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The philosophy of artificial intelligence attempts to answer such questions as:

• Can a machine act intelligently? Can it solve any problem that a person would solve by thinking?
• Can a machine have a mind, mental states and consciousness in the same sense humans do? Can it feel?
• Are human intelligence and machine intelligence the same? Is the human brain essentially a computer?

These three questions reflect the divergent interests of AI researchers, philosophers and cognitive scientists respectively. The answers to these questions depend on how one defines "intelligence" or "consciousness" and exactly which "machines" are under discussion.

Important propositions in the philosophy of AI include:

• Turing's "polite convention": If a machine acts as intelligently as a human being, then it is as intelligent as a human being.
• The Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it."
• Newell and Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."
• Searle's strong AI hypothesis: "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds."
• Hobbes' mechanism: "Reason is nothing but reckoning."
  
  Source: Wikipedia - Philosophy of artificial intelligence

Philosophy of space and time is the branch of philosophy concerned with the issues surrounding the ontology, epistemology, and character of space and time. While such ideas have been central to philosophy from its inception, the philosophy of space and time was both an inspiration for and a central aspect of early analytic philosophy. The subject focuses on a number of basic issues, including—but not limited to—whether or not time and space exist independently of the mind, whether they exist independently of one another, what accounts for time's apparently unidirectional flow, whether times other than the present moment exist, and questions about the nature of identity (particularly the nature of identity over time).

Source: Wikipedia - Philosophy of space and time

In general usage, complexity tends to be used to characterize something with many parts in intricate arrangement. The study of these complex linkages is the main goal of network theory and network science. In science there are at this time a number of approaches to characterizing complexity, many of which are reflected in this article. In a business context, complexity management is the methodology to minimize value-destroying complexity and efficiently control value-adding complexity in a cross-functional approach.

Definitions are often tied to the concept of a "system"—a set of parts or elements which have relationships among them differentiated from relationships with other elements outside the relational regime. Many definitions tend to postulate or assume that complexity expresses a condition of numerous elements in a system and numerous forms of relationships among the elements. At the same time, what is complex and what is simple is relative and changes with time.

Some definitions key on the question of the probability of encountering a given condition of a system once characteristics of the system are specified. Warren Weaver has posited that the complexity of a particular system is the degree of difficulty in predicting the properties of the system if the properties of the system's parts are given. In Weaver's view, complexity comes in two forms: disorganized complexity, and organized complexity. Weaver's paper has influenced contemporary thinking about complexity.

The approaches which embody concepts of systems, multiple elements, multiple relational regimes, and state spaces might be summarized as implying that complexity arises from the number of distinguishable relational regimes (and their associated state spaces) in a defined system.

Some definitions relate to the algorithmic basis for the expression of a complex phenomenon or model or mathematical expression, as is later set out herein.

Source: Wikipedia - Complexity

The Prandtl–Glauert singularity or P.G. singularity, also referred to as a vapor cone, shock collar, or shock egg,^[1] is the point at which a sudden drop in air pressure occurs, generally accepted as the cause of the visible condensation cloud that often surrounds an aircraft traveling at transonic speeds, though there remains some debate. It is an example of a mathematical singularity in aerodynamics.

One view of this phenomenon is that it exhibits the effect of compressibility and the so-called "N-wave". The N-wave is the time variant pressure profile seen by a static observer when a sonic compression wave passes. The overall three-dimensional shock wave is in the form of a cone with its apex at the supersonic aircraft. This wave follows the aircraft. The pressure profile of the wave is composed of a leading compression component (the initial upward stroke of the "N"), followed by a pressure descent forming a rarefaction of the air (the downward diagonal of the "N"), followed by a return to the normal ambient pressure (the final upward stroke of the "N"). The rarefaction may be thought of as the "rebounding" of the compression due to inertial effects.

Source: Wikipedia - Prandtl–Glauert singularity