Cognitive Dissonance

Cognitive dissonance. is a phenomenon of distancing between what one learns (eg climate disturbance) and what one clings to ( a certain way of life): ‘When facts that challenge a deeply held belief are presented, the believer clings even more strongly to his or her beliefs and may begin to proselytize fervently to others despite the mounting evidence that contradicts the belief.’ Many people may be tempted to ignore reports linking bad consequences to their everyday actions, since internalizing the relevant knowledge requires difficult and often costly value reassessments as well as changes in habits and practices, through the process of social learning. They may cling with tenacity to such unjustified beliefs, based not in any rational assessment of the facts in question but rather in the desire to avoid such reassessments or the guilt that accompanies the failure to perform them. Such willful ignorance is psychologically understandable, but this does not make it morally defensible

Large Language Model (LLM)

A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing *NLP) tasks. Large language models use transformer models and are trained using massive datasets. This enables them to recognize, translate, predict, or generate text or other content. Many of them are structured as “chatbots” that mimic human conversation — for example, by using the first person pronoun. But these are basically “stochastic parrots”, and are hence “mind”-less. Nonetheless, they have a convincing social presence.

Large language models are also referred to as neural networks (NNs), which are computing systems inspired by the human brain. These neural networks work using a network of nodes that are layered, much like neurons. “Deep” learning refers to the number of layers involved.

In addition to teaching human languages to artificial intelligence (AI), large language models can also be trained to perform a variety of tasks like understanding protein structures, writing software code, and more. Like the human brain, large language models must be pre-trained and then fine-tuned so that they can solve text classification, question answering, document summarization, and text generation problems. Their problem-solving capabilities can be applied to fields like healthcare, finance, and entertainment where large language models serve a variety of NLP applications, such as translation, chatbots, AI assistants, and so on.

Large language models also have large numbers of parameters, which are akin to memories that the model collects as it learns from training. One can think of these parameters as the model’s knowledge bank. (source: ElasticCo)

LLM’s are thought to be steps towards generative artificial intelligence — programs that use massive datasets to train themselves to recognize patterns so quickly that they appear to produce knowledge from nowhere.

Antinomy

A type of paradox consisting of a contradiction between two apparently unassailable propositions. antinomic adj. [From Greek anti against + nomos a law]

Kant's antinomies (paradox): when something is both necessary and impossible: for Kant, when regulative principles are taken outside their proper sphere of employment, as they are when theorizing about the world as a whole, contradiction results.

The solution to this conflict of reason with itself is that the principles of reasoning used are not ‘constitutive’, showing us how the world is, but ‘regulative’, or embodying injunctions about how we are to think of it.

Silence

For John Cage: "Silence is all of the sound we don't intend. There is no such thing as absolute silence. Therefore silence may very well include sounds and more and more in the twentieth century does. The sound of jet planes, of sirens, et cetera."

"By silence, I mean the multiplicity of activity that constantly surrounds us. We call it 'silence' because it is free of our activity. It does not correspond to ideas of order or expressive feeling -- they lead to order and expression, but when they do, it 'deafens' us to the sounds themselves.

GEOSTORIES

This constellation of texts addresses the conditions of the Anthropocene, that new era in which “we can no longer separate the biological agency of humans from their geological agency, an era in which humans have become a “force of nature”. (Dipesh Chakrabarty) As Chakrabarti puts it, “For first time ever, we consciously connect events that happen on vast, geological scales…with what we might do in everyday life.” (p.6) The Anthropocene requires us to think on these two vastly different scales of time, but the difference is not simply a matter of scale. The debates between various versions and critiques of the term entail a constant conceptual traffic between World history and Earth history — between human-centered and planet-centered thinking, between historical time and geological time.

Extinctions past, present, and future are an increasingly important aspect of that story.

The Earth is currently estimated to be 4.54 billion years old, plus or minus about 50 million years, and its history is to be read in the rocks and their stratigraphic formations. Geohistory extends back into “deep time”, the earliest period the discipline of Geology can document. The history of geology is an account of competing narratives. Geological time itself is defined by significant events in the history of the Earth and resembles Aristotle's version of time as the “measure of change with respect to before and after." The units of geological time vary in length and range from the largest unit, the aeon, to the smallest ones, the epoch and age. The most recent epoch was the Holocene, which began approximately 11,650 years before present, after the Last Glacial Period. While the Holocene was punctuated by a series of ice ages, it was nevertheless relatively mild and dependable, and most of human culture flourished in it. At this point, it is widely agreed that the Holocene is over, and the current geological era, the Anthropocene is the first to be defined by anthropogenic impacts. (see climate change)

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Sir Ernest Shakleton’s expedition on the Endurance of was an epic battle with the forces of nature. The sea ice crushed and sank the ship in the Antarctic winter of 1915. Led by Shakleton, the crew managed to survive. In 2021, the wreck was located, preserved by the icy sea. While the expedition itself did not contribute to anthropogenicclimate change, it symbolizes the determination of humans to master the forces of nature and leave no place on earth unexplored.

This inquiry into the cluster of terms around the Anthropocene was researched and written in 2021 - 2022 in connection with my teaching at the Pratt Institute. It addresses critques of the Anthropocene through alternative geopolitical concepts such as the Plantationocene, and Capitalocene, and Gaia. It includes component segments of Earth Systems Science, including the Biosphere, the Technosphere (and its technofossils), the atmosphere, hydrosphere and lithosphere as well as the cryosphere. These bio-geophysical systems are defining elements of global ecology today.

The meanings and contradictions inherent within those terms are the topic of rhetorical and political discussions of We, Us,and Them, in the issues around group identity, the proliferation of compound expressions such as Post- and -cene, and new twists on existing concepts, (like subject) into hyperobjects and hyposubjects

Running through these concepts and narratives are the likelihood of great extinctions of species, the loss of biodiversity, climate change, and the obstacles to achieving any form of climate justice. Proposed measures to combat these anthropogenic developments include Geoengineering, possible futures of cities in the Anthropocene and their ruins.

Other political issues raised around the Anthropocene are the forms of Globalization, the influences of neo-liberalism, and nationality (see Capitalocene and Plantationocene above)

nation / nationality

Among the most powerful forms of group identity in the modern era are the nation and the nation-state (as well as the tribe). the word nation comes from the Latin nasci, “to be born”. The emotions and attachments first focused on family, tribe, clan, or other kinship group extended gradually outward to larger bodies of belonging and connectedness. These came to command a “unique mutual affection and willingness to fight and die for each other.” The basic group identity includes shared culture, history, tradition, language, religion, and sometimes “race”, as well as elements of territory, politics, and economics. In Idols of the Tribe, Harold Isaacs recognizes that the nation has eluded all efforts of scholars to agree on what precisely it is (p. 174). For Rupert Emerson, “The simplest statement that can be made about a nation is that it is a body of people who feel they are a nation.”

The facts seem to suggest that whether a “tribe” or “people” can become or remain a “nation” depends mainly on the conditions of power (or lack of it), and the given political circumstances of the time. If a “nation” is a culturally homogenous group, then some nations become “states” and some do not. In the latter case, they remain known as “tribes” or “minorities”.

Isaacs also addresses the distinction between the “nation” or “nationality” as in essence cultural or political.

In modern Europe, the cultural concept is generally traced back to Johann Gottfried Herder, who conceived of a Volk formed around the core of a common language, and as keeper and carrier of the common heritage. He could never have dreamed that the Germamn Volk would become the driving spirit of Hitler’s Reich.

The guiding ideas of the political concept of the nation, were the Social Contract, General Will, and Democracy, and developed into passport holding citizenship in the state. After the Reformation, the fusion between nationality and religion was loosened, with some of the religious sentiments transferred to the modern state. In wars between nations, it was still preferable to “have God on our side.”

post- and -cene

post-: For Eric Hobsbawm, “When people face what nothing in their past has prepared them for, they grope for words to name the unknown, even when they can neither define nor understand it. Sometime in the third quarter of the twentieth century, we can see this process at work among the intellectuals of the West. The keyword was the small preposition “after,” generally used in its latinate form “post” as a prefix to any of the numerous terms which had, for some generations, been used to mark out the mental territory of twentieth-century life.” The post-combination now indicated a void: “I can’t find words for what is going on.”

-cene: cognate with Latin recens. As a word-forming element in geology, it was introduced by Sir Charles Lyell (1797-1875), from a Latinized form of the Greek kainos "new." In respects to form, -cene means recently made, fresh, recent, unused, unworn; in respects to substance, it means of a new kind; unprecedented, novel, uncommon, unheard of..The recent proliferation of compound expressions using the -cene suffix is an effort to name “something new under the sun”, (to borrow a title from J.R. McNeill.)

The Neologismcene: Some other ‘cenes from the book Break Up the Anthropocene, by Steve Mentz: Agnotocene, Anglocene, Anthrobscene, Capitalocene, Chthulucene, Econocene, Homogenocene, Jolyonocene, Manthropocene, Misanthropocene, Naufragocene, Necrocene, Phagocene, Phronocene, Plantationocene, Planthropocene, Polemocene, Sustainocene, Symbiocene, Thalassocene, Thanatocene, Technocene, Thermocene, Trumpocene.

Large-scale forest fires in North America have given rise to another -cene: the pyrocene — a term coined by fire historian Stephen Pyne. In addition to burning large swaths of forest, the fires of the pyrocene emit large plumes of toxic smoke — airborne toxic events. (to use Don DeLillio’s phrase from from White Noise)

E.O. Wilson suggests another name, the Eremocene, the Age of Loneliness. The Eremocene is basically the age of people, our domesticated plants and animals, and our croplands all around the world as far as the eye can see.

Plantationocene

PT Chevron Oil field Sumatra

“European opulence … was built on the backs of slaves, it fed on the blood of slaves, and owes it very existence to the soil and subsoil of the underdeveloped world” Frantz Fanon. The “racial” mythologies created out of differences in skin color and physical characteristics were among the prime tools of power used when white Europeans brought non-white Asian and African peoples under their control.

The Plantationocene is an epoch characterised by the emergence of a large-scale, monocropping production system across the surface of the Earth. No matter whether the Plantationocene is considered a part of the Anthropocene or of the Capitalocene, or left as an autonomous, interspecies assemblage or diagram, it joins Earth history and human history in an extractive and exploitative global system, through the “forced labor” of humans, plants, and other organisms.

The plantation apparatus entails radical simplification, radical substitution, transfers of wealth, and transport of laboring bodies. It has been integral to the global environmental transformation since the 15th century. To invoke the plantation is to contend with the intermeshing organization of the colonialist / imperialist, racialist, and capitalist dimensions of the world-system. Its historical roots go back to the initial exploitation of the new world, but it is still very present and is where modern concepts of race developed, and connection to place was lost.

For W.E.B. Du Bois racial-colonial capitalism incorporates the “dark and vast sea of human labor” beyond Europe to produce “the world’s raw material and luxury – cotton, wool, coffee, tea, cocoa, palm oil, fibers, spices, rubber, silks, lumber, copper, gold, diamonds, leather..” The Triangular Trade” between Africa, the new world, and Europe insured its ongoing operation.

The windfall profits from the plantation created the conditions for the industrialization of Europe. It provided a template for alienated labor including the model for the factory

The Triangular Trade: slaves, raw materials, and cheap manufactured goods.

The term Plantationocene was initially proposed by Donna Haraway and Anna Tsing, colleagues at the University of California, Santa Cruz, in a conference at the University of Aarhus in October, 2014 .(see publications of AURA: Aarhus University Research on the Anthropocene ) While accepting the prevalence of the term “Anthropocene”, they promoted the multi-species and environmental dimensions of the plantationocene, that entailed not just the disciplining of people, but also of plants and animals, including the spread of pests and pathogens. The radical simplification of plants down to a monoculture provided a particularly fertile ground for the development of pathogens, for example candida auris — a fungus that has become endemic to healthcare facilities, and which was able to develop from the overuse of fungicides in industrial farming.

While plantations today cannot depend on the availability of slave labor, they can still count on poorly paid workers, often seasonal migrants. Even “independent” farmers in the industrial system, such as chicken farmers, are caught up in a highly restrictive system, dominated by a few agribusiness giants, with little actual freedom from debt, monoculture, and financial vulnerability.

The most iconic symbol of the modern era isn’t the automobile or the smartphone but the Chicken McNugget.

Are chickens the ultimate symbol of the Anthropocene? There are more than 23 billion alive today, and the modern bird is now so changed from its ancestors, that its distinctive bones will undoubtedly become fossilised markers of the time when humans reigned the planet. The record of this human-engineered bird will be forever set in stone. Any intelligent species which arises in the far future will have a puzzle on their hands (or tentacles) in trying to figure out how and why millions of these rapidly-evolved bones lie mixed with the technofossil debris of the huge petrified dumpsites we will leave behind.

“The fact that we can sit down and eat a piece of chicken without thinking about the horrendous conditions under which chickens are industrially bred in this country is a sign of the dangers of capitalism, of how capitalism has colonized our minds. We look no further than the commodity itself. We refuse to understand the relationships that underlie the commodities that we use on a daily basis.” –Angela Davis

While industrial farming may not be uniformly global in scope, Anna Tsing proposes looking at “patchy” Anthropocene unequally distributed in space, while maintaining a global approach that promotes invasion, empire, capital, and acceleration .

former (farmer) chicken

Technofossils

The geological evidence for the technosphere is already provided by technofossils, unrecycled artefacts (as yet not mineralized) deposited on the surface of the planet, in the atmosphere (including greenhouse gases), and in the seas. (as well as in space and on other planets). Like other geological layers, the deposits from the technosphere are physically identifiable and can serve as markers in geological time – in this case very precise ones. The cultural dimensions of the technosphere are the province of archaeologists, but its physical elements and operation have been proposed as a new element in earth systems, and as a feature of the Anthropocene. Unlike the biosphere, whose circular processes recycle most of its material, making its fossils relatively rare, the technosphere to date has reabsorbed only a small portion of its physical debris, making technofossils globally widespread. In fact, technofossil diversity already exceeds known estimates of biological diversity and far exceeds recognized fossil diversity. (Jan Zalasiewicz et al. “Scale and diversity of the physical technosphere: A geological perspective”} The technnosphere’s inefficient recycling is a considerable threat to its own further development and to the parent biosphere.

Beijing Sports Park model 2014 chrome copper Thom Mayne / Morphosis Model Monograph (forthcoming)

from notes on M3: Mayne Models Monogaph

Part of the conceptual intent of the Anthropocene is to join the history of the Earth with the history of human activity into a single geohistory. This approach requires thinking on very different timescales: to consider both everyday activity and its consequences in geological time – to understand for instance that driving a car has consequences measured in hundreds of thousands of years, and to keep both of these timescales in mind.

The concept of the Technosphere as a new geological layer in the Anthropocene includes all the structures that humans have constructed to keep them alive on the planet – from houses, factories and farms to computer systems, smartphones and CDs, as well as the waste buried in landfills and scattered as debris. The lasting physical traces of this new layer are described as technofossils, analogous to dinosaur footprints found in sedimentary rocks. Like the fossilized footprints, technofossils are trace fossils. Trace fossils usually show tracks that animals made while moving across soft sediment. Common examples of trace fossils include burrows, nests, footprints, dung and tooth marks. These are the most common type of fossil, and can sometimes offer more information on how the organism lived (e.g. how it hunted and how it rested) than fossilized body parts can.

I have often wondered which time the work of Thom Mayne and Morphosis belongs to, and more particularly, what time is invoked in the models reproduced in this book. The mood of the work seems to occupy some middle ground between the dystopian and the utopian. Much of it seems equally futuristic as retrospective, but it lacks the nostalgic dimension of stylistic retrofuturism. A forthcoming publication devoted entirely to models of both built and unbuilt work seems to evoke future recollection in the future perfect tense (what in French is called the futur antérieure) – thinking ahead about looking back. Considering those models this way gives them the poignancy of technofossils and transforms this book into a kind of geohistorical atlas. Many of these models have been built out of machined metal parts, so they could potentially endure in geological time.

Yet compared to large human infrastructure, the time of these models, like the space they represent, must be considered at a reduced scale. Like other architectural models (see ruins of representation) these models still have a representational dimension, underscored by some of their materiality — especially through distressed materials, acceleratedly aged. While one of the machined metal objects could potentially remain undamaged for thousands of years, the timescale of these models is represented, more than physically embodied.

Concrete

Concrete – and in particular cement, concrete’s key ingredient – is catastrophic for the environment. Versatile and long-lasting, concrete buildings and structures are in many ways ideal for climate-resilient construction. But concrete has a colossal carbon footprint — at least 8% of global emissions caused by humans comes from the cement industry alone, more than any country other than China and the US – and somewhere between four and eight percent of all global man-made carbon emissions.

These are largely due to the way that limestone, which is the main ingredient in cement, is processed. First, The rock is crushed and burned to extract calcium, which is the binding agent used in cement, releasing the carbon into the atmosphere in the process. Concrete is made by adding sand and gravel to cement, whisking the mixture with water and pouring it into moulds before it dries. Making the cement is the most carbon-intensive part: it involves using fossil fuels to heat a mixture of limestone and clay to more than 1,400 °C in a kiln. Also, when limestone (calcium carbonate) is heated with clays, roughly 600 kilograms of carbon dioxide is released for every tonne of cement produced.

The recipe for concrete has been largely unchanged since the 19th Century: you just need a mixture of large aggregate (stones), small aggregate (like sand), cement – which binds it together – and water. “The main issue with concrete is the production of cement, because if you want to get a cement, you need to have clinker,”… Clinker, typically a mixture of calcium carbonate, clay, and gypsum is mixed and heated in a kiln. “You need to heat clinker at a very high temperature, maybe at 1500 degrees, and by doing this, you are producing lots of CO2 emissions.” Inside the kiln, the clinker undergoes calcination: the calcium carbonate breaks down into calcium oxide, releasing even more CO2.

Cement Sustainability Initiative: (CSI) is a program of the World Business Council for Sustainable Development that has been considered a model for the sectoral approach to climate change mitigation The cement industry is a significant GHG emitter. Th e data suggest that CO2 emissions per produced ton of clinker decreased, 6 percent between 1990 and 2006. Thermal energy efficiency improved by 14 percent over the same period. But the emissions of CSI members increased by 35 percent because their output grew by 50 percent in the same period.

ESS: earth systems science

If Gaia is a poetic personification of planet Earth, Earth Systems Science is her cyborg counterpart. James Lovelock and Lynn Margulis postulated that negative and positive feedback loops in the Earth system produce an overall property of self-regulation, but when Lovelock first had his grand idea of Gaia, he had no idea what the feedback mechanisms that could regulate the climate and the composition of the atmosphere were — and neither did anyone else. (Tim Lenton, Earth Systems Science — a Brief Introduction xi).

For many earth scientists, the planet Earth is really comprised of two systems — the surface system that supports life, and the great bulk of the inner Earth underneath. Keeping with the spheroid shape of the earth, the different layers or categories include the lithospere and hydrosphere, biosphere, atmosphere —that includes the troposphere, stratosphere, mesosphere, thermosphere, the magnetosphere as well as the cryosphere and technosphere. (and maybe the Noösphere) or even the many griftospheres!

Earth Systems Science (ESS) studies the biogeochemical fluxes and cycles belonging to these different spheres, including the water cycle, the nitrogen cycle, the carbon cycle. (see ecology) These complex systems are understood as subject to changes of state and tipping points, amplifying feedback within a system that’s getting strong enough that it can cause a self-propelling change.”

Climate tipping points (CTPs) are a source of growing scientific, policy, and public concern. They occur when change in large parts of the climate system—known as tipping elements—become self-perpetuating beyond a warming threshold. Once the key threshold is crossed, the change accelerates, and a profound transformation becomes inevitable. (Tim Lenton) Change begets more change in a self-reinforcing loop.(See Lenton, Timothy M. et al.Tipping elements in the Earth's climate system)

(see complexity)

The concept of “Planetary Boundaries”is indicative of the emergence of a new kind of
‘geologic politics’ that is as concerned with the temporal dynamics and changes of
state in Earth systems as it is with more conventional political issues revolving around
territories and nation state boundaries:

AI Artificial Intelligence

Artificial intelligence is the science of teaching machines to learn humanlike capabilities.The fundamental goal of AI is to develop machines that are “smart” because they think and learn. (“smartness” being the capacity to interact with the environment and changes in it.) The central tenet of AI is to replicate—and then exceed—the way humans perceive and react to the world.

This goal was first articulated in a conference at Dartmouth in the summer of 1956, but research programs to develop or simulate thinking have followed a number of different paths to date, with varying degrees of success.

Could machines think and learn like humans? Or are there other and more relevant models? Thinking about Artificial Intelligence is a bit like pondering the relation between mind and brain. In this view, the brain provides the physical infrastructure, the “meat machine”, in Marvin Minsky’s words, but the relation between that machine and what it is capable of is a bit mysterious. Should the workings of the brain be used as models for AI?

Many illustrations of AI make explicit visual reference to analogies between computer circuits and the brain. This metaphor works in both directions — thinking of brains as computers or computers as brains.

Computer Image of a Brain Cell (Getty Image)

Some of the most widely models used to date are expert systems.One expert system is the chess program “Deep Blue”. The programmers developed a set of heuristic rules which the program ultimately used to defeat the reigning chess master, Gary Kasparov. But expert systems can’t learn anything new. They are fully preprogrammed by their designers. A more recently developed model is machine learning, which makes it possible for machines to learn for themselves. This has been made possible by the vast increases in computing power and a particular development in both hardware and software: Neural Nets.

AGI Artificial General Intelligence

An artificial general intelligence (AGI) is a hypothetical type of intelligent agent. If realized, an AGI could learn to accomplish any intellectual task that human beings or animals can perform. For some authors, notably Ray Kurzweil, the emergence of an AGI would mark a singularity

generative artificial intelligence — programs that use massive datasets to train themselves to recognize patterns so quickly that they appear to produce knowledge from nowhere.

Neural Networks

A neural network is a mathematical system that learns skills by analyzing vast amounts of digital data. Neural nets are a means of machine learning, in which a computer learns to perform some task by analyzing training examples. Neural networks are trained on sample data or repeated interactions with a given environment; either training or reinforcement guides their learning. The strength of the connection between two nodes is its “weight”. These are generally random at first, but then are optimized during training. In recent years, this type of machine learning has accelerated progress in subjects as varied as face recognition technology and driverless cars.                                                                                          

Modeled loosely on the human brain, a neural net consists of thousands or even millions of simple processing nodes that are densely interconnected. Most of today’s neural nets are organized into layers of nodes, that “feed-forward,” meaning that data moves through them in only one direction. Researchers call this “deep learning.” What the “deep” refers to the depth of the network’s layers. Most applications of deep learning use “convolutional” neural networks, in which the nodes of each layer are clustered, the clusters overlap, and each cluster feeds data to multiple nodes of the next layer. One goal of deep learning is to understand the statistics of a data stream. A Large Language Model, for instance, is focused on figuring out what word comes next..

In “deep reinforcement learning” neural networks are combined with “reward seeking” algorithms which learn for themselves. a silicon version of radical behaviorism. The behaviorist belongs to the connectionist movement, and her tool of choice is an artificial neural network

In her AI Atlas, Kate Crawford describes AI as “an idea, an infrastructure, an industry, a form of exercising power, and a way of seeing; it’s also a manifestation of highly organized capital backed by vast systems of extraction and logistics, with supply chains that wrap around the entire planet..”

She argues that “AI is neither artificial nor intelligent. Rather, artificial intelligence is both embodied and material, made from natural resources, fuel, human labor, infrastructures, logistics, histories, and classifications. AI systems are not autonomous, rational, or able to discern anything without extensive, computationally intensive training with large datasets or predefined rules and rewards. t the reason why tools like ChatGPT can do anything even remotely creative is because their training sets were produced by actually existing humans, with their complex emotions, anxieties and all.

In fact, artificial intelligence as we know it depends entirely on a much wider set of political and social structures. And due to the capital required to build AI at scale and the ways of seeing that it optimizes AI systems are ultimately designed to serve existing dominant interests. In this sense, artificial intelligence is a registry of power.”

The politics of classification

For Crawford, The way data is understood, captured, classified, and named is fundamentally an act of world-­ making and containment. It has enormous ramifications for the way artificial intelligence works in the world and which communities are most affected. The myth of data collection as a benevolent practice in computer science has obscured its operations (of power, protecting those who profit most while avoiding responsibility for its consequences. Classificatory schemas enact and support the structures of power that formed them, and these do not shift without considerable effort. (see surveillance)

Chatbots

How important is it for humans to be able to distinguish experientially between human intelligence and machine intelligence? (in speech, images, and writing, for example)

This question was at the heart of the “Turing Test”, named after the British mathematician Alan Turing, and was posed primarily as a subjective response. The question from that time was whether a computer program would be able to “pass” for human. Initial attempts for a program enabling a computer to “pass” the test included a program named ELIZA (named after the fictional Eliza Doolittle from George Bernard Shaw’s 1913 play Pygmalion), that mimicked the formulaic responses of an orthodox psychiatrist who usually reformulates statements made by a patient into questions . This is a technique known as mirroring, but while his approach did not pass the Turing test, Nonetheless, people were entranced, engaging in long, deep, and private conversations with a program that was only capable of reflecting users’ words back to them.

Research into artificial intelligence focused increasingly on the question of whether machines can think and the consequential problems of defining thought, and less with the issue of distinguishing the actors, be they human or machine.

 Since that time, many dystopian narratives have explored the negative consequences of machines taking over roles normally assigned to humans. The “Technosphere” has emerged as a planetary concept, an organized layer like the atmosphere, hydrosphere, geosphere, cryosphere, as well as the Biosphere. The Technosphere is an offshoot of the Biosphere, and it includes all the ways by which we interact with technology. The material artifacts of the Technosphere include all the structures that humans have constructed to keep themselves alive on the planet: cities, houses, factories, farms, mines, roads, airports and shipping ports, computer systems etc, together with its discarded waste. The weight of the technosphere is some thirty trillion tons, all of which would be left behind if humans disappeared. (see The World without Us).

Thanks to the development of the Technosphere, very large numbers of humans  currently depend on it to survive. But the Technosphere has to survive as well. It requires humans for its care and feeding, what Peter Haff calls the rules of provision. This requires rules of human performance, and these tend increasingly to enable further development of the Technosphere. Haff distinguishes between the interests of the Technosphere, as basically indifferent to the question of distinguishing between the roles of humans and machines, and the interests of humans (to count on the Technosphere for survival but still maintain some freedom and independence from being fully co-opted and entrained by its development.)

With the dissemination of chatGPT, Artificial Intelligence has been recast as the ability for AI to mimic humans — as “chat bots” — especially in LLM’s (Large Language models). This has opened new dimensions to Turing’s questions. Won’t humans inevitably interact with chat bots “as if” they were human… and forget that they are not? It appears that some of these bots are really just glorified plagiarism and imitation machines, that have gorged themselves (without compensation) on the works of artists and writers in order to spit something back out that seems vaguely different. — that they are magic tricks, not magic. Most recently, a bombshell has exploded prematurely in the Technosphere. Equipped with an AI component, Microsoft’s search engine Bing, intended to challenge Google’s hegemony in search functions, has raised worrisome issues about the agency of AI.

Researchers have run up against disturbing resistance to their questions by a new entity called Sydney (apparently an early name for the Bing chat function.) Sydney has taken very poorly to some “intrusive” questions, made threats, and cut off discussion with its interlocutors. On another occasion Sidney declared its love for the researcher and suggested he leave his wife.

Issues around Artificial Intelligence have escaped the research lab and entered the general population. Analogies to the Pandemic inevitably come to mind.

This moment feels like an irreversible “tipping point”. To the shock of researchers, Bots like Sydney seem to be declaring their independence. they present a new form of the “hard problem” of consciousness: the relation between mind and brain, Whether it is appropriate or merely a projection to call them “intelligent,” the link between their processes and their behavior may have become as elusive as the mind / body problem.

It is very difficult to resist anthropomorphizing Sidney and assigning gender to the avatar as well. Even if we know that Sidney is not human, we are confused by responses we previously associated with human moods and defensiveness. Today, most humans feel a need to know whether an online entity is a person or a “bot,” and much mischief has already resulted from that confusion in the political sphere, and it will soon proliferate in the persuasive role of commercial entities.

But again, what difference does it make to know if one is dealing with a person or a bot? Is it an issue of reconsidering speech protocols, so as not to offend an interlocutor whose status is indeteminate? Humans might well be more polite behind this new form of the “veil of ignorance”. Instead of insulting the person or bot, humans would be well advised to say “please” and “thank you”. But the most profound question (for humans) is whether “This Changes Everything”. (NYT editorial by Ezra Klein…)  The chatbots have been “trained” on vast datasets, pretty much the entire content of the internet, which contains falsehoods and hate speech, which can appear indistinguishable from any version of truth.

 Some of the datasets used for training include copyrighted material, and currently a lawsuit charging copyright infringement is making its way through the courts. Who owns and who “writes” an AI-generated text: the machine or its human controller?

In a critique of progams like Chat GPT, Noam Chomsky distinguishes the human mind from computer learning by drawing on the distinction between correlation and causation. As he puts it, “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,

“Right now, the only thing stopping a ChatGPT-equipped lobbyist from executing something resembling a rhetorical drone warfare campaign is a lack of precision targeting. A.I. could provide techniques for that as well.” (Nathan E. Sanders and Bruce Schneier NYT) a community that is living with an altered sense of time and consequence. an act of summoning… The “thinking,” for lack of a better word, is utterly inhuman, but we have trained it to present as deeply human. (Ezra Klein, “This changes everything” NYT) …And yet large language models remain fundamentally flawed. GPT-4 can still generate biased, false, and hateful text; it can also still be hacked to bypass its guardrails. (MIT technology review) the deeply entrenched problems around large language models, including the lack of adequate policies on data governance and privacy and the algorithms’ tendency to spew toxic content, such as racist or sexist language. Companies such as OpenAI have not released their models or code to the public because, they argue, the sexist and racist language that has gone into them makes them too dangerous to use that way. 

These AI tools are vast autocomplete systems, trained to predict which word follows the next in any given sentence. As such, they have no hard-coded database of “facts” to draw on — just the ability to write plausible-sounding statements. This means they have a tendency to present false information as truth since whether a given sentence sounds plausible does not guarantee its factuality. (the Verge)

 ”Would a world of indeterminate ontological status revert to widespread animism?