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Analytic Concepts to Improve our Cognitive Toolkit


 
  
Here are some Analytics Concepts that would improve everybody's cognitive toolkit, selected from answers to Edge Question: WHAT SCIENTIFIC CONCEPT WOULD IMPROVE EVERYBODY'S COGNITIVE TOOLKIT?


Question This years's Edge Question is

WHAT SCIENTIFIC CONCEPT WOULD IMPROVE EVERYBODY'S COGNITIVE TOOLKIT?

Gregory PS: Here are some EDGE answers that I found especially relevant for analytics / data mining professionals.

What are your analytics concepts that would improve cognitive toolkits of scientists and non-scientists? Please comment below


VINOD KHOSLA
Technology Entrepreneur & Venture Capitalist, Khosla Ventures; Formerly General Partner at Kleiner Perkins. Caufield & Byers; Founder, Sun Microsystems
The Black Swan Technology

Think back to the world 10 years ago. Google had just gotten started; Facebook and Twitter didn't exist. There were no smart phones, no one remotely conceived of the possibility of the 100,000 iPhone apps that exist today. The few large impact technologies (versus slightly incremental advances in technologies) that occurred in the past 10 years were black swan technologies. In his book, Nassim Taleb defines a Black Swan as an event of low probability, extreme impact, and with only retrospective predictability

JOHN ALLEN PAULOS
Professor of Mathematics, Temple University, Philadelphia; Author, Irreligion: A Mathematician Explains Why the Arguments ofr God Just Don't Add Up
A Probability Distribution

The notion of a probability distribution would, I think, be a most useful addition to the intellectual toolkits of most people.

DAVID ROWAN
Editor, WIRED magazine's UK Edition
Personal Data Mining

It's time to reclaim the concept of data mining from the marketing industry's microtargeting of consumers, the credit-card companies' anti-fraud profiling, the intrusive surveillance of state-sponsored Total Information Awareness. We need to think more about mining our own output to extract patterns that turn our raw personal datastream into predictive, actionable information. All of us would benefit if the idea of personal data mining were to enter popular discourse.

CHARLES SEIFE
Professor of Journalism, New York University; formerly journalist, Science magazine; Author,Proofiness
Randomness

Our very brains revolt at the idea of randomness. We have evolved as a species to become exquisite pattern-finders - long before the advent of science, we figured out that a salmon-colored sky heralds a dangerous storm, or that a baby's flushed face likely means a difficult night ahead. Our minds automatically try to place data in a framework that allows us to make sense of our observations and use them to understand events and predict them.

The First Law of Randomness: There is such a thing as randomness.

ANDY CLARK
Philosopher and Cognitive Scientist, University of Edinburgh. Author: Supersizing the Mind: Embodiment, Action, and Cognitive Extension
Predictive Coding

The idea that the brain is basically an engine of prediction is one that will, I believe, turn out to be very valuable not just within its current home (computational cognitive neuroscience) but across the board: for the arts, for the humanities, and for our own personal understanding of what it is to be a human being in contact with the world.

CLAY SHIRKY
Social & Technology Network Topology Researcher; Adjunct Professor, NYU Graduate School of Interactive Telecommunications Program (ITP); Author, Cognitive Surplus
Pareto Principle

You see the pattern everywhere: the top 1% of the population control 35% of the wealth. On Twitter, the top 2% of users send 60% of the messages. In the health care system, the treatment for the most expensive fifth of patients create four-fifths of the overall cost. These figures are always reported as shocking, as if the normal order of things has been disrupted, as if the appearance of anything other than a completely linear distribution of money, or messages, or effort, is a surprise of the highest order.

It's not. Or rather, it shouldn't be.

Comments:

Gregory Piatetsky
What about "Identifying False Predictors" - one of the key steps in building any predictive models. In more layman terms, separating correlation from causality


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