Remember.
The article looks at machine learning (ML) and large language models (LLMs), focusing on a concept under development at Google Research called TITAN which is a ML system that can continue learning after its initial training has occurred and it has been put into use. Topics include building the capac...
| Publicado en: | Communications of the ACM Vol. 68; no. 4; pp. 5 - 6 |
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| Autor principal: | |
| Formato: | Opinion |
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Association for Computing Machinery
Apr2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=184138763&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 184138763 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Apr2025 vid: 68 iid: 4 pid: 68 pub: Association for Computing Machinery artinfo: ui: 184138763 10.1145/3720453 ppf: 5 ppct: 1 formats: tig: atl: Remember. aug: au: Cerf, Vinton G. affil: Google, Global Networking, Reston, VA, USA su: Machine learning Language models Federated learning Computer security vulnerabilities sug: subj: Machine learning Language models Federated learning Computer security vulnerabilities ab: The article looks at machine learning (ML) and large language models (LLMs), focusing on a concept under development at Google Research called TITAN which is a ML system that can continue learning after its initial training has occurred and it has been put into use. Topics include building the capacity to forget previously learned information into ML systems and the possible vulnerability of ML systems to hacking and attacks. pubtype: Periodical doctype: Opinion src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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