Computing Machinery and Intelligence

By A. Turing. 1950

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Philosophy

Citations in the corpus

LLaMA: Open and Efficient Foundation Language Models (2023, background): “Because Turing (2009) proposed to measure machine intelligence by using language through the “imitation game”, language modeling has been proposed as a benchmark to measure progress toward artificial intelligence (Mahoney, 1999).”
Experience Grounds Language (2020, background): “Launched into computing when Alan Turing suggested the “Imitation Game” (Turing, 1950), the ﬁrst illustrative example of the test brings to bear an issue that has haunted generative NLP since it was possible.”
WinoGrande (2019, methodology): “The Winograd Schema Challenge (WSC) (Levesque, Davis, and Morgenstern 2011), proposed as an alternative to the Turing Test (Turing 1950), has been used as a benchmark for evaluating commonsense reasoning.”
Measuring Massive Multitask Language Understanding (2020, background): “Some researchers have suggested that the future of NLP evaluation should focus on Natural Language Generation (NLG) (Zellers et al., 2020), an idea that reaches back to the Turing Test (Turing, 1950).”
Towards a Human-like Open-Domain Chatbot (2020, neutral): “Unlike a typical Turing test (Turing, 1950), we tell the human judges upfront that they are about to chat with an experimental chatbot and ask them to label what the chatbot says in terms of sensi-bleness and speciﬁcity.”
All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text (2021, methodology): “This task format, inspired by the Turing (1950) Test, is used to compare the quality of machine-generated text to human-authored text and, as models’ fluency improves, to analyze NLG models’ ability to “fool” readers (Garbacea et al., 2019; Ippolito et al., 2020; Brown et al., 2020).”
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions (2020, methodology): “We note that this is different from passing a Turing test (Turing, 1950): we do not wish to fool humans into believing our agent is human, but instead that our agent should be enjoyed as a speaking partner, which we believe is a simpler problem.”
Semantics derived automatically from language corpora contain human-like biases (2016, background): “Machine learning, exploiting the universality of computation (Turing, 1950), is able to capture the knowledge and computation discovered and transmitted by humans and human culture.”
Imitating Interactive Intelligence (2020, background): “” Historically, Turing argued that a machine would be intelligent if it could interact indistinguishably from a human when paired with a human examiner, a protocol he called “the imitation game,” (Turing, 1950).”
A Neural Conversational Model (2015, background): “Amongst the many limitations, the lack of a coherent personality makes it difficult for our system to pass the Turing test (Turing, 1950).”
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question (2015, methodology): “The Visual Turing Test only gives a rough evaluation of the generated answers.”
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses (2017, background): “Building systems that can naturally and meaningfully converse with humans has been a central goal of artificial intelligence since the formulation of the Turing test (Turing, 1950).”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, background): “Evaluation of such non-goaldriven dialogue systems can be traced back to the Turing test (Turing, 1950), where human judges communicate with both computer programs and other humans over a chat terminal without knowing each other’s true identity.”
Unifying Human and Statistical Evaluation for Natural Language Generation (2019, methodology): “In the spirit of the Turing Test, we could consider using the error rate of a human discriminator f hum instead, often considered the gold standard for evaluation.”
Conversational AI: The Science Behind the Alexa Prize (2018, methodology): “For evaluation, we decided not to focus on the Turing Test (Turing, 1950).”
Should We Trust Algorithms? (2020, background): “…in which performance is compared with human ‘experts,’ sometimes in the form of a Turing Test in which the quality of the judgments of both humans and algorithms are assessed by independent experts, who are blinded as to whether the judgment was made by a human or algorithm (Turing, 1950).”