Pythagoras, the famous Greek philosopher and mathematician, once said, “Everything is a number.” For a long time, people thought the idea is overstretched and metaphoric in his interpretation of creation. How could you convert everything a number, or how numbers could justify the existence of this material world? However, in today’s world of internet and digital computer the maestro’s metaphor is thriving with resounding resonance at every corner of this planet. We can have everything now in numbers – music, art, dance, movies, images of every animate and inanimate object, the three-dimensional flora and fauna – they could be described by numbers – their colors, texture, seamless interactions with the environment – it is the Pythagorean world mirroring our existence as well as aiding its sustenance. This virtual world maintains a symbiotic relationship with our real world. We create it by digitizing the objects and concept, and it recreates our experiences; thus guiding us to face and overcome new challenges and stresses of life. The synthesis of our life with this virtual world is so strong that we have also become numbers in various ways. We now have digital codes for almost everything be it’s a subatomic particle or a large galactic body in the Universe – sometimes using a global standard, sometimes a local number limited to a specific system of operations. Thus our personal identities are numbered in the form of different IDs including in the recent Aadhaar scheme of the Govt. of India intending to provide every citizen of this country a unique identifier (UID).
Digital Characteristics
How digital being differs from our common notion of this material world, in particular in space and time? What we see, feel, touch or listen, appears to us as a continuous stream of experiences over space and time. An object in a space could be of finite size and discrete. But, object occupancy in that space, however infinitesimal it may be, is considered to be continuous. The same is true over time. A period of time interval could be also finite and very precise. But, it has infinite number of time instances (or moments) within it. In digital form a finite space and a finite time interval has a finite number of cells and time instances, respectively. So an object in digital form is described as a collection of cells, occupied by it. Similarly, over a time interval, only discrete events are recorded or observed at a finite number of time instances. Since the cells or time slots are countable and could be specified using numbers (integers) only, the digital representation becomes an ordered set of numbers. Not only the object occupancy, but also various properties or attributes of objects at those locations, e.g. surface texture, volume transparency, color, etc., can be represented in the digital form. The values of these attributes over the digital space or time are encoded by numbers.
Mathematically, we denote the variation of values as functions over space or time or both. Consider an example in one dimension. Let x be a variable, which takes any value within the interval [a,b] in the real axis. We denote f(x) as a function of x. In this case f(x) is a function in continuous space. Its domain is x. We may refer it also as an analog function. An example of a digital function g(i) following f(x) could be in the form of a finite sequence say, g(i)=round(f(a+ik)), i=0,1,…,n, i.e. g(0)=round(f(a)), g(1)=round(f(a+k)), g(2)=round(f(a+2k)), …., g(n)=round(f(a+nk)), such that (a+nk) ≤ b. In this case, rounding of real numbers into an integer value is expressed by the operation round(.). Hence, the values in the sequence are integers. The process, by which a sequence is generated selecting functional values at regular intervals, is known as sampling. The length of the regular interval is known as sampling period. In our example, the sampling period for generating g(i) from f(x) is k. We find that the digital function in some way approximates the analog function f(x). It gives almost the same values at points of x from where the functional values are sampled. Even the intermediate values between two points could be computed by using interpolation techniques, say by imagining a straight-line passing through these two points, and determining the functional value at any intermediate point lying on the straight line. More we take the number of samples within the interval of a and b, better is the approximation. This implies that we need to take smaller sampling period. Suppose these points are plotted on a paper. With decreasing sampling period, the gaps between these points become smaller, and the curve would appear to be closer to that of the analog function. So one may naturally ask a question, how small this sampling period should be so that f(x) could be obtained faithfully from g(i)?
Science of digitization
The answer to the above query comes in the form of a theorem, the Nyquist sampling theorem, named after the American Scientist Harry Nyquist, who discussed it in 1928. Let us understand this theorem intuitively. Consider a sinusoidal function in the form of A.sin(2πmx+ɸ). From its mathematical form, we attribute A to be its amplitude, so that in every cycle the functional value varies from A to –A, and -A to A. The length of the period is 1/m. m is also called the frequency of the function, and ɸ is the phase term. Given a sampled sequence, both the positive to negative and negative to positive transitions of the function could be captured, only when there are at least two samples per cycle. This implies that for sampling a sinusoidal signal, the sampling period should be at most half of its period, or in another form, the sampling frequency (number of samples per unit scale of x) should be at least twice of its frequency. Now by performing Fourier series analysis (introduced by Joseph Fourier, the French mathematician and physicist, in 1830), we express almost any function f(x) as a superposition of a number of sinusoid functions. Each component sinusoid is called a frequency component of the function. In most cases, the number of components is infinite. However, for functions related to a physical system (for example a time varying signal of an electrical circuit), the contributions of high frequency sinusoids become negligible. In that case, beyond a frequency mh we need not bother to sample any component. This requirement guides us to select a sampling frequency no less than 2mh, twice the highest frequency component of the function. Nyquist sampling theorem says that the minimum sampling rate should be twice the bandwidth (the length of the frequency interval of the components) of the function.
The other factor, which is also associated with the approximation of the analog function in digital form, is the conversion of a functional value to an integer. In the example given previously we have used rounding function to get this conversion, which keeps the difference between an actual and the corresponding approximate value within 0.5. Not necessarily rounding is the only method of conversion. For example, truncation could also be another form, in which only whole number part of the real value is retained, by throwing away the fractional part. Even in a more general situation, one may divide the values by a scale factor and keeps only the integral quotient to represent the digital functional value. In the reverse process of computing analog values from their digital counter parts, the same scale factor is used in multiplying with the quantized values. The scale factor is also referred as quantization threshold, and the process of converting real values to integers in this way is called quantization. The reverse of quantization is termed de-quantization. Smaller we take the quantization threshold; less becomes the error of conversion.
Reconstruction Process
Let us consider the other side of the story. Given a digital sequence, how one could get back the analog function? As intuitively we have seen earlier, one may carry out linear interpolation between successive samples to get the values at every point in the domain. However, it is far from a close approximation of the original analog function. This can be understood from the digital representation of the sinusoidal function. As we have discussed, it is sufficient to recover a sinusoid function with two sample values per cycle of it. However, the linear interpolation of successive sample points would provide us a triangular periodic function. So, is there any better way to recover this sinusoidal variation?
The answer to the above question is provided by the linear system theory. In fact the Nyquist sampling theorem is based on the assumption of reconstruction of a function through a linear shift invariant (LSI) system. A system is something, which accepts input in certain form, and provides an output in the same or a different form. It is characterized by its input-output relationship. In a simpler form, a system may be considered as a function, which takes an input (say, x) and provides an output (say, y). However, in general, a system is more than a function. Rather a function could also be its input (say, f(x)). In that case, its output would be another function (say, g(x)). For example, in a radio set the electromagnetic wave (or radio wave) transmitted in free space is its input and the sound played by its speaker is the output. A linear system follows the superposition principle of input-output relationship. It implies, given an input x1 if the output is y1, and for x2 if the output is y2, a linear combination of the inputs (say, ax1 + bx2) produces the same linear combination of their respective outputs (i.e. ay1 + by2). A system is shift invariant, if a shifted input produces the same shift in its respective output. For example, if f(x) produces g(x) in a system, in a shift invariant system f(x-x0) produces g(x-x0). An interesting property of an LSI system is that given an input in the form of a sinusoid function it also produces a sinusoid function of the same frequency as the output. However, the amplitude and phase of the output sinusoid go through changes. Due to linearity, output amplitude changes with the same proportion of the amplitude of the input. This proportion is called gain. Similarly, due to shift invariant property shift in phase of the output sinusoid function from the input at the same frequency remains constant. Hence, an LSI system could be characterized by the variation of gain and phase shift over varying frequencies of input sinusoids. This is known as the transfer function or frequency response of the system. If the frequency response is known, it is possible to determine whether a sinusoid of a specific frequency would provide a significant output response or not. If the gain at that frequency is very low (nearing zero), the output response would be negligible and in all practical purposes it is ignored. As we have already seen that almost any function f(x) can be expressed as an additive combination of sinusoid functions, applying the principle of superposition of a linear system, it is possible to determine the output from its frequency response. In the output, depending upon the gains at varying frequency, some of the sinusoidal components would be present and some of them would be filtered out. As an LSI system is capable of selectively passing and modulating the frequency components by the respective gain factors and phase shifts of its frequency response, the system is also referred as a filter. A filter which suppresses the high frequency components and passes low frequency components till a cut-off frequency ml is called low-pass filter (LPF), whereas a high-pass filter (HPF) passes only high frequency components beyond a cut-off frequency. There is also another class of filter known as band-pass filter (BPF), which allows frequency components within an interval to pass through. The reverse of the BPF is a band-stop filter. A physical LSI system has characteristics similar to those of an LPF or a BPF. This means that there exists a high-frequency cut-off beyond which the sinusoid functions do not respond. For example, the human auditory system responds to sound waves between 20 Hz and 20 KHz. Our color perception is restricted within a band of wavelengths (roughly 390 nm to 710 nm) of electro-magnetic radiation, which we term as the visible spectrum. This property of physical systems enables us to process the input with an LPF or a BPF, and then apply sampling with a sampling frequency at least twice of the frequency band of the processed input.
Now, let us consider the response of an LSI to a sampled digital signal as its input. As an example, we may consider how our auditory channels would respond to chopped off sound waves at regular intervals. As expected such a discrete sequence would be a nuisance to our hearing. This is due to the fact that in this digital form the function has a lot many high frequencies, additionally introduced due to sampling operations. Those frequencies within the allowable range of the LSI would make a chaos in the output. However, the sampled function still has the frequency components of the original function in the same proportions. If the Nyquist sampling rate is used, the original frequency band would be well separated from those additional high-frequencies. Hence to recover the original analog signal, we need to design an appropriate LPF, which allows only the desired frequency components of the sampled function. This is how we get back the function again in the analog domain from its digitized version, i.e. by processing it through successive stages of de-quantization and low pass filtering.
Discrete sensing and perception
It is not that our perception of continuity of any event itself is continuous. Apparently we are immersed into a continuous flow of events and sensation. However, if we consider the anatomy and physiology involved in this process, we find that there is discreteness both in sensation and perception. Our sensory organs receive the stimulation from the environment through different nerve cells. As these cells are discrete, there is an inherent sampling in the process of receiving the stimulation. For example, in our retina of eye, there are about 120 million rod cells and 6 million cone cells. Hence the images formed by the retina are sampled by them. Moreover, the sensation is processed by these cells and transmitted through the visual path to our brain, where the final visual perception takes place for understanding the scene before us. The whole process roughly takes 100 ms, out of which around 3 ms are spent for the transmission of the excitation through the optical nerves. The rest are almost equally shared by processing at the source of reception (retina), and at the final destination (brain). However, due to this latency, our brain is not capable of processing any visual sensation within this period of 100ms, which implies that it works at the rate of 10 scenes per second. This fact is used in cinematography, television sets, etc. Similar discreteness is also observed in other sensory mechanisms. Our auditory nerve takes around 0.4 ms for conducting auditory sensation. Hence, though our ears are sensitive to sound wave with a frequency as high as 20 KHz, our audio perception, for which we have a very little understanding till today, should be limited by a rate of 2.5 KHz. In fact, the speed of cognition is much less as there are processing in the receptors as well as in brain. This is the reason, why we cannot understand any fast playing audio recordings, sometimes used in advertisements broadcast in television channels, where the law dictates a compulsory announcement on financial risks (say, of new investment bonds issued by a company), health hazards, etc. In general, any sensation needs to be carried to brain or muscles through nerves. There is a finite time by which this could be accomplished. Besides, there is a refractory period in our nerve cells, within which it does not accept any new stimulation. In addition, it requires also processing in the source as well as in the destination. All these put a limit in the cognition rate and sometimes, even at the rate of sensation. Hence, not only the creation of digital content of the real world analog entities, such as sound, images, 3-D objects, etc., is guided by the principles of sampling and quantization, but also their realistic rendering takes place by exploiting the discreteness in our sensory and perceptual mechanism. Next we would examine, how through identification of the factors responsible for sensation of depth, direction and color, we can stimulate such illusive sensation from the virtual world.
Creation of illusion in virtual digital world
It is not clear what processing precisely goes in our brain, which empowers us to perceive the depth variations in an object in the 3D world. But we could identify the factors responsible for this sensation. These are from the two images of the same scene formed in the retinas of our left and right eyes, respectively. The corresponding image of a 3D point goes through a lateral shift in the right image, with respect to its position in the left image. This we would be able to sense, if we look at the same scene with one eye (closing the other eye) at a time, first by the left, and the next one by the right eye. It is observed that the amount of shift is inversely proportional to the distance of the point from us. Technically, these shifts are called parallaxes. Our depth sensation has causal relationship with these parallaxes. Hence to provide an illusion of depth in images, in 3D movies, we project two images of the same scene simultaneously on a screen. One image is meant for viewing by the left eye and the other one is for the right eye only. In addition, necessary care is taken so that a viewer does not see them by both eyes at the same time. This is achieved by various means of technological advancements. One is to use polarization of light in the projection of superimposed images, and watching them through polarized glasses.
A processing somewhat similar to visual stimuli goes in our hearing system also. We hear the same sound by our two ears. However, sound wave received by one of them reaches faster than the other. This difference of phase between these two simultaneously received sound waves gives us a sense of direction of the sound source. This principle is used in the recording and playing of stereophonic sound. While recording, two channels are recorded by two separate microphones with a phase shift in their sound tracks, and during production of stereo sound, they are played simultaneously in two different sound speakers. This provides a relative depth variation in the source of sound in our surroundings. However, the sensation becomes more realistic, if we use a stereo headphone attached to our ear, as it reduces the effect of interference of surrounding sources during hearing. Moreover, with this gadget the emulation of phase differed sound tracks in our ear-drums, becomes more flawless.
Compared to depth and direction, our color sensing is better understood. In our retina, we have three types of cone cells. Each of these types acts like a filter, allowing only a certain band of optical wavelengths in the process of stimulation. One category of cones operates around the wavelengths near red colors. The other two allow those around green and blue colors, respectively. Though physically color is the property of the wavelength of light, in our sensation it is perceived by the superimposed stimulation of these three filters. This implies that even an appropriate mixing of these three primary colors, i.e. red, green and blue, respectively, can produce the same color sensation. It is not necessary to have an external simulation of the light energy of the precise wavelength representing the color in the optical spectrum. This enables us to design a system which produces all different colors using just three colored light sources, as opposed to use of infinite number of light sources of pure wavelengths. This principle is used in production of colors in a television set. Even in capturing color information also, we use the same principle. In this case, we use optical filters corresponding to the frequency (or wavelength) response of our cones, and then use optical sensors for capturing colors of an object. This is what is done in color cameras, both in analog and digital forms.
The above are only a few examples by which we realize how information related to the objects and various sensations of this real world could be put in the form of numbers, stored into computer memory, and used in their rendering as and when required. Day by day with the technological advancement we are increasing our power of recording our existence for the posterity in the digital form, so that at any point of time our 3D surroundings including us, are captured digitally with colors, sound, smell, touch, taste, emotion, etc., extending an invitation to others to share our experiences and emotion in this virtual world.
The Real Digital World
So far we talked about a virtual world, which may act as a mirror of our real world, and is capable of recreating itself with reliability and authenticity. In reality in sensing this illusive world, the discreteness in our sensation and perception plays a major role. Naturally one may ask the question, what about our real world? Does it exist with discreteness too? Or, is it inherently continuous? Let us see how modern science explains our existence in its very fundamental forms of matter, force, energy, and life.
The thing which should form the core of our understanding is our objective observation of this world in terms of measurements and quantifications of physical entities. From our school physics we know that basis of all such observations lies in measurements of three fundamental concepts, namely, mass, distance or length, and time. Whereas the last two are related to measurements in space and time, respectively, the first one is a measure related to matter. For a long time as our common intuition dictates, we considered the invariance of these measures in every state of an object anywhere in this universe. In particular for a moving object with uniform velocity, though its velocity is measured with respect to the inertial state of the observer, the measurements related to mass, length and duration are considered to be the same for identical objects or periods in any inertial frame. However, a simple physical fact posed a paradox to this apparently sensible assumption. The speed of light in vacuum for any observer in any state of motion is always measured the same. As the measurement of speed is associated with measurements of distance and time, the foundation of their invariance in any inertial state becomes fragile. Albert Einstein by proposing his famous theory of special relativity resolved this riddle. According to this theory, none of these measurements has any absolute frame of reference. They are all relative with respect to the inertial frame of an observer. The only quantity which is invariant in these frames is the speed of light in vacuum. Hence the length of a speeding vehicle would appear differently to its rider and to an observer standing on the road. The road side watcher would find it shorter than the rider. Even the duration or period between two events occurred within a vehicle would deem to be longer in the frame of reference of the observer at rest (with respect to the road). The measurement of mass of the object also becomes relative. Its value increases for an observer with a relative motion compared to that obtained by an observer who is at rest with respect to the object. We practically do not observe this variation in our common day experiences, as speeds of common objects in our inertial frame (stationary with respect to Earth) are much below the speed of light, and the variations are negligible. But for subatomic particles which move with a very high velocity, the measurements of their masses are shown to follow the laws of special relativity with high accuracy. Their observation for a longer period (their life-times in free space) is also possible due to time dilation in the static reference frame of the observer. Even though these measurements are different in different inertial frames, the laws of nature are the same and uniform in each of them. Hence, though the measurements are relative for a given inertial frame, they are dictated by uniform scales set by their standards. For example, a stationary rod of length of 1cm will always be read the same in every inertial frame. However, there is no apparent discreteness in time and space even in relativistic world. Let us consider whether the same is true for the objects and events, which occupy a certain amount of space for a certain period of time.
Discreteness in material existence
Since the birth of modern civilization, ancient philosophers thought about matters composing of tiny indivisible particles. The Greek philosopher Democritus and his teacher Leucippus named them atoms. In the modern scientific era, the English scientist John Dalton proposed that atoms of elements combine to form compounds and hence they combine in a definite proportion of their masses. Till the end of the nineteenth century this was the single most discovery showing the discreteness in the behavior of material world. However, the scientific world started to wonder on the rule of numbers in nature by observing the periodicity in the molecular weights of elements with similar chemical and physical properties. The famous Russian scientist Dmitri Mendeleev catalogued these elements in a periodic table paving the prediction of existence of many other elements, which had remained undiscovered till his time. It is only at the end of the nineteenth century, subatomic particles were discovered by observing the phenomena of radioactivity (discovered by the French scientist Henry Becquerel in the year 1896) and cathode ray discharge in a vacuum tube. The English scientist J.J. Thomson in 1897 explained the cathode ray as a stream of negatively charged small particles confirming the hypothesis of existence of such a particle by the Anglo-Irish scientist G. Johnstone Stoney, who named it electron in the year 1891. Using radioactive emission Ernest Rutherford in the year 1907 showed the existence of a heavy concentration of positively charged mass within an atom, which he named nucleus, and proposed a model of atom where electrons are revolving around the nucleus. Later in 1918, Rutherford confirmed the positive charge particle in the Hydrogen atom and named it proton. In 1932, another subatomic particle in the nucleus, named neutron, was discovered by the English scientist James Chadwick. The mystery of periodic table became unfolded with these discoveries, as we understand now that the number of protons in the nucleus of an atom does uniquely identify an element with its distinct physical and chemical properties. On the other hand arrangement of electrons (the same number as of protons in a neutral atom) around the nucleus, specifically the number of electrons in the outmost shell, provides explanation to its various chemical properties. As the number of electrons in the outmost shell varies periodically with the increasing number of protons (or the atomic number of an element), we observe similar properties in elements separated by a length of a period, which could be 2, 8, 18, or 32. Even the arrangement of electrons also follows a rule of numbers (called quantum numbers) as they exist at discrete energy levels while revolving around the nucleus.
Within a few decades the simple picture of atoms containing only the above three elementary subatomic particles got shattered by the series of discoveries of many more elementary particles. Of course, electron itself is found to be elementary, and falls under a group of subatomic particles called lepton, which gets affected by electroweak force. In the group there are five more particles (besides electron), they are mu-meson (or muon), the tau-meson, and three types of neutrino. The second group of particles is known as hadron. This group contains more than 100 subatomic particles, which includes proton and neutron also. However, hadrons are formed by more fundamental particles, called quarks. There are six types of quarks, up, down, top, bottom, strange, and charm. For example, a proton is made up of two up-quarks and one down quark, whereas a neutron is composed of one up and two down quarks. The combined family of hadrons and leptons are called fermions. It is not only in their material existence, but also in their interactions, these sub-atomic particles exhibit discreteness. They interact among themselves by exchanging another type of particles, force particles, called bosons. There are four fundamental forces observed in nature, namely strong nuclear force, weak nuclear force, electromagnetic force, and gravitation. Out of these four, the first two are observed within nucleus, and the third one (electro magnetic force) is observed in the microscopic world of atoms and molecules, and also in the macroscopic world. However, gravitational force is so far observed only with the bodies in our macroscopic world. We are yet to explain its presence in the subatomic level. The strong nuclear force is responsible for holding the quarks together in hadrons by exchanging a type of particles known as gluons. There are eight types of gluons. The weak nuclear force is responsible for the decay of large nucleus, and there are three force particles associated with it namely, W+, W-, and Z-bosons, respectively. The electromagnetic force acts through exchange of photons. In 1905, Einstein proposed that the light, which is also a type of electromagnetic radiation, contains stream of photons, each carrying a finite amount of energy (in quanta). In atoms, energy transfer takes place discretely through the exchange of a number of photons. It is similarly hypothesized that gravitational force is also carried out by a force particle, named graviton. However, the existence of graviton is yet to be confirmed experimentally. There is also another particle which is much smaller than any of the above particles predicted theoretically, called Higgs boson. It is hypothesized that the mass of a matter is determined by its interaction with Higgs bosons. Its existence is also yet to be confirmed, as for its generation and observation for a longer duration, one has to collide two hadrons using a very high energy particle accelerator. The recent experimentation in the Large Hadron Collider (LHC) of European Organization of Nuclear Research (CERN) is an attempt towards this. The analysis of the experimental data is yet to be confirmed. However, there is an initial report of success, which requires much more careful investigation by the scientific community.
Life in discrete form
It is not easy to define life. In comparison the definition of a matter is simpler, as we may term it as a substance with a mass. According to this definition, a living being is also a matter. But then, what is the essence of life in it, is a mystery, a very little of which is understood today. However our modern understanding of life started with the observation that it functions in discrete form within the cells of an organism. That the smallest unit of life is a cell, was discovered in the middle of the seventeenth century. In 1665, the English scientist Robert Hooke observed the cellular structure in a cork using a microscope, invented by a Dutch tradesman Antoni van Leeuwenhoek. Inspired by Hooke’s work, Leeuwenhoek on his own extended the microscopic study in other substances, and reported existence of bacteria and protozoa in 1678. Finally the proposition that all living being are grown from pre-existing cells came from the German biologist, Rudolf Virchow in 1858. The most fascinating discovery of discrete nature of transmission of inheritance in the reproduction of organisms came from the work of an Austrian Augustinian friar, Gregor Johann Mendel, considered to be the father of modern Genetics. However, Mendel’s work remained unnoticed for a long time, and rediscovered again in the beginning of the last century with its revolutionary impact in developing the theory of genetic transmission of inheritance, and understanding the fundamental role of genes in cellular metabolism. During the same period of Mendel’s work, Charles Darwin introduced the theory of evolution as the origin of species and variations among them. Evolution and inheritance play an important role in sustenance of life. That is why in a modern perspective life is defined by NASA in their program of astrobiology as “a self-sustainable chemical system capable of undergoing Darwinian evolution.” In view of this, the discrete form of genetic components and their pivotal roles in orchestrating a chain of events in the form of synthesis and interactions of proteins, RNAs, etc., are truly fascinating, as these are essentials in the sustenance of life.
In the second half of the nineteenth century it was understood that the hereditary factors are residing in chromosomes. There are always a fixed number of chromosomes in a cell for a specific organism. For example, a human non-reproductive cell has 23 pairs of chromosomes. Today we know more about its structure, which is a thread of double helical structure consisting of two DNA strands. A DNA is a sequence of four types of nucleotides, namely, adenine, guanine, cytosine and thymine. In this long chain of nucleotides, there exist segments, which are responsible for synthesis of proteins, and RNAs. Each such segment has a specific sequence, determining the amino acid chain of a protein, or a chain of ribo-nucleotides of an RNA. In fact a triplet of nucleotide in that segment is mapped to a unique amino acid in this process of synthesis. These triplets are termed codons, as they carry the protein translation code. There are twenty possible amino acids, and four types of nucleotides. Hence, we have 64 different codons, which are to be mapped to one of these twenties. This shows that there are multiple codons meant for a single amino acid. Moreover, there are a few codons which control the process of synthesis (called transcription followed by translation). They take part in initiation and termination of synthesis. It is amazing that this codon table is almost universal for every living, and extinct species of this earth. As more and more the secret of nature is unfolding, and the technology is moving fast with digital precision and robustness on synthesizing biochemical molecules in our laboratory, the scientists hope today what was unimaginable even a few decades ago, synthesis of life from lifeless inorganic chemical substances. In June 2010, an attempt in a very nascent form was reported to the amazement (and also with some concerns!) to the scientific community. Dr. Craig Venter and his team of twenty scientists of the John Craig Venter Institute, USA, were able to create artificial life by implanting synthetic DNA containing around 850 genes into the cytoplasm of a bacterial cell.
The dual world
We live in a dual world. Nature works with both continuity and discreteness in our real world. We could find this discreteness in existence and interactions among living and non-living substances. On the other hand, they exist with the continuity of space and time. Who knows one day with new findings and new realization of laws of nature assumption on their continuity would not be at stake? In material existence also, the discreteness has a vague boundary. We know that a particle too, has a dual existence of a wave. Hence, it is not possible for us to determine exactly both the position and momentum of a particle. Nature has put limits in the precision of these measurements. At the same time it offers us new challenges to uncover its mystery.
In our social life also we have to face another duality. We have to interact with both the real and virtual world. Day by day, it is becoming difficult to ignore the digital world in our social interactions. This digital world is created by us. It has many facets. It could be bothering your privacy, and monitoring you at every sphere of your activities. Again, it could be refreshing and entertaining. We could fly our imagination in its exploration. It can act as a tool for better understanding our nature, and thus controlling its resources and energy to our advantages. It is true that with the help of digital technology, we are presently going through a very exciting phase of social interaction and information sharing. But it has become possible only through our continued pursuit of knowledge to refine our understanding of this real world in its dual form.
12/02/2012
