#BigData #DataScience @Twitter and @BlackRock: how I can add value to #MachineVision and #FinTech

15 11 27 MeetupThis Meetup @ Twitter in Piccadilly was a treat for me because of Miriam Redi‘s presentation on Machine Vision. She mentioned creativity also in association with MATHEMATICS!

She works at Yahoo Labs and her site is VisionResearchWITCH.com

She went WOW when I showed her the re-visualised image of Cassiopeia which continues to be one of my Favourites among my re-visualisations, because the white square becomes a transparent 3D tower:


Three days later I attended this Meetup @ BlackRock – one of the most impressive offices in the City with these five senses in a Buddha face:

 20151126_190843[1] 20151126_212625[1]

15 11 27 Meetup 2One of the presenters from Alpha wanted to compare the rainfall in Surrey with the results of spending on advertising . So I asked what he’d give me, if I offered him my tool.

Here’s now a Prezi (alternative to PowerPoint) to explain my solutions to the problems of big multi-dimensional data and the insecurity of the future:

My answers:

  1. Generic Forecasting of any Time Series;
  2. Trend Directions for Different Time Periods;
  3. New Qualitative Metrics for Expert Users to define;
  4. Boundaries in Visual Selection Criteria for Expert Users to set before automation;
  5. ‘3dM’ Diagrams for ‘layering’ mult-dimensional time series;
  6. Combining Forecasting with Layering.

By recording a voice over, I produced this video:

And my proposal regarding images:

I turned it into this video.

The required levels of collaboration in (wo)man-machine relationships:

  1. ‘3dM Images’ to Enhance Machine Vision
  2. ‘3dM’ = Visual + Metric 3D
  3. ‘3dM Images’ are Re-Visualisations of ANY image
  4. New Qualitative Metrics
  5. Images can be Compared Numerically
  6. Imaging Technologies can be Compared
  7. To find Reference Technologies for particular Applications and specific Scales
  8. To find the best Resolution for every Application
  9. To find the best Technology for every Scale
  10. Experts + Automation = ‘Smart Knowledge’
  11. Domain and Technology Experts Interpret
  12. Machines Crunch Numbers.

Might you be interested in any of these solutions for YOUR problems and applications?

About Sabine Kurjo McNeill

I'm a mathematician and system analyst formerly at CERN in Geneva and became an event organiser, software designer, independent web publisher and online promoter of Open Justice. My most significant scientific contribution is www.smartknowledge.space
This entry was posted in 3D Metrics, BlackRock, Forecasting, Layering Complex Data and tagged , , , , , , , , , , , , , , , , , , , . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s