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Content exploitation:
men and machines
DG Connect -Unit G1 ā€“ Converging Media and Content
Bruxelles, June 16 2014
Piero Fraternali, Politecnico di Milano
piero.fraternali@polimi.it
What content?
Corporate content
ā€¢ Google is indexing the earth
User generated content
ā€¢ Video IP traffic will be 73% of
all Internet traffic by 2017. The
sum of all forms of video (TV,
VoD, Internet, and P2P) ~ 80-
90% [Cisco, 2013]
ā€¢ 250+ billion photos uploaded
and 350+ million photos
uploaded every day
[Facebook, 2013]
ā€¢ ā€œBig dataā€
is (mostly)
visual data
How to exploit UGC
So many applications But .. so much garbage
The human computation trust circle
people
data
Passive crowdsourcing
Active crowdsourcing
Crowdsourcing optimization
Incentives
Trust computing
Adversarial computing
Provenance tracking
Tampering detection
Uncertainty modeling and reduction
Semantic enrichment
algorithms
Reliability
Optimization
Predictive modeling
Quality guarantee
FOCUS HERE
Example: incentives
ā€¢ Problem: identify 10
balloons anchored in 10
undisclosed locations in
the US, $ 40,000 prize
to the winner
ā€¢ Solution in less than 9
hours
ā€¢ Recursive incentive
mechanism (Nash
equilibrium)
Another (different) incentive scheme
ā€¢ Complex content (3d
with constraints)
ā€¢ Computationally
intractable
ā€¢ Solved with Tetris-like
game
ā€¢ Massive voluntary
online collaboration,
community quality
monitoring
How to compute people Influence &
Trust
How to fight adversaries
Goal
ā€¢ Obtain quality content with
minimum amount of human
and computational
resources
ā€¢ Algorithm can fail
ā€¢ But humans can cheat!
Object detection example
In summary
ā€¢ Exploiting content requires .. good content
ā€¢ Computers and humans can cooperate in new ways
ā€“ More than algorithm optimization
ā€“ More than crowdsourcing
ā€¢ Old problem, but at a new scale
ā€“ "On two occasions I have been asked,
"Pray, Mr. Babbage, if you put into
the machine wrong figures,
will the right answers come out?"...
I am not able rightly to apprehend the kind of confusion of
ideas that could provoke such a question."
ā€“ Charles Babbage, Passages from the Life of a Philosopher
(1864)

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Fraternali concertation june25bruxelles

  • 1. Content exploitation: men and machines DG Connect -Unit G1 ā€“ Converging Media and Content Bruxelles, June 16 2014 Piero Fraternali, Politecnico di Milano piero.fraternali@polimi.it
  • 2. What content? Corporate content ā€¢ Google is indexing the earth User generated content ā€¢ Video IP traffic will be 73% of all Internet traffic by 2017. The sum of all forms of video (TV, VoD, Internet, and P2P) ~ 80- 90% [Cisco, 2013] ā€¢ 250+ billion photos uploaded and 350+ million photos uploaded every day [Facebook, 2013] ā€¢ ā€œBig dataā€ is (mostly) visual data
  • 3. How to exploit UGC So many applications But .. so much garbage
  • 4. The human computation trust circle people data Passive crowdsourcing Active crowdsourcing Crowdsourcing optimization Incentives Trust computing Adversarial computing Provenance tracking Tampering detection Uncertainty modeling and reduction Semantic enrichment algorithms Reliability Optimization Predictive modeling Quality guarantee FOCUS HERE
  • 5. Example: incentives ā€¢ Problem: identify 10 balloons anchored in 10 undisclosed locations in the US, $ 40,000 prize to the winner ā€¢ Solution in less than 9 hours ā€¢ Recursive incentive mechanism (Nash equilibrium)
  • 6. Another (different) incentive scheme ā€¢ Complex content (3d with constraints) ā€¢ Computationally intractable ā€¢ Solved with Tetris-like game ā€¢ Massive voluntary online collaboration, community quality monitoring
  • 7. How to compute people Influence & Trust
  • 8. How to fight adversaries Goal ā€¢ Obtain quality content with minimum amount of human and computational resources ā€¢ Algorithm can fail ā€¢ But humans can cheat! Object detection example
  • 9. In summary ā€¢ Exploiting content requires .. good content ā€¢ Computers and humans can cooperate in new ways ā€“ More than algorithm optimization ā€“ More than crowdsourcing ā€¢ Old problem, but at a new scale ā€“ "On two occasions I have been asked, "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?"... I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question." ā€“ Charles Babbage, Passages from the Life of a Philosopher (1864)