19. Hobbes published The Leviathan, containing a material and combinatorial theory of thinking.
20. Pascal created the first mechanical digital calculating machine (1642).
21. Leibniz improved Pascal's machine to do multiplication & division (1673) and envisioned a universal calculus of reasoning by which arguments could be decided mechanically.
22.
23. George Boole developed a binary algebra representing (some) "laws of thought," published in The Laws of Thought.
24. Charles Babbage and Ada Byron (Lady Lovelace) worked on programmable mechanical calculating machines.
25. Mary Shelley published the story of Frankenstein's monster (1818).
33. Mathematical, logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example when we hear of a bird, we may infer that it can fly. But this conclusion can be reversed when we hear that it is a Penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can be drawn from a set of premises is a monotonic increasing function of the premises. Circumscription is another form of non-monotonic reasoning.
35. This is the area in which AI is farthest from human level, in spite of the fact that it has been an active research area since the 1950s. while there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and other theories of action, yet more new ideas are needed.
37. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic. Programs can only learn what facts or behaviors their formalism can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.
39. Planning programs start with general facts about the world, especially facts about the effects of actions, facts about the particular situation and a statement of a goal. From these they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.
43. This is the study of kinds of things that exist. In AI, the program and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. Emphasis on Ontology begins in the 1990s.
![Chapter 1: An introduction to Artificial Intelligence<br />What exactly is Artificial Intelligence? Artificial Intelligence is the study of how to make computer do things which, at the moment, people do better. This definition is of course somewhat ephemeral because of its reference to the current state of computer science. And it fails to include some areas of potentially very large impact, namely problems that cannot now be solved well by either computer or people. But it provides a good outline of what constitutes Artificial Intelligence, and it avoids the philosophical issues that dominate attempts to define the meaning of either artificial or intelligence.<br />31753917315Artificial Intelligence is the area of engineering focusing on creating machines that can engage on behaviours that human considers intelligence. The ability to create intelligent machines has intrigued humans since ancient time and today with the advent of computer and 50 years of research into AI programing techniques, the dream of smart machines is becoming reality. Researchers are creating systems which can mimic human thoughts, understand speech, beat the best human chess player, and countless other feats never before possible. The military is applying AI logic to its hi-tech systems, and in the near future Artificial Intelligence may impact our lives.<br />-1814195549275John McCarthy00John McCarthyJohn McCarthy, who coined the term ‘Artificial Intelligence’ in 1956, at Massachusetts Institute of Technology, defines it as quot;
the science and engineering of making intelligent machines” <br />Artificial Intelligence can be said a crossbreeding of a lot of fields:<br />PhilosophyLogic, methods of reasoning, mind as physical system, foundations of learning, language, rationality.MathematicsFormal representation and proof, algorithms, computation, (un)decidability, (in)tractabilityStatisticsModeling uncertainty, learning from dataEconomicsUtility, decision theory, rational economic agentsNeuroscienceNeurons as information processing unitPsychology How do people behave, perceive, process cognitive information, represent knowledgeComputer Engineering Building fast computersControl Theory Design systems that maximize an objective function over timeLinguisticsKnowledge representation, grammars<br />683260492760Artificial Intelligence00Artificial Intelligencecenter2617470Artificial Intelligence is a broad topic, consisting of different field from machine vision to expert system.<br />In order to classify machines as “thinking”, it is necessary to define intelligence. To what degree intelligence consists of, for example, solving complex problems, or making generalization and relationship? And what about perception and comprehension? Researches into the areas of learning of language, and of sensory perception have aided scientist in building intelligent machines. One of the most challenging approaches experts facing is building systems that mimic the behavior of human brain, made up of billions of neurons, and arguably the most complex matter in the universe. Perhaps the best way to gauze the intelligence is British computer scientist Alan Turing’s test. He stated that a computer would deserve to be called intelligent if it could deceive a human into believing that it was a human.<br />3801110-1905A computer would need the followings to pass Turing test:<br />Natural language processing: to communicate with examiner.<br />Knowledge representation: to store and retrieve information provided before or during interrogation.<br />3869055226060Alan Turing00Alan TuringAutomated reasoning: to use the stored information to answer questions and to draw new conclusions.<br />Machine learning: to adapt to new circumstances and to detect and extrapolate patterns.<br />Vision (for Total Turing test): to recognize the examiner’s actions and various objects presented by the examiner.<br />Motor control (total test): to act upon objects as requested.<br />Other senses (total test): such as audition, smell, touch, etc.<br />-19157951711960George Boole00George Boole-6354349115Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginning of AI reaches back before electronics, to philosophers and mathematicians such as Boole and other theorizing on principles that were used as the foundation of AI logic. AI really began to intrigue researchers with the invention of computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behavior. Over the next five decades, despite many stumbling blocks, AI has grown from a dozen researchers to thousands of engineers and specialists; and from programs capable of playing checkers, to system designed to diagnose disease.<br />Chapter 2: The history of Artificial Intelligence<br />Evidence of Artificial Intelligence folklore can be traced back to ancient Egypt, but with the development of electronic computer in 1941, the technology finally become available to create machine intelligence. The term Artificial Intelligence was first coined in 1956, at Dartmouth conference, and since then Artificial Intelligence has expanded because of the theories and principles developed by its dedicated researchers. From its birth 4 decades ago, there have been a variety of AI programs, and they have impacted other technological developments.<br />Here the brief history of AI is given:<br />1943McCulloch & Pitts: Boolean circuit model of brain1950Turing's quot;
Computing Machinery and Intelligencequot;
1956Dartmouth meeting: quot;
Artificial Intelligencequot;
adopted1950sEarly AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine1965Robinson's complete algorithm for logical reasoning1966-73AI discovers computational complexity, neural network research almost disappears1969-79Early development of knowledge-based systems1980AI becomes an industry 1986Neural networks return to popularity1987AI becomes a science 1995The emergence of intelligent agents<br />Pre-history of AI:<br />,[object Object]](https://image.slidesharecdn.com/ai-100315090042-phpapp01/85/Artificial-Intelligence-1-320.jpg)
