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Mechanical Turk - Human
Powered Software
Let’s work with
the machines
SIVA MANIKANTESWARAN
How will you automate this?
“There is nothing quite
so useless as doing
with great efficiency
something that should
not be done at all.”
Peter Drucker
Type of challenges
Simple
Complex
Complicated
What is Mechanical Turk?
Do you know?
 "Human Intelligence Tasks".
For example
 Choosing the best among several photographs of a
store-front
 writing product descriptions
 Identifying performers on music CDs
 Crowd Sourcing vs. Mechanical Turk?
Using Mechanical Turk in
cv-web-annotation-toolkit
 Alex Sorokin of University of Illinois
 Collection and Classification of data (Images)
 If you're a researcher collecting a data set, you can use this toolkit
to submit images to Mechanical Turk and pay people to label them
for you
 Teach the robot where your refrigerator and dishwasher are, what
your cups look like, or where your power outlets are
Where 2.0 - Spatial Analysis of
Tweets using Hadoop, Pig, Python
& Mechanical Turk
 Peter Skomoroch, Entrepreneur, Data Scientist, & Product Leader
 Process tweets with location data using Hadoop cluster interactive
Pig session with MapReduce
 Match the exact location with geonames data using python
 Standardize remaining location strings with Mechanical Turk
 Developed Conservatives/Liberals classification of Geo Locations
 Working on Tweet phrases associated with high crime areas to
predict real time level of criminal activity based on Tweets
Written
 Content Moderation
 Sentimental Analysis
 Natural Language
Processing
 Language Translation
Visual
 See and transcript
 Which image is clear ?
 Flag unsuitable images
 Image analysis- Describe what you see?
 Meta data from images
Auditory
Hear and Transcript
Tone analysis – Example - Angry?
Age and Gender guessing
Tone , pitch and other meta data
Human Intelligence Tasks
 Data Collection
 Data Correction
 Data Extraction
 Site Filtering
 Survey
 Open Ended Questions
 Image Filtering
 Image Tagging
 Policy Compliance
 Product categorization
Product Success - Mechanical Turk

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Product Success - Mechanical Turk

  • 1. Mechanical Turk - Human Powered Software Let’s work with the machines SIVA MANIKANTESWARAN
  • 2. How will you automate this? “There is nothing quite so useless as doing with great efficiency something that should not be done at all.” Peter Drucker
  • 5. Do you know?  "Human Intelligence Tasks". For example  Choosing the best among several photographs of a store-front  writing product descriptions  Identifying performers on music CDs  Crowd Sourcing vs. Mechanical Turk?
  • 6. Using Mechanical Turk in cv-web-annotation-toolkit  Alex Sorokin of University of Illinois  Collection and Classification of data (Images)  If you're a researcher collecting a data set, you can use this toolkit to submit images to Mechanical Turk and pay people to label them for you  Teach the robot where your refrigerator and dishwasher are, what your cups look like, or where your power outlets are
  • 7. Where 2.0 - Spatial Analysis of Tweets using Hadoop, Pig, Python & Mechanical Turk  Peter Skomoroch, Entrepreneur, Data Scientist, & Product Leader  Process tweets with location data using Hadoop cluster interactive Pig session with MapReduce  Match the exact location with geonames data using python  Standardize remaining location strings with Mechanical Turk  Developed Conservatives/Liberals classification of Geo Locations  Working on Tweet phrases associated with high crime areas to predict real time level of criminal activity based on Tweets
  • 8. Written  Content Moderation  Sentimental Analysis  Natural Language Processing  Language Translation
  • 9. Visual  See and transcript  Which image is clear ?  Flag unsuitable images  Image analysis- Describe what you see?  Meta data from images
  • 10. Auditory Hear and Transcript Tone analysis – Example - Angry? Age and Gender guessing Tone , pitch and other meta data
  • 11. Human Intelligence Tasks  Data Collection  Data Correction  Data Extraction  Site Filtering  Survey  Open Ended Questions  Image Filtering  Image Tagging  Policy Compliance  Product categorization