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The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI
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| Published in: | Journal of Artificial General Intelligence |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2020
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| Subjects: | |
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| _version_ | 1867301670518521858 |
|---|---|
| collection | WordPress RSS FRELIP Feed Integration |
| container_title | Journal of Artificial General Intelligence |
| description | |
| discipline_display | Artificial Intelligence |
| discipline_facet | Artificial Intelligence |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:83332 |
| institution | FRELIP |
| journal_source_facet | Journal of Artificial General Intelligence |
| publishDate | 2020 |
| publishDateSort | 2020 |
| record_format | rss_article |
| spellingShingle | The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI Artificial Intelligence General Artificial Intelligence |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Artificial Intelligence General Artificial Intelligence Artificial Intelligence General Artificial Intelligence |
| subject_facet | Artificial Intelligence General Artificial Intelligence |
| title | The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI |
| title_auth | The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI |
| title_full | The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI |
| title_fullStr | The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI |
| title_full_unstemmed | The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI |
| title_short | The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI |
| title_sort | the archimedean trap: why traditional reinforcement learning will probably not yield agi |
| topic | Artificial Intelligence General Artificial Intelligence |
| url | https://sciendo.com/article/10.2478/jagi-2020-0004 |