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Time and technology can temper mans creativity, but the desire burns as strong today as ever. Art, literature, and technology; be it paint, paper or steel mans creativity is manifest in everything we do. The crowning jewel for man will be to pass on that spark with which he has been entrusted, robotics, genetic engineering, and their ilk have been trying to create new life from the raw tools with which man is so proficient. It can be said that as Prometheus took fire from the heavens to give to man, so shall man give fire of another kind, and be it biological or made from the cold steel and silicon gateways through which we now travel man will at last, have his legacy. There is a caveat however, with knowledge comes change, with creation comes difference, and with difference comes fear, hatred and discrimination. People have forever shunned that which they do not understand, that which is different from the face they see in the mirror in the morning. Since initial forays into the AI field in 1950 there have been philosophical as well as technical concerns. As technology advanced and the concept of a machine that “thinks” became more and more plausible the philosophy became more apparent. The basic problem we are confronted with is: Can machines think? In his book entitled Philosophical perspectives in artificial intelligence, Martin Ringle calls for “ a logical and semantic analysis of the concepts of ‘thought’, ‘intelligence’, ‘consciousness’, and ‘machine’, rather than an empirical assessment of computer behaviour” (hjhjh,999,2000). Thusly from its birthing AI has been regarded as an unknown, a concept that by its very name challenges nearly every norm and convention we have as individuals and as a society. Thusly because of its inherent alien nature artificial life will be subject to the same prejudices as race, gender and religion, once it is integrated into society and assumes roles associated with humans. As we venture deeper into the realms of artificial life we must uncover not only its potential but also its implications. Artificial Intelligence can be traced back to ancient Egypt, the idea that an inanimate object can be infused with a human “soul” is not new; but in the murky annals of time much of the facts have been lost. Yet the concept of creating thought from material that is by definition lacking all cognizance or consciousness is admittedly not a new idea. The advent of the electronic computer in the early and mid 1940’s gave science its first good glimpse at the future of intelligent machines. The likes of Jules Verne and H.G. Wells had dreamt them up, now the torch had been passed to make them reality. Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really forged. Norbert Weiner was one of the first Americans to make observations on the principle of feedback theory. The most familiar example of feedback theory is the thermostat: It controls the temperature of an environment by gathering the actual temperature of the house, comparing it to the desired temperature, and responding by turning the heat up or down. What was so important about his research into feedback loops was that Wiener theorized that all intelligent behaviors were the result of feedback mechanisms. Mechanisms that could possibly be simulated by machines, this discovery influenced much of early development of AI. When the collective light bulb turned on for the scientific community, it changed things overnight. That an automated or autonomous system could be developed that would respond to stimuli with programmed responses, and perhaps even learn new ones, was astounding. Robots based on insects utilize a collective intelligence, by working in groups toward a cooperative goal, rudimentary communication allows for greater efficiency as a whole. Like an ant colony, an individual ant may not posses the strength or skills to surmount an obstacle, the colony as a whole is sufficient. If a single step is uncertain the AI can create new alternatives based on the raw data, simple yes but right now it is what we have. There are three basic foundations from which AI can be built, Logic trees, List processing and probability theory. I shall discuss each in turn. 1. Logic Trees- when one attempts to create a machine that can “think” persay, we must first examine out own thought processes. Example: “I am hungry” - This is the statement of the issue at hand, the conflict to be resolved or the matter to be addressed. Having established this we must then come up with a series of alternatives each of which will either A. Solve the problem immediately. Or B. Lead us on a path that will eventually solve the problem. The below diagram illustrates this very well: The reactions to what we do, the results we create along the logical path are used as inputs farther down the line. This is exactly what occurs in standards logic gates and transistor-transistor logic units. Neural networking as this is called recycles an output to turn it into input farther down the line. The following equation establishes the mathematical base: This obviously complex and truly cryptic piece of gibberish does nothing essentially more than re-iterate the truth table for a given series of logic gates. Mathematically it is a very elegant and attractive way to put on paper what happens in a computer chip. Once the electronic computer evolved to the point where punch cards and vacuum tubes were a thing of the past, and data storage capacities increased to the level at which programs were no longer forced to skim bytes; science could take the next “logical” steps in the evolution of artificial intelligence. 2. List Processing- also known as LISP, this form of AI has evolved an entire programming language based upon it. LISP is a Unix based program which uses a default shell to process sequential orders. Little more than an advanced “If/Then” processor it allowed both great flexibility and great limitation in its programs. A LISP program started its operation by executing the first instruction in a set, if specific conditions were met it moved on to the next, it also could skip, splice in or modify instructions based on a pre-defined set of criterion. This allowed it to adapt to new inputs and occurrences rapidly and create a simulacrum of rational decision making. Unfortunately it was limited to the set of instructions given to it originally, it was free to adapt and interchange instructions within the bounds of its programming. This unfortunately hugely limited its application as a true “artificial intelligence” LISP has been used in countless industrial applications, it assists doctors in making diagnoses, it allows for greater automation in machinery and on assembly lines. A LISP based system has been proven to be a reliable and effective AI, unfortunately its limitation crop up instantly when a more flexible and “intelligent” system is called for. 3. Probability theory- in its experimental stages, AI programs based on probability equations allow for true machine intelligence. They have very few predefined criterions for a response and are given free reign to both make mistakes and to learn new responses and store inputs as they see fit. They act based on the mathematical chances that a given input will occur, similar to the cache memory on modern motherboards. Based on that probability they select an appropriate response to that input, if they are wrong or the input is something with which they are unprepared t deal with they re-evaluate their position. The basic flaw with modern AI research is thus: they attempt to make a machine into a man or an animal. It is neither, you cannot force a computer to think like a human brain, they are two totally different items, and each overspecialized to its own domain. A computer must think like one, current programming has overlooked that at its core, no matter how neural it’s programming may be it is still a device of 0’s and 1’s. A computer in the same fashion cannot interpret raw data fed into it as our brain would interpret it. We see a rose but a video camera merely sees a collection of colored pixels, our brain interprets the meaning into an object. We cannot force a machine to do so. The next generation of AI must be entirely artificial, it must posses its own unique intelligence, unlike what we or anything we know possesses. If we are to create a device that can think and act for itself and by itself it must be entirely synthetic, there can be no residual humanity, it must recognize that it is NOT a human being, nor is it in any way obligated to emulate its creators. We must keep these two parallel universes separate if we are to succeed. Already unfortunately the initial biases are beginning. To quote J.K Rowlings hero Harry Potter, “one should never trust anything that can think for itself, if you can’t see where it keeps its brain”. Albeit glib it is nonetheless true, many people think of a computer as nothing more than an appliance, a glorified toaster with little more use than e-mail and recipe searches. All too well I think it can be remembered the reactions when women entered the workforce en masse. And again the trepidation when computers, this new “fad” arrived in offices across the world, mechanization of any kind is seen as an encroachment on humanity. Justine Cassell in an article written for the 2000 IAAI (Interactive Applications of Artificial Intelligence) conference states “shared reality—a paradigm in which both human and computer share a real physical space within which to make hand gestures, facial displays, body movements, and real physical objects that can be passed back and forth between the real and virtual world” Scholars have long been trying to quantify the actual differences between “brain” and “mind” as well as the degree to which psychology can be converted into a physical science. Society as an entity seems unwilling to make leaps of judgment or significant paradigm shifts dealing with such concepts. The realms of the physical and the more nebulous sciences of the mind must for the time being remain separate. Once we begin to mesh technology more closely with ourselves as humans we can begin to accept it as a part of ourselves and as a part of our society. While today we do not possess the technology to achieve a truly sentient machine we cannot because of that speculate too deeply as to the results of such an achievement. The image of a cold “Terminator” style robot or perhaps HAL from 2001 is perhaps the exact opposite of the eventual reality. We cannot form opinions without the proper grounding in science, philosophy and indeed, ourselves. Bibliography: Word Count: 1964
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