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Michael Chang
Research Scientist Intern at DeepMind
Michael Chang is a prominent Research Scientist at Google DeepMind, where he is involved in significant projects such as Project Astra and Gemini. He completed his Ph.D. at the University of California, Berkeley, in 2023, specializing in artificial intelligence and deep learning under the guidance of Professors Sergey Levine and Tom Griffiths. His academic journey was supported by the NSF Graduate Research Fellowship, and he has also interned at both DeepMind and Meta AI during his doctoral studies.12
Education and Early Career
- Ph.D.: University of California, Berkeley (2023)
- B.S.: Massachusetts Institute of Technology (2017)
Chang's research focuses on developing neural software abstractions, which aim to enhance the capabilities of deep learning algorithms by allowing them to construct their own abstractions for modeling complex systems. His work has been influential in understanding how traditional software principles can be applied to improve artificial intelligence systems.12
Professional Experience
- Research Scientist: Google DeepMind
- Internships:
- DeepMind (2022)
- Meta AI (2022)
- Various research roles at institutions like MIT and the University of Michigan.
Contributions and Publications
Chang has contributed to numerous publications in the field of AI, with a focus on enhancing machine learning models and their applications in real-world scenarios. He emphasizes the importance of creating AI systems that can autonomously gather and process information without needing human input for data that is already accessible.12
Personal Philosophy
Chang advocates for the No-Push Principle, which posits that machines should autonomously retrieve information rather than relying on humans to provide it. This principle reflects his broader vision for advancing artificial intelligence towards more intuitive and self-sufficient systems.1
In summary, Michael Chang is a leading figure in AI research at Google DeepMind, recognized for his innovative approaches to deep learning and his commitment to developing intelligent systems that enhance human capabilities.
