{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:51:34Z","timestamp":1774965094599,"version":"3.50.1"},"reference-count":12,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,5]],"date-time":"2019-02-05T00:00:00Z","timestamp":1549324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Today\u2019s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI\u2019s \u201cbarrier of meaning\u201d; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.<\/jats:p>","DOI":"10.3390\/info10020051","type":"journal-article","created":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T03:03:05Z","timestamp":1549422185000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Artificial Intelligence Hits the Barrier of Meaning"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8881-3505","authenticated-orcid":false,"given":"Melanie","family":"Mitchell","sequence":"first","affiliation":[{"name":"Department of Computer Science, Portland State University, Portland, OR 97207-0751, USA"},{"name":"Santa Fe Institute, Santa Fe, NM 87501, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,5]]},"reference":[{"key":"ref_1","unstructured":"McCracken, H. 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