CLICK HERE TO DOWNLOAD PPT ON Artificial Intelligence
Artificial Intelligence Presentation Transcript
1.Artificial Intelligence
Artificial Intelligence (AI) is the name given to encoding intelligent or humanistic behaviors in computer software.
Problem: Nobody has created a widely accepted definition of intelligence.
At one time was considered a uniquely human quality.
Now generally accepted to be an animal quality.
Has been linked to tool use, tool creation, learning, adaptation to novel situations, capacity for abstraction.
Problem: Nobody has created a widely accepted definition of artificial intelligence.
Cognitive models attempt to recreate the actual processes of the human brain.
Behavioral models attempt to produce behavior that is reasonable for a situation regardless of how the behavior was produced.
Tend to focus on reasoning, behavior, learning, adaptation.
Artificial Intelligence (AI) is the name given to encoding intelligent or humanistic behaviors in computer software.
Problem: Nobody has created a widely accepted definition of intelligence.
At one time was considered a uniquely human quality.
Now generally accepted to be an animal quality.
Has been linked to tool use, tool creation, learning, adaptation to novel situations, capacity for abstraction.
Problem: Nobody has created a widely accepted definition of artificial intelligence.
Cognitive models attempt to recreate the actual processes of the human brain.
Behavioral models attempt to produce behavior that is reasonable for a situation regardless of how the behavior was produced.
Tend to focus on reasoning, behavior, learning, adaptation.
2.Herbert Simon: We call programs intelligent if they exhibit behaviors that would be regarded intelligent if they were exhibited by human beings.
Elaine Rich: AI is the study of techniques for solving exponentially hard problems in polynomial time by exploiting knowledge about the problem domain.
Elaine Rich and Kevin Knight: AI is the study of how to make computers do things at which, at the moment, people are better.
Avron Barr and Edward Feigenbaum: Physicists ask what kind of place this universe is and seek to characterize its behavior systematically. Biologists ask what it means for a physical system to be living. We in AI wonder what kind of information-processing system can ask such questions.
Claudson Bornstein: AI is the science of common sense.
Douglas Baker: AI is the attempt to make computers do what people think computers cannot do.
Anonymous: Artificial Intelligence is no match for natural stupidity.
3.Ambiguity – Knowledge ultimately represents natural phenomena that are inherently ambiguous. How do we resolve this?
Acquiring Knowledge – How does one combine new and old information?
Relationship to old knowledge.
Abstraction.
Negative learning – can we detect false information or contradictions?
Can we quantify the reliability of the knowledge? “Truth nets” attempt to do this.
Deriving Knowledge, Abstracting Knowledge – Given a set of information, can I derive new information? Reasoning systems and proof systems attempt to do this. Can I group similar knowledge items into a more general single item?
Elaine Rich: AI is the study of techniques for solving exponentially hard problems in polynomial time by exploiting knowledge about the problem domain.
Elaine Rich and Kevin Knight: AI is the study of how to make computers do things at which, at the moment, people are better.
Avron Barr and Edward Feigenbaum: Physicists ask what kind of place this universe is and seek to characterize its behavior systematically. Biologists ask what it means for a physical system to be living. We in AI wonder what kind of information-processing system can ask such questions.
Claudson Bornstein: AI is the science of common sense.
Douglas Baker: AI is the attempt to make computers do what people think computers cannot do.
Anonymous: Artificial Intelligence is no match for natural stupidity.
3.Ambiguity – Knowledge ultimately represents natural phenomena that are inherently ambiguous. How do we resolve this?
Acquiring Knowledge – How does one combine new and old information?
Relationship to old knowledge.
Abstraction.
Negative learning – can we detect false information or contradictions?
Can we quantify the reliability of the knowledge? “Truth nets” attempt to do this.
Deriving Knowledge, Abstracting Knowledge – Given a set of information, can I derive new information? Reasoning systems and proof systems attempt to do this. Can I group similar knowledge items into a more general single item?
4.Artificial Intelligence Challenges
Adaptation – How can I use what I know in new situations? What constitutes a new situation?
Sensing – Sensing is the ability to take in information from the world around you. Virtually all computer systems “Sense” 1’s and 0’s through keyboard, mouse, and serial port.
Perception – Perception is related to sensing, in that the meaning of the thing sensed is discovered. Auto example.
Emotional Intelligence –
“I think therefore I am.” Renee Descartes, about 1640.
“Descartes Error” is a book by Antonio R Damasio, 1995, in which he proposes that traditional rational thought without emotional content fails to create intelligent behavior.
Social Knowledge, Ethics – How do I behave with my teammates, strangers, friend, foe? What are my responsibilities towards others as well as myself?
Adaptation – How can I use what I know in new situations? What constitutes a new situation?
Sensing – Sensing is the ability to take in information from the world around you. Virtually all computer systems “Sense” 1’s and 0’s through keyboard, mouse, and serial port.
Perception – Perception is related to sensing, in that the meaning of the thing sensed is discovered. Auto example.
Emotional Intelligence –
“I think therefore I am.” Renee Descartes, about 1640.
“Descartes Error” is a book by Antonio R Damasio, 1995, in which he proposes that traditional rational thought without emotional content fails to create intelligent behavior.
Social Knowledge, Ethics – How do I behave with my teammates, strangers, friend, foe? What are my responsibilities towards others as well as myself?
5.Proposed AI Systems
Rule Based Behavior – designed behavior specifying sets of conditions and responses.
Finite-State Machines – Graphical representations of the state of systems, with sensory inputs leading to transitions from state to state.
Scripts – attempts to make behavior production tractable by anticipating behaviors that follow certain sequences. “The Restaraunt Script” is a typical example; we expect roughly the same behaviors (be greeted, be seated, order drinks, get drinks, …) no matter what restaurant we are in.
Case-based and Context-Based Reasoning – attempt to reduce search space of possible behaviors by only considering those associated with certain situations or contexts.
Rule Based Behavior – designed behavior specifying sets of conditions and responses.
Finite-State Machines – Graphical representations of the state of systems, with sensory inputs leading to transitions from state to state.
Scripts – attempts to make behavior production tractable by anticipating behaviors that follow certain sequences. “The Restaraunt Script” is a typical example; we expect roughly the same behaviors (be greeted, be seated, order drinks, get drinks, …) no matter what restaurant we are in.
Case-based and Context-Based Reasoning – attempt to reduce search space of possible behaviors by only considering those associated with certain situations or contexts.
6.
Cognitive Models – Attempts to model cognitive processes.
Cognitive Processes – attempt to match human thinking by reproducing human thought processes.
Neural Nets – attempt to match human thinking by reproducing brain synapse structures.
7.Covering Technical AI
Cognitive Models – Attempts to model cognitive processes.
Cognitive Processes – attempt to match human thinking by reproducing human thought processes.
Neural Nets – attempt to match human thinking by reproducing brain synapse structures.
7.Covering Technical AI
8.Acting Humanly: The Turing Test
Acting Humanly: The Turing Test
Acting Humanly: The Turing Test
9.Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes.
Anticipated all major arguments against AI in
following 50 years.
Suggested major components of AI: knowledge,
reasoning, language, understanding, learning.
10.The Turing Test
Turing's original game, described a simple party game involving three players.
Player A is a man,
Player B is a woman and
Player C (who plays the role of the interrogator) is of either sex.
In the Imitation Game, player C is unable to see either player A or player B, & can communicate with them only through written notes
Anticipated all major arguments against AI in
following 50 years.
Suggested major components of AI: knowledge,
reasoning, language, understanding, learning.
10.The Turing Test
Turing's original game, described a simple party game involving three players.
Player A is a man,
Player B is a woman and
Player C (who plays the role of the interrogator) is of either sex.
In the Imitation Game, player C is unable to see either player A or player B, & can communicate with them only through written notes
11.By asking questions of player A and player B, player C tries to determine which of the two is the man and which is the woman.
Player A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator in making the right one.
This was the Original Imitation Game Test.
Player A's role is to trick the interrogator into making the wrong decision, while player B attempts to assist the interrogator in making the right one.
This was the Original Imitation Game Test.
12.Turing proposes that the role of player A be filled by a computer. Thus, the computer's task is to pretend to be a woman and attempt to trick the interrogator into making an incorrect evaluation.
The success of the computer is determined by comparing the outcome of the game when player A is a computer against the outcome when player A is a man.
The success of the computer is determined by comparing the outcome of the game when player A is a computer against the outcome when player A is a man.
13.If, as Turing puts it, "the interrogator decide[s] wrongly as often when the game is played [with the computer] as he does when the game is played between a man and a woman", it may be argued that the computer is intelligent.
The second version appears later in Turing's 1950 paper.
The second version appears later in Turing's 1950 paper.
14. As with the Original Imitation Game Test, the role of player A is performed by a computer, the difference being that the role of player B is now to be performed by a man rather than a woman.
15."Let us fix our attention on one particular digital computer C. Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate program, C can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by a man?"
In this version, both player A (the computer) and player B are trying to trick the interrogator into making an incorrect decision.
In this version, both player A (the computer) and player B are trying to trick the interrogator into making an incorrect decision.
16.Strengths of Turing test
Tractability and simplicity
The power and appeal of the Turing test derives from its simplicity. The philosophy of mind, psychology, and modern neuroscience have been unable to provide definitions of "intelligence" and "thinking" that are sufficiently precise and general to be applied to machines.
The Turing test, at least provides something that can actually be measured.
Tractability and simplicity
The power and appeal of the Turing test derives from its simplicity. The philosophy of mind, psychology, and modern neuroscience have been unable to provide definitions of "intelligence" and "thinking" that are sufficiently precise and general to be applied to machines.
The Turing test, at least provides something that can actually be measured.
17.Weaknesses of Turing test
Turing himself did not explicitly state that the Turing test could be used as a measure of intelligence, or any other human quality.
It may simulate with human behavior but it cant judge intelligent behavior.
Turing himself did not explicitly state that the Turing test could be used as a measure of intelligence, or any other human quality.
It may simulate with human behavior but it cant judge intelligent behavior.
18.It tests only whether the computer behaves like a human being. Since human behavior and intelligent behavior are not exactly the same thing, the test can fail to accurately measure intelligence in two ways:
Some human behavior is unintelligent.
Some intelligent behavior is inhuman.
Some human behavior is unintelligent.
Some intelligent behavior is inhuman.
19.Thinking Humanly: Cognitive Modelling
Not content to have a program correctly solving a problem.
More concerned with comparing its reasoning steps
to traces of human solving the same problem.
Requires testable theories of the workings of the
human mind: cognitive science.
Not content to have a program correctly solving a problem.
More concerned with comparing its reasoning steps
to traces of human solving the same problem.
Requires testable theories of the workings of the
human mind: cognitive science.
20.Thinking Rationally: Laws of Thought
Aristotle was one of the first to attempt to codify “right thinking”, i.e., irrefutable reasoning processes.
Formal logic provides a precise notation and rules for representing and reasoning with all kinds of things in the world.
Obstacles:
- Informal knowledge representation.
- Computational complexity and resources.
21.Acting Rationally
Acting so as to achieve one’s goals, given one’s beliefs.
Does not necessarily involve thinking.
Advantages:
- More general than the “laws of thought” approach.
- More amenable to scientific development than human- based approaches.
Aristotle was one of the first to attempt to codify “right thinking”, i.e., irrefutable reasoning processes.
Formal logic provides a precise notation and rules for representing and reasoning with all kinds of things in the world.
Obstacles:
- Informal knowledge representation.
- Computational complexity and resources.
21.Acting Rationally
Acting so as to achieve one’s goals, given one’s beliefs.
Does not necessarily involve thinking.
Advantages:
- More general than the “laws of thought” approach.
- More amenable to scientific development than human- based approaches.
Posts
0 comments