Dawn Song

@dawnsongtweets

Professor in Computer Science at UC Berkeley; Research in AI, Security, Blockchain; Serial entrepreneur

Bay Area
Vrijeme pridruživanja: ožujak 2012.

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  1. Prikvačeni tweet
    12. stu 2019.

    Thanks and for sharing the story on the importance of building trustworthy AI, privacy technologies, and establishing individual data rights: !

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  2. 30. sij

    Congrats to the launch of , AI Robotics for the real world!

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  3. 23. sij

    Really exciting list of papers for , at the intersection of machine learning and systems!

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  4. 16. sij

    So excited to see the Oasis Network reach this important milestone! Huge thanks to everyone in the Oasis Community who has made this possible!

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  5. 24. stu 2019.

    Great to see the increasing awareness among users about the lack of control for how their data is being used: . Excited to build a new platform with a brilliant team to help change this!

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  6. 17. stu 2019.

    Thanks Joelle Pineau and for having me as the keynote speaker at Facebook Women in AI Summit, sharing my experience with so many great women AI researchers & engineers! Here's the book by I recommended as a must-read for every woman: .

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  7. 9. stu 2019.

    Great speaking with at summit about building a new privacy paradigm where users are data owners instead of serfs: ! And amazing stories from fellow speakers & others!

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  8. 7. stu 2019.

    Really honored to be speaking at summit tomorrow (Nov 8), on technological advancement leading to a new paradigm of data ownership and privacy! Thanks for putting together such an impressive and inspiring program: !

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  9. 4. stu 2019.
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  10. 28. lis 2019.

    Congrats to Joe Near, Lun Wang, and my other co-authors for the Distinguished Paper Award at ACM OOPSLA conference on our work on Duet (), making it easier to automatically verify differentially private machine learning algorithms!

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  11. 19. lis 2019.

    Honored to be part of Wired25 List: . Addressing the tension between data utility and privacy protection is a huge topic, needing better technology, regulation, incentives, business models. Excited to work with the community to explore the frontier!

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  12. 3. lis 2019.

    Really excited about one of the first workshops on using program synthesis for intelligent process automation (part of ) . Submit your work here: deadline Nov 15! $1000 for Best Paper Award.

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  13. 29. ruj 2019.

    Congratulations to Josh Tenenbaum for being named a MacArthur Fellow 2019 () by ! Really well deserved for making steps forward towards answering the question of how human minds understand so much from so little, so quickly!

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  14. 25. ruj 2019.

    Congrats to everyone who submitted to ! 2594 submissions! Over 100 area chairs and 1700 reviewers will be working hard together to make a great program. Also, let us know who you'd like to see as invited speakers!

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  15. 24. ruj 2019.

    It was great fun talking to NPR All Things Considered about adversarial deep learning: . In order to build driverless cars, we need to build more resilient learning systems first. This will need the whole community's effort to succeed!

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  16. 19. ruj 2019.

    Honored to be named on the Female Founders 100 list 100 by ! Excited to be working with a brilliant team, building the platform for privacy-first applications.

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  17. 15. ruj 2019.

    My safe bet: "In 10 years, most chips will have secure enclave capabilities." check out our new paper & release on Keystone, open source secure enclave: ; and the summary on the recent Open Source Secure Enclave workshop at Berkeley:

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  18. 12. ruj 2019.

    Really excited and honored that Science Museum in London is showing our STOP Sign adversarial example on exhibit ("DRIVERLESS: WHO IS IN CONTROL" ) through Oct 2020 - to build autonomous cars, we need to build more resilient learning systems!

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  19. 8. ruj 2019.

    As program co-chair for ICLR2020, I'm really excited that for the first time a major machine learning conference is taking place in Africa! Please submit your best work on deep learning to , deadline on Sep 25!

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  20. 7. ruj 2019.

    Submit your work on privacy-preserving machine learning to Workshop on Federated Learning for Data Privacy and Confidentiality (in Conjunction with NeurIPS 2019) by Sep 9, or join us in Dec!

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