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www.abdn.ac.uk/sras Artificial Intelligence In the Real World Computing Science University of Aberdeen
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www.abdn.ac.uk/sras Artificial Intelligence In the Real World Artificial Intelligence In the Movies
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www.abdn.ac.uk/sras Artificial Intelligence In the Real World Artificial Intelligence In the Movies
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www.abdn.ac.uk/sras Artificial Intelligence In the Real World Artificial Intelligence In the Movies ?
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www.abdn.ac.uk/sras Artificial Intelligence Began in 1956… Great expectations… Machines will be capable, within twenty years, of doing any work that a man can do. Herbert Simon, 1965.
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www.abdn.ac.uk/sras Machines will be capable, within twenty years, of doing any work that a man can do. Herbert Simon, 1965. What Happened?
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www.abdn.ac.uk/sras Machines cant do everything a man can do… People thought machines could replace humans… instead they are usually supporting humans Machines will be capable, within twenty years, of doing any work that a man can do. Herbert Simon, 1965. What Happened?
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www.abdn.ac.uk/sras Machines cant do everything a man can do… People thought machines could replace humans… instead they are usually supporting humans –Healthcare, Science, Government, Business, Military… Machines will be capable, within twenty years, of doing any work that a man can do. Herbert Simon, 1965. What Happened?
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www.abdn.ac.uk/sras Machines cant do everything a man can do… People thought machines could replace humans… instead they are usually supporting humans –Healthcare, Science, Government, Business, Military… Most difficult problems are solved by human+machine –astronomy, nuclear physics, genetics, maths, drug discovery… Machines will be capable, within twenty years, of doing any work that a man can do. Herbert Simon, 1965. What Happened?
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www.abdn.ac.uk/sras Neural Networks Neural Networks are a popular Artificial Intelligence technique Used in many applications which help humans The idea comes from trying to copy the human brain…
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www.abdn.ac.uk/sras Fascinating Brain Facts… 100,000,000,000 = 10 11 neurons -100 000 are irretrievably lost each day! Each neuron connects to 10,000 -150,000 others Every person on planet make 200 000 phone calls –same number of connections as in a single human brain in a day Grey part folded to fit - would cover surface of office desk The gray cells occupy only 5% of our brains –95% is taken up by the communication network between them About 2x10 6 km of wiring (to the moon and back twice) Pulses travel at more than 400 km/h (250 mph) 2% of body weight… but consumes 20% of oxygen All the time! Even when sleeping What about copying neurons in Computers?
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www.abdn.ac.uk/sras Artificial Neural Network (ANN) loosely based on biological neuron Each unit is simple, but many connected in a complex network If enough inputs are received –Neuron gets excited –Passes on a signal, or fires ANN different to biological: –ANN outputs a single value –Biological neuron sends out a complex series of spikes –Biological neurons not fully understood Image from Purves et al., Life: The Science of Biology, 4th Edition, by Sinauer Associates and WH Freeman
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www.abdn.ac.uk/sras Now play with the flash animation to see how synapses work http://www.mind.ilstu.edu/curriculum/neurons_intro/flash_sum mary.php?modGUI=232&compGUI=1828&itemGUI=3160
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www.abdn.ac.uk/sras The Perceptron add weight 1 output input 1 input 2 input 3 input 4 weight 4 (threshold) weight 2 weight 3
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www.abdn.ac.uk/sras The Perceptron add weight 1 output input 1 input 2 input 3 input 4 weight 4 (threshold) weight 2 weight 3 Save Graph and Data
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www.abdn.ac.uk/sras The Perceptron Note: example from Alison Cawsey studentfirst last year maleworks hard Lives in halls First this year 1Richard11010 2Alan11101 3Alison00100 4Jeff01010 5Gail10111 6Simon01110 Save Graph and Data
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.2 Threshold = 0.5 0.2 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 1Richard11010
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www.abdn.ac.uk/sras The Perceptron add 0.15 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.15 0.2 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 1Richard11010
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www.abdn.ac.uk/sras The Perceptron add 0.15 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.15 0.2 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 2Alan11101
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.2 0.25 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 2Alan11101
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.2 0.25 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 3Alison00100
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.2 0.25 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 4Jeff01010
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.2 0.25 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 5Gail10111
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.2 0.25 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 6Simon01110
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.15 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 6Simon01110
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.15 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 1Richard11010
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.15 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 2Alan11101
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.15 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 3Alison00100
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.15 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 4Jeff01010
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www.abdn.ac.uk/sras The Perceptron add 0.2 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.15 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 5Gail10111
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www.abdn.ac.uk/sras The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.15 0.25 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 5Gail10111
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www.abdn.ac.uk/sras The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.15 Threshold = 0.5 0.15 0.25 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 6Simon01110
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www.abdn.ac.uk/sras The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.10 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year 6Simon01110
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www.abdn.ac.uk/sras The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.10 0.20 Note: example from Alison Cawsey Finished
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www.abdn.ac.uk/sras The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.10 0.20 Note: example from Alison Cawsey Finished Ready to try unseen examples
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www.abdn.ac.uk/sras The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.10 0.20 Note: example from Alison Cawsey studentFirst last yearmaleworks hardLives in hallsFirst this year James0101?
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www.abdn.ac.uk/sras The Perceptron add 0.25 _ output 0.10 Threshold = 0.5 0.10 0.20 Simple perceptron works ok for this example but sometimes will never find weights that fit everything In our example: –Important: Getting a first last year, Being hardworking –Not so important: Male, Living in halls Suppose there was an exclusive or - –Important: (male) OR (live in halls), but not both –Cant capture this relationship
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www.abdn.ac.uk/sras Stock Exchange Example Company NameCompany less than 2 years old Paid dividend >10% last year Share price increases in following year 1Robot Components Ltd.110 2Silicon Devices101 3Bleeding Edge Software 000 4Human Interfaces Inc.110 5Data Management Inc.011 6Intelligent Systems110
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www.abdn.ac.uk/sras Multilayer Networks We saw: perceptron cant capture relationships among inputs Multilayer networks can capture complicated relationships
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www.abdn.ac.uk/sras Stock Exchange Example Hidden Layer
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www.abdn.ac.uk/sras Neural Net example: ALVINN Autonomous vehicle controlled by Artificial Neural Network Drives up to 70mph on public highways Note: most images are from the online slides for Tom Mitchells book Machine Learning
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www.abdn.ac.uk/sras Neural Net example: ALVINN Autonomous vehicle controlled by Artificial Neural Network Drives up to 70mph on public highways Note: most images are from the online slides for Tom Mitchells book Machine Learning
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www.abdn.ac.uk/sras ALVINN Input is 30x32 pixels = 960 values 1 input pixel 4 hidden units 30 output units Sharp right Straight ahead Sharp left
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www.abdn.ac.uk/sras ALVINN Input is 30x32 pixels = 960 values 1 input pixel 4 hidden units 30 output units Sharp right Straight ahead Sharp left Learning means adjusting weight values
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www.abdn.ac.uk/sras ALVINN Input is 30x32 pixels = 960 values 1 input pixel 4 hidden units 30 output units Sharp right Straight ahead Sharp left
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www.abdn.ac.uk/sras ALVINN
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www.abdn.ac.uk/sras ALVINN This shows one hidden node Input is 30x32 array of pixel values = 960 values Note: no special visual processing Size/colour corresponds to weight on link
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www.abdn.ac.uk/sras ALVINN This shows one hidden node Input is 30x32 array of pixel values = 960 values Note: no special visual processing Size/colour corresponds to weight on link Output is array of 30 values This corresponds to steering instructions E.g. hard left, hard right
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www.abdn.ac.uk/sras Lets try a more complicated example with the program… In this example well get the program to help us to build the neural network
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten)
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces)
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control - hand-arm-block.mpg
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control –ECG pattern – had a heart attack?
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control –ECG pattern – had a heart attack? –Application for credit card or mortgage
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control –ECG pattern – had a heart attack? –Application for credit card or mortgage –Data Mining on Customers
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control –ECG pattern – had a heart attack? –Application for credit card or mortgage –Other types of Data Mining - Science
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control –ECG pattern – had a heart attack? –Application for credit card or mortgage –Spam filtering
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control –ECG pattern – had a heart attack? –Application for credit card or mortgage –Shape in go
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www.abdn.ac.uk/sras Neural Network Applications Particularly good for pattern recognition –Sound recognition – voice, or medical –Character recognition (typed or handwritten) –Image recognition (e.g. human faces) –Robot control –ECG pattern – had a heart attack? –Application for credit card or mortgage –Data Mining on Customers –Other types of Data Mining –Spam filtering –Shape in Go… and many more!
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www.abdn.ac.uk/sras What are Neural Networks Good For? When training data is noisy, or inaccurate –E.g. camera or microphone inputs Very fast performance once network is trained Can accept input numbers from sensors directly –Human doesnt need to interpret them first
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www.abdn.ac.uk/sras Need a lot of data – training examples Training time could be very long –This is the big problem for large networks Network is like a black box –A human cant look inside and understand what has been learnt –Logical rules would be easier to understand Disadvantages?
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