1. Approaches to A. I. Thinking like humans Cognitive science Neuron level Neuroanatomical level Mind level Thinking rationally Aristotle, syllogisms Logic “Laws of thought” Acting like humans Understand language Play games Control the body The Turing Test Acting rationally Business approach Results oriented Human Rational Thinking Acting

2. (Artificial) Neural Networks Biological inspiration Synthetic networks non-Von Neumann Machine learning Perceptrons – MATH Perceptron learning Varieties of Artificial Neural Networks

3. Brain - Neurons 10 billion neurons (in humans) Each one has an electro-chemical state

4. Brain – Network of Neurons Each neuron has on average 7,000 synaptic connections with other neurons. A neuron “fires” to communicate with neighbors.

5. Modeling the Neural Network

6. von Neumann Architecture Separation of processor and memory. One instruction executed at a time.

7. Animal Neural Architecture von Neumann Separate processor and memory Sequential instructions Birds and bees (and us) Each neuron has state and processing Massively parallel, massively interconnected.

8. The Percepton

9. The Perceptron

10. Perceptrons can be combined to make a network

11. How to “program” a Perceptron?

12. Perceptron Learning Rule InputOutput x1x2x3 1 if avg(x1, x2)>x3, 0 otherwise 12961 -28150 3030 9-0.541 Training data: Valid weights: Perceptron function:

13. Varieties of Artificial Neural Networks Neurons that are not Perceptrons. Multiple neurons, often organized in layers.

14. Feed-forward network

15. Recurrent Neural Networks

16. Hopfield Network

17. On Learning the Past Tense of English Verbs Rumelhart and McClelland, 1980s

18. On Learning the Past Tense of English Verbs

20. Neural Networks Alluring because of their biological inspiration – degrade gracefully – handle noisy inputs well – good for classification – model human learning (to some extent) – don’t need to be programmed Limited – hard to understand, impossible to debug – not appropriate for symbolic information processing