1.- Intelligence & Learning - UPM · CVG-UPM COMPUTER VISION P. Campoy Machine Learning and Neural...
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CVG-UPM COMPUTER VISION Machine Learning & Neural Networks 1.- Intelligence & Learning by Pascual Campoy Computer Vision Group C.A.R. - U.P.M. 2 CVG-UPM COMPUTER VISION Machine Learning and Neural Networks P. Campoy P. Campoy P. Campoy Intelligence and learning What is intelligence? What are intelligent machines? The learning relevance Building intelligent machines Objectives of the subject Applications
Transcript of 1.- Intelligence & Learning - UPM · CVG-UPM COMPUTER VISION P. Campoy Machine Learning and Neural...
CVG-UPMC
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Machine Learning & Neural Networks
1.- Intelligence & Learning
byPascual Campoy
Computer Vision GroupC.A.R. - U.P.M.
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Intelligence and learning
What is intelligence?
What are intelligent machines?
The learning relevance
Building intelligent machines
Objectives of the subject
Applications
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¿ What is intelligence ?
“Intelligence is a very general mental capabilitythat, among other things, involves the ability toreason, plan, solve problems, think abstractly,comprehend complex ideas, learn quickly andlearn from experience. […], it reflects a broaderand deeper capability for comprehending oursurroundings -- "catching on," "making sense" ofthings, or "figuring out" what to do”statement signed by 52 experts in intelligence in the WallStreet Journal & in the Intelligence Journal 1994
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¿ What is intelligence ?
•“Prediction is the real essence of cerebralfunction”, Rodolfo Llinás “I and the vortex”
Recognition, prediction and learning
an intelligent machine has to recognize thesituation and predict the future based on
learned previous experiences
•“Intelligence is the capability of predictingfuture”, Jeff Hawkins “On Intelligence”
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¿ Machine intelligence ?
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¿ Machine intelligence ?
Alan Turing test:• original version: “a machine is said to beintelligent if it can fool a judge as often as aperson in pretending to be a man/woman”• later extended version: “a machine is saidto be intelligent if it can fool a judge to be aperson”
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¿ Machine intelligence ?
•“Chinese room paradigm: suppose that amachine convinces a human Chinese speaker thatthe program is itself a human Chinese speaker, thenthe machine is substitute by a non Chinese speakingperson in a room provided with the same algorithmand information as the machine had. Does he/sheunderstand Chinese? ” de John Searle
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Learning relevance
imagen sólo para la imagen sólo para la mitad izquierda del mitad izquierda del
auditorioauditorio
imagen sólo para la imagen sólo para la mitad derecha del mitad derecha del
auditorioauditorio
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learning relevance
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¿How to build intelligent machines?
Explicit model− top-down methodology
Example learning− boton-up methodology
- paradigms: physical equations expert systems
- paradigms: experimental parameters neural networks
Self-learning
Parallel processing
Distributed Patterns
Signal processing
Programmed
Serial Processing
Symbolic data
High dimensional statespace searching
Learning based systemsKnwoledge based systems
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Ojectives of the subject
Find a model that predicts the right output to anew input, considering outputs to previous inputs.
y
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Learning applications
Pattern Recognition
Control
Identification
Associative memory
Optimization
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Pattern Reconition Applications
Representation, recognition and predictionof the state of a multidimensional inputvector, which can be:- The state of an industrial process from the field
sensors (includes also alarm management)- The state of an electrical net- The atmospheric state from a spread net of sensors- The seismical or geological state- The state of a vehicle (e.g. airplane, car, …)- The commercial situation of a company from its whole
accountability- Patterns in images (e.g. visual, IR, electromagnetic, ..)
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Computer Vision applications
Abnormalcristalitation
preprocessing segmentation feature extraction Recognition
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Your applications
1. Synthetic data
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2. Real data
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Homeworks in Moodle:Files/Exercises
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Exercise 1.1
Print the 2 different handwritten images of thesame number for 4 different numbers
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