Despite advances in technology, computers still can’t come close to the power of the world’s most remarkable computer – the human brain.

Computing machines have seen three phases: the tabulating phase, the programmable phase, and now the new era of computing, the cognitive phase [1]. Tabulating machines performed a fixed task, whereas programmable machines could be reprogrammed to execute different tasks without any change in the hardware. Cognitive machines, however, promise learning and reasoning capabilities.

The possibility of such machines raises the question: is cognition the ultimate test for conscious existence? What do we know about cognition? How do we define intelligence? The questions go on and on. One thing, however, everyone seems to agree with is the fact that understanding the mechanism of human cognition is the key to developing advanced artificial intelligence, and cognitive machines.

Cognitive machines have the ability to learn, and they employ artificial intelligence to “reason.” Artificial intelligence is defined as, “The science of making machines do things that would require intelligence if done by men” [2]. The good news is that cognitive computing is no longer an esoteric pursuit of some futurists. It is, and has been, essential for the operation of many big-data driven processes. The 21st century has been flooded with data coming from almost every aspect of our lives. Technology has made it possible to generate data at an exponential rate. Temperature distribution throughout our buildings, the number of people diagnosed with cancer in the last six months, real-time changes in customer preferences, ethnic profiles of college applicants, and the top three words trending in online conversations at this very moment, are some examples of the kind of data available today.

As the amount of data generated increases, it also becomes harder and harder to process and make sense of the data collected. Our “greedy and ambitious” human nature does not want to waste, and it wants to use every bit of available data. This is where it becomes imperative to have a computing machine that goes beyond performing pre-programmed tasks and learns as it goes, without human interference. For this kind of computing, we need cognition.

The biggest challenge in imitating human cognition is to understand how cognition happens in the brain. It is obviously beyond our ability to monitor such activity that is constantly taking place in our brains, let alone recreating such marvels in the first place. Nevertheless, it will be a great achievement if we can manage to somewhat imitate human cognition, even partially. It would open a whole new era in terms of what can be achieved from a computing standpoint. For instance, the entire curriculum of a college degree can be processed by a cognitive machine in a fraction of a second; such machine can digest the whole of medical literature in a short period of time, provide human doctors with second opinions on their diagnoses [3].

Despite the fact that there have been substantial improvements in designing “intelligent” computing machines, mimicking the hardware of the human brain and simulating its decision-making processes, have posed three fundamental challenges: a hardware with comparable processing power and memory, a software algorithm to implement intelligent behavior, and the necessity of both being self-adapting and self-improving.

First of all, human intelligence has not been fully characterized – its capabilities and limitations are still unknown. This lack of knowledge makes it difficult, and perhaps even impossible, to reduce such intelligence to smaller, or simpler, modules. Therefore, we don’t have a good handle on how to mimic the human brain in a behavioral sense.

The second major problem is that we are still far away from having the hardware on which our “intelligence” software could run. Implementing intelligence in conventional computing machines, evidently, seems to be a futile undertaking. Programmable machines are no match for human brains; even the fastest supercomputers, taking advantage of thousands of processors, is able to mimic just one percent of one second worth of human brain activity-and even that takes 40 minutes [4]. Therefore, cognitive computing machines must incorporate different hardware architecture from conventional computers to achieve cognition comparable to humans. IBM’s SyNAPSE chip is one example of hardware inspired by the brain, and it has the potential to carry out the required, intense computations.

Lastly, the human brain and its cognitive power are constantly changing. Depending on various factors and experiences, our brains can improve or deteriorate; this is also true of our cognitive power. However, such improvement or deterioration could be in the form of a change in the physical structure or the amount of capacity utilized [5]. Such dynamic flexibility, also called Brain Plasticity [6], is essential to our intelligence. At this time, no self-evolving computing hardware has been worked out. However, promising developments have been reported with respect to cognitive computing machines that can learn – that is, they can make deductions and reach conclusions that are not preprogrammed.

Along the way, human supervision will be the ultimate guide in perfecting such imitation. Therefore, human cognition, taken for granted in our daily lives, remains to be the final frontier for our thousands-years long technological journey. Once again, the creation set the boundaries for human development.

Adem G. Aydin holds a Phd degree in Electrical and Computer Engineering. He works as an engineer scientist at IBM.

References

[1] Virginia Rometty, 2013, http://smarterplanet.tumblr.com/post/32816006311/i-b-m-chief-on-watson-cognitive-computing-and-her

[2] Marvin Minsky, 1968, http://www.akri.org/ai/defs.htm

[3] “WellPoint and IBM Announce Agreement to Put Watson to Work in Health Care”, http://www-03.ibm.com/press/us/en/pressrelease/35402.wss

[4] “Largest neuronal network simulation achieved using K computer” http://www.riken.jp/en/pr/press/2013/20130802_1/

[5] William James, The Principles of Psychology

[6] Bryan Kolb and Ian Q. Whishaw, Brain Plasticity and Behavior, Annual Review of Psychology, Vol. 49: 43-64

Robots that “care” are no longer merely science fiction …

Producing machines that look and behave like people seems to be a human project with a long history. Mention of a Jewish Rabbi producing an instance of the legendary golem (a creature understood to possess an active human-like body, while lacking a soul) appeared as early as the 4th century CE. The celebrated 13th-century Muslim engineer Ibn al-Razzaz al-Jazari undoubtedly designed, and may have constructed, what has been described in present-day terms as “the first programmable human-like robotic device” – a spectacular artifact featuring four robotic musicians performing on a floating boat (Nicks 2010). Inspired by animated life-like figures reportedly created by an ancient Greek named Ctesibus, Leonardo da Vinci – around the time in the 1400s at which he began painting his famous Last Supper – also designed a human-like robot resembling a knight in armor. Fascination with the idea of crafting convincing imitations of people appears to have been part of human history for millennia.

In more recent times, though, modern computers – and with them, research introducing so-called “artificial intelligence” (AI) – have begun to give this long-standing fascination some significant new dimensions. Perhaps the most widely recognized contemporary human-like (or, nowadays, “humanoid”) robot is a product of Japanese science and technology named “ASIMO.” Resembling a short (4 ft 3 in) astronaut wearing a backpack, ASIMO represents the fruit of several decades of research and development conducted by the Honda Motor Company. Videos on the company’s official web site show ASIMO climbing stairs, jogging, balancing on one foot, visually recognizing people by name, and serving a tray of beverages to restaurant patrons. Similar examples of this impressive humanoid robot technology exist in other countries as well – e.g., Turkey (Today’s Zaman 2010), United Arab Emirates (Fahad Inc. 2008), and South Korea (Impactlab.net 2008).

Investment by business enterprises in the significant cost and engineering effort required to design and build these curiously humanoid machines constitutes one of the “new dimensions” previously mentioned. Historical figures such as Al-Jazari and da Vinci, after all, were not responding to global marketing prospects with their robotic creations. In contrast, a current Honda Motor Company web site tells us that ASIMO was intended to be more than an attention-catching novelty from the beginning; in fact, it was “created solely to perform tasks to assist people, especially those lacking full mobility” (Honda Robotics 2011). Similarly, the president of a Robotic Industries Association reports that South Korea is “taking the lead in promoting the use of robots for service applications such as elder care” (Burnstein 2009). A former GM of the Microsoft Robotics Group has identified such assistive care as the market that “intrigues” him the most, citing approaching increases in senior populations – and, consequently, heavier burdens upon healthcare systems – as factors that may “present the ‘killer app’ for personal robots” (Foley 2009). A 2009 online report titled “Robot Nurses to Care for Japanese Elderly within Five Years” reports that Warwick University, in England, has undertaken a “three year 2.7 million dollar project to develop a robot nurse,” predicting that “nurses could be delegating tasks to robotic colleagues by 2020” (Zygbotics 2009).

It is important to note that such robotic “colleagues” of human nurses commonly are intended to be suited for fairly intimate kinds of social interactions with people. One finds, for instance, references to robotic assistance in recreation and with feeding, grooming, walking, bathing, etc. (Babyboomercaretaker.com 2007). Accordingly, we encounter another new dimension. Robotic arms have welded and painted in our automobile factories for decades, but the repetitive activities of these familiar industrial robots are profoundly different from interaction with a humanoid machine that helps one’s aging grandmother eat her dinner and take her medicine (perhaps even chatting and playing a card game with her). Moreover, the latter type of robot no longer is mere science fiction; design and construction of machines to perform these kinds of personal human-robot interactions are taking place now.

… and these robots that “care” invite some questions

Considered only as machines meant to assist overburdened nurses with their care of older people, the types of humanoid robots just described might initially be categorized simply as useful new tools. We have reasons to wonder, though, how long those who will be interacting regularly with these life-like robots can be expected to perceive them merely as tools. So-called “animaloid” robots, such as the robotic dog AIBO that was marketed in recent years by the Sony Corporation, admittedly represent a somewhat different class of robotic artifact than the more complex contemporary humanoids such as ASIMO. Nevertheless, empirical studies of human-robot interaction even with AIBO have uncovered some relevant thought-provoking surprises. Not long ago, for example, numerous online postings by owners of AIBO began appearing on Internet forums. One study of these postings noted the following confession by an AIBO owner:

The other day I proved to myself that I do indeed treat him as if he were alive, because I was getting changed to go out, and [AIBO] was in the room, but before I got changed I stuck him in a corner so he didn’t see me! (Friedman, Kahn, and Hagman 2003, 278)

Regardless of whether this posted confession was altogether truthful, its expressed thought of needing modesty in this case clearly alerts us to the potential psychological potency of human interaction with such machines. Abrahamic religions, through their shared accounts of the Garden of Eden, have long recognized appropriateness of modesty between even the primordial man and woman – but application of that sentiment to our dealings with a battery-operated dog suggests how plastic human notions of personhood might be!

For that matter, professional testimony of such plasticity for the specific case of humanoid robots is available in a frequently-quoted set of observations by Professor Sherry Turkle, Director of the MIT Initiative on Technology and Self, at the Massachusetts Institute of Technology. One of her MIT colleagues, widely-recognized roboticist Rodney Brooks, is among the many people who have cited Turkle’s report of her first encounter with his experimental humanoid robot, Cog; note carefully Sherry’s candid description of the experience:

Cog “noticed” me soon after I entered its room. Its head turned to follow me and I was embarrassed to note that this made me happy. I found myself competing with another visitor for its attention. At one point, I felt sure that Cog’s eyes had “caught” my own. My visit left me shaken – not by anything that Cog was able to accomplish but by my own reaction to “him.” For years whenever I had heard Rodney Brooks speak about his robotic “creatures,” I had always been careful to mentally put quotation marks around the word. But now, with Cog, I had found the quotation marks had disappeared. Despite myself and despite my continuing skepticism about this research project, I had behaved as though in the presence of another being. (Brooks 2003, 149)

Professor Turkle’s testimony is consistent with an entire literature of contemporary research in human-robot interaction that suggests a deep human predisposition progressively to accept as peers various machines that convincingly mimic human appearance and autonomous behavior. Her reference to discovering herself behaving as though she were “in the presence of another being” points, in turn, toward some questions that invite our reflection.

First, one might inquire whether (and why) it could matter that humans seem so inclined to regard convincingly humanoid machines as peers. For some people, it apparently does not matter. From his perspective as a practicing Zen Buddhist, for example, robotics engineer Masahiro Mori has argued against insisting upon any profound distinction between persons and robots, noting that there “must also be buddha-nature in the machines and robots that my colleagues and I make” (Mori 1999, 174). In contrast, though, a pilot study has suggested that Abrahamic theistic belief in creation of individual human souls by a personal deity may be related to disapproval of human-robot interaction “with life-like personal robots that requires human acceptance of the robots at intimate levels” (Metzler and Lewis 2008, 22). This finding resonates with a respected voice in modern Christian theology. Paul Tillich, in Volume Three of his monumental Systematic Theology, addresses “objects that are produced by the technical act,” warning that “by virtue of producing and directing mere things” one can lose one’s “character as an independent self” and “become a thing” (74). Again, Jewish theologian and philosopher Martin Buber, widely remembered for his distinction between “I – Thou” and “I – It” relations, issues a similar warning in I and Thou:

And in all the seriousness of truth, hear this: without It man cannot live.

But he who lives with It alone is not a man. (34)

Apparently, we have reasons to expect that individuals belonging to Abrahamic religious traditions may especially feel troubled when they find themselves treating humanoid machines as though they were peers.

Within the Abrahamic religious family, after all, human beings historically have been regarded as spiritually special, and understood as belonging to a category fundamentally different from any technological artifacts that they might construct for amusement, or as tools. Anglican priest (and physicist) John Polkinghorne has emphasized significance of “the mystery of the human person,” which involves “our embodied nature, embedded in the physical world but transcending a merely reductive physicality” (Polkinghorne 1998, 80). Both the mystery and the transcendence that Polkinghorne mentions are punctuated clearly, as well, in the Holy Qur’an: And they will ask thee of the Spirit. SAY: The Spirit proceedeth at my Lord’s command: but of knowledge, only a little to you is given (The Night Journey – Sura 17:85). The theistic perspective of this family of religions tends to link the human person, as a free moral agent, with a spiritual level of reality that is not completely expressible in terms of everyday (macro-level) entities such as rocks and trees – and machines.

It may be pertinent at this point to inquire whether the spiritual level of reality envisioned by these religious faiths might arguably be represented even in current science. To be sure, the robotic and AI technologies upon which we have focused in this essay are discussed almost entirely nowadays with so-called “macro-level” accounts of discrete, individualized entities. Computer scientists typically view all “information processing” executed by contemporary computers as reducible to operations of the celebrated Turing Machine formalism, which imagines an abstract machine successively “reading” well-defined symbols (0 or 1) on an external tape, comparing them with its current internal “state,” and then implementing clearly prescribed (albeit possibly null) changes on the tape and its own internal state. Physicists working with quantum mechanics, however, have discovered a quite different level of reality that requires a so-called “quantum-level” description. The description is expressed mathematically in terms of complex numbers (incorporating an imaginary unit equal to the square root of negative one) and it explores a reality in which individualized entities of the macro-level (this table, that book, etc.) simply are no longer present. An atom may be understood to contain four electrons, but – in principle – one cannot select and track, say, the changing locations over time of a specific individual electron among the four. Pondering this strange new reality, mathematical physicist Roger Penrose has argued (via his Shadows of the Mind) that human consciousness cannot be modeled in terms of the Turing Machine formalism, requiring, instead, the resources of an advanced quantum physics. If the emerging technology of “quantum computers” eventually could yield a machine consistent with Roger Penrose’s understanding of how the human brain operates when we experience consciousness, future robots incorporating such computers might open possibilities for exciting new dialogue between religion and science.

Under present circumstances, though, we can discern the outlines of potential difficulties in the not-so-distant future. Specifically, elderly members of the Abrahamic faiths may find themselves increasingly conflicted in responding to robotic “caregivers.” On one hand, following natural predispositions, they will be inclined to accept the machines as caregivers (dropping the skeptical quotation marks, as Professor Turkle did during her encounter with Cog). At the same time, they may retain their religious worldviews and resist accepting the machines as persons. Will they feel authentically comforted, then, by machines programmed to display “artificial empathy”? Will they discover resolution of their conflict in the following conjecture by noted roboticist Hans Moravec?

So, it may be appropriate to say “God” has granted a soul to a machine when the machine is accepted as a real person by a wide human community. (Moravec 1999, 77)

Indeed, in perhaps the next ten years or so, how will people be using quotation marks to distinguish what they consider authentic from mere “make-believe”? Will they be describing new robot nurses as persons – or as “persons”? Will they decide that the machines care for people – or “care” for people? Will the artifacts be considered capable of moral behavior – or “moral” behavior? Will some older people still understand the granting of souls to be determined by God – or by “God”?

For some of us, these already are important questions.

Theodore Albert Metzler is the Director of Darrell W. Hughes Program for Religion and Science Dialogue, Oklahoma City University.

References

• American Honda Motor Co. Inc. 2010. “Asimo, The World’s Most Advanced Humanoid
• Robot.” Accessed December 21, 2010. http://asimo.honda.com/ .
• Babyboomercaretaker.com. 2007. “Robotics in Nursing.” Accessed January 4, 2011.
• http://www.babyboomercaretaker.com/assistive-technology/robotic-technology/Robotics-In-
• Nursing.html .
• Brooks, Rodney A. 2003. Flesh and Machines: How Robots Will Change Us. New York:
• Vintage Books.
• Buber, Martin. 1987. I and Thou. New York: Macmillan Publishing Company.
• Burnstein, Jeff. 2009. “Robotics and the Big Trends.” Robotics Online. Accessed January 3,
• 2011. http://www.robotics.org/content-detail.cfm/Industrial-Robotics-Feature-
• Article/Robotics-and-the-BigTrends/content_id/1709 .
• Fahad Inc. 2008. “REEM-B: UAE’s First ‘Home-Grown’ Humanoid Robot.” Accessed
• December 21, 2010. http://www.fahad.com/2008/06/reem-b-uaes-first-home-grown-
• humanoid.html .
• Foley, Mary Jo. 2009. “‘Partner bots: The next killer robotics app? (And will Microsoft bite?).”
• ZDNet. Accessed January 3, 2011. http://www.zdnet.com/blog/microsoft/partner-bots-the-
• next-killer-robotics-app-and-will-microsoft-bite/2828 .
• Friedman, Batya, Peter H. Kahn, Jr., and Jennifer Hagman. 2003. “Hardware Companions? –
• What Online AIBO Discussion Forums Reveal about the Human-Robotic Relationship.” CHI
• 2003. ACM. CHI Letters 5.1: 273-280. doi: 10.1145/642611.642660.
• Honda Robotics. 2011. “ASIMO.” Accessed January 3, 2011. http://dreams.honda.com/robotics-
• mobility/ .
• Impactlab.net. 2008. “Mahru II – South Korea’s Humanoid Robot.” Accessed December 21,
• 2010. http://www.impactlab.net/2008/10/14/mahru-ii-south-koreas-humanoid-robot/ .
• Moravec. Hans. 1999. Robot: Mere Machine to Transcendent Mind. New York: Oxford
• University Press.
• Nicks, Victoria. 2010. “History of Robots – Robotics Technology in Automata by Al-Jazari.”
• Suite101.com. Accessed December 10, 2010.
• http://www.suite101.com/content/history-of-robots-robotics-technology-in-automata-by-al-
• jazari-a253819 .
• Metzler, Ted, and Lundy Lewis. 2008. “Ethical Views, Religious Views, and Acceptance of
• Robotic Applications: A Pilot Study.” Technical Report WS-08-05. Menlo Park, CA: AAAI
• Press: 15-22.
• Mori, Masahiro. 1999. The Buddha in the Robot: A Robot Engineer’s Thoughts on Science and
• Religion. Tokyo: Kosei Publishing Co.
• Penrose, Roger. 1994. Shadows of the Mind: A Search for the Missing Science of Consciousness.
• New York: Oxford University Press.
• Polkinghorne, John. 1998. Belief in God in an Age of Science. Binghamton: Vail-Ballou Press.
• Tillich, Paul. 1971. Systematic Theology: Three volumes in one. Chicago: The University of
• Chicago Press.
• Today’s Zaman. 2010. “Meet SURALP, Turkey’s first humanoid robot.” Accessed December
• 21, 2010.
• http://www.todayszaman.com/news-224330-meet-suralp-turkeys-first-humanoid-robot.html .
• Zygbotics. 2009. “Robot Nurses to Care for Japanese Elderly within Five Years.” Accessed May
• 13, 2009. http://www.zygbotics.com/2009/03/27/robot-nurses-to-care-for-japanese-elderly-
• within-five-year/ .

Open heart surgery is performed after putting the patient to sleep under a general anesthetic. The chest is then opened by the surgeon, and the heart is temporarily bypassed or deactivated for the duration of surgery (although in some new techniques like beating heart surgery or minimal invasion heart surgery, the operation is possible without deactivation of the heart) During this period the functions of the heart are performed by an artificial lung mechanism called the heart-lung machine (cardiopulmonary bypass machine). Performing surgery on a working heart cases where there is no facility for beating heart surgery would be like trying to repair the engine of a car while it is in motion. This is why it is necessary to temporarily prevent the functions of the heart during the operation, which requires great care and accuracy.

Stopping the heart

During this procedure the patient is connected to the machine, thin pipes called cannulae are inserted into the main veins which lead to the heart, and thus the blood which goes to the heart is directed into the heart-lung pump, fed with oxygen, and then redirected into the body. Preventing the function of the heart is not a very difficult process. When the heart-lung machine is activated and the blood is cooled and redirected into the blood vessels, the body temperature is reduced to below 30°C, and this lowers the heart rate and assists the heart to stop functioning. The actual stopping of the heart is performed by feeding a serum containing a concentrated solution of potassium ions into the coronary artery, which feeds the heart muscle. Potassium ions are normally found in the human body but in a fixed proportion; potassium is an electrolyte which, if increased, causes a defect in the heart’s rhythm and can lead to ceasing of the heart function. Feeding the coronary artery rapidly with a rich potassium solution causes the heart to stop within a few seconds and allows the surgeon to perform the operation on a non-functioning, motionless heart.

The heart should not be stopped from functioning for a long period, even if the heart-lung pump is performing the function of the heart successfully. Under normal conditions the pump cannot perform the whole duty of the actual heart and lungs. When the body temperature is reduced, there is a reduction of functioning in many organs of the body to such an extent that they almost stop working, especially the brain. This means that every organ freezes and if the patient’s pulse were taken within this period, they would be assessed as dead.

Restarting the heart

To restart the heart following the operation a reversal of the procedure performed at the beginning of surgery is necessary; the temperature of the body is increased to 36.5–37°C again with the help of the heart-lung pump, and at the same time the amount of potassium in the blood is reduced to a normal level. This is usually executed by ensuring the normal function of the kidneys which discard the potassium from the body. This is when the function of the heart is monitored closely because there is a reversal in the process of inducing low body temperature and the excess of potassium which caused the heart to stop. In other words, the barrier which stopped the flowing river is removed; therefore, according to the laws of physics, the trapped fluid should flow again at great speed, and although following surgery the majority of hearts do begin to function again when these procedures are performed, there is unfortunately no actual guarantee. There may be certain complications or even causes which we have not yet discovered, in which case an electric shock of 10–20 joules is delivered directly to the heart muscle to encourage it to function normally. If this is unsuccessful, medication such as adrenalin, which induces the functioning of the heart, is given to the patient. If, following these repeated procedures, there is no effect, and, regardless of all the effort, the heart does not function, then everything is performed again from the beginning, including the operation. But there is always the possibility that the desired result may not be achieved. Human beings always face the prospect of death in daily life, and although there is a very slim chance of death, with such a big operation there is always the possibility.

The result

We are normally totally unaware of the rhythmic incidents, a combination of great harmony, occurring within our bodies. Even breathing, a necessity for every living creature to stay alive, is not an action which we activate and continue of our own will. Sight, hearing, hunger and senses are acts of nature over which we have little direct will or power. Nevertheless, they are all events which we can only describe as divine miracles and what a great blessing it is that none of these complex functions of our bodies have been left to us humans.

The theoretical background of the computer was developed at the beginning of the 20th century, but it was not until the Second World War that progress was made in developing electrical calculating machines. Now we have a new era; the era of computers. In the beginning, the computer was a machine that had a very limited capacity and calculation, and it was only used in a very few important centers. With the passage of time, computers began to be used in business centers, and eventually the production of personal computers became more widespread. Nowadays, we can see many high-tech machines, like handheld PC’s and robot dogs everywhere we look.

In the last fifty years, computer technology has developed rapidly. With respect to this, logical-thinking devices have also been greatly developed. Artificial Intelligence (AI) provides the logical thinking for these types of devices. The goal of AI is to attain the level of logic that “living systems” (i.e. humans) possess. The improvements in AI systems are encouraging to the scientists involved in the field; they now believe that not only can a humanlike machine be built, but, in fact, a machine that is more advanced than humans can be developed. The debate on this subject has separated scientists into two camps. AI advocates claim that in the future people will have the opportunity to make advanced devices that have a better ability to think and decide than humans have. On the other hand, many scientists think that the decision-making mechanism of the human brain contains something that is beyond electronics and that cannot be replicated in an electronic device.

Before going into details, we must first answer the question “what is intelligence?” A being is intelligent if it understands and evaluates some “known data”; if it makes logical inferences and avoids redundant processes and therefore arrives at a sound solution. The famous English mathematician Alan Turing claimed that “if the interrogator cannot distinguish the machine from the human” then the machine is assumed to be intelligent. The Turing Test consists of an interrogator, a machine and a human placed in three rooms. The interrogator is in contact with the human and the machine over text terminals.

The theory of computation was first propounded by Alan Turing in 1936. He described, in basic terms, “The Turing Machine” which is an abstract machine with an unlimited amount of storage space that can go on computing forever without making any mistakes. The Turing Machine only performs three basic operations; reading, writing, and moving the read-write head. According to the Turing Theorem, all computers are Turing equivalent; that is, any process that can be done by a Turing Machine, can be done by a computer and similarly any process that can be done by a computer, can be done by a Turing Machine.

Figure 1 A simple illustration of a Turing Machine

The Turing machine is an abstract model of computer execution and storage that gives a mathematically precise definition of algorithm or “mechanical procedure.”

All computers perform algorithmic processes. Algorithm means a step by step progression. In other words, you have a certain situation. You solve that situation and proceed to another situation that is better than the last one. Using this step by step solution method you are able to reach the goal situation. This is an algorithmic problem. An example will make this easier to understand:

Suppose we have any 10 numbers.

Problem: What is the sum of these numbers?

Figure 2 The algorithm that gives the sum of any given 10 numbers.

As shown above, the sum is “0” in the beginning. A loop with 10 processes is prepared and the next number is read. The number is added to the sum and the algorithm moves to the next number. The process continues until the 10 numbers have been finished. After the process is finished, the result is written.

On the other hand, there are many known problems that do not have any algorithmic solutions. A simple example is given in the following:

Problem: Find a number that is not the sum of three square numbers.

In this problem we had a bit of luck; we just tried 7 and were able to find the solution. Let’s change the problem a little bit:

Problem: Find a number that is not the sum of four square numbers.

The eighteenth-century mathematician Lagrange proved the well-know theorem that every number can be expressed as the sum of four squares. What this means for our computer is that if we were to simply go on in a mindless way trying to find such a number, the computer would simply chug away forever, never finding any answer. In order to solve this problem, therefore, Lagrange had to apply a method that was not algorithmic. Additionally, Penrose states that “there are certain classes of problems that do not have any algorithmic solutions.”

In fact, Turing described the situation where a computer fails to find a solution and therefore does not come to a stop (or a halt) as a “halting problem.” One good example of this, given by Penrose, is the completely deterministic, but non-computable “tiling problem.” We are given tiles called polyominoes and we have to place these tiles on a Euclidian plane

Figure 3. Various sets of polyominoes that will tile the infinite Euclidean plane (reflected-image tiles being allowed).Neither of the polyominoes in set (c), if taken by itself, will tile the plane, however.

In Figure 3 (a), it is obvious to see the tiling of the plane by tiling around a cross. In figure 3 (b) the same condition holds, but in part (c) the tiles cannot tile a plane by themselves, but only together. Another example is shown in the following.

Figure 4. A set of three polyominoes that will tile the plane, but in a way that never repeats.

The plane can be tiled by using three polyominoes, but not in an algorithmic way. In other words, the computer will try to tile the polyominoes by adding around each of them and, since it cannot find a pattern, it will go on forever and will not be able to arrive at a conclusion as to whether or not the polyominoes will tile the plane.

One of the most important factors that separate computers from the human mind is consciousness. Consciousness is the process of understanding. The computer can compute the data given, but it cannot understand what the data means. For example, when one of your friends calls you, you understand that he has called you and you respond. When you switch on a machine, it starts to work. It is not because the machine has understood that you have pressed the button; rather the electronic structure of the machine has been designed to work when you switch it on. The machine cannot understand; it is not conscious. Here are some more examples:

Figure 5. White to play and draw—easy for humans, but Deep Thought took the castle.

In the chess game above, by just playing the king left and right, white can bring the game to a draw. But, at first to make the game a draw, the white player has to understand the situation. Since the computer has no capability to understand, it may think that it would be more profitable to take the castle and therefore it loses the game.

Another example:

Figure 6. White to play and draw—again easy enough for humans, but a normal expert chess computer will take the castle.

There is a great temptation to take the black castle with the white bishop, but the correct thing to do is to pretend that the white bishop is a pawn and use it to create another barrier of pawns. Once you have taught the computer to recognize barriers of pawns, it might be able to solve the first problem, but it would fail on the second because it needs an extra level of understanding. The situation is very easy for a human, but as we mentioned, it is quite difficult for a computer.

These examples are halting problems because both situations have endless algorithms to identify the solution, so basically they need to be understood by an intelligent mechanism. Maybe the chess problems can be solved with enough computation, but again we can make the situation more complex. That is to say, the important thing is not computation, but understanding the situation.

In conclusion, the problems we mentioned above are some of the basic problems that AI has to overcome. The present technology is very far from being similar to the human mind. The human mind is not a simple substance; in fact, quite the contrary, it is an incredibly complex structure. There are many things that play a role in the human mind; it is not easy, perhaps it is even impossible, to build a mechanism that is like the human mind. 

References

• Adami C., Introduction to Artificial Life: Flavors of Artificial Life, 1999.
• Aksoy M. S., Artifical Intelligence, The Fountain, No.4, s.10.
• Artificial life and the Turing Test, Retrieved from World Wide Web: “http://http1.brunel.ac.uk:8080/depts/AI/alife/alife-main.html” http://http1.brunel.ac.uk:8080/depts/AI/alife/alife-main.html, 2000
• Crick F., The Astonishing Hypothesis: The Science Search for the Soul, Charles Scribner’s Sons, 1994.
• Penrose R., Shadows of the Mind: Consciousness and computation, Oxford University Press, 1994.
• Penrose R., Shadows of the Mind: Does Mind have a Place in Classical Physics, Oxford University Press, 1994.
• Penrose R., Shadows of the Mind: Quantum Theory and the Brain, Oxford University Press, 1994.
• Penrose R., Shadows of the Mind: A Search for the Missing Science of Consciousness, Oxford University Press, 1994.
• Petri H.L., Mishkin M. Behaviorism, Cognitivism and the Neuropsychology of Memory, American Scientist, Jan-Feb 1994. s. 3037.
• Searle J.R. Minds, Brains and Computers. Retrieved from World Wide Web:”http://www.siu.edu/~philos/faculty/Manfredi/intro /searle.html” http://www.siu.edu/~philos/faculty/Manfredi/intro/
• searle.html, 2000.
• Interview with Ucoluk G., Can a More Advanced Mechanism than the Human be Built?, 1999.

The theorem published in 1931 by the 25 year old Austrian scientist, Kurt Godel, made a great impact on the scientific circles. Not only did it destroy the hopes of many scientists, but it also initiated a new point of view concerning AI and the mind. This theorem is one of the most important ones to be proven this century, ranking alongside Einstein’s Theory of Relativity and Heisenberg’s Uncertainty Principle. However, very few people know about it. In this article, we will examine in detail the effects of Godel’s theorem on AI.

What is Godel’s Incompleteness Theorem?

As a formal definition, proof is a sequence of well-formed-formulas (wff), each of which is either an axiom or a wff that is derived from preceding wff’s. Godel’s contemporary Hilbert, one of the most famous mathematicians, thought that all proofs in mathematics can be obtained in an automated way (with an axiomatic system) and he started to work on this project. He believed that if he derived all wff’s in basic arithmetic from its own axioms, then he could derive all facts in mathematics using these axioms.

Unfortunately, Godel demonstrated the impossibility of this. First of all, he found a method of translating the syntax of a formal system into arithmetic. Then he formulated the statement, “This formula is improvable in the system,” (G) in arithmetic. Using the same method, he also formulated the negative of the statement G (“This formula is provable in the system”). For the next step, he showed that if the truth value of G was calculated, the truth value of negation of G could also be calculated, causing a contradiction. At the end of his calculations, Godel arrived at two very important consequences:

1. If a formal system that contains minimal arithmetic is consistent, then it is incomplete.

2. Consistency of any formal system containing minimal arithmetic is not internally provable (by using the system’s own rules and formulas).

Surprisingly, even if G were added as a further axiom into the system, a new Godel sentence could be easily found. In other words, no matter how many axioms we add, one can find a Godel sentence that will make the truth value undeterminable.

What Does the Theorem Imply for Artificial Intelligence vs. the Mind?

By examining Godel’s Theorem, one can determine very important consequences for artificial intelligence. An English mathematician, Turing, described an abstract machine called the “Turing Machine.” This is an abstract machine which has an unlimited amount of storage space and which can go on computing forever without making any mistakes. This machine can compute any type of algorithmic problem. According to the Turing Theorem all computers are Turing equivalents. After proposing this, Turing went on to observe that some type of problems have no algorithmic solutions. In the meantime, “the Halting Problem” emerged – the problem of deciding those situations in which a Turing Machine action fails never comes to a halt because of the consequences of the Godel’s Incompleteness Theorem.

It has been proven that a halting problem is computationally insoluble. This leads us to an important conclusion; a computer cannot be the same as the human mind because the non-computational physics of the mind is not available for Turing equivalent machines and the nature of the algorithms is not compatible with the thinking process due to the halting problem.

The argument of the Godelian Case problems made great sense to AI supporters. Godel’s Theorem started a great debate between supporters of AI vs. those of the human mind.

Reviews of the Theorem on AI vs. Mind

Penrose claims that the human mind cannot be compared to artificial intelligence. Penrose bases his claim on Godel’s Incompleteness Theorem. By appealing to the results obtained by Godel (and Turing), mathematical thinking (and hence conscious thinking generally) is something that cannot be encapsulated within any purely computational model of thought. This is the part of Penrose’s argument that his critics have most frequently taken issue with. In addition, he states that there are certain classes of problems that do not have any algorithmic solutions (R. Penrose, 1994, p.29). In fact, Turing described this as the halting problem. Penrose gives an example of the completely deterministic, but non-computable “tiling problem” (R. Penrose, 1994, p.30-33).

Penrose asserted that some mathematical relations required long chains of reasoning before they could be perceived with certainty. But the object of a mathematical proof is to provide such chains of reasoning that each step is indeed something that can be perceived as being “obvious.” He concluded that the endpoint of such reasoning is something that must be accepted as being true, even though it may not, in itself, be at all obvious. One might imagine that it would be possible to list all possible “obvious” steps of reasoning once and for all, so that from that time on everything could be reduced to computation. But, what Godel’s argument shows is that this is not possible. There is no way to eliminate the need for new “obvious” understandings. Thus, mathematical understanding cannot be reduced to blind computation (R. Penrose, 1994, p.56).

Penrose claims that the results of Godel’s

theorem established that human understanding and insight cannot be reduced to any set of computational rules (R. Penrose, 1994, p.65). In the chapter entitled “The Godelian Case” of his book Shadows of the Mind, Penrose supported his idea with Turing’s Halting Problem and showed sound examples on non-computability. At the end of the chapter he answered possible technical objections to his idea based on Godel’s Theorem in details (R. Penrose, 1994, p.64-116).

Penrose believes that there is something beyond computation in the human mind. In Chapter 3 of Shadows of the Mind, he examines the thinking process and non-computability in mathematical thought carefully and uses formal representations (R. Penrose, 1994, p.127-209). Godel’s theorem states that in any sufficiently complex formal system there exists at least one statement that cannot be proven to be true or false. Penrose believes that this would limit the ability of any AI system in its reasoning. He argues that there will always be a statement that can be constructed which is unprovable by the AI system. However, Penrose believes that somehow the human mind can see the truth of such Godel statements directly (R. Penrose, 1989).

Along the same lines as Penrose, Lucas believes that Godel’s theorem seems to prove that the idea of “Mechanism” is false, that is, that minds cannot be seen in terms of machines. He claims that Godel’s theorem must apply to cybernetics, because the essence of being a machine is that it should be a concrete instantiation of a formal system. It follows that for any given machine which is consistent and capable of doing simple arithmetic, there is a formula which it will be incapable of producing as being true (i.e., the formula is improvable in the system but which we can see to be true). It follows that no machine can be a complete or adequate model of the mind, that minds are essentially different from machines. This does not mean that a machine cannot simulate any piece of the mind; it only says that there is no machine that can simulate every piece of the mind. Lucas says that there may be deeper objections. Godel’s theorem applies to deductive systems, and human beings are not confined to making only deductive inferences. Godel’s theorem applies only to consistent systems, and one may have doubts about how far it is permissible to assume that human beings are consistent. Godel’s theorem applies only to formal systems, and there is no a priori bound to human ingenuity which rules out the possibility of our contriving some replica of humanity which is not representable by a formal system (J. Lucas, 1970).

Chalmers examines the situation when a formal system F, which understands the consequences of Godel’s Theorem, is given. According to his claim, F may not be sound, so Godel’s theorem cannot be applied. He specifies that the crucial point of Godel’s argument is not to know “a formal system is sound”; but to determine “if we know that our system is sound.” It follows that we perhaps have a sound system, but we can not conclude that “we know that we have a sound system” (D. J. Chalmers, 1995).

Like Chalmers, McCullough claims that not only artificial intelligence, but also the human mind is tightly related with Godel’s theorem. Godel argument did not prove that human reasoning had to be noncomputable – it only proved that if human reasoning was computable, then it had to either be unsound, or it had to be inherently impossible for a human to know both what a human’s own reasoning powers were and to also know that they were sound. And adds, Penrose dismisses the possibility that a human knows its reasoning powers, but does not know that they are sound. In his paper, McCullough also examines the appliability of Godel’s theorem on non-computable systems and the human mind. According to him, both are possible, by the way he asserts that Penrose’s idea is wrong. Consequently, McCullough agrees with Penrose that human reasoning cannot be formalized in some sense, because humans do not understand their reasoning system well enough to formalize it. This limitation is not due to a lack of human intelligence, but is inherent in any reasoning system that is capable of reasoning about itself. (D. McCullough, 1995).

As a short conclusion, it seems that the discussion between AI vs. mind will last for a long time. But, considering the present situation, AI has a long way to the go in order to achieve the expected skills.

References

• Chalmers, D.J. (1995). “Minds, Machines, and Mathematics”. http://psyche.cs.monash.edu.au/v2/psyche-2-09-chalmers.html

• Godel, K. “On Formally Undecidable Propositions of Principia Mathematica”. http://www.ddc.net/ygg/etext/godel/

• Lucas, J.R. (1970). “Minds, Machines and Godel”. The Freedom of the Will, Oxford: Oxford University Press. http://users.ox.ac.uk/jrlucas/mmg.html

• Maudlin, T. (1995). “Between The Motion And The Act…”. http://psyche.cs.monash.edu.au/v2/psyche-2-02-maudlin.html

• McCarthy, J. (1995). “Awareness and Understanding in Computer Programs”. http://psyche.cs.monash.edu.au/v2/psyche-2-11-mccarthy.html

• McCullough, D. (1995). “Can Humans Escape Godel?”. http://psyche.cs.monash.edu.au/v2/psyche-2-04-mccullough.html

• Penrose, R. (1989). The Emperor’s New Mind. New York: Oxford University Press.

• Penrose, R. (1994). Shadows of the Mind. New York: Oxford University Press.

• Penrose, R. (1996). “Beyond the Doubting of a Shadow”. http://psyche.cs.monash.edu.au/v2/psyche-2-23-penrose.html

• Pysche (1995). An Interdisciplinary Search of Consciousness, Vol. 2, Symposium on Roger Penrose’s Shadows of the Mind. http://psyche.cs.monash.edu.au/psyche-index-v2.html

Metaphors also play an important role in science. Cognitive scientists, who continue to research how language evolved and which tools the mind uses during this process, state that metaphors are widely used tools. Scientists use language, as well as graphs and equations, to explain their models. Therefore metaphors are essential in learning and teaching science. In this article, we will present a few examples of metaphorical thinking in science and how it guides science in new directions that could lead to breakthroughs. We will be concerned mainly with the machine metaphor that affected the way we picture the world.

A short history

Metaphor derives from the Greek verb methaphora (to transport or transfer). George Lakoff, professor of linguistics at the University of California”Berkeley, and philosopher Mark Johnson explain the essence of metaphor as understanding and experiencing one kind of thing in terms of another. We generally use analogies to make less familiar things appear in guises that are more familiar to us. For example, we try to picture time by associating it with a river. Chemists extend their knowledge of a poorly understood compound by finding similarities with familiar compounds. Biologists use biological mechanisms found in organisms to understand similar mechanisms in others. Luigi Galvani associated the transmission of nerve pulses to an electric current.

Metaphor can be defined as the mapping of a source domain onto a target domain. Throughout the history of science, water waves were used prototypically for understanding light waves. People tried to understand light (target domain) in terms of water waves (source domain). This mapping forced scientists to search for a medium”ether”that could propagate light waves, for water waves were propagated in water.

According to the English physicist Norman Campbell, every scientific theory requires the use of models to understand theoretical terms. For example, in order to comprehend the kinetic theory of gas, we must resort to an analogue model gas behaving as if it were composed of point particles randomly moving in a vessel. Metaphorical models help to improve the scientific imagination. But for scientists to take a model seriously, it must be expressible in terms of mathematical equations.

Sometimes, the symbolic representation of a mathematical relation can lead to a metaphor. For example, the space is time metaphor has an interpretation in molecular biology where millions of years (time) are encoded and contained in DNA (space). In addition, metaphors offer insight into mathematical phenomena. The basic example is the number line, which is the result of imagining numbers as points on a line.

The machine metaphor

From the seventeenth to the nineteenth centuries, the dominant metaphor was the machine metaphor: The world is a machine. This connection was made by Galileo (d. 1642), Descartes (d. 1650), Boyle (d. 1691), and Newton (d. 1727). They imagined the universe as a static, predictable machine. This worldview influenced our beliefs and psyche, as well as the way scientists, philosophers, and other intellectuals thought. Scientists started to think of everything in terms of a machine. Muscles were considered to be force-generating machines, nerves to be electronic machines, and photosynthesis to be a solar-powered machine. Lord Kelvin (d. 1907) characterized the universe as a galactic heat engine.

The machine metaphor led to reductionism in philosophy: If we wish to understand how a machine works, we look into its component parts. Descartes’ analytical method of reasoning is based on the assertion that complex phenomena can be understood by reducing them to simple phenomena. Biologists tried to understand living organisms, as well as bodily motions and functions, by reducing them to their constituents. More research was devoted to understanding the nature of genes. Physicist reduced gases’ properties to the motion of atoms or molecules. Locke (d. 1704) attempted to understand society by observing the behavior of individuals.

With the picture of a perfect world-machine, belief in a Creator became a logical necessity, since the machine implies an engineer. Even children know, as a part of their personal experience of reality, that a house implies a builder and a watch a watchmaker. As they study the more intricately complex nature of the human body or the ecology of a forest, it is highly unnatural to tell them to think of all these systems as chance productions of irrational processes.

Theologian William Paley (d. 1805) expressed this creationist view on page 98 of his Natural Theology as: There cannot be a design without a designer; contrivance without a contriver; order without choice; arrangement without a thing capable of arranging … Arrangement, disposition of parts, subserviency of means to an end, relation of instruments to use, imply the presence of intelligence and mind. This Cartesian philosophy connoted the idea that The Hand of God had set the machine in motion at the beginning of creation. As everything had been determined by God already, nothing could be the result of chance. This view is known as determinism.

The computer metaphor

The computer, the current dominant machine, has become the modern era’s dominant metaphor. Visionary physicist Edward Fredkin characterizes the universe as a cosmological computer. The universe appears as a network of a dynamic whole whose parts are essentially interrelated. Modern physicists do not consider the atom a basic building block; rather, it is a web of relations that includes human consciousness in an essential way. Modern biologists describe cells as distributive real-time computers. Making good use of the computer metaphor, they focus more on how neurons work together and how each cell interacts with its environment.

Sociologists study individuals and their social relationships. Psychologists and cognitive scientists use the analogy of a computer to understand the mind. Artificial intelligence is just one consequence of such modeling. The universe is no longer seen as a simple mechanical machine, but as a more complex high-tech computer system.

The computer metaphor supports the view of a dynamic universe more than Newton’s static universe. James Maxwell’s (d. 1879) electrodynamics, the second law of thermodynamics (entropy), and Darwin’s (d. 1882) theory of evolution all involve dynamic concepts. Entropy describes the evolution of inanimate matter and states that inanimate systems tend to go from order to disorder. Entropy is an increase in disorder became a root metaphor at the beginning of the nineteenth century.

Biologists describe evolution as a movement toward increasing order and complexity. Darwinian theory attributes biological complexity to the accumulation of mutations by natural selection. One factor that makes Darwinian theory a naturalistic philosophy more than an empirical science is its claims that all of these dynamic processes are a result of random chance. Natural selection is introduced as the mechanism that minimizes the effect of chance. According to Darwinism, probability governs the universe and there is no need for God.

There are probabilities in the quantum world as well. However, probability and uncertainty show the unpredictability of the outcomes of quantum measurements from a human perspective. This probabilistic vision of universe, contrary to Darwinian theory, deepened the consequences of the machine metaphor: God not only set the machine in motion eons ago, but is still governing the process of creation and changing the probability pattern.

Metaphors and paradigm shifts

According to Thomas Kuhn (d. 1996), metaphorical thinking becomes crucial at periods of scientific revolution and gives way to a paradigm shift. Distant analogies are probably the most useful in times of scientific revolution: Newton’s metaphor of an apple is a moon is of this kind. However, this metaphor became literal after it was understood that the same gravitational force that holds the apple to Earth is the same one that holds the moon in its orbit around Earth.

Kuhn’s paradigm shift and use of metaphors can best be seen in the history of exploring the atom. The atom was conceived first by Democritus (d. c. 370 BCE) as an impenetrable sphere. This analogy was used to explain the behavior of matter until the nineteenth century. Spherical analogy was discarded in favor of Ernest Rutherford’s (d. 1937) analogy between the solar system and the hydrogen atom.

The following interpretations followed the planetary model: The nucleus is more massive than the electron (just as the sun is more massive than the planet), the nucleus attracts the electron, this plus the mass relation causes the electron to revolve around the nucleus, and so on. Object descriptions are disregarded, for there is no attempt to match the nucleus with the sun in color, size, or temperature. Then, the atomic nucleus was analogized to a drop of water instead of to a body like the sun.

Now, in quantum mechanics, every particle is considered to be a probability wave, which is an abstract mathematical quantity. Scientists have not yet been able to put these waves into a metaphorical pictorial model, which makes them difficult to comprehend.

Metaphors in revealed texts

The following Qur’anic verses illustrate the use of metaphors in the Qur’an. Speaking of hypocrites who do not believe in God but nevertheless claim to believe, it states: Their parable is like the parable of one who kindled a fire, but when it had illumined all around him, God took away their light and left them in utter darkness”they do not see. Deaf, dumb (and) blind, so they will not turn back. Or like abundant rain from the cloud in which is utter darkness and thunder and lightning. They put their fingers into their ears because of the thunder peal, for fear of death, and God encompasses the unbelievers. The lightning almost takes away their sight. Whenever it shines on them they walk in it, and when it becomes dark to them they stand still. If God had willed, He would have taken away their hearing and their sight. God has power over all things. O people, serve your Lord, Who created you and those before you so that you may guard (against evil) (2:17-21).

Conclusion

Logic and mathematics are not enough to understand science. We need internal mental pictures to grasp new phenomena. When trying to understand unfamiliar phenomena, the mind thinks of them in metaphorical terms. This not only helps to explore new realms for scientific theories, but also can change the view we hold of the world.

References

• Behe, M. Intelligent Design Theory as a Tool for Analyzing Biomedical Systems. InterVarsity Press, 1999.
• Gentner, D., & Clement, C. (1988). Evidence for relational selectivity in the interpretation of analogy and metaphor. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 22, pp. 307-358). New York: Academic Press.
• Hesse, Mary. Models and Analogies in Science. University of Notre Dame Press: 1966.
• Belal A. Baaquie and Lai Choy Heng, Department of Physics, National University of Singapore Method and Anti-Method in the Sciences: Metaphors and Scientific Creativity. http://www.scholars.nus.edu.sg/natureslaw/methodology/anti_method/11.html
• Lakoff, G. and Johnson. Metaphors We Live By. University of Chicago Press, 1995.
• Morris, Henry, ed. Scientific Creationism. 2d ed. Word Publishing: 1974.
• Paley, William. Natural Theology; or, Evidences of the Existence and Attributes of the Deity. Online at: www.hti.umich.edu.

Another feature of the human visual system is the ease with which interpretation is carried out. We see a scene as it is – trees in a landscape, books on a desk, products in a factory. No obvious deductions are needed and no overt effort is required to interpret each scene. In addition, answers are immediate and available normally within a tenth of a second. The important point is that we are for the most part unaware of the complexities of vision. Seeing is not a simple process.

We are still largely ignorant of the process of human vision. However, man is inventive and he is now trying to get machines to do much of his work for him. For the simplest tasks there should be no particular difficulty in mechanization but for more complex tasks the machine must be given man’s prime sense, i.e. that of vision. Efforts have been made to achieve this, sometimes in modest ways, for well over 30 years. At first, such tasks seemed trivial and schemes were devised for reading, for interpreting chromosome images and so on. But when such schemes were confronted with rigorous practical tests, the problems often turned out to be more difficult.

Computer vision blends optical processing and sensing, computer architecture, mechanics and a deep knowledge of process control. Despite some success, in many fields of application it really is still in its infancy.

With the current state of computing technology, only digital images can be processed by our machines. Because our computers currently work with numerical rather than pictorial data, an image must be converted into numerical form before processing.

The field of machine or computer vision may be sub-divided into six principal areas (1) sensing, (2) pre-processing, (3) segmentation, (4) description (5) recognition and (6) interpretation. Sensing is the process that yields a visual image. The sensor, most commonly a TV camera, acquires an image of the object that is to be recognised or inspected. The digitizer converts this image into an array of numbers, representing the brightness values of the image at a grid of points; the numbers in the array are called pixels. Pre-processing deals with techniques such as noise reduction and the enhancement of details. The pixel array is fed into the processor, a general-purpose or custom-built computer that analyses the data and makes the necessary decisions. Segmentation is the process that partitions an image into objects of interest. The segmentation of images should result in regions which correspond to objects, parts of objects or groups of objects which appear in the image. These features of these entities, along with their positions relative to the entire image, help us to make a meaningful interpretation. Description deals with the computation of features such as size, shape, texture, etc. suitably for differentiating one type of object from another. Recognition is the process that identifies these objects. Finally, interpretation assigns meaning to an ensemble of recognised objects.

Any description of the human visual system only serves to illustrate how far computer vision has to go before it approaches human ability.

In terms of image acquisition, the eye is totally superior to any camera system yet developed. The retina, on which the upside-down image is projected, contains two classes of discrete light receptors – cones and rods. There are between 6 and 7 million cones in the eye, most of them located in the central part of the retina called the fovea. These cones are highly sensitive to colour and the eye muscles rotate the eye so that the image is focused primarily on the fovea. The cones are also sensitive to bright light and do not operate in dim light. Each cone is connected by its own nerve to the brain.

There are at least 75 million rods in the eye distributed across the surface of the retina. They are sensitive to light intensity but not to colour.

The range of intensities to which the eye can adapt is of the order of 1010, from the lowest visible light to the highest bearable glare. In practice the eye per forms this amazing task by altering its own sensitivity depending on the ambient level of brightness.

Of course, one of the ways in which the human visual system gains over the machine is that the brain possesses some 1010 cells (or neurons), some of which have well over 10,000 contacts (or synapses) with other neurons. If each neuron acts as a type of microprocessor, then we have an immense computer in which all the processing elements can operate concurrently. Probably, the largest manmade computer still contains less than a million processing elements, so the majority of the visual and mental processing tasks that the eye-brain system can perform in a flash have no chance of being performed by present-day man-made systems.

Added to these problems of scale is the problem of how to organize such a large processing system, and also how to program it. Clearly, the eye- rain system is partly hard-wired but there is also an interesting capability to program it dynamically by training during active use. This need for a large parallel processing system with the attendant complex control problems illustrates clearly that machine vision must indeed be one of the most difficult intellectual problems to tackle.

Part of the problem lies in the fact that the sophistication of the human visual system makes robot vision systems pale by comparison.

Developing general-purpose computer vision systems has been proved surprisingly difficult and complex. This has been particularly frustrating for vision researchers, who experience daily the apparent ease and spontaneity of human perception.

As can be seen from the information given above, developing a computer-vision system requires knowledge. Hence the eye performs better and better than even the best computer-vision system, the very complex eye-brain system also requires more comprehensive knowledge to build. When man understands and discovers the functioning of the eye-brain system, better computer-vision systems will be developed. That means the eye-brain system (like other systems in the body of human being) is a very deep and rich knowledge source. As this system has links with other systems in the body, it obviously shows that the maker of these systems is One who knows everything about every single part of the whole body, for not only the eye-brain system but the entire body develops accordingly.

So, who can be the maker of this fantastic eye-brain system? If it is said that it is self-creating, this has no meaning because everybody knows that such an important system cannot create itself, just as a computer-vision system cannot give itself existence. As for chance, is it possible for such a system, which is full of knowledge for human being to imitate in order to develop computer- vision systems, to be made by chance at all? Of course not. So who is the maker of the eye-brain system?

The maker or the creator of this system can only be One who has supernatural power. He says in His Holy Book:

‘Have we not made for him (human being) a pair of eyes?’ (The Holy Qur’an 90.8).

Yes, indeed, He made them just as He made the whole of the rest of the cosmos with His unlimited knowledge.

REFERENCES

• DAVIES, B. R. (1990) Machine vision: theory, algorithms, practicalities, Academic Press, New York.
• GOWRISBANKAR, T. R, & BOURBAKIS, N. G. (June 1990) Specifications for the development of a knowledge-based image-interpretation systems, Engineering Applications of Al, Vol. 3, pp.79-9O.
• MAJUMDER, D. D. (1988) Computer vision and knowledge based computer systems, Institution of Electronics and Telecom. Engineers, Vol. 34, No 3, pp. 230-245.
• MULLER. B. & REINHARDT. J. (1990) Neural networks, an introduction. Springer- Verlag Publications, New York.
• RODD, M. G. (1990) Knowledge based vision systems, Knowledge Engineering, Vol. 2. ed, Adeli, II., McGraw [Jill, pp.245-276.
• ROSENFELD, A. (August 1985) Machine vision for industry: tasks, tools and techniques, Image and Vision Computing, Vol. 3, No 3. pp.122-135.
• SANDERSON, 1