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Artificial Intelligence
Laboratory
Boi Faltings
Artificial
Intelligence
Artificial Intelligence
• People are good in the natural world:
• irregular shapes
• reactive
• Modern world is artificial:
• precision machinery
• schedules and interdependencies
• AI: helps people work in the artificial world
My Motivation
• Use computers to solve a problem that
people could not solve otherwise.
Sharing Networks
Sharing Networks
• Routing demands in
circuit-switched
communication
networks.
Sharing Networks
• Routing demands in
circuit-switched
communication
networks.
Sharing Networks
• Routing demands in
circuit-switched
communication
networks.
• Shortest path in
blocking island
graph rather than
network => 40%
more capacity.
Iconomic Systems V1
Iconomic Systems V1
Iconomic Systems V1
• Goal: market
Icononet = routing
tool that increases
capacity by 40%
Iconomic Systems V1
• Goal: market
Icononet = routing
tool that increases
capacity by 40%
• Problem: nobody
wanted to save
capacity.
Recommendation systems
Infer preferences from past behavior.
Collaborative filtering:
use preferences of like-minded users.
Problems:
requires too much information !!
! (30-40 rated items per user)
slow to incorporate new items.
7
Ontology Filtering
Structure user preferences into an
ontology.
=> better accuracy with less information
(5 items)
=> new items can be placed in ontology
and are immediately accessible.
Chorafas award/patent.
8
• Startup company
Prediggo
commercializes
technology.
• Sold to about 40 web
sites so far, including
Moevenpick,
MediaMarkt,
Ricardo, etc.
9
Performance on actual
web sites
• Baskets impacted + 5% to 32%
• Sales facilitated + 5% to 25%
• Conversion rate + 10% to 30%
• Long Tail items + 30% to 95%
10
News recommendation
• Challenges:
• short traces
• items change all the time
• avoid redundant recommendations
• new technique: context trees
Current Work
• Human-level AI
• Social Computing
Human-Level AI
• Can we match human performance on
common sense tasks like natural
language processing, vision, etc.?
• Recent advances (e.g. Watson) make it
seem so.
• Everyone expects big things from big
data...
Limitations of Big Data
• Learning from large corpora has hit a
dead end: only marginal improvements
for huge computation costs.
• Nice feature of commonsense
knowledge: do not need experts,
everybody has it!
• New direction: crowdsource
knowledge acquisition
Crowdsourcing
Platforms
Games
Practical Impact
• Analysis of social network interactions
• Recommendations from review texts
Awesome pool!
9$:(;%*'401'2342'
Nice, clean hotel
567%'41'2338'
Simply filthy …
-./'01'2344'
!"#$% &'(")''
*%+#%,&'
=(("'
?($.:#(7'
@((A'
+#%,'
B"%.7"#7%&&'
<7:%*%&:'
=*(>"%'
!"#$%&'()*&%'
Dirty
towels '
Good
location '
D+%'
E(;'
@%$(AA%7).:#(7
C'
Social Computing
• Intelligence based on learning from
data gathered from individuals.
• Quality of data is crucial!
Great Leap Forward
(China, 1958-60)
• Progress measured by grain/steel production
figures.
• Village chiefs pressured to report inflated grain
production (up to 10 times).
• Result: grain was exported in spite of shortage, at
least 30 million people died.
Subprime Mortgages
(USA,2008)
• Risk of financial products assessed by statistical
models.
• Debt bundled into large packages cut into slices with
different priorities and ratings.
• Banks reverse-engineered models and made risky debt
look unrisky.
• We know the rest...
Big Data...
• Big data can become dangerous!
• We need to ensure its quality:
• filtering
• motivation of data providers
• Focus on crowdsourcing scenarios.
Asking the Crowd
• Collect
information
through the
internet:
• opinion forums
• product reviews
Asking the Crowd
• Collect
information
through the
internet:
• opinion forums
• product reviews
Community Sensing
Community Sensing
• Goal: Maintain a map
of pollution in a city.
Community Sensing
• Goal: Maintain a map
of pollution in a city.
• Need to distribute
sensors in many
places.
Community Sensing
• Goal: Maintain a map
of pollution in a city.
• Need to distribute
sensors in many
places.
• Community sensing:
citizens operate
sensors and get paid
for the data.
Community Sensing
• Goal: Maintain a map
of pollution in a city.
• Need to distribute
sensors in many
places.
• Community sensing:
citizens operate
sensors and get paid
for the data.
Truthfulness
• Reporting values is a lot of work.
• Agents only do it for ulterior motives:
• promote themselves or their friends
• punish their rivals
• take revenge
Truthfulness
• Reporting values is a lot of work.
• Agents only do it for ulterior motives:
• promote themselves or their friends
• punish their rivals
• take revenge
Truthfulness
• Reporting values is a lot of work.
• Agents only do it for ulterior motives:
• promote themselves or their friends
• punish their rivals
• take revenge
Example: music ratings
ratings on Amazon
ratings in controlled experiment
[Hu, Pavlou & Zhang, 2006]
Better Results
• Reward people for their efforts: karma
points, rankings, gifts, or money.
• Scale rewards to encourage truthfulness:
• payoff is a function of report and
outcome.
• truthful report maximizes expected
reward.
Opinion Polls
Monitoring Service
Level Agreements
Current Approaches to QoS Monitoring
QoS Monitoring using Feedback
from Clients
Monitoring by Proxy (expensive)
Client
Monitor
Provider
QoS estimates
Reputation
Mechanism
Monitoring by Sampling (imprecise)
$
Client
QoS
Provider
Client
Decentralized Monitoring (not trustworthy)
Client
(cheap, reliable
and precise)
Provider
Provider
Community Sensing
• Sensors placed in private properties
• Reward them for providing data
(similar to solar energy)
• Incentive scheme rewards accuracy.
Other Work
• Digital health: recommendations from
wearable sensor data.
• Multi-agent optimization.
• Collaboration with privacy guarantees.
• Cognitive information security.