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Blockchain: Fortifying Identity, Finance, and Privacy

The Power of Blockchain Technology

Blockchain technology has emerged as a game-changer in the digital landscape, transforming the way we manage identity, finance, and privacy. At its core, blockchain is a decentralized, immutable, and transparent ledger that enables secure and instant transactions without the need for intermediaries or centralized authorities. This revolutionary technology has the potential to disrupt traditional industries, boost innovation, and empower individuals and communities.

In this article, we will explore how blockchain is fortifying identity, finance, and privacy, and its real-world applications, challenges, and future prospects. We will also discuss the legal, cybersecurity, and social impact implications of blockchain, and how it can contribute to a more equitable and sustainable world.

Blockchain and Identity: A New Era of Digital Identity Management

Identity is a fundamental aspect of our lives, both online and offline. However, traditional identity management systems are often fragmented, insecure, and vulnerable to data breaches and identity theft. Blockchain offers a new paradigm for digital identity management, based on decentralized and self-sovereign identity (SSI) principles.

SSI allows individuals to own, control, and share their identity information securely and selectively, without relying on third-party intermediaries or central authorities. By using blockchain-based identity solutions, individuals can authenticate themselves seamlessly, access services and resources, and protect their privacy and security.

For instance, the Sovrin Network provides a decentralized identity infrastructure that enables trusted and verifiable digital identities, based on open standards and interoperability. Other blockchain-based identity platforms include uPort, Civic, and SelfKey, which offer similar features and benefits.

Blockchain and Finance: Towards a More Transparent and Secure Financial System

Finance is another area where blockchain is making significant strides, by enabling more transparent, efficient, and secure transactions. Blockchain-based finance, also known as decentralized finance (DeFi), is a rapidly growing ecosystem that offers a range of financial services, such as lending, borrowing, trading, and investing, without relying on traditional intermediaries or centralized authorities.

DeFi leverages blockchain’s features, such as smart contracts, tokenization, and interoperability, to provide more accessible and inclusive financial services, especially for underserved and unbanked populations. For example, stablecoins, which are blockchain-based digital currencies pegged to traditional assets, can provide a stable store of value and a more reliable means of exchange, especially in volatile markets.

Other DeFi applications include decentralized exchanges (DEXs), which allow peer-to-peer trading of digital assets without intermediaries, and yield farming, which enables users to earn interest on their crypto holdings by providing liquidity to DeFi protocols. However, DeFi is not without risks, such as smart contract vulnerabilities, liquidity issues, and regulatory challenges.

Blockchain and Privacy: Protecting Personal Data in a Decentralized World

Privacy is a critical aspect of digital life, as it enables individuals to control their personal information and prevent unauthorized access, misuse, or exploitation. However, traditional privacy solutions, such as centralized databases or encryption, have limitations and vulnerabilities that can be exploited by cybercriminals or surveillance agencies.

Blockchain offers a new approach to privacy, based on cryptographic techniques and distributed storage. By using blockchain-based privacy solutions, individuals can protect their data from unauthorized access, maintain anonymity, and ensure data integrity and immutability.

For example, zero-knowledge proofs (ZKPs) are cryptographic protocols that enable parties to prove the validity of a statement without revealing any additional information. ZKPs can be used to authenticate identities, verify transactions, and protect sensitive data without compromising privacy.

Other blockchain-based privacy solutions include homomorphic encryption, ring signatures, and multi-party computation, which offer different levels of privacy and security. However, privacy is not absolute, and there are trade-offs between privacy, usability, and scalability.

How Blockchain Works: The Fundamentals of Distributed Ledgers and Cryptography

To understand how blockchain works, we need to delve into its fundamental principles and components. At its core, blockchain is a distributed ledger that maintains a record of transactions, verified by a network of nodes, without the need for trust or intermediaries.

Each block in the blockchain contains a cryptographic hash of the previous block, creating an immutable and tamper-evident chain of blocks. Transactions are validated and added to the blockchain through consensus mechanisms, such as proof-of-work (PoW) or proof-of-stake (PoS), which incentivize nodes to contribute computing power and verify transactions.

Blockchain also relies on various cryptographic techniques, such as public-key cryptography, hash functions, and digital signatures, to ensure data confidentiality, integrity, and authenticity. These techniques enable secure and transparent transactions, without revealing sensitive information or compromising privacy.

Blockchain technology is not limited to cryptocurrency transactions, but can also be applied to various use cases, such as supply chain management, voting systems, and intellectual property management.

Blockchain Use Cases: Real-World Examples of Blockchain Applications

Blockchain has already demonstrated its potential to transform various industries and domains, from finance and identity to healthcare and energy. Some notable blockchain use cases include:

  • Supply chain management: Blockchain can provide end-to-end visibility and traceability of products, from raw materials to distribution, ensuring authenticity, quality, and compliance.
  • Healthcare: Blockchain can enable secure and interoperable sharing of patient data, as well as tracking of medical supplies and drugs, reducing errors, fraud, and inefficiencies.
  • Energy: Blockchain can facilitate peer-to-peer energy trading, renewable energy certificates, and carbon credits, enabling more sustainable and decentralized energy systems.
  • Gaming: Blockchain can enable secure and transparent ownership, transfer, and trading of in-game assets, as well as provably fair gaming outcomes, enhancing player experience and trust.

These are just a few examples of how blockchain is disrupting traditional industries and enabling new business models and opportunities.

Blockchain Challenges: Overcoming Scalability, Interoperability, and Adoption Hurdles

Despite its potential and benefits, blockchain also faces various challenges and limitations that hinder its widespread adoption and scalability. Some of these challenges include:

  • Scalability: Blockchain’s limited processing power and storage capacity can limit its throughput and transaction speed, especially for large-scale applications.
  • Interoperability: Blockchain’s fragmentation and lack of standardization can hinder its compatibility and integration with other systems and platforms, causing data silos and inefficiencies.
  • Adoption: Blockchain’s complexity and unfamiliarity can deter users and organizations from adopting it, especially in regulated industries or conservative environments.

To overcome these challenges, blockchain developers and researchers are exploring various solutions, such as sharding, sidechains, and interoperability protocols, as well as user-friendly interfaces and educational resources.

The Future of Blockchain: Beyond Cryptocurrencies and Initial Coin Offerings

Blockchain is still at an early stage of development, and its potential is far from fully realized. In the future, blockchain is likely to evolve and expand beyond its current applications and use cases, enabling new forms of value creation, governance, and social impact.

Some possible future developments of blockchain technology include:

  • Decentralized autonomous organizations (DAOs): DAOs are organizations that operate on blockchain-based smart contracts and are governed by their members. DAOs can enable more transparent and democratic decision-making, as well as more efficient and resilient organizations.
  • Internet of Things (IoT): Blockchain can provide secure and decentralized communication and data sharing among IoT devices, enabling more efficient and trustworthy IoT applications, such as smart homes, cities, and factories.
  • Artificial intelligence (AI): Blockchain can enable more secure and transparent training, validation, and deployment of AI models, as well as more accountable and ethical AI systems.

These are just some of the potential future applications of blockchain technology, and the possibilities are limited only by our imagination and creativity.

Blockchain Regulation: Navigating the Legal Landscape of Digital Assets

Blockchain’s decentralized and borderless nature poses significant challenges for regulatory frameworks and compliance measures. However, blockchain also offers opportunities for more efficient and effective regulation, based on transparency, accountability, and innovation.

The regulation of blockchain and digital assets varies across countries and jurisdictions, reflecting different legal, cultural, and economic contexts. Some countries, such as Malta, Switzerland, and Singapore, have adopted blockchain-friendly regulatory frameworks and attracted blockchain startups and investments.

Other countries, such as China and India, have adopted more restrictive policies and regulations, limiting the growth of blockchain and digital assets. However, the global trend is towards more regulatory clarity and convergence, as blockchain becomes more mainstream and recognized as a legitimate technology and asset class.

Blockchain and Cybersecurity: Enhancing Data Protection and Threat Detection

Cybersecurity is a critical aspect of blockchain, as it enables secure and trustworthy transactions and protects users from various threats, such as hacking, phishing, and malware. However, blockchain itself is not immune to cybersecurity risks and vulnerabilities, such as 51% attacks, smart contract bugs, and social engineering.

To enhance blockchain cybersecurity, various measures and solutions are being developed and deployed, such as:

  • Multi-factor authentication: This requires multiple forms of authentication, such as passwords, biometrics, and tokens, to access blockchain accounts and wallets.
  • Cold storage: This refers to storing cryptocurrencies and assets offline, in physical devices or paper wallets, to reduce the risk of online attacks.
  • Anti-money laundering (AML) and know-your-customer (KYC) regulations: These require blockchain-based businesses and exchanges to verify the identity and source of funds of their users, to prevent money laundering and terrorism financing.
  • Cyber threat intelligence (CTI): This involves collecting and analyzing data on cyber threats and vulnerabilities, to proactively detect and prevent attacks on blockchain networks and applications.

Blockchain and Social Impact: Empowering Communities and Reducing Inequality

Blockchain has the potential to contribute to social impact and sustainability goals, by enabling more democratic, transparent, and inclusive systems and applications. Blockchain-based solutions can empower marginalized communities, reduce inequalities, and promote social innovation and entrepreneurship.

For example, blockchain can enable:

  • Financial inclusion: Blockchain-based financial services, such as microlending, can provide access to capital for underserved and unbanked populations, reducing poverty and inequality.
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Exploring the Transhumanist Themes of Ghost in the Shell: Is AI Just a Shell?

The Transhumanist Themes of Ghost in the Shell

Ghost in the Shell, a Japanese manga series written and illustrated by Masamune Shirow, has been a staple of science fiction since its inception in the late 1980s. With a powerful mix of cyberpunk and transhumanist themes, the series has explored the profound implications of artificial intelligence, cyborgs, and human augmentation. In this article, we will delve into the transhumanist themes of Ghost in the Shell and analyze the series’ messages about the future of humanity.

The Evolution of Artificial Intelligence in Ghost in the Shell

One of the most prominent themes in Ghost in the Shell is the evolution of artificial intelligence. The series depicts a world in which AI has become so advanced that it is nearly indistinguishable from human consciousness. The protagonists of the series, members of a cyborg law enforcement unit, must grapple with the ethical implications of creating and interacting with sentient AI.

The Concept of Cyborgs and Augmentation in Ghost in the Shell

Another central theme in Ghost in the Shell is the concept of cyborgs and human augmentation. In this world, it is common for individuals to have cybernetic enhancements that allow them to perform incredible feats of strength, agility, and cognitive ability. However, the series also explores the dark side of this technology, as the line between human and machine becomes increasingly blurred.

The Ethics of Transhumanism in Ghost in the Shell

The ethics of transhumanism are a constant concern in Ghost in the Shell. The series delves into questions about the morality of creating artificial life and the consequences of merging human consciousness with machines. The protagonists must navigate complex ethical dilemmas as they confront the potential dangers of transhumanism.

The Quest for Identity in Ghost in the Shell: Human or Machine?

Ghost in the Shell also explores the quest for identity in a world where the line between human and machine is blurred. The characters struggle to define themselves as either human or machine, and the series raises important questions about what it means to be a conscious being in a world where technology has become so advanced.

The Implications of Consciousness in Ghost in the Shell

The implications of consciousness are a constant concern in Ghost in the Shell. The series explores questions about the nature of consciousness and what it means to be a sentient being. The characters grapple with the possibility that their consciousness may be the result of programming rather than true free will.

The Role of Memories in Shaping Our Identity in Ghost in the Shell

One of the most poignant themes in Ghost in the Shell is the role of memories in shaping our identity. The series explores the idea that our memories are a fundamental part of who we are, and that the loss of memories can be a deeply traumatic experience. The characters must confront the possibility that their memories and identities may be manipulated by external forces, such as artificial intelligence.

The Fear of Losing Humanity in Ghost in the Shell

The fear of losing humanity is a constant theme in Ghost in the Shell. The characters struggle to maintain their humanity as they become increasingly integrated with machines, and the series raises important questions about what it means to be human in a world where technology has become so advanced.

The Boundaries between Real and Virtual Worlds in Ghost in the Shell

Ghost in the Shell also explores the boundaries between real and virtual worlds. The characters must navigate complex virtual environments that are indistinguishable from reality, and the series raises important questions about the nature of reality itself.

The Relevance of Transhumanism in Today’s World: A Reflection on Ghost in the Shell

The themes of transhumanism explored in Ghost in the Shell are more relevant today than ever before. As artificial intelligence and human augmentation become increasingly common, we must grapple with the ethical implications of these technologies and the potential consequences of merging human consciousness with machines.

The Future of Humanity in Ghost in the Shell’s Vision of Transhumanism

Ghost in the Shell presents a vision of the future that is both awe-inspiring and deeply concerning. The series raises important questions about the future of humanity in a world where technology has become so advanced, and the potential consequences of merging human consciousness with machines.

Is AI Just a Shell? Exploring the Transhumanist Themes of Ghost in the Shell

In conclusion, Ghost in the Shell is a powerful exploration of transhumanist themes that raises important questions about the future of humanity. The series presents a vision of the future that is both exhilarating and deeply concerning, and it reminds us that we must grapple with the ethical implications of artificial intelligence, human augmentation, and transhumanism. Ultimately, Ghost in the Shell asks us to consider the question of whether AI is just a shell, or whether it has the potential to become something more.

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Three Thousand Years of Algorithmic Rituals: The Emergence of AI from the Computation of Space

Illustration from Frits Staal, "Greek and Vedic geometry" Journal of Indian Philosophy 27.1 (1999): 105-127.


With topographical memory, one could speak of generations of vision and even of visual heredity from one generation to the next. The advent of the logistics of perception and its renewed vectors for delocalizing geometrical optics, on the contrary, ushered in a eugenics of sight, a pre-emptive abortion of the diversity of mental images, of the swarm of image-beings doomed to remain unborn, no longer to see the light of day anywhere.

—Paul Virilio, The Vision Machine1

1. Recomposing a Dismembered God

In a fascinating myth of cosmogenesis from the ancient Vedas, it is said that the god Prajapati was shattered into pieces by the act of creating the universe. After the birth of the world, the supreme god is found dismembered, undone. In the corresponding Agnicayana ritual, Hindu devotees symbolically recompose the fragmented body of the god by building a fire altar according to an elaborate geometric plan.2 The fire altar is laid down by aligning thousands of bricks of precise shape and size to create the profile of a falcon. Each brick is numbered and placed while reciting its dedicated mantra, following step-by-step instructions. Each layer of the altar is built on top of the previous one, conforming to the same area and shape. Solving a logical riddle that is the key of the ritual, each layer must keep the same shape and area of the contiguous ones, but using a different configuration of bricks. Finally, the falcon altar must face east, a prelude to the symbolic flight of the reconstructed god towards the rising sun—an example of divine reincarnation by geometric means.

The Agnicayana ritual is described in the Shulba Sutras, composed around 800 BCE in India to record a much older oral tradition. The Shulba Sutras teach the construction of altars of specific geometric forms to secure gifts from the gods: for instance, they suggest that “those who wish to destroy existing and future enemies should construct a fire-altar in the form of a rhombus.”3 The complex falcon shape of the Agnicayana evolved gradually from a schematic composition of only seven squares. In the Vedic tradition, it is said that the Rishi vital spirits created seven square-shaped Purusha (cosmic entities, or persons) that together composed a single body, and it was from this form that Prajapati emerged once again. While art historian Wilhelm Worringer argued in 1907 that primordial art was born in the abstract line found in cave graffiti, one may assume that the artistic gesture also emerged through the composing of segments and fractions, introducing forms and geometric techniques of growing complexity. 4In his studies of Vedic mathematics, Italian mathematician Paolo Zellini has discovered that the Agnicayana ritual was used to transmit techniques of geometric approximation and incremental growth—in other words, algorithmic techniques—comparable to the modern calculus of Leibniz and Newton.5 Agnicayana is among the most ancient documented rituals still practiced today in India, and a primordial example of algorithmic culture.

But how can we define a ritual as ancient as the Agnicayana as algorithmic? To many, it may appear an act of cultural appropriation to read ancient cultures through the paradigm of the latest technologies. Nevertheless, claiming that abstract techniques of knowledge and artificial metalanguages belong uniquely to the modern industrial West is not only historically inaccurate but also an act and one of implicit epistemic colonialism towards cultures of other places and other times.6 The French mathematician Jean-Luc Chabert has noted that “algorithms have been around since the beginning of time and existed well before a special word had been coined to describe them. Algorithms are simply a set of step by step instructions, to be carried out quite mechanically, so as to achieve some desired result.”7 Today some may see algorithms as a recent technological innovation implementing abstract mathematical principles. On the contrary, algorithms are among the most ancient and material practices, predating many human tools and all modern machines:

Algorithms are not confined to mathematics … The Babylonians used them for deciding points of law, Latin teachers used them to get the grammar right, and they have been used in all cultures for predicting the future, for deciding medical treatment, or for preparing food … We therefore speak of recipes, rules, techniques, processes, procedures, methods, etc., using the same word to apply to different situations. The Chinese, for example, use the word shu (meaning rule, process or stratagem) both for mathematics and in martial arts … In the end, the term algorithm has come to mean any process of systematic calculation, that is a process that could be carried out automatically. Today, principally because of the influence of computing, the idea of finiteness has entered into the meaning of algorithm as an essential element, distinguishing it from vaguer notions such as process, method or technique.8

Before the consolidation of mathematics and geometry, ancient civilizations were already big machines of social segmentation that marked human bodies and territories with abstractions that remained, and continue to remain, operative for millennia. Drawing also on the work of historian Lewis Mumford, Gilles Deleuze and Félix Guattari offered a list of such old techniques of abstraction and social segmentation: “tattooing, excising, incising, carving, scarifying, mutilating, encircling, and initiating.”9 Numbers were already components of the “primitive abstract machines” of social segmentation and territorialization that would make human culture emerge: the first recorded census, for instance, took place around 3800 BCE in Mesopotamia. Logical forms that were made out of social ones, numbers materially emerged through labor and rituals, discipline and power, marking and repetition.

In the 1970s, the field of “ethnomathematics” began to foster a break from the Platonic loops of elite mathematics, revealing the historical subjects behind computation.10 The political question at the center of the current debate on computation and the politics of algorithms is ultimately very simple, as Diane Nelson has reminded us: Who counts?11 Who computes? Algorithms and machines do not compute for themselves; they always compute for someone else, for institutions and markets, for industries and armies.

Illustration from Frank Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, (Cornell Aeronautical Laboratory, Buffalo NY, 1961).

2. What Is an Algorithm?

The term “algorithm” comes from the Latinization of the name of the Persian scholar al-Khwarizmi. His tract On the Calculation with Hindu Numerals, written in Baghdad in the ninth century, is responsible for introducing Hindu numerals to the West, along with the corresponding new techniques for calculating them, namely algorithms. In fact, the medieval Latin word “algorismus” referred to the procedures and shortcuts for carrying out the four fundamental mathematical operations—addition, subtraction, multiplication, and division—with Hindu numerals. Later, the term “algorithm” would metaphorically denote any step-by-step logical procedure and become the core of computing logic. In general, we can distinguish three stages in the history of the algorithm: in ancient times, the algorithm can be recognized in procedures and codified rituals to achieve a specific goal and transmit rules; in the Middle Ages, the algorithm was the name of a procedure to help mathematical operations; in modern times, the algorithm qua logical procedure becomes fully mechanized and automated by machines and then digital computers.

Looking at ancient practices such as the Agnicayana ritual and the Hindu rules for calculation, we can sketch a basic definition of “algorithm” that is compatible with modern computer science: (1) an algorithm is an abstract diagram that emerges from the repetition of a process, an organization of time, space, labor, and operations: it is not a rule that is invented from above but emerges from below; (2) an algorithm is the division of this process into finite steps in order to perform and control it efficiently; (3) an algorithm is a solution to a problem, an invention that bootstraps beyond the constrains of the situation: any algorithm is a trick; (4) most importantly, an algorithm is an economic process, as it must employ the least amount of resources in terms of space, time, and energy, adapting to the limits of the situation.

Today, amidst the expanding capacities of AI, there is a tendency to perceive algorithms as an application or imposition of abstract mathematical ideas upon concrete data. On the contrary, the genealogy of the algorithm shows that its form has emerged from material practices, from a mundane division of space, time, labor, and social relations. Ritual procedures, social routines, and the organization of space and time are the source of algorithms, and in this sense they existed even before the rise of complex cultural systems such as mythology, religion, and especially language. In terms of anthropogenesis, it could be said that algorithmic processes encoded into social practices and rituals were what made numbers and numerical technologies emerge, and not the other way around. Modern computation, just looking at its industrial genealogy in the workshops studied by both Charles Babbage and Karl Marx, evolved gradually from concrete towards increasingly abstract forms.

Illustration from Frank Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, (Cornell Aeronautical Laboratory, Buffalo NY, 1961).

3. The Rise of Machine Learning as Computational Space

In 1957, at the Cornell Aeronautical Laboratory in Buffalo, New York, the cognitive scientist Frank Rosenblatt invented and constructed the Perceptron, the first operative artificial neural network—grandmother of all the matrices of machine learning, which at the time was a classified military secret.12 The first prototype of the Perceptron was an analogue computer composed of an input device of 20 × 20 photocells (called the “retina”) connected through wires to a layer of artificial neurons that resolved into one single output (a light bulb turning on or off, to signify 0 or 1). The “retina” of the Perceptron recorded simple shapes such as letters and triangles and passed electric signals to a multitude of neurons that would compute a result according to a threshold logic. The Perceptron was a sort of photo camera that could be taught to recognize a specific shape, i.e., to make a decision with a margin of error (making it an “intelligent” machine). The Perceptron was the first machine-learning algorithm, a basic “binary classifier” that could determine whether a pattern fell within a specific class or not (whether the input image was a triangle or not, a square or not, etc.). To achieve this, the Perceptron progressively adjusted the values of its nodes in order to resolve a large numerical input (a spatial matrix of four hundred numbers) into a simple binary output (0 or 1). The Perceptron gave the result 1 if the input image was recognized within a specific class (a triangle, for instance); otherwise it gave the result 0. Initially, a human operator was necessary to train the Perceptron to learn the correct answers (manually switching the output node to 0 or 1), hoping that the machine, on the basis of these supervised associations, would correctly recognize similar shapes in the future. The Perceptron was designed not to memorize a specific pattern but to learn how to recognize potentially any pattern.

The matrix of 20 × 20 photoreceptors in the first Perceptron was the beginning of a silent revolution in computation (which would become a hegemonic paradigm in the early twenty-first century with the advent of “deep learning,” a machine-learning technique). Although inspired by biological neurons, from a strictly logical point of view the Perceptron marked not a biomorphic turn in computation but a topological one; it signified the rise of the paradigm of “computational space” or “self-computing space.” This turn introduced a second spatial dimension into a paradigm of computation that until then had only a linear dimension (see the Turing machine that reads and writes 0 and 1 along a linear memory tape). This topological turn, which is the core of what people perceive today as “AI,” can be described more modestly as the passage from a paradigm of passive information to one of active information. Rather than having a visual matrix processed by a top-down algorithm (like any image edited by a graphics software program today), in the Perceptron the pixels of the visual matrix are computed in a bottom-up fashion according to their spatial disposition. The spatial relations of the visual data shape the operation of the algorithm that computes them.

Because of its spatial logic, the branch of computer science originally dedicated to neural networks was called “computational geometry.” The paradigm of computational space or self-computing space shares common roots with the studies of the principles of self-organization that were at the center of post-WWII cybernetics, such as John von Neumann’s cellular automata (1948) and Konrad Zuse’s Rechnender Raum by (1967).13 Von Neumann’s cellular automata are cluster of pixels, perceived as small cells on a grid, that change status and move according to their neighboring cells, composing geometric figures that resemble evolving forms of life. Cellular automata have been used to simulate evolution and to study complexity in biological systems, but they remain finite-state algorithms confined to a rather limited universe. Konrad Zuse (who built the first programmable computer in Berlin in 1938) attempted to extend the logic of cellular automata to physics and to the whole universe. His idea of “rechnender Raum,” or calculating space, is a universe that is composed of discrete units that behave according to the behavior of neighboring units. Alan Turing’s last essay, “The Chemical Basis of Morphogenesis” (published in 1952, two years before his death), also belongs to the tradition of self-computing structures.14 Turing considered molecules in biological systems as self-computing actors capable of explaining complex bottom-up structures, such as tentacle patterns in hydra, whorl arrangement in plants, gastrulation in embryos, dappling in animal skin, and phyllotaxis in flowers.15

Von Neumann’s cellular automata and Zuse’s computational space are intuitively easy to understand as spatial models, while Rosenblatt’s neural network displays a more complex topology that requires more attention. Indeed, neural networks employ an extremely complex combinatorial structure, which is probably what makes them the most efficient algorithms for machine learning. Neural networks are said to “solve any problem,” meaning they can approximate the function of any pattern according to the Universal Approximation theorem (given enough layers of neurons and computing resources). All systems of machine learning, including support-vector machines, Markov chains, Hopfield networks, Boltzmann machines, and convolutional neural networks, to name just a few, started as models of computational geometry. In this sense they are part of the ancient tradition of ars combinatoria.16

Image from Hans Meinhardt, The Algorithmic Beauty of Sea Shells (Springer Science & Business Media, 2009).

4. The Automation of Visual Labor

Even at the end of the twentieth century, no one would have ever thought to call a truck driver a “cognitive worker,” an intellectual. At the beginning of the twenty-first century, the use of machine learning in the development of self-driving vehicles has led to a new understanding of manual skills such as driving, revealing how the most valuable component of work, generally speaking, has never been merely manual, but also social and cognitive (as well as perceptual, an aspect of labor still waiting to be located somewhere between the manual and the cognitive). What kind of work do drivers perform? Which human task will AI come to record with its sensors, imitate with its statistical models, and replace with automation? The best way to answer this question is to look at what technology has successfully automated, as well as what it hasn’t.

The industrial project to automate driving has made clear (more so than a thousand books on political economy) that the labor of driving is a conscious activity following codified rules and spontaneous social conventions. However, if the skill of driving can be translated into an algorithm, it will be because driving has a logical and inferential structure. Driving is a logical activity just as labor is a logical activity more generally. This postulate helps to resolve the trite dispute about the separation between manual labor and intellectual labor.17 It is a political paradox that the corporate development of AI algorithms for automation has made possible to recognize in labor a cognitive component that had long been neglected by critical theory. What is the relation between labor and logic? This becomes a crucial philosophical question for the age of AI.

A self-driving vehicle automates all the micro-decisions that a driver must make on a busy road. Its artificial neural networks learn, that is imitate and copy, the human correlations between the visual perception of the road space and the mechanical actions of vehicle control (steering, accelerating, stopping) as ethical decisions taken in a matter of milliseconds when dangers arise (for the safety of persons inside and outside the vehicle). It becomes clear that the job of driving requires high cognitive skills that cannot be left to improvisation and instinct, but also that quick decision-making and problem-solving are possible thanks to habits and training that are not completely conscious. Driving remains essentially also a social activity, which follows both codified rules (with legal constraints) and spontaneous ones, including a tacit “cultural code” that any driver must subscribe to. Driving in Mumbai—it has been said many times—is not the same as driving in Oslo.

Obviously, driving summons an intense labor of perception. Much labor, in fact, appears mostly perceptive in nature, through continuous acts of decision and cognition that take place in the blink of an eye.18 Cognition cannot be completely disentangled from a spatial logic, and often follows a spatial logic in its more abstract constructions. Both observations—that perception is logical and that cognition is spatial—are empirically proven without fanfare by autonomous driving AI algorithms that construct models to statistically infer visual space (encoded as digital video of a 3-D road scenario). Moreover, the driver that AI replaces in self-driving cars and drones is not an individual driver but a collective worker, a social brain that navigates the city and the world.19 Just looking at the corporate project of self-driving vehicles, it is clear that AI is built on collective data that encode a collective production of space, time, labor, and social relations. AI imitates, replaces, and emerges from an organized division of social space (according first to a material algorithm and not the application of mathematical formulas or analysis in the abstract).

Animation from Chris Urmson’s, Ted talk “How a Driverless Car Sees the Road.” Urmson is the former chief engineer for Google’s Self-Driving Car Project. Animation by ZMScience

5. The Memory and Intelligence of Space

Paul Virilio, the French philosopher of speed or “dromology,” was also a theorist of space and topology, for he knew that technology accelerates the perception of space as much as it morphs the perception of time. Interestingly, the title of Virilio’s book The Vision Machine was inspired by Rosenblatt’s Perceptron. With the classical erudition of a twentieth-century thinker, Virilio drew a sharp line between ancient techniques of memorization based on spatialization, such as the Method of Loci, and modern computer memory as a spatial matrix:

Cicero and the ancient memory-theorists believed you could consolidate natural memory with the right training. They invented a topographical system, the Method of Loci, an imagery-mnemonics which consisted of selecting a sequence of places, locations, that could easily be ordered in time and space. For example, you might imagine wandering through the house, choosing as loci various tables, a chair seen through a doorway, a windowsill, a mark on a wall. Next, the material to be remembered is coded into discreet images and each of the images is inserted in the appropriate order into the various loci. To memorize a speech, you transform the main points into concrete images and mentally “place” each of the points in order at each successive locus. When it is time to deliver the speech, all you have to do is recall the parts of the house in order.

The transformation of space, of topological coordinates and geometric proportions, into a technique of memory should be considered equal to the more recent transformation of collective space into a source of machine intelligence. At the end of the book, Virilio reflects on the status of the image in the age of “vision machines” such as the Perceptron, sounding a warning about the impending age of artificial intelligence as the “industrialisation of vision”:

“Now objects perceive me,” the painter Paul Klee wrote in his Notebooks. This rather startling assertion has recently become objective fact, the truth. After all, aren’t they talking about producing a “vision machine” in the near future, a machine that would be capable not only of recognizing the contours of shapes, but also of completely interpreting the visual field … ? Aren’t they also talking about the new technology of visionics: the possibility of achieving sightless vision whereby the video camera would be controlled by a computer? … Such technology would be used in industrial production and stock control; in military robotics, too, perhaps.

Now that they are preparing the way for the automation of perception, for the innovation of artificial vision, delegating the analysis of objective reality to a machine, it might be appropriate to have another look at the nature of the virtual image … Today it is impossible to talk about the development of the audiovisual … without pointing to the new industrialization of vision, to the growth of a veritable market in synthetic perception and all the ethical questions this entails … Don’t forget that the whole idea behind the Perceptron would be to encourage the emergence of fifth-generation “expert systems,” in other words an artificial intelligence that could be further enriched only by acquiring organs of perception.20

Ioannis de Sacro Busco, Algorismus Domini, c. 1501. National Central Library of Rome. Photo: Public Domain/Internet Archive. 

6. Conclusion

If we consider the ancient geometry of the Agnicayana ritual, the computational matrix of the first neural network Perceptron, and the complex navigational system of self-driving vehicles, perhaps these different spatial logics together can clarify the algorithm as an emergent form rather than a technological a priori. The Agnicayana ritual is an example of an emergent algorithm as it encodes the organization of a social and ritual space. The symbolic function of the ritual is the reconstruction of the god through mundane means; this practice of reconstruction also symbolizes the expression of the many within the One (or the “computation” of the One through the many). The social function of the ritual is to teach basic geometry skills and to construct solid buildings.21 The Agnicayana ritual is a form of algorithmic thinking that follows the logic of a primordial and straightforward computational geometry.

The Perceptron is also an emergent algorithm that encodes according to a division of space, specifically a spatial matrix of visual data. The Perceptron’s matrix of photoreceptors defines a closed field and processes an algorithm that computes data according to their spatial relation. Here too the algorithm appears as an emergent process—the codification and crystallization of a procedure, a pattern, after its repetition. All machine-learning algorithms are emergent processes, in which the repetition of similar patterns “teach” the machine and cause the pattern to emerge as a statistical distribution.22

Self-driving vehicles are an example of complex emergent algorithms since they grow from a sophisticated construction of space, namely, the road environment as social institution of traffic codes and spontaneous rules. The algorithms of self-driving vehicles, after registering these spontaneous rules and the traffic codes of a given locale, try to predict unexpected events that may happen on a busy road. In the case of self-driving vehicles, the corporate utopia of automation makes the human driver evaporate, expecting that the visual space of the road scenario alone will dictate how the map will be navigated.

The Agnicayana ritual, the Perceptron, and the AI systems of self-driving vehicles are all, in different ways, forms of self-computing space and emergent algorithms (and probably, all of the them, forms of the invisibilization of labor).

The idea of computational space or self-computing space stresses, in particular, that the algorithms of machine learning and AI are emergent systems that are based on a mundane and material division of space, time, labor, and social relations. Machine learning emerges from grids that continue ancient abstractions and rituals concerned with marking territories and bodies, counting people and goods; in this way, machine learning essentially emerges from an extended division of social labor. Despite the way it is often framed and critiqued, artificial intelligence is not really “artificial” or “alien”: in the usual mystification process of ideology, it appears to be a deus ex machina that descends to the world like in ancient theater. But this hides the fact that it actually emerges from the intelligence of this world.

What people call “AI” is actually a long historical process of crystallizing collective behavior, personal data, and individual labor into privatized algorithms that are used for the automation of complex tasks: from driving to translation, from object recognition to music composition. Just as much as the machines of the industrial age grew out of experimentation, know-how, and the labor of skilled workers, engineers, and craftsmen, the statistical models of AI grow out of the data produced by collective intelligence. Which is to say that AI emerges as an enormous imitation engine of collective intelligence. What is the relation between artificial intelligence and human intelligence? It is the social division of labor


Matteo Pasquinelli (PhD) is Professor in Media Philosophy at the University of Arts and Design, Karlsruhe, where he coordinates the research group KIM (Künstliche Intelligenz und Medienphilosophie / Artificial Intelligence and Media Philosophy). For Verso he is preparing a monograph on the genealogy of artificial intelligence as division of labor, which is titled The Eye of the Master: Capital as Computation and Cognition.


Paul Virilio, La Machine de vision: essai sur les nouvelles techniques de representation (Galilée, 1988). Translated as The Vision Machine, trans. Julie Rose (Indiana University Press, 1994), 12.


The Dutch Indologist and philosopher of language Frits Staal documented the Agnicayana ritual during an expedition in Kerala, India, in 1975. See Frits Staal, AGNI: The Vedic Ritual of the Fire Altar, vol. 1–2 (Asian Humanities Press, 1983).


Kim Plofker, “Mathematics in India,” in The Mathematics of Egypt, Mesopotamia, China, India, and Islam, ed. Victor J. Katz (Princeton University Press, 2007).


See Wilhelm Worringer, Abstraction and Empathy: A Contribution to the Psychology of Style (Ivan R. Dee, 1997). (Abstraktion und Einfühlung, 1907).


For an account of the mathematical implications of the Agnicayana ritual, see Paolo Zellini, La matematica degli dèi e gli algoritmi degli uomini (Adelphi, 2016). Translated as The Mathematics of the Gods and the Algorithms of Men (Penguin, forthcoming 2019).


See Frits Staal, “Artificial Languages Across Sciences and Civilizations,” Journal of Indian Philosophy 34, no. 1–2 (2006).


Jean-Luc Chabert, “Introduction,” in A History of Algorithms: From the Pebble to the Microchip, ed. Jean-Luc Chabert (Springer, 1999), 1.


Jean-Luc Chabert, “Introduction,” 1–2.


Gilles Deleuze and Félix Guattari, Anti-Oedipus: Capitalism and Schizophrenia, trans. Robert Hurley (Viking, 1977), 145.


See Ubiratàn D’Ambrosio, “Ethno Mathematics: Challenging Eurocentrism,” in Mathematics Education, eds. Arthur B. Powell and Marilyn Frankenstein (State University of New York Press, 1997).


Diane M. Nelson, Who Counts?: The Mathematics of Death and Life After Genocide (Duke University Press, 2015).


Frank Rosenblatt, “The Perceptron: A Perceiving and Recognizing Automaton,” Technical Report 85-460-1, Cornell Aeronautical Laboratory, 1957.


John von Neumann and Arthur W. Burks, Theory of Self-Reproducing Automata (University of Illinois Press, 1966). Konrad Zuse, “Rechnender Raum,” Elektronische Datenverarbeitung, vol. 8 (1967). As book: Rechnender Raum (Friedrich Vieweg & Sohn, 1969). Translated as Calculating Space (MIT Technical Translation, 1970).


Alan Turing, “The Chemical Basis of Morphogenesis,” Philosophical Transactions of the Royal Society of London B 237, no. 641 (1952).


It must be noted that Marvin Minsky and Seymour Papert’s 1969 book Perceptrons (which superficially attacked the idea of neural networks and nevertheless caused the so-called first “winter of AI” by stopping all research funding into neural networks) claimed to provide “an introduction to computational geometry.” Marvin Minsky and Seymour Papert, Perceptrons: An Introduction to Computational Geometry (MIT Press, 1969).


See the work of twelfth-century Catalan monk Ramon Llull and his rotating wheels. In the ars combinatoria, an element of computation follows a logical instruction according to its relation with other elements and not according to instructions from outside the system. See also DIA-LOGOS: Ramon Llull's Method of Thought and Artistic Practice, eds. Amador Vega, Peter Weibel, and Siegfried Zielinski (University of Minnesota Press, 2018).


Specifically, a logical or inferential activity does not necessarily need to be conscious or cognitive to be effective (this is a crucial point in the project of computation as the mechanization of “mental labor”). See the work of Simon Schaffer and Lorraine Daston on this point. More recently, Katherine Hayles has stressed the domain of extended nonconscious cognition in which we are all implicated. Simon Schaffer, “Babbage’s Intelligence: Calculating Engines and the Factory System,” Critical inquiry 21, no. 1 (1994). Lorraine Daston, “Calculation and the Division of Labor, 1750–1950,” Bulletin of the German Historical Institute, no. 62 (Spring 2018). Katherine Hayles, Unthought: The Power of the Cognitive Nonconscious (University of Chicago Press, 2017).


According to both Gestalt theory and the semiotician Charles Sanders Peirce, vision always entails cognition; even a small act of perception is inferential—i.e., it has the form of an hypothesis.


School bus drivers will never achieve the same academic glamor of airplane or drone pilots with their adventurous “cognition in the wild.” Nonetheless, we should acknowledge that their labor provides crucial insights into the ontology of AI.


Virilio, The Vision Machine, 76.


As Stall and Zellini have noted, among others, these skills also include the so-called Pythagorean theorem, which is helpful in the design and construction of buildings, demonstrating that it was known in ancient India (having been most likely transmitted via Mesopotamian civilizations).

In fact, more than machine “learning,” it is data and their spatial relations “teaching.”