Machine learning challenges and impact: an interview with Thomas dietterich
Open accessAbstract
Abstract Machine learning is the driving force of the hot artificial intelligence (AI) wave. In an interview with NSR, Prof. Thomas Dietterich, the distinguished professor emeritus of computer science at Oregon State University in the USA, the former president of Association of Advancement of Artificial Intelligence (AAAI, the most prestigious association in the field of artificial intelligence) and the founding president of the International Machine Learning Society, talked about exciting recent advances and technical challenges of machine learning, as well as its big impact on the world.
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DL algorithms are capable of learning more correct features compared to human-designed ones.
“On the other hand, DL algorithms are capable of learning more correct features compared to human-designed ones (Zhou et al., 2018).”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Zhou et al., 2018
For scenarios where there is a big gap between the inputs and the outputs, DL models are able to perform much better than traditional ML models.
“Hence, for scenarios where there is a big gap between the inputs (such as images) and the outputs (such as objects), DL models are able to perform much better than traditional ML models (Zhou et al., 2018).”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Zhou et al., 2018
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- Authors
- Zhou, Z.-H.
- Affiliations
- Oregon State University (US)
- Corresponding author
- Zhou, Zhi-Hua
- Venue
- National Science Review
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-05-02
- Volume
- 5
- Issue
- 1
- Pages
- 54-58
- ISSN
- 2053-714X (ISSN-L)
- 2095-5138
- OpenAlex
- W2611054605
- Cited by
- 17
- References
- 0
- Citations by year
- 2024: 2 · 2023: 2 · 2022: 1 · 2021: 4 · 2020: 3 · 2019: 2 · 2018: 1 · 2017: 2
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- National Science Review (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- machine learning, artificial intelligence, technical challenges, recent advances, societal impact of AI, AAAI
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