Better Algorithms through Faster Math: The search for new algorithms that could reduce the time needed for multiplication is now at the center of data science.
Saved in:
| Title: | Better Algorithms through Faster Math: The search for new algorithms that could reduce the time needed for multiplication is now at the center of data science. |
|---|---|
| Authors: | Greengard, Samuel |
| Source: | Communications of the ACM. Jun2023, Vol. 66 Issue 6, p11-13. 3p. 1 Color Photograph. |
| Subjects: | Algorithms, Algorithm research, Multiplication, Matrix multiplications, Machine learning, Deep learning, Artificial intelligence, DeepMind Technologies Ltd. |
| Abstract: | The article focuses on the search for algorithms that can reduce the time needed for multiplication problems within the fields of machine learning (ML), deep learning (DL), and artificial intelligence (AI). Topics discussed include the history of matrix multiplication and the data science group DeepMind and their ML approach AlphaTensor. |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 163907041 AccessLevel: 6 PubType: Periodical PubTypeId: serialPeriodical PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Better Algorithms through Faster Math: The search for new algorithms that could reduce the time needed for multiplication is now at the center of data science. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Greengard%2C+Samuel%22">Greengard, Samuel</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Communications+of+the+ACM%22">Communications of the ACM</searchLink>. Jun2023, Vol. 66 Issue 6, p11-13. 3p. 1 Color Photograph. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithm+research%22">Algorithm research</searchLink><br /><searchLink fieldCode="DE" term="%22Multiplication%22">Multiplication</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+multiplications%22">Matrix multiplications</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22DeepMind+Technologies+Ltd%2E%22">DeepMind Technologies Ltd.</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The article focuses on the search for algorithms that can reduce the time needed for multiplication problems within the fields of machine learning (ML), deep learning (DL), and artificial intelligence (AI). Topics discussed include the history of matrix multiplication and the data science group DeepMind and their ML approach AlphaTensor. |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=163907041 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3591213 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 3 StartPage: 11 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Algorithm research Type: general – SubjectFull: Multiplication Type: general – SubjectFull: Matrix multiplications Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: DeepMind Technologies Ltd. Type: general Titles: – TitleFull: Better Algorithms through Faster Math: The search for new algorithms that could reduce the time needed for multiplication is now at the center of data science. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Greengard, Samuel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00010782 Numbering: – Type: volume Value: 66 – Type: issue Value: 6 Titles: – TitleFull: Communications of the ACM Type: main |
| ResultId | 1 |