Introduction to Artificial Intelligence : History and Applications
What is Artificial Intelligence (AI) ?
Artificial Intelligence is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.
Intelligent behaviors
- Everyday tasks : recognize a friend, recognize who is calling, translate from one language to another, interpret a photograph, talk, cook a dinner
- Formal tasks : prove a logic theorem, geometry, calculus, play chess, checkers, or Go
- Expert tasks : engineering design, medical designers, financial analysis
Intelligence is:
- The ability to reason
- The ability to understand
- The ability to create
Can we produce a machine with all these abilities? The answer is no, so then what is AI ?
“The art of creating machines that perform functions that require intelligence when performed by people” – Kurzwell,1990
“The science of making computers, do things that require intelligence like humans”- Minskey “AI is the study of how to make computers do things at which, at the moment, people are better”- Elaine Rich
AI Today
- Diagnose lymph- node diseases
- Monitor space shuttle missions
- Automatic vehicle control
- Large- scale scheduling
- Detection of money laundering
- Classify astronomical objects
- Speech understanding systems
- Beat world’s best players in chess, checkers, and backgammon
AI Topics
- Robotics
- Search
- Planning
- Machine learning
- Image processing
- Expert systems
- Natural Language Processing
ELIZA
A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test which was Coded at MIT during 1964- 1966 by Joel Weizenbaum. The first script was DOCTOR which was a simple collection of syntactic patterns not unlike regular expressions. Each pattern had an associated reply which might include bits of the input.
Asimov’s laws of Robotics
The three laws of robots are :
- A robot may not injure a human being or, through inaction, allow a human being
to come to harm. - A robot must obey the orders given it by human beings.
- A robot must protect its own existence.
Brief history of AI
- 1943 McCulloch & Pitts: Boolean circuit model of brain
- 1950 Turing’s “Computing Machinery and Intelligence”
- 1956 Dartmouth meeting: “Artificial Intelligence” adopted
- 1950s Early AI programs, including Samuel’s checkers program, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine
- 1965 Robinson’s complete algorithm for logical reasoning
- 1966—73 AI discovers computational complexity, neural network research almost disappears
- 1969—79 early development of knowledge-based systems
- 1980– AI becomes an industry
- 1986– Neural networks return to popularity
- 1987– AI becomes a science
- 1995– The emergence of intelligent agents
Applications of AI
- Game playing: IBM’s Deep Blue became the first computer program to defeat the world champion in a chess match when it bested Garry Kasparov by a score of 3.5 to 2.5 in an exhibition match in 1997.
- Autonomous control: The Alvinn computer vision system was trained to steer a car to keep it following a lane. It was placed in CMU’s NavLab computer-controlled minivan and used to navigate across the United States—for 2850 miles it was in control of steering the vehicle 98% of the time. A human took over the other 2%, mostly at exit ramps. NavLab has video cameras that transmit road images to Alvinn, which then computes the best direction to steer, based on experience from previous training runs. • Language understanding and problem solving:
- Business Intelligence
- Medical Diagnosis
- Scientific Analysis
- Weather forecasting
- And many more
Approaches of AI
- Acting humanly
- Thinking humanly
- Acting rationally
- Thinking rationally
Acting Humanly
- Machine is required to act as humans do.
- Turing Test, proposed by Alan Turing (1950). Turing’s definition of intelligent behavior as the ability to achieve human-level performance in all cognitive tasks.
- The test involves an interrogator who interacts with one human and one machine. Within a given time the interrogator has to find out which of the two the human is, and which one the machine.
- Issue to solve in order to pass the test using a terminal: natural language processing, knowledge representation automated reasoning, machine learning.
- Additional requirements for the “total Turing test”: computer vision, speech recognition, speech synthesis, robotics.Critics of Turing test
- Test is not reproducible, amenable or constructive to mathematical analysis as it is more important to study the underlined principles of intelligence than to duplicate example
- Trying to evaluate machine intelligence in terms of human intelligence is fundamental mistake. It focuses too much on the behavior of conversation
Factors required to pass Turing Test
- Natural Language Processing: To communicate easily.
- Knowledge Representation: To store facts and rules.
- Automated Reasoning: To draw conclusion from stored knowledge.
- Machine Learning: To adopt new circumstances and detect pattern.
Thinking Humanly
- It is the action or process of acquiring knowledge and understanding thought, experience or the sense. Machine needs to understand how humans think.
- It is mainly concerned with the investigation, development of human behavior and the working of human mind in the computer.
- Example: General Problem Solver (GPS) developed by Newell & Simon in 1961 attempted to synthesize the human solving process. Compares the steps of the problem with the reasoning of human subjects solving the same problem.
- Critics: Lacks scientific theories of internal activities of brain. Level of abstraction of the approach is vague too.
Thinking rationally
- Aristotle made first attempt to harness “right thinking”. Famous example: “Socrates is a
man; all men are mortal; therefore Socrates is mortal.” - Formal logic (late nineteenth, early twentieth century’s) provides a precise notation of
statements of all kinds of things and relations between them. - Programs that can find the solution to a logical problem if one exists have been around
since 1965. Given enough memory and time, they should be able to solve everything. - Critics: It is not easy to take informal knowledge and state in the formal terms required
by logical notation specially when knowledge is less than 100% certain - There is big difference between being able to solve a problem in principle and doing so in
practice
Acting rationally (Rational agent approach)
- Rational agent: acts to achieve the best outcome on when there is uncertainty of the best
expected outcome - Rational agent does not necessarily involve thinking. Example, reflex action
- More general than laws of thought approach
- More amenable to scientific development then approach based on human
behaviors.
