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Byoung-Tak Zhang

Humans and Machines in the Evolution of AI in Korea

This column recounts the evolution of AI research inKorea. I tell a story of agents, that is, humans andmachines, that shaped the AI landscape. I sketch the

formation and transformation of the AI communities fromthe 1980s to 2010s. The whole story consists of five periods:settlement, exploration, diversification, unification, andgrowth. I also describe recent activities in AI, along withgovernmental funding circumstances and industrial inter-est. The column does not intend to be complete thoughevery effort will be made to strike a balance of coverage anddepth in the limited space.

A History of AI in KoreaIt took quite some time for AI to be established as a field inKorea. Only in the 1970s did the computer and electronicsindustry start and universities institute computer sciencedepartments ., software companies were founded in theearly 1980s. The first AI organization in Korea was estab-lished in December 1985 as a special interest group of theKorea Society for Information Science (KISS), which is theprecursor of the current Korea Artificial Intelligence Socie-ty (KAIS). Excited by the vision of AI, the major universitiesin the country had each created at least one AI group by

� Artificial intelligence in Korea is currentlyprospering. The media are regularly reportingAI-enabled products such as smart advisors,personal robots, autonomous cars, and human-level intelligence machines. The IT industry isinvesting in deep learning and AI to maintainthe global competitive edge in its services andproducts. The Ministry of Science, ICT, andFuture Planning (MSIP) has launched newfunding programs in AI and cognitive science toimplement the government’s newly adoptedendeavor of building a creative economy andsoftware-centered society. However, AI did notalways flourish as it now does. Similar to thehistory of AI worldwide, AI research and indus-try in Korea have faced both ups and downs intheir history.

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1990. One of the early AI groups was the Natural Lan-guage Processing and Artificial Intelligence Lab atSeoul National University (SNU) founded by Yung-Taek Kim in the early 1980s. This was followed by AIgroups at KAIST by Jin-Hyung Kim and at other uni-versities. Most of these first-generation AI researcherswere newly graduated computer scientists who didtheir graduate studies abroad. The research topicswere wide-ranging, including expert systems,machine translation, natural language understand-ing, heuristic search, logical reasoning, and knowl-edge representation (Kang et al. 1987, Yoo and Kim1992).

Exploration (1990–1995)It is ironic that the new AI groups were founded inKorea when, globally, the AI boom had begun to runout of steam. The worldwide AI winter affected Koreatoo. The term AI had adopted a new connotation of“too ambitious to be practical.” On one hand, thisled the natural language processing (NLP) people tobranch out of the special interest group (SIG) AI toform a SIG on Language Technology with an empha-sis on technology. On the other hand, new approach-es to AI were emerging that were complementary oran alternative to the traditional symbolic AIapproach. These included the bioinspired AI models,such as neural networks, genetic algorithms, andfuzzy logic. “Neural,” “fuzzy,” and “genetic” havebeen the buzzwords for commercial advertisementsof home appliance makers, such as Samsung, LG, andDaewoo. Much industrial research in the early 1990sfocused on intelligent control, speech recognition,and handwritten character recognition using neuralnetworks. In 1992, the SIG on Neurocomputing wasestablished under the Korea Society for InformationScience. Neurocomputing-based AI was considered asa new generation of AI and many universities estab-lished related labs. The Neurocomputing SIG alsocovered other nature-inspired approaches such asevolutionary computation, genetic algorithms, andartificial life.

Diversification (1995–2005) During this period, AI research turned its focus ontoproblem solving from developing basic technologies.Two new SIGs were founded, the Computer Visionand Pattern Recognition SIG in 1997 and the Bioin-formation Technology SIG in 2002, the latter being afollow-up and extension of the Neurocomputing SIG.Many IT startups were founded, and promoted inpart by governmental funding strategies. Althoughnot many of them eventually survived the 1997financial crisis in Korea and Asian countries, the ven-ture boom spurred the interest and importance of AItechnologies for industries such as informationretrieval, e-commerce, and data mining. In particu-lar, the importance of machine learning was recog-nized for Internet business, text mining, customer

relationship management, and life sciences. As aresult, the application platform/range of AI diversi-fied in many different ways. A large number of intel-ligent agents were developed for information filter-ing, comparison shopping, recommender systems,and smart user interfaces (Lee 2007, Seo and Zhang2000). Other AI funding included the three projectsthat started in 2002–2004 under the NationalResearch Laboratory (NRL) Program of KOSEF thatsponsored university research labs for five years. Thetopics of the three projects were machine learning forbiodata mining, visual pattern recognition, and nat-ural language processing, respectively. At the basicscience level a closer collaboration between AI andcognitive science began by launching the Interna-tional Conference on Cognitive Science ICCS (Zhang1997).

Unification (2005–2010) By 2005 there were four AI-related SIGs under theKorea Information Science Society: SIG ArtificialIntelligence, SIG Language Technology, SIG Com-puter Vision and Pattern Recognition, and SIG Bioin-formation Technology. Each SIG was a closed circleand there was not much interaction between theSIGs. However, the emerging research field ofmachine learning provided a trigger to collaborateamong the SIGs. This was clear when a machinelearning tutorial was organized as the Joint Work-shop ILVB (for Intelligence, Language, Vision, andBioinformation) in 2005 at Seoul National Universi-ty. It attracted more than 400 attendants, which wasmore than three times the number the organizersexpected. Following the success of ILVB 2005, ILVBhas been held annually. In November 2010, theannual ILVB led to the official establishment of theKorean Society for Computational Intelligence(KSCI), which was renamed in 2013 as the Korea Arti-ficial Intelligence Society (KAIS). The first three pres-idents of KAIS are Byoung-Tak Zhang (2010-2013),Hyeran Byun (2013-2015), and Seong-Whan Lee(2015-). The founding of KAIS is an important eventsince the AI community now has an authoritativesociety.

Growth (2010~)KAIS initiated a diverse range of efforts to promoteinteraction between AI researchers and collaborationbetween academia and industry. It launched newsymposia, such as the Annual Symposium on Real-Life Intelligence since 2010, the Symposium on Cog-nition and Artificial Intelligence in 2012, and theSymposium on Brain and Artificial Intelligence in2013. It also started the Winter School of PatternRecognition and Machine Learning in 2011. In 2012the first AI Expo was held and more than 50 AI labsin Korea presented their work and shared ideas. In2013 the Global Frontier Forum on Human-LevelMachine Learning in the Post Smartphone Era was

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held to promote the cross-fertilization of AI and relat-ed areas.

Currently, the demand of the industry for AIresearchers is much bigger than the supply academiacan provide. Global IT companies, such as Samsung,are looking for AI graduates in deep learning, dia-logue understanding, computer vision, and context-aware services for smartphones, tablets, wearabledevices, and home appliances. AI technology playsan increasing role in making differences among thecompetitive smart-device vendors. Recent examplesof AI, such as Apple Siri, IBM Watson, Google Car,Facebook DeepFace, Microsoft Cortana, AmazonEcho, MIT Jibo, and SoftBank Pepper have drawn theattention of Korean companies to the importance ofAI-enabled technologies. In addition to the IT com-panies, the global car companies like Hyundai andKia had started to invest in smart cars. Telecom com-panies, such as SK Telecom, KT, and LG U+, build on

new businesses based on smart technologies. Websearch and social media companies, like Naver,Daum, and Kakao are employing more and more AIexperts. Game companies such as Nexon andNCSOFT are also active in recruiting AI researchers.

Contemporary Research ProjectsHere I give a flavor of current AI activities in Korea bydescribing some sample projects. One is the ExoBrainproject which started in 2014 with funding from theMinistry of Science, ICT, and Future Planning (web-page at exobrain.kr). It is planned for nine years andthe total budget is $90 million. The ExoBrain con-sortium consists of national research institutes (ETRIand KITECH), companies, and universities. The goalis to develop natural language dialogue systems forknowledge communications between humans andmachines in specific domains. ExoBrain (see figure 1)

Figure 1. The ExoBrain Project.

Machine MachineCollaborating

Machine Collaborating

Machine Reading

Machine Reasoning

Machine Studying

Machine Learning

Inference

Evolvable AI

PredictionMachine Predicting

Cars and Robots Big Data Decision Making Consulting Medical QA

Human Knowledge and Machine Intelligence

StructuredBig Data

UnstructuredBig Data

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is a digital extension or augmentation of the biolog-ical brain. It contains knowledge of deep under-standing, question and answering, and knowledgeproduction in natural languages (Ryu, Jang, and Kim2014). Applications include call center Q&As, health-care advisors, and big data consultants. ExoBrain willalso be used for natural language communicationwith humanoid robots, smart cars, digital signage,and wearable devices. The project develops tech-nologies for machine reading, machine learning,machine reasoning, and machine collaborating. Thefirst phase investigates core technologies for conver-sations with general knowledge. The second phaseconcentrates on developing applied technologies forconsulting in medical, legal, financial domains. Thethird phase develops multilingual systems.

Another nine-year AI project started in 2015. Thisproject, named DeepView, aims to understand visualdata from surveillance cameras. Some other projectsdeal with integrated vision-language problems. For

example, the Videome project investigates technolo-gies for automatically building knowledge bases fromdigital videos. A deep learning technique was devel-oped to build hypernetwork models of concept hier-archies from the pictures and sentences in video data(Ha, Kim, and Zhang 2015).

In 2015 the Ministry of Science and Technology(MSIP) launched the Software Star Lab Program, andAI was chosen as one of the five thrust areas. In thelong run, a total of five AI projects (AI Star Labs) willbe selected and sponsored for eight years. In the firstyear (2015), two projects (labs) were selected. One isthe Autonomous Learning and Thinking Agent(ALTA) project (see figure 2), which aims to developopen source cognitive agents that learn and thinkautonomously based on wearable devices that arepart of the Internet of Things (IoT). The ALTA AI ker-nel is a neurocognitive memory system that inte-grates perception, action, and cognition componentscoupled organically in a cognitive cycle and supports

Figure 2. The Autonomous Learning and Thinking Agent (ALTA) Project.

Packages

Process Scheduler

Integrated Controller

Database

Hardware Drivers

ALTA Kernel

LifeMap CogMap ActMap

LearningModels

InferenceModels &

Applications

CoreModules

AgentKernel

GUI BasedInterface

EEG Inter-processCommunication

Mediator MultimodalSensor Data

Synchronizer

Deep-Network-

based PatternAnalyzer

MultigraphSLAM-basedUser Activity

Mapping

Topology-Preserving

Self-organizedMap

Real-TimeSelf-

organizedNeural

Network

POMDPbased

SequentialDecision Model

SequentialBayesianInference

Long/Short-term

ObjectiveDecision Model

Environment-Adaptive

RecommendSystem

Higher-orderRecurrent

Hypernetwork

HierarchicalModularCorticalNetwork

DeepRecurrent

NeuralNetwork

Episodic and SemanticMemory

Recall

API

SDK

Smart Devices,Wearables and

Computing HW

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autonomous learning of and inference on three lev-els of knowledge maps (LifeMap, CogMap, andActMap) for personalized lifelong services. Potentialservice scenarios include cognitive advisors, life com-panions, home robots, autonomous cars, and healthcare. A world-wide consortium of Open Source Cog-nitive Architecture (OSCA) is being established to col-laborate on developing the ALTA cognitive agent.

ProspectsI have reviewed the history of AI research in Koreaand some contemporary research projects. Startingfrom the formation of SIG AI in 1985, AI in Korea wasdiversified into four SIGs on intelligence, language,vision, and bio, and grown separately. The SIGs werethen unified in 2010 to form the Korea ArtificialIntelligence Society. Recently, the learning-based AIapproach has gained in importance. In recognitionof it, in 2014 the Ministry of Science, ICT, and FuturePlanning funded a machine learning center. A totalof 12 researchers participate in the center projectfrom 5 universities: Seoul National University, KoreaUniversity, Yonsei University, KAIST, and Postech.The focal topic is human-level lifelong machinelearning (Zhang 2013). Spurred by the Center, KAISlaunched a new SIG for Machine Learning.

There are new developments. The AI communitystarts to investigate the agents that deal with physi-cal sensor data in addition to the traditional text-based data. This trend is accelerated by the widespread of mobile computing, wearable devices,autonomous robots, and smart cars. This is especial-ly remarkable in Korea since much of its economyrelies on the electronics and car industries. The com-ing age of smart machines and the Internet of Things(IoT) is expected to rely more on physical AI systems.

Another development is the transition frommachine-oriented AI to human-oriented AI andhumanlike machine intelligence. Scientists talk,more often than before, to each other to find com-mon interests between AI and cognitive science. TheNational Association of Cognitive Science Industries(NACSI) was founded in 2014 to promote industry-related convergence R&D at the intersection of artifi-cial intelligence, brain science, and cognitive science.An international forum is being organized to estab-lish a master plan for the so-called Mind MachineProject that aims to innovate core technologies forintegrative intelligence systems such as personalrobots and cognitive agents. The Korean Institute ofInformation Scientists and Engineers (KIISE) hasrecently organized the First International Symposiumon Perception, Action, and Cognitive Systems (PACS-2015). The PACS will serve as a global venue for fron-tier research in embodied cognitive systems, humancognition, and machine cognition to achievehuman-level artificial intelligence.

ReferencesHa, J.-W.; Kim, K.-M.; and Zhang, B.-T. 2015. AutomatedConstruction of Visual-Linguistic Knowledge via ConceptLearning from Cartoon Videos. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015).Palo Alto, CA: AAAI Press.

Kang, S.-S.; Shim, K.-S.; Zhang, B.-T.; Kwon, H.-C.; Woo, C.-S.; and Kim, Y.-T. 1987. An English-Korean System forHuman Assisted Language Translation. In Proceedings of the1987 IEEE Region 10 Conference (TENCON’87), 509–515. Pis-cataway, NJ: Institute for Electrical and Electronics Engi-neers.

Lee, J. 2007. A Data-Centered Integration Framework forIntelligent Service Robots. In Proceedings of the 16th IEEEInternational Symposium on Robot and Human InteractiveCommunication (ROMAN 2007), 357–362. Piscataway, NJ:Institute for Electrical and Electronics Engineers.dx.doi.org/10.1109/ROMAN.2007.4415109

Ryu, P. M.; Jang, M.-G.; and Kim, H.-K. 2014. Open DomainQuestion Answering Using Wikipedia-Based KnowledgeModel. Information Processing & Management 50(5): 683–692. dx.doi.org/10.1016/j.ipm.2014.04.007

Seo, Y.-W., and Zhang, B.-T. 2000. Learning User’s Prefer-ences by Analyzing Browsing Behavior, In Proceedings ofInternational Conference on Autonomous Agents (Agents 2000),381–387. Richland, SC: International Foundation forAutonomous Agents and Multiagent Systems.

Yoo, S., and Kim, I. K. 1992. DIAS1: An Expert System forDiagnosing Automobiles with Electronic Control Units.International Journal of Expert Systems with Applications 4(1):69–78. dx.doi.org/10.1016/0957-4174(92)90042-Q

Zhang, B.-T. 1997. Active Learning Agents with ArtificialNeural Brains. Paper presented at the First InternationalConference on Cognitive Science (ICCS’97), Seoul Nation-al University, Seoul, Korea.

Zhang, B.-T. 2013. Information-Theoretic Objective Func-tions for Lifelong Learning. In Lifelong Machine Learning:Papers from the 2013 AAAI Spring Symposium. TechnicalReport SS-13-05. Palo Alto, CA: AAAI Press.

Byoung-Tak Zhang is a professor of computer science anddirector of the Institute for Cognitive Science (ICS) and theCognitive Robotics and Artificial Intelligence Center (CRA-IC), Seoul National University, Seoul, Korea. He has workedas a research scientist at the German National ResearchCenter for Computer Science (1992–1995) and a visitingprofessor at MIT CSAIL and Department of Brain and Cog-nitive Sciences (2003–2004) and Samsung Advanced Insti-tute of Technology (2007–2008). Zhang served as thefounding president of the Korea Artificial Intelligence Soci-ety (KAIS) for 2010–2013. He received his Ph.D. in com-puter science from University of Bonn, Germany, in 1992and his B.S. and M.S. in computer science and engineeringfrom Seoul National University, in 1986 and 1988, respec-tively.