[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-503300-105":59,"doc-detail-503300-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","deepiq-a-human-inspired-ai-system-for-solving-iq-test-problems","DeepIQ: A Human-Inspired AI System for Solving IQ Test Problems","","DeepIQ presents a neural network method for solving Raven’s Progressive Matrices, one of the most common IQ test formats. The system integrates a deep autoencoder for feature-based representations, an ensemble of shallow multilayer perceptrons to detect feature differences, and a scoring module to evaluate candidate answers. DeepIQ learns RM-solving principles about similarity across shape, rotation, size, and shading in a domain-independent way, enabling transfer to unseen figures or different IQ task types with no additional training. Experiments on multiple datasets show strong results, including large gains over human average scores on difficult RM subsets and over 90% accuracy in odd-one-out tasks.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/deepiq-a-human-inspired-ai-system-for-solving-iq-test-problems/503300/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/deepiq-a-human-inspired-ai-system-for-solving-iq-test-problems/503300.png","ImageObject",300,407,{"name":92,"@type":93},"Alexandar","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the DeepIQ system address?","Question",{"text":112,"@type":113},"DeepIQ targets solving Raven’s Progressive Matrices, a widely used human IQ test format.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is DeepIQ structured internally?",{"text":117,"@type":113},"It combines a deep autoencoder for feature representation, an ensemble of shallow multilayer perceptrons to detect feature differences, and a scoring module to assess candidate answers.",{"name":119,"@type":110,"acceptedAnswer":120},"What makes DeepIQ able to generalize to new IQ instances?",{"text":121,"@type":113},"DeepIQ learns principles based on similarity of figures in shape, rotation, size, and shading, allowing transfer to instances built from different figures or other IQ problem types without additional training.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},503300,1791296371,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},962090768658,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","DeepIQ: A Human-Inspired AI System for Solving  \nIQ Test Problems  \nJacek Ma´ndziuk  \nFaculty of Mathematics and Information Science Warsaw University of Technology, Warsaw, Poland [mandziuk@mini.pw.edu.pl](mandziuk@mini.pw.edu.pl)  \nAdam Z˙ychowski  \nFaculty of Mathematics and Information Science Warsaw University of Technology, Warsaw, Poland [a.zychowski@mini.pw.edu.pl](a.zychowski@mini.pw.edu.pl)  \nAbstract—This paper presents a neural network approach to solving the most common type of human IQ test problems – Raven's Progressive Matrices (RMs). The proposed DeepIQ system is composed of three modules: a deep autoencoder which is trained to learn a feature-based representation of various figure images used in IQ tests, an ensemble of shallow multilayer perceptrons applied to detection of feature differences, and a scoring module use for assessment of candidate answers. DeepIQ is able to learn the underlying principles of solving RMs (the importance of similarity of figures in shape, rotation, size or shading) in a domain-independent way, that allows its subsequent application to test instances constructed based on a different set of figures, never seen before, or another type of IQ problem, with no requirement for additional training. This transfer learning property is of paramount importance due to scarce availability of the real data, and is demonstrated in the paper on two different RM data sets, as well as two distinct types of IQ tasks (solving RMs and odd-one-out problems). Experimental results are promising, excelling human average scores by a large margin on the most challenging subset of RM instances and exceeding 90% accuracy in odd-one-out tests.  \nI. INTRODUCTION  \nIn psychology, there exist well-established and generally accepted methods to evaluate human intelligence by means of psychometric tests (IQ tests) measuring the intelligence quotient (IQ) . In the case of Artificial Intelligence (AI), such comprehensive methods are yet to be developed, except for the well-known Turing Test [1] . The applicability of the Turing Test is, however, limited to assessing certain aspects of presumably intelligent, human-like machine behavior. Moreover, the assessment is subjective, and does not allow for quantitative evaluation. In 2003 Bringsjord and Schimanski attempted to bridge this gap and introduced the concept of Psychometric AI [2] which points to psychometric tests as a valid approach to defining and evaluating IQ of the AI systems and a viable alternative to task-oriented assessment methods. Related work. In the mainstream AI / Machine Learning (ML) literature the number of papers related to solving IQ tests by machines is rather limited, although the topic gained visible interest in recent years. A survey article [3] on computer models solving intelligence-requiring problems mentions 30 papers, half of which have appeared since 2011 . The first attempt  \nAccepted for the International Joint Conference on Neural Networks (IJCNN 2019) - Budapest, Hungary, July 2019  \nwas made over 50 years ago by [4] who designed a computer program for detecting regularities in figures in the so-called geometric-analogy problems (similar to IQ test instances) . Subsequent papers were concentrated on various types of intelligence tests, for instance completion of sequences of numbers [5], words-related tests [6], verbal tests [7], Bennett's Mechanical Comprehension Tests [8] or general approaches to solving various types of IQ tests [9], [10] .  \nThe proposed DeepIQ system is evaluated on the problems which follow the rule-based construction principles of the most popular IQ tests - Raven's Progressive Matrices [11], referred to as RMs hereafter. There are three main reasons that motivated our choice of RMs as a testbed problem. Firstly, machine learning and testing on real IQ test examples is practically impossible due to their scarce public accessibility, as psychologists do not make them publicly available. Secondly, the most popular h","cbCaim9etBuiNTUd","https://ap.wps.com/l/cbCaim9etBuiNTUd","pdf",418880,"English","# Introduction\n## Psychometric AI and IQ testing\n## Related work in machine IQ solving\n## Why Raven's Progressive Matrices","[{\"question\":\"What problem does the DeepIQ system address?\",\"answer\":\"DeepIQ targets solving Raven’s Progressive Matrices, a widely used human IQ test format.\"},{\"question\":\"How is DeepIQ structured internally?\",\"answer\":\"It combines a deep autoencoder for feature representation, an ensemble of shallow multilayer perceptrons to detect feature differences, and a scoring module to assess candidate answers.\"},{\"question\":\"What makes DeepIQ able to generalize to new IQ instances?\",\"answer\":\"DeepIQ learns principles based on similarity of figures in shape, rotation, size, and shading, allowing transfer to instances built from different figures or other IQ problem types without additional training.\"}]","DeepIQ: A Human-Inspired AI System for Solving IQ Test Problems | PDF",1791053401]