186 research outputs found
The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence
This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing
The Turing Deception
This research revisits the classic Turing test and compares recent large
language models such as ChatGPT for their abilities to reproduce human-level
comprehension and compelling text generation. Two task challenges --
summarization, and question answering -- prompt ChatGPT to produce original
content (98-99%) from a single text entry and also sequential questions
originally posed by Turing in 1950. We score the original and generated content
against the OpenAI GPT-2 Output Detector from 2019, and establish multiple
cases where the generated content proves original and undetectable (98%). The
question of a machine fooling a human judge recedes in this work relative to
the question of "how would one prove it?" The original contribution of the work
presents a metric and simple grammatical set for understanding the writing
mechanics of chatbots in evaluating their readability and statistical clarity,
engagement, delivery, and overall quality. While Turing's original prose scores
at least 14% below the machine-generated output, the question of whether an
algorithm displays hints of Turing's truly original thoughts (the "Lovelace
2.0" test) remains unanswered and potentially unanswerable for now
Towards a more natural and intelligent interface with embodied conversation agent
Conversational agent also known as chatterbots are computer programs which are designed to converse like a human as much as their intelligent allows. In many ways, they are the embodiment of Turing's vision. The ability for computers to converse with human users using natural language would arguably increase their usefulness. Recent advances in Natural Language Processing (NLP) and Artificial Intelligence (AI) in general have advances this field in realizing the vision of a more humanoid interactive system. This paper presents and discusses the use of embodied conversation agent (ECA) for the imitation games. This paper also presents the technical design of our ECA and its performance. In the interactive media industry, it can also been observed that the ECA are getting popular
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The Turing test as interactive proof
In 1950, Alan Turing proposed his eponymous test based on indistinguishability of verbal behavior as a replacement for the question "Can machines think?" Since then, two mutually contradictory but well-founded attitudes towards the Turing Test have arisen in the philosophical literature. On the one hand is the attitude that has become philosophical conventional wisdom, viz., that the Turing Test is hopelessly flawed as a sufficient condition for intelligence, while on the other hand is the overwhelming sense that were a machine to pass a real live full-fledged Turing Test, it would be a sign of nothing but our orneriness to deny it the attribution of intelligence. The arguments against the sufficiency of the Turing Test for determining intelligence rely on showing that some extra conditions are logically necessary for intelligence beyond the behavioral properties exhibited by an agent under a Turing Test. Therefore, it cannot follow logically from passing a Turing Test that the agent is intelligent. I argue that these extra conditions can be revealed by the Turing Test, so long as we allow a very slight weakening of the criterion from one of logical proof to one of statistical proof under weak realizability assumptions. The argument depends on the notion of interactive proof developed in theoretical computer science, along with some simple physical facts that constrain the information capacity of agents. Crucially, the weakening is so slight as to make no conceivable difference from a practical standpoint. Thus, the Gordian knot between the two opposing views of the sufficiency of the Turing Test can be cut.Engineering and Applied Science
Optimising Humanness: Designing the best human-like Bot for Unreal Tournament 2004
This paper presents multiple hybridizations of the two best
bots on the BotPrize 2014 competition, which sought for the best humanlike
bot playing the First Person Shooter game Unreal Tournament 2004.
To this aim the participants were evaluated using a Turing test in the
game. The work considers MirrorBot (the winner) and NizorBot (the
second) codes and combines them in two different approaches, aiming to
obtain a bot able to show the best behaviour overall. There is also an
evolutionary version on MirrorBot, which has been optimized by means
of a Genetic Algorithm. The new and the original bots have been tested
in a new, open, and public Turing test whose results show that the evolutionary
version of MirrorBot apparently improves the original bot, and
also that one of the novel approaches gets a good humanness level.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Can machines think? The controversy that led to the Turing test
Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess-playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing refer to gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely, that Turing's 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones
Can machines think? The controversy that led to the Turing test
Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess-playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing refer to gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely, that Turing's 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones
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