The Experts below are selected from a list of 5304 Experts worldwide ranked by ideXlab platform
Katja Hofmann - One of the best experts on this subject based on the ideXlab platform.
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navigation Turing Test ntt learning to evaluate human like navigation
arXiv: Artificial Intelligence, 2021Co-Authors: Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja HofmannAbstract:A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. We address these limitations through a novel automated Navigation Turing Test (ANTT) that learns to predict human judgments of human-likeness. We demonstrate the effectiveness of our automated NTT on a navigation task in a complex 3D environment. We investigate six classification models to shed light on the types of architectures best suited to this task, and validate them against data collected through a human NTT. Our best models achieve high accuracy when distinguishing true human and agent behavior. At the same time, we show that predicting finer-grained human assessment of agents' progress towards human-like behavior remains unsolved. Our work takes an important step towards agents that more effectively learn complex human-like behavior.
Tomer Ullman - One of the best experts on this subject based on the ideXlab platform.
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a minimal Turing Test
Journal of Experimental Social Psychology, 2018Co-Authors: John Mccoy, Tomer UllmanAbstract:Abstract We introduce the Minimal Turing Test, an experimental paradigm for studying perceptions and meta-perceptions of different social groups or kinds of agents, in which participants must use a single word to convince a judge of their identity. We illustrate the paradigm by having participants act as conTestants or judges in a Minimal Turing Test in which conTestants must convince a judge they are a human, rather than an artificial intelligence. We embed the production data from such a large-scale Minimal Turing Test in a semantic vector space, and construct an ordering over pairwise evaluations from judges. This allows us to identify the semantic structure in the words that people give, and to obtain quantitative measures of the importance that people place on different attributes. Ratings from independent coders of the production data provide additional evidence for the agency and experience dimensions discovered in previous work on mind perception. We use the theory of Rational Speech Acts as a framework for interpreting the behavior of conTestants and judges in the Minimal Turing Test.
Robert M French - One of the best experts on this subject based on the ideXlab platform.
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dusting off the Turing Test
Science, 2012Co-Authors: Robert M FrenchAbstract:Hold up both hands and spread your fingers apart. Now put your palms together and fold your two middle fingers down till the knuckles on both fingers touch each other. While holding this position, one after the other, open and close each pair of opposing fingers by an inch or so. Notice anything? Of course you did. But could a computer without a body and without human experiences ever answer that question or a million others like it? And even if recent revolutionary advances in collecting, storing, retrieving, and analyzing data lead to such a computer, would this machine qualify as “intelligent”?
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the Turing Test the first 50 years
Trends in Cognitive Sciences, 2000Co-Authors: Robert M FrenchAbstract:Abstract The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turing's classic article from its publication to the present. The changing perception of the Turing Test over the last 50 years has paralleled the changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960s to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now.
Antoni Gomila - One of the best experts on this subject based on the ideXlab platform.
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a minimal Turing Test reciprocal sensorimotor contingencies for interaction detection
Frontiers in Human Neuroscience, 2020Co-Authors: Pamela Barone, Manuel G Bedia, Antoni GomilaAbstract:In the classical Turing Test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short time lapses, so that they appear as mutually contingent, as reciprocal. In other words, the experience of interaction takes place when sensorimotor patterns are contingent upon one's own movements, and vice versa. I react to your movement, you react to mine. When I notice both components, I come to experience an interaction. Therefore, we designed a "minimal" Turing Test to investigate how much information is required to detect these reciprocal sensorimotor contingencies. Using a new version of the perceptual crossing paradigm, we Tested whether participants resorted to interaction detection to tell apart human from machine agents in repeated encounters with these agents. In two studies, we presented participants with movements of a human agent, either online or offline, and movements of a computerized oscillatory agent in three different blocks. In each block, either auditory or audiovisual feedback was provided along each trial. Analysis of participants' explicit responses and of the implicit information subsumed in the dynamics of their series will reveal evidence that participants use the reciprocal sensorimotor contingencies within short time windows. For a machine to pass this minimal Turing Test, it should be able to generate this sort of reciprocal contingencies.
Sam Devlin - One of the best experts on this subject based on the ideXlab platform.
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navigation Turing Test ntt learning to evaluate human like navigation
arXiv: Artificial Intelligence, 2021Co-Authors: Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja HofmannAbstract:A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. We address these limitations through a novel automated Navigation Turing Test (ANTT) that learns to predict human judgments of human-likeness. We demonstrate the effectiveness of our automated NTT on a navigation task in a complex 3D environment. We investigate six classification models to shed light on the types of architectures best suited to this task, and validate them against data collected through a human NTT. Our best models achieve high accuracy when distinguishing true human and agent behavior. At the same time, we show that predicting finer-grained human assessment of agents' progress towards human-like behavior remains unsolved. Our work takes an important step towards agents that more effectively learn complex human-like behavior.
