Artificial Intelligence (AI) is a big field. many scientists tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers.
Here we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
We call ourselves Homosapiens man the wise because our intelligence is so important
to us. For thousands of years, we have tried to understand how we think; that is, how a mere handful of matter can perceive, understand, predict, and manipulate a world far larger and more complicated than itself. The field of artificial intelligence, or Al, goes further still an attempts not just to understand but also to build intelligent entities.
AI is one of the newest fields in science and engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956. Along with molecular biology, AI is regularly cited as the "field I would most like to be in" by scientists in other disciplines. A student in physics or mathematics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest. AI, on the other hand, still has openings for several full-time Einsteins and Edisons.
We have claimed that AI is exciting, but we have not said what it is.
There are many ways to define AI but rather here we would be more concerned on a few topics. And are:thought processes and reasoning.
THE FOUNDATIONS OF ARTIFICIAL INTELLIGENCE:
Philosophy,mathematics,economics,neuroscience,psychology and a bunch of more subjects made successful for the evaluation of AI.
"if the organism carries a "small-scale model' of external reality and of its own possible actions within its head, it is able to try out various alternatives, conclude which is the best of them, react to future situations before they arise, utilize the knowledge of past events in dealing with the present and future, and in every way to react in a much fuller, safer, and more competent manner to the emergencies which face it."
- (Craik, 1943)
The birth of artificial intelligence (1956):
Princeton was home to another influential figure in AI, John McCarthy. After receiving his PhD there in 1951 and working for two years as an instructor, McCarthy moved to Stanford and then to Dartmouth College, which was to become the official birthplace of the field. McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring together U.S. researchers interested in automata theory, neural nets, and the study of intel-ligence_ They organized a two-month workshop at Dartmouth in the summer of 1956. The proposal states:1°
We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hamp-shire_ The study is to proceed on the basis of the conjecture that every aspect of learning or arty other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together fur a summer.
There were 10 attendees in all, including Trenchard More from Princeton, Arthur Samuel from IBM, and Ray Solomonoff and Oliver Selfridge from MIT.
Two researchers from Carnegie Tech,11 Allen Newell and Herbert Simon, rather stole the show. Although the others had ideas and in some cases programs for particular applications such as checkers, Newell and Simon already had a reasoning program, the Logic Theorist (LT), about which Simon claimed, "We have invented a computer program capable of drinking non-numerically, and thereby solved the venerable mind—body problem."12 Soon after the workshop, the program was able to prove most of the theorems in Chapter 2 of Russell and Whitehead's Principia Mathematica. Russell was reportedly delighted when Simon showed him that the program had come up with a proof for one theorem that was shorter than the one in Principia The editors of the Journal of Symbolic Logic were less impressed; they rejected a paper coauthored by Newell, Simon, and Logic Theorist.
The Dartmouth workshop did not lead to any new breakthroughs, but it did introduce all the major figures to each other. For the next 20 years, the field would be dominated by these people and their students and colleagues at MIT, CMU, Stanford, and IBM.
Looking at the proposal for the Dartmouth workshop (McCarthy , 1955), we can see why it was necessary for Al to become a separate field. Why couldn't all the work done in AI have taken place under the name of control theory or operations research or decision theory. which, after all, have objectives similar to those of Al? Or why isn't Al a branch of mathematics? The first answer is that AI from the start embraced the idea of duplicating human faculties such as creativity, self-improvement, and language use. None of the other fields were addressing these issues. The second answer is methodology. Al is the only one of these fields that is clearly a branch of computer science (although operations research does share an emphasis on computer simulations), and AI is the only field to attempt to build machines that will function autonomously in complex, changing environments.
A dose of reality:
"It is not my aim to surprise or shock you but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover,their ability to do these things is going to increase rapidly until—in a visible future—the range of problems they can handle will be coextensive with the range to which the human mind has been applied."
- Herbert Simon in 1957
AI adopts the scientific method:
Recent years have seen a revolution in both the content and the methodology of work in artificial intelligence. It is now more common to build on existing theories than to propose brand-new ones, to base claims on rigorous theorems or hard experimental evidence rather than on intuition, and to show relevance to real-world applications rather than toy examples. AI was founded in part as a rebellion against the limitations of existing fields like control.
In the early period of Al it seemed plausible that new forms of symbolic computation, e.g., frames and semantic networks, made much of classical theory obsolete. this led to a form of isolationism in which Al became largely separated from the rest of computer science. This isolationism is currently being abandoned. There is a recognition that machine learning should not be isolated from information theory, that uncertain reasoning should not be isolated from stochastic modeling, that search should not be isolated from classical optimization and control, and that automated reasoning should not be isolated from formal methods and static analysis.
Here we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
We call ourselves Homosapiens man the wise because our intelligence is so important
to us. For thousands of years, we have tried to understand how we think; that is, how a mere handful of matter can perceive, understand, predict, and manipulate a world far larger and more complicated than itself. The field of artificial intelligence, or Al, goes further still an attempts not just to understand but also to build intelligent entities.
AI is one of the newest fields in science and engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956. Along with molecular biology, AI is regularly cited as the "field I would most like to be in" by scientists in other disciplines. A student in physics or mathematics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest. AI, on the other hand, still has openings for several full-time Einsteins and Edisons.
We have claimed that AI is exciting, but we have not said what it is.
There are many ways to define AI but rather here we would be more concerned on a few topics. And are:thought processes and reasoning.
THE FOUNDATIONS OF ARTIFICIAL INTELLIGENCE:
Philosophy,mathematics,economics,neuroscience,psychology and a bunch of more subjects made successful for the evaluation of AI.
"if the organism carries a "small-scale model' of external reality and of its own possible actions within its head, it is able to try out various alternatives, conclude which is the best of them, react to future situations before they arise, utilize the knowledge of past events in dealing with the present and future, and in every way to react in a much fuller, safer, and more competent manner to the emergencies which face it."- (Craik, 1943)
The birth of artificial intelligence (1956):
Princeton was home to another influential figure in AI, John McCarthy. After receiving his PhD there in 1951 and working for two years as an instructor, McCarthy moved to Stanford and then to Dartmouth College, which was to become the official birthplace of the field. McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring together U.S. researchers interested in automata theory, neural nets, and the study of intel-ligence_ They organized a two-month workshop at Dartmouth in the summer of 1956. The proposal states:1°
We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hamp-shire_ The study is to proceed on the basis of the conjecture that every aspect of learning or arty other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together fur a summer.
There were 10 attendees in all, including Trenchard More from Princeton, Arthur Samuel from IBM, and Ray Solomonoff and Oliver Selfridge from MIT.
Two researchers from Carnegie Tech,11 Allen Newell and Herbert Simon, rather stole the show. Although the others had ideas and in some cases programs for particular applications such as checkers, Newell and Simon already had a reasoning program, the Logic Theorist (LT), about which Simon claimed, "We have invented a computer program capable of drinking non-numerically, and thereby solved the venerable mind—body problem."12 Soon after the workshop, the program was able to prove most of the theorems in Chapter 2 of Russell and Whitehead's Principia Mathematica. Russell was reportedly delighted when Simon showed him that the program had come up with a proof for one theorem that was shorter than the one in Principia The editors of the Journal of Symbolic Logic were less impressed; they rejected a paper coauthored by Newell, Simon, and Logic Theorist.
The Dartmouth workshop did not lead to any new breakthroughs, but it did introduce all the major figures to each other. For the next 20 years, the field would be dominated by these people and their students and colleagues at MIT, CMU, Stanford, and IBM.
Looking at the proposal for the Dartmouth workshop (McCarthy , 1955), we can see why it was necessary for Al to become a separate field. Why couldn't all the work done in AI have taken place under the name of control theory or operations research or decision theory. which, after all, have objectives similar to those of Al? Or why isn't Al a branch of mathematics? The first answer is that AI from the start embraced the idea of duplicating human faculties such as creativity, self-improvement, and language use. None of the other fields were addressing these issues. The second answer is methodology. Al is the only one of these fields that is clearly a branch of computer science (although operations research does share an emphasis on computer simulations), and AI is the only field to attempt to build machines that will function autonomously in complex, changing environments.
A dose of reality:
"It is not my aim to surprise or shock you but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover,their ability to do these things is going to increase rapidly until—in a visible future—the range of problems they can handle will be coextensive with the range to which the human mind has been applied."
- Herbert Simon in 1957
AI adopts the scientific method:
Recent years have seen a revolution in both the content and the methodology of work in artificial intelligence. It is now more common to build on existing theories than to propose brand-new ones, to base claims on rigorous theorems or hard experimental evidence rather than on intuition, and to show relevance to real-world applications rather than toy examples. AI was founded in part as a rebellion against the limitations of existing fields like control.
In the early period of Al it seemed plausible that new forms of symbolic computation, e.g., frames and semantic networks, made much of classical theory obsolete. this led to a form of isolationism in which Al became largely separated from the rest of computer science. This isolationism is currently being abandoned. There is a recognition that machine learning should not be isolated from information theory, that uncertain reasoning should not be isolated from stochastic modeling, that search should not be isolated from classical optimization and control, and that automated reasoning should not be isolated from formal methods and static analysis.
