1. The Von Neumann Model The approach to solving problems is to process a set of instructions and data stored in locations. The instructions are processed SERIALLY. This model is NOT suited to solving many types of problem.. e.g. decide whether an image is of a male or a female IF hair is long AND wearing makup AND … THEN the face is female ELSE the face is male END IF

2. Neural nets Neural networks Neuron net Etc. A brain cell ‘neuron’; note the other terms dendrite & axon We have seen that a ‘life experience’ in the brain is reckoned to form connection amongst the neurons, of varying strengths.. This pattern is attained using an iterative process - learning eye brain

3. Rather than neurons being arranged randomly as in the brain, neural networks are more commonly engineered in layers. A typical ‘pattern matching’ one may look like the arrangement below. e.g. Photo sensitive cells for vision Digitise sound for NLP Conclusion as to what object is The system would have to be trained with feedback.

4. Suppose the net is deciding whether it was seeing an arrow facing up or down e.g. Photo sensitive cells for vision Conclusion arrow is UP or DOWN If the system get it wrong, inform it. ↓ ↑

5. Below is an engineered neuron cell. It copies the brain cell. Note that there can be different input weightings

6. Why are neural networks considered to be a branch of AI? Which aspects of human intelligence do they model?neural network ability to learn: yes, algorithms have been developed which allow a neural network to adjust its own weights, so that its performance can improve; creativity: neural networks can be used to solve problems which have defeated an algorithmic approach; however, uses are more in pattern recognition rather than creative areas at present; reasoning: a neural network can arrive at an answer, but is unable to show the reasoning which it used to justify its answer. use of language: neural networks have been very successful in speech recognition and in speech production; vision: neural networks are very effective in extracting useful information from vision systems; problem solving: neural networks are effective in a wide range of problem-solving situations; adapting to new situations: most neural networks are designed and trained for a particular situation; they are flexible enough to cope with new input data, but could not be transferred from one type of problem (e.g. speech recognition) to a completely different problem (e.g. playing chess) without being redesigned.