1 Lecture 35 Brief Introduction to Main AI Areas (cont’d) Overview  Lecture Objective: Present the General Ideas on the AI Branches Below  Introduction to Main Areas of AI (cont’d):  Natural Language Processing  Introduction to Robotics  Neural Network  Machine Learning  Preview: Brief Introduction to Operating Systems

2 Lecture 35 I.Knowledge Representation l E.g., Facts:“bird isa animal” “bird has wings” » Logical Rules: “has-wings -> can-fly » (exception: isa penguin)” l Hierarchies and Relationships… animal bird pigeon living thing penguin colour blue (.5)colour grey (.5) wings feathers isa has

3 Lecture 35 Introduction to Natural Language Processing  Natural language refers to the languages that people speak (e.g., English).  Natural Language Processing deals with programming computers to understand natural human languages.  The goals of natural language processing is to train computers to be able to:  interpret human language  translate documents as humans would  Probably the single most challenging problem in computer science is to develop computers that can understand natural languages. So far, the complete solution to this problem has proved elusive, although a great deal of progress has been made.  Natural language was one of the first things that AI was envisioned to do, but it was found to be very difficult. It never succeeded as was hoped, as there is too much context and real world knowledge required.  Examples:  “John saw the elephant with a telescope.”  “The house was built by the river.”  “The house was built by the workers.”  “Time flies like an arrow.”  The context is assumed to resolve ambiguity. Other times it provides real information as well.

4 Lecture 35 Introduction to Robotics  Programming computers to see and hear and react to other sensory stimuli  Robots are now widely used in factories to perform high-precision jobs such as welding and riveting (fixing nails & tightening bolts)  They are also used in special situations that would be dangerous for humans -- for example, in cleaning toxic wastes or defusing bombs.  Although great advances have been made in the field of robotics during the last decade, robots are still not very useful in everyday life, as they are too clumsy to perform ordinary household chores.

5 Lecture 35 Introduction to Neural Networks  The human brain is so powerful because of its ability to process information in parallel. The brain consists of billions of neurons, each connected to thousands of others. Each neuron acts as its own processing unit, and works together with other neurons, exchanging information to create our perception, motor and creative abilities.  The human brain is said to contain about neurons and about interconnections (roughly equivalent to the number of characters in 10 billion books each of 350 pages)!

6 Lecture 35 Introduction to Neural Networks (cont’d)  Neural Networks: are AI systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains. A neural network uses adaptive algorithms, that “learn” based on training data.

7 Lecture 35 Introduction to Neural Networks (cont’d)  An ANN can be viewed as a graph in which the nodes represent neurons and the arcs represent axons (i.e., the interneuronal connections).  The nodes in an ANN get weighted input from other nodes according to the pattern of connectivity expressed in the graph.  The weight represent the importance of the interconnections and in the ANN are initially assigned random values.  When the sum of all inputs on a given node exceeds a certain threshold, the node fires and an output value is propagated through the preset interconnections of the graph.  Neural networks provides good solutions for recognition and classification problems

8 Lecture 35 Introduction to Machine Learning  What is learning?  "Learning denotes changes in a system that enable a system to do the same task more efficiently the next time.”  The ability of the system to improve its behavior  It could be reorganization of existing knowledge  Why it is hard?  Intelligence implies that an organism or machine must be able to adapt to new situations.  It must be able to learn to do new things.  This requires knowledge acquisition, inference, updating/refinement of knowledge base, acquisition of heuristics, applying faster searches, etc.

9 Lecture 35 Learning Paradigms  Rote Learning : memorizing  Inductive vs. deductive learning:  inductive: from special cases to general rules  deductive: from general rules to special cases  Supervised Learning:  give the training set with the right answer, the teacher evaluates the student answer and give the right answer.  Reinforcement Learning:  the teacher evaluates the students paper and give a grade to his paper without telling the correct answer to the student.  Unsupervised Learning:  You browse the web pages and you categorize them according to certain trend you develop while reading these web pages. No teacher.  Discovery : Unsupervised, specific goal not given  Reinforcement : Only feedback (positive or negative reward) given at end of a sequence of steps. Requires assigning reward to steps by solving the credit assignment problem--which steps should receive credit or blame for a final result?