The Abstract and Impenetrable Nature of The Mind: Debunking Machine State Functionalism

Nico Wada, 260669707 PHIL 306 Friday October 14, 2016 The Abstract and Impenetrable Nature of The Mind: Debunking Machine State Functionalism and Machine Intelligence In Hilary Putnam’s The Nature of Mental States, Putnam relates all organisms capable of feeling pain to computing machines in a theory known as Machine State Functionalism, in which the mind is comparable to an application or a software and the brain is similar to a device’s hardware. According to Putnam, an organism arrives at a particular mental state in a manner analogous to how a computer completes a certain function by implementing complex processes through programmed mechanical principles. Machine State Functionalism allows for multiple realization, meaning that a myriad of distinct species with varied brain states are permitted to share mental states under this theory. Because of the theory’s support for multiple realization and definition of a mental state as a functional translation of input into output, a Machine State Functionalist posits that a machine with correctly implemented software can be considered intelligent. In this paper, I will explore the relation between Machine State Functionalism and machine intelligence and reference John Searle’s objection involving the Chinese Room Experiment. Then, I will argue against the possibility of machine intelligence and demonstrate that the computational description of mental states under Machine State Functionalism is a flawed and radically liberal understanding of what constitutes intelligence.

While I hold that machine intelligence is impossible, a Machine State Functionalist must reason that machines can possess mental states and therefore, be deemed intelligent. Putnam defines a mental state as “the state of receiving sensory inputs which play a certain role in the

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Functional Organization of the organism” (Putnam, 1967, 78). In other words, Putnam compares the operation of human minds to computers. A computer software is wired with mechanical principles that allow the machine to carry out tasks. A human mind, similarly, filters environmental stimuli, translates the input in a functional manner, and arrives at a particular mental state. Because minds are mechanisms under Putnam’s Machine State Functionalism and there are various ways to construct a mechanism, a machine enabled with properly advanced software is capable of possessing mental states and therefore having a mind. Having a mind means the possibility of cognitive functions such as holding beliefs, hopes, and pains. Therefore, if a machine has a mind, it must also be intelligent, and a Machine State Functionalist must believe in the possibility of machine intelligence. Putnam’s Functionalism can be successfully applied to describe basic mental states, such as physical pain or hunger, yet the theory assumes too much responsibility and fails to explain other, more complex states of mind and general intelligence, as I will demonstrate in a later paragraph. While some forms of intelligence require processes of computation and translation, his definition of what constitutes a mental state is a liberal generalization.

John Searle provides an objection to Putnam’s theory on mental states and the concomitant belief in machine intelligence using his famous Chinese Room Experiment.

Mentioned in his 1984 text Minds, Brains, and Science, the Chinese Room Experiment describes a scenario where an individual is locked in a room with several baskets full of Chinese symbols and a rule book in English for manipulating the Chinese symbols. The individual can use the rule book to produce coherent Chinese sentences, yet Searle explains that the “rules specify the manipulations of the symbols purely formally, in terms of their syntax, not their !2

semantics” (Searle, 1984, 9). Therefore, while outwardly appearing to be a fluent Chinese speaker, the individual in the room does not truly understand the meanings of the Chinese words he produces. According to Searle, an intelligent being must have both syntactical and semantical knowledge: “Understanding a language, or indeed, having mental states at all, involves more than just having a bunch of formal symbols” (Searle, 1984, 9). Searle’s statement is in direct opposition to Putnam’s definition of a mental state, as Searle states bluntly: “The computational properties of the brain are simply not enough to explain its functioning to produce mental states” (Searle, 1984, 9). Searle claims that minds are capable of thought, feelings, and emotions which all extend beyond the coded and instructional world of syntax. Because he posits that computer programs have operations that must be defined syntactically, Searle discredits any concept of machine intelligence. To Searle, no matter how powerful a computer may be, it will only be able to simulate a human mind and never duplicate it. I agree wholeheartedly with Searle that computers are capable of only syntactical functions and that a being capable of syntax alone can never be intelligent.

Because some critics respond to Searle’s Chinese Room Experiment by stating that language is essentially a process of translation and that the individual in the room does understand Chinese to some extent, I will illustrate Searle’s point using a separate example of my own. Suppose that a robot has no vision whatsoever but possesses an incredibly sharp sense of smell and can identify any object by its scent within a mile radius. Through a functional process where the input is the scent of the object and the output is the identification of that object, the robot navigates the world in such a strategic way that unknowing outsiders would be convinced that the robot can see. In many ways, this robot is like the man in the Chinese Room, feigning a !3

skill that it does not actually possess through a syntactical process. Blindness is universally defined as lacking sight. Therefore, this robot is blind despite being equipped with a coded program that replicates sight. Blindness does not equate to visual intelligence, demonstrating that manipulation of scent into sight or squiggles into Chinese characters in Searle’s example cannot even be fathomed as the same forms of intelligence.

Because my definition of intelligence is discordant with that of a Machine State Functionalist’s and more in line with Searle's, I have difficulty accepting the notion of machine intelligence as a Machine State Functionalist would. I argue that intelligence is the process, or capacity, of using information to adapt to ever-changing requirements. While I concede that intelligence varies in its complexity and some forms of intelligence, such as reflexive body movements, are functionally organized, I hold that intelligence can never be algorithmically conceived in a machine. In relation to Searle’s emphasis that a thinking organism must possess semantic understanding of its knowledge, I reason that any intelligent being must be capable of creativity and spontaneity in thought. Machine intelligence is therefore inconceivable as a machine’s software is wired with predetermined functions. Due to these coded instructions, the machine lacks the ability to adapt to infinite possibilities of randomness or behave outside of its limitations.

I will use the example of emotional pain to examine my point and its incompatibility with Machine State Functionalism. Putnam uses the mental state of pain to detail how the mind works. Pain occurs when an organism with “organs whose function is to detect damage to the body” receive from its environment inputs which “represent a condition that the organism assigns a high disvalue to” (78). Putnam’s example is clear and conceivable regarding a case of !4

physical pain where, for example, sensors in the hand detect damage when it lands on a hot stove and triggers a state of pain. Machine State Functionalism fails to explain, however, the mental state of emotional pain. Suppose a husband and wife lost their first child as an infant two decades ago. Though a sufficient amount of time has passed, the husband is occasionally wracked by haunting memories of his loss and finds himself in emotional pain. The husband is clueless as to why some days he feels this pain and other days he does not. Emotional pain proves to contain an abstract element distinct from physical pain and cannot be explained by Machine State Functionalism. While a hand rests on a hot stove for just a moment before sensors in the hand trigger a functional state of physical pain, the cause of the husband’s torment is ever-present yet only occasionally triggers emotional pain. Because of this inconsistency, I cannot accept mental states as purely functional processes and must reject Machine State Functionalism as an overgeneralized theory.

I contend that the mind is unique in its impenetrability and its abstraction and may therefore never be condensed into a computer software. My argument draws heavily on Jerry A. Fodor’s description of modularity referenced in his 1985 text, The Modularity of Mind.

Modularity is Fodor’s thesis that the mind contains independent input systems that are restricted in the types of information that they can consult when engaged. In a sense, Modularity relates to Putnam’s Functionalism as both theories illustrate that certain mental states can be described as analytical processes of input and output reception. Fodor, however, uses the word “dumb” to describe perceptual processes and reflexes, which he labels as encapsulated in modular systems and occasionally distant from higher cognitive abilities. According to Fodor, machines are dumb because “computational systems…are informationally encapsulated in this sense” (Fodor, 1985, !5

3). I agree with Fodor and Searle in the sense that machine intelligence is purely modular and syntactical. Fodor describes higher cognitive processes, such as thinking, as “not modular” and “in particular, not encapsulated” (3). Complex mental states contain an abstract quality that renders them both indecipherable and incondensable. In a sense, I relate the mind to the weather.

While an algorithm can predict the weather to an almost flawless degree, the weather itself is not an algorithm, but rather a complex, abstract, and ever-adapting force much like the human mind.

In his text, Putnam describes mental processes in terms of input reception and translation.

Directly comparing the mind to computer software, he claims that organisms arrive at particular mental states through processing stimuli and sensory data through principled systems. Since a Machine State Functionalist sees the mind as operating systematically, a supporter of this theory must believe in the possibility of machine intelligence. Similar to the ideas of Searle and Fodor, I posit that mental states cannot be reduced to mere analytical processes and therefore discredit Machine State Functionalism as a viable theory. No machine can ever be intelligent because no code or embedded function, no matter how complex, can ever decipher the abstract and impenetrable content that constitutes the human mind.

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