machine

A machine is any device that uses energy to perform some activity. In common usage, the meaning is that of a device having parts that perform or assist in performing any type of work. A simple machine is a device that transforms the direction or magnitude of a force without consuming any energy. The word "machine" is derived from the Latin word machina.^[1]

Usage

Historically, a device required moving parts to be classified as a machine; however, the advent of electronics technology has led to the development of devices without moving parts that are considered machines—the computer being the most obvious example.^[1]

"Engines" are machines that convert heat or other forms of energy into mechanical energy. For example, in an internal combustion engine the expansion of gases caused by the heat from an exothermic chemical reaction results in a force being applied to a movable component, such as a piston or turbine blade.^[2]

Machines are ubiquitous in a wide variety of industrial, commercial, residential and transportation applications. Those employing hydraulics are especially useful in manufacturing and construction.

Types of machines and related components

Philosophy

Interest in machines and the way we describe them has grown as the result of several lines of inquiry:

(i) The application of mathematics to biology. The biologist and mathematician Robert Rosen has done some intriguing work in attempting to talk about the relationship between a living system on the one hand, and on the other any representation of that system in mathematical or scientific terms that scientists or thinkers develop. In Rosen's conception, all machines can be codified as systems of inferential entailment, that is to say, they can be fully captured in propositions or in material equivalents that logically relate. A classic case is that of a mathematical machine such as invented by Turing in 1936 which has subsequently received material realisation in the common computer. These are all inferentially entailed systems. Rosen laboured to establish the idea that all systems of inferential entailment rely on external causes at some point whereas living systems are causally closed. In this way Rosen attempted to set up a modeling relation between systems of causal entailment (living systems) and systems of inferential entailment(all mathematical or scientific attempts to model living systems). Rosen was adamant that the fact that living things exhibit causal closure, that is, do not require external cause for metabolism or repair (he called these "M-R systems") placed them in a unique category that could never be captured or represented by a formal system of inferential entailment. For criticism of Rosen's views, see below.

(ii) The application of philosophy to both machines and machine-like systems in the living world.

(a) The role of philosopher Immanuel Kant. Rosen's position is criticised by A.J.Wells ^[3] in his monograph "In Defence of Mechanism". Wells identifies Kant's Indirect realism as playing a strong role in Rosen's inability to 'know' of mechanisms in actual nature. Rosen makes explicit references to Kant and Kantian terminology but the net effect on his perception of machines is made clear when he says: "The causal relations manifested by a natural system provide the orderliness required of the ambience. Inferential entailment in a formal system is a way of providing the orderliness required of the self. The art of bringing the two into correspondence, through the establishing of a definite modeling relation between them is the articulation of the former within the latter; it is science itself"^[4]. It seems that a standard answer to the question as to why we can see machines in nature is to say that it has to do with our own subjectivity and accomplishments, and that we view the world via engineering or technological metaphors. This kind of answer has a Kantian ring about it. It is possible however to argue that the mechanisms detected in the living world (e.g. bat sonar) truly exist in an objective observer-independent way, though to make such a statement may imply a critique of modern assumptions about origins.

(b) The role of Alan Turing. The father of the modern computer in the 1930's described a machine (the Turing machine) which he derived from considering a human performing basic calculations where the human uses a pencil and a piece of paper. From this basic model he derived a mathematical and logical description of what a computer is (and the limits of what it can compute), and went on to use both this novel formulation of his, together with a trick Gödel and Georg Cantor had developed by which Turing could provide the answer to a mathematical challenge issued by David Hilbert in 1928. In the process, the issue of Artificial Intelligence is raised, especially how we as humans can detect such intelligence (the Turing Test). It should be noted however, that in Turing's work, and in the work of many in this area, there is an assumption about the nature of the human mind that is not stated. The Turing Test itself, simple and straightforward though it may appear to be, presupposes a certain philosophy of mind which people by and large are inclined to accept at face value viz that the human mind is like a computer. What this means is that machine models such as these for all their utility, are able to exert subtle influences over the way think about ourselves and the world. It is quite likely that Turing's language in his seminal 1936 paper where he modeled a human mind via a mathematical machine and where he said of the iterative condition: "The behaviour of the computer at any moment is determined by the symbols which he is observing, and his “state of mind” at that moment"^[5] actually led to the use of the phrase 'brain states' so common today . Such language is a reminder of the way in which machine thinking has been a lens through which organisms have been viewed, rightly or wrongly.

(iii) The role of machine logic in design arguments. The jury is still out as to whether it is permissible to describe the existence of biological machines using the language of human-designed machines. Some philosophers of science like Barbara Forrest & Paul R. Gross imply that such language cannot be used in a strong sense because that would suggest deliberate engineering design which they regard as impossible from a Neo-Darwinian perspective.^[6] Others like zoologist Richard Dawkins are quite happy to describe such systems as machines and he is even willing to talk about them as being designed, though he intends his readers to understand that the designer is in fact blind natural selection.^[7] The matter is discussed at length in "Interdisciplinary Dialogue"^[8].^{[clarification needed]} In the literature, Rube-Goldberg machine illustrations are used for and against design arguments depending on the assumptions made. The concept of Irreducible Complexity expressed by biochemist Michael Behe is an attempt to formalise a logic of machines. This concept has been strongly criticised by Neo-Darwinians, and equally strongly defended usually by those who have difficulty in accepting that the modern synthesis can account for all observation of design in nature.

(iv) Correspondence of logic between living and humanly engineered systems. Paleontologist Simon Conway Morris writes: "Not only is the integrity and integration of living systems quite astonishing, but attempts to employ machine-like analogies soon run into difficulties. To be sure we refer to motors, switches, transport mechanisms, fluid flow, pumps and electricity, but the reality is that organisms have a subtlety and efficiency far beyond any machine we can build"^[9]. This argument is countered, so some think, by examining the remarkable correspondence such living systems enjoy with formal systems, especially in the logic of operation. Engineers can understand and appreciate this perhaps more readily than most, and this leads to a consideration of block diagrams. When designing a system, engineers often produce a drawing that details the logic of what is proposed. This is done via a flowpath arrangement of boxes or blocks each of which is labeled with its contributing function. In essence, such a block diagram details an embodied principle(s) of operation. However, the biological literature does not often make use of block diagram logic to assist in the communication of research into living systems perhaps due to background assumptions regarding machines. Occasionally, however, one will read that "Just as springs and ratchets can store or release energy and rectify motion in physical systems, their analogs can perform similar functions in biological systems"^[10]. The line of argument pursued by the logic of correspondence is impressed not so much by differences of "subtlety and efficiency", but by similarities of logical operation between formal systems and their living counterparts (for more discussion on block diagram correspondence see ^[8]. This could provide a different basis for evaluating Dawkin's observation that "Whenever humans have a good idea, zoologists have grown accustomed to finding it anticipated in the animal kingdom. Examples...include echo-ranging (bats), electrolocation (platypus), the dam (beaver), parabolic reflector (limpets), the infrared, heat-seeking sensor (some snakes), the hypodermic syringe (wasps, snakes and scorpions), the harpoon (cnidarians) and jet propulsion (squids)"^[11].

(v) Machines and Brains. Philosopher Hilary Putnam in 1982 in a book titled "Reason, Truth and History" wrote about 'The Brain in a Vat', an idea which was immortalised in the film The Matrix. But can a computer perfectly read the 'thoughts' and 'desires' of the brain, so that if it desires a steak, or wishes to paint a picture, or thinks thoughts of love, the computer can detect this, "understand" it, and respond appropriately? There is an assumption in all this about what machines can do, and about what brains are doing when they are experiencing love or seeing the colour red in their imagination, or remembering the smell of burning rubber. If you assume a machine can have internal states that correspond to this, then additionally you have to assume that the human brain can be accessed by the machine. Furthermore, it may be the case that we can never actually know whether machines have consciousness or only simulating consciousness; and we might never know what our brains are really doing when we experiencing love or seeing the colour red in their imagination, or remembering the smell of burning rubber. We cannot assume that we could ever describe this in mathematical or engineering or scientific terms.

See also

• Automation
• Engineering
• History of technology
• Naturoid
• Technology

References

Further reading

• Oberg, Erik; Franklin D. Jones, Holbrook L. Horton, and Henry H. Ryffel (2000). ed. Christopher J. McCauley, Riccardo Heald, and Muhammed Iqbal Hussain. ed. Machinery's Handbook (26th edition ed.). New York: Industrial Press Inc.. ISBN 0-8311-2635-3.