Artificial Intelligence Master at UPC: some experience on applying AI to real world problems
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Transcript of Artificial Intelligence Master at UPC: some experience on applying AI to real world problems
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Artificial Intelligence solutions forArtificial Intelligence solutions for real world problems real world problems
Departament de Llenguatges i Sistemes Informàtics (UPC)Departament de Llenguatges i Sistemes Informàtics (UPC)
26/05/201026/05/2010
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ContentsContents
What is Artificial Intelligence useful for?
Some examples of using AI
AI and Medicine
AI in Industrial Processes
AI for on-line, real-time Text Translation
AI for flexible, adaptive on-line systems
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What is Artificial Intelligent useful for?What is Artificial Intelligent useful for?
To create computational systems with some human-like capabilities.
ReasoningReasoning
ClassificationClassification
Decision makingDecision making
Learning/adaptationLearning/adaptation
Human communicationHuman communication
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What is Artificial Intelligent useful for?What is Artificial Intelligent useful for?
Classical applications include:
Decision support systems / Expert SystemsDecision support systems / Expert Systems Data MiningData Mining Natural Language ProcessingNatural Language Processing RoboticsRobotics
… But there are lots of new applications coming from latest technologies! (in special Internet):
Automatic user profilingAutomatic user profiling Recommender systemsRecommender systems Social networkingSocial networking e-Commercee-Commerce Future Internet, Internet of Things, Cloud Computing…Future Internet, Internet of Things, Cloud Computing…
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Some examples of using AISome examples of using AI
The following are just some examples of our own experience in the use of AI to solve real problems.
Hotels
Museums
Restaurants
Map generator
Weather
Public Transport
CinemasLegal body
policies
Inte
rfa
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Recommendation System
Social modeler
System
User modeler
feedback
feedback
feed
feed
Route planner Booking/payment
Traffic
The Webcontent
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AI and MedicineAI and Medicine
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AI to assist patients’ autonomyAI to assist patients’ autonomy
The problem: European population is becoming older Danger of unsustainable healthcare Need to find technologies that will assist
elders in their daily life, incresing theirautonomy
Idea: Development of intelligent, semi-autonomousintelligent, semi-autonomous
assistive devices assistive devices for persons with disabilities(both cognitive and/or motor).
• These persons will reach a sufficient degree of autonomy with a high level of safety and comfort.
Approach: Robotics, Agent-oriented technologies, Robotics, Agent-oriented technologies,
Ambient Intelligence, User ProfilingAmbient Intelligence, User Profiling.
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Ambient Intelligence
AI to assist patients’ autonomyAI to assist patients’ autonomy
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AI to assist patients’ autonomyAI to assist patients’ autonomy
Multiagent Systems
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AI to assist patients’ autonomyAI to assist patients’ autonomy
step 3
INPUT:
real time forces anduser relative position
Real Time Simulation
Application:
Design and verification of strategiesfor user intent detection
OUTPUT
Enviroment motionrelative to IW
Relevant datavisual feedback
sensor information
INPUT: path and timing Direct Model
Application:
Design of motor torques strategies
step 1
OUTPUT:
forcesand torques
step 2
INPUT:
programmedforces anduser relativeposition
Inverse Model
Application:
Verification of motor torques strategies
OUTPUT: path and timing
sensor information
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AI to assist patients’ autonomyAI to assist patients’ autonomyWho shall I contact to know more?Who shall I contact to know more?
Ulises Cortés Cristian Barrue Guiem Bosch
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AI for Brain Tumor DiagnosisAI for Brain Tumor Diagnosis
The problem: Brain tumour diagnosis is a sensitive and
complex task usually left to specialized radiologists (IDI).
Due to the anatomical constraints of these pathologies, experts’ decision making often relies upon information acquired through non-invasive measurement methods.
• MRI (neuroimaging) The interpretability of the results is
paramount in brain tumour diagnosis. Idea:
Machines learn to better recognise tumors through recognise tumors through MRS MRS (neurospectra), and help doctors in diagnosis
Approach: Data Mining and rule extraction techniquesData Mining and rule extraction techniques
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AI for Brain Tumor DiagnosisAI for Brain Tumor Diagnosis
Rule extraction from raw data
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AI for Brain Tumor DiagnosisAI for Brain Tumor DiagnosisWho shall I contact to know more?Who shall I contact to know more?
Alfredo Vellido Angela Nebot Rene Alquezar
INTERPRET INTERPRET ToolTool
INTERPRET INTERPRET ToolTool
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AI in Industrial ProcessesAI in Industrial Processes
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AI for textile machinery AI for textile machinery configurationconfiguration
The Problem: European textile industries need to
innovate to compete with other countries and open to new markets
Innovate means create new fabrics• Mix different materials• Mix them in different, unprecedented ways
A critical step is the textile machine setup• Manual set-up can take weeks of trial and error• It can also be costly in terms of raw materials
Idea: A systems that allows the machines to auto-configure machines to auto-configure
themselvesthemselves when presented with a description of the target fabric
Approach: Use Case-Based ReasoningCase-Based Reasoning.
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Raw materials parameters Machinery settings parameters End product parameters
RP1 RP2 RP3? RP4 RP5?
RP1 RP2 RP3 RP4 RP5
MP1? MP2 MP3 MP4 MP5
MP1 MP2 MP3 MP4 MP5
EP1 EP2? EP3 EP4 EP5?
EP1 EP2 EP3 EP4 EP5
CBR System
Diam
eter
Fiber Fib
er
dens
ity
Cyl
inde
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twis
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Porosity
Predicted value
Requiredvalue
To make easier the production of new advanced textile products Prediction of the required unknown parameters To reduce the economical cost and time required for thetextile machinery set-up Doing less tests in the textile machines
Case-Based Reasoning: Using previous process experiential knowledge
AI for textile machinery configurationAI for textile machinery configuration
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AI for textile machinery configurationAI for textile machinery configurationWho shall I contact to know more?Who shall I contact to know more?
Miquel Sànchez-Marré Beatriz Sevilla
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AI for on-line, real-time Text TranslationAI for on-line, real-time Text Translation
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AI for on-line, real-time Text TranslationAI for on-line, real-time Text Translation
The Problem: EU is a union of states
with several languagesused
Continuous need of translations from one tothe other.
Idea: to develop a set of tools tools
for translating texts for translating texts between multiple languages in real time with high quality.
Approach: Use multilingual grammars based on semantic interlinguasmultilingual grammars based on semantic interlinguas.
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AI for on-line, real-time Text TranslationAI for on-line, real-time Text Translation
Necesito que me pases los resultados
Ik begrijp je niks!
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AI for on-line, real-time Text TranslationAI for on-line, real-time Text TranslationWho shall I contact to know more?Who shall I contact to know more?
Lluís Màrquez David Farwell Cristina España Horacio Rodríguez Xavier Carreras Lluís Padró
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AI for flexible, adaptive on-line systemsAI for flexible, adaptive on-line systems
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AI for flexible, adaptive on-line systemsAI for flexible, adaptive on-line systems Problem:
New generations of networked service applications should be able to:
• communicate and reconfigure at runtime• adapt to their environment• dynamically combine sets of building
block services into new applications
Idea: The mechanismsmechanisms used today to organise
the vastly complex interdependencies found in human, social, economic in human, social, economic behaviour behaviour will be essential to structuring to structuring future distributed software systems distributed software systems
Approach: To bring together Agent TechnologyAgent Technology, Organizational Organizational
TheoryTheory and new technologies on Model Driven design Model Driven design to create a framework for software and services engineering
Hotels
Museums
Restaurants
Map generator
Weather
Public Transport
CinemasLegal body
policies
Inte
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Recommendation System
Social modeler
System
User modeler
feedback
feedback
feed
feed
Route planner Booking/payment
Traffic
The Webcontent
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Methodology
Fram
ework
Coordination level:- coordination patterns- task allocation- actor expectation
Organizational level:- norms and regulations- organizational structure- communication ontology- evaluation indicators
WSWS
WS
WS
WSnewWS
Existing platformsExisting servicesNew servicesService interactions
SDSD
SD
SD
SD SD
Service level:- semantic service description (SD)- standards specification
actor
actor
actor
actor
role
dynamic assignment
Functional instantiation
role role role
actual deployment
WHY?(motivations)
WHAT?(possible actions, plans)
HOW?(available services)
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AI for flexible, adaptive on-line systemsAI for flexible, adaptive on-line systems
A set of services is selected to fulfill a user request.
The service selected for the “find museum info” task fails …
No alternate service isfound for the task re-plan
A new set of services is invoked and the results merged to fulfill the user request.
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AI for flexible, adaptive on-line systemsAI for flexible, adaptive on-line systemsWho shall I contact to know more?Who shall I contact to know more?
Javier Vázquez Sergio Álvarez Roberto Confalonieri Sofia Panagiotidi Juan Carlos Nieves Ignasi Gómez
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Some collaborationsSome collaborations
3scale Networks S.L. Sisltech TMT Telecom Factory Techideas Qporama ASCAMM ISOCO Servei Català de la Salut Agencia Catalana de l’Aigua Ajuntament de Barcelona Institut Guttmann Organització Mundial de la Salut Telefònica I+D IBM Labs UK. Fujitsu Gmbh
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ConclussionsConclussions
AI allows to create smarter, more flexible computational systems that can:
Help humans in cognitive tasksHelp humans in cognitive tasks Adapt to human needsAdapt to human needs Communicate with humansCommunicate with humans Dynamically adapt to changesDynamically adapt to changes
There are several application areas when AI is key.
The Future Internet is one of them!The Future Internet is one of them!
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