{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:39:32Z","timestamp":1723016372832},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>AI benchmarking becomes an increasingly important task. As suggested by many researchers, Intelligence Quotient (IQ) tests, which is widely regarded as one of the predominant benchmarks for measuring human intelligence, raises an interesting challenge for AI systems. For better solving IQ tests automatedly by machines, one needs to use, combine and advance many areas in AI including knowledge representation and reasoning, machine learning, natural language processing and image understanding. Also, automated IQ tests provides an ideal testbed for integrating symbolic and sub-symbolic approaches as both are found useful here. Hence, we argue that IQ tests, although not suitable for testing machine intelligence, provides an excellent benchmark for the current development of AI research. Nevertheless, most existing IQ test datasets are not comprehensive enough for this purpose. As a result, the conclusions obtained are not representative. To address this issue, we create IQ10k, a large-scale dataset that contains more than 10,000 IQ test questions. We also conduct a comparison study on IQ10k with a number of state-of-the-art approaches.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/846","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"6110-6116","source":"Crossref","is-referenced-by-count":4,"title":["How Well Do Machines Perform on IQ tests: a Comparison Study on a Large-Scale Dataset"],"prefix":"10.24963","author":[{"given":"Yusen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University"}]},{"given":"Fangyuan","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University"}]},{"given":"Haodi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University"}]},{"given":"Guozheng","family":"Rao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University"}]},{"given":"Zhiyong","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanghai Research Center for Brain Science and Brain-Inspired Intelligence\/Zhangjiang Laboratory"},{"name":"School of Natural and Computational Sciences, Massey University"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:52:12Z","timestamp":1564285932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/846"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/846","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}