Kersting, Kristian (2024)
Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines.
In: Frontiers in Big Data, 2018, 1
doi: 10.26083/tuprints-00015715
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
Machine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL). However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short. These fields share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines. What kind of computation and how to program it? This is not the right question. Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them. They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Kersting, Kristian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines |
| Sprache: | Englisch |
| Publikationsjahr: | 5 März 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 19 November 2018 |
| Ort der Erstveröffentlichung: | Lausanne |
| Verlag: | Frontiers Media S.A. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Big Data |
| Jahrgang/Volume einer Zeitschrift: | 1 |
| Kollation: | 4 Seiten |
| DOI: | 10.26083/tuprints-00015715 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/15715 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | Machine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL). However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short. These fields share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines. What kind of computation and how to program it? This is not the right question. Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them. They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them. |
| Freie Schlagworte: | machine learning, artificial intelligence, deep learning, computation, learning methods |
| ID-Nummer: | Artikel-ID: 6 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-157154 |
| Zusätzliche Informationen: | Specialty section: This article was submitted to Machine Learning and Artificial Intelligence, a section of the journal Frontiers in Big Data |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Künstliche Intelligenz und Maschinelles Lernen Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Hinterlegungsdatum: | 05 Mär 2024 13:33 |
| Letzte Änderung: | 07 Mär 2024 09:36 |
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| Suche nach Titel in: | TUfind oder in Google |
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