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 {"tstamp": 1722445177.4698, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1722445177.0449, "finish": 1722445177.4698, "ip": "", "conv_id": "25390507c1a14ef1b32bb1574a1c3f26", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "Which test was devised to determine whether robots can think?", "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)."]], "corpus": "wikipedia"}
 {"tstamp": 1722445233.9867, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722445233.7863, "finish": 1722445233.9867, "ip": "", "conv_id": "456d3c377f934513b2f5289db44c4e18", "model_name": "GritLM/GritLM-7B", "prompt": "Which test was devised to determine whether robots can think?", "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 \"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: \"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.\nHistory"]], "corpus": "wikipedia"}
 {"tstamp": 1722445253.6548, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1722445253.4763, "finish": 1722445253.6548, "ip": "", "conv_id": "e1ca08ee16e14522ab72653bcd36b8b7", "model_name": "GritLM/GritLM-7B", "prompt": "remote desktop connection denied because the user account is not authorized?", "output": [["remote desktop connection denied because the user account is not authorized?", "Superuser Stackexchange\n\nQ: How to grant remote desktop right to a user in Windows Server 2008? I create a user and add it to group Remote Desktop Users but I cannot still remote using mstsc. The error message said the user/group doesn't have the right to remote.\nMy question is how can I grant this right to the group?\nThanks!\n\nA: Check in the group policy editor (gpedit.msc) under Computer Config > Windows Settings > Security Settings > Local Policies > User Rights Assignment. Find the entry for \"Allow log on through remote desktop services\" and \"deny log on through remote desktop services\", and see if the groups in question are in either of those categories. Deny permissions will usually override allow permissions.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722445343.7295, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722445342.5281, "finish": 1722445343.7295, "ip": "", "conv_id": "96be454fd12343d0af9d7fa31a927266", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "output": [["A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "Title: Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach\n\nAbstract: Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation."]], "corpus": "arxiv"}
+{"tstamp": 1722445343.7295, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722445342.5281, "finish": 1722445343.7295, "ip": "", "conv_id": "4ab9f7d1179f49ff8c1b741dd7ab39ef", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "output": [["A study on improving traffic forecasting accuracy by combining future traffic volume with historical data.", "Title: Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction\n\nAbstract: While deep learning has shown success in predicting traffic states, most methods treat it as a general prediction task without considering transportation aspects. Recently, graph neural networks have proven effective for this task, but few incorporate external factors that impact roadway capacity and traffic flow. This study introduces the Roadway Capacity Driven Graph Convolution Network (RCDGCN) model, which incorporates static and dynamic roadway capacity attributes in spatio-temporal settings to predict network-wide traffic states. The model was evaluated on two real-world datasets with different transportation factors: the ICM-495 highway network and an urban network in Manhattan, New York City. Results show RCDGCN outperformed baseline methods in forecasting accuracy. Analyses, including ablation experiments, weight analysis, and case studies, investigated the effect of capacity-related factors. The study demonstrates the potential of using RCDGCN for transportation system management."]], "corpus": "arxiv"}