To attract and retain talent from all backgrounds, new educational models and mentorship programmes are needed in machine intelligence, says Shannon Wongvibulsin.
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Acknowledgements
This work was supported by the Johns Hopkins School of Medicine Medical Scientist Training Program (National Institutes of Health: Institutional Predoctoral Training Grant — T32), National Institutes of Health: Ruth L. Kirschstein Individual Predoctoral NRSA for MD/PhD: F30 Training Grant, the Johns Hopkins Individualized Health (inHealth) Initiative, and the Hopkins Engineering Applications & Research Tutorials (HEART) programme.
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Wongvibulsin, S. Educational strategies to foster diversity and inclusion in machine intelligence. Nat Mach Intell 1, 70–71 (2019). https://doi.org/10.1038/s42256-019-0021-8
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DOI: https://doi.org/10.1038/s42256-019-0021-8
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