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Green AI - AI for Sustainability and Sustainability of AI (GREENAI)5 ECTS
(englische Bezeichnung: Green AI - AI for Sustainability and Sustainability of AI)

Modulverantwortliche/r: Eva Dorschky, Björn Eskofier
Lehrende: Eva Dorschky, Björn Eskofier


Startsemester: SS 2022Dauer: 1 SemesterTurnus: halbjährlich (WS+SS)
Präsenzzeit: 30 Std.Eigenstudium: 120 Std.Sprache: Englisch

Lehrveranstaltungen:


Empfohlene Voraussetzungen:

Basic knowledge in machine learning is required to take part in the seminar. Students are expected to have completed one or more basic courses, such as PR, PA, IntroPR, DL, MTLS, or equivalent.

Inhalt:

Can we use AI to combat global climate change? How can advances in machine learning and data science help to monitor climate crises and to conserve nature? What is the role of AI in reducing greenhouse gas emissions in the manufacturing industries, transportation infrastructure, agriculture, and power sector?

In this seminar, we will develop and discuss future perspectives of AI for sustainability, considering the sustainability of AI itself. Current advances in machine learning, particularly deep learning, are enabling new applications but are accompanied by an exponential increase in computational cost and thus significant carbon emissions (Schwartz et al., 2020; Vinuesa et al., 2020). In this seminar, we will learn about important aspects of improving the sustainability of machine learning algorithms.

This seminar offers a different perspective on machine learning as taught in other courses, namely its role in global climate change. This aspect is becoming increasingly important in research, but also in industry. Therefore, this seminar provides the following items:

  • Introduction to "Green AI" versus "Red AI"

  • Guests talks on related research topics

  • Group discussions on future prospects of AI, specifically machine learning

  • Best practices for literature review and scientific presentations

  • Literature review on Green AI in certain areas in groups

  • Scientific talk of each student on one specific topic

Lernziele und Kompetenzen:

Students will analyze

  • the opportunities that AI offers to combat global climate change

  • the negative impact of AI on global climate change

  • current research topics in the field of "Green AI"

Students will be able to

  • discuss and work in a group

  • perform and write a literature review

  • give a scientific presentation

Literatur:

Schwartz, Roy et al. (2020). “Green ai”. In: Communications of the ACM 63.12, pp. 54– 63.
Vinuesa, Ricardo et al. (2020). “The role of artificial intelligence in achieving the Sustainable Development Goals”. In: Nature communications 11.1, pp. 1–10.

Organisatorisches:

Registration via email: eva.dorschky@fau.de


Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:
Das Modul ist im Kontext der folgenden Studienfächer/Vertiefungsrichtungen verwendbar:

  1. Artificial Intelligence (Master of Science)
    (Po-Vers. 2021s | TechFak | Artificial Intelligence (Master of Science) | Gesamtkonto | Hauptseminar | Green AI - AI for sustainability and sustainability of AI)
  2. Informatik (Bachelor of Science)
    (Po-Vers. | TechFak | Informatik (Bachelor of Science) | Gesamtkonto | Hauptseminar | Green AI - AI for sustainability and sustainability of AI)
  3. Informatik (Bachelor of Science)
    (Po-Vers. | TechFak | Informatik (Bachelor of Science) | Hauptseminar | Green AI - AI for sustainability and sustainability of AI)
  4. Informatik (Bachelor of Science)
    (Po-Vers. 2009w | TechFak | Informatik (Bachelor of Science) | Gesamtkonto | Hauptseminare, Praktika, Bachelorarbeit | Hauptseminar | Green AI - AI for sustainability and sustainability of AI)
  5. Informatik (Bachelor of Science)
    (Po-Vers. 2022w | TechFak | Informatik (Bachelor of Science) | Gesamtkonto | Hauptseminar | Green AI - AI for sustainability and sustainability of AI)
  6. Informatik (Master of Science)
    (Po-Vers. 2010 | TechFak | Informatik (Master of Science) | Gesamtkonto | Hauptseminar, Projekt, Masterarbeit | Hauptseminar | Green AI - AI for sustainability and sustainability of AI)
  7. Medizintechnik (Master of Science)
    (Po-Vers. 2018w | TechFak | Medizintechnik (Master of Science) | M7 Flexibles Budget Technische Fakultät | Green AI - AI for sustainability and sustainability of AI)
  8. Medizintechnik (Master of Science)
    (Po-Vers. 2019w | TechFak | Medizintechnik (Master of Science) | Modulgruppen M1, M2, M3, M5, M7 nach Studienrichtungen | Studienrichtung Medizinische Bild- und Datenverarbeitung | Flexibles Budget / Flexible budget | Green AI - AI for sustainability and sustainability of AI)
  9. Medizintechnik (Master of Science)
    (Po-Vers. 2019w | TechFak | Medizintechnik (Master of Science) | Modulgruppen M1, M2, M3, M5, M7 nach Studienrichtungen | Studienrichtung Medizinelektronik | Flexibles Budget / Flexible budget | Green AI - AI for sustainability and sustainability of AI)
  10. Medizintechnik (Master of Science)
    (Po-Vers. 2019w | TechFak | Medizintechnik (Master of Science) | Modulgruppen M1, M2, M3, M5, M7 nach Studienrichtungen | Studienrichtung Medizinische Produktionstechnik, Gerätetechnik und Prothetik | Flexibles Budget / Flexible budget | Green AI - AI for sustainability and sustainability of AI)

Studien-/Prüfungsleistungen:

Green AI - AI for sustainability and sustainability of AI (Prüfungsnummer: 76141)

(englischer Titel: Green AI)

Untertitel: AI for Sustainability and Sustainability of AI

(englischer Untertitel: AI for Sustainability and Sustainability of AI)

Prüfungsleistung, Seminarleistung, Dauer (in Minuten): 20, benotet, 5 ECTS
Anteil an der Berechnung der Modulnote: 100.0 %
weitere Erläuterungen:
The grade will be based on a written report (40%) and scientific talk (60%). Active participation in discussions will be rewarded with a bonus.
Prüfungssprache: Englisch

Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023, 2. Wdh.: keine Wiederholung
1. Prüfer: Björn Eskofier

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