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Develop Artificial Brain with Knowledge Learning and Language Ability

Founded in 1998, KAIST’s Cognitive Information Lab is committed to comprehending human intellectual activities and developing an information system that will enable computers to assist in human learning activities. The Lab’s research objectives are to learn about human intelligence and its knowledge processing mechanisms; to develop knowledge information processing technology that functions similarly to the manner in which humans think, based on a brain information processing mechanism; and to study the core foundational technology for the development of an artificial brain.

With the development of artificial brains, humans would be able to leave relatively simple knowledge and information processing tasks to artificial brains, and be able to focus on creative activities in the arts and sciences. As this development would enable a more intelligent knowledge information system that could infer relationships between learning and information, it would also materialize talking intelligent software agents based on an integrated human abilities model. These could include, for example, question-answer assistants, lecture preparation assistants, essay and report graders, and other knowledge information processing with the ability to intelligently infer relationships between information.

To make the artificial brain a reality, an Autonomous Mental Development model, which would enable machines to automatically acquire knowledge, is necessary. An Autonomous Mental Development model studies artificial knowledge, which ranges from unstructured, illogical situation data to structured, logical knowledge information, based on the brain’s information processing mechanism. During this development process, a number of language resources are needed and are produced.

To learn from both unstructured, illogical language resources and structured, logical language resources, an understanding of language resources, such as concept recognition and relation recognition between concepts, is necessary. The Autonomous Mental Development model learns new knowledge and information, including concept relations knowledge, definitions of newly recognized terminology, and relations between sentences, through causality inference, hierarchy inference, and other inference processes based on the understanding of language. This new hierarchical knowledge and information is again stored by the memory model based on the brain information processing of the artificial brain, and is used in active learning system development, the comprehension of newly-inputted information, and in artificial intelligence applications.

Research Area
- Human Languages Engineering
Install language ability to enable a computer to communicate with businesses and people
Meaning-Unit Comprehension Morphological Analysis, Terminology Recognition, Foreign Word Recognition, Named Entity Recognition, Word Sense Disambiguation
Dependence Structure Comprehension Syntactic Analysis, Dependency Structure Analysis, Noun Phrase/Verb Phrase Recognition
Concept Relations Comprehension Hierarchy Relation, Polysemy Relation, Causality Relation
Application Knowledge Learning and Extraction, Information Search, Document Classification


- Artificial Brain
Study a information-processing model that imitates the information-processing model of the human brain
Causality Inference Causality Relations between Terms Inference, Causality Relations between Sentences Inference
Hierarchy Relation Inference Terminology Hierarchy Relation Inference, Specificity Comparison, Automatic Ontology Construction, Ontology Mapping
Knowledge Expression Concept Relations Expression, Knowledge Acquisition and Inference
Application Question-Answer based on Knowledge, Intelligent Agents


- Knowledge Mining
**Summarization , Grading Essays, Student-tailored Education, Etc.

- Information Search and Multimedia Search

Application of Language Processing in Brain Science
- Knowledge Acquisition : Resource -> Artificial Knowledge
Research Area: Terminology Extraction, Event Recognition, Study of Knowledge Representation

-Knowledge Search: Knowledge Exploration: Knowledge Search and Inference
Research Area: High-capacity Data Search, Meanings Comprehension, Information Search, Causality Inference.

- Answer Creation: Resource -> Language Representation
Research Area: Definitions and Explanation Formation, Video-clip Generation, Automatic Abstraction, Powerpoint Generation