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Enregistrement W2973450498 · doi:10.1353/vcr.2019.0015

Seeming Human: Artificial Intelligence and Victorian Realist Character by Megan Ward

2019· article· en· W2973450498 sur OpenAlexvenueno aff
Tara K. MacDonald

Notice bibliographique

RevueVictorian review · 2019
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueHistory of Science and Medicine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCharacter (mathematics)RealismVerisimilitudeEpistemologyAestheticsSociologySituatedPosthumanismHumanismReflexivityLiteraturePhilosophyArtComputer scienceArtificial intelligenceSocial science

Résumé

récupéré en direct d'OpenAlex

Reviewed by: Seeming Human: Artificial Intelligence and Victorian Realist Character by Megan Ward Tara MacDonald (bio) Seeming Human: Artificial Intelligence and Victorian Realist Character by Megan Ward; pp. 216. Ohio State UP, 2018. $84.81 cloth. What does the work of Anthony Trollope and Alan Turing have in common? More than you might think, according to Megan Ward’s innovative new book, Seeming Human. It is an original and playful account of the ways in which we can see mid-twentieth-century forms of artificial intelligence as both “afterlives of and theories for realist character” (99). Ward emphasizes that critics have read the history of the realist novel largely through its use of formal techniques of interiority, privileging free indirect discourse and interior monologue. She argues that while we are comfortable with acknowledging realism’s self-reflexivity, we are less comfortable admitting the fictionality, or the “non-human-ness,” of fictional characters (2). Since characters are words on a page that are meant to seem human, Ward argues [End Page 287] that intelligent machines are more useful than actual humans in offering us a theory of character verisimilitude. Such claims allow her to attend to aspects of realist character that have been considered anti-realist or awkward, such as mechanicity and flatness. She suggests that we have situated these traits outside of realism not because they are inherently anti-realist but, rather, because we do not want to identify such qualities as human. Rather than sticking to the humanist tradition, we might imagine realist character as part of the history of the posthuman. Each chapter pairs an aspect of fictional character—development, predictability, flatness, and mind—with forms of AI. Chapter 1 uses the cybernetic feedback loop to explore repetitive middle-class domesticity in novels by Charlotte Mary Yonge, Elizabeth Gaskell, and Margaret Oliphant. Chapter 2 pairs information theory, specifically the stochastic system, with patterns of predictability in Dickens’s Bleak House, Collins’s No Name, and Braddon’s Lady Audley’s Secret. Turing’s imitation game, better known as the Turing Test, is used to read Trollope’s so-called flat characters Lizzie Eustace and Ferdinand Lopez in chapter 3. And, finally, chapter 4 contrasts two models of artificial mind—the perceptron and the physical symbol system—with Thomas Hardy’s and Henry James’s differing representations of human consciousness. The brief coda, on David Lodge’s novel Thinks . . . (2001), serves as a somewhat odd conclusion to the book. Seeming Human offers various new ways to conceive of character and specifically focuses on how we can think about character representation outside of narrative techniques and even cultural values that stress psychological interiority. In chapter 1, the feedback loop, which “need not result in transformation to show adaptation,” is used to show that development, for characters, need not be synonymous with change (23). Instead, development can happen through automated daily routine. In Yonge’s The Daisy Chain (1856), she argues, characters’ reliance on domestic routine—a project of edifying self-making—does not make them any less realist. Ward’s notion that embodied acts of routine do not always gesture to or symbolize a hidden self, but instead, are the self is the first of her attempts to separate character from interiority. Indeed, this is key to the second chapter, which focuses on mystery and sensation fiction. She convincingly points out that characters in sensation novels have been dismissed as superficial or repetitive, but that if we reorient our attention from psychological depth to an understanding of the human as part of a system, then these novels have much to tell us about the formation of character. She builds on, and challenges, Alex Woloch’s work on character systems, resisting the idea that secondary or flat characters only help to differentiate the protagonist in novels like Bleak House; instead, characters like Skimpole also work to “accrete meaning across the surface of the novel” (51). Thinking about character as information circulation, the chapter offers clever close readings of Lady Dedlock’s portrait, of blank spaces in No Name, and of the multiple versions of Lady Audley. [End Page 288] Building on these claims, chapter 3 presents “a theory of realist...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,911
Score d'incertitude au seuil0,996

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,050
Tête enseignante GPT0,271
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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