History of discussion about singularity

Vernor Vinge, a science fiction writer, first wrote about the vision of technological singularity and coined the term in 1993. He wrote, "Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended."
Ray Kurzweil, inventor and futurist, is a fervid proponent of technological singularity. Kurzweil predicts the timeline of singularity as follows:

• By 2019, a $1000 PC will have the computing power of the human brain. It will be capable of performing 20 million billion calculations.
• By 2029, a $1K PC will be a thousand times more powerful than the human brain; the human brain itself will be successfully reverse engineered.
• 2045 is singularity: machines will have surpassed humans in intelligence and in fact will have created next-generation robots even smarter than themselves. We should either merge with our creations or step out of their way. Immortality!
• By 2055, $1K of computing power will equal the processing power of all the humans on the planet.

In 2011, Ray Kurzweil sponsored a movie/documentary about singularity, titled "Transcendent Man," which has been screened in five major cities in the U.S., as well as London. In December 2012, Kurzweil was hired by Google as a director of engineering to "work on new projects involving machine learning and language processing."
In 2000, Bill Joy, a well known computer scientist and the primary figure behind the BSD operating system (on which MacOS was built on) and the widely used Java programming language, joined this discussion. In a Wired magazine article, "Why the future doesn’t need us," Joy declared, in what some have described as a "neo-Luddite" position, that he was convinced that growing advances in genetic engineering and nanotechnology would pose severe risks to humanity.

Arguments and counterarguments about the feasibility of singularity

Proponents of singularity often cite Moore’s law to support their claim. Moore’s law states, crudely, that the capacity of computer chips doubles every two years. That is, the speed and capability of computers grows at an exponential speed. Such an exponential growth is a powerful enabler. Consider the series 1,2,4,8,16,32… The small increments in the beginning may be misleading about the overall speed of the series’ growth. The 20th element in this series would be 1 million. The 266th element in this series is 1080, which is more than the number of atoms in the universe.
Proponents of singularity argue that thanks to this exponential growth, the processing powers of computers will reach such high levels in the next few decades that it will be possible to simulate the human brain in high fidelity. The workings of each neuron in the brain will be simulated in real time, achieving a full simulation of the brain. At that point, the computer will essentially have the equivalent of human intelligence. In the succeeding years, with the increase in capacity, the computer intelligence will be several folds ahead of human intelligence.
Opponents of the feasibility of singularity cite that exponential growth is hard to sustain. Exponential growth is seen in the beginning of a series, but then due to limitations/adversities, most series will level off and stay constant. An example of this is the population of rabbits. Initially, the increase is exponential; however, due to scarcity of food sources and an abundance of predators, the population stabilizes around a constant. Therefore, opponents of singularity argue that the exponential progress of computer processing speeds will similarly hit a brick wall. At the chip level, physical issues such as heating will make exponential speedup unsustainable. At the cluster level, latency, consistency, and scalability issues will also prevent exponential growth.
Underlying all of Kurzweil’s ideas regarding the progress of technology and the singularity is the Law of Accelerating Returns. This Law states that technological progress occurs exponentially instead of linearly, meaning that each new advancement enables several higher advancements instead of just one higher advancement, and, concordantly, every year brings more useful inventions and discoveries than were made in the last. The first generation artificial intelligence (AI) approaches failed, but simulating a human brain may work if we know the workings of the brain in excruciating detail. As a promising development, recently, “deep learning” and “deep neural networks” technologies achieved great success in image and speech recognition tasks.
However, the opponents of singularity like to point out that the workings of the brain as a whole are still a big mystery. We have information about the rough mechanism of how a neuron works. An excited neuron can transmit a signal to a neighboring neuron through its synapses. But, there is no clear explanation about how thought occurs from this process. Brain-scanning techniques are improving, as they are based on computers, but the brain may throw us more complex surprises as we learn more about it.
In fact, much of the brain power comes about through organic materials, and the very low-level analog physical interactions between these materials. These physical phenomena could be close to impossible to model/simulate in a digital environment. Henry Markram, lead researcher of the "Blue Brain Project" for simulating mammal brains at the molecular level, has stated that "it is not [their] goal to build an intelligent neural network." He claimed, “[That would] be very difficult because, in the brain, every molecule is a powerful computer and we would need to simulate the structure and function of trillions upon trillions of molecules as well as all the rules that govern how they interact. You would literally need computers that are trillions of times bigger and faster than anything existing today."
Another relevant question is whether we can develop the parallel processing architectures needed to support the parallel processing that goes on in the brain. The brain uses far more parallel processing than exists in most classical computing designs.
Even if a computer successfully simulates the human brain, whether such a computer design will be “scalable” to two times, ten times, or even one hundred times the brain’s normal power is an unknown; for the human brain’s computation power may be inherently unscalable. Also, if a computer models the human brain, human emotions would also be modeled. Would the resulting computer be stable? As it scales up, would it become existential and suicidal, or perhaps become an arrogant killer?

The aftermath of singularity

Several questions are raised about the aftermath of singularity. Can a downloaded personality replace the spirit? How does this equate to living forever? Singularity promises are similar to claiming that you can live forever by cloning yourself. One copy dies, but another digital copy survives. But it is clear that the copies are different entities.
And it is also clear that this is not true immortality. If we stretch singularity’s approach to immortality a little further, we can argue that humans can achieve immortality through their work or art. And to this idea Woody Allen provided the best response: "I don’t want to achieve immortality through my work. I want to achieve it by not dying."

References

• Vernor Vinge, “The Coming Technological Singularity: How to Survive in the Post-Human Era”, 1993, available from https://www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html
• Ray Kurzweil, ”The singularity is near: When humans transcend biology”, Penguin books, 2005.
• Bill Joy, “Why the future doesn’t need us”, Wired 8 (04), 2000.  
• Henry Markram, “The blue brain project”, Nature Reviews Neuroscience, 7 (2), 153–160, 2006.

For this tree, the seed is intelligence, water is determination, air is patience, and effort is excitement. One thing to consider is that imagination and fantasy are not the same concept. Whereas imagination is the seed of realistic, logical plans and projects designed in accordance with long term goals regarding life and the future, fantasy involves short term projections out of proportion with reality, emotion, and rational thought. In this sense, there is no imagination that does not sprout its roots towards the goal and does not disperse the seeds of excitement to the human soul. This is because imagination is like a pile of emotions that sustain us in the years ahead. It is the energy that powers the heart, the mystery juice which injects excitement into our minds, a framework of desires that bind us to life. It helps us to believe that one man’s expectations for life are as good as his dreams; his earnings will match his excitement. Imagination and the resulting “excitement” is a great blessing that brings out treasures, love, and skills hidden inside us. Above all, imagination can help us to achieve success in all of our endeavors. This is true of science, too.

Defining scientific success can be tricky. It requires effort, hard work, and observation to achieve scientific success. But most importantly, it requires intelligence which is a blessing. Just like a licensed driver controlling a car, the brain’s performance improves with the skill of its driver. To maximize the brain’s potential, it is very important to integrate the heart and conscience with the use of intelligence.

Can we explain the reality that there is only a limited number of scientists because they were born as such or inherited this quality genetically? The reality is thousands of babies are born with brain capacities similar to that of scientists’ in terms of anatomy, histology, physiology, and biochemistry. Even though human intelligence is partly determined by inheritance, there are other factors. If the case was as simple as inheritance, would Einstein say, “I do not have any special skill, but I am passionately curious”? When Thomas Edison said, “1% of genius is inspiration and 99% is sweat,” he captured perfectly that one needs more than just intelligence to achieve success – one also needs to maximize that intelligence through hard work.

Scholar Bediuzzaman Said Nursi summarized the stages of knowledge development of the human mind in seven steps. “One first imagines something (tahayyul), then conceives of it, and clothes it in a form (tasawwur). Afterwards, one reasons and reflects on this thing (taaqqul), then confirms it (tasdiq), and then has full conviction of it (iz’an). Then they fully support it (iltizam); then they become committed or devoted to it (itiqad)” (The Words, Gleams of Truth).

Each of these stages is subject to different rulings. They form the ladder to higher meanings. Thus, if a person is unaware of the step occupied, he/she ends up remaining in the lower stages and acts according to the necessities of that particular level.

Therefore if we do not want to leave our scientific approach at the dimension of “sophistry,” which starts with imagination (tahayyul), we have to continue our intellectual and ideological journey all the way to the end. Imagination, which is often overlooked, bears the seed of intelligence, which is a blessing offered from the infinite ocean of God’s wisdom. Imagination is the unprinted photo of our intellectual aptitudes. How do you like to print and uncover your dreams?

Various studies exist about these compartments – which controls the expression of the soul through the body, which is responsible for memory, intelligence, belief, hatred and love. The brain’s two hemispheres have different functions and specialize in different areas. For example, linguistic abilities and tongue functions are usually controlled by the left hemisphere, while musical abilities and the ability to comment and analyze complex images are controlled by the right hemisphere. However, it is not possible to state that the forebrain functions independently from the hemispheres of the brain. The Wernicke’s area, located on the upper tail of the temporal lobe, is responsible for language comprehension, and the Angular Gyrus that is a mediator between sound and vision, is responsible for linguistic and mathematical operations. For this reason, the Angular Gyrus is accepted to be related with superior consciousness and intelligence. Scientists have proven that intelligence and consciousness decrease more dramatically upon the destruction of these areas when compared to the destruction of the forebrain. Therefore, for the spiritual functions to be able to operate in the body, it’s essential that these areas of the brain function.

The more accepted view, at least today, is that spiritual characteristics and personality function independent of the brain. A convincing proof disregarding this view is that during many tumor surgeries, even though the body’s vital activities remain intact, after removing tissue from the brain, spiritual characteristics sometimes degrade. Another proof is the surgery performed to remove the forebrain of depressed patients (a prefrontal lobotomy). The patients vital activities were not disrupted upon surgery and their mood improved for a while. For a short amount of time, patients stopped suffering from serious neurological and psychiatric disturbances. However, when the patients were observed several years after the surgery, it was concluded that the patients lacked the functions of the forebrain and these surgeries were abandoned. The most prevalent side effects related to the removal of the forebrain were:

• Loss of the ability to solve complex social problems,
• Loss of the ability to take sequential steps in order to resolve a complex problem,
• Loss of the ability to multitask,
• Loss of determination, effort and desire to do activities,
• Loss of the ability for a community response when faced with a problem,
• Loss of ethical values and the feeling of shame,
• Loss of coherent thought even though the ability to speak and understand what is said is not lost,
• Instant emotional changes: from kindness to frustration, passion, violence and insanity,
• Inability to use artistic or naturally existing talents for the sake of a purpose.

These dysfunctions made it apparent that there was a correlation between the forebrain and spiritual characteristics.

Single neurons, not from the human brain, were inoculated in the lab and an experiment was carried out.. Studies showed that these neurons were not a means to highly intellectual capabilities, such as intelligence; whereas when neurons taken from the forebrain were inoculated and experimented on, it was found that they were a means to uniquely human capabilities like intelligence. Complex problem solving and the capacity to make discoveries were found to be correlated with the forebrain. It is not possible to explain such a fact without taking the soul into consideration. Spiritual characteristics such as sadness, happiness, joy, peace, patience, compassion, and love are all the results of complex interactions within the brain. However, it is not convincing to say that they are only results of electrical or chemical interactions within the brain.

The prefrontal area is where all thoughts coming from lower brain areas (such as sight, hearing, and feeling cortex areas, and the thalamus and hypothalamus) are gathered and processed to be enriched. Many information and memories gathered from different parts of the brain come together in the prefrontal area to be synthesized into deeper thoughts. Active memory is when different pieces of information are synthesized together and form a thought; and this thought is later acted upon. This allows to accurately surmise future events, and to plan for them.

It also allows for a rapid, appropriate response to the signals gathered by the five sensory channels. It allows for predicting the consequences of actions before performing them and for resolving problems of a complex medical, mathematical, ethical, moral, or philosophical basis. It can also postpone emotional responses for an appropriate time, measure the consequences of verbal and physical communications, use will power to measure the ethical and moral consequences of actions, and use information gathered from all channels to diagnose a problem. People who have damaged prefrontal cortexes, have serious difficulty synthesizing information and processing it to form coherent thoughts (active memory). This situation shows us that the prefrontal cortex enables consciousness and highly intellectual actions.

Another function of the prefrontal cortex is to enable focus about a specific topic. Taking this into consideration, the prefrontal cortex is semi related to attention. An existing proof for this is that people with damaged prefrontal cortexes are unable to concentrate and their focus is easily disturbed. Individuals whose prefrontal cortex has been removed or damaged can acquire a spiritual consciousness; however due to their handicap, they are unable to express their thoughts in a serial and sensible fashion for more than a minute. They can be easily distracted from the original subject. The prefrontal cortex allows an individual to accomplish a process of thought despite distractions, and allows for communication between the soul and the mind.

To sum it up, the prefrontal cortex is correlated with intellectual characteristics such as consciousness, intelligence, and self control, and also relates to how well we perform our moral values. For example, if the prefrontal cortex is unable to function, the individual does not feel shame anymore. This makes people believe shame is related only to the prefrontal cortex. However, the brain is merely a bridge between the body and the soul. Our brain is a bridge and curtain for our soul, which is the source of our spiritual characteristics. When we physically have a problem with our brain, the soul carries on its existence as before, however it is unable to express its consciousness, intellect, and will power in this material world.

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

“Good grades pay off literally. Teachers have long said that success is its own reward. But these days, some students are finding that good grades can bring them cash and luxury gifts… Baltimore schools chief Andres Alonso last week promised to spend more than $935,000 to give high school students as much as $110 each to improve their scores on state graduation exams… The most ambitious experiment began in September, when seven states-Arkansas, Alabama, Connecticut, Kentucky, Massachusetts, Virginia and Washington-won spots in an Exxon/Mobil-funded program that, in most cases, pays students $100 for each passing grade on advanced placement (AP) college-prep exams.” (USA Today, 01/27/2008)

The above is just one example of how some educators have developed additional incentives to motivate their students in hopes of them increasing their performance in school. A majority of students have gone through educational systems in which such rewards are present, albeit not as extreme as in the example above. In such systems, money, student awards, honorary degrees or advanced placement opportunities aim to improve student performance and motivation toward meeting expected goals through increased competition. Some of these rewards may not be tangible: simple praise, public appreciation and positive feedbacks are examples of intangible or verbal rewards. Both tangible and intangible rewards are advocated and implemented by many educators. Today they constitute a crucial part of our educational system. The reasoning behind rewarding or praising students is simple: If one reinforces a behavior by bestowing a reward for it, it is believed that behavior is more likely to be developed and maintained by the recipients of the reward, but is this really the case?

Some psychologists, known as behaviorists, believe that human nature is utilitarian: behaviors are influenced by their consequences. The use of reinforcement and punishment to control and manipulate human behavior reflects this view. The former allows educators to increase the likelihood of a behavior, while the latter is employed to decrease its likelihood. They argue that one can improve and nurture a particular behavior through reinforcement. An educator with a behaviorist point of view, therefore, believes that learning can be achieved in part by reinforcing desired behaviors with rewards, medals, degrees, grades or praises.

Even though these concepts have been valued and applied widely by many educators for decades, there are some psychologists and educators that do not agree with the current use of either tangible or verbal rewards for educational purposes. Their reasoning is based in part on motivation theory. According to motivation theory, people are motivated by either extrinsic or intrinsic drives. Extrinsic motivation is defined as any reinforcement to support, develop or maintain a behavior provided by external conditions, such as rewarding a student with money or an award. Intrinsic motivation, in contrast, implies doing an activity for its own sake because it is inherently interesting or enjoyable. The most important distinction between intrinsic and extrinsic motivation lies in their source: an internal (intrinsic) versus external (extrinsic) source of motivation. Money, praise, awards or promotions are examples of external motivators, which are used to nurture some behaviors, while inner willingness and ambition, are sources of intrinsic motivation (Ryan & Deci, 2000).

There are educators who advocate motivation theory but disagree with the idea of rewarding their students since they believe that external rewards undermine intrinsic motivation. As students are extrinsically rewarded, they are more likely to lose their intrinsic motivation to sustain their behavior. Empirical studies have shown that students who did not receive any tangible rewards previously will begin to associate their behavior to rewards they have been given, eventually leading students to neglect performing an activity for its own sake when there is no reward present.

Amabile, Hennesey and Grossman (1986) examined the influence of rewards and perceptions of activity on creativity. In their study, children were rewarded with the opportunity to take pictures with instant cameras after they completed a task requiring creativity. For young children this reward was pretty attractive. The perception of the activity was manipulated by calling the activity “play,” “work” or by not specifying its nature. They found that children with the perception of play but without the promise of a reward showed a higher degree of creative performance than other children who were promised a reward. It appears that the “label” of the activity and the existence of rewards influences the creative performance of children. Another phenomenon considered in this study was the variation of the children’s behavior in activities in which they were given choice and no choice at all. The authors showed that in activities where participants were not given options, rewarded participants outperformed non-rewarded participants while non-rewarded participants were superior to rewarded participants in choice situations. There were three educational implications discovered as a result of this study:

1. The way a teacher presents an activity or responsibility counts! The presentation of an activity as “play” is correlated with higher performance than if it is presented as “work” or if its nature is not specified.
2. Rewards may undermine activities requiring intrinsic motivation. Intrinsic motivation is important for creativity. Biographies of inventors show that inventions are the result of perennial attempts and thus require a high degree of intrinsic motivation. Therefore, educators need to think twice about whether rewarding their students encourages or suppresses creativity in a given situation.
3. Rewards are detrimental when people have intrinsically chosen to do something, but not necessarily when an action is required of them. This point leaves some room for rewarding students. A good portion of the educational curriculum might be outside of students’ interests. For those kinds of activities, rewards may work to motivate them to participate in these activities, however, if students choose an activity, and if they have already had the motivation to do it, rewards will most likely not improve their performance.

This study shows that educators need to answer the following three questions before deciding whether rewards should be used:

1. Do the students already have intrinsic motivation to complete this activity?
2. Did the students choose to complete this activity?
3. Has someone else already asked or encouraged them to complete this activity?

Another study by Mueller and Dweck (1998) revealed the influence of praise on performance. In a culture of appreciation, praise is expected to encourage people and motivate them to be more successful. This study questions whether praising students is necessary and how praise can affect students. The authors of this study designed six experiments to examine the effects of praise for intelligence and praise for effort and how they influenced the participants. The authors discovered that when praising students for their intelligence the students’ achievements and motivation were lower than those students who were praised for their effort. Children whose intelligence was praised cared more about performance goals while children whose effort was praised cared more about learning. Moreover, the students whose intelligence was praised displayed lower levels of task persistence and enjoyment, increased low-ability attributions, and worse task performance when they experienced failure than children praised for their effort. Another crucial result of the study concerns the concept of intelligence. The same group of students, who were praised for their intelligence, considered their intelligence to be a fixed trait whereas the other group considered it a flexible trait. While the first group (students who were praised for their intelligence) tried to look smart even by sacrificing learning, the second group (students who were praised for effort) attempted to learn new things even if doing so was difficult or risky. Another interesting result was in the discrepancy in performance between the two groups of students when they encountered difficulty in their tasks. The study found that the performance of the students in the first group was impaired in comparison to the second group whose performance increased.

Considering the controversy surrounding rewarding versus not rewarding, the authors of this study focused on the issue of “what to praise.” Praising some traits such as intelligence, ability or creativity might be detrimental for the traits’ continuity in students while praise for endeavoring towards utilizing their intelligence; creativity or any other ability appears to increase the likelihood of learning. This issue raises some serious questions about the current practices of gifted education. Today, most education systems in the United States and other countries “identify” gifted students through tests and other means of determining intelligence, some even group students in separate classes. These students are well aware why they are being treated in this way. In other words, the system itself is continuously providing implicit rewards for the students identified as gifted. From Dweck’s point of view, this policy would impair learning because the student’s performance is not rewarded, while their “intelligence” is.

Another important question raised in this study is the “appropriate time of rewarding.” In research by Kamins and Dweck (1999), the influence of praise and criticism was observed in several ways: when directed variously toward the person performing an activity, toward the process of the activity itself, and toward the activity’s outcome. In their research some of the participants were praised with “I’m very proud of you”; some as “That’s the right way to do it,” whereas others were told: “You found a good way to do it, can you think of other ways that may also work?” When these groups were compared, the researchers found that the process-praised group was superior in rating the products of their work in comparison to the outcome-praised group. It was also noticed in that participants that were members of the process-praised group had increased self-esteem and were more persistent than the other groups. Those findings were the same when criticism was used in place of praise. The lesson to take from this research is clear: we should provide feedback, praise or criticism, about the process rather than the person and the end product.

There are other side effects of rewarding students. When we reward, we actually teach two different things in a very subtle way. First, we teach the student to do something for the purpose of another thing rather than for its own sake. Second, we teach the student not to do anything for free. Both of these have important drawbacks: we kill their enthusiasm and intrinsic motivation because they have been given another reason to do their activities and we implicitly redirect their valuation to external things that then could cause them to undervalue the task itself. This means that they will no longer repeat the same behavior when the rewards are discontinued. There is a paradox here. On the one hand, behaviorist psychologists defined learning as an overt change in behavior. Since the behavior (or learning) fades away later along with the rewards, we cannot argue that rewards did make a permanent influence on behavior. On the other hand, by using these methods we teach students to be independent individuals who do not care about others. Especially in today’s conditions, when many jobless, homeless, and hungry people are trying to survive all around the world, self-centered individualism should not be the desired outcome of education. Infusing people with seemingly innocent notions of “money talks” or “There ain’t no such thing as a free lunch,” might function as a rationale for unethical and socially irresponsible behaviors. Educators should not bolster a self-centered individualism by excessively rewarding students. Such rewarding may contribute to the kind of irresponsibility that allows for the kinds of unwise decisions that lead us to the current recession.

The overall point that I have advocated in this article is not an argument strictly against rewarding. Rather, knowing more about the individual, the conditions, and possible consequences related to rewards are vital for an effective education. How can this be achieved? An example is the treatment of Prophet Muhammad (peace be upon him) toward a child who was making fun of the Adhan (the Muslim call to prayer). He approached one of them and praised one of them by saying, “What a beautiful voice that you have.” Caressing his hair, he continued by asking the boy “Would you mind to give the call to the prayer in the masjid with such a beautiful voice?” He took the child to the masjid and this child, known as Abu Mansura, later became a well-known muezzin (the individual who makes the Muslim call to prayer). This example stands as evidence of the importance of rewarding. On the other hand, in traditional Sufi thought, the purpose of praying and good works is not merely entering Heaven or avoiding Hell, instead, acquiring the acceptance and approval of God is seen as the ultimate goal for a person. Such a purpose may not apply to everyone, because the majority of people may need a tangible reward to do something, just like majority of students want to achieve a certain goal primarily in order to gain rewards, however, higher levels of spirituality can be developed toward an overarching goal that can be achieved by only a select few. Therefore, these two examples do not in fact conflict. The messages derived from educational and psychological literature, as well as religious teachings, seems to converge, even though rewards may help most people to perform better, rewarding is not helpful and even detrimental to a minority group of people who already display high levels of motivation and ambition to achieve.

As a result, rewarding is not necessarily helpful or detrimental. Educators should give up applying their ready-made methods to all students. Ideally, intrinsically motivated students should not receive external rewards because this would switch their focus to material prizes. When needed, educators should try to provide feedback directed toward the process rather than the outcome or the person. Also, they should foster student’s efforts rather than their ability. The decision about rewarding should be based on knowledge regarding the person to be rewarded, the target or direction, and even the timing of the reward. Otherwise, for some individuals the rewards might produce an effect diametrically opposed to its intended purpose.

References

• Skinner, B. F. 1953, Science and Human Behavior. New York: The Free Press.
• Kamins, M. L. & Dweck, C. S. 1999. “Person versus process praise and criticism: Implications for contingent self-worth and coping.” Developmental Psychology, 35, 835-847.
• Mueller, C. M. and Dweck, C. S. 1998. “Intelligence praise can undermine motivation and performance.” Journal of Personality and Social Psychology, 75, 33-52.
• Ryan, R. M. & Deci, E. L. (2000). “Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions.” Contemporary Educational Psychology, 25, 54-67.
• Amabile, T. M., Hennessey, B. A. & Grossman, B. S. (1986). “Social Influences on Creativity: The Effects of Contracted-For Reward.” Journal of Personality and Social Psychology, 50, 14-23.

As cultural differences continue to be an important part of life, we need to examine how different cultures make sense of the world in terms of the meanings that represent the mind, and within which the concept of intelligence is defined. First, I will explore some basic philosophical views about the conception of intelligence which I believe constitute the root of cultural constructions of the notion.

Historical conceptions of intelligence

Some scholars of the past, such as philosophers, writers and theologians, explored intelligence but did not attempt to define the nature of intelligence. For example, in the Odyssey, Homer distinguished between good looks and good thinking. He remarked that one may have a poor physical appearance but may speak in an articulate way. Another man may be handsome but lack the ability to communicate well with others. Plato made his comments regarding the nature of intelligence in Theaetetus. In the dialogue, Socrates asks Theaetetus to imagine that there exists in the mind of man a block of wax that is of different sizes in different men. The block of wax can also differ in hardness, moistness and purity. Socrates suggests that when the wax is pure and sufficiently deep, the mind will learn easily and will not be subject to confusion. Because impressions in the wax are clear, it only will think things that are true. However, when the wax is impure or very soft or very hard, there will be defects in the intellect. People whose wax is soft will be good at learning but be apt to forget. People whose wax is hard will be slow to learn but will retain what they learn. People whose wax is shaggy or gritty, or whose wax is a mixture of earth and dung will have only indistinct impressions. Those with hard wax will have the same because there will be no depth to their thoughts (Sternberg 1985).

In the Posterior Analytics Book, Aristotle conceived of intelligence in terms of “quick wit.” For example, an intelligent person seeing someone in conversation with a man of wealth might conclude quickly that the person is seeking to borrow money from the wealthy man.

Cultural conceptualization of intelligence

In recent years, researchers have found that people in Eastern and Western cultures often have fundamentally different ideas about intelligence. Richard Nisbett, in The Geography of Thought, argues that these differences are due to the different cognitive styles of both cultures, including how intelligence is understood. He suggests that people in Western countries tend to view intelligence as a means for individuals to devise categories and to engage in rational debate, while people in Eastern cultures see it as a way for members of community to recognize contradiction and complexity and to play their social roles successfully.

I think, as Nisbett says these differences between Eastern and Western views of intelligence are related to differences in the basic cognitive processes of people in those cultures. In most cases, Western notions of intelligence are not shared by other cultures. For example, at the mental level, the Western emphasis on speed of mental processing (Sternberg 1981) is not shared by many cultures. In contrast, people in Eastern countries may even be suspicious of the quality of work done very quickly and emphasize depth rather than speed.

Similarly, Chen (1994) found three factors underlying Chinese conceptualizations of intelligence: nonverbal reasoning ability, verbal reasoning ability and memory. These factors differ substantially from American people’s conceptualizations of intelligence which have mostly mental attributions, such as: practical problem solving, verbal ability and social competence (Sternberg 1981).

About Eastern notions of intelligence, Das (1994) has suggested that in Buddhist and Hindu philosophies, intelligence involves waking up, noticing, recognizing, understanding, and comprehending but also includes such things like determination, mental effort, and even feelings and opinions (Sternberg and Kaufman 1998). In a related study, Yang and Sternberg reviewed Chinese philosophical conceptions of intelligence. The Confucian perspective emphasizes the characteristic of benevolence and of doing what is right. Again, Taoist culture emphasizes the importance of humility, freedom from conventional standards of judgment, and full knowledge of oneself which appears well-correlated with Plato’s “Know yourself” notion.
Differences between cultures in conceptions of intelligence have been recognized for some time. In a study, Gill and Keats (1980) reported that Australian University students value academic skills and the ability to adapt to new situations as critical to intelligence, while Malay students emphasize practical skills, such as speed and creativity, as well as both social and cognitive attributes in their conceptions of intelligence.

Studies done in Africa provide evidence of substantial differences in the notion of intelligence. Serpell (1974, 1977, and 1982) found that people in Zambia emphasize social responsibilities, cooperativeness, and obedience as important to intelligence; intelligent children are expected to be respectful of adults. In Zimbabwe, the word intelligence means to be prudent and cautious, especially in social relationships (Dasen 1984). As in many Eastern countries, service to the family and community, politeness and respect for elders are seen as the key to intelligence.

In his study. The Geography of Thought, researcher Nisbett, compares the Greeks and the Chinese in order toshow the different cognitive styles of East and West, which in turn causes different conceptions of intelligence. The Ancient Greeks were known for their strong sense of agency, the ability to exercise free will, while the Chinese found their belief system in harmony. The Greeks also had a curiosity about the nature of the world. They were not satisfied just to make systematic observations about the world; they were also interested in the underlying principles of their observations. Debate and confrontation were discouraged in Chinese society, whereas they were encouraged in Greece. The Eastern way of life is based on the principle that the life is ever-changing and filled with contradictions. It was interrelationships that defined the Chinese; who they were with would define who they were and what roles they were to fulfill. The sense of identity was defined by social context, by individual attributes while in Greece, objects and people would be analyzed separately. These fundamental differences in thought patterns have implications that extend to all aspects of life.

At this point, the differences seen in the individualistic culture of the West and the collectivistic (Petersen 2004) culture of the East need more attention to provide a better picture of the notion of intelligence in terms of its cultural construction.

Different thinking styles of East and West

There are some assumptions which are thought to be true by most Westerners. That is, they are individuals with characteristics that make them distinctive; moreover, we want to be unique. Westerners are in control of their own behavior and feel better when they believe that they can choose and control the outcomes of their actions. They are goal-oriented and success-driven (Nisbett 2004), and relationships can sometimes interfere with attaining success. Personal success and feeling positive about oneself are important for the sense of well-being.

Conversely, Easterners are less concerned with personal success; they are far more group driven (Nisbett 2004). Their sense of well-being is related to their being in harmony with those around them; moreover, rules that apply to relationships are not universal, instead relationships are dictated by the context and are unique to the roles each holds in that context. This collective sense of the East extends also to language. For example, in the Japanese language, there are many words for “I,” each dependent on the situation. Who “I” am is different when I am with a boss, co-worker, family member, or friend. In my home country, in Turkey which has strong Eastern roots; there is still the tradition of using “We” instead of ‘I’ in an effort to be humble and avoid egocentric ownership of success. In contrast, Americans rarely take context into account when asked for self descriptions. Moreover, Americans are more likely to overestimate their personal attributes (Nisbett 2004). On the other hand, it is the goal of harmony and fulfilling one’s role in social life to achieve collective success that prevails in Eastern culture.

Another notion of Eastern culture that has a substantial effect on its cultural conceptions of true intelligence and progress is self -criticism which is regarded as part of learning. Self-criticism is taught to Japanese children to help them learn how to solve problems and improve relationships with others. Again, in Turkey, the sayings of the Prophet Muhammad, peace be upon him, and some other Islamic scholars affect people’s conceptualizations of progress and success, such as, “Happy are those whose own faults preoccupy them too much to think of the faults of others” and “The strongest among you is the one who controls his anger.”

Differences in culture start early in life and continue throughout life. Western parents encourage their children to be independent. Generally they do this by focusing attention on objects. In Eastern cultures, parents focus attention on social relationships and feelings. When children grow up, the differences can be seen in experiments, for example, that Easterners’ have a superior ability to be aware of the emotions of groups of others (Peterson 2004). One theory that accounts for this difference is communication styles. In the West, it is the communicator who is responsible for making the information clear to the listener. In the East, it is the responsibility of the hearers to understand what they are told. The implication of these two contrasting styles can leave Americans feeling that Easterners are difficult to “read” because of their subtle and indirect communication styles. Conversely, Easterners may feel Americans are so direct that they may even be thought to be rude (Nisbett 2004).

Not surprisingly, the East and West have very different styles of dealing with conflict. As in ancient China, debate is still uncommon in the East. Because argument and debate pose a threat to group harmony, what Westerners consider controversial topics would not be brought up in conversation in the East (Becker 1986). Moreover, while debate is part of the rhetoric of science in the West, a skill Westerners are taught throughout their education, it is new to many Easterners that come to the West to pursue a scientific career.
Easterners and Westerners also differ in their perception of control. In Western culture, feeling in control promotes a feeling of well-being to a much greater extent than it does for Easterners (Nisbett 2004). On the other hand, Westerners have more difficulty tolerating ambiguous situations than Easterners. Westerners tend to believe that if things are going to change, it will be in the same direction and at the same level, whereas Easterners see the world as a complex dynamic system (Peterson 2004).

It is at this point that researchers (Nisbett, Peng, Choi, Norenzayan 2001) comment that Easterners appear to think more holistically, paying greater attention to relationship and context, relying more on experience-based knowledge than abstract logic and showing more tolerance for contradiction. Westerners are more analytic in their thinking, tending to separate objects from their context, to avoid contradictions and to rely more heavily on formal logic.

Conclusion

Taken all together, it is certain that people who grow up in different cultures do not just think about different things but they think differently. Thus, the connotations of the word intelligence do not only include a particular set of mental functions but also some value-based conceptions of appropriateness, such as competence, helpfulness, and so on. So, for example, when someone tells a parent that their child is very intelligent, they may be talking about child’s education, or their good relations with others, and sometimes their being a good listener, or lots of other meanings which all depend on the context of the culture in which people live.

References

• Becker, C. B., (1986). Reasons for the lack of argumentation and debate in the Far East. International Journal of Intercultural Relations, 10, 75–92
• Chen, M. J. (1994). Chinese and Ausralian concepts of intelligence. Psychology and Developing Societies, 6, 101–117
• Choi, Nisbett, Norenzayan, Peng (2001). Culture of Thought. Psychological Review: 108, 291–310
• Curtis Mary E.; Glaser Robert. Changing conceptions of intelligence. Review of Research in Education, Vol. 9 (1981), 111–148
• Das, J.P. (1994). Eastern views of intelligence. In R.J. Sternberg (Ed.), Encyclopedia of Human Intelligence (Vol.1, pp.387–391).New York: Macmillan
• Dasen, P. (1984). The cross- cultural study of intelligence: Piaget and Baoule. International Journal of Psychology, 19,407–434
• Gill R, Keats DM. 1980. Elements of intellectual competence: judgments by Australian and Malay university students. Journal of Cross – Cult. Psychology. 11:233–43
• Nisbett, Richard E. (2004). The Geography of Thought: How Asians and Westerners Think Differently…and Why. New York: Free Press
• Norenzayan, A., CHOI, i., & Nisbett, R. E (1994). Eastern and Western perceptions of causality for social behavior: Lay theories about personalities and social situations. In D. Prentice & D. Miller ( Ed.s), Cultural divides: Understanding and overcoming group conflict (pp.239–272). New York: Sage.
• Serpell, R. (1974). Aspects of intelligence in a developing country. African Social Research, No.17, 576–596
• Serpell, R. (1994). The cultural construction of intelligence. In W. J. Lonner & R. M. Malpass ( Eds.), Psychology and culture. Boston: Allyn & Bacon.
• Sternberg, R. J. (1990). Metaphors of mind: Conceptions of the nature of the intelligence. New York: Cambridge U. Press
• Sternberg, R. J., Conway, B. E., Ketron, J. L., & Bernstein, M. (1981). People’s conceptions of intelligence. Journal of Personality and Social Psychology, 41, 37–55
• Sternberg, R. J., & Kaufman, J. C. (1998). Human abilities. Annual Review of Psychology, 49,479–502
• Sternberg, R. J. (1985c). Implicit theories of intelligence, creativity, wisdom. Journal of Personality and Social Psychology, 49, 607–627
• Yang, S. R. & Sternberg, R. J. (1997a). Conceptions of intelligence in ancient Chinese philosophy. Journal of Theoretical and Philosophical Psychology, 17, 101–119

Ants and Ecosystem³

The abundance of ants on earth is legendary. They live almost everywhere except very cold places such as Antarctica and Greenland. A worker is less than one-millionth the size of a human being, yet ants taken collectively rival people as dominant organisms on the land. Lean against a tree almost anywhere and the first creature that crawls on you will probably be an ant. Stroll down a suburban sidewalk with your eyes fixed on the ground, counting the different kinds of animals you see. The ants will win hands down. The British entomologist⁴ C. B. Williams once calculated that the number of insects alive on earth at a given moment is one million trillion, 1018. If, to take a conservative figure, one percent of this host is ants, their total population is ten thousand trillion. Individual workers weigh on average between one to five milligrams, according to the species. When combined, all ants in the world taken together weigh about as much as all human beings. But being so finely divided into tiny individuals, this biomass⁵ saturates the terrestrial environment.⁶

Ants absolutely dominate in rainforests, which are the most biologically diverse ecosystems on earth. Rainforests are so diverse that in a single leguminous tree (a relative to beans and peas) in Peru, 43 species of ants belonging to 26 genera⁷ were found, about equal to the ant fauna⁸ of the British Isles. In a single square mile of tropical forest in Peru or Brazil, there may be 1,500 or more species of butterflies-twice the total number found in the United States and Canada combined.⁹ In Amazon rainforests ants and termites together compose nearly a third of the animal biomass. In other words, when all kinds of animals, large and small, from jaguars to monkeys down to roundworms and mites, are weighed, nearly a third of the weight consists of the flesh of ants and termites.

All of the ants, composing in formal taxonomic classification the family Formicidae of the order Hymenoptera, contain about 9,500 species known to science and at least twice that number of species remaining to be discovered, most of which are confined to the tropics. The total number of species of social insects is about 13,500 out of a grand total of 750,000 insect species that have been recognized to date by biologists. These numbers show that social insects seem to constitute 2 percent of all insects yet, in terms of biomass, social insects are half or more of all insects. Why are ants and other social insects so successful in the terrestrial environment? Their strength comes from their social organization.¹⁰ In addition to the question of why ants and other highly social insect species have been so successful, it is also important to understand how such a large collection of individuals maintains order and collectively accomplishes tasks without producing chaos. With potentially thousands of individual ants to coordinate, how do they make decisions regarding who does what and when, especially critical decisions regarding reproduction?¹¹ These questions become even more intriguing when you realize that ants have quite limited sensory devices to experience the world. They also have relatively simple nervous systems that process only a limited number of stimuli and are aware of only a few minutes to a few hours into the past.¹²

What is Collective Intelligence?¹³

Intelligence can be defined simply as the ability to solve problems. One system is more intelligent than another system if in a given time interval it can solve more problems, or find better solutions to the same problems. A group can then be said to exhibit collective intelligence if it can find more or better solutions than the whole of all solutions that would be found by its members working individually.

All organizations, whether they are firms, institutions or sporting teams, are created on the assumption that their members can do more together than they could do alone. Yet, most organizations have a hierarchical structure, with one individual at the top directing the activities of the other individuals at the levels below. Although no president, chief executive or general can oversee or control all the tasks performed by different individuals in a complex organization, one might still suspect that the intelligence of the organization is somehow merely a reflection or extension of the intelligence of its hierarchical head. This is no longer the case in small, closely interacting groups such as soccer or football teams, where the “captain” rarely gives orders to the other team members. The movements and tactics that emerge during a soccer match are not controlled by a single individual, but result from complex sequences of interactions. Still, they are simple enough for an individual to comprehend, and since soccer players are intrinsically intelligent individuals, it may appear that the team is not really more intelligent than its members.

With the growing interest in complex adaptive systems, artificial life, swarms, and simulated societies, the concept of “collective intelligence” is coming more and more to the fore. The basic idea is that a group of individuals (e.g. people, insects, robots etc.) can be smart in a way that none of its members is. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules.

How do ants succeed?

Now we have lots of questions to ask about the success of ants as a group. How do they govern? Who is the ruler? How do they foresee the future? How do they elaborate plans and preserve equilibrium? These, indeed, are puzzling questions. Every single ant in a colony seems to have its own agenda, and yet an insect colony looks so organized. The seamless integration of all individual activities does not seem to require a supervisor. For example, leaf-cutter ants cut leaves from plants and trees to grow fungi. Workers forage for leaves hundreds of meters away from the nest, literally organizing highways to and from their foraging sites. Weaver ant workers form chains of their own bodies, allowing them to cross wide gaps and pull stiff leaf edges together to form a nest. Several chains can join to form a bigger one over which workers run back and forth. In their moving phase, army ants organize impressive hunting raids, involving up to 200,000 workers, during which they collect thousands of prey.¹⁴

A harvester ant colony performs many tasks: It must collect and distribute food, build a nest, and care for the eggs, larvae, and pupae. It lives in a changing world to which it must respond. When there is a windfall of food, more foragers are needed. When the nest is damaged, extra effort is required for quick repairs. Task allocation is the process that results in certain workers engaged in specific tasks, in numbers appropriate to the current situation. Task allocation is a solution to a dynamic problem and thus it is a process of continual adjustment. It operates without any central or hierarchical control to direct individual ants into particular tasks. Although “queen” is a term that reminds us of human political systems, the queen is not an authority figure. She lays eggs and is fed and cared for by the workers. She does not decide which worker does what. In a harvester ant colony, many feet of intricate tunnels and chambers and thousands of ants separate the queen, surrounded by interior workers, from the ants working outside the nest and using only the chambers near the surface. It would be physically impossible for the queen to direct every worker’s decision about which task to perform and when. Consider the commercially available ant farms being sold. Since it’s forbidden to transfer ant queens, in the US ant farms are sold with only worker ants. Still they work in harmony. They build their nest, they build bridges, they collect food and they defend their colony. They do all these things without a queen. The absence of central control may seem counterintuitive, because we are accustomed to hierarchically organized social groups in many aspects of human societies, including universities, businesses, governments, orchestras and armies. This mystery underlies the ancient and pervading fascination of social insect colonies.

No ant is able to assess the global needs of the colony, or to count how many workers are engaged in each task and decide how many should be allocated differently. The capacity of an individual is limited. It cannot make complicated assessments. It probably cannot remember anything for very long. Its behavior is based on what it perceives in its immediate environment. Each worker needs to make only fairly simple decisions. There is abundant evidence, throughout physics, the social sciences and biology that such simple behavior by individuals can lead to predictable patterns in the behavior of the group. It should be possible to explain task allocation in a similar way, as the consequence of simple decisions by individuals.

Though ant colonies must respond to changing conditions, the response does not have to be perfect. It is not like clockwork, or an army, each unit snapping into place so the whole system ticks on without a hitch. There must be enough ants to collect food, often enough for the colony to survive and grow. The appropriate range of numbers should be allocated over a set of similar occasions. If the colony did not get enough food today, perhaps it will tomorrow. The process results in more or less the right number of ants engaged in the appropriate task, often enough for the colony to carry on.

Maximizing the number of ants that perform each task may not always be best for the colony. A task allocation problem for a human city is how to get the right number of firefighters to the scene of a fire. It may be a waste to have too many firefighters on the city payroll. Too many ants allocated to each task may be expensive for a colony if the excess ants could be doing something more useful than waiting around when they are not needed.

The most difficult thing to grasp about task allocation is that it is not a deterministic process even at the individual level. An ant does not respond the same way every time to the same stimulus; nor do colonies. Some events influence the probabilities that certain ants will perform certain tasks, and this regularity leads to predictable tendencies rather than perfectly deterministic outcomes. The ant is jostled in a stream of events that send it sometimes into one task, sometimes another. Task allocation is not a system in which each ant awaits the crucial event that defines its status forever. Like a twig in a turbulent river, an ant may tend to go in one direction, but there are many places it could get washed ashore, to be picked up and then swept in another direction altogether.

Stories about totalitarian societies, inexorable armies, and voracious monsters are often told as stories about ants. But ants have no dictators, no generals and no evil masterminds. In fact, there are no leaders at all.

In short, the basic mystery about ant colonies is that there is no management. A functioning organization with no one in charge is so unlike the way humans operate as to be virtually inconceivable. There is no central control. No insect issues commands to another or instructs it to do things in a certain way. No individual is aware of what must be done to complete any colony task. Each ant scratches and prods its way through the tiny world of its immediate surroundings. Ants meet each other, separate, go about their business. Somehow these small events create a pattern that drives the coordinated behavior of colonies.¹⁵

Elements of Collective Intelligence¹⁶

More is different. This old slogan of complexity theory actually has two meanings that are relevant to our ant colonies. First, the statistical nature of ant interaction demands that there is a critical mass of ants for the colony to make intelligent assessments of its global state. Ten ants roaming across the desert floor will not be able to accurately judge the overall need for foragers or nest-builders, but two thousand will do the job admirably. Individual ants do not know that they are prioritizing pathways between different food sources when they lay down a pheromone¹⁷ gradient near a pile of nutritious seeds. In fact, if we only studied individual ants in isolation, we’d have no way of knowing that those chemical secretions were part of an overall effort to create a mass distribution line, carrying comparatively huge quantities of food back to the nest. It is only by observing the entire system at work that the global behavior becomes apparent.

Ignorance is usually useful for ants. The simplicity of the ant language-and the relative stupidity of the individual ants-is, as the computer programmers say, a feature but not a bug. Emergent systems can grow unwieldy when their component parts become excessively complicated. Better to build a densely interconnected system with simple elements, and let the more sophisticated behavior trickle up. That is why an ant does not respond to all stimuli around her, namely she ignores until she decides that the stimulus is strong enough to be responded to.

Encourage random encounters. Decentralized systems such as ant colonies rely heavily on the random interactions of ants exploring a given space without any predefined orders. Their encounters with other ants are individually arbitrary, but because there are so many individuals in the system, those encounters eventually allow individuals to gauge and alter the state of the colony itself. Without those haphazard encounters, the colony would not be capable of stumbling across new food sources or of adapting to new environmental conditions.

Look for patterns in the signs. While the ants do not need an extensive vocabulary and are capable of syntactical formulations, they do rely heavily on patterns in the semiochemicals they detect. A gradient in a pheromone trail leads them toward a food source, while encountering a high ratio of nest-builders to foragers encourages them to switch tasks. This knack for pattern detection allows meta-information to circulate through the colony mind: signs about signs. Smelling the pheromones of a single forager ant means little, but smelling the pheromones of fifty foragers imparts information about the global state of the colony.

Pay attention to your neighbors. This may well be the most important lesson that the ants have to give us, and the one with the most far-reaching consequences. You can restate it as “Local information can lead to global wisdom.” The primary mechanism of swarm logic is the interaction between neighboring ants in the field: ants stumbling across each other, or each other’s pheromone trails, while patrolling the area around the nest. Adding ants to the overall system will generate more interactions between neighbors and will consequently enable the colony to solve problems and regulate itself more effectively. Without neighboring ants stumbling across one another, colonies would be just a senseless assemblage of individual organisms-a swarm without logic.

Conclusion

Ants, first of all, have something to teach us about how nature works. Any system whose behavior arises from the interactions of its components has something in common with ant colonies. Using ants and other social insects as models, computer scientists have developed software agents that cooperate to solve complex problems, such as the rerouting of traffic in a busy telecom network or internet. Another example, the famous traveling salesman problem, in which a salesman tries to find the shortest and fastest route between many cities, is almost impossible to solve definitively. But with the methods inspired by ants the problem can be solved at least approximately, because ants are very good at finding the shortest path between the food and the nest collectively. Collective robotics borrowed from collective intelligence in ant colonies is being used to manage systems composed of lots of robots in synchronization.

Nature is a book to be read by the people who approach it to live in harmony, not to dominate. We are not the owners of the beautiful things around us, but observers searching for signs which reveal the wisdom behind them.

Footnotes

1. Haskins C.P., Of Ants and Men, Prentice-Hall Inc., 1939.
2. Huxley J., Ants, AMS Press, 1969.
3. An ecosystem is a grouping of plants, animals, and other organisms interacting with each other and with the environment in such a way as to perpetuate the grouping more or less indefinitely.
4. The scientific discipline in which ants are studied is called myrmecology and it is one of the branches of the study of insects, entomology.
5. Biomass is the total weight of all living organisms in a biological environment.
6. Holldobler B. and Wilson E.O., Journey to the Ants, Harvard University Press, 1994.
7. The word Genera is the plural of genus. Genus is a taxonomic category ranking below a family and above a species and generally consisting of a group of species exhibiting similar characteristics.
8. Fauna (Flora) is the animals (plants) of a particular region or period, considered as a group.
9. http://www.savenature.org/images/pdfs/ecoandinsects.pdf
10. Holldobler and Wilson, ibid.
11. Bonabeau E., Dorigo M., and Theraulaz G., Swarm Intelligence: From Natural to Artificial System, Santa Fe Institute Studies in the Sciences of Complexity, Oxford University Press, NY:1999.
12. Holldobler and Wilson, ibid.
13. Heylighen, F. “Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map,” Computational & Mathematical Organization Theory. 1999, Vol. 5, no. 3, pp. 253-280.
14. Bonabeau et al, ibid.
15. Gordon D., Ants at Work, W. W. Norton. 1999.
16. Johnson S., Emergence Simon & Schuster. 2001.
17. The pheromone is the semiotic chemical ants use to communicate with each other and with other colonies. Every colony has its own odor. That is why ants can recognize their sisters from the same colony easily.

In this scenario, according to one school of thought, as there is no source of knowledge but intellect for human beings, then there is no alternative with which we can identify the solutions of human problems. We have to go along life’s journey under the guidance of the intellect, bearing the distress of each abortive effort.

A second school of thought opposes this view. It states that intellect alone is not enough to reveal the truths of life and/or to solve the problems. Rather, knowledge is also a source; one can differentiate between right and wrong. Moreover, humans should be able to reach the destination safely and save themselves from the fruitless and exhaustive efforts of the intellect. But unlike animals, knowledge is not an instinctive characteristic of all human beings. Humanity has been given such knowledge through selected persons; this knowledge is called revelation.

Revelation does not exclude human intellect; rather, it respects it and claims that just as the human eye needs light to see, so too does the intellect depend on the light of revelation to be able to view the world correctly.

After having looked at these two different points of views, it is time to compare them.

Today, if human intellect (even after thousands of failures) has discovered the true solution to all problems, then there is no need for human beings to take on revelation as a panacea. The intention of revelation was to solve the problems of life. If such problems have been solved without the assistance of revelation, then it is useless to discuss intellect or revelation.

But what if human intellect is yet to discover the true solution of the problems of life and is yet entangled in the whirlpool of its experiments? Then, it is worth investigating whether humans should continue pursuing these answers with the intellect or whether they should verify the claim of the revelation by adopting its approach for dealing with problematic situations.

Today’s world carries the burden of diverse problems-social unrest, political instability, wide-spread poverty and destitution, prostitution, homicide, drug addiction, alcoholism, the disintegration of the family, juvenile delinquency, terrorism, suicides and AIDS. The figures and forecasts of this virulent disease are intimidating, bearing in mind that prostitution and pornography are not only permitted in many countries worldwide, but are becoming money-spinning sources of living, particularly in the West. This is aggravated by the actuality that there is at present an ever-increasing rise in the international trafficking of children for these purposes. Moreover, infidelity is also on the rise, along with soaring crime rates in leading democratic and formerly communist countries. Undeniable facts illustrate that man-made systems that are devoid of Divine guidance have done more damage than good to humanity. It appears that the ambitions of knowledge and contentment have not been realized.

In philosophy and contemporary sciences-natural, behavioral, and social-the source of knowledge is limited to the human intellect and its five senses. Revelation is dismissed out of hand, and is not considered to be a source of knowledge; it is renounced as being nothing more than a parable or a superstition. This refusal to accept revelation as a source of knowledge is a phenomenon that has both historical and philosophical antecedents.

It is the natural attribute of human intelligence to identify and strive to destroy disorders that exist in and near it; this is part of the “quest for truth.” Astoundingly, regardless of humanity’s efforts to bring order by abolishing these disorders, they are in fact increasing the world over. It is the same intelligence that causes the disorders and the attempts to correct them; the only difference is the level of perception. Intelligence, if it is improved, or not disturbed, believes that it can alter the situation of the world by evangelizing good behavior. Any amount of teaching or intimidation will only bring about a provisional alteration in behavior; yet it is only a fundamental or basic change in humanity’s frame of mind that can change the world.

If we are not able to elucidate “why things are the way they are” by means of principles that are the results of the intellect, then the only alternative, other than abandoning our pursuit, is to search for the true principles as the source. So, one might be led to inquire, where do we turn for an appropriate understanding of reality and the purpose of life itself then? If we cannot rely on the knowledge or findings of any human source, then where can we discover a dependable source of knowledge?

Moreover, knowledge that is gained through the endeavor of the intellect does not exist in isolation, but rather is linked with the economics and politics of the culture from which it arises, as well as all the rest of that culture. The thoughts, attitudes, feelings, values, motives, purposes, goals, modes of action and organization, rituals, and institutions of a society are all interrelated and all affect one another. The attention to nature, the idea that there are causes for things, that there is an underlying harmony and something constant behind change and diversity, and that the Universe is regulated by laws are attitudes that result from religion. These are pre-requisites that must be fulfilled before science can begin.

In fact, the Qur’an attaches great worth to human intellect. It appeals to the intellect of its addressees in order to persuade them about its legitimacy as the word of God. It denounces those who fail to employ their intellects as being undeserving of God’s blessings, and being no better than animals. Furthermore, those who do not have the appropriate mental ability-for example, children before they reach the age of puberty and the mentally ill-are not expected to fulfill the requirements imbedded in the message of Islam according to the Prophet.

Nevertheless, these facts about the intellect do not signify that human intellect does not have its limitations. It can be swayed to believe, for instance, what opposes its own instincts. Unrestrained eagerness for a definite model, equally, can cause the intellect to be prejudiced to what is undeserving of endorsement. There are, likewise, some intriguing problems which are beyond its grasp. All these limitations restrain the efficiency of human intellect if used without restrictions. It appears to yearn-given these restrictions-for external guidance. Divine Revelation in the form of the messages of the prophets (peace and blessings be upon them) is this very guidance. The Qur’an and Sunna (way of life of the Prophet) represent the final version of this guidance. The Qur’an has given an account of the connection between Divine Revelation and human intellect as being light upon light (Qur’an 24:35); that is, the guidance of human intellect is principally a light, although with defects (and therefore not bright), while the Divine revelation in the form of the Qur’an is a brighter light to compensate for the imperfections of the intellect. Hence, a brighter light (the Divine revelation) exhibits the path to a less bright light (the human intellect).

Islam, being the final source of revealed knowledge in the modern world, also equips us with the techniques to differentiate between good and evil. It does not base our knowledge of wickedness and virtue on mere intellect, desire, intuition, or experience that are derived through the senses; these frequently experience changes and alterations and therefore fail to offer explicit and unchanging ethical norms. As an alternative, Islam provides us with an unbiased source, the Divine revelation, as is obvious in the Book of Allah and the Sunnah of the Prophet. This source recommends a standard of moral conduct that is lasting and universal, holding true in every age and under all circumstances.

References

• Barbour, Ian G., “Religion in an Age of Science”, HarperCollins: New York, 1991.
• Hooykaas, R., “Religion and the Rise of Modern Science”, Scottish Academic Press, 1972.

Psychologists have uncovered and grouped intelligence into three main groups: abstract intelligence (the ability to understand and manipulate using verbal and mathematical symbols), concrete intelligence (the ability to understand and manipulate with objects), and social intelligence (the ability to understand and relate to people).¹

Emotional intelligence (EI) has its roots in the concept of “social intelligence,” first identified by E.L. Thorndike in 1920. The influence of this concept on popular culture and the academic community has been rapid and widespread.

As we all know, emotions are a fundamental part of who we are, and play an important part in our relationships with others. They cannot be removed from the picture. However, most of us have traditionally been conditioned “to leave our emotions at home,” believing that in order to be more effective we need to base all our strategies and decisions only upon cold, logical “intelligence.”²

In fact, to do so often guarantees that the suppressed emotions will flare, causing increased conflict and affecting the mood of the individual negatively. But what if we continue to expand our knowledge of emotions in a different way altogether, as another kind of “intelligence,” beyond reason and logic? An intelligence that – if we could learn to access it – could become nothing less than a touchstone to greater collaboration, giving us greater influence with others, motivating us to be more productive and effective.

It could be said that the greatest distance there is is the distance between the mind (IQ) and the heart (EQ). By learning about and utilizing EI, we will be able to shorten that distance, and we will be working with astonishing capacity and effectiveness. Let us address some important questions and issues about Emotional Intelligence.

What Is EI?

When Salovey and Mayer coined the term “emotional intelligence” (EI) in 1990,³ they were aware of the previous work on non-cognitive aspects of intelligence. They described emotional intelligence as “a form of social intelligence that involves the ability to monitor one’s own and others’ emotions, to discriminate among them, and to use the information to guide one’s thinking and actions.”⁴

Esther Orioli and Robert Cooper state the following in explaining EI: “EI is far more than being ‘nice’ to people. It is the ability to sense, understand, and effectively apply the power and acumen of emotions as a source of energy, information, creativity, trust, and connection.”⁵

Another way to describe EI is the ability to sense and use emotions to more effectively manage ourselves and influence positive outcomes in our relationships.

Why Do We Need EI?

Primitive emotional responses of our EI hold the keys to better health, experiencing greater joy and closer connections, achieving stronger leadership, a clearer vision, greater efficacy, higher goals, expanding our personal power, enhancing our self-awareness, augmenting learning, magnifying clarity, ensuring healthier relationships and finer perception, and finally, the provision of greater satisfaction. These emotional reflexes, rarely conscious but powerful, motivate our choices. In short, we need EI for utilizing the power of emotions.

What Are the Competencies of EI?

In Goleman’s model of EI there are five key steps:⁶ self-awareness, self-regulation, self-motivation, empathy, and effective relationships; each includes a set of different competencies. All the competencies to be successful are not necessary, but the two core competencies (emotional self-awareness and accurate self-assessment) are essential. These are the two foundational abilities upon which all others rest (see Chart).

1. Self-Awareness

The cornerstone or foundation of EI is self-awareness. It supports all the other EI skills. Self-awareness must come first because if we do not know ourselves or what we are feeling, how can we possibly know the needs of others or understand someone else and how they feel?

The more we know about ourselves, the better we are able to control and choose what kind of behavior we will display in any given situation. Self-awareness is about knowing where we are now and where we want to go, so that we will be willing to change to get there. Without self-awareness, our emotions can blind us and guide us to do things or to become people we do not want to be.

To become more self-aware, one must follow these steps:

• Learn the difference between thoughts and feelings. It is extremely important to know the difference between “I think” and “I feel” if you are to know yourself better.

• Ask yourself how you are feeling throughout the day and be honest. If your heart races or you blush or if you are short of breath – this is usually a gut reaction. Ask yourself, “What is the feeling behind it?” Name that feeling – fear, anxiety, love, eagerness.

• Be open to input from others. Friends and associates can often enlighten us about our behavior.

2. Self-Regulation

While we are listening and learning from our gut feelings as a step toward self-awareness, self-regulation is regulating those same feelings and managing them so that they do more good than harm. Self-regulation is giving the rational side time to temper our feelings when needed. It also helps us to act intentionally rather than reactively. When we act intentionally, we mean what we say rather. The other side of the coin is to just spout off without thinking and later regret an impulsive act.

Here are some tips to help you with self-regulation:

• Monitor your self-talk (what you say to yourself in your mind).

• Accept responsibility for your emotional responses in your life. When you are feeling that you are accountable, you are acknowledging your own power.

• Anticipate emotional “triggers” and prepare to manage them.

• Reframe an irritating situation into a problem-solving exercise. When you encounter a situation that provokes an undesirable emotional response, decrease your anger by focusing on the behavior. Reframe it to make the behavior the problem, and not the person.

• Use humor!

• Never underestimate the power of taking deep breaths. Increasing the flow of oxygen to the brain eases tension, clarifies thinking and has a relaxing effect on our psyche and body. It also gives you a moment to collect your thoughts and to think before speaking.

• Remove yourself from the situation and keep moving. There are major benefits in distancing yourself from a bad situation and re-directing your energy into a new activity. This will help you regain your perspective, increase your alertness and re-energize.

Unlike Intelligence Quotient, which is fixed for life, Emotional Quotient can be continually developed to enable people to increase their awareness of their selves and others, to develop.

3. Self-Motivation

Self-motivation is directing the power of our emotions toward a purpose that will motivate and inspire us. It is also visualizing the achievement of a goal and taking whatever the necessary steps are to get there. Various ways to bolster your self-motivation are: — Be aware of how you explain setbacks to yourself… stay realistic. Realize that you can control and choose what you are thinking and feeling. Increase your persistence by keeping things in perspective. — Connect your goals with your values to get energized. Keep your eyes on the goal and follow through so you can enjoy the satisfaction of completing whatever projects you’ve begun. — Strive for reaching a —flow— state while working on projects. — Visualize! — Keep learning! The pursuit of knowledge will build on your areas of strength. You will be more valuable and versatile.

4. Empathy

Once we have become more honest and intentional with our emotions, it is time to look outward. EI is both tuning into our feelings and tuning into the feelings of those around us. It means responding to others appropriately, with sensitivity and compassion. Empathy is being able to see a problem from another person’s perspective. It is good to acknowledge other people’s emotions while still remembering that those are their emotions, not ours. Empathy begins with listening. This means making a connection with people. Individuals who lack empathy are more focused on their needs and pay little or no attention to anyone else’s. Empathy is the glue that will bind a group together to work successfully. Some of the techniques for enhancing your empathy are: — Look for nonverbal cues as well as listening for verbal ones. Studies show that words account for only 7% of communication. Tone and the speed of speech accounts for 38% of the message, while 55% is unspoken and revealed through body language, such as posture, eye contact, facial expressions, and so on. — Share and be honest about your feelings. Good communication leads to trust. — Your spoken and unspoken messages should be consistent. You want what you are saying to uphold what you are doing. This proves that you are being honest, or authentic, which builds trust. — Take the kinder road whenever possible. There are many ways to deliver opinions and criticism. You can be honest and still give positive feedback, which increases confidence. Construc-tive feedback increases competence. — Try to see a situation from the other person’s perspective. Empathy is about imagining what it would be like to walk in someone else’s shoes. We ought to assume that everyone is doing the best they can with the resources that they have at the moment.

5. Effective Relationships

Mastering the abilities of self-awareness, self-regulation, self-motivation, and empathy paves the way to attaining greater skill in effective relationships. This competency is about interacting with people successfully and being adept at managing emotions in others. With heightened social skills, leaders are better communicators and better collaborators. Some EI techniques for having more effective relationships at work: — Share your passion and enthusiasm for your job and the organization’s vision — it’s contagious! Keep the vision in view by relating your own excitement for a project or goal. — Create an inspiring work environment. If you show honesty, trust, and appreciation toward your team, you create the perfect environment for them to do their best work. — Engage in creative brainstorming. Not only is brainstorming good for generating fresh ideas, but the process builds rapport and trust among team members. This could smooth the way to future collaborations because of the creative bond that has been formed. — Be willing to coach or mentor others and be open to being coached yourself. This is the most important relationship skill in the workplace. By sharing your knowledge and expertise with other team members, you are nurturing the next generation. And by allowing someone to coach you, you are showing that you are receptive to others’ ideas and that you do not —know it all.— In conclusion, quite simply the EI competencies of intra-personal and inter-personal skills are central to our life and are a different way of being intelligent. They are the tools that our brains use to define ourselves, to shape the meaning of major concepts like love, success, and happiness. EI has been found to be more important than IQ, technical ability, or experience in determining life success. Unlike IQ, which is fixed for life, EQ can be continually developed to enable people to increase their awareness of their selves and others, to develop self-management strategies, and to connect with others to create collaboration and harmony. In practice, EI also enables people to tap into their true power and creativity and to consciously choose the most effective response to any situation rather than merely reacting impulsively.

Footnotes

1. Ruisel, I., Social Intelligence: Conception and Methodological Problems, 34(4-5), 281-296.
2. Emotional Intelligence Workbook, CRM Learning, Carlsbad, 2001.
3. Salovey, P., & Mayer, J. Emotional Intelligence, Imagination, Cognition, and Personality, 1990.
4. Salovey, P., & Mayer, J., The Intelligence of Emotional Intelligence, 1993, 17, 433-442. 5 See http://www.qmetricseq.com/
5. Goleman, D., Emotional Intelligence, Bantam Books, New York, 1995.

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