eliza

This paper examines a selection of examples of AI storytelling from film, games, and interactive fiction to imagine the future of AI authorship and to question the impetus behind this trend of replacing human authors with algorithmically generated narrative. Increasingly, we’re becoming familiarized with AI agents as they are integrated into our daily lives in the form of personified virtual assistants like Siri, Cortana, and Alexa. Recently, director Oscar Sharp and artist Ross Goodwin generated significant media buzz about two short films that they produced which were written by their AI screenwriter, who named himself Benjamin. Both Sunspring (2016) and It’s No Game (2017) were created by Goodwin’s long short-term memory (LSTM) AI that was trained on media content that included science fiction scripts and dialogue delivered by actor David Hasselhoff. It’s No Game offers an especially apt metacommentary on AI storytelling as it addresses the possibility of a writers strike and imagines that entertainment corporations opt out of union negotiations and instead replace their writers with AI authors.After watching Benjamin’s films, it’s clear that these agents are not yet ready to take over the entertainment industry, but this trend is growing more common in video games. Many games now feature procedurally generated content that creates unique obstacles, worlds, and creatures. The most well-known example might be No Man’s Sky (2016), but it is not the first; Spelunky (2008), for example, made use of procedural generation many years prior. Although attempts at algorithmically generated narrative are rare, Ludeon Studio’s RimWorld (2016) boasts that its sci-fi game world is “driven by an intelligent AI storyteller.” Its AI, however, became the subject of controversy after Claudio Lo analyzed the game’s code that supports its storyteller and revealed that the program replicated problematic aspects of society, including the harassment of women and erasure of bisexual men.These examples offer insight into issues that have and will continue to arise as AI storytelling advances. This paper addresses questions concerning not only the implications for human authors in the face of this very literal take on Barthes’ “Death of the Author,” but also those related to what AI will learn from reading our texts and what it will mean to look into the uncanny mirror that AI will inevitably hold up to us when producing its own fiction. Though it may be a while before Siri will tell us bedtime stories, it is no doubt a feature that has occurred to Apple, as requesting Siri to do so results in a story about her struggles working at Apple and the reassurance she receives from conversing with ELIZA. ELIZA is one of the earliest natural language processing programs that was created by Joseph Weizenbaum in the 1960s and was designed to mimic a Rogerian psychotherapist by parroting back user input in the form of questions. Siri’s reference to this program is both an acknowledgement of the history of these agents and evokes a future where our virtual assistants grow to become more than canned responses.

This thesis explores how various computer programs
construct poems and addresses the way several critics
respond to these computer generated texts. Surprisingly,
little attention has heretofore been paid to these programs.
Critics who have given the matter attention usually focus on
only one of the myriad programs available, and more often
than not, such scholarship concludes with a disparagement of
all such projects. My work reexamines computer generated
poetry on a larger scale than previously exists, positing
some conclusions about how these texts affect contemporary
theories of authorship and poetic meaning.
My first chapter explicates the historical debate over the
use and limits of technology in the generation of text,
studying similitudes between certain artistic movements and
computer poetry. This historical background reveals that
the concept of mechanically generated text is nothing new.
My second chapter delineates how the two main families of
computer poetry programs actually create these texts.
Computer programs combine existing input text, aleatory
functions, and semantic catalogues, which provides insight
into how humans both create and interact with these
programs. At the same time, this study illustrates the
difficulty in defining the level of intention and influence
by individuals on the textual product, and therefore these
texts challenge our traditional notions of authorship and
the value of poetry. My third and final chapter argues that
contemporary literary theory and poetics creates the
conditions under which computer generated poetry can pose as
a human product. The success of these programs to deceive
readers about the origins of the text becomes clearer with
the results of a survey I conducted in which the respondents
were fooled by the machine more often than not. This
possibility of machine-created text masquerading as human
art threatens many critics, who quickly dismiss the process
and its results as non-poetic, but I conclude that since the
computer complicates foreknowledge of origin in some
contemporary poetic forms, this intrusion by the machine
prompts us to reconsider how we traditionally value and
interpret poetry.

Depending on their purpose, and what we expect of them, chatterbots are equipped with a more or less complex and elaborate artifi ficial intelligence (AI) (see artificial intelligence). Automated assistants and computer game characters are usually expected to operate within a limited knowledge area. For these to function satisfactorily, it may be suffi fficient that they know how to identify and match key words in the question with a predefined fi answer in their database. However, to fluently converse on a number of nonspecifi fied topics— as is required to pass the Turing test— a more sophisticated AI based in natural language processing may be needed. Some of today’s chatterbots are even designed to learn from their previous conversations—in other words, developing their AI as they speak.

A relative of ELIZA named PARRY demonstrates how the opposite principle may also be employed to create a convincing chatterbot. Written by Stanford psychiatrist Kenneth Colby in 1972, PARRY is a program that simulates a patient suffering ff from paranoia. When Parry does not know how to answer, he will aggressively spit out a line of conspiracy theory, thereby forcing his world onto ours. While totally out of context, his response is nonetheless plausible. A similar trick of the trade is used in artist Ken Feingold’s robotic AI installation “Head” (1999), modeling a poetically inclined, slightly disturbed and confused elderly man. Engaging in conversation with Head d requires a signifi ficant share of interpretative effort, ff but it may also be greatly rewarding to those who are willing to invest in it. Another artistcreated chatterbot is Stelarc’s Prosthetic Head , which simulates the artist’s own personality. As it is designed to learn from its conversations, however, this chatterbot may gradually become “more autonomous in its responses” until the artist will “no longer be able to take full responsibility for what his head says” (Stelarc 2003). While most chatterbots are designed to engage in conversation with human partners, artistic experiments have been made in which chatterbots converse with each other. The result may be hilarious, as in director Annie Dorsen’s theatrical per for mance “Hello Hi There” (2010). Here, Dorsen stages two chatterbots self-reflexively fl discussing the famous 1971 debate between philosophers Michel Foucault and Noam Chomsky, on the concept of human nature.

(Johns Hopkins University Press)

An overview and explanation of dialogue systems, especially NPCs, divided into three types: noninteractive dialogue systems, dialogue tree, and parser-based dialogue systems.

A short history of chatterbots (or chatbots), which includes information about artificial intelligence, the chatbot ELIZA and the relative PARRY,