{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T08:06:58Z","timestamp":1761552418905,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":5,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032090072"},{"type":"electronic","value":"9783032090089"}],"license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Scale is hopefully not all you need.<\/jats:p>\n                  <jats:p>My personal AI mission: Productionizing Graph Neural Networks \u2013 a set of models with strong inductive bias that require less data and are easier to interpret.<\/jats:p>","DOI":"10.1007\/978-3-032-09008-9_16","type":"book-chapter","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T07:56:19Z","timestamp":1761551779000},"page":"138-143","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Let\u2019s Talk AI with AI Expert Matthias Fey"],"prefix":"10.1007","author":[{"given":"Matthias","family":"Fey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Steffen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"16_CR1","unstructured":"Fey, M.: On the power of message passing for learning on graph-structured data. Ph.D. Thesis. TU Dortmund (2022)"},{"key":"16_CR2","unstructured":"Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey https:\/\/arxiv.org\/abs\/2312.10997"},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Hamilton, W.L.: Graph Representation Learning. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-031-01588-5","DOI":"10.1007\/978-3-031-01588-5"},{"key":"16_CR4","unstructured":"Minaee, S., et al.: Large language models: a survey. https:\/\/arxiv.org\/abs\/2402.06196"},{"key":"16_CR5","unstructured":"Smith, S.L., et al.: ConvNets match vision transformers at scale. In: Proceedings of the CVPR 2023. IEEE (2023)"}],"container-title":["Lecture Notes in Computer Science","Let\u2019s Talk AI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09008-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T07:56:22Z","timestamp":1761551782000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09008-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,28]]},"ISBN":["9783032090072","9783032090089"],"references-count":5,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09008-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,28]]},"assertion":[{"value":"28 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}