Using near misses to teach concepts to a human intelligence system
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119546 |
| _version_ | 1811079309079609344 |
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| author | Barnwell, Jake A. (Jake Allen) |
| author2 | Patrick H. Winston. |
| author_facet | Patrick H. Winston. Barnwell, Jake A. (Jake Allen) |
| author_sort | Barnwell, Jake A. (Jake Allen) |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T11:13:09Z |
| format | Thesis |
| id | mit-1721.1/119546 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:13:09Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1195462019-04-11T05:10:27Z Using near misses to teach concepts to a human intelligence system Barnwell, Jake A. (Jake Allen) Patrick H. Winston. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 63). If we are to understand how we can build artificial intelligence machines that are able to organically process and acquire knowledge like humans do, we must first understand how to teach a human intelligence system to model and learn generic concepts without resorting to mechanistic hand-coding of such concepts. Such an achievement in human intelligence is an important stride towards the realization of Minsky's hypothetical "baby machine." Genesis is a story-understanding system that reads and analyzes stories in simple English, as humans do. Genesis can recognize certain sequences of events as particular manifestations of some user-defined concepts, such as revenge or physical retaliation. However, both the structure and definition of these high-level concepts must be explicitly provided by the user, and must be exactly matched using concept patterns against sequences of events in a story. I propose that this paradigm is unlike how humans learn such concepts, and instead, such concepts are learned by example, not by explicit definition. To introduce this organic, example-driven paradigm to Genesis, I have created STUDENT, a system that ingests a small series of positive and negative examples of concepts and builds an internal model for these concepts. By aligning similar examples of concepts together, I have constructed refined concept patterns which encompass multiple, different manifestations of the concepts, without requiring a human to explicitly define them. Adding this capability to Genesis enables it to learn concepts by example from a teacher. This behavior emulates how maturing humans learn concepts. Achieving this ability to emulate a human's learning process for a new idea brings us one step closer to modeling human intelligence and story understanding. by Jake A. Barnwell. M. Eng. 2018-12-11T20:39:35Z 2018-12-11T20:39:35Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119546 1076272866 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 63 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Barnwell, Jake A. (Jake Allen) Using near misses to teach concepts to a human intelligence system |
| title | Using near misses to teach concepts to a human intelligence system |
| title_full | Using near misses to teach concepts to a human intelligence system |
| title_fullStr | Using near misses to teach concepts to a human intelligence system |
| title_full_unstemmed | Using near misses to teach concepts to a human intelligence system |
| title_short | Using near misses to teach concepts to a human intelligence system |
| title_sort | using near misses to teach concepts to a human intelligence system |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119546 |
| work_keys_str_mv | AT barnwelljakeajakeallen usingnearmissestoteachconceptstoahumanintelligencesystem |