Download presentation
Presentation is loading. Please wait.
Published byNicole Connolly Modified over 10 years ago
1
Artificial Intelligence In the Real World Computing Science University of Aberdeen
2
Artificial Intelligence In the Real World Artificial Intelligence In the Movies
3
Artificial Intelligence In the Real World Artificial Intelligence In the Movies
4
Artificial Intelligence In the Real World Artificial Intelligence In the Movies ?
5
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.
6
What Happened? Machines will be capable, within twenty years, of doing any work that a man can do. Herbert Simon, 1965.
7
What Happened? 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.
8
What Happened? 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.
9
What Happened? 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 my 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.
10
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…
11
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?
12
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 Biological Inspiration
13
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 (Maybe this is a bit too long – about 3 or 4 mins)
14
The Perceptron add weight 1 output input 1 input 2 input 3 input 4 weight 4 (threshold) weight 2 weight 3
15
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
16
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
17
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 year maleworks hard Lives in halls First this year 1Richard11010
18
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 year maleworks hard Lives in halls First this year 1Richard11010
19
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 year maleworks hard Lives in halls First this year 2Alan11101
20
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 year maleworks hard Lives in halls First this year 2Alan11101
21
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 year maleworks hard Lives in halls First this year 3Alison00100
22
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 year maleworks hard Lives in halls First this year 4Jeff01010
23
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 year maleworks hard Lives in halls First this year 5Gail10111
24
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 year maleworks hard Lives in halls First this year 6Simon01110
25
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 year maleworks hard Lives in halls First this year 6Simon01110
26
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 year maleworks hard Lives in halls First this year 1Richard11010
27
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 year maleworks hard Lives in halls First this year 2Alan11101
28
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 year maleworks hard Lives in halls First this year 3Alison00100
29
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 year maleworks hard Lives in halls First this year 4Jeff01010
30
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 year maleworks hard Lives in halls First this year 5Gail10111
31
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 year maleworks hard Lives in halls First this year 5Gail10111
32
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 year maleworks hard Lives in halls First this year 6Simon01110
33
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 year maleworks hard Lives in halls First this year 6Simon01110
34
The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.10 0.20 Finished
35
The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.10 0.20 Finished Ready to try unseen examples
36
The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 0.10 Threshold = 0.5 0.10 0.20 studentfirst last year maleworks hard Lives in halls First this year James0101?
37
The Perceptron add 0.25 _ output First last year _ Male _ hardworking _ Lives in halls 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
38
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
39
Multilayer Networks We saw: perceptron cant capture relationships among inputs Multilayer networks can capture complicated relationships
40
Stock Exchange Example Hidden Layer
41
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
42
Neural Net example: ALVINN Autonomous vehicle controlled by Artificial Neural Network Drives up to 70mph on public highways
43
Neural Net example: ALVINN Input is 30x32 pixels = 960 values 1 input pixel 4 hidden units 30 output units Sharp right Straight ahead Sharp left
44
Neural Net example: 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
45
Neural Net example: ALVINN Input is 30x32 pixels = 960 values 1 input pixel 4 hidden units 30 output units Sharp right Straight ahead Sharp left
46
Neural Net example: ALVINN
47
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
48
Neural Net example: ALVINN Output is array of 30 values This corresponds to steering instructions E.g. hard left, hard right 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
49
Lets try a more complicated example with the program… In this example well get the program to help us to build the neural network
50
Neural Network Applications Particularly good for pattern recognition
51
Neural Network Applications Particularly good for pattern recognition Sound recognition – voice, or medical
52
Neural Network Applications Particularly good for pattern recognition Sound recognition – voice, or medical Character recognition (typed or handwritten)
53
Neural Network Applications Particularly good for pattern recognition Sound recognition – voice, or medical Character recognition (typed or handwritten) Image recognition (e.g. human faces)
54
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
55
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?
56
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
57
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
58
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 - Science
59
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
60
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
61
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!
62
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
63
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 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 Precise logical rules would be easier to understand Disadvantages?
Similar presentations
© 2024 SlidePlayer.com Inc.
All rights reserved.