A Psychoanalytic View of the Web, AI, and Cookies

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A Psychoanalytic View of the Web, AI, and Cookies Tolga Yalur

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A Psychoanalytic View of the Web, AI, and Cookies Tolga Yalur

1. The Informative Discourse 2. Language in Artificial Intelligence 3. The Cookie Dispositif

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I The Informative Discourse

In psychoanalytic theory, discourses construct the subject in the reality, the social other, the invisible whole, which, in its complete form, reproduces itself whenever the subject speaks as. In Jacques Lacan's (1972) view, this intersubjectivity is structured upon the function of the self where the specular report to the Other in narcissism is pressing. Sigmund Freud (1920) pointed to the question of the indestructibility of the unconscious desire to mark its essential character in reiteration and reproduction that cause the unrest in civilization. Freud was not walking down a spiritualist path but questioning the structure of discursive determining the subject in social reality.

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This book advances the psychoanalytic notion of dis‐ course to rationalize the subject on the web of reality. Psychoanalytic theory answers questions of reality in various networks that Freud revealed and Lacan methodized, where he conceptualized the subject of reality in discourses. What I term as "the informative discourse" has the symbolizing mechanism to register, diagnose, detect, decode the patterns or patternize the subject’s information for desire, need and consumption behaviors that is called objet(a) in psychoanalysis. The subject depends upon the information he/she creates or the subject is conditioned by the prescribed information, which is what the discourse contextually might store in databases to make sense of the subject’s de‐ sire, need and various behaviors. Reality In psychoanalytic theory, the coherence of reality is in question. Lacan's conception of reality is triple: the Real, the Symbolic and the Imaginary, which are three different

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senses. The triple unity of Reality in Lacan’s oeuvre is based upon the sign, more or less demonstrating the equivalence of these units in a hole that is the symptom (Lacan 1936, 1953, 1967). The Real is what is strictly unthinkable, what would at least be a departure, what would make a hole in the register of the Symbolic. And it would make it possible to question what the real is about the triple reality that conveys a sense. This sense is there only to be lessened to the function that supports the unconscious, the function that is structured as language. The equivocation of this function is symbolic, not sense. Sense is other than the symbolic and support the Imaginary, everything that is represented for the human being who cannot grasp the whole, such as the psychosomatic whole‐ ness, but instead needs reflexive objects to get a glimpse at the bodily unity, mastery over the human as psychosomatic being. The human being conceives reality because of being in the illusion of it. Psychoanalytic theory conceives the human reality in these essential registers to answer

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questions of the unconceived (the real) and the singular experience (imaginary) that report the subject through speech (the symbol). The subject's registration into reality is the step toward a primordial question in terms of the trespass from the conscious to the uncon‐ scious. To diagnose what happens in the Real in the symptom:"Symbolism is through which the symptom returns in language. As Freud manifested as the essential reality, there are symptoms, missed acts, inscriptions. These are symbols that are even specifically organized in language, functioning from an equivalent of the signifier and the signified: the very structure of language" (Lacan, 1953). The experience, the essence of psychoanalytic speech is the trespass of the forces that give balance to being human: the objet (a). The symbol of the objet (a) is “the object there”. When the object is no longer there, it is incarnated in duration, devoid of self. By the same token, the object is there for the subject S, all the while. Here is the report of the symbol to the fact that all that is human is considered as such. The Real manifests itself not only in the analytic experience if the notion of

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symptom was introduced. Long before Freud, Marx made it the sign of something that is what is wrong, in the Real. Marx was able to work on the symptom as long as the symptom was the affect of the Symbolic in the Real, reflected in the Imaginary, making up a hole. As long as the unconscious says everything that responds to the symptom. The uncon‐ scious is responsible for reducing the symptom to the symbolic register, which is everything that makes sense in words in the human consciousness and the unconscious, by means of human imaginary reports to others to enter into the symbolic order of reality as subject. "The symptom is not the novelty of Freud’s introduc‐ tion to psychoanalysis but Marx’s. Marx's capitalist discourse, inasmuch as it is the determination of the master’s discourse, discovers the fact of the symptom. Capitalism departing from the master’s discourse is what seems to distinguish the political outcomes from the Marxist critique of the discourse of semblance. If there is

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something revolutionary in Freud, though the word “revolutionary” might be misused, it is the fact that he prioritized one function which is the function in common with Marx: it is to consider a number of facts as symptoms. The dimension of the symptom is that it speaks, even to those who do not know how to hear it. The symptom also does not say everything to those who know how to hear it." (Lacan, 1971) Karl Marx's theory is the theory of capitalist discourse and the symptom of this discourse is the power of the poor, the proletariat. "For they do not know what they do," is the quote from Marx to diagnose the fact that the poor does not know, nor does the poor own the means of production that is held by the upper classes to determine the reality, but nonetheless the poor does the work of reproducing not only capitalism as the economic system but also the social reality, for which, Marx adopted the term "false consciousness". "The dimension of the symptom is that it speaks, even to those who do not know how to hear it," said Lacan for Marx's discovery of it:

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"The symptom does not say everything to those who know how to hear it. This dimension of the symptom is the turning point in a regis‐ter that has been resonating around the theme of cognition. The theory of cognition explains the register to constitute the formulations of science, for which the physics formulates regressions. With the evolution of science, the human is in the condition of being on the path to some verity, which demonstrates a heterogeneity of the double register. Except that in my teach‐ ing, the coherence of this register is in question." (La‐ can, 1971). Lacan's rework on reality is based upon the context of the psychoanalytic discourse where the analysand speaks and the analyst keeps silent most of the time, and obviously the psychoanalytic discourse is not only personal but political as well. As I unpack, Lacan had to compare his work with Marx', and the psychoanalytic subject with the worker. "Speech" is the psychoanalytic specialization that led Lacan to formulate the "discourse". Discours is the term

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for "speech" in French but psychoanalytically means more than that. When the analysand speaks, it is with signs, symbols, the level of interpretation for the analyst. The cognitive, conscious and unconscious functions of speech is the discourse in psychoanalytic theory. The Other (L'Autre, A), the symbolic speaks in the form of the unconscious word. This “speech” is discursive, banned and censored, distorted, stopped, captured, profoundly ignored for the subject by the interposition of the imaginary report of the object (a) to another object, of the self to the other. The essentially alienated report of the subject to the symbolic in the discourse, the Big Other. Would the discourse of the subject be coherent in the Other, the symbolism where all the discourses settle? In the realm of the Other, a fully consistent discourse is not possible. The function of the objet(a) responds to the coherence of the verity nowhere but in the Other. The objet(a) indi‐ cates well that the affect of the meaning of something that claims to symbolize lack, cannot be a signifier. The

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psychoanalytic discourse expresses this lack in the symbolizer with S(A) in terms of “Che vuoi?”. The “Che?” means: what does the Other (A) want? I de‐ mand. Nothing can be expressed in terms of the func‐ tion of the subject of lack except to double it because the discourse does not question it. This duplicity of the report to the Other duplicates what is represented as the discourse, the speech of what is represented as a demand. To formulate, the subject S is put in a con‐ junction with the hallmark ◊ and demand D, articulated as such, S ◊ D. The function of s(A) is what happens to the subject’s affect in speech. The description of the subject begins in the scale of what represents one signifier for another signifier. The Other's desire is the spring of imaginary identification, expressed in a symbolic mode which intersects with the imaginary. Lacan uses formulae to inscribe the report of the subject to the symbolic register of reality, the Other, the loci of the Subject (S) in the symbolic. These are dis‐ courses that are mostly based upon Lacan’s discover‐ ies and

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formulations as the psychoanalyst who both teaches and philosophizes the psychoanalytic theory. In Lacan’s practice, what is constituted in the analysis is the virtual discursive report. In psychoanalytic dis‐ course, the objet(a) is the matter that goes around affectively, exceeding the extent of the human mind to the symbolic. What is essential is the symbolizer S1 in the formulae, in the structure of mathematical logic of the discourse. The structure is something that first presents itself as a group of factors, forming a co-variant whole. S1 needs to have the discourse function with language. To conceive the efficiency of language in determining the subject through these discourses as varieties of the master’s discourse, Lacan deploys the semblance, the verity, Other, product. What the factors of language produce is the surplus of jouissance, informed in the use of S1. The capitalist discourse is untenable and inclined to crises more than other discourses. The capitalist discourse is the substitute for the master’s dis‐ course, and if there has been a crisis, that’s the crisis of the capitalist discourse. The very small

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inversion be‐ tween the S1 and the S, the subject. Because it works too fast to be consumed, however, there is virtually no chance that anything serious will happen in the course of the analytical discourse, apart from being random. The function of discourse supports itself with four privileged places, one of which remains unnamed: the semblance. The master signifier (S1) replaces the mas‐ ter’s discourse, knowledge (S2) might occupy it in the university discourse, the split subject (S) might be there in the hysteric’s discourse, surplus-jouissance (a) in the psychoanalytic discourse.

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Diagrams of Discourses

In the University Discourse, where is the S in the scholar conditioned at the scientific production level. The direction of the arrow means that the report does not come from anywhere, it was produced at the scientific stage of the discursive mechanism. In the University Discourse, knowledge (S2) is the verity subjecting the master symbolizer (S1). In the Hysteric's Discourse, the teacher is situated as the master. In the Psychoanalytic

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Discourse, the ana‐ lyst leads and has to keep in place what it is about the function of the object a. The psychoanalyst supports the function until the other recognizes it. In the formu‐ lae, the structure of the Psychoanalytic Discourse is the opposite of the Master’s Discourse. At the level of the master's discourse, the master introduces One to the world, and commands S2. To obey is to know. In Hegel’s dialectic, the slave knows. The Surplus-jouissance Marx taught the capitalist discourse to describe the surplus value that's equivalent to the surplus-jouis‐ sance in psychoanalytic theory. The surplus-value is what makes capitalism work. These similar terms do not have the same sense. The function of language is in question there. Who does the work in language is Marx. He is the analyzer, not the analysand. If Marx is questioned in terms of the work of language, the term surplus-value in language could’ve turned into the surplus-jouissance in psychoanalysis. His capitalist discourse relies heavily on

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technological advances, a contradicting, complex and evolutionary mode of production whose survival is based upon the exploitation of alienated labor and crises. Marx’s innovation is this function in which he situates labor. He did not discover labor for sure, but he conceptualized a labor theory of value to demon‐ strate the surplus-value. "A subject is what can be represented by one signifier for another

signifier,"

is

Lacan's

motto

passim.

Psychoanalytic discourse demonstrates how the surplusjouissance is represented by the enunciation, as an affect by the very discourse. In the discourse on the function of the renunciation of jouissance that the term of the objet(a) is introduced. There is the function with‐ in the function of renunciation, that is, renunciation, through the affect in the psychoanalytic discourse, decides the degree of surplus- jouissance that situates the objet(a). The objet(a) is the essential object of the function of the surplus-jouissance. In the psychoana‐ lytic jargon, the

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excess of jouissance is loss for the subject of discourse who is far from being sufficient. "The subject is no longer identical to oneself, and hence is lost without joy, which is 'surplus-jouissance'. The loss is strictly connected to the entry into the dis‐ cursive play of symbolisms representing everything as thought. The subject is quite unable to name the symptom around this loss, the surplus-jouissance. Freud's theory of repetition designates this: Nothing is identifiable in the jouissance. In virtue of the sign, something else replaces the object; the trait that marks it. Nothing can happen there without an object being lost." (Lacan, 1968) The formula S ◊ a demonstrates this well. What hap‐ pens to the ratio of one signifier S1 to another signifier S2 is that the subject S, represented by S1, is not sufficient since any signifier is connected to only another objet(a). The formula depicts the ratio of the reiteration of the signifier S in S1 → S2 in the discursive dia‐ grams, representing the subject in relation to him‐

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self/herself, leading to the (a). There happens some‐ thing that is neither the subject nor the object, but S ◊ a. From there onwards the other signifiers represent in the ratio S1 → S2 that introduces the metonymy as the condition of the subject’s being. Lacan connects his motto to the Marxian notion of "commodity" in the capitalist discourse through the compatibility of the surplus jouissance with the surplus value detected in the capitalist discourse. What Marx deciphered as the economic reality of the exchange value is represented by the use value. The difference of these two incompatible values yields the surplus value. Like the free markets where some object of human work is described as a commodity, incorporating something of the surplus value, the surplus-jouissance is what allows the isolation of the function of the ob‐ jet(a). Jouissance is the substance of everything in psycho‐ analysis. Its scope introduces the function of surplusjouissance, in Marx’s terms, the surplus value. Lacan

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attributes the invention of surplus-jouissance to Marx, if not dis‐ covery. In this terminological report is the function of the objet(a). This objet(a) is what Marx's discourse in‐ vents as the surplus value. The same goes for the surplus-value before it appeared in Marx’s discourse in its rigour. Eventually, what is revealed after these discoveries is the affect of the discourse. The objet(a) is the affect of the psychoanalytic discourse. Is it an artifice created by the psychoanalytic discourse? The ques‐ tion of the artifice is modified and suspended, which discovers its mediation in the fact that what is discovered in the discourse’s affect has already appeared as the affect in the psychoanalytic experience as a symp‐ tom, a turning point described by the objet(a). Labor was not a new notion in Marx's introduction of objet(a) in commodification, nor was the renuncia‐ tion of jouissance. The meaning of labor and jouis‐ sance are similar, but revealing the renunciation is the essence of the

psychoanalytic

discourse.

The

function

of

surplus-jouissance depicts this essence and demonstrates

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an affect of the discourse in the renunciation of jouissance. Labor market totalizes merits and values, organization of choices, preferences in the do‐ main of the Other. This is what refers to an ordinal, even cardinal structure. In the field of the Other, the discursive symbolism detains the subject's means to enjoy. Marx conceived the capitalist markets in the field of the Other, asking to obey and consume, and then do it again. In Lacan's "A subject is what can be represented by one signifier for another signifier," the subject is asked in the capitalist discourse by the Oth‐ er to buy this or that for the illusion of being an ade‐ quate subject through consumption objects (a) that are replete with the excess of meanings and symbols from logos to language for the subject to perform beyond the needs but for the jouissance of illusory being the almighty S, which is the 'false consciousness' Marx termed and Lacan deciphered as 'misrecognition', both of which refers to the alienated subject.

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"For Marx, the surplus value, as the surplus-jouissance in analytic discourse, restitutes the capitalist dis‐ course, which cannot be appeased by a meta‐ language. The surplus value is the cause of the desire that an economy makes its principle: that of extensive production, therefore the insatiable lack-to-enjoy. The surplus value is accumulated to increase the means of production in the name of capital. It extends consump‐ tion, otherwise this production would be in vain, precisely from its ineptitude to procure a jouissance. Mas‐ ter’s discourse gives meaning

to

the term 'surplus-jouissance' in the

psychoanalytic discourse, which is equivalent to the surplus value, an essential spring in Marx’s capitalist discourse. The capitalist discourse is not the master’s discourse as such, but a variety of it." (Lacan, 1972) For Lacan, language is what produces the surplusjouissance, and the term that applies to this jouissance is the cause of desire: objet a. That objet a as a term is the true support of everything functional in selecting each object in desire. In psychoanalytic theory, objet(a)

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concerns the essence of the subject. The object cause of desire is what represents the subject for another signifier, another structure. Discourse is the order of language that happens through language and functions as a social connection. Marx's subject is in the place of the objet(a), the loci of the essential function of mar‐ ket capitalism. Freud tracked the discontent that inflects the implacable capitalist discourse. He did it down to the unconscious where something is working. The capitalist discourse, in complementing itself with the ideology of the class struggle, only induces the exploited to compete on the exploitation of principle.

The Informative Discourse

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Jacques Lacan's notion of discourse works well to rationalize the psychoanalytic subject on the web. La‐ can’s method answers the question of reality in various networks. What could be termed as the informative discourse has the symbolic mechanism to register, diagnose, detect, decode the patters or patternize the subject’s

information (S1) for desire, need and

consumption behaviors, objet(a). The user is the Subject S who depends upon the information he/she creates on the web, the S1. S1 is what the informative discourse contextually might store in databases (S2) to make sense of the subject’s desire, need and consumption behaviors, objet(a). The S is discursively (S1) connected to the S2 through the object report (a). The difference is that there is no master in the informative discourse, which makes the whole web sound like the hysteric’s discourse. There is no obvious master in the informative dis‐ course but sets of logics that could make verities concerning the subject information. S2, unlike Lacan’s for‐ mer discourses, does not stand for a One mastery in the

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discourse, but the symbolic as the Other that varies a lot in the web. In other words, there is no uni‐ versal discourse on the internet but small incompatible varieties of knowledge that claim to offer and represent what is true and what is real. To make things easier, I would conceptualize S2 as such. The S, through the S1, reports to the a, that is tailored by S2 for S where the subject’s desires and needs might be influenced, triggered, in brief, affected by and large, a term Lacan also makes sense of in his lectures and discourses. S has in no way an access to the S2, deprived of the means of making verities and confronted with, and represented by, small objects of desire and needs in‐ stead. Sigmund Freud discusses the question of affect in his description of the unheimlich, the uncanny, the feeling of otherness that differs from the most common, the most ordinary, the most familiar appearances. The affect of the unheimlisch is the symbolized feeling at the moment when these appearances are not familiar any more but other. Affect is somehow excursive, but not repressed at

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all (Lacan). Freud conceives the affect as something that is not repressed, that remains excursive. The irruption of the unheimlich is closely connect‐ ed to the verbal symbolism. Imagined from the outside world, the characteristic of repression is always an affect. In this, words apparently comprise a cognizance of the world. Unsurprisingly, artificial intelligence is one of the areas to see the formulation of informative discourse. Artificial intelligence is used and not only in writing, imag‐ ing, music. The function at work behind the hereable and the visible concerns the discourse. The question would be if there are any ideologies and economy-poli‐ tics involved in these constructs. Affect plays a crucial role in these constructs, especially in music. The affect might be better verbalized where rewording might alter the meaning that represents a wish. There is symbolism in the wish that is represented in, for example, the psychological tags and moods chosen in the AI music composition. There are no words in terms of symbol‐ ism in any form of music. Instead, there are tags, moods and genres that symbolize

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the affect. This pos‐ sibility of the affective discourse in AI composed music complies with the algorithms of the informative dis‐ course, which is symbolism. With learning the affect, AI composers even imitate human composers. For the user who is presented with the options of tags, moods and genres, the affective composition of the objet(a) is constrained by the means of expression that connect the user's wish symbolized to the desire, the affective that replaces the desire in symbolism. The Freudian impression would be that these AI music composers are predominantly constructed through pre-learned and pre-coded emotions, feelings, genres and types. If there are psychological issues of the user that would be a matter of psychoanalytic discourse. The AI com‐position of music would concern the informative discourse. The questions concerning the informative discourse would be: (how) does the informative discourse on the web learn about the subject’s conscious and unconscious needs and wants? Are all the settings available to the

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subject on the internet enough to have an answer about the

reality

of

human

desires

and

subjective

characteristics? Are the logics in what I describe as the informative discourse predetermined before they learn about the subject for real? What’s the matter with all the circuits of information on the internet? The logics of and in the object the internetworks, the subject is the user represented by the information he/she creates (I, noted by S1) distinguished from the information tailored for the user (I’), where the difference is what might be termed as surplus information (I- I’). The former, I, refers to what the latter saves before registration toward tailored suggestion I’, that is, whatever the subject might be interested in. The I-I’ difference is the theoretical void that is filled in with the senses of information through these information circuits of I-I’. On the internet of informative objects, the common components are texts, sounds, and images, which refer to symbols that make sense the audio and visual dimensions in various contexts of the internet.

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Discursive Learning Jean-Francois Lyotard notes that there can be techno‐ logical memory in the sense that human “writing” has memory (1991, p. 48). Information refers to writing and encoding bits about the pattern of information that is described as "processed". The informative discourse tags interests and suggestions after this information. Machine learning concerns the logic of processing and informing the "I", user information prior to I’. It is the procedure to perform cognition on the information. In simple words, intelligent agents do the correlations in the information and deploy these correlations for fur‐ ther information. New textual or visual information is learned in comparison to the pre-learned symbols and close meanings. In this sense, I-I’ is not static. The recognition process refers to extracting data from the information. Recognition agents use statistical models to predict patterns in the user-generated information to store those patterns into datasets. Sensors of cognition may or

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may not register objects in the in‐ formation correctly. For example, if the subject’s information input (I) is the word for or an image of a “beetlejuice,” the discursive function of the machine has to compare it with all the “beetlejuice”s archived in the database, from "beetle"s to "juice"s, and then pointing out that it is a “beetlejuice” based upon the patterns in its appearance. However, if there is no information about “beetlejuice” in the database, it should be added by supervisors. The process of being included in the database aims to balance by repeated coding. Machine learning performs an object analysis of the information: it unpacks the knowledge of the information recorded in the database, into smaller units. For example, if the symbol of the “beetlejuice” contextually means something invasive, functions make inference accordingly. The output is what the subject is aimed at with the I’, the information that is designated for the subject’s use, desire, need, consumption. The Beetlejuice

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Language, sound, and image are fundamental registers in machine learning. The patterns in these registers are recognized

and

data-formed

in databases. When

language, such as words and sentences in a type-written or verbal text, indicates textual symbols, images point to the imaginary of the web server’s space. New textual or visual inputs are machine-learned by regularly rechecking previously informed inputs. In this sense, inputs are variable. Another variable in these inputs is the context of the input recognized. The meaning of the word “beetlejuice” may converge to close meanings and its general meaning. In general, symbol‐ ic data preserved in the data structure through ma‐ chine learning will be devoid of an initial context (the sentence or image within which the "beetlejuice" was originally informed). As the machine keeps recognizing var‐ ious words in different contexts, it reaches the balanced data of what a “beetlejuice” is. Each reiterated input is bound to the context of a new “beetlejuice,” that is, to the time when a new "beetlejuice" is recognized and the differ‐ence between the pre-informed symbol of “beetlejuice” and

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the informed "beetlejuice" as a variant. Symbols may exist independent of inputs after they are stored in the sets. The semblance of "beetlejuice"s is never complete with reference to the discourse. The referent is not the "beetlejuice", the informative object, since what it means is that this referent is not static but kinetic. The semblance in which the symbolizer, that is, the word "beetlejuice", is iden‐ tical to itself is the information base for the term, the semblance of "beetlejuice" in essence. A semblance is manifest in every repetition. Something with an ambivalent mark and point of departure refers to the “accumulation of symbolizers”. The “semblance” is not a semblance of anything but the sense of the objective genitive: it is the semblance of its very object through which the economy of discursive learning functions. The logic of the hypothetical "beetlejuice" represents the value “True” or “False”, to merge with what is being reported as True. The discourse is the semblance of what the affirmation offers to advance.

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The subject of web does not appear of unless signi‐ fiers in S1→ S2 has been connected somewhere; the subject as such is produced from the meaningful information (the product a), and the split subject of S ◊ a never masters this. The web discourse makes the semblance. If what is informed is true of being what it is in objective and verbal terms, it is the very object of the discourse that the semblance poses, and hence, the senseless S1, of what is articulated in the discourse. The wealth of language is revealed where a logic exceeds everything in‐ formable with it, detaches from the materializable. Machine learning can be described as a domain of networks in the server space where intelligent agents recognize and register human users into a symbolic logic that correlates data in large sets. As mentioned above, the computing machine has been built on a logic that is historically related to intellectual labor to show two intertwining spaces

of

human-machine

inter‐

action

in

the

development of machine learning: user- generated information. Pattern recognition and recur‐ rent networks are common in machine learning agents that are deployed

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in social media platforms where humans use digital profiles. The informative discourse in machine learning could be conceived in terms of as artificial agents if not artificial beings with consciousness. They are more or less extensions of subjectivity and agency but toward limit‐ ed objectives depending on the context of the dis‐ course. The information I’ in the discourse mediate the user to the first party of the context where the user is conditioned and the third parties, such as a corpora‐ tion, a brand, a real person, an artificial agent. In other words, I’ represent the user for other internet information depending on the context. For in‐ stance, if the context is a dating application, then the discourse is expected to offer the best match to the user. If the context is a non-profit organization, then the informative discourse might lead the user to phil‐ anthropic communities. For sure, there is ideology in the very practice of making sense of the subject’s in‐ formation to be tailored for I’. The gap between I-I’ is the surplus-information.

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There

are

firms,

academies,

news

outlets,

and

government agencies that might discern this user information I, even for I’. The questionably informative ideology in these social entities, would save the I-I’ circuits for such materializations as tracking, profiling, and sur‐ veillance, functioning to materialize the I ideologically. Here the cultural codes might be required with or with‐ out legal bases as well to maintain materializing the subject in line with such cultural norms within the limits of their worldviews or simply the ideology.

Ideology

means

formations

in culture,

economics, politics, and so on: life practices, views, discourses,

ideas

that or‐

ganize and represent

individuals and communities, and decide the dimensions of the social, political, and eco‐ nomic relation in these communities. The trouble with the user information here would be stereotypically profiling, nuancing patterns to surveil the subject, violation of information rights, such as copyright. The firms prepare agreements, and the

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governments are asked to take measures against these risks. Indeed, firms and governments have distinct agencies that are responsible for auditing the trust and credibility of the institutions, to protect subject rights and prevent subject information from being exploited. Firms also like the subject's trust in social institutions or advertisement agencies’ ideals of being objective. Information codes and subject agreements, however, are found to be puzzling. Because of these menaces of materialization, and loose privacy policies of social entities in the 21st century has become a private-publicacademic ideology. Platforms and marketing firms work on the subject’s consumption

behaviors,

government

agencies and

academics like to research social movements and mobility, as well as health. Because social entities are private firms and the subject is a customer of these firms, evaluating the subject information might differ. Acade‐ mics to write research articles, governments to re‐ search health, firms to measure consumption. The question is

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that large pools of subject information might be exploited through

materializations

that

make

perplexing

information and representations out of in‐ formation. The absence of a representation might infect the reality of an event, if not deprive the reality of the idéa that I’ inform. The absence between an event and its representation, deprives the subject’s objective information about an event of environment and history. To put another way, ideologies relate and turn discrete subject information into formations without thinking about the history of information. When "logics" inform an idéa about the subject, this becomes an ideologic materialization. Enumerating, sorting, performing have been conceived as common mental tasks since René Descartes’ (1641) cogito ergo sum (“I think; therefore, I am”) put con‐ sciousness at the center of the human being. The cartesian idea of cogito privileged the internal, that is, the conscious mental performance of thought through which the human being can access information in the external world, such as perceiving things through a conscious

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logic for being in the world. From cogito to the modern digital media, human thinking and being in the external world has been influential in technology. The logic of digital information is allegorized through the human mental performance of thinking via modern theories of mind. The internet of objects reflects pre- learned codes on the subject information to re-inform the subject information. Subject information hence be‐ comes huge and non-formed material, something that Sigmund Freud might have described as “id”, not the conscious subject. The history of computers advanced with the modern theory of mind and cognition, as well as cybernetics. Jacques Lacan (1936, 1967) demonstrated that cultur‐ al symbolics have independent realities that spread so‐ cial codes, rules, meanings to the individual in his courses on the Freudian ego, where taught a course on cybernetics to theorize symbolic logics to explore the finite logics in the cybernetic circuits (Lacan, 1955). Language is the symbolic dimension of the computer, as Lacan’s view of reality has a lot in common with his expression of the

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symbolic

circuits

in

cybernetics.

He

viewed

a

pre-subjective, symbolic realm of texts, forms, and meanings around the human being. The symbolic is the social, intersubjective dimension of reality, which has a logic in any place wherever. It is the realm where a person must become the subject of “reality”, before becoming the ‘thinking subject’. The symbolic is there before the individual becomes the conscious subject in the world. The subject is the discursive agent of signs when symbolic logic is the realm of ideologies. Informative re‐ search of the informative discourse therefore describes the symbolic logic through which the subject’s informative mind has a report to the discursive formations in the I-I’ circuits. Performances, codes, and practices

refer

re-depicting

to

symbolic

discerned

logics

patterns.

in

learning,

Mind,

through

verisimilitude between the symbolic and the cybernetic, for Lacan, refers to the symbolic of encoded patterns,

40

representing

learned

patterns

from

the

subject

information. The Surplus-Information (I-I’) as Jouissance and Labor In Grundrisse (1858) to Capital (1867), Marx informed the process in which non-human machinery might re‐ place human labor through instrumentalization of science and technology. He conceived the machine as “constant capital” that cannot create value, but it is a product of worker’s labor. Unlike machinery, only the worker, a “variable capital,” can create surplus value. Technology as a product of human labor is no foreign to Marx. In “Machinery and Modern Industry”, he referred to technology as a totality, not the human experience of particular technologies. The capitalist deployment of technology has the determining power on the economic and social consciousness of the society. For Hannah Arendt, it is not if the human being gets to be subjugated to the machine but how the machine conditions the human world (1958, p. 151). Arendt's (1958) notion of 'homo faber' describes the human being “qua creator" of

41

the world and artifacts, which sees everything in terms of utility and as means to ends. The human being works through and with machines. Theodor Adorno and Max Horkheimer (1972) conceptualized instrumentality of technology by means of the reduction of logic to a tool in terms of production and consumption of commodities in which machines function through the logic that alienates the worker from the labor in the commodity form. The productive force of technology in capitalism is the means of production owned by the bourgeoisie at the cost of workers, whose labor is materialized in the commodity. Marx described the alienating logic as dehumanizing. Marx and Marxists consider the logic of the accumula‐ tion of money and power that grows through the exploitation of the proletariat (M-C-M’) in describing the capital that instrumentalizes everything. For Pierre Bourdieu (1986), capital goes beyond the sphere of money

and

permeates

into

culture.

Bourdieu

conceptualized "symbolic capital" for the realms of capital that are economic, cultural, political, social, and

42

scientific. The symbolic capital, though the term is not psychoanalytic, is inclusive of the digital capital as labor. Given the context that capitalism is a social and cultural formation, capitalist instrumentalization of digital tech‐ nologies refers to a phase of the mode of production of culture and life, the advances in digital technologies regarding politics, economics, culture, ideology and exploitation. Yann Moulier-Boutang (2011) approached digital capitalism in transforming the work and the class struggle

where

the

surplus

value

is

extracted.

Moulier-Boutang used the term cognitive capitalism for the logic of globalism that weakens the working class‐ es in

market

globalization

through

digitalization,

deindustrialization, globally dividing the human labor in terms of the material (commodity production in the south) and the digital (knowledge production in the north), former doing the material production of what latter

digitally formulates. For Carlo Vercellone,

cognitive capitalism should be understood as a “knowledge- based economy” that is informed by the norms and accumulation of value (Vercellone 2007,

43

p.14). Forces of production would take the knowledgeform, as well as the form of real, social life. Marx's description of the machine in terms of the wearisomeness, mental drudgery, and fatigue of work‐ ing with the machines at his time refers to the combi‐ nation of all the “simple instruments, set in motion by a single motor, constitutes a machine.” An intellectual worker at his time, he was interested in measuring and calculating labor in terms of quantities. For this, the term he used was commodification to refer to objectification in consumption culture where objects are materialized by the human being but turns into a completely distinct and alienated thing in capitalist markets. He referred to this in his simple formulation of Money- Commodity-Money’ (M-C-M’) circuits. The I-I' circuits on the web are similar to this. Marx described immate‐ rial labor for such materializations as intellectual work that are actually the information that the user creates. Technically, what is stored by the user’s consent is the material with value. Because of the alienating distance between the user and

44

the databases, the outcome is indisputably capitalist commodification in the market logic of the internet. In Marx’s view of objects in capitalism, what it means to make, to use, and to buy an object are different, which, in brief, indicates the discrepancy that he termed as the surplus-value. I-I’ in this regard, is the immeasurable material value. Lacan (1968) compared this theory of value in terms applied in psychoanalytic practice, such as need, de‐ sire, and jouissance. Lacan’s version works to grasp the idea of the subjective desire that all the information economy on

the

internet

wants

to

trigger.

Namely, the

surplus-jouissance in French is the pleasure or the libidinal energy that cannot be measured with techno‐ logical gadgets. Well, interesting it may sound, Lacan conceives jouissance exactly as Marx conceived labor value. The former fits well into the theoretical user subjectivity in the capitalist discourses in the internet, since it is very confusing to differentiate I-I’.Lacan’s

45

term helps see the difference I-I’ in the making of informative realities. Though I is immaterial, I’ in the forms of suggestions, advertisements, news etc. can be thought of as fiction‐ ally materialized objects. Marx’s notion of object in this sense refers to what the subject texts, which is materialized. Freud thought of the unfamiliar in the familiar in a similar manner to what I have been conceptualizing in the I-I’ circuits. I is the conscious whereas I’ is the unconscious that is uncanny, even an output of a very alienating mechanism that creates the surplus informa‐ tion. The surplus value regards the subject desires, retained for the desire. The discursive mechanism in the informative discourse responds to the I that would follow I’ in the circuit (technically I-I’-I- I’... nth I’) and create the nth I’ with the nth surplus. In this, the surplus- value is crafted by moderating the user information I. In one way or another, these logics demonstrate that the subject in the I is alienated through the subject in the I’ to the degree of nth I’. The idea is

46

that the discursive functions can answer human desires. The subject’s de‐ sire is privileged in the information in order to prevent the subject's reality. The idea is that the "I" can be more efficient if materialized for the nth time. This in‐ formation behaviorism is a consumerism that does not even let the information speak for itself. The discrepancies between the subject S of information and the ob‐ jet(a) of knowledge that are the outputs of the discursive mechanism do not require blaming the informative discourse for everything. Eventually, they make the de‐ sire real, where information I alienates the subject S in I-... nth I’ circuits. What happens in I-... nth I’ circuit depends on another variable: time. The subject information I, at the time t, is archived in the database contextually. The moment the subject creates information (I) on the platform, this information is discerned in comparison to the prelearned I concerning the subject. How do the discourses learn the codes of the subject? It may be that the subject was in that specific context before, already confirmed the

47

discursive criteria, that the subject preferred to be anonymous, that the subject used a private means to remain anonymous. Depending on the context, the pre-learned codes might vary or not even include the subject as such. Conclusion The informative discourse depends upon the Web where there are varieties for the subject. I would conceive the notion of the web that would be a wholly individuated discourse as well for the user subject through singular quantum computers where all the artificial intelligences make the whole internet into a web only for the singular user’s subjectivity. That theoretical approach might incorporate even more confusing quantum formulation of the subject of the discourse. Here the subject might not be alienated from the surplus of the information that represents the subject through the symbolic mechanism. I would describe this as incorporation since the very idea

48

concerns corporating the subject in the reality where he/she owns the means of the web making.

49

References Arendt, H. (1958). The Human Condition. Chicago: The University of Chicago Press. Bourdieu, P. (1986). The forms of capital. In: Richard‐ son, J., Handbook of Theory and Research for the Sociology of Education. Westport, CT: Greenwood: 241– 58. Descartes, René (1641). Meditations. Freud, Sigmund (1920). Beyond the Pleasure Principle. Horkheimer, Max and Theodor W. Adorno (1972). Dialectic of enlightenment. Lacan, Jacques (1936). “Principe de réalité" Lacan, J (1955). “Psychanalyse et Cybernetiqué” Lacan, J (1967) . "La psychanalyse dans ses rapports avec la réalité" Lacan, J (1953). “Le symbolique, l’imaginaire et le réel” Lacan, J (1968). D’un Autre à l’autre

50

Lacan, J (1971). D'un Discours qui ne serait pas du semblant Lacan, J (1972). “Du Discours Psychanalytique” Lyotard, J. F. (1991). Logos and Techne or Telegraphy. In Inhuman: Reflections on Time. Stanford University Press, (pp. 47–57). Marx, K. (1858). The grundrisse, (pp. 690–712). Marx, K. (1977[1867]). Capital (B. Fowkes, Trans.). New York: Vintage. Moulier-Boutang, Y (2011). Cognitive capitalism. Polity. Vercellone, C (2007). From formal subsumption to general intellect: Elements for a Marxist reading of the thesis of cognitive capitalism. Historical materialism, 15(1), 13–36.

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II Language in Artificial Intelligence

"Every sign, linguistic or nonlinguistic, spoken or written... in a small or large unit, can be cited, put between quotation marks; in so doing it can break with every given context, engendering an infinity of new contexts in a manner which is absolutely illimitable. This does not imply that the mark is valid outside of a context, but on the contrary that there are only contexts without any center or absolute anchoring [ancrage]. This citationality, this duplication or duplicity, this iterability of the mark is neither an accident nor an anomaly, it is that (normal/abnormal) without which a mark could not even have a function called “normal.” What would a mark be that could not be cited? Or one whose origins would not get lost along the way?" (Derrida 1988, 12)

52

Sigmund Freud (1925) alludes to writing and difference that create the absence-presence pair with reference to cognitive tools such as the Wunderblok. The difference of absence to presence, presence to absence. They are never separate indeed in Freudian theory. That's why the repressed returns uncannily, through the symptoms of what is absent and in the spaces where it is present. The absence of the repressed in this sense is not represented but represenced in traces. There are traces, traits of the absent, which doesn't turn wholly or make the absent fully present. The absent is never absent. The repressed concerns the archived in Jacques Derrida's Archive Fever, especially inscription. In the Spectes of Marx, the absent is Marx with all of his ghostly presence in the scene of writing. In the Freudian example, the magnet under the surface of the tablet keeps the inscribed magnet dust on the surface in symbolic forms. The surface could be undusted but the traces of the previous symbolic forms could be discerned. Because these cognitive gadgets function with the human users, they are allegories of the human psy‐che for Freud in terms of the preconscious,

53

the uncon‐ scious, and the conscious. The archive does what I de‐scribed as the represencing in the inscription. The Freudian impression of speech is always there in the idea of inscription (to give an example, the writer of this book is inscribing his voice archives in writing these paragraphs). Traces in writing and speech is a fundamental subject in language learning in artificial intelligence. Jacques Derrida’s deconstructive view concerns the performative in the inscription of these, the ‘written’ marks that exist independently of performance. In his light, where‐ as traces cannot perform, living systems do so. Derri‐ da’s concern with traces overlaps with our concern with machine-derived and manipulated traces. Language processing-based computational models deal with traces, not what language does things to living people. Accordingly, the question as to how computational means can be used to classify and manipulate traces and elements is significant. We argue that they can be treated

54

as performative and tagged to elements and variables to pick out ‘speech act types’ and types of contexts. A central concept of the theory of language as imitated and reiterated performance is performativity, highlighted by Jacques Derrida in ‘Signature Event Context’ (1988). Derrida

critiques

John

L.

Austin’s

concept

of

performative utterances in How To Do Things With Words, where Austin has put forward that performativi‐ ty in languaging is based on action that includes ‘con‐ text’ and ‘consciousness’ and human use of ‘illocu‐ tionary’ force. Austin identifies two forms of interrelat‐ ed utterances: constative utterances that define some‐ thing and performative utterances that refer to the act of performing what is said.Derrida concentrates on not action, but signs and speech traces. In this way, he turns to the issue of “the conscious presence of the intention of the speaking subject in the totality of his speech act,” which, he claims, is a highly significant ingredient of performativity (Derrida 1988, 14). He uses the term ‘author’ for the origin and the norm of the language

55

(which he tends to associate with writing) and, further, illustrates how the author’s intention can be decontextualized in different uses of language by the speaking subject’s conscious intention and memory traces. All elements, Derrida stresses, are replete with ‘reiteration’ and ‘recitation.’ For there to be a success‐ ful or an unsuccessful speech, respectively, language should be cited and reiterated. Repetition of consta‐ tives, in Derrida’s view, slightly alter the meaning of the speech acts in ways that are context sensitive. This is Derrida’s perspective of performativity. For Derrida, communication is not only the mediation of thinking, but also what he calls an ‘original move‐ ment’ of traces of the previous meanings of the same. The performative lies in the movement within any given contextual limitation. Derrida stresses the contextual difference of meaning "as accidental, exterior, one which teaches us nothing about the linguistic phenomenon being considered” (1988, 14). For Derrida, the claim that does not include the possibility of being "quoted" is that

56

uttered by an actor on a stage, performative utterance becomes void and deprived of any possibility of a variable meaning understood contextually. Performance is replete with variability, and iterability, of context. This general iterability means that an utterance is coded: Could a performative utterance succeed if its formula‐ tion did not repeat a “coded” or iterable utterance, or in other words, if the formula I pronounce in order to open a meeting, launch a ship or a marriage was not identifiable as conforming with an iterable model, if it were not then identifiable in some way as a “citation”? (Derrida 1988, 18) For Derrida, reiterability, re-citeability of any semiotic sign/trace is an inherent characteristic of language. The origin and intention become insignificant as this reiteration proceeds, allowing the sign to be used for new possibilities. Iterability does not simply mean that any performative utterance is a repetition of a norm. Different contexts yield different results, and a reitera‐ tion cannot be pure. This general iterability assumes that there is

57

residual for the return of the same in the process of variation between each item re-uttered. Each re-uttered item will vary in the process of constating. The differential residual will make it impossible for a re-presenting or an absolute return of the original in which the spectral author, or the linguistic sign’s past, is displaced since it becomes insignificant. Language Learning A never-ending goal of the research on human–ma‐ chine interaction has been to achieve a state where humans and computers may have natural conversations. In recent decades, there have been overwhelm‐ ing advances in the competency of computers to recognize, parse and understand language and images, and generate responses in the context of conversations, especially by the help of the new practical deep neural network models developed in relatively indepen‐ dent laboratories of MIT and DFKI. In particular, when it comes to the use of these state-of-the-art models in daily life, it is imperative

58

to take into account the devel‐ opments in the laboratories of major commercial technology firms. In these laboratories, there are quite a few models that understand natural language in constrained domains, goal-oriented dialog contexts, no matter how stilted digitized voices of these models might sound. Yet, even with the current advanced models developed in these laboratories, we still see artificial agents that generate speech in a limited num‐ ber of situations for limited number of purposes, such as reserving a place in a restaurant in the case of Google’s Duplex, or playing chess in the case of IBM’s Watson (Danaher 2018). In particular, machine learn‐ ing-based natural language processing systems are still struggling to model simple human-like communi‐ cation, such as conversations. They do not go beyond constant contextual constraints to engage in a flowing conversation that can force the artificial agent (bot or AI assistant) to adjust to human agents’ natural linguis‐ tic systems instead of language systems adapting to the artificial agent.

59

The following unpacks the recurrent nets or recurrent neural networks (RNN) for the treatment of “natural” in the process of designing and developing artificial agents. Recurrent nets are designed for pattern recognition in data, such as text, numerical data, or images. The algebraic functions of these nets are enhanced by the repetitive insertion of similar data in‐ puts in a way as to get close to the way human memo‐ ry operates to use language in the context of conver‐ sation. In other words, RNNs take the input as some‐ thing they have already recognized. For instance, a present case of an input would be someone speaking to Amazon’s Alexa to play a particular song on Spotify (“Alexa, can you play Billie Eilish’s ‘Bad Guy’”), and Alexa completes this task by parsing the words, recognizing the traces of each word and the entire sentence structure by means of its previously recorded audio dataset, as well as grammar structures. In this sense, these models have two sorts of inputs at work: the one from the past (encoded data) and the one at the present (input data), which determine how recurrent nets are supposed to respond to new inputs.

60

This procedure is accepted as the same as humans do in real life and is termed ‘natural language processing’. This paper questions the treatment of the “natural” in RNN models in particular, and in machine learning-based NLP models in general. As we will see, “recurrent” or repetitive dimension of language learning requires an examination on the performative aspects of language traces (as opposed to the formal abstraction of models that appeal to the “natural”). The article begins with a description of NLP architecture deployed in the research on machine learning, with an overview of the particular neural networks that have been found applicable for developing algorithmic agents toward commercial purposes. The next section outlines the processes through which language gets recorded and indexed into big datasets, and employed by recurrent neural networks based on machine learning. Then, in the third section, the article takes a theoretical detour through Jacques Derrida’s rework of per‐ formativity underlines the trouble with the natural, and to propose the

61

performative as a better concept to grasp the functions of recurrent nets. Human-like Performance Human-like performance is a common denominator in broad definitions of AI. One of the most cited books in the field, Russell and Norvig’s Artificial Intelligence: A Modern Approach, elaborate a number of leading definitions and find out that ‘doing like a human’ and ‘hu‐ man rationality’ are common characteristics (Russell and Norvig 2016). This is no surprise, since these definitions follow Alan Turing’s footsteps, and that Turing’s ideal computer was based on imitating human-like speech (Turing 1950). Various versions of Turing’s fa‐ mous test illustrate why certain schools approach AI as an embodiment with human-like traits, and thus treat these projected embodiments of humanlike traits as natural fixes. Contemporary research on the develop‐ ment of AI relies on a similar understanding of the hu‐ man mind and sign systems, which, in most

62

current cases, show a synthesis of language and articulation, imitation and data processing. Natural language processing is a comprehensive prac‐ tice

of

predominantly

statistical

and

broadly

computational techniques used in the process of analyzing, parsing and representing language (Allen 2006). NLP research generally started in the 1950s thanks to Turing’s work, which provided the research with the basic criteria, the Turing Test, toward successful intelligence models (Chowdhury 2003). NLP techniques have evolved from earlier models that processed sentences in minutes in the 1950s to the search engines that process large texts in seconds in the 2000s (Cambria 2014). NLP techniques are an integral part of digital tools from computers to smartphones that provide them with various tasks at multiple levels such as parsing language, sentence breaking, part-of-speech tag‐ ging (POS), named-entity recognition (NER), optical character recognition (OCR) and machine translation. Recent NLP research concentrates on machine learn‐ ing methods due

63

to the growth of machine learning mechanisms and algorithms in the applied fields, such as pattern recognition. In earlier models, NLP tasks in‐ corporated simplistically trained support vector ma‐ chines (SVM) to arrive at the threshold between (two) groups of linguistic data (e.g., sentences) whose distance from each particular datum point (e.g., words) is maximized, disregarding many points as outliers. In the last decade, however, NLP tasks have been supported by various neural

networks

with

sophisticated

vector

representations, and the research has yielded powerful outcomes of using these networks on NLP tasks. This increasing use of neural networks is a result of ma‐ chine learning methods that allow the automated aspect of representing language by learning, which tran‐ scends traditional machine learning techniques that rely heavily on manual touches (1) (Socher and Perely‐ gin; Collobert et al.).

64

NLP systems are based upon a common process of initial training of their network models. Engineers in this process supervise the learning function of the system’s network and the production of linguistic output after processing an input. Breaching points between linguis‐ tic actions (talking) and performance goals (talking in a particular context for a particular task) are fulfilled manually (Conneau et al. 2017). Unsupervised learning is also another method deployed. In this case, after a process of supervised learning, an NLP network learns the

logic

of

computation

and

processing

data

autoregressively, that is, by using the computations of the previous step in the computations of the next step. In this case, while certain tasks and performance goals are given for the initial supervision, breaching points between

65

linguistic actions (talking) are connected. For recent examples, see Kelly and IBM (2018) At first glance, this interactive approach to research brings forward not only the importance of machine learning, but also the significance of conducting hu‐ man-like interactions (Bennett et al. 2003). However, ‘interaction’ is a form/trace mediated exchange that is separated from action, how humans feel or how hu‐ mans use multimodal activity. A closer look at the infrastructure of NLP models thus opens up the question of ‘natural’ as in the human world. Once asked, it will be argued that misassociations with the ‘natural’ place unnecessary limits on the architecture of NLP systems. Furthermore, in mistreating the human-like as a fixed norm, as in the unchanging laws of physics, NLP systems also incorporate the ‘context’ component into machinic models as a constant as opposed to a vari‐ able. There are multiple machine learning methods that do the work of processing various forms of data through

66

dimensions that are composed of representations. Re‐ cent methods and models that have been employed to master the “natural” come forward with regard to the design of commercial AI products. The model of recur‐ rent neural networks (RNNs) has been put into opera‐ tion in the designs. Facebook’s Mechanical Turker De‐ scent researchers Yang et al. and Google Duplex engi‐ neers propose the interactive process as a better method to produce natural language since humans learn a language in a natural environment (Yang et al. 2017; Leviathan and Matias 2018). It is therefore imperative to scrutinize

the

architecture

of

recurrent

nets,

a

fundamental feature of major machine learning research on language process‐ ing. We will unpack the relations between recurrent nets and the “natural” to clarify the trouble with the ‘context’ component of these nets. Recurrent neural networks Whether recorded data are distributed similar to the ‘human world’ or not is a prominent issue in NLP

67

systems. Given that the data are not significantly different from those found in the human world, it is hypothe‐ sized to be reliable. The distributional hypothesis is re‐ lated to the context component of any NLP system. Distributions represent words with similar meanings by vectors, and the hypothesis makes the strong assump‐ tion that these words will only be uttered this way in similar contexts. The distributional vectors function to grasp the neighbors of a particular word within some borders known as windows. This procedure called “word embedding” computes similarities between vec‐ tor representations through various formulations such as cosine. Usually, these embeddings are obtained through initial pieces of training in large datasets, by subsidiary goals such as forecasting a word used in a particular context (Mikolov et al. 2010). Context is pictured as a psychological force that ‘works’ to get se‐ mantic information from the data. Because windowing reduces the number of dimensions, word embeddings are useful in deciding on the context for NLP tasks. Machine learning-based NLP models in general, and RNNs in

68

particular, represent not only words, but also sentences employing

these distributed representations (word

embeddings). RNNs perform the same computational representation sequentially, that is, they do the same computation in each step of the sequence, and every other step de‐ pends on the computation of the previous step of the sequence. Previous computations function as memory traces that explain the present state of the information. Popular NLP tasks such as textual predictions in search engines, machine translation, use this modeling (Mikolov et al. 2010). Because

RNNs

process

linguistic

information

sequentially, their algorithms can recognize sequentiality as embedded in language. RNNs parse language into units of a sequence, and these units represent charac‐ ters, words or sentences. In essence, RNN uses a lexi‐ cal semantic approach to such units. Depending on the meaning of use, each new unit might change its semantic

69

meaning based on the previous unit (like compound words). RNNs can capture various mean‐ ings through the mediation of its re- adjustable and sequential function to model texts of any length from words to longer documents (Tang et al. 2015). Various versions of the Turing Test have shown why re‐ search on AI strives to compose models with natural human

characteristics

such as language. In the

representative version of the test, there are three participa‐ tors: an interrogator, a human and a machine. If the interrogator cannot tell who is human and who is the machine, or if the machine can trick the interrogator, it passes the test as “intelligent.” Turing proposed his digital computer model as a self-learning machine using an audio dataset recorded to tape. The earlier models of imitative AI relied on the mediation of data between the machine and the human, or the mediation between “intelligences” on the basis of lingual signs (see Shannon and Weaver 1949).

70

Turing’s proposal for intelligence did not explicitly refer to an AI’s understanding of natural language in a dialog but, rather, how language could be learned and imitat‐ ed to pass the test. In this vein, the Turing design concentrates on mere imitation without recurrence. RNN, however, is designed not only to imitate the natural, but also approximate and fixate the natural through repetition. Their design incorporates learning the architecture of language. Performing beyond imitation requires repetition of similar patterns in different con‐ texts. It thus demands their embedded ability to learn structures in language. Repetition of the learned patterns and then putting together small pieces to make a meaningful whole, using this larger structure in another step and then reiterating this process help RNN algorithms

build

exhaustive structures of words,

sentences and dialogs from a limited dataset. The infrastructure of this particular NLP model highlights these

themes

of

linguistic

performance

and

contextualization, as well as an orientation toward an ultimate goal (for any NLP framework, this process of

71

objectifying

a task with a goal is known as

incentivization). The whole is associated with differing treatments of gathering, processing and generating data. RNN architecture focuses on language as “natural”. Despite innovative and powerful techniques, we argue that the concept of “natural” needs to be problematized by taking a performative approach to language. Jacques Derrida’s articulation of performativity would offer an analytical tool for such a goal. Linguistic performativity provides us with a variable of context. In this way, rather than offer a fixed meaning of “conver‐ sation as context”, one can see the differing meanings of each word, sentences and texts not only within a given dataset, but also in their interactively generated equivalents. As such, we will now turn to a survey of relevant aspects of Jacques Derrida’s view of the per‐ formative. The results show a problem immanent to the RNN architecture in particular and, it is suggested, NLP systems in general.

72

As long as context as a variable is not included in NLP models, there is no possibility of the new. Derrida’s de‐constructive approach, however, opens up a contextu‐ al or even creative approach to language. The only in‐ vention would be the re-invention of speech act, followed by a supervised narrowing that is sensitive to context. In the case of Facebook’s and Google’s AI re‐ searches, the proposal is that of simple contexts run by enormous data, and this is because the researchers conceive unusual events to be usual in big data (Yang et al. 2017; Michaely et al. 2017; Oord et al. 2017). The data structure of RNNs is inevitably encoded at the level of algorithm, grammar, subject– object relation and the editorial code, and thus the possibility of new contexts is always limited by these operations.

Hence,

RNNs

function

through

contextualization, as evidenced by their linguistic nature. This restrains their ability to investigate the critical domain upon which contextual variations parasitize the original data and produce the new. What this means for us is that even those intentions and contexts that are not included in the master algorithm of RNNs can be

73

included through the artificial agent’s entrance into the game of linguistic performativity that constitutes the agent’s ability to do what it is not encoded to do. Conclusion In casting off the possibility of the interactive performance, the research on AI underrepresents the contextual spirals that are needed for the master game plan. The problem with NLP is that it treats the world as constants in the “natural” used in physics. This paper has argued that humans do not live in such a world; we live, perform, reiterate and recite traces, and language is no exception. The ways we inhabit the world allows what Alan Turing calls imitation, and what Jacques Derrida calls repeating with differences. Each time we repeat a linguistic trace in the present, we do it differently from what we did in the past. The context, or space, revolves around the words and sentences in relation to the previous uses. NLP’s language systems fail to grasp the contextual spirals, an integral part of human performance

74

and languaging, since speech traces, as a consequence of actions/activity, have to be performative. References Allen JF (2006) Natural language processing. Encyclo‐ pedia of cognitive science Austin JL (1962) How to do things with words. The William James lectures delivered at Harvard University in 1955. Clarendon Press, Oxford Bennett IM, Babu BR, Morkhandikar K, Gururaj P (2003) US Patent no. 6,665,640. US Patent and Trademark Office, Washing‐ ton, DC Chowdhury GG (2003) Natural language processing. Ann Rev Inf Sci Technol 37(1):51–89 Conneau A, Kiela D, Schwenk H, Barrault L, Bordes A (2017) Supervised learning of universal sentence representations from natural language inference data. arXiv preprint. arXiv:1705.02364 Danaher J (2018) Toward an ethics of ai assistants: an initial framework. Philos Technol 31(4):629–653

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Derrida J (1988) Signature event context. Limited Inc. Northwestern University Press, Evanston Derrida J (1993) Specters of Marx: The State of the Debt, the Work of Mourning and the New International Freud, S (1925) A Note upon the 'Mystic Writing Pad IBM (2018) The new AI innovation equation. IBM Blog. https://ibm.com/watson/advantage-reports/future-ofartificial-intelligence/ai-innovation-equation.html Kelly K, IBM (2018) What’s next for AI? Q&A with the co-founder of Wired Kevin Kelly. IBM Blog. https://ib‐m.com/watson/advantage-reports/future-of-arti ficial- intelligence/kevin-kelly.html Leviathan Y, Matias Y (2018) Google duplex: An ai sys‐ tem for accomplishing real-world tasks over the phone. Google AI Blog. https://ai.googleblog.co m/2018/05/duplex-ai-system-for-natural-conversa‐ tion.html Michaely AH, Zhang X, Simko G, Parada C, Aleksic P (2017) Keyword spotting for Google assistant using contextual speech recognition. In: Automatic speech recognition and understanding workshop (ASRU), 2017 IEEE. IEEE, pp 272–278

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III The Cookie Dispositif

Cookies (http cookies, web cookies, browser cookies, internet cookies), like the bakery kind, are composed of bits of information, but unlike the bakery kind, they register, identify, profile, and categorize the internet user's information. Cookies are not meant to be artificial intelligence but they are intelligent agents designat‐ ed to have logical responses to in terms of the user in‐ formation. There is the context of the server hosting the website that the user visits, and the cookies match the user preferences to serve information tailored for the user after the user gives consent or sets the con‐ sent preferences. The most common cookie is the one used for authentication information such as passwords and usernames. The tracking cookies are what concerns the most in this chapter since these cookies save the user

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information to individuate the userâ's subjective traces. This function of the cookies has been questioned in terms not only of privacy but also of the ide‐ ology around information. This chapter tracks the ways how cookies learn about the user's conscious and unconscious needs and wants? Are all the cookie settings enough to have an answer about the

reality

of

the

user

desires

and

subjective

characteristics? Are the logics in what I de‐ scribe as the cookie dispositif predetermined before they learn about the subject for real? What's the matter with all the circuits of information on the internet? Technically I resort to the psychoanalytic concepts, mainly that of the dispositif, as the method to answer these questions. I point to the logics of and in the cookies, to distinguish the user information (I) from the cookie in‐ formation (I'). The former, I, refers to what the latter saves before registration toward tailored suggestion I', that is, whatever the user might be interested in. The I- I' difference is the theoretical void that is filled in with the

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senses of information through these information circuits of I-I'. In the internet of objects, the common components are texts, sounds, and images, which refer to symbols that make sense the audio and visual dimensions in various contexts of the internet. Jean Louis Baudry conceptualized this psychoanalytic notion of dispositif (Baudry 1975) to see how audiences get identify with sights, characters, colors, sound and so on and so forth on the screen, where he suggested that the viewer experience is not so ideologically deterministic, that is, the subject is not depen‐ dent upon the dispositif of reality-making but does not have the access to the means of materializing that reality. Baudry's term has been adapted to a number of research in humanities and psychoanalytic studies of film and arts (Zajc 2015). It would not be an exaggera‐ tion to conceive of the internet cookies through the no‐ tion dispositif. The very idea of the dispositif is to re‐ veal the mechanisms and logics in knotting the contextualized subject into the reality represented. In the internet, there is no specific

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context other than websites, platforms, applications etc. The context is varying, but in all of the contexts the logic of the cookies does not vary significantly. What could be termed as the cookie dispositif is an object which is very small and might have been disregarded, even undermined in digital research. Cookies are the symbolic mechanisms that register and patternize the internet user's information toward consump‐ tion behaviors. To put simply, what a cookie dispositif does with the user information is pretty similar to how a human being bakes a cookie. The ingredients are the intermediate data, processed or not, that have to be crafted through logics and directions that apply to bak‐ ing a cookie. Well, similarly, after giving consent or managing through options, the user leaves the fate of the information

to the cookies' decisions, responses,

suggestions. Cookies bake the input of the information (I) as an output (I') for the user to do this or that, like this or that, go there to see and read this or that, buy this, buy that and so on. They register the subject information to

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discern patterns in it, and store this discerned information distinctly for future uses. Learning to Cookie In formation refers to an encoded bit about the pattern of an information processed. The cookie dispositif tags interests and suggestions after this information. Machine learning concerns the logic of informing the I, the process of the user information prior to I'. It is the procedure to perform cognition on the information. In simple words, intelligent agents do the correlations in the information and deploy these correlations for further information. New textual or visual information is learned in comparison to the pre-learned symbols and close meanings. In this sense, I-I' is not static. The recognition process refers to extracting data from the information. Recognition agents use statistical models to predict patterns in the user-generated information to store those patterns into datasets. Sensors of cogni‐ tion may or may not register objects in the information correctly. For

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example, if an information input is the word for or an image of a "cookies," the dispositif has to compare it with all the "cookies" stored in the data‐ base, and then pointing out that it is a "cookie" based on the patterns in its appearance. However, if there is no information about "cookie" in the database, it should be added by supervisors. The process of being included in the database aims to balance by repeated coding. Machine learning performs an object analysis of the information: it unpacks the information it knows into smaller units. For example, if the "cookie" means invasive, the learning codes make inference according to the context. From recognition to predic‐tive-texts on smartphones, this recognition function exists in virtually every digital machine and application. The difference between how a human recognizes an image and how a pattern recognition agent analyzes the same image is basically twofold. A pattern recogni‐ tion agent does not have the ease brought on by re‐ duction that humans achieve by operating at the level of the imaginary

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dimension of reality, but instead simu‐ lates pattern cognition through reduction, by processing. Language, sound and image are fundamental registers in machine learning. The patterns in these registers are recognized

and

data-formed

in databases. When

language, such as words and sentences in a typewritten or verbal text, indicates textual symbols, images point to the imaginary of the web server's space. New textual or visual inputs are machine-learned by regularly checking previously informed inputs. In this sense, in‐ puts are kinetic and variable. Another variable in these inputs is the context of the input recognized. The meaning of the word "cookie" may converge to close meanings and common sense. Symbolic data served in the data structure through machine learning will be devoid of an initial context (the sentence or im‐ age within which the cookie was originally informed). As the machine keeps recognizing various cookies in different contexts, it reaches the balanced data of what a “cookie” is. Each reiterated input is limited by the context of a new

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"cookie," that is, to the place and time when a new cookie is recognized and the difference between the pre-learned symbol of "cookie" and the informed cookie as a variant. Symbols may exist independent of inputs after they are stored in the sets. Machine learning can be described as a domain of networks in the server space where intelligent agents such as cookies recognize and register human users into a symbolic logic that correlates data in large sets. As mentioned above, the computing machine has been built on a logic that is historically related to intellectual labor to show two intertwining spaces of hu‐ man-machine interaction in the development of ma‐ chine learning: user-generated information. Pattern recognition and recurrent networks are common in ma‐ chine learning agents that are deployed in social media platforms where humans use digital profiles. The cook‐ ie dispositif in this sense could be viewed as artificial intelligence agents if not artificial beings with con‐ sciousness. They are more or less extensions of the user subjectivity and

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agency but toward limited objectives depending on the context. Cookies mediate the user to the first party of the context where the user is conditioned and the third parties, such as a corpora‐ tion, a brand, a real person, an artificial agent. In other words, cookies represent the user for other internet subjects depending on the context. For instance, if the context is a dating application, then cookies are expected to offer the best match to the user. If the con‐ text is a non-profit organization, then the cookies might lead the user to philanthropic communities. For sure, there is ideology in the very practice of making sense of information. Ideology The term "ideology" originates from the ancient Greek: "idéā" (ἰδέα) and "logíā" (λογῐ́ᾱ) idea and logos. "Idéā" means "form", "law" and "pat‐ tern", and derives from ideiv (ἰδεῖν), meaning "to see". Logos means logic, calculation, and word, and the studies of these. The

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description of ideology therefore would be "logics" of the "dispositif" in terms of laws of patterns and forms. Ideological discerning of the subject information, I, for the material practices of ideolo‐ gies, I". Indeed, more than cookies, there are firms, academies, news outlets, and government agencies that might dis‐ cern this user information I, even for I'. The question‐ ably informative ideology in these social dispositifs, these social entities, would save the I-I' circuits for such materializations as tracking, profiling and sur‐ veillance, functioning to materialize the I ideologically. Here, the cultural codes might be required with or with‐ out legal bases as well to maintain materializing the subject in line with such cultural norms within the limits of their worldviews or simply the ideology. Ideology means formations in culture, economics, politics, and so on: life practices, views, discourses, ideas that organize and represent individuals and communities, and decide the dimensions of the social, political and eco‐ nomic relation in these communities.

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The trouble with the user information here would be stereotypically profiling, nuancing patterns to surveil the subject, loose rights to privacy. The firms prepare agreements, and the governments are asked to take measures against these risks. Indeed, firms and governments have distinct agencies that are responsible for auditing the trust and credibility of the dispositifs, to protect subject rights and prevent subject informa‐ tion from being exploited. Firms also like the subject's trust in social dispositifs or advertisement agencies, ideals of being

objective.

Information

codes

and

subject

agreements, however, are found to be puzzling. Because of these menaces of materialization, and loose privacy policies of social dispositifs, the dis‐ positifs in the 21st century has become a private-pub‐

lic-academic

ideology. Some troubles researched in the literature in terms of these circuits of I-I' include the exposure to misinformation/disinformation that might lead to and strengthen ideologic prejudices and bias‐ es; the user anxieties

about

connection

might

result

in

disindividuation and distraction; advertisements featured

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to users on digital platforms might cause excessive consumption (Turkle 2016). While Dominic Pettman (2016) sees the "distraction" as an adverse but looks at distraction as a mode of subjectivity that has not lost much of a focus, Shoshana Zuboff (2019) expresses the seizure of her user experiences for commercial purposes as "numbing”, and Wendy Chun (2017) looks at the very biases in recognizing data as well as what an I-I' circuit does to the user. What I-I' ideology does can be briefly seen in terms of discrimination, economic inequalities, and arguably social categorization. Platforms and advertising firms work on the sub‐ ject's consumption

behaviors,

government

agencies and

academics like to research social movements and mobility, as well as health. Because social dispositifs are private firms and the subject is a customer of these firms, evaluating

the

subject

information

might

differ:

academics to write research articles, governments to research health, firms to measure consumption. The question is that large pools of subject information might

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be

exploited

through

materializations

that

make

perplexing information and representations out of information. The absence of a representation might infect the reality of an event, if not deprive the reality of the idéā that I' inform. The absence between an event and its representation,

deprives

the

subject’s

objective

information about an event of envi‐ ronment and history. To put another way, dispositif ide‐ ologies relate and turn discrete subject information into formations without thinking about the history of information. When dispositifs inform an idéa about the subject, this becomes a materialization. Enumerating, sorting, performing have been conceived as common mental tasks since René Descartes' cogito ergo sum ("I think; therefore, I am") put consciousness at the center of the human being. The cartesian idea of cogito privileged the internal, that is, the conscious mental performance of thought through which the hu‐ man being can access information in the external world, such as perceiving things through a conscious logic for being

90

in the world. From cogito to the modern digital media, human thinking and being in the external world has been influential in technology. The logic of digital information is allegorized through the human mental performance of thinking via modern theories of mind. Cybernetics emerged from the theories of the mind in the early 20th century. The internet of objects reflects pre-learned codes on the subject information to re-inform the subject information. Subject informa‐ tion hence becomes huge and non-formed material, something that Sigmund Freud might have described as "id," not the conscious subject. The history of computer advanced with the modern theory of mind and cognition, as well as cybernetics. Jacques Lacan (1955) argued that cultural symbolics have "autonomous realities" that spread social codes, rules, meanings to the individual in his course on the Freudian ego, where taught a course on cybernetics to theorize symbolic logics to explore the finite logics in the cybernetic circuits. Language is the symbolic di‐ mension of the computer, as Lacan's view of reality has a

91

lot in common with his expression of the cybernetic circuits. He viewed a pre-subjective, symbolic realm of texts, forms, and meanings around the human being. The symbolic is the social, intersubjective dimension of reality, which has a logic in any place wherever. It is the realm where a person must become the subject of "reality", before becoming the "thinking subject". The symbolic is there before the individual becomes the conscious subject in the world. The subject is the discursive agent of signs, when symbolic logic is the realm of ideologies. Informative research of cookies therefore describes the symbolic logic through which the subject's informative mind has a report to the discursive formations in the I-I' circuits. Performances, codes, and practices refer to symbolic logics in learning, re- depicting discerned patterns. Mind, through verisimilitude between the symbolic and the cybernetic, for Lacan, refers to the symbolic of en‐ coded patterns, representing learned patterns from the subject information.

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Surplus-information Karl Marx (1867) described a machine in terms of the wearisomeness, mental drudgery and fatigue of work‐ ing with the machines at his time. To Marx, the combi‐ nation of all the "easy instruments, set in motion by a single motor, constitutes a machine." An intellectual worker at his time, he was interested in measuring and calculating labor in terms of quantities. For this, the term he used was commodification to refer to objectifi‐ cation in consumption culture where objects are materialized by the human being but turns into a completely distinct and alienated thing in capitalist markets. He referred to this in his simple formulation of Money- Commodity-Money' (M-C-M') circuits. The I-I' circuits on the internet are similar to this. Marx described immaterial labour for such materializations as intellectual work that is actually the information that the user creates. Technically, what is stored by the user's consent is the material with value. Because of the alienating distance

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between the user and the databases, the outcome is indisputably capitalist commodification in the market logic of the internet (Flisfeder 2015). In Marx's view of objects in capitalism, what it means to make, to use, and to buy an object are different, which, in brief, indicates the discrepancy that he termed as the surplus-value. I-I' in this regard, is the immeasurable material value. Jacques Lacan compared this theory of value in terms applied in psychoanalytic practice, such as need, desire, and jouissance. Lacan's version works to grasp the idea of the subjective desire that all the cookie economy in the internet wants to trigger. Namely, the surplus- jouissance in French is the pleasure or the libidinal energy that cannot be measured with technological gadgets. Well, interesting it may sound, Lacan conceives jouissance exactly as Marx conceived labour value. The former fits well into the theoretical user subjectivity in the capitalist dispositifs in the inter‐ net, since it is very confusing to differentiate I-I'. What the cookies do I', is to digitally extend the psychic dispositif. In one way or another, Lacan's term helps see the difference I-I' in the making of

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informative realities. Though I is immaterial, I' in the forms of suggestions, advertisements, news etc. can be thought of as fictional materialized-objects. Conclusion Marx's notion of object in this sense refers to what the subject texts, which is materialized. Freud thought of the unfamiliar in the familiar in a similar manner to what I have been conceptualizing in the I-I' circuits. I is the conscious whereas I’ is the unconscious that is uncanny, even an output of a very alienating mechanism that creates the surplus in‐ formation. The surplus value regards the subject de‐ sires, retained for the desire. Cookies respond to the I that would follow I' in the circuit (technically I-I'-I-I' to I- nth I'), and create the nth I' with the nth surplus. In this, the surplus-value is crafted by moderating the user in‐ formation I. In one way or another, these logics demonstrate that the subject in the I is alienated through the subject in the I' to the degree of nth I'. The idea is that dispositifs can answer human

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desires. The subject's desire is privileged in the information in order to prevent the subject's reality. The idea is that I can be more efficient if materialized for the nth time. This information behaviorism is a consumerism that does not even let the information speak for itself. The distinction between the subject and the cookie is vital, though it is not to say that cookies are to blame for everything. Eventually, they make the desire real, where information I alienates the subject in I-I' circuits. This paper has briefly viewed the cookie dispositif to demonstrate the logics in the registration and cognition of the internet user's information. What differs in the internet is the context in which the user is conditioned vis-a-vis the cookies, as well as the time and the re‐volving circuits of information, I-I'. Materializations in these circuits go beyond the mere desires but look for predicting the subject's future on the internet. Not far from this is the predictive research in informat‐ ics that refers to the aforementioned codes that dis‐ cern and

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(de)subjectivize patterns in the I, which could be deployed to predict a future of materialization. What is presumed in this is that the subject needs to be kept within the internet of things only for a future output.

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Cookiegraphy Baudry, JL (1975) Le dispositif. Communications, 23(1): 56-72. Chun, WHK (2017) Digital and New Media. A Companion to Critical and Cultural Theory, 387-401. Descartes, R (1641) Meditations. Flisfeder, M (2015) The entrepreneurial subject and the objectivization of the self in social media. South Atlantic Quarterly, 114(3): 553-570. Lacan, J (1955) Psychanalyse et Cybernétique. Marx, K (1867) Capital Vol. 1. Pettman, D (2016) Infinite distraction. John Wiley & Sons. Turkle, S (2016) Reclaiming conversation: The power of talk in a digital age. Penguin. Zajc, M (2015) Social media, prosumption, and dispositives: New mechanisms of the construction of subjectivity. Journal of Consumer Culture, 15(1): 2847. Zuboff, S (2019) Surveillance capitalism and the challenge of collective action. New labor forum Vol. 28, No. 1, pp. 10- 29.

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A Psychoanalytic View of the Web, AI, and Cookies Tolga Yalur

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