Transcript Lecture Notes

AI – Week 21
Sub-symbolic AI
One: Neural Networks
Lee McCluskey, room 3/10
Email [email protected]
http://scom.hud.ac.uk/scomtlm/cha2555/
Neural Networks
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Neural Networks
Up to now: Symbolic AI
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Knowledge Representation is explicit and composite – features
(eg objects, relations ..) of the representation map to feature of
the world
Processes often based on heuristic search, matching, logic
reasoning, constraints handling
Good for simulating “high level cognitive” tasks such as
reasoning, planning, problem solving, high level learning,
language and text processing ..
The Representation
OnTop(A,B)m
A
The World
B
Neural Networks
Up to now: Symbolic AI
Benefits:
- AI/KBs can be engineered and maintained like in software
engineering
- Behaviour can be predicted and explained eg using logic
reasoning
Problems:
• Reasoning tends to be “brittle” – easily broken by incorrect /
approximate data
• Not so good for simulating low level (reactive) animal
behaviour where the inputs are noisy / incomplete
Neural Networks
Neural Networks
• Neural Networks (NNs) are networks of neurons, for example, as
found in real (i.e. biological) brains. Artificial Neurons are crude
approximations of the neurons found in brains. They may be
physical devices, or purely mathematical constructs.
• Artificial Neural Networks (ANNs) are networks of Artificial Neurons,
and hence constitute crude approximations to parts of real brains.
ANNs =~ a parallel computational system consisting of many
simple processing elements connected together in a specific way in
order to perform a particular task.
BENEFITS:
• Massive parallelism makes them very efficient
• They can learn and generalize from training data – so
there is no need for knowledge engineering or a
complex understanding of the problem.
• They are fault tolerant – this is equivalent to the
“graceful degradation” found in biological systems, and
noise tolerant – so they can cope with noisy inaccurate
inputs
Learning in Neural Networks
There are many forms of neural networks. Most operate by
passing neural ‘activations’ – processed firing states
through a network of connected neurons.
One of the most powerful features of neural networks is their
ability to learn and generalize from a set of training data.
They adapt the strengths/weights of the connections
between neurons so that the final output activations are
correct. (e.g. like catching a ball, learning to balance)
We will consider:
1. Supervised Learning (i.e. learning with a teacher)
2. Reinforcement learning (i.e. learning with limited
feedback)
BRAINS VS COMPUTERS
“My Brain is a Learning
Neural Network” Terminator 2
1. There are approximately 10 billion neurons in the human cortex,
compared with 10s of thousands of processors in the most
powerful parallel computers.
2. Each biological neuron is connected to several thousands of other
neurons, similar to the connectivity in powerful parallel
computers.
3. Lack of processing units can be compensated by speed. The
typical operating speeds of biological neurons is measured in
milliseconds (10-3 s), while a silicon chip can operate in
nanoseconds (10-9 s).
4. The human brain is extremely energy efficient, using
approximately 10-16 joules per operation per second, whereas the
best computers today use around 10-6 joules per operation per
second.
5. Brains have been evolving for tens of millions of years,
computers have been evolving for tens of decades.
Neural Networks
Very Very Simple Model of an Artificial Neuron
(McCulloch and Pitts 1943)
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A set of synapses (i.e. connections) brings in activations (inputs) from other
neurons.
A processing unit sums the inputs x weights, and then applies a transfer
function using a “threshold value” to see if the neuron “fires”.
An output line transmits the result to other neurons (output can be binary or
continuous). If the sum does not reach the threshold, output is 0.
NNs: we don’t have to design them, they
can learn their weights
Consider the simple Neuron Model:
1. Supply a set of values for the input (x1 … xn)
2. An output is achieved and compared with the known target
(correct/desired) output (like a “class” in learning from
example).
3. If the output generated by the network does not match the
target output, the weights are adjusted.
4. The process is repeated from step 1 until the correct output
is generated.
This is like supervised learning / learning from examples
Real Example: Pattern Recognition Pixel Grid
What’s missing here?
1 output node indicates
two classes.
Dimension: n = 5 x 8 = 40
Simple Example: Boolean Functions
Learn
Example viewed as a Decision Problem
x2
x1
Separating line (decision boundary).
One Layer Neuron not very powerful …!
XOR – Linearly Non-separable
x2
x1
Classes cannot be separated by a single decision boundary.
Perceptrons
 k is the k th threshold value
To determine whether the jth output node should fire, we
calculate the value
 n

sgn   wi , j xi   j 
 i 1

If this value exceeds 0 the neuron will fire otherwise it will
not fire.
Conclusions
• The McCulloch-Pitts / Perceptron neuron
models are crude approximations to real
neurons that performs a simple summation and
threshold function on activation levels.
• NNs are particularly good at Classification
Problems where the weights are learned
• Powerful NNs can be created using multilayers – next term
Next week – Reinforcement Learning
Neural Networks