The Three Amigos of Big Data – Interface, Intelligence & Inference


Image

  • The other day I was thinking how to reason about the Analytics & Big Data eco system & came up with a few monikers
    • We do have a few interesting architectural artifacts connecting these monikers with the appropriate domains. May be I will share them in a future blog
    • For now, back to the essential monikers …
  • Syntactically Big Data has three Vs – the Volume, Velocity & Variety.
    • A very useful viewpoint that helps us to manage the beast, … but it does nothing for deriving value …
  • Semantically the 3 Cs – Context, Connectedness & the Convergence make a lot of sense
    • Context is King. Naturally It has many faces:
      • Personal Context, Social Context, Enterprise Context, Consumer Context and so forth
      • Came across an interesting post on Context being the future – mobility and context would rule in terms of personal apps
    • Connectedness is an essential step to mine Smart Data out of Big Data
  • Architecturally, I like the Three Amigos : Interface, Intelligence & Inference 
    • Interface is key – whether it is interface with wearable devices or visualization of data
      • Interface also includes Augmented Cognition as well as NLP/NLU
    • Intelligence comes from applying Analytics, Machine Learning, Modern AI, Deep Learning et al to Big Data
    • Inference, of course is the piece that makes it all worthwhile – Models, Reasoning Engines, Learning Machines, Boltzman Machines all fit in tis category …

In short, it is time we pay attention to the 3Cs & 3 Is of Analytics & Big Data

What says thee ? Am I making any sense ?

Ref: Thanks to http://www.american-buddha.com/cia.threeamigos10.htm for the image

About these ads

5 thoughts on “The Three Amigos of Big Data – Interface, Intelligence & Inference

  1. Can you explain a bit more about the distinction between intelligence and inference? I don’t get why machine learning counts as intelligence and yet Models, Reasoning Engines, Learning Machines, Boltzman Machines all count as inference. Most of the latter count as elements of machine learning don’t they?

    • Good point Tim. Machine Learning is a generic term and I am using it to mean algorithms. The engines,models et al have the function of actually delivering inference, of course using the algorithms. I know it is not precise.

  2. Pingback: Deep Learning for the masses | My missives

  3. Pingback: Of Building Data Products | My missives

  4. Pingback: Is it still “Artificial” Intelligence, if our Computers learn -to think- from the workings of our Brain ? | My missives

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 )

Twitter picture

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

Facebook photo

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

Google+ photo

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

Connecting to %s