Artificial Intelligence (AI)
AI can be defined as a part of computer science that is concerned with the designing of intelligent computer systems, i.e., systems that exhibit characteristics we associate with intelligence in human behavior.
The goal of AI is to develop computers that can think, see, hear, walk, talk, and feel.
What Computers Can Do Better Than People?
- Numerical computation: Fast & accurate
- Information storage: Voluminous amounts
- Repetitive operations: Not getting bored (??)
However, these are mechanical mindless activities and cannot be regarded as ‘intelligent’ tasks.
What People Can Do Better Than Computers?
Activities that involve intelligence include:
- Understanding
- Common sense reasoning
- Natural language processing and generation
- Planning & Design
- Learning (e.g., from mistakes, by analogy, by experience or examples)
- Emotions
What is “Intelligence”?
Intelligence has the ability:
- To respond to situations very flexibly
- To make sense out of ambiguous messages
- To recognize the relative importance of different elements of a situation
An intelligent agent interacts with the environment, receives state information through sensors, and makes decisions that can be carried out by actuators based on sensor data. The important part is that AI is able to map sensors to actuators through control policy/rules.
Human Intelligence vs. AI
| Human Intelligence | AI |
|---|---|
| Natural intelligence | Intelligences possessed by machines |
| Intuition, common sense, judgment, creativity, beliefs, etc. | Ability to simulate human behavior & cognitive processes |
| Ability to demonstrate their intelligence by communicating effectively | Capture & preserve human expertise |
| Probable reasoning & critical thinking | Flexibly response – ability to comprehend large amounts of data quickly |
Computer Intelligence
Computer Intelligence involves:
- Natural language processing to communicate successfully in a human language.
- Knowledge representation to store what it knows or hears.
- Automated reasoning to answer questions and draw new conclusions.
- Machine learning to adapt to new circumstances and detect patterns.
To pass the total Turing test, a robot will need computer vision and speech recognition to perceive the world, and robotics to manipulate objects and move about.
Can a Machine Think?
This can be answered by the following “tests” for a machine (i.e., the program/software).
The Alan Turing Test
- Invented by Alan Turing.
- An interrogator tries to distinguish between a machine and a human by asking them questions.
- If the interrogator cannot make a decision within a certain time, the machine is considered intelligent.
- If the computer succeeds in fooling the interrogator, i.e., the interrogator cannot distinguish the machine from the human, then the machine may be assumed to be “intelligent.”
ELIZA
ELIZA is an early natural language processing computer program created in the 1960s at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.
History of AI
- 1950: Turing Test; General problem-solving methods.
- 1956: Dartmouth Conference; Proposed launch of Joint Research on AI.
- 1960s: AI established as a research field with a focus on knowledge bases.
- 1963: Newell & Simon built General Problem Solver (GPS).
- 1965: DENDRAL developed by Feigenbaum at Stanford University.
- 1970s: AI commercialization began; MYCIN developed at Stanford University.
- 1972: PROLOG developed by Alain Colmerauer at University of Marseilles.
- 1981: Artificial neural networks; ICOT invented Concurrent Prolog.
- 1990: Intelligent agents; Software that performs assigned tasks on the user’s behalf.
AI Languages
Conventional Systems vs AI
| Conventional Systems | AI Systems |
|---|---|
| Procedural | Declarative |
| Numerical processing | Symbolic processing |
| Algorithmic | Heuristic programming |
| Rigid syntax | More natural syntax |
Regular Programming vs AI Programming
Regular Programming (Algorithmic):
- Input: Sequence of alphanumeric symbols
- Processing: Manipulation of stored symbols using algorithms
- Output: Sequence of alphanumeric symbols on media such as a screen, paper, or disk
AI Programming (Heuristics):
- Input: Sight, sound, touch, smell, or taste
- Processing: Knowledge representation, pattern matching, search, logic, problem solving & learning
- Output: Printed language, synthesized speech, manipulation of physical objects, or locomotion (movement in space)
Symbolic Processing
Symbolic Processing deals with symbolic, non-algorithmic methods of problem solving.
Heuristics
Heuristics is a branch of Computer Science that deals with ways of representing knowledge using symbols rather than numbers and with rules-of-thumb for processing information. It is developed through intuition, experience, and judgment and represents guidelines for system operation.
Language Levels for AI Problem Solving
Symbol Level
Concerns the particular formalisms used to represent knowledge, such as logic or production rules, and the structures used to organize knowledge.
Knowledge Level
Addresses what queries/questions will be asked, how new knowledge can be added or updated, what objects and relations are necessary, and whether the system can reason despite the incompleteness of information.
Essential Requirements for AI Language
- Support of Symbolic Computation: Implementation of operations on symbolic data.
- Flexibility of Control: Rule-based systems allow for non-step-by-step execution.
- Support of Exploratory Programming Methodologies: AI programming is inherently exploratory.
- Late Binding & Constraint Propagation: Problems may require values to remain unknown until sufficient information is gathered.
- Clear and Well-defined Semantics: New languages with mathematical formalisms like logic (Prolog) can provide clarity.
Languages
AI Languages
In Europe and Japan, Prolog is preferred, while in America, Lisp is often used.
Prolog:
- Good for rapid prototyping.
- Allows writing algorithms by augmenting logical sentences.
Lisp:
- Flexible and adapts to changing programming styles.
- Enables writing complex programs easily and quickly.