Optimization Problem Calculus Without Given Area

A Preface for Understanding Artificial Intelligence

If we compare our life style and requirements of the day with not more than 50 years back then one conclusion is obvious that we are evolved not merely in terms of knowledge but also in stance of requirements, needs and dependency over the external entities.

In short it can be said that as the domain of our knowledge expanded it also changed the scale of our dependency over the external entities, and to cop up with this dynamic world of knowledge one of the tools to assist came into being called “computers”.

There is very interesting thing related with computers.

Before the 1920s, computers were human clerks that performed computations. They were usually under the lead of a physicist. Many thousands of computers were employed in commerce, government and research establishment. Most of these computers were women and they were known to have a degree in calculus. Some performed astronomical calculations for calendars [1].

It gives us a clue of Artificial Intelligence. Clue is; most of the jobs that man was associated with, now machines are give the responsibility to perform, because machines can work on those things effortlessly. And it is said that if knowledge is the power then of course computer is an amplifier of this power. But we will see that now this domain of required knowledge is changing and know the definition of Knowledge in terms of computer is evolved from mindlessly performing some actions to reasoning and thinking and in this regard I am afraid computer is not a very good amplifier, yet.

If we broader our domain from computer system to the machines all around us then again it wont be unnatural or unusual, man has always manipulated the nature to cater its needs and these all mechanical development around us is a good piece of confirmation. Man maneuver the nature simply be the faculty of curiosity that is very much inherited by a child. When it opens its eyes, starts hearing voices, etc. You must have observed this phenomenon.

Computers can do many awesome things that are in the domain of computation but now man has again pushed its limits and challenged the nature in its endeavor of exploration.

For Aristotle, the most fascination aspect of nature was change. In his Physics, he defined his “ philosophy of nature” as the “ study of things that change “. [2]

Although there is no standard definition of Artificial Intelligence and many experts have suggested different definitions relevant to their work on it.

We can say that Artificial Intelligence is the study of intelligence that can be generated in the entities that do not inherit it. Although it is a definition by a student so do not solely reply on it.

INTELLIGENCE is something like fire, it is thought of a divine gift, randomly delivered in the form of lightning, forest fire or burning of lava. European peasants would insert a wooden frill in round hole and rotate it briskly between their palms to produce fire, Ancient Greeks used lenses or concave mirrors to concentrate the sun’s rays and burning glasses were also used by Mexican Aztecs and the Chinese’s. Same is the case with Intelligence, chess playing and theorem-proving were thought as subject to intellect but now intelligent computers can defeat their masters in chess playing and even can have tremendous achievements at master level chess championships. Now it is quest of man to encage intelligence into the machines of iron and steel.

A more formal definition of Artificial Intelligence given by Elaine:

“ Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better”. [3]

So it is clear now that Artificial Intelligence is not the study to make computers efficient in what it can perform, already but it is a study of how to empower computers to do things at which at the moment people out class computers.

Computers are better than people in numerical computation, information storage, repetitive operation now what’s left, the core area of Artificial Intelligence i.e. Intelligent Behavior, even an ordinary person can perform better than a super computer. Dr. Seuss in her book writes that even the most super supercomputer lacks the reasoning capacity of a child engrossed. [4]

We do much more than just process information we understand it. We make sense out of what we see and hear; we use common sense to make our way through a world that sometimes seems highly illogical. [5]

So now our quest is streamlined but still there is a long way ahead to reach our destination. Before that do you know what intelligence is? Well most of us don’t or probably almost all of us.

But for the sake of abstract idea regarding intelligence I list some characteristic indispensable for an intelligence system, these characteristics are suggested by Douglas Hofstadter in a list of “essential abilities for Intelligence. He says for an intelligent system it needs:

· “To respond to situations very flexible”.

· “ To make sense out of ambiguous or contradictory messages”

· “To recognize the relative importance of different elements of a situation.”

· “To find similarities between situations despite differences which may separate them.”

· “To draw distinctions between situations despite similarities which may link them.”[6]

Mathematics can describe in great detail the technique for multiplying two numbers together because they can be described in intricate detail; computer performs them easily. On the other hand if an activity comes so naturally to you that you don’t have to think about it at all, you may have great difficult in describing exactly how you did it. What did you have for dinner last night?

Now, can you list the mental steps you went though to remember what you ate? [7]

And definitely learning is required to be intelligent. But when our computers are used-to with predefined instructions, when computer are already dictated what to do, how to do, and when to do, then is it learning. I don’t think so. Learning is basically reasoning and analyzing and in short concatenation new processed information to our already held knowledge database. Although, it is a big concern that how this knowledge need to be represented in computer system so that it can be retrieved for intelligent actions. One of the proposed actions taken by researchers is using Artificial Neural Networks. Those are closely resembled the human brain and activities taking place in it. And again it is very interesting thing that okay its fine computer ‘knows’ a lot of things but that’s what we know. How does computer know what it knows?

Another concern could be that why mimic human intelligence, when we all know that people don’t do things exact or optimal. Well the main reason to do so is this that we want our computers to perform activities sufficiently acceptable we don’t require them to be Intelligent and optimal at the same time although it is desired but just for a start it is not required. And to serve our energies to make computers Optimal and Intelligent, scientists are working over how to avoid “Artificial Stupidities”.

Following are the some research fields of Artificial Intelligence by John McCarthy [8]:

Logical AI

What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language.

Search

AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem-proving program. Discoveries are continually made about how to do this more efficiently in various domains.

Pattern recognition

When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most

Representation

Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.

Inference

From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s.

Common sense knowledge and reasoning

This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed.

Learning from experience

Programs do that. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic.

Planning

Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.

Epistemology

This is a study of the kinds of knowledge that are required for solving problems in the world.

Ontology

Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on ontology begins in the 1990s.

Heuristics

A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal.

Genetic programming

Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations.

Right now it can be said that this field is evolving and still there is much more to be done. Currently, we have many questions but only some of them are answered. But being scientist we are not afraid to ask because question is the key to unlock the secrets of the nature.

I hope this preliminary introduction will give you better understanding about this field and its concerns.

“When the only tool you have is a hammer, every problem you encounter trends to resemble a nail”;

(Source – Unknown)

References:

1. wikipedia/History of computer science.

2. Artificial Intelligence, 3rd Edition. By George F. Luger and William A. Stubblefield, Page No. 65.

3. Understanding Artificial Intelligence by Henery C Mishkoff..

4. Herald Net. Published: Sunday, January 30, 2005. The A.I. equation.

5. Understanding Artificial Intelligence by Henery C Mishkoff.

6. An Eternal Golden Braid by Douglas Hofstadter. New York, vintage 1980, Page No. 6.

7. Understanding Artificial Intelligence by Henery C Mishkoff.

8. www-formal.stanford.edu/jmc/whatisai/whatisai.html

About the Author

Born in UAE

Primary education from Pakistan

Secondary education from UAE

undergraduate in computing from Pakistan

Optimization with Calculus 1