Origin of big data

Here are three links that are worth your time.

  • Who came up with the name big data? The an­swer is much older than you think. Even the “three Vs” of big data (volume, ve­lo­city, vari­ety) date back to 2001.
  • JC Penney’s park­ing lots are empty­ing. The de­cline neatly cor­rel­ates with their stock price. Satellite im­agery is also be­ing used to fore­cast “… things like pover­ty (by track­ing build­ing height and rooftop ma­ter­i­al), oil in­vent­ory (by look­ing at im­ages of tanks and drilling rigs), and ag­ri­cul­tur­al yields (by ob­serving crops and ana­lyz­ing weather data)”
  • JP Morgan uses ma­chine learn­ing to auto­mat­ic­ally “read” thou­sands of con­tracts and in­ter­pret them. This gets us much closer to the Bill Gates mile­stone: “… when com­puters can read and un­der­stand in­form­a­tion like hu­mans do.”

Soon, we can leave the read­ing to the ma­chines and go out Saturday night.

Cultures of a data science team

Here are a few links that are worth your time:

  • If you’re build­ing a data sci­ence team, read The Two Cultures of ML Systems to learn about the pit­falls in pro­duc­tion­ising data sci­ence. Every para­graph makes a very per­tin­ent point.
  • You can im­prove the wis­dom of crowds. Ask people to vote. Also ask what oth­ers will vote for. Pick the an­swer that is more pop­ular than people pre­dict. (Nature: A Solution to the Single Question Crowd Wisdom Problem. The full text is not open.)

For our tech­nic­ally minded friends, here are a few more:

  • The Data Stack is a col­lec­tion of tools used in the data sci­ence eco­sys­tem, ran­ging from data sourcing to pro­cessing to ana­lys­is to visu­al­isa­tion
  • This is a short in­tro­duc­tion to an­om­aly de­tec­tion. When ex­plor­ing data, an­om­alies and out­liers in­vari­ably pro­duce in­ter­est­ing stor­ies
  • TensorFlow 1.0 is out. It’s faster, and fea­tures an easy to use in­ter­face. The Python API looks more like NumPy

Most of the above work on Python.

Analytics in the restaurant industry

Here are three links that are worth your time.

Lastly, here’s a map of the most well-funded AI star­tups in each of the the US states.