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In mathematics and computer science, an algorithm is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. By making use of artificial intelligence, algorithms can perform automated deductions and use mathematical and logical tests to divert the code execution through various routes. Using human characteristics as descriptors of machines in metaphorical ways was already practiced by Alan Turing with terms such as "memory", "search" and "stimulus".
In science and history, consilience is the principle that evidence from independent, unrelated sources can "converge" on strong conclusions. That is, when multiple sources of evidence are in agreement, the conclusion can be very strong even when none of the individual sources of evidence is significantly so on its own. Most established scientific knowledge is supported by a convergence of evidence: if not, the evidence is comparatively weak, and there will not likely be a strong scientific consensus.
Falsifiability is a standard of evaluation of scientific theories and hypotheses that was introduced by the philosopher of science Karl Popper in his book The Logic of Scientific Discovery (1934). He proposed it as the cornerstone of a solution to both the problem of induction and the problem of demarcation. A theory or hypothesis is falsifiable if it can be logically contradicted by an empirical test that can potentially be executed with existing technologies. Popper insisted that, as a logical criterion, it is distinct from the related concept "capacity to be proven wrong" discussed in Lakatos's falsificationism. Even being a logical criterion, its purpose is to make the theory predictive and testable, thus useful in practice.
Philosophy of science is a branch of philosophy concerned with the foundations, methods, and implications of science. The central questions of this study concern what qualifies as science, the reliability of scientific theories, and the ultimate purpose of science. This discipline overlaps with metaphysics, ontology, and epistemology, for example, when it explores the relationship between science and truth. Philosophy of science focuses on metaphysical, epistemic and semantic aspects of science. Ethical issues such as bioethics and scientific misconduct are often considered ethics or science studies rather than the philosophy of science.
An ontological commitment of a language is one or more objects postulated to exist by that language. The 'existence' referred to need not be 'real', but exist only in a universe of discourse. As an example, legal systems use vocabulary referring to 'legal persons' that are collective entities that have rights. One says the legal doctrine has an ontological commitment to non-singular individuals.
Usability can be described as the capacity of a system to provide a condition for its users to perform the tasks safely, effectively, and efficiently while enjoying the experience. In software engineering, usability is the degree to which a software can be used by specified consumers to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use.
Inductive reasoning is a method of reasoning in which a body of observations is considered to derive a general principle. It consists of making broad generalizations based on specific observations. Inductive reasoning is distinct from deductive reasoning. If the premises are correct, the conclusion of a deductive argument is certain; in contrast, the truth of the conclusion of an inductive argument is probable, based upon the evidence given.
"Computing Machinery and Intelligence" is a seminal paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.
Structured Systems Analysis and Design Method (SSADM) is a systems approach to the analysis and design of information systems. SSADM was produced for the Central Computer and Telecommunications Agency, a UK government office concerned with the use of technology in government, from 1980 onwards.
In philosophy of science and philosophy of mind, cognitive closure is the proposition that human minds are constitutionally incapable of solving certain perennial philosophical problems. Owen Flanagan calls this position anti-constructive naturalism or the "new mysterianism" and the primary advocate of the hypothesis, Colin McGinn, calls it transcendental naturalism acknowledging the possibility that solutions may be knowable to an intelligent non-human of some kind. According to McGinn, such philosophical questions include the mind-body problem, identity of the self, foundations of meaning, free will, and knowledge, both a priori and empirical.
Naturalized epistemology is a collection of philosophic views concerned with the theory of knowledge that emphasize the role of natural scientific methods. This shared emphasis on scientific methods of studying knowledge shifts focus to the empirical processes of knowledge acquisition and away from many traditional philosophical questions. There are noteworthy distinctions within naturalized epistemology. Replacement naturalism maintains that traditional epistemology should be abandoned and replaced with the methodologies of the natural sciences. The general thesis of cooperative naturalism is that traditional epistemology can benefit in its inquiry by using the knowledge we have gained from the cognitive sciences. Substantive naturalism focuses on an asserted equality of facts of knowledge and natural facts.
Troubleshooting is a form of problem solving, often applied to repair failed products or processes on a machine or a system. It is a logical, systematic search for the source of a problem in order to solve it, and make the product or process operational again. Troubleshooting is needed to identify the symptoms. Determining the most likely cause is a process of elimination—eliminating potential causes of a problem. Finally, troubleshooting requires confirmation that the solution restores the product or process to its working state.
Backward induction is the process of reasoning backwards in time, from the end of a problem or situation, to determine a sequence of optimal actions. It proceeds by examining the last point at which a decision is to be made and then identifying what action would be most optimal at that moment. Using this information, one can then determine what to do at the second-to-last time of decision. This process continues backwards until one has determined the best action for every possible situation at every point in time. Backward induction was first used in 1875 by Arthur Cayley, who uncovered the method while trying to solve the infamous Secretary problem.
Glossary of Unified Modeling Language (UML) terms provides a compilation of terminology used in all versions of UML, along with their definitions. Any notable distinctions that may exist between versions are noted with the individual entry it applies to.
Ariadne's thread, named for the legend of Ariadne, is solving a problem by multiple means—such as a physical maze, a logic puzzle, or an ethical dilemma—through an exhaustive application of logic to all available routes. It is the particular method used that is able to follow completely through to trace steps or take point by point a series of found truths in a contingent, ordered search that reaches an end position. This process can take the form of a mental record, a physical marking, or even a philosophical debate; it is the process itself that assumes the name.
In mathematical optimization and computer science, heuristic is a technique designed for solving a problem more quickly when classic methods are too slow or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut.
Medical diagnosis is the process of determining which disease or condition explains a person's symptoms and signs. It is most often referred to as diagnosis with the medical context being implicit. The information required for diagnosis is typically collected from a history and physical examination of the person seeking medical care. Often, one or more diagnostic procedures, such as medical tests, are also done during the process. Sometimes posthumous diagnosis is considered a kind of medical diagnosis.
A hypothesis is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words "hypothesis" and "theory" are often used interchangeably, a scientific hypothesis is not the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis proposed for further research in a process beginning with an educated guess or thought.
Mobile device forensics is a branch of digital forensics relating to recovery of digital evidence or data from a mobile device under forensically sound conditions. The phrase mobile device usually refers to mobile phones; however, it can also relate to any digital device that has both internal memory and communication ability, including PDA devices, GPS devices and tablet computers.
A guess is a swift conclusion drawn from data directly at hand, and held as probable or tentative, while the person making the guess admittedly lacks material for a greater degree of certainty. A guess is also an unstable answer, as it is "always putative, fallible, open to further revision and interpretation, and validated against the horizon of possible meanings by showing that one interpretation is more probable than another in light of what we already know". In many of its uses, "the meaning of guessing is assumed as implicitly understood", and the term is therefore often used without being meticulously defined. Guessing may combine elements of deduction, induction, abduction, and the purely random selection of one choice from a set of given options. Guessing may also involve the intuition of the guesser, who may have a "gut feeling" about which answer is correct without necessarily being able to articulate a reason for having this feeling.
References
- Richard L. Burden, J. Douglas Faires (2000). Numerical Analysis, 7th ed. Brooks/Cole. ISBN 0-534-38216-9.
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