasks rather than merely observing.

most software applications don’t foster learning and engagement. In fact, they have the opposite effect. That’s because taking the steps necessary to promote the development and maintenance of expertise almost always entails a sacrifice of speed and productivity.

Learning requires inefficiency. Businesses, which seek to maximize productivity and profit, would rarely accept such a trade-off. Individuals, too, almost always seek efficiency and convenience.

Abstract concerns about the fate of human talent can’t compete with the allure of saving time and money.

The small island of Igloolik, off the coast of the Melville Peninsula in the Nunavut territory of northern Canada, is a bewildering place in the winter.

, Inuit hunters have for some 4,000 years ventured out from their homes on the island and traveled across miles of ice and tundra to search for game. The hunters’ ability to navigate vast stretches of the barren Arctic terrain, where landmarks are few, snow formations are in constant flux, and trails disappear overnight, has amazed explorers and scientists for centuries. The Inuit’s extraordinary way-finding skills are born not of technological prowess—they long eschewed maps and compasses—but of a profound understanding of winds, snowdrift patterns, animal behavior, stars, and tides.

The Igloolik hunters have begun to rely on computer-generated maps to get around. Adoption of GPS technology has been particularly strong among younger Inuit, and it’s not hard to understand why.

But as GPS devices have proliferated on Igloolik, reports of serious accidents during hunts have spread. A hunter who hasn’t developed way-finding skills can easily become lost, particularly if his GPS receiver fails.

The routes so meticulously plotted on satellite maps can also give hunters tunnel vision, leading them onto thin ice or into other hazards a skilled navigator would avoid.

An Inuit on a GPS-equipped snowmobile is not so different from a suburban commuter in a GPS-equipped SUV: as he devotes his attention to the instructions coming from the computer, he loses sight of his surroundings. He travels “blindfolded,” as Aporta puts it

A unique talent that has distinguished a people for centuries may evaporate in a generation.

Computer automation severs the ends from the means. It makes getting what we want easier, but it distances us from the work of knowing. As we transform ourselves into creatures of the screen, we face an existential question: Does our essence still lie in what we know, or are we now content to be defined by what we want?

each of these is valuable in its own way.

The cult of randomized controlled trials also neglects a rich body of potential hypotheses.

This is what artificial intelligence and machine learning are. Facebook is a series of algorithms and goals aimed at maximizing engagement with Facebook. That’s why it’s worth hundreds of billions of dollars. It has a vast army of computer scientists and programmers whose job it is to make that machine more efficient.

the Facebook engine is designed to scope you out, take a psychographic profile of who you are and then use its data compiled from literally billions of humans to serve you content designed to maximize your engagement with Facebook.

Put in those terms, you barely have a chance.

Of course, Facebook can come in and say, this is damaging so we’re going to add some code that says don’t show this dieting/fat-shaming content but girls 18 and under. But the algorithms will find other vulnerabilities

So what to do? The decision of all the companies, if not all individuals, was just to lie. What else are you going to do? Say we’re closing down our multi-billion dollar company because our product shouldn’t exist?

why exactly are you creating a separate group of subroutines that yanks Facebook back when it does what it’s supposed to do particularly well? This, indeed, was how the internal dialog at Facebook developed, as described in the article I read. Basically, other executives said: Our business is engagement, why are we suggesting people log off for a while when they get particularly engaged?

what it makes me think about more is the conversations at Tobacco companies 40 or 50 years ago. At a certain point you realize: our product is bad. If used as intended it causes lung cancer, heart disease and various other ailments in a high proportion of the people who use the product. And our business model is based on the fact that the product is chemically addictive. Our product is getting people addicted to tobacco so that they no longer really have a choice over whether to buy it. And then a high proportion of them will die because we’ve succeeded.

. The algorithms can be taught to find and address an infinite numbers of behaviors. But really you’re asking the researchers and programmers to create an alternative set of instructions where Instagram (or Facebook, same difference) jumps in and does exactly the opposite of its core mission, which is to drive engagement

You can add filters and claim you’re not marketing to kids. But really you’re only ramping back the vast social harm marginally at best. That’s the product. It is what it is.

there is definitely an analogy inasmuch as what you’re talking about here aren’t some glitches in the Facebook system. These aren’t some weird unintended consequences that can be ironed out of the product. It’s also in most cases not bad actors within Facebook. It’s what the product is. The product is getting attention and engagement against which advertising is sold

How good is the machine learning? Well, trial and error with between 3 and 4 billion humans makes you pretty damn good. That’s the product. It is inherently destructive, though of course the bad outcomes aren’t distributed evenly throughout the human population.

The business model is to refine this engagement engine, getting more attention and engagement and selling ads against the engagement. Facebook gets that revenue and the digital roadkill created by the product gets absorbed by the society at large

Facebook is like a spectacularly profitable nuclear energy company which is so profitable because it doesn’t build any of the big safety domes and dumps all the radioactive waste into the local river.

in the various articles describing internal conversations at Facebook, the shrewder executives and researchers seem to get this. For the company if not every individual they seem to be following the tobacco companies’ lead.

Ed. Note: TPM Reader AS wrote in to say I was conflating Facebook and Instagram and sometimes referring to one or the other in a confusing way. This is a fair

I spoke of them as the same intentionally. In part I’m talking about Facebook’s corporate ownership. Both sites are owned and run by the same parent corporation and as we saw during yesterday’s outage they are deeply hardwired into each other.

the main reason I spoke of them in one breath is that they are fundamentally the same. AS points out that the issues with Instagram are distinct because Facebook has a much older demographic and Facebook is a predominantly visual medium. (Indeed, that’s why Facebook corporate is under such pressure to use Instagram to drive teen and young adult engagement.) But they are fundamentally the same: AI and machine learning to drive engagement. Same same. Just different permutations of the same dynamic.

Of course, defining terms is of primary importance, a task that has proven impossible when discussing the nuances of consciousness, which is effectively the power we’re attempting to imbue our machines with.

What type of machines are we creating if we only recognize a “sort of” intelligence under the hood of our robots? For over a century, dystopian novelists have envisioned an automated future in which our machines best us. This is no longer a future scenario.

In school, one learns about “the scientific method”—usually a straightforward set of steps, along the lines of “ask a question, propose a hypothesis, perform an experiment, analyze the results.”

That method works in the classroom, where students are basically told what questions to pursue. But real scientists must come up with their own questions, finding new routes through a much vaster landscape.

Since science began, there has been disagreement about how those routes are charted. Two twentieth-century philosophers of science, Karl Popper and Thomas Kuhn, are widely held to have offered the best accounts of this process.

For Popper, Strevens writes, “scientific inquiry is essentially a process of disproof, and scientists are the disprovers, the debunkers, the destroyers.” Kuhn’s scientists, by contrast, are faddish true believers who promulgate received wisdom until they are forced to attempt a “paradigm shift”—a painful rethinking of their basic assumptions.

Working scientists tend to prefer Popper to Kuhn. But Strevens thinks that both theorists failed to capture what makes science historically distinctive and singularly effective.

Sometimes they seek to falsify theories, sometimes to prove them; sometimes they’re informed by preëxisting or contextual views, and at other times they try to rule narrowly, based on t

Why do scientists agree to this scheme? Why do some of the world’s most intelligent people sign on for a lifetime of pipetting?

Strevens thinks that they do it because they have no choice. They are constrained by a central regulation that governs science, which he calls the “iron rule of explanation.” The rule is simple: it tells scientists that, “if they are to participate in the scientific enterprise, they must uncover or generate new evidence to argue with”; from there, they must “conduct all disputes with reference to empirical evidence alone.”

, it is “the key to science’s success,” because it “channels hope, anger, envy, ambition, resentment—all the fires fuming in the human heart—to one end: the production of empirical evidence.”

Strevens arrives at the idea of the iron rule in a Popperian way: by disproving the other theories about how scientific knowledge is created.

The problem isn’t that Popper and Kuhn are completely wrong. It’s that scientists, as a group, don’t pursue any single intellectual strategy consistently.

Exploring a number of case studies—including the controversies over continental drift, spontaneous generation, and the theory of relativity—Strevens shows scientists exerting themselves intellectually in a variety of ways, as smart, ambitious people usually do.

“Science is boring,” Strevens writes. “Readers of popular science see the 1 percent: the intriguing phenomena, the provocative theories, the dramatic experimental refutations or verifications.” But, he says,behind these achievements . . . are long hours, days, months of tedious laboratory labor. The single greatest obstacle to successful science is the difficulty of persuading brilliant minds to give up the intellectual pleasures of continual speculation and debate, theorizing and arguing, and to turn instead to a life consisting almost entirely of the production of experimental data.

Ultimately, in fact, it was good that the geologists had a “splendid variety” of somewhat arbitrary opinions: progress in science requires partisans, because only they have “the motivation to perform years or even decades of necessary experimental work.” It’s just that these partisans must channel their energies into empirical observation. The iron rule, Strevens writes, “has a valuable by-product, and that by-product is data.”

Science is often described as “self-correcting”: it’s said that bad data and wrong conclusions are rooted out by other scientists, who present contrary findings. But Strevens thinks that the iron rule is often more important than overt correction.

Eddington was never really refuted. Other astronomers, driven by the iron rule, were already planning their own studies, and “the great preponderance of the resulting measurements fit Einsteinian physics better than Newtonian physics.” It’s partly by generating data on such a vast scale, Strevens argues, that the iron rule can power science’s knowledge machine: “Opinions converge not because bad data is corrected but because it is swamped.”

Why did the iron rule emerge when it did? Strevens takes us back to the Thirty Years’ War, which concluded with the Peace of Westphalia, in 1648. The war weakened religious loyalties and strengthened national ones.

Two regimes arose: in the spiritual realm, the will of God held sway, while in the civic one the decrees of the state were paramount. As Isaac Newton wrote, “The laws of God & the laws of man are to be kept distinct.” These new, “nonoverlapping spheres of obligation,” Strevens argues, were what made it possible to imagine the iron rule. The rule simply proposed the creation of a third sphere: in addition to God and state, there would now be science.

Strevens imagines how, to someone in Descartes’s time, the iron rule would have seemed “unreasonably closed-minded.” Since ancient Greece, it had been obvious that the best thinking was cross-disciplinary, capable of knitting together “poetry, music, drama, philosophy, democracy, mathematics,” and other elevating human disciplines.

We’re still accustomed to the idea that a truly flourishing intellect is a well-rounded one. And, by this standard, Strevens says, the iron rule looks like “an irrational way to inquire into the underlying structure of things”; it seems to demand the upsetting “suppression of human nature.”

Descartes, in short, would have had good reasons for resisting a law that narrowed the grounds of disputation, or that encouraged what Strevens describes as “doing rather than thinking.”

In fact, the iron rule offered scientists a more supple vision of progress. Before its arrival, intellectual life was conducted in grand gestures.

Descartes’s book was meant to be a complete overhaul of what had preceded it; its fate, had science not arisen, would have been replacement by some equally expansive system. The iron rule broke that pattern.

Strevens sees its earliest expression in Francis Bacon’s “The New Organon,” a foundational text of the Scientific Revolution, published in 1620. Bacon argued that thinkers must set aside their “idols,” relying, instead, only on evidence they could verify. This dictum gave scientists a new way of responding to one another’s work: gathering data.

it also changed what counted as progress. In the past, a theory about the world was deemed valid when it was complete—when God, light, muscles, plants, and the planets cohered. The iron rule allowed scientists to step away from the quest for completeness.

The consequences of this shift would become apparent only with time

In 1713, Isaac Newton appended a postscript to the second edition of his “Principia,” the treatise in which he first laid out the three laws of motion and the theory of universal gravitation. “I have not as yet been able to deduce from phenomena the reason for these properties of gravity, and I do not feign hypotheses,” he wrote. “It is enough that gravity really exists and acts according to the laws that we have set forth.”

What mattered, to Newton and his contemporaries, was his theory’s empirical, predictive power—that it was “sufficient to explain all the motions of the heavenly bodies and of our sea.”

Descartes would have found this attitude ridiculous. He had been playing a deep game—trying to explain, at a fundamental level, how the universe fit together. Newton, by those lights, had failed to explain anything: he himself admitted that he had no sense of how gravity did its work

by authorizing what Strevens calls “shallow explanation,” the iron rule offered an empirical bridge across a conceptual chasm. Work could continue, and understanding could be acquired on the other side. In this way, shallowness was actually more powerful than depth.

Quantum theory—which tells us that subatomic particles can be “entangled” across vast distances, and in multiple places at the same time—makes intuitive sense to pretty much nobody.

Without the iron rule, Strevens writes, physicists confronted with such a theory would have found themselves at an impasse. They would have argued endlessly about quantum metaphysics.

ollowing the iron rule, they can make progress empirically even though they are uncertain conceptually. Individual researchers still passionately disagree about what quantum theory means. But that hasn’t stopped them from using it for practical purposes—computer chips, MRI machines, G.P.S. networks, and other technologies rely on quantum physics.

One group of theorists, the rationalists, has argued that science is a new way of thinking, and that the scientist is a new kind of thinker—dispassionate to an uncommon degree.

As evidence against this view, another group, the subjectivists, points out that scientists are as hopelessly biased as the rest of us. To this group, the aloofness of science is a smoke screen behind which the inevitable emotions and ideologies hide.

At least in science, Strevens tells us, “the appearance of objectivity” has turned out to be “as important as the real thing.”

The subjectivists are right, he admits, inasmuch as scientists are regular people with a “need to win” and a “determination to come out on top.”

But they are wrong to think that subjectivity compromises the scientific enterprise. On the contrary, once subjectivity is channelled by the iron rule, it becomes a vital component of the knowledge machine. It’s this redirected subjectivity—to come out on top, you must follow the iron rule!—that solves science’s “problem of motivation,” giving scientists no choice but “to pursue a single experiment relentlessly, to the last measurable digit, when that digit might be quite meaningless.”

If it really was a speech code that instigated “the extraordinary attention to process and detail that makes science the supreme discriminator and destroyer of false ideas,” then the peculiar rigidity of scientific writing—Strevens describes it as “sterilized”—isn’t a symptom of the scientific mind-set but its cause.

The iron rule—“a kind of speech code”—simply created a new way of communicating, and it’s this new way of communicating that created science.

Other theorists have explained science by charting a sweeping revolution in the human mind; inevitably, they’ve become mired in a long-running debate about how objective scientists really are

In “The Knowledge Machine: How Irrationality Created Modern Science” (Liveright), Michael Strevens, a philosopher at New York University, aims to identify that special something. Strevens is a philosopher of science

Compared with the theories proposed by Popper and Kuhn, Strevens’s rule can feel obvious and underpowered. That’s because it isn’t intellectual but procedural. “The iron rule is focused not on what scientists think,” he writes, “but on what arguments they can make in their official communications.”

Like everybody else, scientists view questions through the lenses of taste, personality, affiliation, and experience

geologists had a professional obligation to take sides. Europeans, Strevens reports, tended to back Wegener, who was German, while scholars in the United States often preferred Simpson, who was American. Outsiders to the field were often more receptive to the concept of continental drift than established scientists, who considered its incompleteness a fatal flaw.

Strevens’s point isn’t that these scientists were doing anything wrong. If they had biases and perspectives, he writes, “that’s how human thinking works.”

Eddington’s observations were expected to either confirm or falsify Einstein’s theory of general relativity, which predicted that the sun’s gravity would bend the path of light, subtly shifting the stellar pattern. For reasons having to do with weather and equipment, the evidence collected by Eddington—and by his colleague Frank Dyson, who had taken similar photographs in Sobral, Brazil—was inconclusive; some of their images were blurry, and so failed to resolve the matter definitively.

it was only natural for intelligent people who were free of the rule’s strictures to attempt a kind of holistic, systematic inquiry that was, in many ways, more demanding. It never occurred to them to ask if they might illuminate more collectively by thinking about less individually.

In the single-sphered, pre-scientific world, thinkers tended to inquire into everything at once. Often, they arrived at conclusions about nature that were fascinating, visionary, and wrong.

How Does Science Really Work?Science is objective. Scientists are not. Can an “iron rule” explain how they’ve changed the world anyway?By Joshua RothmanSeptember 28, 2020

I don’t know if that sounds too complicated. But the point is, it’s not just giving up domination. It’s giving up this sense of separateness in favor of a sense of kinship. And those people who do often wonder how they failed to see how much continuity there is in the more-than-human world with the human world.

to go from terror into being and into that sense that the experiment is sacred, not this one outcome of the experiment, is to immediately transform the way that you think even about very fundamental social and economic and cultural things. If the experiment is sacred, how can we possibly justify our food systems, for instance?

when I first went to the Smokies and hiked up into the old growth in the Southern Appalachians, it was like somebody threw a switch. There was some odd filter that had just been removed, and the world sounded different and smelled different.

richard powersYeah. In human exceptionalism, we may be completely aware of evolutionary continuity. We may understand that we have a literal kinship with the rest of creation, that all life on Earth employs the same genetic code, that there is a very small core of core genes and core proteins that is shared across all the kingdoms and phyla of life. But conceptually, we still have this demented idea that somehow consciousness creates a sanctity and a separation that almost nullifies the continuous elements of evolution and biology that we’ve come to understand.

if we want to begin this process of rehabilitation and transformation of consciousness that we are going to need in order to become part of the living Earth, it is going to be other kinds of minds that give us that clarity and strength and diversity and alternative way of thinking that could free us from this stranglehold of thought that looks only to the maximizing return on investment in very leverageable ways.

richard powersIt amazed me to get to the end of the first draft of “Bewilderment” and to realize how much Buddhism was in the book, from the simplest things.

I think there is nothing more science inflected than being out in the living world and the more-than-human world and trying to understand what’s happening.

And of course, we can combine this with what we were talking about earlier with death. If we see all of evolution as somehow leading up to us, all of human, cultural evolution leading up to neoliberalism and here we are just busily trying to accumulate and make meaning for ourselves, death becomes the enemy.

And you’re making the point in different ways throughout the book that it is the minds we think of as unusual, that we would diagnose as having some kind of problem or dysfunction that are, in some cases, are the only ones responding to the moment in the most common sense way it deserves. It is almost everybody else’s brain that has been broken.

it isn’t surprising. If you think of the characteristics of this dominant culture that we’ve been talking about — the fixation on control, the fixation on mastery, the fixation on management and accumulation and the resistance of decay — it isn’t surprising that that culture is also threatened by difference and divergence. It seeks out old, stable hierarchies — clear hierarchies — of control, and anything that’s not quite exploitable or leverageable in the way that the normal is terrifying and threatening.

And the more I looked for it, the more it pervaded the book.

ezra kleinI’ve heard you say that it has changed the way you measure a good day. Can you tell me about that?richard powersThat’s true.I suppose when I was still enthralled to commodity-mediated individualist market-driven human exceptionalism — we need a single word for this

And since moving to the Smokies and since publishing “The Overstory,” my days have been entirely inverted. I wake up, I go to the window, and I look outside. Or I step out onto the deck — if I haven’t been sleeping on the deck, which I try to do as much as I can in the course of the year — and see what’s in the air, gauge the temperature and the humidity and the wind and see what season it is and ask myself, you know, what’s happening out there now at 1,700 feet or 4,000 feet or 5,000 feet.

let me talk specifically about the work of a scientist who has herself just recently published a book. It’s Dr. Suzanne Simard, and the book is “Finding the Mother Tree.” Simard has been instrumental in a revolution in our way of thinking about what’s happening underground at the root level in a forest.

it was a moving moment for me, as an easterner, to stand up there and to say, this is what an eastern forest looks like. This is what a healthy, fully-functioning forest looks like. And I’m 56 years old, and I’d never seen it.

the other topics of that culture tend to circle back around these sorts of trends, human fascinations, ways of magnifying our throw weight and our ability and removing the last constraints to our desires and, in particular, to eliminate the single greatest enemy of meaning in the culture of the technological sublime that is, itself, such a strong instance of the culture of human separatism and commodity-mediated individualist capitalism— that is to say, the removal of death.

Why is it that we have known about the crisis of species extinction for at least half a century and longer? And I mean the lay public, not just scientists. But why has this been general knowledge for a long time without public will demanding some kind of action or change

And when you make kinship beyond yourself, your sense of meaning gravitates outwards into that reciprocal relationship, into that interdependence. And you know, it’s a little bit like scales falling off your eyes. When you do turn that corner, all of the sources of anxiety that are so present and so deeply internalized become much more identifiable. And my own sense of hope and fear gets a much larger frame of reference to operate in.

I think, for most of my life, until I did kind of wake up to forests and to trees, I shared — without really understanding this as a kind of concession or a kind of subscription — I did share this cultural consensus that meaning is a private thing that we do for ourselves and by ourselves and that our kind of general sense of the discoveries of the 19th and 20th century have left us feeling a bit unsponsored and adrift beyond the accident of human existence.

The largest single influence on any human being’s mode of thought is other human beings. So if you are surrounded by lots of terrified but wishful-thinking people who want to believe that somehow the cavalry is going to come at the last minute and that we don’t really have to look inwards and change our belief in where meaning comes from, that we will somehow be able to get over the finish line with all our stuff and that we’ll avert this disaster, as we have other kinds of disasters in the past.

I think what was happening to me at that time, as I was turning outward and starting to take the non-human world seriously, is my sense of meaning was shifting from something that was entirely about me and authored by me outward into this more collaborative, reciprocal, interdependent, exterior place that involved not just me but all of these other ways of being that I could make kinship with.

And I think I was right along with that sense that somehow we are a thing apart. We can make purpose and make meaning completely arbitrarily. It consists mostly of trying to be more in yourself, of accumulating in one form or another.

I can’t really be out for more than two or three miles before my head just fills with associations and ideas and scenes and character sketches. And I usually have to rush back home to keep it all in my head long enough to get it down on paper.

for my journey, the way to characterize this transition is from being fascinated with technologies of mastery and control and what they’re doing to us as human beings, how they’re changing what the capacities and affordances of humanity are and how we narrate ourselves, to being fascinated with technologies and sciences of interdependence and cooperation, of those sciences that increase our sense of kinship and being one of many, many neighbors.

And that’s an almost impossible persuasion to rouse yourself from if you don’t have allies. And I think the one hopeful thing about the present is the number of people trying to challenge that consensual understanding and break away into a new way of looking at human standing is growing.

And when you do subscribe to a culture like that and you are confronted with the reality of your own mortality, as I was when I was living in Stanford, that sense of stockpiling personal meaning starts to feel a little bit pointless.

And I just head out. I head out based on what the day has to offer. And to have that come first has really changed not only how I write, but what I’ve been writing. And I think it really shows in “Bewilderment.” It’s a totally different kind of book from my previous 12.

the marvelous thing about the work, which continues to get more sophisticated and continues to turn up newer and newer astonishments, is that there was odd kind of reciprocal interdependence and cooperation across the species barrier, that Douglas firs and birches were actually involved in these sharing back and forth of essential nutrients. And that’s a whole new way of looking at forest.

she began to see that the forests were actually wired up in very complex and identifiable ways and that there was an enormous system of resource sharing going on underground, that trees were sharing not only sugars and the hydrocarbons necessary for survival, but also secondary metabolites. And these were being passed back and forth, both symbiotically between the trees and the fungi, but also across the network to other trees so that there were actually trees in wired up, fungally-connected forests where large, dominant, healthy trees were subsidizing, as it were, trees that were injured or not in favorable positions or damaged in some way or just failing to thrive.

so when I was still pretty much a card-carrying member of that culture, I had this sense that to become a better person and to get ahead and to really make more of myself, I had to be as productive as possible. And that meant waking up every morning and getting 1,000 words that I was proud of. And it’s interesting that I would even settle on a quantitative target. That’s very typical for that kind of mindset that I’m talking about — 1,000 words and then you’re free, and then you can do what you want with the day.

there will be a threshold, as there have been for these other great social transformations that we’ve witnessed in the last couple of decades where somehow it goes from an outsider position to absolutely mainstream and common sense.

I am persuaded by those scholars who have showed the degree to which the concept of nature is itself an artificial construction that’s born of cultures of human separatism. I believe that everything that life does is part of the living enterprise, and that includes the construction of cities. And there is no question at all the warning that you just gave about nostalgia creating a false binary between the built world and the true natural world is itself a form of cultural isolation.

Religion is a technology to discipline, to discipline certain parts of the human impulse. A lot of the book revolves around the decoded neurofeedback machine, which is a very real literalization of a technology, of changing the way we think

one of the things I think that we have to take seriously is that we have created technologies to supercharge some parts of our natural impulse, the capitalism I think should be understood as a technology to supercharge the growth impulse, and it creates some wonders out of that and some horrors out of that.

richard powersSure. I base my machine on existing technology. Decoded neurofeedback is a kind of nascent field of exploration. You can read about it; it’s been publishing results for a decade. I first came across it in 2013. It involves using fMRI to record the brain activity of a human being who is learning a process, interacting with an object or engaged in a certain emotional state. That neural activity is recorded and stored as a data structure. A second subsequent human being is then also scanned in real time and fed kinds of feedback based on their own internal neural activity as determined by a kind of software analysis of their fMRI data structures.

And they are queued little by little to approximate, to learn how to approximate, the recorded states of the original subject. When I first read about this, I did get a little bit of a revelation. I did feel my skin pucker and think, if pushed far enough, this would be something like a telepathy conduit. It would be a first big step in answering that age-old question of what does it feel like to be something other than we are

in the book I simply take that basic concept and extend it, juke it up a little bit, blur the line between what the reader might think is possible right now and what they might wonder about, and maybe even introduce possibilities for this empathetic transference

ezra kleinOne thing I loved about the role this played in the book is that it’s highlighting its inverse. So a reader might look at this and say, wow, wouldn’t that be cool if we had a machine that could in real time change how we think and change our neural pathways and change our mental state in a particular direction? But of course, all of society is that machine,

Robin and Theo are in an airport. And you’ve got TVs everywhere playing the news which is to say playing a constant loop of outrage, and disaster, and calamity. And Robbie, who’s going through these neural feedback sessions during this period, turns to his dad and says, “Dad, you know how the training’s rewiring my brain? This is what is rewiring everybody else.”

ezra kleinI think Marshall McLuhan knew it all. I really do. Not exactly what it would look like, but his view and Postman’s view that we are creating a digital global nervous system is a way they put it, it was exactly right. A nervous system, it was such the exact right metaphor.

the great insight of McLuhan, to me, what now gets called the medium is the message is this idea that the way media acts upon us is not in the content it delivers. The point of Twitter is not the link that you click or even the tweet that you read; it is that the nature and structure of the Twitter system itself begins to act on your system, and you become more like it.If you watch a lot of TV, you become more like TV. If you watch a lot of Twitter, you become more like Twitter, Facebook more like Facebook. Your identities become more important to you — that the content is distraction from the medium, and the medium changes you

it is happening to all of us in ways that at least we are not engaging in intentionally, not at that level of how do we want to be transformed.

richard powersI believe that the digital neural system is now so comprehensive that the idea that you could escape it somewhere, certainly not in the Smokies, even more remotely, I think, becomes more and more laughable. Yeah, and to build on this idea of the medium being the message, not the way in which we become more like the forms and affordances of the medium is that we begin to expect that those affordances, the method in which those media are used, the physiological dependencies and castes of behavior and thought that are required to operate them and interact with them are actual — that they’re real somehow, and that we just take them into human nature and say no, this is what we’ve always wanted and we’ve simply been able to become more like our true selves.

Well, the warpage in our sense of time, the warpage in our sense of place, are profound. The ways in which digital feedback and the affordances of social media and all the rest have changed our expectations with regard to what we need to concentrate on, what we need to learn for ourselves, are changing profoundly.

If you look far enough back, you can find Socrates expressing great anxiety and suspicion about the ways in which writing is going to transform the human brain and human expectation. He was worried that somehow it was going to ruin our memories. Well, it did up to a point — nothing like the way the digital technologies have ruined our memories.

my tradition is Jewish, the Sabbath is a technology, is a technology to create a different relationship between the human being, and time, and growth, and productive society than you would have without the Sabbath which is framed in terms of godliness but is also a way of creating separation from the other impulses of the weak.

Governments are a technology, monogamy is a technology, a religiously driven technology, but now one that is culturally driven. And these things do good and they do bad. I’m not making an argument for any one of them in particular. But the idea that we would need to invent something wholly new to come up with a way to change the way human beings act is ridiculous

My view of the story of this era is that capitalism was one of many forces, and it has become, in many societies, functionally the only one that it was in relationship with religion, it was in relationship with more rooted communities.

it has become not just an economic system but a belief system, and it’s a little bit untrammeled. I’m not an anti-capitalist person, but I believe it needs countervailing forces. And my basic view is that it doesn’t have them anymore.

the book does introduce this kind of fable, this kind of thought experiment about the way the affordances that a new and slightly stronger technology of empathy might deflect. First of all, the story of a little boy and then the story of his father who’s scrambling to be a responsible single parent. And then, beyond that, the community of people who hear about this boy and become fascinated with him as a narrative, which again ripples outward through these digital technologies in ways that can’t be controlled or whose consequences can be foreseen.

I’ve talked about it before is something I’ve said is that I think a push against, functionally, materialism and want is an important weight in our society that we need. And when people say it is the way we’ll deal with climate change in the three to five year time frame, I become much more skeptical because to the point of things like the technology you have in the book with neural feedback, I do think one of the questions you have to ask is, socially and culturally, how do you move people’s minds so you can then move their politics?

You’re going to need something, it seems to me, outside of politics, that changes humans’ sense of themselves more fundamentally. And that takes a minute at the scale of billions.

richard powersWell, you are correct. And I don’t think it’s giving away any great reveal in the book to say that a reader who gets far enough into the story probably has this moment of recursive awareness where they, he or she comes to understand that what Robin is doing in this gradual training on the cast of mind of some other person is precisely what they’re doing in the act of reading the novel “Bewilderment” — by living this act of active empathy for these two characters, they are undergoing their own kind of neurofeedback.

The more we understand about the complexities of living systems, of organisms and the evolution of organisms, the more capable it is to feel a kind of spiritual awe. And that certainly makes it easier to have reverence for the experiment beyond me and beyond my species. I don’t think those are incommensurable or incompatible ways of knowing the world. In fact, I think to invoke one last time that Buddhist precept of interbeing, I think there is a kind of interbeing between the desire, the true selfless desire to understand the world out there through presence, care, measurement, attention, reproduction of experiment and the desire to have a spiritual affinity and shared fate with the world out there. They’re really the same project.

richard powersWell, sure. If we turn back to the new forestry again and researchers like Suzanne Simard who were showing the literal interconnectivity across species boundaries and the cooperation of resource sharing between different species in a forest, that is rigorous science, rigorous reproducible science. And it does participate in that central principle of practice, or collection of practices, which always requires the renunciation of personal wish and ego and prior belief in favor of empirical reproduction.

I’ve begun to see people beginning to build out of the humbling sciences a worldview that seems quite spiritual. And as you’re somebody who seems to me to have done that and it has changed your life, would you reflect on that a bit?

So much of the book is about the possibility of life beyond Earth. Tell me a bit about the role that’s playing. Why did you make the possibility of alien life in the way it might look and feel and evolve and act so central in a book about protecting and cherishing life here?

richard powersI’m glad that we’re slipping this in at the end because yes this framing of the book around this question of are we alone or does the universe want life it’s really important. Theo, Robin’s father, is an astrobiologist.

Imagine that everything happens just right so that every square inch of this place is colonized by new forms of experiments, new kinds of life. And the father trying to entertain his son with the story of this remarkable place in the sun just stopping him and saying, Dad, come on, that’s asking too much. Get real, that’s science fiction. That’s the vision that I had when I finished the book, an absolutely limitless sense of just how lucky we’ve had it here.

one thing I kept thinking about that didn’t make it into the final book but exists as a kind of parallel story in my own head is the father and son on some very distant planet in some very distant star, many light years from here, playing that same game. And the father saying, OK, now imagine a world that’s just the right size, and it has plate tectonics, and it has water, and it has a nearby moon to stabilize its rotation, and it has incredible security and safety from asteroids because of other large planets in the solar system.

they make this journey across the universe through all kinds of incubators, all kinds of petri dishes for life and the possibilities of life. And rather than answer the question — so where is everybody? — it keeps deferring the question, it keeps making that question more subtle and stranger

For the purposes of the book, Robin, who desperately believes in the sanctity of life beyond himself, begs his father for these nighttime, bedtime stories, and Theo gives him easy travel to other planets. Father and son going to a new planet based on the kinds of planets that Theo’s science is turning up and asking this question, what would life look like if it was able to get started here?

in general what he argues is that if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working and you see that from Marr's highest level.

it's basically in the spirit of Marr's analysis. So when you're studying vision, he argues, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything.

"Good Old Fashioned AI," as it's labeled now, made strong use of formalisms in the tradition of Gottlob Frege and Bertrand Russell, mathematical logic for example, or derivatives of it, like nonmonotonic reasoning and so on. It's interesting from a history of science perspective that even very recently, these approaches have been almost wiped out from the mainstream and have been largely replaced -- in the field that calls itself AI now -- by probabilistic and statistical models. My question is, what do you think explains that shift and is it a step in the right direction?

AI and robotics got to the point where you could actually do things that were useful, so it turned to the practical applications and somewhat, maybe not abandoned, but put to the side, the more fundamental scientific questions, just caught up in the success of the technology and achieving specific goals.

The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability... ..in engineering? Chomsky: ...But away from understanding.

I was very skeptical about the original work. I thought it was first of all way too optimistic, it was assuming you could achieve things that required real understanding of systems that were barely understood, and you just can't get to that understanding by throwing a complicated machine at it.

if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.

Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is.

if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.

the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach.

take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don't stop to ask if the wiring diagram is the right level of abstraction -- maybe it's no

if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine.

that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.