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 {"tstamp": 1731355846.2801, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1731355844.0218, "finish": 1731355846.2801, "ip": "", "conv_id": "daf4edf93dd24e7685c3392c74ca3c5a", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Artificial general intelligence\n\nMany interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider additional traits such as imagination (the ability to form novel mental images and concepts) and autonomy.\nComputer-based systems that exhibit many of these capabilities exist (e.g. see computational creativity, automated reasoning, decision support system, robot, evolutionary computation, intelligent agent). However, no consensus holds that modern AI systems possess them to an adequate degree.\nPhysical traits\nOther capabilities are considered desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include:\nthe ability to sense (e.g. see, hear, etc.), and\nthe ability to act (e.g. move and manipulate objects, change location to explore, etc.)\nThis includes the ability to detect and respond to hazard.\nTests for human-level AGI\nSeveral tests meant to confirm human-level AGI have been considered, including:\nThe Turing Test (Turing)\nA machine and a human both converse unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time. Note: Turing does not prescribe what should qualify as intelligence, only that knowing that it is a machine should disqualify it. The AI Eugene Goostman, imitating a 13-year-old boy, achieved Turing's estimate of convincing 33% of judges that it was human in 2014.\nThe Robot College Student Test (Goertzel)"]], "corpus": "wikipedia"}
 {"tstamp": 1731355995.5178, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1731355995.2357, "finish": 1731355995.5178, "ip": "", "conv_id": "c484ef3462504b4cb119fbd36e937c99", "model_name": "BM25", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which 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 \"AI\" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in \"AI\".\nAbout 800,000 \"AI\"-related U.S. job openings existed in 2022.\nPhilosophy\nDefining artificial intelligence\nAlan Turing wrote in 1950 \"I propose to consider the question 'can machines think'?\" He advised changing the question from whether a machine \"thinks\", to \"whether or not it is possible for machinery to show intelligent behaviour\". 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 \"actually\" thinking or literally has a \"mind\". Turing notes that we can not determine these things about other people but \"it is usual to have a polite convention that everyone thinks.\""]], "corpus": "wikipedia"}
 {"tstamp": 1731355995.5178, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1731355995.2357, "finish": 1731355995.5178, "ip": "", "conv_id": "47661538c4d54b4f893c821e69945ec0", "model_name": "Salesforce/SFR-Embedding-2_R", "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": 1731357872.8336, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1731357870.5195, "finish": 1731357872.8336, "ip": "", "conv_id": "808427ce00b1405b90ac0f58ce58393c", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "first order optimization with multiple EMAs", "output": [["first order optimization with multiple EMAs", "Title: The effect of the annealing temperature on the local distortion of La$_{0.67}$Ca$_{0.33}$MnO$_3$ thin films\n\nAbstract: Mn $K$-edge fluorescence data are presented for thin film samples (3000~\\AA) of Colossal Magnetoresistive (CMR) La$_{0.67}$Ca$_{0.33}$MnO$_3$: as-deposited, and post-annealed at 1000 K and 1200 K. The local distortion is analyzed in terms of three contributions: static, phonon, and an extra, temperature-dependent, polaron term. The polaron distortion is very small for the as-deposited sample and increases with the annealing temperature. In contrast, the static distortion in the samples decreases with the annealing temperature. Although the local structure of the as-deposited sample shows very little temperature dependence, the change in resistivity with temperature is the largest of these three thin film samples. The as-deposited sample also has the highest magnetoresistance (MR), which indicates some other mechanism may also contribute to the transport properties of CMR samples. We also discuss the relationship between local distortion and the magnetization of the sample."]], "corpus": "arxiv"}
+{"tstamp": 1731357872.8336, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1731357870.5195, "finish": 1731357872.8336, "ip": "", "conv_id": "425b80930aaf4c9ab1afe12cdb938bad", "model_name": "text-embedding-004", "prompt": "first order optimization with multiple EMAs", "output": [["first order optimization with multiple EMAs", "Title: Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima\n\nAbstract: Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms typically converge only to a single solution. While this can be counteracted by applying niching strategies, the number of optima is nonetheless trivially bounded by the population size. Estimation-of-distribution algorithms (EDAs) are an alternative, maintaining a probabilistic model of the solution space instead of a population. Such a model is able to implicitly represent a solution set far larger than any realistic population size. To support the study of how optimization algorithms handle large sets of optima, we propose the test function EqualBlocksOneMax (EBOM). It has an easy fitness landscape with exponentially many optima. We show that the bivariate EDA mutual-information-maximizing input clustering, without any problem-specific modification, quickly generates a model that behaves very similarly to a theoretically ideal model for EBOM, which samples each of the exponentially many optima with the same maximal probability. We also prove via mathematical means that no univariate model can come close to having this property: If the probability to sample an optimum is at least inverse-polynomial, there is a Hamming ball of logarithmic radius such that, with high probability, each sample is in this ball."]], "corpus": "arxiv"}