Artificial intelligence (AI) aims to develop systems that behave like humans through techniques like natural language processing, robotics, computer vision, and expert systems. Expert systems capture knowledge as rules or networks to diagnose issues like medical conditions, while neural networks mimic the human brain through interconnected nodes that can learn from examples without being explicitly programmed. Overall, AI research involves developing systems that can match or exceed human capabilities in complex, unrestricted domains using methods like search, learning, reasoning, and self-correction.

1. What is Artificial Intelligence?
• AI is the effort to develop systems that can
behave/act like humans.
• Turing Test
• The problem = unrestricted domains
– human intelligence vastly complex and broad
– associations, metaphors, and analogies
– common sense
– conceptual frameworks

2. Elements of AI
• Natural Language Processing
• Robotics
• Perceptive Systems (Vision)
• Expert Systems

3. How are Machines Intelligent?
• Constrained Heuristic Search
– How do you play chess?
• first move = 20 possible
• second move = 400 possible
• 7th move = 1,280,000,000 possible
– Depth First vs. Breath First Searching
– Ability to Learn

4. Decision Tree

5. Depth First Search

6. Breath First Search

7. Expert Systems
• Capture knowledge of an expert.
• Represent Knowledge as a
– rule base
• if then rules
– semantic net
• hierarchy
– frames
• shared characteristics, IS-A relationships

8. Expert System Successes
• XCON - configures systems for DEC
• Prospector - an mining expert
• MYCIN - infectious blood diseases
• EMYCIN - Empty MYCIN

9. Elements of Expert System Shell
• Knowledge Base
– rules
• Working Memory
– facts of current case
• Inference Engine
– applies rules using current set of facts
• Explanation Facility
• CLIPS

10. Neural Networks & The Brain
• Base on architecture of human brain
– Neurons connected by axons & dendrites
– 100 billion neurons
– 1,000 dendrites per neuron
– 100,000 billion synapses
– 10 million billion interconnectons per second

11. How a Neuron Works
Impulses
come from
other neurons.
When sum of
inputs reaches
a threshold,
neuron fires.
Sending
impulses
to next
level of
neurons.

12. An Artificial Neural Network
Inputs Hidden Output
w
w
w
w
w
w

13. Neural Networks, NN
• NNs learn by using a training set and
adjusting the weights on each connection.
• NNs do not have to be “told” explicit
relationship rules.
• NNs can work with partial inputs.
• NNs cannot explain their results.
• NNs can take a long time to train.
• A NN demonstration