What is Artificial Intelligence? How Does AI Work?

example understanding spoke the natural language, medical diagnosis, circuit design, learning, self-adaptation, reasoning, chess-playing, proving math theories, etc.

• Artificial Intelligence is a process of making a machine or a program that
• Learn and understand like a human.
• Acts like a human (Turing test).
• Thinks like a human (human-like patterns of thinking steps).
• Acts or thinks rationally (logically, correctly).

It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods.

• Artificial Intelligence is the study of how to make computers just like humans. That means how to make computers do things that people do better
• Some problems used to be thought of as AI but are now considered note. g., compiling Fortran(suited to numeric computation and scientific computing) in 1955,
• symbolic mathematics (manipulate mathematical equations) in 1965 proving math theories.
• Artificial Intelligence is the study and design of intelligent agents where an intelligent agent is a system that interacts with its environment and takes actions that maximize its chances of success.

INTELLIGENCE

Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals, and some machines. Artificial Intelligence is the study of how to make computers make things which at the moment people do better. Examples: Speech recognition, Smell, Face, Object, Intuition, Inferencing, Learning new skills, Decision making, Abstract thinking.

History of Artificial Intelligence

• Artificial Intelligence has roots in several scientific disciplines
• computer science and engineering (hardware and software)
• philosophy (rules of reasoning)
• mathematics (logic, algorithms, optimization)
• cognitive science and psychology (modeling high-level human/animal thinking)
• neural science (model low level human/animal brain activity)
• linguistics

The birth of AI (1943 -1956)

Pitts and McCulloch (1943): a simplified mathematical model of neurons (resting/firing states) can realize all propositional logic primitives (can compute all Turing computable functions)

1. Allen Turing: Turing machine and Turing test (1950).
2. Claude Shannon: information theory; the possibility of chess-playing computers.

Early enthusiasm (1952 -1969)

1956 Dartmouth conferenceJohn McCarthy (Lisp); Marvin Minsky (first neural network machine); Alan Newell and Herbert Simon (GPS);

Emphasize on intelligent general problem solvingGSP (means-ends analysis); Lisp (Artificial Intelligence programming language); Resolution by John Robinson (the basis for automatic theorem proving); heuristic search (A*, AO*, game tree search)

Emphasis on knowledge (1966 -1974)

Knowledge-based systems (1969 -1999)

Artificial Intelligence became an industry (1980 -1989)

Programming languages for Artificial Intelligence

The programs for AI problems can be written with procedural languages like PASCAL or declaration languages like PROLOG. Generally, relational languages like PROLOG or LISP are preferred for symbolic reasoning in AI. However, if the program requires much arithmetic computation (say for the purpose of uncertainty management), then procedural languages would be preferred.

Recently some shells are available, where the user needs to submit knowledge only and the shall offer the implementation of both symbolic processing simultaneously.

Easy Problems in AI

It’s been easier to mechanize many of the high-level cognitive tasks we usually associate with “intelligence” in people

Example for — symbolic integration, proving theorems, playing chess, some aspect of medical diagnosis, etc.

Hard Problems in AI

It’s been very hard to mechanize tasks that animals can do easily catching prey and avoiding predators.

Examples of AI problems

Expert Consulting Systems

A key problem in the development of an Expert Consulting System is how to represent and use the knowledge that human experts in these subjects obviously possess and use. This problem is more difficult by the fact that the expert knowledge in any important field is imprecise and uncertain.

Finding proof of a mathematical theorem requires the following intelligence. Requires the ability to make deductions from hypothesis.

It deals with the problems of controlling the physical actions of a Mobile Robot.

Automatic Programming

In automatic programming, a system takes in a high-level description of what program is to accomplish and produce a program.

Perceptional Problems

Computers are made to see their surroundings by fitting T.V inputs.

Also, they are made to hear speaking voices by providing microphone inputs.

But it requires the processing of large base knowledge about the things being perceived.

Natural Language Processor

This field is concerned with the efforts of making computers understand spoken and written languages.

To understand sentences about a topic, it is necessary not only a lot about the vocabulary and grammar but also a good deal about the topic so that unstated assumptions can be recognized.

Task Domains of Artificial Intelligence

Mundane Tasks:

Mathematics: Geometry, logic, Proving properties of programs

Engineering ( Design, Fault finding, Manufacturing planning)

GPS (General Problem Solver): Goal not just to produce human-like behavior (like ELIZA), but to produce a sequence of steps of the reasoning process that was similar to the steps followed by a person in solving the same task.

Develop formal models of knowledge representation, reasoning, learning, memory, problem-solving, that can result in algorithms. There is often an emphasis on provably correct systems, and guarantee finding an optimal solution.

For a given set of inputs, generate an

appropriate output that is not necessarily correct but gets the job done.

A heuristic(heuristic rule, heuristic method) is a rule of thumb, strategy, trick, simplification, or any other kind of device which drastically limits the search for solutions in large problem spaces. Heuristics do not guarantee optimal solutions; in fact, they do not guarantee any solution at all: All that can be said for a useful heuristic is that it offers solutions that are good enough most of the time.

Not interested in how you get results, just the similarity to what human results are.

Exemplified by the Turing Test (Alan Turing, 1950).

Acting Humanly: The Turing Test

Alan Turing (1912–1954)

“Computing Machinery and Intelligence” (1950)

• Three rooms contain a person, a computer, and an interrogator.
• The interrogator can communicate with the other two by teleprinter.
• The interrogator tries to determine which is the person and which is the machine.
• The machine tries to fool the interrogator into believing that it is the person.
• If the machine succeeds, then we conclude that the machine can think.

Data

A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test.

• Coded at MIT during 1964–1966 by Joel Weizenbaum.
• The first script was DOCTOR.
• The script was a simple collection of syntactic patterns not unlike regular expressions
• Each pattern had an associated reply which might include bits of the input (after simple transformations (my your).

What can AI systems do?

• Here are some example applications
• Computer vision: face recognition from a large set
• Robotics: autonomous (mostly) automobile
• Natural language processing: simple machine translation
• Expert systems: medical diagnosis in a narrow domain
• Spoken language systems:~1000 word continuous speech
• Planning and scheduling: Hubble Telescope experiments
• Learning: text categorization into ~1000 topics
• Games: Grand Master level in chess (world champion), checkers, etc.

What can’t AI systems do yet?

• Understand natural language robustly (e.g., read and understand articles in a newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Construct plans in dynamic real-time domains
• Refocus attention in complex environments
• Perform life-long learning

How is AI research done?

AI research has both theoretical and experimental sides. The experimental side has both basic and applied aspects.

There are two main lines of research:

• One is biological, based on the idea that since humans are intelligent, AI should study humans and imitate their psychology or physiology.
• The other is phenomenal, based on studying and formalizing common sense facts about the world and the problems that the world presents to the achievement of goals.

The two approaches interact to some extent, and both should eventually succeed. It is a race, but both racers seem to be walking. [John McCarthy]

Branches of AI

1. Logical AI
2. Search
3. Natural language processing
4. pattern recognition
5. Knowledge representation
6. Inference From some facts, others can be inferred.
7. Automated reasoning
8. Learning from experience
9. Planning To generate a strategy for achieving some goal
10. Epistemology This is a study of the kinds of knowledge that are required for solving problems in the world.
11. Genetic programming
12. Emotions???

Originally published at https://www.softwarequery.com.