Functionalism (philosophy of mind)

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Functionalism is a view in the theory of the mind. It states that mental states (beliefs, desires, being in pain, etc.) are constituted solely by their functional role – that is, they have causal relations to other mental states, numerous sensory inputs, and behavioral outputs. Functionalism developed largely as an alternative to the identity theory of mind and behaviorism.

Functionalism is a theoretical level between the physical implementation and behavioral output.^[2] Therefore, it is different from its predecessors of Cartesian dualism (advocating independent mental and physical substances) and Skinnerian behaviorism and physicalism (declaring only physical substances) because it is only concerned with the effective functions of the brain, through its organization or its "software programs".

Since mental states are identified by a functional role, they are said to be realized on multiple levels; in other words, they are able to be manifested in various systems, even perhaps computers, so long as the system performs the appropriate functions. While computers are physical devices with electronic substrate that perform computations on inputs to give outputs, so brains are physical devices with neural substrate that perform computations on inputs which produce behaviors.

Multiple realizability

An important part of some accounts of functionalism is the idea of multiple realizability. Since, according to standard functionalist theories, mental states are the corresponding functional role, mental states can be sufficiently explained without taking into account the underlying physical medium (e.g. the brain, neurons, etc.) that realizes such states; one need only take into account the higher-level functions in the cognitive system. Since mental states are not limited to a particular medium, they can be realized in multiple ways, including, theoretically, within non-biological systems, such as computers. In other words, a silicon-based machine could, in principle, have the same sort of mental life that a human being has, provided that its cognitive system realized the proper functional roles. Thus, mental states are individuated much like a valve; a valve can be made of plastic or metal or whatever material, as long as it performs the proper function (say, controlling the flow of liquid through a tube by blocking and unblocking its pathway).

However, there have been some functionalist theories that combine with the identity theory of mind, which deny multiple realizability. Such Functional Specification Theories (FSTs) (Levin, § 3.4), as they are called, were most notably developed by David Lewis^[3] and David Malet Armstrong.^[4] According to FSTs, mental states are the particular "realizers" of the functional role, not the functional role itself. The mental state of belief, for example, just is whatever brain or neurological process that realizes the appropriate belief function. Thus, unlike standard versions of functionalism (often called Functional State Identity Theories), FSTs do not allow for the multiple realizability of mental states, because the fact that mental states are realized by brain states is essential. What often drives this view is the belief that if we were to encounter an alien race with a cognitive system composed of significantly different material from humans' (e.g., silicon-based) but performed the same functions as human mental states (e.g., they tend to yell "Yowzas!" when poked with sharp objects, etc.) then we would say that their type of mental state is perhaps similar to ours, but too different to say it's the same. For some, this may be a disadvantage to FSTs. Indeed, one of Hilary Putnam's^[5][6] arguments for his version of functionalism relied on the intuition that such alien creatures would have the same mental states as humans do, and that the multiple realizability of standard functionalism makes it a better theory of mind.

Types

Machine-state functionalism

Artistic representation of a Turing machine.

The broad position of "functionalism" can be articulated in many different varieties. The first formulation of a functionalist theory of mind was put forth by Hilary Putnam^[5][6] in the 1960s. This formulation, which is now called machine-state functionalism, or just machine functionalism, was inspired by the analogies which Putnam and others noted between the mind and the theoretical "machines" or computers capable of computing any given algorithm which were developed by Alan Turing (called Turing machines). It should be noted that Putnam himself, by the mid-1970s, had begun questioning this position. The beginning of his opposition to machine-state functionalism can be read about in his Twin Earth Thought Experiment.

In non-technical terms, a Turing machine is not a physical object, but rather an abstract machine built upon a mathematical model. Typically, a Turing Machine has a horizontal tape divided into rectangular cells arranged from left to right. The tape itself is infinite in length, and each cell may contain a symbol. The symbols used for any given "machine" can vary. The machine has a read-write head that scans cells and moves in left and right directions. The action of the machine is determined by the symbol in the cell being scanned and a table of transition rules that serve as the machine's programming. Because of the infinite tape, a traditional Turing Machine has an infinite amount of time to compute any particular function or any number of functions. In the below example, each cell is either blank (B) or has a 1 written on it. These are the inputs to the machine. The possible outputs are:

• Halt: Do nothing.
• R: move one square to the right.
• L: move one square to the left.
• B: erase whatever is on the square.
• 1: erase whatever is on the square and print a '1.

An extremely simple example of a Turing machine which writes out the sequence '111' after scanning three blank squares and then stops as specified by the following machine table:

	State One	State Two	State Three
B	write 1; stay in state 1	write 1; stay in state 2	write 1; stay in state 3
1	go right; go to state 2	go right; go to state 3	[halt]

This table states that if the machine is in state one and scans a blank square (B), it will print a 1 and remain in state one. If it is in state one and reads a 1, it will move one square to the right and also go into state two. If it is in state two and reads a B, it will print a 1 and stay in state two. If it is in state two and reads a 1, it will move one square to the right and go into state three. If it is in state three and reads a B, it prints a 1 and remains in state three. Finally, if it is in state three and reads a 1, then it will stay in state three.

The essential point to consider here is the nature of the states of the Turing machine. Each state can be defined exclusively in terms of its relations to the other states as well as inputs and outputs. State one, for example, is simply the state in which the machine, if it reads a B, writes a 1 and stays in that state, and in which, if it reads a 1, it moves one square to the right and goes into a different state. This is the functional definition of state one; it is its causal role in the overall system. The details of how it accomplishes what it accomplishes and of its material constitution are completely irrelevant.

The above point is critical to an understanding of machine-state functionalism. Since Turing machines are not required to be physical systems, "anything capable of going through a succession of states in time can be a Turing machine".^[7] Because biological organisms “go through a succession of states in time”, any such organisms could also be equivalent to Turing machines.

According to machine-state functionalism, the nature of a mental state is just like the nature of the Turing machine states described above. If one can show the rational functioning and computing skills of these machines to be comparable to the rational functioning and computing skills of human beings, it follows that Turing machine behavior closely resembles that of human beings.^[8] Therefore, it is not a particular physical-chemical composition responsible for the particular machine or mental state, it is the programming rules which produce the effects that are responsible. To put it another way, any rational preference is due to the rules being followed, not to the specific material composition of the agent.

Psycho functionalism

A second form of functionalism is based on the rejection of behaviorist theories in psychology and their replacement with empirical cognitive models of the mind. This view is most closely associated with Jerry Fodor and Zenon Pylyshyn and has been labeled psychofunctionalism.

The fundamental idea of psychofunctionalism is that psychology is an irreducibly complex science and that the terms that we use to describe the entities and properties of the mind in our best psychological theories cannot be redefined in terms of simple behavioral dispositions, and further, that such a redefinition would not be desirable or salient were it achievable. Psychofunctionalists view psychology as employing the same sorts of irreducibly teleological or purposive explanations as the biological sciences. Thus, for example, the function or role of the heart is to pump blood, that of the kidney is to filter it and to maintain certain chemical balances and so on—this is what accounts for the purposes of scientific explanation and taxonomy. There may be an infinite variety of physical realizations for all of the mechanisms, but what is important is only their role in the overall biological theory. In an analogous manner, the role of mental states, such as belief and desire, is determined by the functional or causal role that is designated for them within our best scientific psychological theory. If some mental state which is postulated by folk psychology (e.g. hysteria) is determined not to have any fundamental role in cognitive psychological explanation, then that particular state may be considered not to exist . On the other hand, if it turns out that there are states which theoretical cognitive psychology posits as necessary for explanation of human behavior but which are not foreseen by ordinary folk psychological language, then these entities or states exist.

Analytic functionalism

A third form of functionalism is concerned with the meanings of theoretical terms in general. This view is most closely associated with David Lewis and is often referred to as analytic functionalism or conceptual functionalism. The basic idea of analytic functionalism is that theoretical terms are implicitly defined by the theories in whose formulation they occur and not by intrinsic properties of the phonemes they comprise. In the case of ordinary language terms, such as "belief", "desire", or "hunger", the idea is that such terms get their meanings from our common-sense "folk psychological" theories about them, but that such conceptualizations are not sufficient to withstand the rigor imposed by materialistic theories of reality and causality. Such terms are subject to conceptual analyses which take something like the following form:

Mental state M is the state that is preconceived by P and causes Q.

For example, the state of pain is caused by sitting on a tack and causes loud cries, and higher order mental states of anger and resentment directed at the careless person who left a tack lying around. These sorts of functional definitions in terms of causal roles are claimed to be analytic and a priori truths about the submental states and the (largely fictitious) propositional attitudes they describe. Hence, its proponents are known as analytic or conceptual functionalists. The essential difference between analytic and psychofunctionalism is that the latter emphasizes the importance of laboratory observation and experimentation in the determination of which mental state terms and concepts are genuine and which functional identifications may be considered to be genuinely contingent and a posteriori identities. The former, on the other hand, claims that such identities are necessary and not subject to empirical scientific investigation.

Homuncular functionalism

Homuncular functionalism was developed largely by Daniel Dennett and has been advocated by William Lycan. It arose in response to the challenges that Ned Block's China Brain (a.k.a. Chinese nation) and John Searle's Chinese room thought experiments presented for the more traditional forms of functionalism (see below under "Criticism"). In attempting to overcome the conceptual difficulties that arose from the idea of a nation full of Chinese people wired together, each person working as a single neuron to produce in the wired-together whole the functional mental states of an individual mind, many functionalists simply bit the bullet, so to speak, and argued that such a Chinese nation would indeed possess all of the qualitative and intentional properties of a mind; i.e. it would become a sort of systemic or collective mind with propositional attitudes and other mental characteristics. Whatever the worth of this latter hypothesis, it was immediately objected that it entailed an unacceptable sort of mind-mind supervenience: the systemic mind which somehow emerged at the higher-level must necessarily supervene on the individual minds of each individual member of the Chinese nation, to stick to Block's formulation. But this would seem to put into serious doubt, if not directly contradict, the fundamental idea of the supervenience thesis: there can be no change in the mental realm without some change in the underlying physical substratum. This can be easily seen if we label the set of mental facts that occur at the higher-level M1 and the set of mental facts that occur at the lower-level M2. Given the transitivity of supervenience, if M1 supervenes on M2, and M2 supervenes on P (physical base), then M1 and M2 both supervene on P, even though they are (allegedly) totally different sets of mental facts.

Since mind-mind supervenience seemed to have become acceptable in functionalist circles, it seemed to some that the only way to resolve the puzzle was to postulate the existence of an entire hierarchical series of mind levels (analogous to homunculi) which became less and less sophisticated in terms of functional organization and physical composition all the way down to the level of the physico-mechanical neuron or group of neurons. The homunculi at each level, on this view, have authentic mental properties but become simpler and less intelligent as one works one's way down the hierarchy.

Mechanistic functionalism

Mechanistic functionalism, originally formulated and defended by Gualtiero Piccinini^[9] and Carl Gillett^[10][11] independently, augments previous functionalist accounts of mental states by maintaining that any psychological explanation must be rendered in mechanistic terms. That is, instead of mental states receiving a purely functional explanation in terms of their relations to other mental states, like those listed above, functions are seen as playing only a part—the other part being played by structures— of the explanation of a given mental state.

A mechanistic explanation^[12] involves decomposing a given system, in this case a mental system, into its component physical parts, their activities or functions, and their combined organizational relations.^[9] On this account the mind remains a functional system, but one that is understood mechanistically. This account remains a sort of functionalism because functional relations are still essential to mental states, but it is mechanistic because the functional relations are always manifestations of concrete structures—albeit structures understood at a certain level of abstraction. Functions are individuated and explained either in terms of the contributions they make to the given system^[13] or in teleological terms. If the functions are understood in teleological terms, then they may be characterized either etiologically or non-etiologically.^[14]

Mechanistic functionalism leads functionalism away from the traditional functionalist autonomy of psychology from neuroscience and towards integrating psychology and neuroscience.^[15] By providing an applicable framework for merging traditional psychological models with neurological data, mechanistic functionalism may be understood as reconciling the functionalist theory of mind with neurological accounts of how the brain actually works. This is due to the fact that mechanistic explanations of function attempt to provide an account of how functional states (mental states) are physically realized through neurological mechanisms.

Physicalism

There is much confusion about the sort of relationship that is claimed to exist (or not exist) between the general thesis of functionalism and physicalism. It has often been claimed that functionalism somehow "disproves" or falsifies physicalism tout court (i.e. without further explanation or description). On the other hand, most philosophers of mind who are functionalists claim to be physicalists—indeed, some of them, such as David Lewis, have claimed to be strict reductionist-type physicalists.

Functionalism is fundamentally what Ned Block has called a broadly metaphysical thesis as opposed to a narrowly ontological one. That is, functionalism is not so much concerned with what there is than with what it is that characterizes a certain type of mental state, e.g. pain, as the type of state that it is. Previous attempts to answer the mind-body problem have all tried to resolve it by answering both questions: dualism says there are two substances and that mental states are characterized by their immateriality; behaviorism claimed that there was one substance and that mental states were behavioral disposition; physicalism asserted the existence of just one substance and characterized the mental states as physical states (as in "pain = C-fiber firings").

On this understanding, type physicalism can be seen as incompatible with functionalism, since it claims that what characterizes mental states (e.g. pain) is that they are physical in nature, while functionalism says that what characterizes pain is its functional/causal role and its relationship with yelling "ouch", etc. However, any weaker sort of physicalism which makes the simple ontological claim that everything that exists is made up of physical matter is perfectly compatible with functionalism. Moreover, most functionalists who are physicalists require that the properties that are quantified over in functional definitions be physical properties. Hence, they are physicalists, even though the general thesis of functionalism itself does not commit them to being so.

In the case of David Lewis, there is a distinction in the concepts of "having pain" (a rigid designator true of the same things in all possible worlds) and just "pain" (a non-rigid designator). Pain, for Lewis, stands for something like the definite description "the state with the causal role x". The referent of the description in humans is a type of brain state to be determined by science. The referent among silicon-based life forms is something else. The referent of the description among angels is some immaterial, non-physical state. For Lewis, therefore, local type-physical reductions are possible and compatible with conceptual functionalism. (See also Lewis's mad pain and Martian pain.) There seems to be some confusion between types and tokens that needs to be cleared up in the functionalist analysis.

Criticism

China brain

Ned Block^[16] argues against the functionalist proposal of multiple realizability, where hardware implementation is irrelevant because only the functional level is important. The "China brain" or "Chinese nation" thought experiment involves supposing that the entire nation of China systematically organizes itself to operate just like a brain, with each individual acting as a neuron. (The tremendous difference in speed of operation of each unit is not addressed.). According to functionalism, so long as the people are performing the proper functional roles, with the proper causal relations between inputs and outputs, the system will be a real mind, with mental states, consciousness, and so on. However, Block argues, this is patently absurd, so there must be something wrong with the thesis of functionalism since it would allow this to be a legitimate description of a mind.
Some functionalists believe China would have qualia but that due to the size it is impossible to imagine China being conscious.^[17] Indeed, it may be the case that we are constrained by our theory of mind^[18] and will never be able to understand what Chinese-nation consciousness is like. Therefore, if functionalism is true either qualia will exist across all hardware or will not exist at all but are illusory.^[19]

The Chinese room

The Chinese room argument by John Searle^[20] is a direct attack on the claim that thought can be represented as a set of functions. The thought experiment asserts that it is possible to mimic intelligent action without any interpretation or understanding through the use of a purely functional system. In short, Searle describes a person who only speaks English who is in a room with only Chinese symbols in baskets and a rule book in English for moving the symbols around. The person is then ordered by people outside of the room to follow the rule book for sending certain symbols out of the room when given certain symbols. Further suppose that the people outside of the room are Chinese speakers and are communicating with the person inside via the Chinese symbols. According to Searle, it would be absurd to claim that the English speaker inside knows Chinese simply based on these syntactic processes. This thought experiment attempts to show that systems which operate merely on syntactic processes (inputs and outputs, based on algorithms) cannot realize any semantics (meaning) or intentionality (aboutness). Thus, Searle attacks the idea that thought can be equated with following a set of syntactic rules; that is, functionalism is an insufficient theory of the mind.
As noted above, in connection with Block's Chinese nation, many functionalists responded to Searle's thought experiment by suggesting that there was a form of mental activity going on at a higher level than the man in the Chinese room could comprehend (the so-called "system reply"); that is, the system does know Chinese. Of course, Searle responds that there is nothing more than syntax going on at the higher-level as well, so this reply is subject to the same initial problems. Furthermore, Searle suggests the man in the room could simply memorize the rules and symbol relations. Again, though he would convincingly mimic communication, he would be aware only of the symbols and rules, not of the meaning behind them.

Inverted spectrum

Another main criticism of functionalism is the inverted spectrum or inverted qualia scenario, most specifically proposed as an objection to functionalism by Ned Block.^[16][21] This thought experiment involves supposing that there is a person, call her Jane, that is born with a condition which makes her see the opposite spectrum of light that is normally perceived. Unlike normal people, Jane sees the color violet as yellow, orange as blue, and so forth. So, suppose, for example, that you and Jane are looking at the same orange. While you perceive the fruit as colored orange, Jane sees it as colored blue. However, when asked what color the piece of fruit is, both you and Jane will report "orange". In fact, one can see that all of your behavioral as well as functional relations to colors will be the same. Jane will, for example, properly obey traffic signs just as any other person would, even though this involves the color perception. Therefore, the argument goes, since there can be two people who are functionally identical, yet have different mental states (differing in their qualitative or phenomenological aspects), functionalism is not robust enough to explain individual differences in qualia.^[22]

David Chalmers tries to show^[23] that even though mental content cannot be fully accounted for in functional terms, there is nevertheless a nomological correlation between mental states and functional states in this world. A silicon-based robot, for example, whose functional profile matched our own, would have to be fully conscious. His argument for this claim takes the form of a reductio ad absurdum. The general idea is that since it would be very unlikely for a conscious human being to experience a change in its qualia which it utterly fails to notice, mental content and functional profile appear to be inextricably bound together, at least in the human case. If the subject's qualia were to change, we would expect the subject to notice, and therefore his functional profile to follow suit. A similar argument is applied to the notion of absent qualia. In this case, Chalmers argues that it would be very unlikely for a subject to experience a fading of his qualia which he fails to notice and respond to. This, coupled with the independent assertion that a conscious being's functional profile just could be maintained, irrespective of its experiential state, leads to the conclusion that the subject of these experiments would remain fully conscious. The problem with this argument, however, as Brian G. Crabb (2005) has observed, is that it begs the central question: How could Chalmers know that functional profile can be preserved, for example while the conscious subject's brain is being supplanted with a silicon substitute, unless he already assumes that the subject's possibly changing qualia would not be a determining factor? And while changing or fading qualia in a conscious subject might force changes in its functional profile, this tells us nothing about the case of a permanently inverted or unconscious robot. A subject with inverted qualia from birth would have nothing to notice or adjust to. Similarly, an unconscious functional simulacrum of ourselves (a zombie) would have no experiential changes to notice or adjust to. Consequently, Crabb argues, Chalmers' "fading qualia" and "dancing qualia" arguments fail to establish that cases of permanently inverted or absent qualia are nomologically impossible.

A related critique of the inverted spectrum argument is that it assumes that mental states (differing in their qualitative or phenomenological aspects) can be independent of the functional relations in the brain. Thus, it begs the question of functional mental states: its assumption denies the possibility of functionalism itself, without offering any independent justification for doing so. (Functionalism says that mental states are produced by the functional relations in the brain.) This same type of problem—that there is no argument, just an antithetical assumption at their base—can also be said of both the Chinese room and the Chinese nation arguments. Notice, however, that Crabb's response to Chalmers does not commit this fallacy: His point is the more restricted observation that even if inverted or absent qualia turn out to be nomologically impossible, and it is perfectly possible that we might subsequently discover this fact by other means, Chalmers' argument fails to demonstrate that they are impossible.

Twin Earth

The Twin Earth thought experiment, introduced by Hilary Putnam,^[24] is responsible for one of the main arguments used against functionalism, although it was originally intended as an argument against semantic internalism. The thought experiment is simple and runs as follows. Imagine a Twin Earth which is identical to Earth in every way but one: water does not have the chemical structure H₂O, but rather some other structure, say XYZ. It is critical, however, to note that XYZ on Twin Earth is still called "water" and exhibits all the same macro-level properties that H₂O exhibits on Earth (i.e., XYZ is also a clear drinkable liquid that is in lakes, rivers, and so on). Since these worlds are identical in every way except in the underlying chemical structure of water, you and your Twin Earth doppelgänger see exactly the same things, meet exactly the same people, have exactly the same jobs, behave exactly the same way, and so on. In other words, since you share the same inputs, outputs, and relations between other mental states, you are functional duplicates. So, for example, you both believe that water is wet. However, the content of your mental state of believing that water is wet differs from your duplicate's because your belief is of H₂O, while your duplicate's is of XYZ. Therefore, so the argument goes, since two people can be functionally identical, yet have different mental states, functionalism cannot sufficiently account for all mental states.

Most defenders of functionalism initially responded to this argument by attempting to maintain a sharp distinction between internal and external content. The internal contents of propositional attitudes, for example, would consist exclusively in those aspects of them which have no relation with the external world and which bear the necessary functional/causal properties that allow for relations with other internal mental states. Since no one has yet been able to formulate a clear basis or justification for the existence of such a distinction in mental contents, however, this idea has generally been abandoned in favor of externalist causal theories of mental contents (also known as informational semantics). Such a position is represented, for example, by Jerry Fodor's account of an "asymmetric causal theory" of mental content. This view simply entails the modification of functionalism to include within its scope a very broad interpretation of input and outputs to include the objects that are the causes of mental representations in the external world.

The twin earth argument hinges on the assumption that experience with an imitation water would cause a different mental state than experience with natural water. However, since no one would notice the difference between the two waters, this assumption is likely false. Further, this basic assumption is directly antithetical to functionalism; and, thereby, the twin earth argument does not constitute a genuine argument: as this assumption entails a flat denial of functionalism itself (which would say that the two waters would not produce different mental states, because the functional relationships would remain unchanged).

Meaning holism

Another common criticism of functionalism is that it implies a radical form of semantic holism. Block and Fodor^[21] referred to this as the damn/darn problem. The difference between saying "damn" or "darn" when one smashes one's finger with a hammer can be mentally significant. But since these outputs are, according to functionalism, related to many (if not all) internal mental states, two people who experience the same pain and react with different outputs must share little (perhaps nothing) in common in any of their mental states. But this is counter-intuitive; it seems clear that two people share something significant in their mental states of being in pain if they both smash their finger with a hammer, whether or not they utter the same word when they cry out in pain.

Another possible solution to this problem is to adopt a moderate (or molecularist) form of holism. But even if this succeeds in the case of pain, in the case of beliefs and meaning, it faces the difficulty of formulating a distinction between relevant and non-relevant contents (which can be difficult to do without invoking an analytic-synthetic distinction, as many seek to avoid).

Triviality arguments

According to Ned Block, if functionalism is to avoid the chauvinism of type-physicalism, it becomes overly liberal in "ascribing mental properties to things that do not in fact have them".^[16] As an example, he proposes that the economy of Bolivia might be organized such that the economic states, inputs, and outputs would be isomorphic to a person under some bizarre mapping from mental to economic variables.^[16]

Hilary Putnam,^[25] John Searle,^[26] and others^[27][28] have offered further arguments that functionalism is trivial, i.e. that the internal structures functionalism tries to discuss turn out to be present everywhere, so that either functionalism turns out to reduce to behaviorism, or to complete triviality and therefore a form of panpsychism. These arguments typically use the assumption that physics leads to a progression of unique states, and that functionalist realization is present whenever there is a mapping from the proposed set of mental states to physical states of the system. Given that the states of a physical system are always at least slightly unique, such a mapping will always exist, so any system is a mind. Formulations of functionalism which stipulate absolute requirements on interaction with external objects (external to the functional account, meaning not defined functionally) are reduced to behaviorism instead of absolute triviality, because the input-output behavior is still required.

Peter Godfrey-Smith has argued further^[29] that such formulations can still be reduced to triviality if they accept a somewhat innocent-seeming additional assumption. The assumption is that adding a transducer layer, that is, an input-output system, to an object should not change whether that object has mental states. The transducer layer is restricted to producing behavior according to a simple mapping, such as a lookup table, from inputs to actions on the system, and from the state of the system to outputs. However, since the system will be in unique states at each moment and at each possible input, such a mapping will always exist so there will be a transducer layer which will produce whatever physical behavior is desired.

Godfrey-Smith believes that these problems can be addressed using causality, but that it may be necessary to posit a continuum between objects being minds and not being minds rather than an absolute distinction. Furthermore, constraining the mappings seems to require either consideration of the external behavior as in behaviorism, or discussion of the internal structure of the realization as in identity theory; and though multiple realizability does not seem to be lost, the functionalist claim of the autonomy of high-level functional description becomes questionable.^[29]

Anapoiesis

The general theory of adaptive biological systems, named practopoiesis (meaning creation of actions), has been used to derive a theory that explains mental operations as an adaptive process. Much like species adapt through evolution and an organism adapts through development, the theory of anapoiesis (meaning re-creation) proposes that a thought is a process of adaptation to the immediate environment. This is performed by fast physiological machinery that can operate within a few 100s of milliseconds and relies on the mechanisms of neural adaptation. A key difference between anapoietic approach and the functional approach is that for anapoietic process much of the information needed for the mental operations is located outside the organism. If mental operations are an adaptive process, they do not juggle symbols internally (like a computer) but make guesses of what changes should be made to the nervous system and then test them against the environment.

The mechanisms of anapoiesis offer a solution to the problem of the Chinese Room posed by John Searle.

Philosophy of artificial intelligence

From Wikipedia, the free encyclopedia

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

• Can a machine act intelligently? Can it solve any problem that a person would solve by thinking?
• Are human intelligence and machine intelligence the same? Is the human brain essentially a computer?
• Can a machine have a mind, mental states, and consciousness in the same way that a human being can? Can it feel how things are?

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

Important propositions in the philosophy of AI include:

• Turing's "polite convention": If a machine behaves as intelligently as a human being, then it is as intelligent as a human being.^[2]
• 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."^[3]
• Newell and Simon's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."^[4]
• 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."^[5]
• Hobbes' mechanism: "For 'reason' ... is nothing but 'reckoning,' that is adding and subtracting, of the consequences of general names agreed upon for the 'marking' and 'signifying' of our thoughts..."^[6]

Can a machine display general intelligence?

Is it possible to create a machine that can solve all the problems humans solve using their intelligence? This question defines the scope of what machines will be able to do in the future and guides the direction of AI research. It only concerns the behavior of machines and ignores the issues of interest to psychologists, cognitive scientists and philosophers; to answer this question, it does not matter whether a machine is really thinking (as a person thinks) or is just acting like it is thinking.^[7]
The basic position of most AI researchers is summed up in this statement, which appeared in the proposal for the Dartmouth workshop of 1956:

• Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.^[3]

Arguments against the basic premise must show that building a working AI system is impossible, because there is some practical limit to the abilities of computers or that there is some special quality of the human mind that is necessary for thinking and yet cannot be duplicated by a machine (or by the methods of current AI research). Arguments in favor of the basic premise must show that such a system is possible.

The first step to answering the question is to clearly define "intelligence".

Intelligence

The "standard interpretation" of the Turing test.^[8]

Turing test

Alan Turing^[9] reduced the problem of defining intelligence to a simple question about conversation. He suggests that: if a machine can answer any question put to it, using the same words that an ordinary person would, then we may call that machine intelligent. A modern version of his experimental design would use an online chat room, where one of the participants is a real person and one of the participants is a computer program. The program passes the test if no one can tell which of the two participants is human.^[2] Turing notes that no one (except philosophers) ever asks the question "can people think?" He writes "instead of arguing continually over this point, it is usual to have a polite convention that everyone thinks".^[10] Turing's test extends this polite convention to machines:

• If a machine acts as intelligently as human being, then it is as intelligent as a human being.

One criticism of the Turing test is that it is explicitly anthropomorphic^{[citation needed]}. If our ultimate goal is to create machines that are more intelligent than people, why should we insist that our machines must closely resemble people?^{[This quote needs a citation]} Russell and Norvig write that "aeronautical engineering texts do not define the goal of their field as 'making machines that fly so exactly like pigeons that they can fool other pigeons'".^[11]

Intelligent agent definition

Simple reflex agent

Recent A.I. research defines intelligence in terms of intelligent agents. An "agent" is something which perceives and acts in an environment. A "performance measure" defines what counts as success for the agent.^[12]

• If an agent acts so as to maximize the expected value of a performance measure based on past experience and knowledge then it is intelligent.^[13]

Definitions like this one try to capture the essence of intelligence. They have the advantage that, unlike the Turing test, they do not also test for human traits that we^[who?] may not want to consider intelligent, like the ability to be insulted or the temptation to lie^{[dubious – discuss]}. They have the disadvantage that they fail to make the commonsense^{[when defined as?]} differentiation between "things that think" and "things that do not". By this definition, even a thermostat has a rudimentary intelligence.^[14]

Arguments that a machine can display general intelligence

The brain can be simulated

An MRI scan of a normal adult human brain

Hubert Dreyfus describes this argument as claiming that "if the nervous system obeys the laws of physics and chemistry, which we have every reason to suppose it does, then .... we ... ought to be able to reproduce the behavior of the nervous system with some physical device".^[15] This argument, first introduced as early as 1943^[16] and vividly described by Hans Moravec in 1988,^[17] is now associated with futurist Ray Kurzweil, who estimates that computer power will be sufficient for a complete brain simulation by the year 2029.^[18] A non-real-time simulation of a thalamocortical model that has the size of the human brain (10¹¹ neurons) was performed in 2005^[19] and it took 50 days to simulate 1 second of brain dynamics on a cluster of 27 processors.

Few^[quantify] disagree that a brain simulation is possible in theory, even critics of AI such as Hubert Dreyfus and John Searle.^[20] However, Searle points out that, in principle, anything can be simulated by a computer; thus, bringing the definition to its breaking point leads to the conclusion that any process at all can technically be considered "computation". "What we wanted to know is what distinguishes the mind from thermostats and livers," he writes.^[21] Thus, merely mimicking the functioning of a brain would in itself be an admission of ignorance regarding intelligence and the nature of the mind^{[citation needed]}.

Human thinking is symbol processing

In 1963, Allen Newell and Herbert A. Simon proposed that "symbol manipulation" was the essence of both human and machine intelligence. They wrote:

• A physical symbol system has the necessary and sufficient means of general intelligent action.^[4]

This claim is very strong: it implies both that human thinking is a kind of symbol manipulation (because a symbol system is necessary for intelligence) and that machines can be intelligent (because a symbol system is sufficient for intelligence).^[22] Another version of this position was described by philosopher Hubert Dreyfus, who called it "the psychological assumption":

• The mind can be viewed as a device operating on bits of information according to formal rules.^[23]

A distinction is usually made^{[by whom?]} between the kind of high level symbols that directly correspond with objects in the world, such as and and the more complex "symbols" that are present in a machine like a neural network. Early research into AI, called "good old fashioned artificial intelligence" (GOFAI) by John Haugeland, focused on these kind of high level symbols.^[24]

Arguments against symbol processing

These arguments show that human thinking does not consist (solely) of high level symbol manipulation. They do not show that artificial intelligence is impossible, only that more than symbol processing is required.

Gödelian anti-mechanist arguments

In 1931, Kurt Gödel proved with an incompleteness theorem that it is always possible to construct a "Gödel statement" that a given consistent formal system of logic (such as a high-level symbol manipulation program) could not prove. Despite being a true statement, the constructed Gödel statement is unprovable in the given system. (The truth of the constructed Gödel statement is contingent on the consistency of the given system; applying the same process to a subtly inconsistent system will appear to succeed, but will actually yield a false "Gödel statement" instead.) More speculatively, Gödel conjectured that the human mind can correctly eventually determine the truth or falsity of any well-grounded mathematical statement (including any possible Gödel statement), and that therefore the human mind's power is not reducible to a mechanism.^[25] Philosopher John Lucas (since 1961) and Roger Penrose (since 1989) have championed this philosophical anti-mechanist argument.^[26] Gödelian anti-mechanist arguments tend to rely on the innocuous-seeming claim that a system of human mathematicians (or some idealization of human mathematicians) is both consistent (completely free of error) and believes fully in its own consistency (and can make all logical inferences that follow from its own consistency, including belief in its Gödel statement)^{[citation needed]}. This is provably impossible for a Turing machine^{[clarification needed]} (and, by an informal extension, any known type of mechanical computer) to do; therefore, the Gödelian concludes that human reasoning is too powerful to be captured in a machine^{[dubious – discuss]}.
However, the modern consensus in the scientific and mathematical community is that actual human reasoning is inconsistent; that any consistent "idealized version" H of human reasoning would logically be forced to adopt a healthy but counter-intuitive open-minded skepticism about the consistency of H (otherwise H is provably inconsistent); and that Gödel's theorems do not lead to any valid argument that humans have mathematical reasoning capabilities beyond what a machine could ever duplicate.^[27][28][29] This consensus that Gödelian anti-mechanist arguments are doomed to failure is laid out strongly in Artificial Intelligence: "any attempt to utilize (Gödel's incompleteness results) to attack the computationalist thesis is bound to be illegitimate, since these results are quite consistent with the computationalist thesis."^[30]

More pragmatically, Russell and Norvig note that Gödel's argument only applies to what can theoretically be proved, given an infinite amount of memory and time. In practice, real machines (including humans) have finite resources and will have difficulty proving many theorems. It is not necessary to prove everything in order to be intelligent^{[when defined as?]}.^[31]

Less formally, Douglas Hofstadter, in his Pulitzer prize winning book Gödel, Escher, Bach: An Eternal Golden Braid, states that these "Gödel-statements" always refer to the system itself, drawing an analogy to the way the Epimenides paradox uses statements that refer to themselves, such as "this statement is false" or "I am lying".^[32] But, of course, the Epimenides paradox applies to anything that makes statements, whether they are machines or humans, even Lucas himself. Consider:

• Lucas can't assert the truth of this statement.^[33]

This statement is true but cannot be asserted by Lucas. This shows that Lucas himself is subject to the same limits that he describes for machines, as are all people, and so Lucas's argument is pointless.^[34]
After concluding that human reasoning is non-computable, Penrose went on to controversially speculate that some kind of hypothetical non-computable processes involving the collapse of quantum mechanical states give humans a special advantage over existing computers. Existing quantum computers are only capable of reducing the complexity of Turing computable tasks and are still restricted to tasks within the scope of Turing machines.^{[citation needed][clarification needed]}. By Penrose and Lucas's arguments, existing quantum computers are not sufficient, so Penrose seeks for some other process involving new physics, for instance quantum gravity which might manifest new physics at the scale of the Planck mass via spontaneous quantum collapse of the wave function. These states, he suggested, occur both within neurons and also spanning more than one neuron.^[35] However, other scientists point out that there is no plausible organic mechanism in the brain for harnessing any sort of quantum computation, and furthermore that the timescale of quantum decoherence seems too fast to influence neuron firing.^[36]

Dreyfus: the primacy of unconscious skills

Hubert Dreyfus argued that human intelligence and expertise depended primarily on unconscious instincts rather than conscious symbolic manipulation, and argued that these unconscious skills would never be captured in formal rules.^[37]
Dreyfus's argument had been anticipated by Turing in his 1950 paper Computing machinery and intelligence, where he had classified this as the "argument from the informality of behavior."^[38] Turing argued in response that, just because we do not know the rules that govern a complex behavior, this does not mean that no such rules exist. He wrote: "we cannot so easily convince ourselves of the absence of complete laws of behaviour ... The only way we know of for finding such laws is scientific observation, and we certainly know of no circumstances under which we could say, 'We have searched enough. There are no such laws.'"^[39]

Russell and Norvig point out that, in the years since Dreyfus published his critique, progress has been made towards discovering the "rules" that govern unconscious reasoning.^[40] The situated movement in robotics research attempts to capture our unconscious skills at perception and attention.^[41]  Computational intelligence paradigms, such as neural nets, evolutionary algorithms and so on are mostly directed at simulated unconscious reasoning and learning. Statistical approaches to AI can make predictions which approach the accuracy of human intuitive guesses. Research into commonsense knowledge has focused on reproducing the "background" or context of knowledge. In fact, AI research in general has moved away from high level symbol manipulation or "GOFAI", towards new models that are intended to capture more of our unconscious reasoning. Historian and AI researcher Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."^[42]

Can a machine have a mind, consciousness, and mental states?

This is a philosophical question, related to the problem of other minds and the hard problem of consciousness. The question revolves around a position defined by John Searle as "strong AI":

• A physical symbol system can have a mind and mental states.^[5]

Searle distinguished this position from what he called "weak AI":

• A physical symbol system can act intelligently.^[5]

Searle introduced the terms to isolate strong AI from weak AI so he could focus on what he thought was the more interesting and debatable issue. He argued that even if we assume that we had a computer program that acted exactly like a human mind, there would still be a difficult philosophical question that needed to be answered.^[5]

Neither of Searle's two positions are of great concern to AI research, since they do not directly answer the question "can a machine display general intelligence?" (unless it can also be shown that consciousness is necessary for intelligence). Turing wrote "I do not wish to give the impression that I think there is no mystery about consciousness… [b]ut I do not think these mysteries necessarily need to be solved before we can answer the question [of whether machines can think]."^[43] Russell and Norvig agree: "Most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis."^[44]

There are a few researchers who believe that consciousness is an essential element in intelligence, such as Igor Aleksander, Stan Franklin, Ron Sun, and Pentti Haikonen, although their definition of "consciousness" strays very close to "intelligence." (See artificial consciousness.)

Before we can answer this question, we must be clear what we mean by "minds", "mental states" and "consciousness".

Consciousness, minds, mental states, meaning

The words "mind" and "consciousness" are used by different communities in different ways. Some new age thinkers, for example, use the word "consciousness" to describe something similar to Bergson's "élan vital": an invisible, energetic fluid that permeates life and especially the mind.  Science fiction writers use the word to describe some essential property that makes us human: a machine or alien that is "conscious" will be presented as a fully human character, with intelligence, desires, will, insight, pride and so on. (Science fiction writers also use the words "sentience", "sapience," "self-awareness" or "ghost" - as in the Ghost in the Shell manga and anime series - to describe this essential human property). For others^[who?], the words "mind" or "consciousness" are used as a kind of secular synonym for the soul.

For philosophers, neuroscientists and cognitive scientists, the words are used in a way that is both more precise and more mundane: they refer to the familiar, everyday experience of having a "thought in your head", like a perception, a dream, an intention or a plan, and to the way we know something, or mean something or understand something^{[citation needed]}. "It's not hard to give a commonsense definition of consciousness" observes philosopher John Searle.^[45] What is mysterious and fascinating is not so much what it is but how it is: how does a lump of fatty tissue and electricity give rise to this (familiar) experience of perceiving, meaning or thinking?

Philosophers call this the hard problem of consciousness. It is the latest version of a classic problem in the philosophy of mind called the "mind-body problem."^[46] A related problem is the problem of meaning or understanding (which philosophers call "intentionality"): what is the connection between our thoughts and what we are thinking about (i.e. objects and situations out in the world)? A third issue is the problem of experience (or "phenomenology"): If two people see the same thing, do they have the same experience? Or are there things "inside their head" (called "qualia") that can be different from person to person?^[47]

Neurobiologists believe all these problems will be solved as we begin to identify the neural correlates of consciousness: the actual relationship between the machinery in our heads and its collective properties; such as the mind, experience and understanding. Some of the harshest critics of artificial intelligence agree that the brain is just a machine, and that consciousness and intelligence are the result of physical processes in the brain.^[48] The difficult philosophical question is this: can a computer program, running on a digital machine that shuffles the binary digits of zero and one, duplicate the ability of the neurons to create minds, with mental states (like understanding or perceiving), and ultimately, the experience of consciousness?

Arguments that a computer cannot have a mind and mental states

Searle's Chinese room

John Searle asks us to consider a thought experiment: suppose we have written a computer program that passes the Turing test and demonstrates "general intelligent action." Suppose, specifically that the program can converse in fluent Chinese. Write the program on 3x5 cards and give them to an ordinary person who does not speak Chinese. Lock the person into a room and have him follow the instructions on the cards. He will copy out Chinese characters and pass them in and out of the room through a slot. From the outside, it will appear that the Chinese room contains a fully intelligent person who speaks Chinese. The question is this: is there anyone (or anything) in the room that understands Chinese? That is, is there anything that has the mental state of understanding, or which has conscious awareness of what is being discussed in Chinese? The man is clearly not aware. The room cannot be aware. The cards certainly aren't aware. Searle concludes that the Chinese room, or any other physical symbol system, cannot have a mind.^[49]
Searle goes on to argue that actual mental states and consciousness require (yet to be described) "actual physical-chemical properties of actual human brains."^[50] He argues there are special "causal properties" of brains and neurons that gives rise to minds: in his words "brains cause minds."^[51]

Related arguments: Leibniz' mill, Davis's telephone exchange, Block's Chinese nation and Blockhead

Gottfried Leibniz made essentially the same argument as Searle in 1714, using the thought experiment of expanding the brain until it was the size of a mill.^[52] In 1974, Lawrence Davis imagined duplicating the brain using telephone lines and offices staffed by people, and in 1978 Ned Block envisioned the entire population of China involved in such a brain simulation. This thought experiment is called "the Chinese Nation" or "the Chinese Gym".^[53] Ned Block also proposed his Blockhead argument, which is a version of the Chinese room in which the program has been re-factored into a simple set of rules of the form "see this, do that", removing all mystery from the program.

Responses to the Chinese room

Responses to the Chinese room emphasize several different points.

• The systems reply and the virtual mind reply:^[54] This reply argues that the system, including the man, the program, the room, and the cards, is what understands Chinese. Searle claims that the man in the room is the only thing which could possibly "have a mind" or "understand", but others disagree, arguing that it is possible for there to be two minds in the same physical place, similar to the way a computer can simultaneously "be" two machines at once: one physical (like a Macintosh) and one "virtual" (like a word processor).
• Speed, power and complexity replies:^[55] Several critics point out that the man in the room would probably take millions of years to respond to a simple question, and would require "filing cabinets" of astronomical proportions. This brings the clarity of Searle's intuition into doubt.
• Robot reply:^[56] To truly understand, some believe the Chinese Room needs eyes and hands. Hans Moravec writes: 'If we could graft a robot to a reasoning program, we wouldn't need a person to provide the meaning anymore: it would come from the physical world."^[57]
• Brain simulator reply:^[58] What if the program simulates the sequence of nerve firings at the synapses of an actual brain of an actual Chinese speaker? The man in the room would be simulating an actual brain. This is a variation on the "systems reply" that appears more plausible because "the system" now clearly operates like a human brain, which strengthens the intuition that there is something besides the man in the room that could understand Chinese.
• Other minds reply and the epiphenomena reply:^[59] Several people have noted that Searle's argument is just a version of the problem of other minds, applied to machines. Since it is difficult to decide if people are "actually" thinking, we should not be surprised that it is difficult to answer the same question about machines.

A related question is whether "consciousness" (as Searle understands it) exists. Searle argues that the experience of consciousness can't be detected by examining the behavior of a machine, a human being or any other animal. Daniel Dennett points out that natural selection cannot preserve a feature of an animal that has no effect on the behavior of the animal, and thus consciousness (as Searle understands it) can't be produced by natural selection. Therefore either natural selection did not produce consciousness, or "strong AI" is correct in that consciousness can be detected by suitably designed Turing test.

Is thinking a kind of computation?

The computational theory of mind or "computationalism" claims that the relationship between mind and brain is similar (if not identical) to the relationship between a running program and a computer. The idea has philosophical roots in Hobbes (who claimed reasoning was "nothing more than reckoning"), Leibniz (who attempted to create a logical calculus of all human ideas), Hume (who thought perception could be reduced to "atomic impressions") and even Kant (who analyzed all experience as controlled by formal rules).^[60] The latest version is associated with philosophers Hilary Putnam and Jerry Fodor.^[61]
This question bears on our earlier questions: if the human brain is a kind of computer then computers can be both intelligent and conscious, answering both the practical and philosophical questions of AI. In terms of the practical question of AI ("Can a machine display general intelligence?"), some versions of computationalism make the claim that (as Hobbes wrote):

• Reasoning is nothing but reckoning^[6]

In other words, our intelligence derives from a form of calculation, similar to arithmetic. This is the physical symbol system hypothesis discussed above, and it implies that artificial intelligence is possible. In terms of the philosophical question of AI ("Can a machine have mind, mental states and consciousness?"), most versions of computationalism claim that (as Stevan Harnad characterizes it):

• Mental states are just implementations of (the right) computer programs^[62]

This is John Searle's "strong AI" discussed above, and it is the real target of the Chinese room argument (according to Harnad).^[62]

Other related questions

Alan Turing noted that there are many arguments of the form "a machine will never do X", where X can be many things, such as:

Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humor, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make someone fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new.^[63]

Turing argues that these objections are often based on naive assumptions about the versatility of machines or are "disguised forms of the argument from consciousness". Writing a program that exhibits one of these behaviors "will not make much of an impression."^[63] All of these arguments are tangential to the basic premise of AI, unless it can be shown that one of these traits is essential for general intelligence.

Can a machine have emotions?

If "emotions" are defined only in terms of their effect on behavior or on how they function inside an organism, then emotions can be viewed as a mechanism that an intelligent agent uses to maximize the utility of its actions. Given this definition of emotion, Hans Moravec believes that "robots in general will be quite emotional about being nice people".^[64] Fear is a source of urgency. Empathy is a necessary component of good human computer interaction. He says robots "will try to please you in an apparently selfless manner because it will get a thrill out of this positive reinforcement. You can interpret this as a kind of love."^[64] Daniel Crevier writes "Moravec's point is that emotions are just devices for channeling behavior in a direction beneficial to the survival of one's species."^[65]

However, emotions can also be defined in terms of their subjective quality, of what it feels like to have an emotion. The question of whether the machine actually feels an emotion, or whether it merely acts as if it is feeling an emotion is the philosophical question, "can a machine be conscious?" in another form.^[43]

Can a machine be self-aware?

"Self awareness", as noted above, is sometimes used by science fiction writers as a name for the essential human property that makes a character fully human. Turing strips away all other properties of human beings and reduces the question to "can a machine be the subject of its own thought?" Can it think about itself? Viewed in this way, it is obvious that a program can be written that can report on its own internal states, such as a debugger.^[63] Though arguably self-awareness often presumes a bit more capability; a machine that can ascribe meaning in some way to not only its own state but in general postulating questions without solid answers: the contextual nature of its existence now; how it compares to past states or plans for the future, the limits and value of its work product, how it perceives its performance to be valued-by or compared to others.

Can a machine be original or creative?

Turing reduces this to the question of whether a machine can "take us by surprise" and argues that this is obviously true, as any programmer can attest.^[66] He notes that, with enough storage capacity, a computer can behave in an astronomical number of different ways.^[67] It must be possible, even trivial, for a computer that can represent ideas to combine them in new ways. (Douglas Lenat's Automated Mathematician, as one example, combined ideas to discover new mathematical truths.)

In 2009, scientists at Aberystwyth University in Wales and the U.K's University of Cambridge designed a robot called Adam that they believe to be the first machine to independently come up with new scientific findings.^[68] Also in 2009, researchers at Cornell developed Eureqa, a computer program that extrapolates formulas to fit the data inputted, such as finding the laws of motion from a pendulum's motion.

Can a machine be benevolent or hostile?

This question (like many others in the philosophy of artificial intelligence) can be presented in two forms. "Hostility" can be defined in terms function or behavior, in which case "hostile" becomes synonymous with "dangerous". Or it can be defined in terms of intent: can a machine "deliberately" set out to do harm? The latter is the question "can a machine have conscious states?" (such as intentions) in another form.^[43]
The question of whether highly intelligent and completely autonomous machines would be dangerous has been examined in detail by futurists (such as the Singularity Institute). (The obvious element of drama has also made the subject popular in science fiction, which has considered many differently possible scenarios where intelligent machines pose a threat to mankind.)

One issue is that machines may acquire the autonomy and intelligence required to be dangerous very quickly. Vernor Vinge has suggested that over just a few years, computers will suddenly become thousands or millions of times more intelligent than humans. He calls this "the Singularity."^[69] He suggests that it may be somewhat or possibly very dangerous for humans.^[70] This is discussed by a philosophy called Singularitarianism.

In 2009, academics and technical experts attended a conference to discuss the potential impact of robots and computers and the impact of the hypothetical possibility that they could become self-sufficient and able to make their own decisions. They discussed the possibility and the extent to which computers and robots might be able to acquire any level of autonomy, and to what degree they could use such abilities to possibly pose any threat or hazard. They noted that some machines have acquired various forms of semi-autonomy, including being able to find power sources on their own and being able to independently choose targets to attack with weapons. They also noted that some computer viruses can evade elimination and have achieved "cockroach intelligence." They noted that self-awareness as depicted in science-fiction is probably unlikely, but that there were other potential hazards and pitfalls.^[69]

Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions.^[71] The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions.^[72][73]

The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue.^[74] They point to programs like the Language Acquisition Device which can emulate human interaction.

Some have suggested a need to build "Friendly AI", meaning that the advances which are already occurring with AI should also include an effort to make AI intrinsically friendly and humane.^[75]

Can a machine have a soul?

Finally, those who believe in the existence of a soul may argue that "Thinking is a function of man's immortal soul." Alan Turing called this "the theological objection". He writes

In attempting to construct such machines we should not be irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing mansions for the souls that He creates.