| dc.contributor.author | Wright, Randall S. | |
| dc.date.accessioned | 2025-12-18T18:54:34Z | |
| dc.date.available | 2025-12-18T18:54:34Z | |
| dc.date.issued | 2024-11-05 | |
| dc.identifier.issn | 0895-6308 | |
| dc.identifier.issn | 1930-0166 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/164409 | |
| dc.description.abstract | In a recent MIT News article titled “Explained: Generative AI,” Adam Zewe (Citation2023) writes
But what do people really mean when they say ‘generative AI?’
Before the generative AI boom of the past few years, when people talked about AI, typically they were talking about machine-learning models that can learn to make a prediction based on data. For instance, such models are trained, using millions of examples, to predict whether a certain X-ray shows signs of a tumor or if a particular borrower is likely to default on a loan.
Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on. | en_US |
| dc.publisher | Taylor & Francis | en_US |
| dc.relation.isversionof | https://doi.org/10.1080/08956308.2024.2394379 | en_US |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivatives | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
| dc.source | Taylor & Francis | en_US |
| dc.title | What I Don’t Get About AI . . . | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Wright, R. S. (2024). What I Don’t Get About AI . . . Research-Technology Management, 67(6), 47–50. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Office of Strategic Alliances and Technology Transfer. Corporate Relations | en_US |
| dc.relation.journal | Research-Technology Management | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.identifier.doi | https://doi.org/10.1080/08956308.2024.2394379 | |
| dspace.date.submission | 2025-12-18T18:48:14Z | |
| mit.journal.volume | 67 | en_US |
| mit.journal.issue | 6 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |