Saturday, April 25, 2015



Friday, April 24, 2015

The Belief of The Atheists




             It is important at the beginning of this essay to first define the meaning of some basic words used in it. So, we will first define  the concepts of  two radically opposed mental attitudes of modern man. The fundamental questions of our times still remains the same :  are we alone in this universe or is there some transcendent reality which is not part of this universe but the  first cause of it's existence. In other words we can say that the philosophical notion of  the Transcendent Being is the  philosophical foundation of what in most important of a living religion is called God.  A believer in God  we call a theist. However in our times there is a spreading and  developing attitude of some people who are completely negative and they call themselves atheists. Atheism is a radical denial of any existence of any being beyond or above nature. All reality available to us  is known only within the  confines of so called  experience of material reality of some kind. Many atheists believe that the future of human knowledge  will be a final  proof of their attitude. This essay will be an analysis of the nature and the grounds   of atheism and an attempt to show that atheism is a belief  and a  mental   dogmatic attitude. Of course there are many different kinds of atheism steming from various sources in individual persons. Every atheist has his or her "reasons" for rejecting belief in a Transcendent Creator of reality. Nature is  totally, ontologically  and existentially dependent on  the Creator in it's existence and actions. So we have two worldviews radically opposed to each other. The question whether God exists or not is by no means outdated or old fashioned or as some would say obsolete. Any one who has at least some basic knowledge of this question must realize immediately that it is  the very central  problem embracing the whole  existence  of humanity, it's purpose or lack of it and any kind of meaning if such meaning exists. Why we exist,  live, do things, some times suffer and finally die.  For example if God exists and created the universe and humanity then this God has some plans for each human being and therefore each individual human has special personal destiny . A human being  is within theistic belief  a  spirit in a body. This spirit  is  what in theistic religion is called  a limited image of God. Death to which each person is subject is then not necessarily the absolute end : annihilation . Briefly saying there is an immortal principle in man and therefore the brief existence on earth is only a short growing of each person towards eternal destiny. Just to mention briefly one religion , Christianity. The whole center of Christian belief rests on incarnation of Christ into human life, His teaching  , His death and most importantly His Resurrection . Every Christian has to become a second Christ, that is  he/she has to imitate in thoughts and actions  the example of  God who is present in Christ. Within   this perspective  anything that happens in my life here on earth  whether it is pleasant and good  or perhaps the opposite that is difficult full of frustration and suffering  is  only a preliminary transition towards eternity and eternal life with God.


              On the other hand  atheists  display  a  tremendous belief  within the life here and now  that is  between  birth and the grave. Therefore they   must create their own meanings, their own beliefs and  ideals  and purpose within the context of  a life without any relationship  to anything  transcendent.


              For an atheist  life is  a simple product of the energies of matter, and  humanity is unintended  byproduct of the cosmic material tensions. Humanity within the context of the cosmos  is an unimportant  and accidental byproduct of biological evolution, which also is a part of the evolution of the whole cosmos from the moment of so called Big Bang . An  individual person will always be busy to create  his/her own  idols  or so called values  without which no human would exist and live at all. The atheist must therefore be self made  creator of him/herself . The problem for any thinking atheist is the meaning of his/her existence. He/she has no answer  to the questions:  Why I exist ? and What for do I exist at all?. Is there any  permanent  meaning of what I do  and live through here  or is this all a sheer  accidental incident of  impersonal matter and energy of the cosmos in it's blind movements.   If someone is an honest atheist, he/she must, in order to go on living  become a  strong

believer in some  reality or some value for which it is worth living,  acting, believing. Most atheists  will  therefore say that  they believe in science,  that science is the only way of knowing , any other  way to know  is unreliable and  science becomes a new kind of idolatry of the modern man. Science itself is a wonderful accomplishment of the human mind. It already changed in many ways  human lives and holds  many promises for  improving  the  conditions    of  life. The atheists however who  say  that they respect    science are making of science an atheistic  credo  and  matter  becomes  the reality replacing  God, the Creator.


          The claim of the atheists is according at least to some of them (for example Richard Dawkins and his friends) is based on  the belief that science is  the only way  to know reality and in this belief they would accept anything whatsoever even the most improbable  tenets  as long as they are labeled with the word "scientifically established." They  absolutely reject any reality transcending the scientific method  of investigation. Richard Dawkins especially shows a vigorous active attitude against any religious belief as based not  on scientific grounds but on prejudice and therefore false. He believes religion to be the very source of the worst evil  for mankind. His fundamental belief comes from the theory of evolution which according to his thinking explains completely and totally the history and nature of man and also by extrapolation the history of the whole universe. He believes also that mankind would be far happier  without any religious belief which only diverts human interest and efforts towards an not existing  phantom. When one reads his books  one is amazed at the  tremendous faith of this unbeliever.      Let's examine   whether  the scientific theories accepted by most scientists today  require less faith than the teaching of some religions.


        " The modern mind  is  selectively  skeptical. We have  no problem believing  the entire universe  came out of a pinpoint. But if told  that five thousand meals once came out of the small basket we exclaim: That's impossible!" ( from the play Abide for Me Many Days, Edmund Rose James, 1992)


                  Suppose you accept  as I do  the origin of the universe in the Big Bang model proposed by Alan Guth of Massachusetts  Institute of Technology you believe   that  around fifteen billion years ago all  material of the cosmos was contained in no more than a pinpoint. Time and space did not exist yet. Nothing was  old or new  because such words were not defined yet. You believe that the universe  has been created virtually out of nothing, you believe that space itself was "inflating" many thousands of times  faster than light, you also believe that during the early moments  the material  of creation  was 100 trillion times denser than water.  Next you believe that within  few seconds after the initial explosion the  particles began generating matter  which immediately began to collide and  annihilate  themselves  back into nonexistence,    vanishing  mysteriously the way they came.  


         The only reason our universe exists is that this process of annihilation   was slightly uneven  and it left a small amount   of  matter over antimatter by one part in 100 million.


         Believing in a Big Bang is well founded because of  heavily favored  hypothesis compared with which statements about the origin of the universe  which came from the  Bible or Koran and not from Massachusetts Institute of Technology would be  laughed out as  obscure and absurd mythology.  The rather recent   "superstring"  theory with at least ten dimensions  somehow "folded" into probability structures millions of times  smaller than atoms developed by Edward Witten of the Institute for Advanced Study at Princeton was glorified in the New York Times Magazine  as an invention of a  generation leading mind.


             If a man believes in the miracles of Christ  he must be mentally deficient, let few psychiatrists  examine his brain. But if a scientist talks about several  invisible dimensions in nature "folded " into  probabilities give him a chair of  professorship in  an outstanding university!  ( Eastebrook, Gregg, Beside Still Waters, William Morrow and Company, Inc., New York 1998, chapter II, Spirit and Science) 


           Things will become clearer if we examine  closely what is science. The intrinsic dynamism of a human mind  is directed towards understanding of not only  immediate phenomena,  but also the ultimate questions providing intelligibility  to the most urgent  questions  of man's  condition , meaning of his/her existence, and also his/her destiny as a human being. Part of  this dynamism towards intelligibility is what we understand by science . The scientific method has it's own limitations because  it remains  within  the sphere of the measurable and tangible  and also repeatable experimentations. The discovery of  facts of nature are the material out of which the scientific  hypotheses and theories are built. It is essential to remember that discovery of facts in themselves is necessary but not sufficient without interpretation.  That is why  a scientist uses universal concepts in which  he/she transcends   the here and now of the sense data. However  scientific hypotheses and theories are provisional in nature and never give absolute truth, but are only strong approximations  to what is. 


         It is important to remember that science does not exist by itself.  It is made by individual scientists in different specified fields of knowledge. Scientists are of course human beings and they also have their preconceived attitudes to the question what is true , what is possible and what is false. Beneath every scientific problem there always remain the crucial element of values  and meaning . None of those two is provided  by the scientific method alone. Since the scientist is a human being  he/she must have some personal vision of reality  and consequently into the  understanding of existence he/she puts his/her all human  reality with possible deviations , prejudices and unexamined attitudes  . Where science  has done it's work and cannot do more there comes in philosophy and  it's drive towards universal intelligibility  available to the human mind.   










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Thursday, April 23, 2015

The Understanding Machine: Can Intelligent Machines Understand Language?

Budimir Zdravkovic
The City College of New York

1. Understanding 
A long-debated problem in the philosophy of artificial intelligence concerns whether intelligent machines are able to understand their own tasks. Some intelligent machine-engineers and researchers might say that whether a machine understands the tasks it is performing has no implications as to its pragmatic function, which concerns how efficiently the machine can execute these tasks. But for certain tasks an intelligent machine must surely understand the tasks it is performing.  An ‘understanding machine’ may be the only kind of machine that stands a chance of passing the Turing Test: an understanding machine would be able to consistently compose meaningful sentences and thereby communicate efficiently—the way humans communicate.
An understanding machine might even be able to produce brilliant works that rival renowned writers like Shakespeare. I say this because language is determined by both semantics and syntax, if the machine does not possess any semantic properties it must rely on syntax and specific software that correlates words. Such a machine would only be able to compose a limited number of intelligent sentences, however, and this would be the equivalent of a human manipulating the words of a language he or she does not understand. Considering this it seems a little far-fetched to expect a syntactic machine to consistently compose meaningful sentences.  An intelligent human who does not have knowledge of a new language would be incapable of so composing consistently intelligent sentences, and it seems that the expectation of syntactic machines to perform such a task is not grounded in a reasonable assumption.

The assumption is that syntactic machines are able to communicate intelligently without understanding a language but the standard we use for comparing intelligence is that of humans, for there is no further recourse. If an intelligent human cannot compose meaningful sentences without understanding the language at hand then by what logic are we to assume that an intelligent machine could? The assumption completely neglects the important function of understanding and semantics in intelligent communication, which is obviously evident in humans.
Even though the mechanism behind how semantics and understanding emerges in human cognition is still poorly understood, one cannot say that the mechanism that allows humans to understand language is irrelevant in intelligent communication. If the Turing Test is a measure of artificial intelligence, machines that understand language would surely have the best chance of passing the Turing Test. The problem of engineering such a machine becomes apparent when one delves into the nature of meaning and the emergence of meaning from electrical impulses—though there are certain human attributes that an intelligent machine needs to possess in order to understand language.
2. The Emergence of Meaning
In the ‘Chinese Room’ thought experiment John Searle proposed that artificially intelligent machines, even if conscious, would be unable to understand language on account of the way they function. Computers manipulate symbols and words based on syntactic instructions pre-written in their programs. The computer is following instructions, and though it does this very well, there is no human learning or understanding involved in the computer’s processing these instructions or its generating outputs. It seems that it is hard to comprehend how an intelligent being begins to understand language; and this is of course expected since the understanding of language relies on the emergence of meaning. One has to understand the meaning of words in order to understand language. The hurdle of engineering a computer that understands language is concerned with the emergence of meaning.
There is no plausible mechanism or an objectively satisfying explanation that describes how meaning emerges from electrical impulses. This applies whether we are talking about the electrical impulses in a functional computer or the electrical impulses generated by human neurophysiology. As humans we know things mean something to us; we have meaningful experiences but such are not reducible to our neurophysiology. 
John Searle demonstrated that the emergence of meaning is not reducible to the instructions coded in computer software when he created the ‘Chinese Room’ thought experiment. Searle showed that a computer which is efficient at generating language could never apprehend the meaning of the words and sentences it is generating. Searle asks the reader: At what point in this mechanism, which is modelled after typical computer software, does meaning emerge? The conclusion is obvious: if the machine only follows instructions and never grasps the meaning of words, it will never learn what they denote and as a consequence it will never be able to understand language.
Similarly we can ask where meaning emerges in our neurophysiology. Can we say that a single neuron can have a meaningful experience when it is stimulated by electricity? What about a small network of neurons? We can then further extend the network to include millions or billions of neurons and we can still ask the question of emergent meaning: when are these neurons capable of having a meaningful experience? We can infinitely add more neurons and make the neural network infinitely more complex; but it appears we will never know how meaning emerges from neurons, since their number or complexity of network provides us with nothing by way of a plausible mechanism for the emergence of meaning. We can always code more instructions into the computer software and make it infinitely more complex, as Searle proposed, but it seems semantics will never emerge from this.
Although how meaning emerges from a physical or a biophysical mechanism is a puzzling and confusing question, we can think about how the meaning of words begins to emerge during development. We can take a simple example from our everyday experience and observe how children learn new languages. Children learn to speak before they learn to read; they do not have the convenience of consulting dictionaries. In truth we learn very few words by consulting dictionaries, and if we think about the emergence of language in general, it must have originated before words were institutionally defined: we learn language by associating words with experiences, phenomena, and concepts.
The way people understand words is also highly abstract. Understanding does not emerge solely within associations between objects and words; it emerges as a web of associations between a concrete object, its abstract representation (which does not have a substance or form), and the term that describes the object. This abstract representation is what we call conceptual understanding. Our conceptual understanding of a tree, for instance, is not represented by any specific physical example of a tree; it is represented by our abstract understanding of it, understanding as a collection of properties or information that pertain to specific trees.
This web of associations is crucial. If we associate a word and a concept but fail to associate both word and concept with an actual object, we are stuck with abstract definitions which would seem like a collection of information that does not apply to anything specific or actual. In such a case we could learn about the word ‘tree’ and what the properties of a tree might be, but we would not recognize a tree if we were to come across one. On the other hand if we associate the word and the actual object, but fail to associate the word and the actual object with the concept, we would then be unable to collectively identify several different types of trees, or a picture of a tree, even though we are able to identify a specific. The understanding of what words mean requires a web of associations between concepts, objects, and words; an intelligent machine intelligent enough to understand language must thus possess this ability to create webs of associations.
3. The Problem of Precise Definitions 
The meaning of a word is associated with a the phenomenon it denotes. We can, however, semantically associate a given word with other words that describe or define the word in question. We often attempt to provide a precise definition of a word in terms of others: for instance we can identify the word ‘tree,’ the object, and the concept with which it is associated, but we cannot describe what the word actually means, precisely, unless we use other words to provide an accurate description. We might say the tree is a living organism, it has branches, or it has leaves, and so on.
Language acquisition through precise definitions is impossible, however—since if we fail to associate words with anything else, other than words, we must be stuck in a cycle of ignorance (which we are not). If we start looking for the precise definition of any given word, we have to understand it in terms of other words, but since we do not understand what these others mean we have to look for their respective definition as well. We look endlessly for definitions of words we do not understand, and if we fail to associate these with anything beyond lingual terms, we fail to understand what any of those words might mean.
We may think of a given word W, and this word is precisely defined by another set of words, W1. But because each of these words in W1 must also be defined precisely, there has to be yet another set of words, W2, which defines those in W1. And again, since the set of words in W2 require precise definition, there must be a further set of words, W3, that defines those in W2adinfinitum. 
The problem here is that once we start to define words in terms of other words we end up going into an infinite regress of definitions in order to find the precise definition of any given example. We define endlessly without ever hitting that precise definition. This is why words are naturally vague and ambiguous: they cannot be defined precisely.
So it is clear that one cannot initially learn the meaning of words in terms of others, since this kind of learning is impossible as demonstrated above. Someone who attempts to learn the meaning of words solely by rifling through dictionaries falls into an infinite regress of definitions which appear ultimately meaningless to the reader. Language has to be learned through associations of words with concepts and actual objects.  The intelligent machine that understands language must possess some kind of sensor that allows it to make associations between words and objects. Computers can easily make these associations for they are simple relations between concrete symbols and objects. It is difficult, however, to know whether the intelligent machine is able to form a concept from this association. The major problem with conceptual formation is that the process requires semantics. It is a mental process that requires a certain understanding in order to make successful associations between concept, word, and object. This understanding relates to how and why a collection of properties, or information, pertains to a specific object.
4. The Learning Machine 
Learning is crucial for language semantics. From what we know about language acquisition, one cannot develop the semantics and the understanding of language without first learning that language. Previously I mentioned why learning is crucial for language acquisition and why language needs to be learned by the association of actual objects with concepts and words. The process of learning a language is also the process which allows us to begin understanding language. Language acquisition is therefore developmental by nature: it is something that must develop through an interaction of organism and environment.
Learning the meaning of words through association with others is problematic. Such a method of learning does not allow for understanding on account of the reasons stated above. Similarly we can conceive of a Turing machine bearing database of all words, books, and encyclopedias ever written: this machine has an endless supply of literature; it has enough computing power to associate words with other words and words with sentences; and from these correlations it is able to compose intelligible discourse. But such a computer would be
unable still to understand the meaning of the words it uses. It can only correlate words with other words, and words with sentences, but under these conditions semantics and understanding cannot emerge. The machine knows how words correlate but it cannot know how they are correlated to the phenomenon they are intended to denote.
What if we installed a sensory device and we programmed the machine to associate words with objects? In such a case the computer might make intelligible associations between such things, but as stated above the machine must also have a conceptual understanding of the words it uses. Nor can organisms that understand language rely on a static, pre-determined code to learn language because one cannot program the inductive knowledge necessary for language acquisition using a static pre-written code. The machine might have an infinite number of terms and sensory devices installed but if it cannot encounter the actual phenomena the words are meant to denote it cannot have perception and understanding of what those phenomena might mean: it cannot understand how the words are related to existing objects. It lacks the capacity to acquire inductive knowledge.
If we posit that a machine can understand without acquiring inductive knowledge it would be similar to stating that the machine can somehow know or understand facts about the world without encountering or observing them. It seems a little ridiculous to claim that a machine can acquire such ‘magical’ or ‘psychic’ knowledge, especially when discussing the kind of knowledge necessary to language acquisition.
A machine that runs on a static pre-written code is incapable of acquiring inductive knowledge because upon observation or encounter of a particular fact the machine must undergo changes in processing or operation. The state of the machine has to transition from ‘not knowing that particular fact’ to      
‘knowing that particular fact.’ Learning a language requires some degree of creative self-processing because learning something new entails the altering of one’s processing. Such changes are regularly observed in organisms that have the capacity to learn and acquire inductive knowledge—not machines.
I will admit that the term ‘creative self-processing’ is somewhat vague. A creative self-processing machine would, for instance, alter its own pre-written processing—and we could always wonder at what point a machine, or its environment, might be altering its own processing. But can we say that a machine that evolves and acquires linguistic skills through association of words and concepts with its environment is learning in a human way? The machine does not have a pre-written program for language, but it picks up language as it interacts with its environment and the speakers it encounters. We could say that such a machine is ‘learning,’ but if it is doing so by association can we assume that the machine understands? We might assume that it understands but that this interpretation of ‘understanding’ is inaccurate for a number of reasons (and I will go into detail about this later).
Other important aspects in human learning are related to the plasticity of the brain. During development and learning the brain’s connectivity undergoes various changes, new connections, and the formation of new synapses. We know that as new neural synapses are being formed this phenomenon will alter the activity of individual neurons. Within the human brain we are not only concerned with decentralized processing, but also the fact that the processing units so applied tend to change as the human brain learns. There are myriad factors involved in human learning, such as epigenetic factors, which alter gene expression in the neural cells—once again altering the nature of the processing.
I would like to emphasize that human learning is a dynamic system where constant changes occur in the processing and these changes have an effect on how humans come to learn and understand language. These kinds of changes are the kind of changes that we would expect a creative self-processing machine to undergo.
5. Is the Learning Machine an Understanding Machine? 
Previously I stated that association is necessary for understanding languages, but in this I did not mean it is sufficient: learning is required as well. Now we can ask if learning by association is sufficient for understanding. Once again the answer is probably ‘no.’ We know that both learning and association are important to language acquisition, but we are still a long way from making sense of the understanding machine. The emergence of meaning still remains a mystery. Learning by association does not guarantee that a conceptual understanding (the kind of understanding required for language semantics) will emerge. As a consequence we cannot at this stage know when a machine begins to form concepts. Conceptual formation is the necessary element of artificial intelligence that remains vague and poorly defined in the physical sciences. One cannot define a concept or the emergence of a concept physically: as I mentioned earlier the associations between concepts and words requires understanding. When one understands the concept of the word, then the meanings of that word begin to emerge.
Other difficulties that pose obstacles for an understanding machine are related to the means of perceiving phenomena. We know, for instance, that there are computers that play chess and possess learning algorithms but is it reasonable to claim these computers understand what chess is? Let us assume for argument’s sake that these machines have a conceptual understanding of it: but we must now think about all the other things the machine must understand in order to grasp chess the way we do. I do not think we can claim that chess-learning computers have an adequate understanding of chess on account of their lack of a wide enough understanding of the cultural context in which chess is played and the context in which chess is defined as a leisure game or a sport. The chess-playing computer cannot therefore understand chess, or rather it cannot understand chess the way we understand it. For us, however, understanding appears to emerge from perceiving an integrated network of phenomena and experiences. A machine that lacks such perception will be unable to understand things the way we do; and it would be ridiculous for us to expect the machine to understand phenomena in such a way if we do not share a common means of perception with it.
If we want to build a machine that understands language, we have to ask ourselves what else the machine must understand besides language. Like chess, language is probably not an isolated phenomenon; it is integrated with many other aspects of our phenomenal experience which we must ourselves understand before we can start developing our understanding of language. Humans must have a certain mechanism for perceiving language; and in order for an intelligent machine to understand it the way we do, it must share with us a common perception it currently lacks.
To summarize I have identified two properties which are crucial in order for the machine to understand language. One is the process of creating a web of associations between words, objects, and concepts; the second is the machine’s capacity to learn by association or, in other words, to acquire inductive knowledge. The difficulty in building a machine that understands concepts presents a major hurdle in building engineering something that grasps the meaning of language, and phenomena in general. Building a computer that understands concepts and knows the meaning of words is currently beyond the scope of scientific engineering, since there is no plausible physical mechanism that explains the emergence of meaning. Finally, the understanding of language requires a more integrated phenomenal experience and a human perception of language—none of which intelligent machines currently possess.

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Privileged Species: Are humans the accidental products?

Published on Mar 24, 2015
Are humans the accidental products of a blind and uncaring universe? Or are they the beneficiaries of a cosmic order that was planned beforehand to help them flourish? Privileged Species is a 33-minute documentary by Discovery Institute that explores growing evidence from physics, chemistry, biology, and related fields that our universe was designed for large multi-cellular beings like ourselves. Featuring geneticist and author Michael Denton, the documentary investigates the special properties of carbon, water, and oxygen that make human life and the life of other organisms possible, and it explores some of the unique features of humans that make us a truly privileged species.

20120517 West Lake Part 4 Beauty in all seasons CCTV News - CNTV English

20120517 West Lake Part 4 Beauty in all seasons CCTV News - CNTV English

Engaging the New Atheism - John Lennox

Thoughts for Reflection

Thoughts for Reflection

1.         Why should everyone need philosophy? Do you think it necessary or a matter of option? Why?
2.         What is philosophy all about? Why?
3.         Why not “science” alone?
4.         What is precisely involved in the quest for meaning? Do you agree with V.E. Frankl, and Camus? How about Tolstoy's crisis? What about yourself?
5.         Does the fact that we search for meaning prove that there really must be some meaning to life? Suppose life is meaningless?
6.         Why are some philosophical problems “perennial”?
7.         Why go through the whole quest yourself? Why not accept ready-made answers? After all they may be true?

FRANKL, VICTOR E., MAN'S SEARCH FOR MEANING. Washington Square, 1963.   

 JASPER S, Karl. Way to Wisdom. New Haven, Yale University Press 1954,  The Great  Philosophers, New York, Harcourt, Brace & World, Inc., 1962

Kaplan, Abraham. The New World of Philosophy. New York. Random House, 1961

Maugham, W. Somerset. The Razor's Edge. Pocket Books, 1946

Ortega y Gasset, José. What is Philosophy? New York. Norton, 1960

Plato. The Apology of Socrates. Any edition of Plato's Dialogues

Dimnet, Ernest. The Art of Thinking. Connecticut, Fawcett Public, Inc
Durant, Will. The Story of Philosophy. New York, Washington Square Press, 1961

Maritain, Jacques. An Introduction to Philosophy. New York, Sheed and Ward, 1962
Runes, Dagobert D. The Art of Thinking. New York, Philosophical Library, 1961
Machan, Tibor R. Introduction to Philosophical Inquiries. Boston, Alyn and Bacon, Inc., 1977.

Schweitzer, Albert. Out of My Life and Thought. Holt, Rinheart and Winston, 1961.

Taylor, A.E, Socrates. Anchor, 1963.

Descartes, René. Discourse on Method. Any edition.

Bolt, Robert. A Man for All Seasons. New York, Random House, 1962 (Vintage Books

Distanceprofessionallearning ( launched a new website featuring a  number of articles, academic topics in philosophy, religions, languages the website contains links to various scientific sources. The website is compatible with mobile devices, allowing easy access via smart phone, tables and personal computers. The website contains links to Facebook, Twitter and others. If you have any questions or problems share with us so we can look for solution together.,

Faith Has Its Reasons | Part 2 | John Lennox, PhD