Artificial Intelligence: Research and Collaborative Possibilities a presentation by: Dr. Ernest L. McDuffie, Assistant Professor Department of Computer Science, Florida State University at the: First Annual Africa-America Cooperative Workshop in Computational Science & Engineering University of the Western Cape Cape Town, South Africa 21 July 2000

What is Intelligence? n A single faculty or just a collection of distinct and unrelated abilities? n Exactly what happens when learning occurs? n What is intuition? n What is self-awareness? n Can intelligence be inferred from observable behavior, or does it require evidence of a particular internal mechanism? n How is knowledge represented in nerve tissue? n Is it even possible to achieve intelligence on a computer, or does an intelligent entity require the richness of sensation and experience that might be found only in a biological existence?

What is AI? n The study of intelligent behavior. n The goal being a theory of intelligence that can account for the behavior of all naturally occurring intelligent entities. n Then use the theory to guide the creation of artificial entities capable of intelligent behavior.

AI Fields n Natural Language Processing n Game Playing n Automatic Theorem Proving n Pattern Recognition & Computer Vision n Expert Systems n Modeling Forms of Reasoning n Automatic Learning n Robotics

Knowledge n We assume that intelligent entities have knowledge about their environment. n What can we say about such knowledge? What forms can it take? What are its limits? How is it used? How is it acquired? n We are beginning to understand how neurons process simple signals, but how the brain processes and represents knowledge is still not well understood.

How Computers Represent Knowledge n There are two major ways we think of machines having knowledge of the world. n Clarification about the distinction between the two is on going. n They are implicit or procedural knowledge and explicit or declarative knowledge.

Implicit Knowledge n In a computer this type of knowledge takes the form of stored procedures. n The knowledge would manifest itself when the procedure is run. n In humans it is often called tacit knowledge and can be difficult or impossible to describe. n It is difficult to easily modify this type of knowledge in a computer.

Explicit Knowledge n Complex tasks that we usually think of as requiring intelligence tend to use explicit knowledge representations. n A tabular database of salary data would be one example of explicit knowledge. n Particularly useful are explicit representations that can be interpreted as making declarative statements.

Explicit is Better for AI n It is much easier to make changes to explicit knowledge then to implicit. n It can be used for many different purposes, even for ones not anticipated when the knowledge was put together. n A knowledge base does not have to be repeated or specifically designed for each new application. n It can be extended by reasoning processes that derive additional knowledge.

Efficiency vs. Flexibility n Using declarative knowledge usually is more costly and slower than is directly applying procedural knowledge. n Declarative knowledge can also be accessed by introspective programs so a machine can then answer questions about what it knows. n Generally, we give up efficiency to gain flexibility and vice versa.

AI Needs Both n Procedural and Declarative types of knowledge. n Most flexible kinds of intelligence seem to depend strongly on declarative knowledge. n AI has concerned itself more and more with this type of knowledge. n Procedural knowledge still has a role to play.

Computer Learning n To assimilate new information or procedures without a programmer writing a new program. n This is different from discovery programs like those designed to formulate new mathematical theorems. n A range of different techniques are used in computer learning programs.

Some Techniques Are: n Induction - learning by generalization from specific examples. n Candidate Elimination - a specific method of induction; testing rules and a method for generating new one. n Genetic Algorithms - finding better and better versions of rules/programs/strings by using random repeated mutations and selection. n Neural Net - a method of training to modify the connections between neurons; back propagation.

Progress Has Been Slow n Learning from experience is difficult in any domain that is not very restricted or has formal contexts. n It seems that even simple animals like flies or slugs have better learning ability. n Studies of these types of animals have been used as background for some neural net approaches.

A New Direction - MIT n Alternative Essences of Intelligence n An attempt at building complex machines with human like capabilities. n Four essences - development, social interaction, physical coupling to the environment, and integration. n Dr. Rodney Brooks, Director AI Lab, MIT.

My Research n Temporal Reasoning Allen Relationships n Automatic Scheduling Lots of manufacturing applications n Second Generation Hybrid Expert Systems Combining learning and decision making n Applied AI to real world problems Network security, intrusions detection

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