Paper on Artificial Intelligence
An Introduction to Artificial Intelligence.
Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think".
In order to classify machines as "thinking", it is necessary to define intelligence. To what degree does intelligence consist of, for example, solving complex
problems, or making generalizations and relationships? And what about perception and comprehension? Research into the areas of learning, of language, and of sensory perception have aided scientists in building intelligent machines. One of the most challenging approaches facing experts is building systems that mimic the behavior of the human brain, made up of billions of neurons, and arguably the most complex matter in the universe. Perhaps the best way to gauge the intelligence of a machine is British computer scientist Alan Turing's test. He stated that a computer would deserves to be called intelligent if it could deceive a human into believing that it was human.
Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics,
to philosophers and mathematicians such as Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began to intrigue researchers with the invention of the computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behavior. Over the next four decades, despite many stumbling blocks, AI has grown from a dozen researchers, to thousands of engineers and specialists; and from programs capable of playing checkers, to systems designed to diagnose disease.
AI has always been on the pioneering end of computer science. Advanced-level computer languages, as well as computer interfaces and word-processors owe their existence to the research into artificial intelligence. The theory and insights brought about by AI research will set the trend in the future of computing. The products available today are only bits and pieces of what are soon to follow, but they are a movement towards the future of artificial intelligence. The advancements in the quest for artificial intelligence have, and will continue to affect our jobs, our education, and our lives.
The History of Artificial Intelligence
Timeline of major AI events
Outline:
1. Introduction
Introduction:
Evidence of Artificial Intelligence folklore can be traced back to ancient Egypt, but with the development of the electronic computer in 1941, the technology finally became available to create machine intelligence. The term artificial intelligence was first coined in 1956, at the Dartmouth conference, and since then Artificial Intelligence has expanded because of the theories and principles developed by its dedicated researchers. Through its short modern history, advancement in the fields of AI have been slower than first estimated, progress continues to be made. From its birth 4 decades ago, there have been a variety of AI programs, and they have impacted other technological advancements.
The Era of the Computer:
In 1941 an invention revolutionized every aspect of the storage and processing of information. That invention, developed in both the US and Germany was the electronic computer. The first computers required large, separate air-conditioned rooms, and were a programmers nightmare, involving the separate configuration of thousands of wires to even get a program running.
The 1949 innovation, the stored program computer, made the job of entering a program easier, and advancements in computer theory lead to computer science, and eventually Artificial intelligence. With the invention of an electronic means of processing data, came a medium that made AI possible.
The Beginnings of AI:
Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really observed. Norbert Wiener was one of the first Americans to make observations on the principle of feedback theory feedback theory. The most familiar example of feedback theory is the thermostat: It controls the temperature of an environment by gathering the actual temperature of the house, comparing it to the desired temperature, and responding by turning the heat up or down. What was so important about his research into feedback loops was that Wiener theorized that all intelligent behavior was the result of feedback mechanisms. Mechanisms that could possibly be simulated by machines. This discovery influenced much of early development of AI.
In late 1955, Newell and Simon developed The Logic Theorist, considered by many to be the first AI program. The program, representing each problem as a tree model, would attempt to solve it by selecting the branch that would most likely result in the correct conclusion. The impact that the logic theorist made on both the public and the field of AI has made it a crucial stepping stone in developing the AI field.
In 1956 John McCarthy regarded as the father of AI, organized a conference to draw the talent and expertise of others interested in machine intelligence for a month of brainstorming. He invited them to Vermont for "The Dartmouth summer research project on artificial intelligence." From that point on, because of McCarthy, the field would be known as Artificial intelligence. Although not a huge success, (explain) the Dartmouth conference did bring together the founders in AI, and served to lay the groundwork for the future of AI research
Knowledge Expansion
Knowledge Expansion
In the seven years after the conference, AI began to pick up momentum. Although the field was still undefined, ideas formed at the conference were re-examined, and built upon. Centers for AI research began forming at Carnegie Mellon and MIT, and a new challenges were faced: further research was placed upon creating systems that could efficiently solve problems, by limiting the search, such as the Logic Theorist. And second, making systems that could learn by themselves.
In 1957, the first version of a new program The General Problem Solver(GPS) was tested. The program developed by the same pair which developed the Logic Theorist. The GPS was an extension of Wiener's feedback principle, and was capable of solving a greater extent of common sense problems. A couple of years after the GPS, IBM contracted a team to research artificial intelligence. Herbert Gelerneter spent 3 years working on a program for solving geometry theorems.
While more programs were being produced, McCarthy was busy developing a major breakthrough in AI history. In 1958 McCarthy announced his new development; the LISP language, which is still used today. LISP stands for LISt Processing, and was soon adopted as the language of choice among most AI developers.
In 1963 MIT received a 2.2 million dollar grant from the United States government to be used in researching Machine-Aided Cognition (artificial intelligence). The grant by the Department of Defense's advanced research projects Agency (ARPA), to ensure that the US would stay ahead of the Soviet Union in technological advancements. The project served to increase the pace of development in AI research, by drawing computer scientists from around the world, and continues funding.
The Multitude of programs
The next few years showed a multitude of programs, one notably was SHRDLU. SHRDLU was part of the microworlds project, which consisted of research and programming in small worlds (such as with a limited number of geometric shapes). The MIT researchers headed by Marvin Minsky demonstrated that when confined to a small subject matter, computer programs could solve spatial problems and logic problems. Other programs which appeared during the late 1960's were STUDENT, which could solve algebra story problems, and SIR which could understand simple English sentences. The result of these programs was a refinement in language comprehension and logic.
Another advancement in the 1970's was the advent of the expert system. Expert systems predict the probability of a solution under set conditions. For example:
Because of the large storage capacity of computers at the time, expert systems had the potential to interpret statistics, to formulate rules. And the applications in the market place were extensive, and over the course of ten years, expert systems had been introduced to forecast the stock market, aiding doctors with the ability to diagnose disease, and instruct miners to promising mineral locations. This was made possible because of the systems ability to store conditional rules, and a storage of information.
During the 1970's Many new methods in the development of AI were tested, notably Minsky's frames theory. Also David Marr proposed new theories about machine vision, for example, how it would be possible to distinguish an image based on the shading of an image, basic information on shapes, color, edges, and texture. With analysis of this information, frames of what an image might be could then be referenced. another development during this time was the PROLOGUE language. The language was proposed for In 1972,
During the 1980's AI was moving at a faster pace, and further into the corporate sector. In 1986, US sales of AI-related hardware and software surged to $425 million. Expert systems in particular demand because of their efficiency. Companies such as Digital Electronics were using XCON, an expert system designed to program the large VAX computers. DuPont, General Motors, and Boeing relied heavily on expert systems Indeed to keep up with the demand for the computer experts, companies such as Teknowledge and Intellicorp specializing in creating software to aid in producing expert systems formed. Other expert systems were designed to find and correct flaws in existing expert systems.
The Transition from Lab to Life
The impact of the computer technology, AI included was felt. No longer was the computer technology just part of a select few researchers in laboratories. The personal computer made its debut along with many technological magazines. Such foundations as the American Association for Artificial Intelligence also started. There was also, with the demand for AI development, a push for researchers to join private companies. 150 companies such as DEC which employed its AI research group of 700 personnel, spend $1 billion on internal AI groups.
Other fields of AI also made there way into the marketplace during the 1980's. One in particular was the machine vision field. The work by Minsky and Marr were now the foundation for the cameras and computers on assembly lines, performing quality control. Although crude, these systems could distinguish differences shapes in objects using black and white differences. By 1985 over a hundred companies offered machine vision systems in the US, and sales totaled $80 million.
The 1980's were not totally good for the AI industry. In 1986-87 the demand in AI systems decreased, and the industry lost almost a half of a billion dollars. Companies such as Teknowledge and Intellicorp together lost more than $6 million, about a third of there total earnings. The large losses convinced many research leaders to cut back funding. Another disappointment was the so called "smart truck" financed by the Defense Advanced Research Projects Agency. The projects goal was to develop a robot that could perform many battlefield tasks. In 1989, due to project setbacks and unlikely success, the Pentagon cut funding for the project.
Despite these discouraging events, AI slowly recovered. New technology in Japan was being developed. Fuzzy logic, first pioneered in the US has the unique ability to make decisions under uncertain conditions. Also neural networks were being reconsidered as possible ways of achieving Artificial Intelligence. The 1980's introduced to its place in the corporate marketplace, and showed the technology had real life uses, ensuring it would be a key in the 21st century.
Methods used to create intelligence.
Outline:
1. Introduction
5. Constraints
3. Problem Solving
1. Chess
4. Conclusion
Introduction
In the quest to create intelligent machines, the field of Artificial Intelligence has split into several different approaches based on the opinions about the most promising methods and theories. These rivaling theories have lead researchers in one of two basic approaches; bottom-up and top-down. Bottom-up theorists believe the best way to achieve artificial intelligence is to build electronic replicas of the human brain's complex network of neurons, while the top-down approach attempts to mimic the brain's behavior with computer programs.
Neural Networks and Parallel Computation
The human brain is made up of a web of billions of cells called neurons, and understanding its complexities is seen as one of the last frontiers in scientific research. It is the aim of AI researchers who prefer this bottom-up approach to construct electronic circuits that act as neurons do in the human brain. Although much of the working of the brain remains unknown, the complex network of neurons is what gives humans intelligent characteristics. By itself, a neuron is not intelligent, but when grouped together, neurons are able to
pass electrical signals through networks.
pass electrical signals through networks.
The neuron "firing", passing a signal to the next in the chain.
Research has shown that a signal received by a neuron travels through the dendrite region, and down the axon. Separating nerve cells is a gap called the synapse. In order for the signal to be transferred to the next neuron, the signal must be converted from electrical to chemical energy. The signal can then be received by the next neuron and processed.
Warren McCulloch after completing medical school at Yale, along with Walter Pitts a mathematician proposed a hypothesis to explain the fundamentals of how neural networks made the brain work. Based on experiments with neurons, McCulloch and Pitts showed that neurons might be considered devices for processing binary numbers. An important back of mathematic logic, binary numbers (represented as 1's and 0's or true and false) were also the basis of the electronic computer. This link is the basis of computer-simulated neural networks, also know as Parallel computing.
A century earlier the true / false nature of binary numbers was theorized in 1854 by George Boole in his postulates concerning the Laws of Thought. Boole's principles make up what is known as Boolean algebra, the collection of logic concerning AND, OR, NOT operands. For example according to the Laws of thought the statement: (for this example consider all apples red)
· Apples are red-- is True
· Apples are red AND oranges are purple-- is False
· Apples are red OR oranges are purple-- is True
· Apples are red AND oranges are NOT purple-- is also True
Boole also assumed that the human mind works according to these laws, it performs logical operations that could be reasoned. Ninety years later, Claude Shannon applied Boole's principles in circuits, the blueprint for electronic computers. Boole's contribution to the future of computing and Artificial Intelligence was immeasurable, and his logic is the basis of neural networks.
McCulloch and Pitts, using Boole's principles, wrote a paper on neural network theory. The thesis dealt with how the networks of connected neurons could perform logical operations. It also stated that, one the level of a single neuron, the release or failure to release an impulse was the basis by which the brain makes true / false decisions. Using the idea of feedback theory, they described the loop which existed between the senses ---> brain ---> muscles, and likewise concluded that Memory could be defined as the signals in a closed loop of neurons. Although we now know that logic in the brain occurs at a level higher then McCulloch and Pitts theorized, their contributions were important to AI because they showed how the firing of signals between connected neurons could cause the brains to make decisions. McCulloch and Pitt's theory is the basis of the artificial neural network theory.
Using this theory, McCulloch and Pitts then designed electronic replicas of neural networks, to show how electronic networks could generate logical processes. They also stated that neural networks may, in the future, be able to learn, and recognize patterns. The results of their research and two of Weiner's books served to increase enthusiasm, and laboratories of computer simulated neurons were set up across the country.
Two major factors have inhibited the development of full scale neural networks. Because of the expense of constructing a machine to simulate neurons, it was expensive even to construct neural networks with the number of neurons in an ant. Although the cost of components have decreased, the computer would have to grow thousands of times larger to be on the scale of the human brain. The second factor is current computer architecture. The standard Von Neuman computer, the architecture of nearly all computers, lacks an adequate number of pathways between components. Researchers are now developing alternate architectures for use with neural networks.
Even with these inhibiting factors, artificial neural networks have presented some impressive results. Frank Rosenblatt, experimenting with computer simulated networks, was able to create a machine that could mimic the human thinking process, and recognize letters. But, with new top-down methods becoming popular, parallel computing was put on hold. Now neural networks are making a return, and some researchers believe that with new computer architectures, parallel computing and the bottom-up theory will be a driving factor in creating artificial intelligence.
Top Down Approaches; Expert Systems
Because of the large storage capacity of computers, expert systems had the potential to interpret statistics, in order to formulate rules. An expert system works much like a detective solves a mystery. Using the information, and logic or rules, an expert system can solve the problem. For example it the expert system was designed to distinguish birds it may have the following:
Charts like these represent the logic of expert systems. Using a similar set of rules, experts can have a variety of applications. With improved interfacing, computers may begin to find a larger place in society.
Chess
AI-based game playing programs combine intelligence with entertainment. On game with strong AI ties is chess. World-champion chess playing programs can see ahead twenty plus moves in advance for each move they make. In addition, the programs have an ability to get progressably better over time because of the ability to learn. Chess programs do not play chess as humans do. In three minutes, Deep Thought (a master program) considers 126 million moves, while human chessmaster on average considers less than 2 moves. Herbert Simon suggested that human chess masters are familiar with favorable board positions, and the relationship with thousands of pieces in small areas. Computers on the other hand, do not take hunches into account. The next move comes from exhaustive searches into all moves, and the consequences of the moves based on prior learning. Chess programs, running on Cray super computers have attained a rating of 2600 (senior master), in the range of Gary Kasparov, the Russian world champion.
Frames
On method that many programs use to represent knowledge are frames. Pioneered by Marvin Minsky, frame theory revolves around packets of information. For example, say the situation was a birthday party. A computer could call on its birthday frame, and use the information contained in the frame, to apply to the situation. The computer knows that there is usually cake and presents because of the information contained in the knowledge frame. Frames can also overlap, or contain sub-frames. The use of frames also allows the computer to add knowledge. Although not embraced by all AI developers, frames have been used in comprehension programs such as Sam.
Conclusion
This page touched on some of the main methods used to create intelligence. These approaches have been applied to a variety of programs. As we progress in the development of Artificial Intelligence, other theories will be available, in addition to building on today's methods.
What we can do with AI
We have been studying this issue of AI application for quite some time now and know all the terms and facts. But what we all really need to know is what can we do to get our hands on some AI today. How can we as individuals use our own technology? We hope to discuss this in depth (but as briefly as possible) so that you the consumer can use AI as it is intended.
First, we should be prepared for a change. Our conservative ways stand in the way of progress. AI is a new step that is very helpful to the society. Machines can do jobs that require detailed instructions followed and mental alertness. AI with its learning capabilities can accomplish those tasks but only if the worlds conservatives are ready to change and allow this to be a possibility. It makes us think about how early man finally accepted the wheel as a good invention, not something taking away from its heritage or tradition.
Secondly, we must be prepared to learn about the capabilities of AI. The more use we get out of the machines the less work is required by us. In turn less injuries and stress to human beings. Human beings are a species that learn by trying, and we must be prepared to give AI a chance seeing AI as a blessing, not an inhibition.
Finally, we need to be prepared for the worst of AI. Something as revolutionary as AI is sure to have many kinks to work out. There is always that fear that if AI is learning based, will machines learn that being rich and successful is a good thing, then wage war against economic powers and famous people? There are so many things that can go wrong with a new system so we must be as prepared as we can be for this new technology.
However, even though the fear of the machines are there, their capabilities are infinite Whatever we teach AI, they will suggest in the future if a positive outcome arrives from it. AI are like children that need to be taught to be kind, well mannered, and intelligent. If they are to make important decisions, they should be wise. We as citizens need to make sure AI programmers are keeping things on the level. We should be sure they are doing the job correctly, so that no future accidents occur.
AIAI Teaching Computers Computers
Does this sound a little Redundant? Or maybe a little redundant? Well just sit back and let me explain. The Artificial Intelligence Applications Institute has many project that they are working on to make their computers learn how to operate themselves with less human input. To have more functionality with less input is an operation for AI technology. I will discuss just two of these projects: AUSDA and EGRESS.
AUSDA is a program which will exam software to see if it is capable of handling the tasks you need performed. If it isn't able or isn't reliable AUSDA will instruct you on finding alternative software which would better suit your needs. According to AIAI, the software will try to provide solutions to problems like "identifying the root causes of incidents in which the use of computer software is involved, studying different software development approaches, and identifying aspects of these which are relevant to those root causes producing guidelines for using and improving the development approaches studied, and providing support in the integration of these approaches, so that they can be better used for the development and maintenance of safety critical software."
Sure, for the computer buffs this program is a definitely good news. But what about the average person who think the mouse is just the computers foot pedal? Where do they fit into computer technology. Well don't worry guys, because us nerds are looking out for you too! Just ask AIAI what they have for you and it turns up the EGRESS is right down your alley. This is a program which is studying human reactions to accidents. It is trying to make a model of how peoples reactions in panic moments save lives. Although it seems like in tough situations humans would fall apart and have no idea what to do, it is in fact the opposite. Quick Decisions are usually made and are effective but not flawless. These computer models will help rescuers make smart decisions in time of need. AI can't be positive all the time but can suggest actions which we can act out and therefor lead to safe rescues.
So AIAI is teaching computers to be better computers and better people. AI technology will never replace man but can be an extension of our body which allows us to make more rational decisions faster. And with Institutes like AIAI- we continue each stay to step forward into progress.
AUSDA is a program which will exam software to see if it is capable of handling the tasks you need performed. If it isn't able or isn't reliable AUSDA will instruct you on finding alternative software which would better suit your needs. According to AIAI, the software will try to provide solutions to problems like "identifying the root causes of incidents in which the use of computer software is involved, studying different software development approaches, and identifying aspects of these which are relevant to those root causes producing guidelines for using and improving the development approaches studied, and providing support in the integration of these approaches, so that they can be better used for the development and maintenance of safety critical software."
Sure, for the computer buffs this program is a definitely good news. But what about the average person who think the mouse is just the computers foot pedal? Where do they fit into computer technology. Well don't worry guys, because us nerds are looking out for you too! Just ask AIAI what they have for you and it turns up the EGRESS is right down your alley. This is a program which is studying human reactions to accidents. It is trying to make a model of how peoples reactions in panic moments save lives. Although it seems like in tough situations humans would fall apart and have no idea what to do, it is in fact the opposite. Quick Decisions are usually made and are effective but not flawless. These computer models will help rescuers make smart decisions in time of need. AI can't be positive all the time but can suggest actions which we can act out and therefor lead to safe rescues.
So AIAI is teaching computers to be better computers and better people. AI technology will never replace man but can be an extension of our body which allows us to make more rational decisions faster. And with Institutes like AIAI- we continue each stay to step forward into progress.
No worms in these Apples
by Adam Dyess
Apple Computers may not have ever been considered as the state of art in Artificial Intelligence, but a second look should be given. Not only are today's PC's becoming more powerful but AI influence is showing up in them. From Macros to Voice Recognition technology, PC's are becoming our talking buddies. Who else would go surfing with you on short notice- even if it is the net. Who else would care to tell you that you have a business appointment scheduled at 8:35 and 28 seconds and would notify you about it every minute till you told it to shut up. Even with all the abuse we give today's PC's they still plug away to make us happy. We use PC's more not because they do more or are faster but because they are getting so much easier to use. And their ease of use comes from their use of AI.
All Power Macintoshes come with Speech Recognition. That's right- you tell the computer to do what you want without it having to learn your voice. This implication of AI in Personal computers is still very crude but it does work given the correct conditions to work in and a clear voice. Not to mention the requirement of at least 16Mgs of RAM for quick use. Also Apple's Newton and other hand held note pads have Script recognition. Cursive or Print can be recognized by these notepad sized devices. With the pen that accompanies your silicon note pad you can write a little note to yourself which magically changes into computer text if desired. No more complaining about sloppy written reports if your computer can read your handwriting. If it can't read it though- perhaps in the future, you can correct it by dictating your letters instead.
Macros provide a huge stress relief as your computer does faster what you could do more tediously. Macros are old but they are to an extent, Intelligent. You have taught the computer to do something only by doing it once. In businesses, many times applications are upgraded. But the files must be converted. All of the businesses records but be changed into the new software's type. Macros save the work of conversion of hundred of files by a human by teaching the computer to mimic the actions of the programmer. Thus teaching the computer a task that it can repeat whenever ordered to do so.
AI is all around us all but get ready for a change. But don't think the change will be harder on us because AI has been developed to make our lives easier.
The Scope of Expert Systems
As stated in the 'approaches' section, an expert system is able to do the work of a professional. Moreover, a computer system can be trained quickly, has virtually no operating cost, never forgets what it learns, never calls in sick, retires, or goes on vacation. Beyond those, intelligent computers can consider a large amount of information that may not be considered by humans.
But to what extent should these systems replace human experts? Or, should they at all? For example, some people once considered an intelligent computer as a possible substitute for human control over nuclear weapons, citing that a computer could respond more quickly to a threat. And many AI developers were afraid of the possibility of programs like Eliza, the psychiatrist and the bond that humans were making with the computer. We cannot, however, over look the benefits of having a computer expert. Forecasting the weather, for example, relies on many variables, and a computer expert can more accurately pool all of its knowledge. Still a computer cannot rely on the hunches of a human expert, which are sometimes necessary in predicting an outcome.
In conclusion, in some fields such as forecasting weather or finding bugs in computer software, expert systems are sometimes more accurate than humans. But for other fields, such as medicine, computers aiding doctors will be beneficial, but the human doctor should not be replaced. Expert systems have the power and range to aid to benefit, and in some cases replace humans, and computer experts, if used with discretion, will benefit human kind.
REFERENCE
Library of Essays:
What we can do with AI--Adam Dyess
Apple Computers and AI--Adam Dyess
The scope of Expert Systems-- Tim Dumm
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