The tyranny of words

Some reflections during a wake period at 1:30 a.m. this morning... 

Mulling on the tyranny of thought as a ruminating mind calms down and refuge is found in a quiet space from which words rise like wisps or vapors, a space free of subjects and objects in which there can be no hurry. 

Grateful to be experiencing an aging process that enables a dedifferentiating 82 year old brain to experience a return towards its youth, letting go of the clouds of senolytic discourse that have come to clutter it and increasingly experience being the calm and quiet space from which everything rises. 

Feeling sympathy for public intellectuals whose writing I follow, immersed in their addiction to words as they oblige themselves, many due to financial necessity, to keep writing a stream that includes mediocre as well as brilliant work, each generating a rivulet in the streams of discourse diverging and merging in an infosphere that is becoming increasingly contaminated by the words of robots that duplicate and obscure their efforts.  Grateful for the retired professor’s pension that  permits optional association with, or dissociation from, this world of words.

The other shoe is about to drop in 2024 - important closure moments in the state of the world.

I want to pass on a clip (to which I have added some definitions in parentheses) from Venkatest Rao’s most recent Ribbonfarm Studio installment in which he argues that the other shoe is about to drop for many narratives in 2024, a year that feels much more exciting than 1984, 1994, 2004, or 2014.

With the Trump/Biden election, the other shoe is about to drop on the arc that began with the Great Weirding (radical global transformations that unfolded between 2016 and 2020). This arc has fuzzy beginnings and endings globally, but is clearly heading towards closure everywhere. For instance, the recent election in India, with its chastening message for the BJP, has a 10-year span (2014-24). In the EU and UK, various arcs that began with events in Greece/Italy and Brexit are headed towards some sort of natural closure.

Crypto and AI, two strands in my mediocre computing series (the other two being robotics and the metaverse), also seem to be at an other-shoe-drops phase. Crypto, after experiencing 4-5 boom-bust cycles since 2009, is finally facing a triple test of geopolitical significance, economic significance, and “product” potential (as in international agreement that “stable coins” as well as fiat currencies are valid vehicles for managing debt and commerce). It feels like in the next year or two we’ll learn if it’s a technology that’s going to make history or remain a sideshow. AI is shifting gears from a rapid and accelerating installation phase of increasing foundational capabilities to a deployment phase of productization, marked by Apple’s entry into the fray and a sense of impending saturation in the foundational capabilities.

Various wars (Gaza, Ukraine) and tensions (Taiwan) are starting to stress the Westphalian model of the nation state for real now. That’s the other shoe dropping on a 400-year-long story (also a 75 year long story about the rules-based international order, but that’s relatively less interesting).

Economically, we’re clearly decisively past the ZIRPy (Zero Interest Rate Policy) end of the neoliberal globalization era that began in the mid-80s. That shoe has already dropped. What new arc is starting is unclear — the first shoe of the new story hasn’t dropped yet. Something about nonzero interest rates in an uncertain world marked by a mercantilist resource-grabbing geopolitical race unfolding in parallel to slowly reconfiguring global trade patterns.

Managing the human herd

 This post is a dyspeptic random walk through thoughts triggered by the front page photograph of the Wall Street journal of June 17, 2024, showing Masses of pilgrims embarked on a symbolic stoning of the devil in Saudi Arabia under the soaring summer heat. Such enormous mobs of people are those most easily roused to strong emotions by charismatic speakers.

How are the emotions and behaviors of such enormous clans of humans be regulated in a sane and humane way? Can this be accomplished outside of authoritarian or oligarchical governance? Might such governance establish its control of the will and moods of millions through the unnoticed infiltration of AI into all aspects of their daily life (cf. Apple's recent AI announcements). Will the world come to be ruled by a "Book of Elon"? 

Or might we be moving into a world of decentralized everything? a bottom up emergence of consensus governance the from the mosh pit of web 3, cryptocurrencies and stablecoins? The noble sentiments of the Etherium Foundation/ notwithstanding, the examples we have to date of 'rules of the commons' are the chaos of Discord, Reddit, and other social media where the sentiments of idiots and experts jumble together in an impenetrable cacophony.  

Who or what is going to emerge to control this mess? How long will the "permaweird" persist?  

 

Empty innovation: What are we even doing?

I came across an interesting commentary by "Tante" on innovation, invention, and progress (or the lack thereof) in the constant churning, and rise and fall, of new ideas and products in the absence of questions like "Why are we doing this?" and "Who is profiting?". In spite of the speaker's arrogance and annoying style, I think it is worth a viewing.

The future of life.

I want to pass on this science magazine review of Jamie Metzl's new book "Superconvergence: How the Genetics, Biotech, and AI Revolutions Will Transform Our Lives, Work, and World". Metzel is founder of the One Shared World organization. Check out its website here.

On the night of 4 July 1776, the Irish immigrant and official printer to the Continental Congress John Dunlap entered his Philadelphia print-shop and began to typeset the first printed version of a document that was to become the enduring North Star of the “American experiment.” It comprised an ideological handbook for its utopian aspirations and a codification of purported essential self-evident ground truths that included the equality of all men and the rights to life, liberty, and the pursuit of happiness. By the morning, Dunlap had produced an estimated 200 copies of the American Declaration of Independence, which Abraham Lincoln would later refer to as a “rebuke and a stumblingblock… to tyranny and oppression.”

In his erudite, optimistic, and timely book Superconvergence, the futurist Jamie Metzl laments the lack of any such authoritative reference to inform our exploration of an equally expansive, intriguing, and uncharted territory: humankind’s future. Replete with unprecedented opportunities and existential risks hitherto unimaginable in life’s history, the new world we are entering transcends geographical boundaries, and—as a result of humankind’s global interdependencies—it must, by necessity, exist in a no-man’s-land beyond the mandates of ideologies and nation-states. Its topography is defined not by geological events and evolution by natural selection so much as by the intersection of several exponential human-made technologies. Most notably, these include the generation of machine learning intelligence that can interrogate big data to define generative “rules” of biology and the post- Darwinian engineering of living systems through the systematic rewriting of their genetic code.

Acknowledging the intrinsic mutability of natural life and its ever-changing biochemistry and morphology, Metzl is unable to align himself with UNESCO’s 1997 Universal Declaration on the Human Genome and Human Rights. To argue that the current version of the human genome is sacred is to negate its prior iterations, including the multiple species of human that preceded us but disappeared along the way. The sequences of all Earth’s species are in a simultaneous state of being and becoming, Metzl argues. Life is intrinsically fluid.

Although we are still learning to write complex genomes rapidly, accurately, without sequence limitation, and at low cost, and our ability to author novel genomes remains stymied by our inability to unpick the generative laws of biology, it is just a matter of time before we transform biology into a predictable engineering material, at which point we will be able to recast life into desired forms. But while human-engineered living materials and biologically inspired devices offer potential solutions to the world’s most challenging problems, our rudimentary understanding of complex ecosystems and the darker sides of human nature cast long shadows, signaling the need for caution.

Metzl provides some wonderful examples of how artificial species and bioengineering, often perceived as adversaries of natural life, could help address several of the most important issues of the moment. These challenges include climate change, desertification, deforestation, pollution (including the 79,000-metric-ton patch of garbage the size of Alaska in the Pacific Ocean), the collapse of oceanic ecosystems, habitat loss, global population increase, and the diminution of species biodiversity. By rewriting the genomes of crops and increasing the efficiency of agriculture, we can reduce the need to convert additional wild habitats into farmland, he writes. Additionally, the use of bioengineering to make sustainable biofuels, biocomputing, bio foodstuffs, biodegradable plastics, and DNA information–storing materials will help reduce global warming.

Meanwhile, artificial intelligence (AI) can free up human time. By 2022, DeepMind’s AlphaFold program had predicted the structures of 214 million proteins—a feat that would have taken as long as 642 million years to achieve using conventional methods. As Metzl comments, this places “millions of years back into the pot of human innovation time.” The ability to hack human biology using AI will also have a tremendous impact on the human health span and life span, not least through AI-designed drugs, he predicts.

Metzl is right when he concludes that we have reached a “critical moment in human history” and that “reengineered biology will play a central role in the future of our species.” We will need to define a new North Star—a manifesto for life—to assist with its navigation. Metzl argues for the establishment of a new international body with depoliticized autonomy to focus on establishing common responses to shared global existential challenges. He suggests that this process could be kick-started by convening a summit aimed at establishing aligned governance guidelines for the revolutionary new technologies we are creating.

The pathologies of the educated elites

Another really nice opinion piece from David Brooks, who notes the consequences, as we have moved from the industrial age to the information age, of progressive energy moving from the working class to the universities, especially the elite universities.

I’ve looked on with a kind of dismay as elite university dynamics have spread across national life and politics, making America worse in all sorts of ways. Let me try to be more specific about these dynamics.

The first is false consciousness. To be progressive is to be against privilege. But today progressives dominate elite institutions like the exclusive universities, the big foundations and the top cultural institutions...This is the contradiction of the educated class. Virtue is defined by being anti-elite. But today’s educated class constitutes the elite, or at least a big part of it...This sort of cognitive dissonance often has a radicalizing effect. When your identity is based on siding with the marginalized, but you work at Horace Mann or Princeton, you have to work really hard to make yourself and others believe you are really progressive. You’re bound to drift further and further to the left to prove you are standing up to the man...elite students...are often the ones talking most loudly about burning the system down.

The second socially harmful dynamic is what you might call the cultural consequences of elite overproduction...the marketplace isn’t producing enough of the kinds of jobs these graduates think they deserve...Peter Turchin argued that periods of elite overproduction lead to a rising tide of social decay as alienated educated-class types wage ever more ferocious power struggles with other elites...The spread of cancel culture and support for decriminalizing illegal immigration and “defunding the police” were among the quintessential luxury beliefs that seemed out of touch to people in less privileged parts of society. Those people often responded by making a sharp countershift in the populist direction, contributing to the election of Donald Trump and to his continued political viability today...elite overproduction induces people on the left and the right to form their political views around their own sense of personal grievance and alienation. It launches unhappy progressives and their populist enemies into culture war battles that help them feel engaged, purposeful and good about themselves, but ...these battles are often more about performative self-validation than they are about practical policies that might serve the common good.

The third dynamic is the inflammation of the discourse. The information age has produced a vast cohort of people (including me) who live by trafficking in ideas — academics, journalists, activists, foundation employees, consultants and the various other shapers of public opinion...Nothing is more unstable than a fashionable opinion. If your status is defined by your opinions, you’re living in a world of perpetual insecurity, perpetual mental and moral war...French sociologist Pierre Bourdieu...argued that just as economic capitalists use their resource — wealth — to amass prestige and power, people who form the educated class and the cultural elite, symbolic capitalists, use our resources — beliefs, fancy degrees, linguistic abilities — to amass prestige, power and, if we can get it, money...symbolic capitalists turned political postures into power tools that enable them to achieve social, cultural and economic might...battles for symbolic consecration are now the water in which many of us highly educated Americans swim. In the absence of religious beliefs, these moral wars give people a genuine sense of meaning and purpose.

Brooks notes a number of potential ways of countering these dynamics, all of which require the educated class. progressive or not, to address the social, political and economic divides it has unwittingly created. But he also cites another, perhaps more likely, path:

Perhaps today’s educated elite is just like any other historical elite. We gained our status by exploiting or not even seeing others down below, and we are sure as hell not going to give up any of our status without a fight.

 Brooks then points to a forthcoming book, al-Gharbi’s “We Have Never Been Woke.” al-Gharbi notes:

...today’s educated-class activists are conveniently content to restrict their political action to the realm of symbols. In his telling, land acknowledgments — when people open public events by naming the Indigenous peoples who had their land stolen from them — are the quintessential progressive gesture...It’s often non-Indigenous people signaling their virtue to other non-Indigenous people while doing little or nothing for the descendants of those who were actually displaced...while members of the educated class do a lot of moral preening, their lifestyles contribute to the immiserations of the people who have nearly been rendered invisible — the Amazon warehouse worker, the DoorDash driver making $1.75 an hour after taxes and expenses.

 Brooks concludes:

That rumbling sound you hear is the possibility of a multiracial, multiprong, right/left alliance against the educated class. Donald Trump has already created the nub of this kind of movement but is himself too polarizing to create a genuinely broad-based populist movement. After Trump is off the stage, it’s very possible to imagine such an uprising....The lesson for those of us in the educated class is to seriously reform the system we have created or be prepared to be run over.

 

 

 

Protecting scientific integrity in an age of generative AI

I want to pass on the full text of an editorial by Blau et al in PNAS, the link points to the more complete open source online version containing acknowledgements and references:

Revolutionary advances in AI have brought us to a transformative moment for science. AI is accelerating scientific discoveries and analyses. At the same time, its tools and processes challenge core norms and values in the conduct of science, including accountability, transparency, replicability, and human responsibility (1–3). These difficulties are particularly apparent in recent advances with generative AI. Future innovations with AI may mitigate some of these or raise new concerns and challenges.

 

With scientific integrity and responsibility in mind, the National Academy of Sciences, the Annenberg Public Policy Center of the University of Pennsylvania, and the Annenberg Foundation Trust at Sunnylands recently convened an interdisciplinary panel of experts with experience in academia, industry, and government to explore rising challenges posed by the use of AI in research and to chart a path forward for the scientific community. The panel included experts in behavioral and social sciences, ethics, biology, physics, chemistry, mathematics, and computer science, as well as leaders in higher education, law, governance, and science publishing and communication. Discussions were informed by commissioned papers detailing the development and current state of AI technologies; the potential effects of AI advances on equality, justice, and research ethics; emerging governance issues; and lessons that can be learned from past instances where the scientific community addressed new technologies with significant societal implications (4–9).

 

Generative AI systems are constructed with computational procedures that learn from large bodies of human-authored and curated text, imagery, and analyses, including expansive collections of scientific literature. The systems are used to perform multiple operations, such as problem-solving, data analysis, interpretation of textual and visual content, and the generation of text, images, and other forms of data. In response to prompts and other directives, the systems can provide users with coherent text, compelling imagery, and analyses, while also possessing the capability to generate novel syntheses and ideas that push the expected boundaries of automated content creation.

 

Generative AI’s power to interact with scientists in a natural manner, to perform unprecedented types of problem-solving, and to generate novel ideas and content poses challenges to the long-held values and integrity of scientific endeavors. These challenges make it more difficult for scientists, the larger research community, and the public to 1) understand and confirm the veracity of generated content, reviews, and analyses; 2) maintain accurate attribution of machine- versus human-authored analyses and information; 3) ensure transparency and disclosure of uses of AI in producing research results or textual analyses; 4) enable the replication of studies and analyses; and 5) identify and mitigate biases and inequities introduced by AI algorithms and training data.

Five Principles of Human Accountability and Responsibility

To protect the integrity of science in the age of generative AI, we call upon the scientific community to remain steadfast in honoring the guiding norms and values of science. We endorse recommendations from a recent National Academies report that explores ethical issues in computing research and promoting responsible practices through education and training (3). We also reaffirm the findings of earlier work performed by the National Academies on responsible automated research workflows, which called for human review of algorithms, the need for transparency and reproducibility, and efforts to uncover and address bias (10).

 

Building upon the prior studies, we urge the scientific community to focus sustained attention on five principles of human accountability and responsibility for scientific efforts that employ AI:

1.

Transparent disclosure and attribution

Scientists should clearly disclose the use of generative AI in research, including the specific tools, algorithms, and settings employed; accurately attribute the human and AI sources of information or ideas, distinguishing between the two and acknowledging their respective contributions; and ensure that human expertise and prior literature are appropriately cited, even when machines do not provide such citations in their output.

 

Model creators and refiners should provide publicly accessible details about models, including the data used to train or refine them; carefully manage and publish information about models and their variants so as to provide scientists with a means of citing the use of particular models with specificity; provide long-term archives of models to enable replication studies; disclose when proper attribution of generated content cannot be provided; and pursue innovations in learning, reasoning, and information retrieval machinery aimed at providing users of those models with the ability to attribute sources and authorship of the data employed in AI-generated content.

2.

Verification of AI-generated content and analyses

Scientists are accountable for the accuracy of the data, imagery, and inferences that they draw from their uses of generative models. Accountability requires the use of appropriate methods to validate the accuracy and reliability of inferences made by or with the assistance of AI, along with a thorough disclosure of evidence relevant to such inferences. It includes monitoring and testing for biases in AI algorithms and output, with the goal of identifying and correcting biases that could skew research outcomes or interpretations.

 

Model creators should disclose limitations in the ability of systems to confirm the veracity of any data, text, or images generated by AI. When verification of the truthfulness of generated content is not possible, model output should provide clear, well-calibrated assessments of confidence. Model creators should proactively identify, report, and correct biases in AI algorithms that could skew research outcomes or interpretations.

3.

Documentation of AI-generated data

Scientists should mark AI-generated or synthetic data, inferences, and imagery with provenance information about the role of AI in their generation, so that it is not mistaken for observations collected in the real world. Scientists should not present AI-generated content as observations collected in the real world.

 

Model creators should clearly identify, annotate, and maintain provenance about synthetic data used in their training procedures and monitor the issues, concerns, and behaviors arising from the reuse of computer-generated content in training future models.

4.

A focus on ethics and equity

Scientists and model creators should take credible steps to ensure that their uses of AI produce scientifically sound and socially beneficial results while taking appropriate steps to mitigate the risk of harm. This includes advising scientists and the public on the handling of tradeoffs associated with making certain AI technologies available to the public, especially in light of potential risks stemming from inadvertent outcomes or malicious applications.

 

Scientists and model creators should adhere to ethical guidelines for AI use, particularly in terms of respect for clear attribution of observational versus AI-generated sources of data, intellectual property, privacy, disclosure, and consent, as well as the detection and mitigation of potential biases in the construction and use of AI systems. They should also continuously monitor other societal ramifications likely to arise as AI is further developed and deployed and update practices and rules that promote beneficial uses and mitigate the prospect of social harm.

 

Scientists, model creators, and policymakers should promote equity in the questions and needs that AI systems are used to address as well as equitable access to AI tools and educational opportunities. These efforts should empower a diverse community of scientific investigators to leverage AI systems effectively and to address the diverse needs of communities, including the needs of groups that are traditionally underserved or marginalized. In addition, methods for soliciting meaningful public participation in evaluating equity and fairness of AI technologies and uses should be studied and employed.

 

AI should not be used without careful human oversight in decisional steps of peer review processes or decisions around career advancement and funding allocations.

5.

Continuous monitoring, oversight, and public engagement

Scientists, together with representatives from academia, industry, government, and civil society, should continuously monitor and evaluate the impact of AI on the scientific process, and with transparency, adapt strategies as necessary to maintain integrity. Because AI technologies are rapidly evolving, research communities must continue to examine and understand the powers, deficiencies, and influences of AI; work to anticipate and prevent harmful uses; and harness its potential to address critical societal challenges. AI scientists must at the same time work to improve the effectiveness of AI for the sciences, including addressing challenges with veracity, attribution, explanation, and transparency of training data and inference procedures. Efforts should be undertaken within and across sectors to pursue ongoing study of the status and dynamics of the use of AI in the sciences and pursue meaningful methods to solicit public participation and engagement as AI is developed, applied, and regulated. Results of this engagement and study should be broadly disseminated.

A New Strategic Council to Guide AI in Science

We call upon the scientific community to establish oversight structures capable of responding to the opportunities AI will afford science and to the unanticipated ways in which AI may undermine scientific integrity.

 

We propose that the National Academies of Sciences, Engineering, and Medicine establish a Strategic Council on the Responsible Use of Artificial Intelligence in Science.^* The council should coordinate with the scientific community and provide regularly updated guidance on the appropriate uses of AI, especially during this time of rapid change. The council should study, monitor, and address the evolving uses of AI in science; new ethical and societal concerns, including equity; and emerging threats to scientific norms. The council should share its insights across disciplines and develop and refine best practices.

 

More broadly, the scientific community should adhere to existing guidelines and regulations, while contributing to the ongoing development of public and private AI governance. Governance efforts must include engagement with the public about how AI is being used and should be used in the sciences.

 

With the advent of generative AI, all of us in the scientific community have a responsibility to be proactive in safeguarding the norms and values of science. That commitment—together with the five principles of human accountability and responsibility for the use of AI in science and the standing up of the council to provide ongoing guidance—will support the pursuit of trustworthy science for the benefit of all.

 

Is it a fact? The epistemic force of language in news headlines.

From Chuey et al. in PNAS (open source):  

Significance

Headlines are an influential source of information, especially because people often do not read beyond them. We investigated how subtle differences in epistemic language in headlines (e.g., “believe” vs. “know“) affect readers’ inferences about whether claims are perceived as matters of fact or mere opinion. We found, for example, saying “Scientists believe methane emissions soared to a record in 2021” led readers to view methane levels as more a matter of opinion compared to saying “Scientists know…” Our results provide insight into how epistemic verbs journalists use affect whether claims are perceived as matters of fact and suggest a mechanism contributing to the rise of alternative facts and “post-truth” politics.

Abstract

How we reason about objectivity—whether an assertion has a ground truth—has implications for belief formation on wide-ranging topics. For example, if someone perceives climate change to be a matter of subjective opinion similar to the best movie genre, they may consider empirical claims about climate change as mere opinion and irrelevant to their beliefs. Here, we investigate whether the language employed by journalists might influence the perceived objectivity of news claims. Specifically, we ask whether factive verb framing (e.g., "Scientists know climate change is happening") increases perceived objectivity compared to nonfactive framing (e.g., "Scientists believe [...]"). Across eight studies (N = 2,785), participants read news headlines about unique, noncontroversial topics (studies 1a–b, 2a–b) or a familiar, controversial topic (climate change; studies 3a–b, 4a–b) and rated the truth and objectivity of the headlines’ claims. Across all eight studies, when claims were presented as beliefs (e.g., “Tortoise breeders believe tortoises are becoming more popular pets”), people consistently judged those claims as more subjective than claims presented as knowledge (e.g., “Tortoise breeders know…”), as well as claims presented as unattributed generics (e.g., “Tortoises are becoming more popular pets”). Surprisingly, verb framing had relatively little, inconsistent influence over participants’ judgments of the truth of claims. These results demonstrate how, apart from shaping whether we believe a claim is true or false, epistemic language in media can influence whether we believe a claim has an objective answer at all.

Impact of our built environment on our microbiome and health

Bosch et al. do a perspective in PNAS (open source) pointing out that:

...contemporary built environments are steadily reducing the microbial diversity essential for human health, well-being, and resilience while accelerating the symptoms of human chronic diseases including environmental allergies, and other more life-altering diseases.

Here is their abstract:

There is increasing evidence that interactions between microbes and their hosts not only play a role in determining health and disease but also in emotions, thought, and behavior. Built environments greatly influence microbiome exposures because of their built-in highly specific microbiomes coproduced with myriad metaorganisms including humans, pets, plants, rodents, and insects. Seemingly static built structures host complex ecologies of microorganisms that are only starting to be mapped. These microbial ecologies of built environments are directly and interdependently affected by social, spatial, and technological norms. Advances in technology have made these organisms visible and forced the scientific community and architects to rethink gene–environment and microbe interactions respectively. Thus, built environment design must consider the microbiome, and research involving host–microbiome interaction must consider the built-environment. This paradigm shift becomes increasingly important as evidence grows that contemporary built environments are steadily reducing the microbial diversity essential for human health, well-being, and resilience while accelerating the symptoms of human chronic diseases including environmental allergies, and other more life-altering diseases. New models of design are required to balance maximizing exposure to microbial diversity while minimizing exposure to human-associated diseases. Sustained trans-disciplinary research across time (evolutionary, historical, and generational) and space (cultural and geographical) is needed to develop experimental design protocols that address multigenerational multispecies health and health equity in built environments.

Ancient origins of aspects of instrumental and song melodies distinctive from those of language.

 A global collaboration from many cultures shows that songs and instrumental melodies are slower and higher and use more stable pitches than speech, suggesting evolutionary origins universal to all humans that cannot simply be explained by culture. The numerous samples of music collected could be arranged in a musi-linguistic continuum from instrumental music to spoken language.

Both music and language are found in all known human societies, yet no studies have compared similarities and differences between song, speech, and instrumental music on a global scale. In this Registered Report, we analyzed two global datasets: (i) 300 annotated audio recordings representing matched sets of traditional songs, recited lyrics, conversational speech, and instrumental melodies from our 75 coauthors speaking 55 languages; and (ii) 418 previously published adult-directed song and speech recordings from 209 individuals speaking 16 languages. Of our six preregistered predictions, five were strongly supported: Relative to speech, songs use (i) higher pitch, (ii) slower temporal rate, and (iii) more stable pitches, while both songs and speech used similar (iv) pitch interval size and (v) timbral brightness. Exploratory analyses suggest that features vary along a “musi-linguistic” continuum when including instrumental melodies and recited lyrics. Our study provides strong empirical evidence of cross-cultural regularities in music and speech.

Think AI Can Perceive Emotion? Think Again.

Numerous MindBlog posts have presented the work and writing of Elizabeth Feldman Barrett (enter Barrett in the search box in the right column of this web page). Her book, "How Emotions Are Made," is the one I recommend when anyone asks me what I think is the best popular book on how our brains work.  Here I want to pass on her piece on AI and emotions in the Sat. May 18 Wall Street Journal. It collects together the various reasons that AI can not, and should not, be used for detecting our emotional state from our facial expressions or other body language.  Here is her text: 

Imagine that you are interviewing for a job. The interviewer asks a question that makes you think. While concentrating, you furrow your brow and your face forms a scowl. A camera in the room feeds your scowling face to an AI model, which determines that you’ve become angry. The interview team decides not to hire you because, in their view, you are too quick to anger. Well, if you weren’t angry during the interview, you probably would be now.

This scenario is less hypothetical than you might realize. So-called emotion AI systems already exist, and some are specifically designed for job interviews. Other emotion AI products try to create more empathic chatbots, build more precise medical treatment plans and detect confused students in classrooms. But there’s a catch: The best available scientific evidence indicates that there are no universal expressions of emotion.

In real life, angry people don’t commonly scowl. Studies show that in Western cultures, they scowl about 35% of the time, which is more than chance but not enough to be a universal expression of anger. The other 65% of the time, they move their faces in other meaningful ways. They might pout or frown. They might cry. They might laugh. They might sit quietly and plot their enemy’s demise. Even when Westerners do scowl, half the time it isn’t in anger. They scowl when they concentrate, when they enjoy a bad pun or when they have gas.

Similar findings hold true for every so-called universal facial expression of emotion. Frowning in sadness, smiling in happiness, widening your eyes in fear, wrinkling your nose in disgust and yes, scowling in anger, are stereotypes—common but oversimplified notions about emotional expressions.

Where did these stereotypes come from? You may be surprised to learn that they were not discovered by observing how people move their faces during episodes of emotion in real life. They originated in a book by Charles Darwin, “The Expression of the Emotions in Man and Animals,” which proposed that humans evolved certain facial movements from ancient animals. But Darwin didn’t conduct careful observations for these ideas as he had for his masterwork, “On the Origin of Species.” Instead, he came up with them by studying photographs of people whose faces were stimulated with electricity, then asked his colleagues if they agreed.

In 2019, the journal Psycho--logical Science in the Public Interest engaged five senior scientists, including me, to examine the scientific evidence for the idea that people express anger, sadness, fear, happiness, disgust and surprise in universal ways. We came from different fields—psychology, neuroscience, engineering and computer science—and began with opposing views. Yet, after reviewing more than a thousand papers during almost a hundred videoconferences, we reached a consensus: In the real world, an emotion like anger or sadness is a broad category full of variety. People express different emotions with the same facial movements and the same emotion with different facial movements. The variation is meaningfully tied to a person’s situation.

In short, we can’t train AI on stereotypes and expect the results to work in real life, no matter how big the data set or sophisticated the algorithm. Shortly after the paper was published, Microsoft retired the emotion AI features of their facial recognition software.

Other scientists have also demonstrated that faces are a poor indicator of a person’s emotional state. In a study published in the journal Psychological Science in 2008, scientists combined photographs of stereotypical but mismatched facial expressions and body poses, such as a scowling face attached to a body that’s holding a dirty diaper. Viewers asked to identify the emotion in each image typically chose what was implied by the body, not the face— in this case disgust, not anger. In a study published in the journal Science in 2012, the same lead scientist showed that winning and losing athletes, in the midst of their glory or defeat, make facial movements that are indistinguishable.

Nevertheless, these stereotypes are still widely assumed to be universal expressions of emotion. They’re in posters in U.S. preschools, spread through the media, designed into emojis and now enshrined in AI code. I recently asked two popular AIbased image generators, Midjourney and OpenAI’s DALL-E, to depict “an angry person.” I also asked two AI chatbots, OpenAI’s ChatGPT and Google’s Gemini, how to tell if a person is angry. The results were filled with scowls, furrowed brows, tense jaws and clenched teeth.

Even AI systems that appear to sidestep emotion stereotypes may still apply them in stealth. A 2021 study in the journal Nature trained an AI model with thousands of video clips from the internet and tested it on millions more. The authors concluded that 16 facial expressions are made worldwide in certain social contexts. Yet the trainers who labeled the clips with emotion words were all English--speakers from a single country, India, so they effectively transmitted cultural stereotypes to a machine. Plus, there was no way to objectively confirm what the strangers in the videos were actually feeling at the time.

Clearly, large data sets alone cannot protect an AI system from applying preconceived assumptions about emotion. The European Union’s AI Act, passed in 2023, recognizes this reality by barring the use of emotion AI in policing, schools and workplaces.

So what is the path forward? If you encounter an emotion AI --product that purports to hire skilled job candidates, diagnose anxiety and depression, assess guilt or innocence in court, detect terrorists in airports or analyze a person’s emotional state for any other purpose, it pays to be skeptical. Here are three questions you can ask about any emotion AI product to probe the scientific approach behind it.

Is the AI model trained to account for the huge variation of real-world emotional life? Any individual may express an emotion like anger differently at different times and in different situations, depending on context. People also use the same movements to express different states, even nonemotional ones. AI models must be trained to reflect this variety.

Does the AI model distinguish between observing facial movements and inferring meaning from these movements? Muscle movements are measurable; inferences are guesses. If a system or its designers confuse description with inference, like considering a scowl to be an “anger expression” or even calling a facial movement a “facial expression,” that’s a red flag.

Given that faces by themselves don’t reveal emotion, does the AI model include abundant context? I don’t mean just a couple of signals, such as a person’s voice and heart rate. In real life, when you perceive someone else as emotional, your brain combines signals from your eyes, ears, nose, mouth, skin, and the internal systems of your body and draws on a lifetime of experience. An AI model would need much more of this information to make reasonable guesses about a person’s emotional state.

AI promises to simplify decisions by providing quick answers, but these answers are helpful and justified only if they draw from the true richness and variety of experience. None of us wants important outcomes in our lives, or the lives of our loved ones, to be determined by a stereotype.

The Happiness Gap Between Left and Right

 I want to pass on a few clips from a recent Thomas Edsall essay, followed by a condensed version of the longer piece provided by Chat GPT 4:

Why is it that a substantial body of social science research finds that conservatives are happier than liberals?...psychologists and other social scientists have begun to dig deeper into the underpinnings of liberal discontent — not only unhappiness but also depression and other measures of dissatisfaction.

One of the findings emerging from this research is that the decline in happiness and in a sense of agency is concentrated among those on the left who stress matters of identity, social justice and the oppression of marginalized groups.

There is, in addition, a parallel phenomenon taking place on the right as Donald Trump and his MAGA loyalists angrily complain of oppression by liberals who engage in a relentless vendetta to keep Trump out of the White House.

There is a difference in the way the left and right react to frustration and grievance. Instead of despair, the contemporary right has responded with mounting anger, rejecting democratic institutions and norms.

Here is my edited version of a Chat GPT4 4-fold condensation of Edsall's essay: 

…surveys have consistently shown that those on the right of the political spectrum enjoy a higher self-reported sense of happiness compared to their counterparts on the left. The reasons are as complex as they are intriguing.

Conservatives tend to view the social and economic systems as just and fair, where hard work is rewarded and natural hierarchies are maintained. This perspective shields them from much of the anger or dissatisfaction that might arise from witnessing social or economic inequalities. They see market outcomes and social stratifications as generally fair and based on merit, which fosters a sense of contentment or acceptance of their circumstances.

On the other hand, liberals are more likely to perceive social and economic systems as flawed or unfair, nurturing a sense of injustice and dissatisfaction. This ideological stance makes them more sensitive to the inequities and imperfections of society, which can manifest as frustration, sadness, or a pervasive sense of being wronged. The liberal focus on social justice, equity, and the protection of marginalized groups, while morally compelling, can also be a source of continuous discontent and agitation as these goals are often far from being realized.

Recent psychological research has started to probe deeper into these disparities, shifting the focus from documenting differences to understanding their underlying causes. This body of work suggests that the liberal emphasis on identity and the systemic oppression of marginalized groups can sometimes lead to a feeling of disempowerment. By defining themselves in terms of victimhood and systemic barriers, liberals might inadvertently undermine their sense of personal agency, which is closely linked to psychological well-being.

The current political climate, especially with the rise of Donald Trump and his brand of populism, has also highlighted a stark difference in how frustration and grievance are expressed across the political spectrum. While liberals might internalize their discontent, leading to despair and dejection, many conservatives have channeled their frustrations into anger and defiance. This is exemplified by the significant number of Republicans who view Democrats not just as political opponents, but as outright enemies, and who believe in the necessity of strong, even authoritarian leadership to preserve their way of life.

This divergence in emotional response is not without consequences. As observed in various studies and polls, more than twice as many Republicans as Democrats believe that extreme measures, including violence, might be necessary to protect the nation from its leaders. This growing acceptance of force and the bending of democratic norms and institutions reflect a profound shift in conservative sentiment, fueled by perceived threats to their traditional values and way of life.

The implications of these ideological and psychological divides extend beyond mere political debates to affect the very fabric of individual well-being. Scholars like Jamin Halberstadt and Timothy A. Judge argue that a focus on systemic injustices and an external locus of control can significantly dampen happiness and self-esteem. Liberals, with their emphasis on the collective and the structural, might find themselves feeling powerless and disillusioned, while conservatives, with their focus on individualism and personal accountability, maintain a more optimistic and empowered outlook.

Moreover, the phenomenon of concept creep, as discussed by Nick Haslam, illustrates another layer of complexity. This expansion of definitions around harm and abuse, often driven by liberal ideologies, has increased sensitivity to various issues, which while raising awareness, also intensifies feelings of vulnerability and injustice. This heightened sensitivity can lead to an atmosphere where free speech and expression are more heavily scrutinized, further complicating the landscape of political and social discourse.

In conclusion, the happiness gap between conservatives and liberals is a multifaceted issue that reflects deeper ideological beliefs and psychological orientations. While conservatives may find comfort in a worldview that sees the social order as just and self-determined, liberals' commitment to challenging this order and addressing systemic injustices, though noble, may paradoxically contribute to their own discontent. This dynamic interplay between ideology and well-being underscores the profound impact of our political beliefs on our personal lives, shaping not only how we view the world but also how we experience it.

 

 

Age of Revolutions

I've finished reading through Fareed Zakaria’s recent magisterial book: “Age of Revolutions Progress and Backlash from 1600 to the Present.” I was so impressed with the concluding pages of the book, titled "The Infinite Abyss" that I have re-read it several times, and was about to attempt a summary of its main points before checking and finding, sure enough, that Chat GPT4 and Anthropic Claude saved me at least an hour of work by performing their quite adequate responses to my prompt: "Writing as professional historian please write an approximately 1000 word summary of the conclusion of Fareed Zakaria’s recent book..." Claude generated 466 words, and ChatGPT 622. I pass on this slightly edited version of Claude's response: 

Zakaria reflects on how revolutions and rapid social change, despite bringing progress, have also generated widespread anxiety and backlash throughout history. Even the most successful revolutions like those in the Netherlands, Britain, and America faced deep resistance. The failed French Revolution instilled a lasting fear of radical change.  

Today, this dynamic plays out with populist leaders like Trump, Orban, and Bolsonaro pushing back against globalization and liberal values out of a desire to reassert national culture. However, Zakaria argues this crisis of global liberalism stems from rapidly transforming societies upending traditional ways of life, leaving many feeling unmoored despite material progress.  

Freedom and autonomy have come at the expense of eroding the authority and traditions that once bound communities together. This vacuum has allowed populism, nationalism, and authoritarianism to offer an "escape from freedom" by subjugating the individual to a higher cause like nation or religion. But Zakaria doubts such ideologies can truly fill the "hole in the heart" created by modernity, as human beings still fundamentally desire freedom for themselves.  

The liberal democratic state avoided the pitfalls of revolutionary excess and top-down imposition of change by gradually reforming within the fabric of society. Change unfolded more organically rather than being abruptly imposed. Zakaria calls for continuing this approach - persuading and educating people rather than forcing change upon them.  

He argues both the left and right have become susceptible to illiberal revolutionary impulses - the right's outright rejection of liberal democracy, and the left's desire to discard liberalism's rules like free speech to immediately achieve their vision of justice. Zakaria warns such extremism, convinced of its virtue, inevitably leads to coercion, violence and destruction.  

Instead, Zakaria advocates gradual, compromising reform that takes into account the whole of society. He points to past examples where conservatives eventually accepted previous liberal advances they had opposed. Like the British Conservatives after 1832 who made peace with democratization under the idea "reform, that you may preserve." Today, Biden has modestly pivoted to address some populist economic concerns to preserve the overall liberal project.  

Ultimately, Zakaria argues liberalism must imbue itself with moral meaning and instill a sense of pride to fill the void left by eroding tradition and religion. Its ideals of freedom, rights, and democracy should inspire the same passion as past causes that built civilizations. Without recovering that confidence in its driving vision, modern liberal civilization risks declining into cynicism and disillusion, paving the way for a new "dark ages" as its predecessors did when they lost faith in their animating principles.  

Human history's greatest tradition liberated the human spirit and mind. Zakaria urges not losing sight of its unfinished project amid the inevitable struggles of politics and social change. Liberalism's greatest achievements, he asserts, still lie ahead if its sublime vision can be reclaimed.  

Evolutionarily conserved neural responses to affective touch transcend consciousness and change with age

Interesting work from Charbonneau et al. in macaque monkeys on the affective (gentle, pleasant) touch pathways that in humans use a different neural network than pathways of discriminative touch:

Significance

Affective touch is thought to be a critical substrate for the formation of the social relationships which exist as a foundation for primate societies. Although grooming behavior in monkeys appears to recapitulate features of affective touch behavior in humans, it is unknown whether affective touch activates the same neural networks in other primate species and whether this activation requires conscious perception or changes across the lifespan. We stimulated lightly anesthetized macaques at affective (slow) and discriminative (fast) touch speeds during the acquisition of functional MRI data. We demonstrate evolutionarily conserved activation of interoceptive neural networks which change significantly in old age.

Abstract

Affective touch—a slow, gentle, and pleasant form of touch—activates a different neural network than which is activated during discriminative touch in humans. Affective touch perception is enabled by specialized low-threshold mechanoreceptors in the skin with unmyelinated fibers called C tactile (CT) afferents. These CT afferents are conserved across mammalian species, including macaque monkeys. However, it is unknown whether the neural representation of affective touch is the same across species and whether affective touch’s capacity to activate the hubs of the brain that compute socioaffective information requires conscious perception. Here, we used functional MRI to assess the preferential activation of neural hubs by slow (affective) vs. fast (discriminative) touch in anesthetized rhesus monkeys (Macaca mulatta). The insula, anterior cingulate cortex (ACC), amygdala, and secondary somatosensory cortex were all significantly more active during slow touch relative to fast touch, suggesting homologous activation of the interoceptive-allostatic network across primate species during affective touch. Further, we found that neural responses to affective vs. discriminative touch in the insula and ACC (the primary cortical hubs for interoceptive processing) changed significantly with age. Insula and ACC in younger animals differentiated between slow and fast touch, while activity was comparable between conditions for aged monkeys (equivalent to >70 y in humans). These results, together with prior studies establishing conserved peripheral nervous system mechanisms of affective touch transduction, suggest that neural responses to affective touch are evolutionarily conserved in monkeys, significantly impacted in old age, and do not necessitate conscious experience of touch.

Collective behavior from surprise minimization

A fascinating model for collective behavior from Heins et al.:

Significance

We introduce a model of collective behavior, proposing that individual members within a group, such as a school of fish or a flock of birds, act to minimize surprise. This active inference approach naturally generates well-known collective phenomena such as cohesion and directed movement without explicit behavioral rules. Our model reveals intricate relationships between individual beliefs and group properties, demonstrating that beliefs about uncertainty can shape collective decision-making accuracy. As agents update their generative model in real time, groups become more sensitive to external perturbations and more robust in encoding information. Our work provides fresh insights into understanding collective dynamics and could inspire strategies in the study of animal behavior, swarm robotics, and distributed systems.

Abstract

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and “social forces” such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference—without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.

How the US is destroying young people's future.

When I wake up in the morning, I frequently remind myself to be grateful for the luck of having been born in 1942, and being able to ride the crest of a number of fortunate external circumstances that made my generation vastly better off than those who followed. I was in high school in the late 50s when Sputnik happened, fueling a huge increase in federal research funding that, powered my laboratory research career how our vision works. Both my parents and myself were clients of state governments or universities that offered generous retirement plans and pensions, and and the ability to set aside tax deferred money to invest for later years.

This has lead to the situation succinctly described in the following TED video done by Scott Galloway, who teaches at NYU, transcript of talk is here.  (It was sent to me by my 49 year old son, an senior E-commerce digital solutions architect, whose expectations about the future are vastly more modest than mine were when I was his age.) One of the most striking graphics in the video shows how the increase in household wealth of those 70 and older has increased by 11% since 1989, while it has decreased by 5% for those under 40. 

 

Blueprint - Nicholas Christakis on the evolutionary origins of a good society

This opinion piece by Frank Bruni in the NYTimes motivated me to download and read Nicholas Christakis' Magnum Opus “Blueprint” (very much in the 'everything you need to know about humans' spirit of Sapolsky's "Behave" and Harari's "Sapiens," and "Homo Deus," and "21 Lessons," all books that I have made the subject of previous posts.). It echoes Pinker's emphasis on the more positive aspects of human nature and progress. It is a very engaging read, and not amenable to a simple summary, but here is a bit from his introduction:

How can people be so different from—even go to war with—one another and yet also be so similar? The fundamental reason is that we each carry within us an evolutionary blueprint for making a good society.

Genes do amazing things inside our bodies, but even more amazing to me is what they do outside of them. Genes affect not only the structure and function of our bodies; not only the structure and function of our minds and, hence, our behaviors; but also the structure and function of our societies. This is what we recognize when we look at people around the world. This is the source of our common humanity.

Natural selection has shaped our lives as social animals, guiding the evolution of what I call a “social suite” of features priming our capacity for love, friendship, cooperation, learning, and even our ability to recognize the uniqueness of other individuals. Despite all the trappings and artifacts of modern invention—our tools, agriculture, cities, nations—we carry within us innate proclivities that reflect our natural social state, a state that is, as it turns out, primarily good, practically and even morally. Humans can no more make a society that is inconsistent with these positive urges than ants can suddenly make beehives.

I believe that we come to this sort of goodness just as naturally as we come to our bloodier inclinations. We cannot help it. We feel great when we help others. Our good deeds are not just the products of Enlightenment values. They have a deeper and prehistoric origin. The ancient tendencies that form the social suite work together to bind communities, specify their boundaries, identify their members, and allow people to achieve individual and collective objectives while at the same time minimizing hatred and violence. For too long, in my opinion, the scientific community has been overly focused on the dark side of our biological heritage: our capacity for tribalism, violence, selfishness, and cruelty. The bright side has been denied the attention it deserves.

(The above is a repost of MindBlog's 6/3/19 post)

Another Big History - why the West is WEIRD (Western, educated, industrialized, rich, democratic)

Alas, I usually end up reading reviews of books rather than the books themselves. Here I want to pass on clips from Shulevitz's review in The Atlantic: of Joseph Henrich's theory-of-everything type book: "The WEIRDest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous." Henrich directs Harvard’s Department of Human Evolutionary Biology.

Consider this the latest addition to the Big History category, popularized by best sellers such as Jared Diamond’s Guns, Germs, and Steel: The Fates of Human Societies and Yuval Noah Harari’s Sapiens: A Brief History of Humankind. The outstanding feature of the genre is that it wrangles all of human existence into a volume or two, starting with the first hominids to rise up on their hind legs and concluding with us, cyborg-ish occupants of a networked globe. Big History asks Big Questions and offers quasi-monocausal answers. Why and how did humans conquer the world? Harari asks. Cooperation. What explains differences and inequalities among civilizations? Diamond asks. Environment, which is to say, geography, climate, flora and fauna. Henrich also wants to explain variation among societies, in particular to account for the Western, prosperous kind.

One culture... is different from the others, and that’s modern WEIRD (“Western, educated, industrialized, rich, democratic”) culture. Henrich’s ambition is tricky: to account for Western distinctiveness while undercutting Western arrogance. He rests his grand theory of cultural difference on an inescapable fact of the human condition: kinship, one of our species’ “oldest and most fundamental institutions.”...Higher-order institutions—governments and armies as well as religions—evolved from kin-based institutions...The Catholic Church changed all that. As of late antiquity, Europeans still lived in tribes, like most of the rest of the world. But the Church dismantled these kin-based societies with what Henrich calls its “Marriage and Family Program,..it meant quashing pagan practices such as polygamy, arranged marriages (Christian matrimony was notionally consensual, hence the formula “I do”), and above all, marriages between relatives, which the Church was redefining as incest.. ..Forced to find Christian partners, Christians left their communities. Christianity’s insistence on monogamy broke extended households into nuclear families. The Church uprooted horizontal, relational identity, replacing it with a vertical identity oriented toward the institution itself...Formerly, property almost always went to family members. The idea now took hold that it could go elsewhere. At the same time, the Church urged the wealthy to ensure their place in heaven by bequeathing their money to the poor—that is, to the Church, benefactor to the needy...The Church, thus enriched, spread across the globe...Loosened from their roots, people gathered in cities. There they developed “impersonal prosociality”—that is, they bonded with other city folk. They wrote city charters and formed professional guilds. Sometimes they elected leaders, the first inklings of representative democracy.

Why, if Italy has been Catholic for so long, did northern Italy become a prosperous banking center, while southern Italy stayed poor and was plagued by mafiosi? The answer, Henrich declares, is that southern Italy was never conquered by the Church-backed Carolingian empire. Sicily remained under Muslim rule and much of the rest of the south was controlled by the Orthodox Church until the papal hierarchy finally assimilated them both in the 11th century. This is why, according to Henrich, cousin marriage in the boot of Italy and Sicily is 10 times higher than in the north, and in most provinces in Sicily, hardly anyone donates blood (a measure of willingness to help strangers), while some northern provinces receive 105 donations of 16-ounce bags per 1,000 people per year.

Henrich’s most consequential—and startling—claim is that WEIRD and non-WEIRD people possess opposing cognitive styles. They think differently. Standing apart from the community, primed to break wholes into parts and classify them, Westerners are more analytical. People from kinship-intensive cultures, by comparison, tend to think more holistically.

Henrich is more persuasive when applying his theory of cumulative culture to the evolution of ideas. Democracy, the rule of law, and human rights “didn’t start with fancy intellectuals, philosophers, or theologians,” Henrich writes. “Instead, the ideas formed slowly, piece by piece, as regular Joes with more individualistic psychologies—be they monks, merchants, or artisans—began to form competing voluntary associations” and learned how to govern them. Toppling the accomplishments of Western civilization off their great-man platforms, he erases their claim to be monuments to rationality: Everything we think of as a cause of culture is really an effect of culture, including us.

 

(The above is a repost of MindBlog's 1/18/21 Post)

Are we the cows of the future?

One of the questions posed by Yuval Harari in his writing on our possible futures is "What are we to do with all these humans who are, except for a small technocratic elite, no longer required as the means of production?" Esther Leslie, a professor of political aesthetics at Birkbeck College, University of London, does an essay on this issue, pointing out that our potential futures in the pastures of digital dictatorship — crowded conditions, mass surveillance, virtual reality — are already here. You should read her essay, and I passon just a few striking clips of text:

...Cows’ bodies have historically served as test subjects — laboratories of future bio-intervention and all sorts of reproductive technologies. Today cows crowd together in megafarms, overseen by digital systems, including facial- and hide-recognition systems. These new factories are air-conditioned sheds where digital machinery monitors and logs the herd’s every move, emission and production. Every mouthful of milk can be traced to its source.

And it goes beyond monitoring. In 2019 on the RusMoloko research farm near Moscow, virtual reality headsets were strapped onto cattle. The cows were led, through the digital animation that played before their eyes, to imagine they were wandering in bright summer fields, not bleak wintry ones. The innovation, which was apparently successful, is designed to ward off stress: The calmer the cow, the higher the milk yield.

A cow sporting VR goggles is comedic as much as it is tragic. There’s horror, too, in that it may foretell our own alienated futures. After all, how different is our experience? We submit to emotion trackers. We log into biofeedback machines. We sign up for tracking and tracing. We let advertisers’ eyes watch us constantly and mappers store our coordinates.

Could we, like cows, be played by the machinery, our emotions swayed under ever-sunny skies, without us even knowing that we are inside the matrix? Will the rejected, unemployed and redundant be deluded into thinking that the world is beautiful, a land of milk and honey, as they interact minimally in stripped-back care homes? We may soon graze in the new pastures of digital dictatorship, frolicking while bound.

Leslie then describes the ideas of German philosopher and social critic Theodor Adorno:

Against the insistence that nature should not be ravished by technology, he argues that perhaps technology could enable nature to get what “it wants” on this sad earth. And we are included in that “it.”...Nature, in truth, is not just something external on which we work, but also within us. We too are nature.

For someone associated with the abstruseness of avant-garde music and critical theory, Adorno was surprisingly sentimental when it came to animals — for which he felt a powerful affinity. It is with them that he finds something worthy of the name Utopia. He imagines a properly human existence of doing nothing, like a beast, resting, cloud gazing, mindlessly and placidly chewing cud.

To dream, as so many Utopians do, of boundless production of goods, of busy activity in the ideal society reflects, Adorno claimed, an ingrained mentality of production as an end in itself. To detach from our historical form adapted solely to production, to work against work itself, to do nothing in a true society in which we embrace nature and ourselves as natural might deliver us to freedom.

Rejecting the notion of nature as something that would protect us, give us solace, reveals us to be inextricably within and of nature. From there, we might begin to save ourselves — along with everything else.

(The above is a repost of MindBlog's 1/7/21 post)