As the many different sectors of biological science push on for a unified theory of how exactly the human body functions, one major subject has been considerably ignored until recently: how do neurobiological process within the brain create subjective conscious states? When you stand outside, the wind creates a sensation as it brushes aside the skin of your face. This sensation is essentially a series of neural firings beginning at the sensory receptors within your skin, which then transfer up the spine and into the brain. The basic problem we now encounter here is how the brain interprets these sensations. As the wind creates a stimulus, the body acts accordingly, modifying various internal biological functions to compensate for the temperature, intensity, ect, but at the same time this stimulus might create feelings of pleasure or pain, like or dislike –an internal conscious reaction that is strictly subjective to the individual, qualia. This “mystery” of consciousness has opened the doors to a countless number of philosophical and scientific theories, but an overwhelming number of gaps in understanding still prevent us from formulating a solid, unified basis of how exactly these feelings of sentience can amount. Two such theories, both coined by John Searle and elaborated on by many others, are Strong and Weak AI.

Strong AI states that the mind is a computer program and the brain is the hardware in which the program is implemented on (Searle 9). Philosopher Daniel Dennett is a strong advocate of such a theory. According to Dennett, we have stimulus inputs, such as pressure applied to the skin and in response we have “reactive dispositions.” These responses are determined by “discriminative states” which monitor the input stimulus and, in turn, create reactive output in the form of behavior (Searle 99-100). This system is essentially the same as an electronic gauge for monitoring atmospheric pressure, with discriminative states and reactive dispositions serving as the computer programs that interpret environmental variables as barometric output. Strong AI describes the belief that these conscious states are computable, and if we did indeed have a computer powerful enough to model the human brain, the results would be nothing more than a series of stimuli (input) plugged into a problem solving computational algorithm (the brain) to produce a solution (output).

The problem with this hypothesis is that it completely removes the brains ability to assign semantic content to this external (and internal) stimuli, with examples such as pressure applied to the skin and the resulting behavior a mere syntactical representation of a computable physical universe. A machine that has the complexity and form of a human being is a human being.

Weak AI is not the negation of Strong AI, but a refutation of the product in which the premise is based on. This theory is defined by the belief that “brain processes cause consciousness, and these processes can be simulated on a computer. But the computational simulation by itself does not guarantee consciousness” (Searle 60). Weak AI takes a more cautious route in stating that computational models are “a useful tool in doing simulations of the mind,” but these simulations are strictly epistemic, void of any subjective content due to the syntactical nature of their creation. Searle uses many examples to back up this statement. Deep Blue, an IBM developed supercomputer, recently challenged and beat the world’s grandmasters at Chess. This victory was not the “threat to human dignity” that much of the news media was claiming it to be, but a series of ingenious programming techniques devised by the mathematicians in charge of system development (58). Utilizing a kind of “brute force”, it defeated its opponents by systematically evaluating the potential moves it could make in reference to the opponents previous moves and the current/future layout of the chessboard. If X piece moves to (x, y) location, Z will result (or not result). The fact remains that through simulation, nothing more than a manipulation of individual symbols has occurred –syntax in the absence of semantic content. He then proceeds with an explanation of Roger Penrose’s application of Godels incompleteness theorem to the mystery of conscious thought. At 25, Kurt Godel published a paper that put forth a revolutionary argument entitled ‘On the Formally Undecidable Propositions of Principia Mathematica and Related Systems.’ Regarded as a milestone in the areas of mathematics and logic, this argument was constructed on two premises: a) ‘Within any consistent formal system, there will be a sentence which can neither be proved true nor false,’ and b) ‘The consistency of a formal system of arithmetic cannot be proved within that system’ (Watson 271). By use of “the proof of the insolvability of the halting problem” first made by Alan Turning in relation to Godels theorem, Penrose presents an example to insist that there are areas of human conscious thinking that cannot be simulated by a computer. Suppose we had a set of computational procedures A that is sound, and through observation we can see that some computations such as Ck(k) do not stop. A, which could encompass any number of methods in procedure, is not sufficient in understanding that Ck(k) will not stop. It is observer dependent. And if the brain is indeed just a series of computation procedures A, it would be unable to construct Ck(k) which is in itself a contradiction (Searle 66-67). Therefore, “Human mathematicians are not using a knowably sound algorithm in order to ascertain mathematical truth” (Penrose 76). Through this simplistic summery of an incredibly difficult problem we can see a direct reflection of the premise in which Weak AI seeks agreement with, as well as a rejection of the conscious model that Strong AI suggests.

These arguments for Strong and Weak AI serve as only two methods for seeking an understanding of the complex problems that we face when trying to understand the nature of consciousness. The brains ability to perceive and interpret stimuli is something that many have sought to understand, from classic dualists such as Galileo and Descartes who saw a definitive, irreducible separation between the brain and the mind, to the reductive scientific approaches of Searle, Penrose, and Dennett. This research and the technological application of such research has allowed us to emancipate the mystery of consciousness from the shadowy corner of philosophical inquiry, to bridge the metaphysical and the physical by means of empirical objectivism not known to generations past. By enlisting in the scrutiny of such technologies, science is able to seek an understanding of the causative biological processes behind the emergent properties that the brain creates, utilizing computational models to suggest procedure. But one has to understand that this procedure is indeed just a model, again strictly syntactical, and the implementation of such a model is nullified without an application –the application lying within the philosophical contexts of subjective experience. One can see immediately that there is a circle of relations between the two, with science providing the empirical evidence of sound objective fact and philosophy unifying those individual constituents of fact into a form that is not mathematical, but human. Will this union help us to understand the products that AI could manifest itself into when AI is indeed actualized? Hopefully, but let us remember that Skynet did not exactly present the technological utopia its researchers were predicting….

Works Cited

Searle, John. The Mystery of Consciousness. New York; The New York Review of Books, 1997.

Penrose, Roger. Shadows of the Mind. Oxford; Oxford University Press, 1994.

Watson, Peter. The Modern Mind: An Intellectual History of the 20th Century. New York; HarperCollins, 2001.