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- 1. G51IAI Introduction to AI Andrew Parkes Neural Networks 1
- 2. Neural Networks • AIMA – Section 20.5 of 2003 edition • Fundamentals of Neural Networks : Architectures, Algorithms and Applications. L, Fausett, 1994 • An Introduction to Neural Networks (2nd Ed). Morton, IM, 1995
- 3. Brief History • Try to create artificial intelligence based on the natural intelligence we know: • The brain – massively interconnected neurons
- 4. G5G51IAI1IAI Neural NetworksNeural Networks Neural Networks
- 5. Natural Neural Networks • Signals “move” via electrochemical signals • The synapses release a chemical transmitter – the sum of which can cause a threshold to be reached – causing the neuron to “fire” • Synapses can be inhibitory or excitatory
- 6. Natural Neural Networks • We are born with about 100 billion neurons • A neuron may connect to as many as 100,000 other neurons
- 7. Natural Neural Networks • McCulloch & Pitts (1943) are generally recognised as the designers of the first neural network • Many of their ideas still used today e.g. – many simple units, “neurons” combine to give increased computational power – the idea of a threshold
- 8. G5G51IAI1IAI Neural NetworksNeural Networks Modelling a Neuron • aj :Activation value of unit j • wj,i :Weight on link from unit j to unit i • ini :Weighted sum of inputs to unit i • ai :Activation value of unit i • g :Activation function ∑= j jiji aWin ,
- 9. G5G51IAI1IAI Neural NetworksNeural Networks Activation Functions • Stept(x) = 1 if x ≥ t, else 0 threshold=t • Sign(x) = +1 if x ≥ 0, else –1 • Sigmoid(x) = 1/(1+e-x )
- 10. Building a Neural Network 1. “Select Structure”: Design the way that the neurons are interconnected 2. “Select weights” – decide the strengths with which the neurons are interconnected – weights are selected so get a “good match” to a “training set” – “training set”: set of inputs and desired outputs – often use a “learning algorithm”
- 11. Neural Networks • Hebb (1949) developed the first learning rule – on the premise that if two neurons were active at the same time the strength between them should be increased
- 12. Neural Networks • During the 50’s and 60’s many researchers worked, amidst great excitement, on a particular net structure called the “perceptron”. • Minsky & Papert (1969) demonstrated a strong limit on the power of perceptrons – saw the death of neural network research for about 15 years • Only in the mid 80’s (Parker and LeCun) was interest revived because of their learning algorithm for a better design of net – (in fact Werbos discovered algorithm in 1974)
- 13. Basic Neural Networks • Will first look at simplest networks • “Feed-forward” – Signals travel in one direction through net – Net computes a function of the inputs
- 14. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks Neurons in a McCulloch-Pitts network are connected by directed, weighted paths -1 2 2X1 X2 X3 Y
- 15. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks If the weight on a path is positive the path is excitatory, otherwise it is inhibitory -1 2 2X1 X2 X3 Y
- 16. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks The activation of a neuron is binary. That is, the neuron either fires (activation of one) or does not fire (activation of zero). -1 2 2X1 X2 X3 Y
- 17. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks For the network shown here the activation function for unit Y is f(y_in) = 1, if y_in >= θ else 0 where y_in is the total input signal received θ is the threshold for Y -1 2 2X1 X2 X3 Y
- 18. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks Originally, all excitatory connections into a particular neuron have the same weight, although different weighted connections can be input to different neurons Later weights allowed to be arbitrary -1 2 2X1 X2 X3 Y
- 19. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks Each neuron has a fixed threshold. If the net input into the neuron is greater than or equal to the threshold, the neuron fires -1 2 2X1 X2 X3 Y
- 20. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks The threshold is set such that any non-zero inhibitory input will prevent the neuron from firing -1 2 2X1 X2 X3 Y
- 21. Building Logic Gates • Computers are built out of “logic gates” • Can we use neural nets to represent logical functions? • Use threshold (step) function for activation function – all activation values are 0 (false) or 1 (true)
- 22. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks AND Function 1 1X1 X2 Y AND X1 X2 Y 1 1 1 1 0 0 0 1 0 0 0 0 Threshold(Y) = 2
- 23. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks AND FunctionOR Function 2 2X1 X2 Y OR X1 X2 Y 1 1 1 1 0 1 0 1 1 0 0 0 Threshold(Y) = 2
- 24. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks AND NOT Function -1 2X1 X2 Y AND NOT X1 X2 Y 1 1 0 1 0 1 0 1 0 0 0 0 Threshold(Y) = 2
- 25. G5G51IAI1IAI Neural NetworksNeural Networks Simple Networks AND OR NOT Input 1 0 0 1 1 0 0 1 1 0 1 Input 2 0 1 0 1 0 1 0 1 Output 0 0 0 1 0 1 1 1 1 0
- 26. G5G51IAI1IAI Neural NetworksNeural Networks Simple Networks t = 0.0 y x W = 1.5 W = 1 -1
- 27. G5G51IAI1IAI Neural NetworksNeural Networks Perceptron • Synonym for Single- Layer, Feed-Forward Network • First Studied in the 50’s • Other networks were known about but the perceptron was the only one capable of learning and thus all research was concentrated in this area
- 28. G5G51IAI1IAI Neural NetworksNeural Networks Perceptron • A single weight only affects one output so we can restrict our investigations to a model as shown on the right • Notation can be simpler, i.e. ∑= j WjIjStepO 0
- 29. G5G51IAI1IAI Neural NetworksNeural Networks What can perceptrons represent? AND XOR Input 1 0 0 1 1 0 0 1 1 Input 2 0 1 0 1 0 1 0 1 Output 0 0 0 1 0 1 1 0
- 30. G5G51IAI1IAI Neural NetworksNeural Networks What can perceptrons represent? 0,0 0,1 1,0 1,1 0,0 0,1 1,0 1,1 AND XOR • Functions which can be separated in this way are called Linearly Separable • Only linearly separable functions can be represented by a perceptron • XOR cannot be represented by a perceptron
- 31. G5G51IAI1IAI Neural NetworksNeural Networks What can perceptrons represent? Linear Separability is also possible in more than 3 dimensions – but it is harder to visualise
- 32. XOR • XOR is not “linearly separable” – Cannot be represented by a perceptron • What can we do instead? 1. Convert to logic gates that can be represented by perceptrons 2. Chain together the gates • Make sure you understand the following – check it using truth tables X1 XOR X2 = (X1 AND NOT X2) OR (X2 AND NOT X1)
- 33. G5G51IAI1IAI Neural NetworksNeural Networks The First Neural Neural Networks XOR Function 2 2 2 2 -1 -1 Z1 Z2 Y X1 X2 XOR X1 X2 Y 1 1 0 1 0 1 0 1 1 0 0 0 X1 XOR X2 = (X1 AND NOT X2) OR (X2 AND NOT X1)
- 34. Single- vs. Multiple-Layers • Once we chain together the gates then we have “hidden layers” – layers that are “hidden” from the output lines • Have just seen that hidden layers allow us to represent XOR – Perceptron is single-layer – Multiple layers increase the representational power, so e.g. can represent XOR • Generally useful nets have multiple-layers – typically 2-4 layers
- 35. Expectations • Be able to explain the terminology used, e.g. – activation functions – step and threshold functions – perceptron – feed-forward – multi-layer, hidden layers – linear separability • XOR – why perceptrons cannot cope with XOR – how XOR is possible with hidden layers
- 36. Questions?

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