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Artificial Intelligence
What is the (physical) basis of intelligence?
• The Brain (it “thinks”)
• The Computer (it “calculates”)
• The Physical World (it “is”)
• Symbols point to signifieds
What is Intelligence?
Pattern Recognition
Hofstadter’s Idea
• “What if” driving down a country road you meet a swarm of bees
• Lucky my window wasn’t open
• Lucky I wasn’t on my bike
• If I was a deer I would have been killed
• Pity they weren’t £10 notes
• Lucky those bees weren’t made of cement
Two Approaches
• Connexionism
• Computationalism
Nature Inspired Computing
Artificial Neural Nets - Symbiosis between computer and cognitive sciences
Cajal -1-
Cajal
Golgi
Cajal + Golgi indentification of
independent neurons by staining,
microscopy and looking. (Nobel
Prize 1906)
Cajal -2-
Rat Neurons
Investigation of Single Neurons
Microelectrode recording of Biological
Neuron activation using tungsten electrode
Hubel and Weisel. Nobel Prize 1958
Photomicrograph: Height = 1mm.
Biological Neurons
synapse
axon
dendrites
Signal flow
Signal shape
Big Neurological principle #1 Neurons work using
electricity, not blood or other special goo
Single Neuron
In 1
“activation”
In 2
D
In 3
“activation”
“input”
In 4
C
A
Big Neurological principle #2 “Integrate and Fire”
Inputs summed. If above threshold output fires.
B
“threshold”
Learning in Neural Nets
A
B
Before Learning
A
B
After Learning
Big Neurological principle #3 “Hebbian Learning” Synapse
strength increases if both cells A and B are firing
Brains Minds and Computers
Brains
Computers
• Work using Electricity
• Have inputs and outputs
• Work using Electricity
• Have inputs and outputs
• Can learn by experience
• Can be taught
• Can be programmed
• ? Can they learn ?
• ? Can they be taught ?
So do we understand brains? Yep. Do we therefore understand Minds?
Nope.
Artificial Neurons
“output”
inputs
D
In 1
output
In 2
“input”
C
In 3
A
B
“threshold”
Learning Logical Gates
Ouput neuron fires only when sum
is greater than the threshold
A
Threshold =
?
AND - gate
B
A
Threshold =
?
OR - gate
B
A
B
O
0
0
0
0
1
0
1
0
0
1
1
1
A
B
O
0
0
0
0
1
1
1
0
1
1
1
1
Training an Artificial Neural Net
eyes
right
left
motors
Back Propagation of Errors
right
left
eyes
motors
1
0.5
eyes
right
left
motors
1
0.5
Neural Net Solver
Medical Application
Flu
cough
headache
Neural Net
Medical Diagnosis
Meningitis
Cough
Headache
Flu
Pneuomonia
Not ill
Cough
1
Headache
1
1
Flu
“Classical” Medical Diagnosis
If ( (symptom ! = cough) && (symptom != headache) )
illness = no illness;
else if ( (symptom ! = cough) && (symptom == headache) )
illness = meningitis;
else if ( (symptom == cough) && (symptom != headache) )
illness = pneumonia;
else if ( (symptom == cough) && (symptom == headache) )
illness = flu;
Rule-based Learning “ if … then …. else … “
CBP 2009-10
Comp 3104 The Nature of
Computing
48
ECG Interpretation
QRS amplitude
R-R interval
SV tachycardia
QRS duration
Ventricular tachycardia
AVF lead
LV hypertrophy
S-T elevation
RV hypertrophy
Myocardial infarction
P-R interval
NNets vs Expert Systems
Rule-based Exp. Syst.
Bayesian Nets
Classification Trees
Neural Nets
Regression Models
Modeling
Effort
Examples
Needed
Explanation
Provided
high
high
low
low
high
low
low
high
high
moderate
high
moderate
“high”
low
moderate