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{: 1722383029.5017, : , : , : [, ], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "9826d760f4aa4601a6b6e28ee0718e0a", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383038.1191, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "08467728adfa4952859812b86a5bde25", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \ is difficult to define, Turing chooses to \ Turing describes the new form of the problem in terms of a three-person game called the \, in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory0_corpuswikipedia1_conv_idb2f2ea5803034d63aade7af0b53f9abd1_model_nameintfloat/e5-mistral-7b-instruct1_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383057.2164, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "f1e1c1a164ab4e08be9d2c84a89d9c11", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. 0_corpuswikipedia1_conv_id30a00be0d6db47acbf3bf576a6daaa1e1_model_namevoyage-multilingual-21_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe test was introduced by Turing in his 1950 paper \ while working at the University of Manchester. It opens with the words: \thinking\replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\imitation game\Are there imaginable digital computers which would do well in the imitation game?\machines can think\mind\understanding\consciousness\]], : } |
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{: 1722383064.7812, : , : , : [, ], : , : , : , : , : [[, ]], : , : , : , : , : [[, ]], : } |
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{: 1722383072.0497, : , : , : [, ], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "35db3663bdc6465b9087866de3621e44", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \ is difficult to define, Turing chooses to \ Turing describes the new form of the problem in terms of a three-person game called the \, in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory1_corpuswikipedia |
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tstamptask_typeretrievaltypetievotemodelsip0_conv_id13109613431c4387a6ba2e7bd4439ba10_model_nameintfloat/multilingual-e5-large-instruct0_promptWhich test was devised to determine whether robots can think?0_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe test was introduced by Turing in his 1950 paper \ while working at the University of Manchester. It opens with the words: \thinking\replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\imitation game\Are there imaginable digital computers which would do well in the imitation game?\machines can think\mind\understanding\consciousness\]], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383088.2854, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "7ee034e1c01f41d49942294d6a42ebfa", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "0_corpus": "wikipedia", "1_conv_id": "5795df7731524c0296dec7979813697c", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior.1_corpuswikipedia |
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tstamptask_typeretrievaltypetievotemodelsip0_conv_id75cd6dfbf2cf4d3ab8bf40ddc8207d1d0_model_nametext-embedding-3-large0_promptWhich test was devised to determine whether robots can think?0_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe test was introduced by Turing in his 1950 paper \ while working at the University of Manchester. It opens with the words: \thinking\replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\imitation game\Are there imaginable digital computers which would do well in the imitation game?\machines can think\mind\understanding\consciousness\]], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383113.394, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "1afcc4f253384375a3497f1799740a55", "0_model_name": "voyage-multilingual-2", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \ is difficult to define, Turing chooses to \ Turing describes the new form of the problem in terms of a three-person game called the \, in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory0_corpuswikipedia1_conv_id00ce4beec46b49bb98b13a0b89048b4e1_model_nameintfloat/multilingual-e5-large-instruct1_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe test was introduced by Turing in his 1950 paper \ while working at the University of Manchester. It opens with the words: \thinking\replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\imitation game\Are there imaginable digital computers which would do well in the imitation game?\machines can think\mind\understanding\consciousness\]], : } |
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{: 1722383122.4956, : , : , : [, ], : , : , : , : , : [[, ]], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383130.1443, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "f59e5bae7b3a40ffb9512d8a3f87205e", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \ is difficult to define, Turing chooses to \ Turing describes the new form of the problem in terms of a three-person game called the \, in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory0_corpuswikipedia1_conv_ideac1a61583894349a5199093944020551_model_namevoyage-multilingual-21_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe test was introduced by Turing in his 1950 paper \ while working at the University of Manchester. It opens with the words: \thinking\replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\imitation game\Are there imaginable digital computers which would do well in the imitation game?\machines can think\mind\understanding\consciousness\]], : } |
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{: 1722383143.97, : , : , : [, ], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "50be6cc0d8344d38bc7a6ddc3aa56d37", "1_model_name": "text-embedding-004", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).1_corpuswikipedia |
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tstamptask_typeretrievaltypeleftvotemodelsip0_conv_idc813550e8ae045a8b3d32f00751be6890_model_nameintfloat/e5-mistral-7b-instruct0_promptWhich test was devised to determine whether robots can think?0_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "0_corpus": "wikipedia", "1_conv_id": "5f54cc7f0d3e482f9bafa85e281fdac1", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383163.0997, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5c1461c05577427daa977ecf86528580", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "History of robots\n\nIt features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata. 0_corpuswikipedia1_conv_id307bbc5403b74fa6b3584ec804a64e431_model_nameembed-english-v3.01_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe test was introduced by Turing in his 1950 paper \ while working at the University of Manchester. It opens with the words: \thinking\replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\imitation game\Are there imaginable digital computers which would do well in the imitation game?\machines can think\mind\understanding\consciousness\]], : } |
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{: 1722383173.9883, : , : , : [, ], : , : , : , : , : [[, AI\AI\AI\I propose to consider the question 'can machines think'?\thinks\whether or not it is possible for machinery to show intelligent behaviour\actually\mind\it is usual to have a polite convention that everyone thinks.\]], : , : , : , : , : [[, nearly equal to human performance\]], : } |
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{: 1722383197.0779, : , : , : [, ], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "34a7dc7fdfad44b1966dad58424b74ad", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \ should not be confused with \ as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383205.5485, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "7f6026b42beb4e1599533d3880f4235c", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).0_corpuswikipedia1_conv_id6cfa70702c3444b3abf7c0c66e34b5401_model_nameAlibaba-NLP/gte-Qwen2-7B-instruct1_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \"strong AI hypothesis\" should not be confused with \"strong AI\" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \ In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test.1_corpuswikipedia |
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tstamptask_typeretrievaltypetievotemodelsip0_conv_idd0c2bbc549044d0f96953e7e6b6fe99a0_model_nameembed-english-v3.00_promptWhich test was devised to determine whether robots can think?0_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe test was introduced by Turing in his 1950 paper \ while working at the University of Manchester. It opens with the words: \thinking\replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\imitation game\Are there imaginable digital computers which would do well in the imitation game?\machines can think\mind\understanding\consciousness\]], : , : , : , : , : [[, ]], : } |
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{: 1722383236.588, : , : , : [, ], : , : , : , : , : [[, ]], : , : , : , : , : [[, ]], : } |
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{: 1722383247.863, : , : , : [, ], : , : , : , : , : [[, Computing Machinery and Intelligence\I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \ This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \.\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \, \, or \, regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "dcfc68f4723b4003b74e01c4fc914804", "1_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "1_prompt": "Which test was devised to determine whether robots can think?", "1_output": [["Which test was devised to determine whether robots can think?", "Chinese room\n\nSearle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.\nSearle's \ should not be confused with \ as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.\nTuring test\nThe Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question \"can machines think?\" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383256.029, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "de85d02935494deaacb803069c129482", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \ is difficult to define, Turing chooses to \ Turing describes the new form of the problem in terms of a three-person game called the \, in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory0_corpuswikipedia1_conv_id5ee58329351f4a42b51abd844d5c10021_model_namejinaai/jina-embeddings-v2-base-en1_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Cognitive test\n\nCognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence.1_corpuswikipedia |
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tstamptask_typeretrievaltypetievotemodelsip0_conv_id06f5c27330dc45a1b8d79a0da79800820_model_nameBM250_promptWhich test was devised to determine whether robots can think?0_outputWhich test was devised to determine whether robots can think?Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \ around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \.\nAbout 800,000 \-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \ He advised changing the question from whether a machine \, to \. He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \ thinking or literally has a \. Turing notes that we can not determine these things about other people but \0_corpuswikipedia1_conv_id4639b98707f04bac85718774607f15ec1_model_nameBAAI/bge-large-en-v1.51_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383278.1255, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "11c326bdd41745b7a30a6dab39f56f2d", "0_model_name": "mixedbread-ai/mxbai-embed-large-v1", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \"nearly equal to human performance\" (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior.0_corpuswikipedia1_conv_idf5fc217bd84943a693a844131ca83e051_model_nameBM251_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \ around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \.\nAbout 800,000 \-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \ He advised changing the question from whether a machine \, to \. He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \ thinking or literally has a \. Turing notes that we can not determine these things about other people but \1_corpuswikipedia |
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tstamptask_typeretrievaltypetievotemodelsip0_conv_idf063e3a0894c474c88d23025388288ce0_model_nameBM250_promptWhich test was devised to determine whether robots can think?0_outputWhich test was devised to determine whether robots can think?Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \ around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \.\nAbout 800,000 \-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \ He advised changing the question from whether a machine \, to \. He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \ thinking or literally has a \. Turing notes that we can not determine these things about other people but \0_corpuswikipedia1_conv_idea878ed70e704075aa7e7b919e5b53031_model_nametext-embedding-0041_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic)."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383299.3142, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "b707c853c46e493883ca27f6537e9c85", "0_model_name": "Salesforce/SFR-Embedding-2_R", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).0_corpuswikipedia1_conv_idc1c7dcd952fe4752935f84630f9d3c4d1_model_namemixedbread-ai/mxbai-embed-large-v11_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Progress in artificial intelligence\n\nSub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)\nObject recognition\nVarious robotics tasks that may require advances in robot hardware as well as AI, including:\nStable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)\nHumanoid soccer\nSpeech recognition: \ (2017)\nExplainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.\nMany tests of fluid intelligence (2020)\nBongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)\nVisual Commonsense Reasoning (VCR) benchmark (as of 2020)\nStock market prediction: Financial data collection and processing using Machine Learning algorithms\nAngry Birds video game, as of 2020\nVarious tasks that are difficult to solve without contextual knowledge, including:\nTranslation\nWord-sense disambiguation\nProposed tests of artificial intelligence\nIn his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.\nThe Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior."]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722383949.909, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "69f750d38fef44f08199d0014cbc0ddf", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Which test was devised to determine whether robots can think?", "0_output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).0_corpuswikipedia1_conv_id27fccfcabec0417290bec4ac237824571_model_nameBM251_promptWhich test was devised to determine whether robots can think?1_outputWhich test was devised to determine whether robots can think?Artificial intelligence\n\nIn the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in \ around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \.\nAbout 800,000 \-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \ He advised changing the question from whether a machine \, to \. He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is \ thinking or literally has a \. Turing notes that we can not determine these things about other people but \1_corpuswikipedia |
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tstamptask_typeretrievaltyperightvotemodelsip0_conv_id40cbc8e2f6bf4dc6ba1f9a2a9d21eefe0_model_namevoyage-multilingual-20_promptWhat is the meaning of life? 0_outputWhat is the meaning of life? Meaning of life\n\nTo be fruitful and multiply. ()\nTo obtain freedom. ()\nTo fill the Earth and subdue it. ()\nTo serve humankind, to prepare to meet and become more like God, to choose good over evil, and have joy.\n\u02f9He is the One\u02fa Who created death and life in order to test which of you is best in deeds. And He is the Almighty, All-Forgiving. \u2014\nTo worship God and enter heaven in afterlife.\nTo love, to feel, to enjoy the act of living\nTo love more.\nTo love those who mean the most. Every life you touch will touch you back.\nTo treasure every enjoyable sensation one has.\nTo seek beauty in all its forms.\nTo have fun or enjoy life.\nTo seek pleasure and avoid pain.\nTo be compassionate.\nTo be moved by the tears and pain of others, and try to help them out of love and compassion.\nTo love others as best we possibly can.\nTo eat, drink, and be merry.\nTo have power, to be better\nTo strive for power and superiority.\nTo rule the world.\nTo know and master the world.\nTo know and master nature.\nTo help life become as powerful as possible.0_corpuswikipedia1_conv_id0c6543fff61e41939f5206b477ffe1871_model_namemixedbread-ai/mxbai-embed-large-v11_promptWhat is the meaning of life? 1_outputWhat is the meaning of life? Meaning of life\n\nThe meaning of life pertains to the inherent significance or philosophical meaning of living (or existence in general). There is not a definitive answer, and thinking or discourse on the topic is sought in the English language through the question, \ (or the related \ or \). There have been many proposed answers to these questions from many different cultural and ideological backgrounds. The search for life's meaning has produced much philosophical, scientific, theological, and metaphysical speculation throughout history. Different people and cultures believe different things for the answer to this question. Opinions vary on the usefulness of using time and resources in the pursuit of an answer. Excessive pondering can be indicative of, or lead to, an existential crisis.\nThe meaning of life can be derived from philosophical and religious contemplation of, and scientific inquiries about, existence, social ties, consciousness, and happiness. Many other issues are also involved, such as symbolic meaning, ontology, value, purpose, ethics, good and evil, free will, the existence of one or multiple gods, conceptions of God, the soul, and the afterlife. Scientific contributions focus primarily on describing related empirical facts about the universe, exploring the context and parameters concerning the \"how\" of life. Science also studies and can provide recommendations for the pursuit of well-being and a related conception of morality. An alternative, humanistic approach poses the question, \"What is the meaning of my life?\""]], "1_corpus": "wikipedia"} |
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{"tstamp": 1722385900.9648, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "62bf4d34067d48fab317ab0ef26dcc9c", "0_model_name": "BM25", "0_prompt": "how to write a good abstract", "0_output": [["how to write a good abstract", "Tex Stackexchange\n\nQ: Missing abstract with amsart class I'm not very good at LaTeX but I've been using it to write my dissertation. Have just come to the end and trying to format everything correctly. Whenever I try to put my Abstract paragraph into the form \\begin{abstract} (writing here) \\end{abstract} and click play the whole abstract section just disappears. What am I doing wrong?\n\\documentclass[12pt]{amsart}\n\n\\begin{document}\n\n\\begin{abstract}\n(my abstract here)\n\\end{abstract}\n\\end{document}\n\n\nA: The amsart class does some Mysterious Stuff with abstracts: it stores the abstract in a box to be used in the title page. To get it to appear, issue the magic command \\maketitle after the abstract.\n(It will then complain about a missing \\title ... )\n\\documentclass[12pt]{amsart}\n\n\n\\begin{document}\n\n\\begin{abstract}\n(my abstract here)\n\\end{abstract}\n\n\\maketitle\nSome text\n\\end{document}\n\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "81d3d31b53db4678a92dea8dedda3e3f", "1_model_name": "intfloat/e5-mistral-7b-instruct", "1_prompt": "how to write a good abstract", "1_output": [["how to write a good abstract", "Mathoverflow Stackexchange\n\nQ: How to write an abstract for a math paper? How would you go about writing an abstract for a Math paper? I know that an abstract is supposed to \"advertise\" the paper. However, I do not really know how to get started. Could someone tell me how they go about writing an abstract?\n\nA: *\n\n*Avoid notation if possible. Notation makes it really hard to search electronically.\n\n*Put the subject in context, e.g., \"In a recent paper, T. Lehrer introduced the concept of left-bifurcled rectangles. He conjectured no such rectangles exist when the number of bifurcles $n$ is odd.\"\n\n*State your results, in non-technical language, if possible. \"In this paper we show the existence of left-bifurcled rectangles for all prime $n$.\"\n\n*Mention a technique, if there is a new one: \"Our methods involve analytic and algebraic topology of locally euclidean metrizations of infinitely differentiable Riemannian manifolds\".\n\n*Never, ever, ever, cite papers in the bibliography by giving citation numbers; the abstract is an independent entity that should stand on its own.\n"]], "1_corpus": "stackexchange"} |
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{"tstamp": 1722389811.1554, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "d85582bd3b174bf2ad9cd3445a06139b", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "Who are the authors of the original vae paper ", "0_output": [["Who are the authors of the original vae paper ", "Title: GAN and VAE from an Optimal Transport Point of View\n\nAbstract: This short article revisits some of the ideas introduced in arXiv:1701.07875 and arXiv:1705.07642 in a simple setup. This sheds some lights on the connexions between Variational Autoencoders (VAE), Generative Adversarial Networks (GAN) and Minimum Kantorovitch Estimators (MKE)."]], "0_corpus": "arxiv", "1_conv_id": "c19cebcaeafc4bdc912783fa3ba27b24", "1_model_name": "text-embedding-3-large", "1_prompt": "Who are the authors of the original vae paper ", "1_output": [["Who are the authors of the original vae paper ", "Title: AVAE: Adversarial Variational Auto Encoder\n\nAbstract: Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets."]], "1_corpus": "arxiv"} |
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{"tstamp": 1722389883.6233, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "16a2429e632846798e0182b7b5c57d67", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "What is xpool cross modal language video attention", "0_output": [["What is xpool cross modal language video attention", "Title: B\\\"acklund--Darboux transformations in Sato's Grassmannian\n\nAbstract: We define B\\\]], : , : , : , : , : [[, ]], : } |
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{: 1722391947.5916, : , : , : [, ], : , : , : , : , : [[, ]], : , : , : , : , : [[, ]], : } |
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{: 1722392012.5695, : , : , : [, ], : , : , : , : , : [[, ]], : , : , : , : , : [[, ]], : } |
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{: 1722392051.0052, : , : , : [, ], : , : , : , : , : [[, ]], : , : , : , : , : [[, ]], : } |
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{: 1722392088.2313, : , : , : [, ], : , : , : , : , : [[, Doc\]], : , : , : , : , : [[, Pacific Railroad\Overland Route\Last Spike\Golden Spike\]], : } |
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{: 1722392115.6318, : , : , : [, ], : , : , : , : , : [[, ]], : , : , : , : , : [[, ]], : } |
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{: 1722392139.5942, : , : , : [, ], : , : , : , : , : [[, Vee\muscle.\]], : , : , : , : , : [[, Vee\muscle.\]], : } |
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{: 1722401702.1092, : , : , : [, ], : , : , : , : , : [[, bla\]], : , : , : , : , : [[, <div class='rounded_box'><div class='rounded_box_content'><div class='rounded_box_top'></div> \n str << yield\n str << \\n raw str\nend\n\nThe way I have it now returns the content properly wrapped in the HTML string, but not before rendering any erb in the rounded_box block (e.g. in this case the target.text is rendered twice, once wrapped, once not).\nIs there a better way to do this? For simplicity, I'd like to avoid using content_tag, but if that's the only/best way I can do that.\n\nA: Call capture on the block instead of yield:\ndef rounded_box(&block)\n str = \ \n str << capture(&block)\n str << \\n raw str\nend\n\n1_corpusstackexchange |
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tstamptask_typeretrievaltyperightvotemodelsip0_conv_idb183c66d1a864220b961fd3aab8a27860_model_nameAlibaba-NLP/gte-Qwen2-7B-instruct0_promptmachine learning ssm models mambda0_outputmachine learning ssm models mambdaTitle: Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures\n\nAbstract: Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts.0_corpusarxiv1_conv_id076e03e824ba49ca95245f716214aec31_model_nameBAAI/bge-large-en-v1.51_promptmachine learning ssm models mambda1_outputmachine learning ssm models mambdaTitle: Vision Mamba: A Comprehensive Survey and Taxonomy\n\nAbstract: State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy."]], "1_corpus": "arxiv"} |
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{"tstamp": 1722403592.4887, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "6f8b359afa074fe9b99960410ca68854", "0_model_name": "embed-english-v3.0", "0_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "0_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: MTEB: Massive Text Embedding Benchmark\n\nAbstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb."]], "0_corpus": "arxiv", "1_conv_id": "ed9faaf063f642cd872d3b354dbe1376", "1_model_name": "sentence-transformers/all-MiniLM-L6-v2", "1_prompt": "I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "1_output": [["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "Title: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows\n\nAbstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes."]], "1_corpus": "arxiv"} |
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{"tstamp": 1722404064.2696, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1f792446858649febdcd92f6bf7b0b37", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "Research on combining sentiment analysis and market data for volatility forecasting.", "0_output": [["Research on combining sentiment analysis and market data for volatility forecasting.", "Title: Volatility forecasting using Deep Learning and sentiment analysis\n\nAbstract: Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. To classify public sentiment, we use a Convolutional Neural Network, which obtained data from Reddit global news headlines. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day's volatility to make forecasts. We employed this method on the past volatility of the S&P500 and the major BRICS indices to corroborate its effectiveness. Our results demonstrate that including sentiment can improve Deep Learning volatility forecasting models. However, in contrast to return forecasting, the performance benefits of including sentiment appear for volatility forecasting appears to be market specific.0_corpusarxiv1_conv_id50604820dc7b45a784d897f43d88f88f1_model_nametext-embedding-0041_promptResearch on combining sentiment analysis and market data for volatility forecasting.1_outputResearch on combining sentiment analysis and market data for volatility forecasting.Title: A Sentiment Analysis Approach to the Prediction of Market Volatility\n\nAbstract: Prediction and quantification of future volatility and returns play an important role in financial modelling, both in portfolio optimization and risk management. Natural language processing today allows to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. The findings suggest that there is evidence of correlation between sentiment and stock market movements: the sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. Also, in a surprising finding, for the sentiment found in Twitter comments we obtained a correlation coefficient of -0.7, and p-value below 0.05, which indicates a strong negative correlation between positive sentiment captured from the tweets on a given day and the volatility observed the next day. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modelling, based on Latent Dirichlet Allocation, to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modelling our classifier achieved a directional prediction accuracy for volatility of 63%."]], "1_corpus": "arxiv"} |
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