The Numerati

Mind Map of the basic concepts and ideas presented in the book, "The Numerati" by Stephen Baker.

Get Started. It's Free
or sign up with your email address
The Numerati by Mind Map: The Numerati

1. Book Info

1.1. The Numerati

1.2. by Stephen Baker

1.3. Houghton Mifflin Company - 2008

1.4. Amazon Link

2. Shopper

2.1. Semantic detail

2.1.1. contextual information

2.2. Chapter Quotes

2.2.1. In the online word, businesses no longer look at us as herds but as vast collections of individuals -- each of us represented by scores of equations. p42

2.2.2. Discouraging unwanted shoopers is far easier on the Internet p52 As merchants learn more about us...easier to determine who to reward and punish

2.3. Buckets

2.4. ** Spam vs. Medicine

2.4.1. David Heckerman - Microsoft

2.4.2. Created program to detect email spam

2.4.3. Spammers change message slightly to break through

2.4.4. If tool could detect mutations in spam, might also work in medicine

2.4.5. Shifted focus to HIV research

2.5. Consumer modelling

2.5.1. Initially used averages

2.5.2. Clustering software works better

3. Worker

3.1. Office workers historically less productive and not easily measured

3.2. MSFT patent in 2006 to measure worker vital signs

3.3. IBM

3.3.1. Samer Takrit

3.3.2. stochastic analysis attempts to tie predictions to random events

3.3.3. modelling employee performance big brother? performance DNA better match of skills to tasks

3.3.4. catalog everything (gestures, skills, etc.) - turn into numbers

3.3.5. challenge - create taxonomy to catalog skills of 300K IBM workers

3.3.6. Second Life IBM created virtual world on SL in Nov. 2006 In the future, all business processes will be simulated

3.4. WARP

3.4.1. baseball wins above replacement player

3.4.2. compares cost variance of replacement player and the variance in wins to determine ROI

3.5. George Dantzig

3.5.1. created simplex algorithm

3.5.2. recipe to guide intelligent decision making

3.5.3. optimization

3.6. Chapter Quotes

3.6.1. Look at us one way and we are stocks. Change the perspective, and we're machine parts. p23

3.6.2. The work force is too big, the world too vast and complicated for managers to get a grip on their workers the old-fashioned way -- by talking to people who know people who know people. (p34)

4. Voter

4.1. Republican and Democrate fail to describe most of us

4.2. Monitor - What in the world were they thinking?

4.2.1. What does the future hold?

4.2.2. How will people fare in the future?

4.2.3. What skills and abilities will be needed to achieve success in the future?

4.3. Example of building campaign focused on five values

4.3.1. Extending opportunity to others

4.3.2. Working within a community

4.3.3. Achieving independence

4.3.4. Focusing on family

4.3.5. Defining righteousness

4.4. Voter groups

4.4.1. Barn Raisers

4.4.2. Right-click (technofriendly)

4.4.3. Hearth Keeper

4.4.4. Civic Sentries

4.4.5. Inner Compass

4.4.6. Crossing Guards

4.4.7. Still Waters

4.4.8. Bootstrapper (committed to individual initiative)

4.4.9. Stand Pats (long for a return to past values)

4.5. Simplex Triangle

5. Blogger

5.1. Millions of people voluntarily broadcast their lives daily via blogs, YouTube, etc.

5.2. Relevance vs. Timeliness

5.3. Data mining of blog content - timely; spot development of trends, issues, hot topics

5.4. Natural language processing and machine language

5.5. Sarcasm stumps the machine

5.6. Impact of spam blogs (splogs) on analytics

6. Terrorist

7. Patient

8. Lover

9. Conclusion

10. Other Info

11. Companies/Individuals

11.1. Tacoda - Dan Morgan

11.2. IBM - Samer Takriti

12. Overview

13. Contributors to this Map

13.1. John Michl

14. Key Concepts/ Quotes

14.1. What kind of science gets it wrong 25% of the time?

14.2. While truth is vital in the world of machines, by nature it is approximate -- based on probability.

14.3. Truth, therefore, is not make-or-break-it for The Numerati. They triumph if they come up with better, quicker, or cheaper answers than the status quo. (p90)


14.4.1. If a tool to predict if a person was a pedophile was accurate 85% of the time (and wrong 15%), should it be used?