AI as Instrument of Knowledge Extractivism
By Vladan Joler and Matteo Pasquinelli
(2020)
1. Some enlightenment regarding the project to mechanise reason.
The Nooscope is a cartography of the limits of artificial intelligence, intended as a provocation to both computer science and the humanities. Any map is a partial perspective, a way to provoke debate. Similarly, this map is a manifesto — of AI dissidents. Its main purpose is to challenge the mystifications of artificial intelligence. First, as a technical definition of intelligence and, second, as a political form that would be autonomous from society and the human. 1 In the expression ‘artificial intelligence’ the adjective ‘artificial’ carries the myth of the technology’s autonomy: it hints to caricatural ‘alien minds’ that self-reproduce in silico but, actually, mystifies two processes of proper alienation: the growing geopolitical autonomy of hi-tech companies and the invisibilization of workers’ autonomy worldwide. The modern project to mechanise human reason has clearly mutated, in the 21st century, into a corporate regime of knowledge extractivism and epistemic colonialism.2 This is unsurprising, since machine learning algorithms are the most powerful algorithms for information compression.
The purpose of the Nooscope map is to secularize AI from the ideological status of ‘intelligent machine’ to one of knowledge instrument. Rather than evoking legends of alien cognition, it is more reasonable to consider machine learning as an instrument of knowledge magnification that helps to perceive features, patterns, and correlations through vast spaces of data beyond human reach. In the history of science and technology, this is no news: it has already been pursued by optical instruments throughout the histories of astronomy and medicine.3 In the tradition of science, machine learning is just a Nooscope, an instrument to see and navigate the space of knowledge (from the Greek skopein ‘to examine, look’ and noos ‘knowledge’).
Borrowing the idea from Gottfried Wilhelm Leibniz, the Nooscope diagram applies the analogy of optical media to the structure of all machine learning apparatuses. Discussing the power of his calculus ratiocinator and ‘characteristic numbers’ (the idea to design a numerical universal language to codify and solve all the problems of human reasoning), Leibniz made an analogy with instruments of visual magnification such as the microscope and telescope. He wrote: ‘Once the characteristic numbers are established for most concepts, mankind will then possess a new instrument which will enhance the capabilities of the mind to a far greater extent than optical instruments strengthen the eyes, and will supersede the microscope and telescope to the same extent that reason is superior to eyesight.’4 Although the purpose of this text is not to reiterate the opposition between quantitative and qualitative cultures, Leibniz’s credo need not be followed. Controversies cannot be conclusively computed. Machine learning is not the ultimate form of intelligence.
Instruments of measurement and perception always come with inbuilt aberrations. In the same way that the lenses of microscopes and telescopes are never perfectly curvilinear and smooth, the logical lenses of machine learning embody faults and biases. To understand machine learning and register its impact on society is to study the degree by which social data are diffracted and distorted by these lenses. This is generally known as the debate on bias in AI, but the political implications of the logical form of machine learning are deeper. Machine learning is not bringing a new dark age but one of diffracted rationality, in which, as it will be shown, an episteme of causation is replaced by one of automated correlations. More in general, AI is a new regime of truth, scientific proof, social normativity and rationality, which often does take the shape of a statistical hallucination. This diagram manifesto is another way to say that AI, the king of computation (patriarchal fantasy of mechanised knowledge, ‘master algorithm’ and alpha machine) is naked. Here, we are peeping into its black box.
2. The assembly line of machine learning: Data, Algorithm, Model.
The history of AI is a history of experiments, machine failures, academic controversies, epic rivalries around military funding, popularly known as ‘winters of AI.’5 Although corporate AI today describes its power with the language of ‘black magic’ and ‘superhuman cognition’, current techniques are still at the experimental stage.6 AI is now at the same stage as when the steam engine was invented, before the laws of thermodynamics necessary to explain and control its inner workings, had been discovered. Similarly, today, there are efficient neural networks for image recognition, but there is no theory of learning to explain why they work so well and how they fail so badly. Like any invention, the paradigm of machine learning consolidated slowly, in this case through the last half-century. A master algorithm has not appeared overnight. Rather, there has been a gradual construction of a method of computation that still has to find a common language. Manuals of machine learning for students, for instance, do not yet share a common terminology. How to sketch, then, a critical grammar of machine learning that may be concise and accessible, without playing into the paranoid game of defining General Intelligence?
As an instrument of knowledge, machine learning is composed of an object to be observed (training dataset), an instrument of observation (learning algorithm) and a final representation (statistical model). The assemblage of these three elements is proposed here as a spurious and baroque diagram of machine learning, extravagantly termed Nooscope.7 Staying with the analogy of optical media, the information flow of machine learning is like a light beam that is projected by the training data, compressed by the algorithm and diffracted towards the world by the lens of the statistical model.
The Nooscope diagram aims to illustrate two sides of machine learning at the same time: how it works and how it fails — enumerating its main components, as well as the broad spectrum of errors, limitations, approximations, biases, faults, fallacies and vulnerabilities that are native to its paradigm.8 This double operation stresses that AI is not a monolithic paradigm of rationality but a spurious architecture made of adapting techniques and tricks. Besides, the limits of AI are not simply technical but are imbricated with human bias. In the Nooscope diagram the essential components of machine learning are represented at the centre, human biases and interventions on the left, and technical biases and limitations on the right. Optical lenses symbolize biases and approximations representing the compression and distortion of the information flow. The total bias of machine learning is represented by the central lens of the statistical model through which the perception of the world is diffracted.
The limitations of AI are generally perceived today thanks to the discourse on bias —the amplification of gender, race, ability, and class discrimination by algorithms. In machine learning, it is necessary to distinguish between historical bias, dataset bias, and algorithm bias, all of which occur at different stages of the information flow.9 Historical bias (or world bias) is already apparent in society before technological intervention. Nonetheless, the naturalisation of such bias, that is the silent integration of inequality into an apparently neutral technology is by itself harmful.10 Paraphrasing Michelle Alexander, Ruha Benjamin has called it the New Jim Code: ‘the employment of new technologies that reflect and reproduce existing inequalities but that are promoted and perceived as more objective or progressive than the discriminatory systems of a previous era.’11Dataset bias is introduced through the preparation of training data by human operators. The most delicate part of the process is data labelling, in which old and conservative taxonomies can cause a distorted view of the world, misrepresenting social diversities and exacerbating social hierarchies (see below the case of ImageNet).
Algorithmic bias (also known as machine bias, statistical bias or model bias, to which the Nooscope diagram gives particular attention) is the further amplification of historical bias and dataset bias by machine learning algorithms. The problem of bias has mostly originated from the fact that machine learning algorithms are among the most efficient for information compression, which engenders issues of information resolution, diffraction and loss.12 Since ancient times, algorithms have been procedures of an economic nature, designed to achieve a result in the shortest number of steps consuming the least amount of resources: space, time, energy and labour.13 The arms race of AI companies is, still today, concerned with finding the simplest and fastest algorithms with which to capitalise data. If information compression produces the maximum rate of profit in corporate AI, from the societal point of view, it produces discrimination and the loss of cultural diversity.
While the social consequences of AI are popularly understood under the issue of bias, the common understanding of technical limitations is known as the black box problem. The black box effect is an actual issue of deep neural networks (which filter information so much that their chain of reasoning cannot be reversed) but has become a generic pretext for the opinion that AI systems are not just inscrutable and opaque, but even ‘alien’ and out of control.14 The black box effect is part of the nature of any experimental machine at the early stage of development (it has already been noticed that the functioning of the steam engine remained a mystery for some time, even after having been successfully tested). The actual problem is the black box rhetoric, which is closely tied to conspiracy theory sentiments in which AI is an occult power that cannot be studied, known, or politically controlled.
3. The training dataset: the social origins of machine intelligence.
Mass digitalisation, which expanded with the Internet in the 1990s and escalated with datacentres in the 2000s, has made available vast resources of data that, for the first time in history, are free and unregulated. A regime of knowledge extractivism (then known as Big Data) gradually employed efficient algorithms to extract ‘intelligence’ from these open sources of data, mainly for the purpose of predicting consumer behaviours and selling ads. The knowledge economy morphed into a novel form of capitalism, called cognitive capitalism and then surveillance capitalism, by different authors.15 It was the Internet information overflow, vast datacentres, faster microprocessors and algorithms for data compression that laid the groundwork for the rise of AI monopolies in the 21st century.
What kind of cultural and technical object is the dataset that constitutes the source of AI? The quality of training data is the most important factor affecting the so-called ‘intelligence’ that machine learning algorithms extract. There is an important perspective to take into account, in order to understand AI as a Nooscope. Data is the first source of value and intelligence. Algorithms are second; they are the machines that compute such value and intelligence into a model. However, training data are never raw, independent and unbiased (they are already themselves ‘algorithmic’).16 The carving, formatting and editing of training datasets is a laborious and delicate undertaking, which is probably more significant for the final results than the technical parameters that control the learning algorithm. The act of selecting one data source rather than another is the profound mark of human intervention into the domain of the ‘artificial’ minds.
The training dataset is a cultural construct, not just a technical one. It usually comprises input data that are associated with ideal output data, such as pictures with their descriptions, also called labels or metadata.17 The canonical example would be a museum collection and its archive, in which artworks are organised by metadata such as author, year, medium, etc. The semiotic process of assigning a name or a category to a picture is never impartial; this action leaves another deep human imprint on the final result of machine cognition. A training dataset for machine learning is usually composed through the following steps: 1) production: labour or phenomena that produce information; 2) capture: encoding of information into a data format by an instrument: 3) formatting: organisation of data into a dataset: 4) labelling: in supervised learning, the classification of data into categories (metadata).
Machine intelligence is trained on vast datasets that are accumulated in ways neither technically neutral nor socially impartial. Raw data does not exist, as it is dependent on human labour, personal data, and social behaviours that accrue over long periods, through extended networks and controversial taxonomies.18 The main training datasets for machine learning (NMIST, ImageNet, Labelled Faces in the Wild, etc.) originated in corporations, universities, and military agencies of the Global North. But taking a more careful look, one discovers a profound division of labour that innervates into the Global South via crowdsourcing platforms that are used to edit and validate data.19 The parable of the ImageNet dataset exemplifies the troubles of many AI datasets. ImageNet is a training dataset for Deep Learning that has become the de facto benchmark for image recognition algorithms: indeed, the Deep Learning revolution started in 2012 when Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton won the annual ImageNet challenge with the convolutional neural network AlexNet.20 ImageNet was initiated by computer scientist Fei-Fei Li back in 2006.21 Fei-Fei Li had three intuitions to build a reliable dataset for image recognition. First, to download millions of free images from web services such as Flickr and Google. Second, to adopt the computational taxonomy WordNet for image labels.22 Third, to outsource the work of labelling millions of images via the crowdsourcing platform Amazon Mechanical Turk. At the end of the day (and of the assembly line), anonymous workers from all over the planet were paid few cents per task to label hundreds of pictures per minute according to the WordNet taxonomy: their labour resulted in the engineering of a controversial cultural construct. AI scholars Kate Crawford and artist Trevor Paglen have investigated and disclosed the sedimentation of racist and sexist categories in ImageNet taxonomy: see the legitimation of the category ‘failure, loser, nonstarter, unsuccessful person’ for a hundred arbitrary pictures of people.23
The voracious data extractivism of AI has caused an unforeseeable backlash on digital culture: in the early 2000s, Lawrence Lessig could not predict that the large repository of online images credited by Creative Commons licenses would a decade later become an unregulated resource for face recognition surveillance technologies. In similar ways, personal data is continually incorporated without transparency into privatised datasets for machine learning. In 2019 artist and AI researcher Adam Harvey for the first time disclosed the nonconsensual use of personal photos in training datasets for face recognition. Harvey’s disclosure caused Stanford University, Duke University and Microsoft to withdraw their datasets amidst a major privacy infringement scandal.24 Online training datasets trigger issues of data sovereignty and civil rights that traditional institutions are slow to counteract (see the European General Data Protection Regulation).25 If 2012 was the year in which the Deep Learning revolution began, 2019 was the year in which its sources were discovered to be vulnerable and corrupted.
4. The history of AI as the automation of perception.
The need to demystify AI (at least from the technical point of view) is understood in the corporate world too. Head of Facebook AI and godfather of convolutional neural networks Yann LeCun reiterates that current AI systems are not sophisticated versions of cognition, but rather, of perception. Similarly, the Nooscope diagram exposes the skeleton of the AI black box and shows that AI is not a thinking automaton but an algorithm that performs pattern recognition. The notion of pattern recognition contains issues that must be elaborated upon. What is a pattern, by the way? Is a pattern uniquely a visual entity? What does it mean to read social behaviours as patterns? Is pattern recognition an exhaustive definition of intelligence? Most likely not. To clarify these issues, it would be good to undertake a brief archaeology of AI.
The archetype machine for pattern recognition is Frank Rosenblatt’s Perceptron. Invented in 1957 at Cornell Aeronautical Laboratory in Buffalo, New York, its name is a shorthand for ‘Perceiving and Recognizing Automaton.’26 Given a visual matrix of 20×20 photoreceptors, the Perceptron can learn how to recognise simple letters. A visual pattern is recorded as an impression on a network of artificial neurons that are firing up in concert with the repetition of similar images and activating one single output neuron. The output neuron fires 1=true, if a given image is recognised, or 0=false, if a given image is not recognised.
The automation of perception, as a visual montage of pixels along a computational assembly line, was originally implicit McCulloch and Pitt’s concept of artificial neural networks.27 Once the algorithm for visual pattern recognition survived the ‘winter of AI’ and proved efficient in the late 2000s, it was applied also to non-visual datasets, properly inaugurating the age of Deep Learning (the application of pattern recognition techniques to all kinds of data, not just visual). Today, in the case of self-driving cars, the patterns that need to be recognised are objects in road scenarios. In the case of automatic translation, the patterns that need to be recognised are the most common sequences of words across bilingual texts. Regardless of their complexity, from the numerical perspective of machine learning, notions such as image, movement, form, style, and ethical decision can all be described as statistical distributions of pattern. In this sense, pattern recognition has truly become a new cultural technique that is used in various fields. For explanatory purposes, the Nooscope is described as a machine that operates on three modalities: training, classification, and prediction. In more intuitive terms, these modalities can be called: pattern extraction, pattern recognition, and pattern generation.
Rosenblatt’s Perceptron was the first algorithm that paved the way to machine learning in the contemporary sense. At a time when ‘computer science’ had not yet been adopted as definition, the field was called ‘computational geometry’ and specifically ‘connectionism’ by Rosenblatt himself. The business of these neural networks, however, was to calculate a statistical inference. What a neural network computes, is not an exact pattern but the statistical distribution of a pattern. Just scraping the surface of the anthropomorphic marketing of AI, one finds another technical and cultural object that needs examination: the statistical model. What is the statistical model in machine learning? How is it calculated? What is the relationship between a statistical model and human cognition? These are crucial issues to clarify. In terms of the work of demystification that needs to be done (also to evaporate some naïve questions), it would be good to reformulate the trite question ‘Can a machine think?’ into the theoretically sounder questions ‘Can a statistical model think?’, ‘Can a statistical model develop consciousness?’, et cetera.
5. The learning algorithm: compressing the world into a statistical model.
The algorithms of AI are often evoked as alchemic formulas, capable of distilling ‘alien’ forms of intelligence. But what do the algorithms of machine learning really do? Few people, including the followers of AGI (Artificial General Intelligence), bother to ask this question. Algorithm is the name of a process, whereby a machine performs a calculation. The product of such machine processes is a statistical model (more accurately termed an ‘algorithmic statistical model’). In the developer community, the term ‘algorithm’ is increasingly replaced with ‘model.’ This terminological confusion arises from the fact that the statistical model does not exist separately from the algorithm: somehow, the statistical model exists inside the algorithm under the form of distributed memory across its parameters. For the same reason, it is essentially impossible to visualise an algorithmic statistical model, as is done with simple mathematical functions. Still, the challenge is worthwhile.
In machine learning, there are many algorithm architectures: simple Perceptron, deep neural network, Support Vector Machine, Bayesian network, Markov chain, autoencoder, Boltzmann machine, etc. Each of these architectures has a different history (often rooted in military agencies and corporations of the Global North). Artificial neural networks started as simple computing structures that evolved into complex ones which are now controlled by a few hyperparameters that express millions of parameters.28 For instance, convolutional neural networks are described by a limited set of hyperparameters (number of layers, number of neurons per layer, type of connection, behaviour of neurons, etc.) that project a complex topology of thousands of artificial neurons with millions of parameters in total. The algorithm starts as a blank slate and, during the process called training, or ‘learning from data’, adjusts its parameters until it reaches a good representation of the input data. In image recognition, as already seen, the computation of millions of parameters has to resolve into a simple binary output: 1=true, a given image is recognised; or 0=false, a given image is not recognised.29
Attempting an accessible explanation of the relationship between algorithm and model, let’s have a look at the complex Inception v3 algorithm, a deep convolutional neural network for image recognition designed at Google and trained on the ImageNet dataset. Inception v3 is said to have a 78% accuracy in identifying the label of a picture, but the performance of ‘machine intelligence’ in this case can be measured also by the proportion between the size of training data and the trained algorithm (or model). ImageNet contains 14 million images with associated labels that occupy approximately 150 gigabytes of memory. On the other hand, Inception v3, which is meant to represent the information contained in ImageNet, is only 92 megabytes. The ratio of compression between training data and model partially describes also the rate of information diffraction. A table from the Keras documentation compares these values (numbers of parameters, layer depth, file dimension and accuracy) for the main models of image recognition.30 This is a brutalist but effective way to show the relation between model and data, to show how the ‘intelligence’ of algorithms is measured and assessed in the developer community.
Documentation for individual models
| Model | Size | Top-1 Accuracy | Top-5 Accuracy | Parameters | Depth |
|---|---|---|---|---|---|
| Xception | 88 MB | 0.790 | 0.945 | 22,910,480 | 126 |
| VGG16 | 528 MB | 0.713 | 0.901 | 138,357,544 | 23 |
| VGG19 | 549 MB | 0.713 | 0.900 | 143,667,240 | 26 |
| ResNet50 | 98 MB | 0.749 | 0.921 | 25,636,712 | – |
| ResNet101 | 171 MB | 0.764 | 0.928 | 44,707,176 | – |
| ResNet152 | 232 MB | 0.766 | 0.931 | 60,419,944 | – |
| ResNet50V2 | 98 MB | 0.760 | 0.930 | 25,613,800 | – |
| ResNet101V2 | 171 MB | 0.772 | 0.938 | 44,675,560 | – |
| ResNet152V2 | 232 MB | 0.780 | 0.942 | 60,380,648 | – |
| InceptionV3 | 92 MB | 0.779 | 0.937 | 23,851,784 | 159 |
| InceptionResNetV2 | 215 MB | 0.803 | 0.953 | 55,873,736 | 572 |
| MobileNet | 16 MB | 0.704 | 0.895 | 4,253,864 | 88 |
| MobileNetV2 | 14 MB | 0.713 | 0.901 | 3,538,984 | 88 |
| DenseNet121 | 33 MB | 0.750 | 0.923 | 8,062,504 | 121 |
| DenseNet169 | 57 MB | 0.762 | 0.932 | 14,307,880 | 169 |
| DenseNet201 | 80 MB | 0.773 | 0.936 | 20,242,984 | 201 |
| NASNetMobile | 23 MB | 0.744 | 0.919 | 5,326,716 | – |
| NASNetLarge | 343 MB | 0.825 | 0.960 | 88,949,818 | – |
Statistical models have always influenced culture and politics. They did not just emerge with machine learning: machine learning is just a new way to automate the technique of statistical modelling. When Greta Thunberg warns ‘Listen to science.’ what she really means, being a good student of mathematics, is ‘Listen to the statistical models of climate science.’ No statistical models, no climate science: no climate science, no climate activism. Climate science is indeed a good example to start with, in order to understand statistical models. Global warming has been calculated by first collecting a vast dataset of temperatures from Earth’s surface each day of the year, and second, by applying a mathematical model that plots the curve of temperature variations in the past and projects the same pattern into the future.31 Climate models are historical artefacts that are tested and debated within the scientific community, and today, also beyond.32 Machine learning models, on the contrary, are opaque and inaccessible to community debate. Given the degree of myth-making and social bias around its mathematical constructs, AI has indeed inaugurated the age of statistical science fiction. Nooscope is the projector of this large statistical cinema.
6. All models are wrong, but some are useful.
‘All models are wrong, but some are useful’ — the canonical dictum of the British statistician George Box has long encapsulated the logical limitations of statistics and machine learning.33 This maxim, however, is often used to legitimise the bias of corporate and state AI. Computer scientists argue that human cognition reflects the capacity to abstract and approximate patterns. So what’s the problem with machines being approximate, and doing the same? Within this argument, it is rhetorically repeated that ‘the map is not the territory’. This sounds reasonable. But what should be contested is that AI is a heavily compressed and distorted map of the territory and that this map, like many forms of automation, is not open to community negotiation. AI is a map of the territory without community access and community consent.34
How does machine learning plot a statistical map of the world? Let’s face the specific case of image recognition (the basic form of the labour of perception, which has been codified and automated as pattern recognition).35 Given an image to be classified, the algorithm detects the edges of an object as the statistical distribution of dark pixels surrounded by light ones (a typical visual pattern). The algorithm does not know what an image is, does not perceive an image as human cognition does, it only computes pixels, numerical values of brightness and proximity. The algorithm is programmed to record only the dark edge of a profile (that is to fit that desired pattern) and not all the pixels across the image (that would result in overfitting and repeating the whole visual field). A statistical model is said to be trained successfully when it can elegantly fit only the important patterns of the training data and apply those patterns also to new data ‘in the wild’. If a model learns the training data too well, it recognises only exact matches of the original patterns and will overlook those with close similarities, ‘in the wild’. In this case, the model is overfitting, because it has meticulously learnt everything (including noise) and is not able to distinguish a pattern from its background. On the other hand, the model is underfitting when it is not able to detect meaningful patterns from the training data. The notions of data overfitting, fitting and underfitting can be visualised on a Cartesian plane.
The challenge of guarding the accuracy of machine learning lays in calibrating the equilibrium between data underfitting and overfitting, which is difficult to do because of different machine biases. Machine learning is a term that, as much as ‘AI’, anthropomorphizes a piece of technology: machine learning learns nothing in the proper sense of the word, as a human does; machine learning simply maps a statistical distribution of numerical values and draws a mathematical function that hopefully approximates human comprehension. That being said, machine learning can, for this reason, cast new light on the ways in which humans comprehend.
The statistical model of machine learning algorithms is also an approximation in the sense that it guesses the missing parts of the data graph: either through interpolation, which is the prediction of an output y within the known interval of the input x in the training dataset, or through extrapolation, which is the prediction of output y beyond the limits of x, often with high risks of inaccuracy. This is what ‘intelligence’ means today within machine intelligence: to extrapolate a non-linear function beyond known data boundaries. As Dan McQuillian aptly puts it: ‘There is no intelligence in artificial intelligence, nor does it learn, even though its technical name is machine learning, it is simply mathematical minimization.’36
It is important to recall that the ‘intelligence’ of machine learning is not driven by exact formulas of mathematical analysis, but by algorithms of brute force approximation. The shape of the correlation function between input x and output y is calculated algorithmically, step by step, through tiresome mechanical processes of gradual adjustment (like gradient descent, for instance) that are equivalent to the differential calculus of Leibniz and Newton. Neural networks are said to be among the most efficient algorithms because these differential methods can approximate the shape of any function given enough layers of neurons and abundant computing resources.37 Brute-force gradual approximation of a function is the core feature of today’s AI, and only from this perspective can one understand its potentialities and limitations — particularly its escalating carbon footprint (the training of deep neural networks requires exorbitant amounts of energy because of gradient descent and similar training algorithms that operate on the basis of continuous infinitesimal adjustments).38
7. World to vector.
The notions of data fitting, overfitting, underfitting, interpolation and extrapolation can be easily visualised in two dimensions, but statistical models usually operate along multidimensional spaces of data. Before being analysed, data are encoded into a multi-dimensional vector space that is far from intuitive. What is a vector space and why is it multi-dimensional? Cardon, Cointet and Mazière describe the vectorialisation of data in this way:
A neural network requires the inputs of the calculator to take on the form of a vector. Therefore, the world must be coded in advance in the form of a purely digital vectorial representation. While certain objects such as images are naturally broken down into vectors, other objects need to be ‘embedded’ within a vectorial space before it is possible to calculate or classify them with neural networks. This is the case of text, which is the prototypical example. To input a word into a neural network, the Word2vec technique ‘embeds’ it into a vectorial space that measures its distance from the other words in the corpus. Words thus inherit a position within a space with several hundreds of dimensions. The advantage of such a representation resides in the numerous operations offered by such a transformation. Two terms whose inferred positions are near one another in this space are equally similar semantically; these representations are said to be distributed: the vector of the concept ‘apartment’ [-0.2, 0.3, -4.2, 5.1…] will be similar to that of ‘house’ [-0.2, 0.3, -4.0, 5.1…]. […] While natural language processing was pioneering for ‘embedding’ words in a vectorial space, today we are witnessing a generalization of the embedding process which is progressively extending to all applications fields: networks are becoming simple points in a vectorial space with graph2vec, texts with paragraph2vec, films with movie2vec, meanings of words with sens2vec, molecular structures with mol2vec, etc. According to Yann LeCun, the goal of the designers of connectionist machines is to put the world in a vector (world2vec).39
Multi-dimensional vector space is another reason why the logic of machine learning is difficult to grasp. Vector space is another new cultural technique, worth becoming familiar with. The field of Digital Humanities, in particular, has been covering the technique of vectorialisation through which our collective knowledge is invisibly rendered and processed. William Gibson’s original definition of cyberspace prophesized, most likely, the coming of a vector space rather than virtual reality: ‘A graphic representation of data abstracted from the banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding.’40
It must be stressed, however, that machine learning still resembles more craftsmanship than exact mathematics. AI is still a history of hacks and tricks rather than mystical intuitions. For example, one trick of information compression is dimensionality reduction, which is used to avoid the Curse of Dimensionality, that is the exponential growth of the variety of features in the vector space. The dimensions of the categories that show low variance in the vector space (i.e. whose values fluctuate only a little) are aggregated to reduce calculation costs. Dimensionality reduction can be used to cluster word meanings (such as in the model word2vec) but can also lead to category reduction, which can have an impact on the representation of social diversity. Dimensionality reduction can shrink taxonomies and introduce bias, further normalising world diversity and obliterating unique identities.42
8. The society of classification and prediction bots.
Most of the contemporary applications of machine learning can be described according to the two modalities of classification and prediction, which outline the contours of a new society of control and statistical governance. Classification is known as pattern recognition, while prediction can be defined also as pattern generation. A new pattern is recognised or generated by interrogating the inner core of the statistical model.
Machine learning classification is usually employed to recognise a sign, an object, or a human face, and to assign a corresponding category (label) according to taxonomy or cultural convention. An input file (e.g. a headshot captured by a surveillance camera) is run through the model to determine whether it falls within its statistical distribution or not. If so, it is assigned the corresponding output label. Since the times of the Perceptron, classification has been the originary application of neural networks: with Deep Learning this technique is found ubiquitously in face recognition classifiers that are deployed by police forces and smartphone manufacturers alike.
Machine learning prediction is used to project future trends and behaviours according to past ones, that is to complete a piece of information knowing only a portion of it. In the prediction modality, a small sample of input data (a primer) is used to predict the missing part of the information following once again the statistical distribution of the model (this could be the part of a numerical graph oriented toward the future or the missing part of an image or audio file). Incidentally, other modalities of machine learning exist: the statistical distribution of a model can be dynamically visualised through a technique called latent space exploration and, in some recent design applications, also pattern exploration.43
Machine learning classification and prediction are becoming ubiquitous techniques that constitute new forms of surveillance and governance. Some apparatuses, such as self-driving vehicles and industrial robots, can be an integration of both modalities. A self-driving vehicle is trained to recognise different objects on the road (people, cars, obstacles, signs) and predict future actions based on decisions that a human driver has taken in similar circumstances. Even if recognising an obstacle on a road seems to be a neutral gesture (it’s not), identifying a human being according to categories of gender, race and class (and in the recent COVID-19 pandemic as sick or immune), as state institutions are increasingly doing, is the gesture of a new disciplinary regime. The hubris of automated classification has caused the revival of reactionary Lombrosian techniques that were thought to have been consigned to history, techniques such as Automatic Gender Recognition (AGR), ‘a subfield of facial recognition that aims to algorithmically identify the gender of individuals from photographs or videos.’44