With the rapid emergence of digital devices, an unstoppable, invisible force is changing human lives in incredible ways. That force is data. We generated more data in 2017 than in all the previous 5,000 years of human history.

The massive gathering and analyzing of data in real time is allowing us to address some of humanity’s biggest challenges but as Edward Snowden and the release of NSA documents have shown, the accessibility of all this data comes at a steep price.

This documentary captures the promise and peril of this extraordinary knowledge revolution.

Press the play button below to listen here or visit the show page at DataDriven.tv

Show Notes

Frank and Andy talk with Kira Wetzel about girls+data, running, Harry Potter, and the stream of consciousness.

Links

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Notable Quotes

  • Frank’s home improvement (2:15)
  • Kira’s LinkedIn profile (4:00)
  • Girls and Data (4:30)
  • Data Point: Girls and Data at SQL Saturday Charlotte (5:12)
  • Tableau (6:00)
  • SAP = “Say a Prayer” (9:30)
  • Tourist-y stuff (12:00)
  • “Quaint” (13:00)
  • “Data found me.” (13:30)
  • TI-99, Q*bert, and BurgerTime (16:40)
  • Girls and Data (18:45)
  • “Try your vegetables” (22:00)
  • Andy is right here… (22:15)
  • Shoutout to SQL Cruise (Now Tech Outbound)! (26:15)
  • Richmond SQL Saturday is 24 Mar 2018 (29:00)
  • Running, sleeping, and eating (30:30)
  • Girls on the Run (31:20)
  • Technology is literally changing the world everywhere you look. (32:05)
  • VR Vacations (33:55)
  • A stealthy Microsoft Store visit… (35:45)
  • Running calms the mind. (38:15)
  • @girlsanddata (39:00)
  • Stream of Consciousness and the Harry Potter question, again (41:00)
  • Kira listens to Data Driven (42:45)
  • Is “novitiate” the name of a young wizard? (44:00)

In this talk, Bin Yu, professor at UC Berkley, discusses the intertwining importance and connections of three principles of data science.

The three principles will be demonstrated in the context of two neuroscience projects and through analytical connections. In particular, the first project adds stability to predictive models used for reconstruction of movies from fMRI brain signals to gain interpretability of the predictive models.

The second project employs predictive transfer learning and stable (manifold) deep dream images to characterize the difficult V4 neurons in primate vision cortex. Our results lend support, to a certain extent, to the resemblance to a primate brain of Convolutional Neural Networks (CNNs).