It was only a decade ago that a phenomenon called the “Internet” came along and changed the way we communicated, did business and conducted our lives. Now, computer technology has become an essential and significant part of our daily lives. But how did it all start and where is it heading?

I entered the world of computers at an early age. I had an Atari 800 XL, the third version of the Atari introduced in 1983, and I was doing some basic programming. The computer contained a full 64K of memory, 1.8 MHz processing power (CPU), and looked like a bulky keyboard. I enjoyed spending a lot of time with games and programming; however I felt very limited with its capabilities. After more than 20 years, from time to time I still feel the same about my 2.8 GHz eight-core desktop computer with 8 GB of memory. While the computers are getting faster and more powerful, our need for computing power is also increasing just to complete our daily tasks at home and work. I always wondered how it all started and where we are heading with computer technology.

Over forty years ago, Gordon Moore, Intel Co-founder, predicted the number of transistors incorporated in a chip to double every 24 months. This is popularly known as Moore’s law, and used countless times by futurists [1] and science fiction writers. For the last half century, computer’s functionality and performance increased in line with Moore’s law, while costs are decreasing. However, fundamental barriers in semiconductor technology are emerging including the power needs and limits of manufacturing in atomic dimensions. Computer industry is already working on technologies to keep Moore’s Law alive.

Intel is already experimenting with 3D transistors that are smaller, faster and more energy efficient using a 22nm (nanometer, 10-12 m) manufacturing process compared to today’s 32nm systems. This will be a significant step forward to build more transistors onto silicon chips. Another approach will be by replacing the silicone in transistors. In 2010, IBM showcased a graphene transistor running at 100 GHz, with a potential of up to 1000 GHz. A graphene layer is only one atom thick with a honeycomb-like structure of carbon atoms. The graphene has unique electrical, optical, mechanical and thermal properties with promising applications in many industries.

In 1971, a theoretical prediction was made for the missing link in electronics, memristor, a fourth element to supplement resistor, capacitor and inductor, which form the basis of today’s electronic devices. HP’s demonstration in 2010 shows a resistor with memory that remembers the electric voltage applied even when the power is turned off. Requiring very little energy to store information promises to run ten times more power efficient and ten times faster than current counterparts.

In 1994, Leonard Adelman [2] proposed DNA computing to solve the famous, “shortest path problem”. Since then, many approaches have been made to utilize the properties of DNA for computing. In 2010, researchers at California Institute of Technology demonstrated a DNA computer, most advanced to date, which can calculate square roots. This approach could be put in use inside living organisms, and perform vital tasks such as disease detection. The promise of DNA computers depends on the parallel processing capabilities of DNA molecules that can try many possibilities at once with low power requirements [3]. Further advancements in DNA based computers in the coming decades will bring faster and low-powered computers to our daily lives.

In 1981, the famous physicist Richard Feynman speculated the possibility of computers obeying quantum mechanical laws that might best simulate the real-world quantum systems. This is a big challenge even for today’s fastest supercomputers. Since then, researchers are pacing towards building quantum computers that rely on quantum mechanics to conduct operations. Quantum computers can use properties like entanglement [4] and qubits. In entanglement, particles behave identically independent of the distance between them, and qubits act as both memory and state of the entanglement. Shor’s Algorithm, formulated by Peter Shor in 1994 for prime factorization is a powerful example of quantum computing that allows breaking encryption algorithms like RSA encryption more effectively and quickly than today’s super computers. Quantum computers can perform at much higher speeds than traditional computers, and are able to solve more complex problems. Recent developments in quantum computing such as quantum photonic chips [5], and first commercial quantum computer by D-Wave shows that we might be closer than we think to have our very own quantum computer in the coming decades.

While greater shift in computing might stem from the change in underlying technology in processing the information, computer form factors (desktop, laptop, tablet, phone, etc.) have a more direct effect in our daily use of computers. Human computer interaction changed significantly with the introduction of smartphones (e.g. iPhone, Android phones), and tablets (e.g. iPad), moving from keyboards and mouse as the primary input methods towards touch screens.

Today’s touch screens, although providing infinite ways of input structures available on their screens, lack tactile feedback when compared to keyboards. New keyboard designs with small screens on each key opens infinite customization of the input, but are still limited to initial design of the key (e.g. usually cubic). Recent developments in touchscreen designs enable users to feel clicks, vibrations and other tactile input by using “haptic technology”. Haptic technology takes advantage of the user’s sense of touch and provides feedback by applying forces, vibrations, or motions to the user. As seen in early prototypes, flexible screens and electronics will provide a more realistic feel for human computer interaction by shifting their forms to a specific shape (e.g. game pad, key, and wheel) in the coming decades.

Another level in human computer interaction even eliminates user’s touch. Apple introduced Siri, a smart virtual assistant, in 2011 as a part of their iOS operating system for iPhones and iPads. Siri is capable of analyzing user’s complex audio inputs to carry out many tasks including scheduling a meeting, creating a reminder, typing and sending SMS messages, and many other functions available in smart phones. Microsoft introduced Kinect in 2010, a motion-sensing device that enables users to control and interact with the game console using gestures and spoken commands. The Kinect interprets specific gestures by using an infrared projector and camera to track the movement of objects and individuals in three dimensions.

Samsung introduced a 46’’ transparent display using LCD technology in 2010, and demonstrated flexible displays in CES 2011. Transparent and flexible displays will easily find use in wearable electronics such as contact lenses and glasses. An obvious application of transparent display is Augmented Reality (AR) where information is displayed on top of real world images. While today’s smartphones and tablets allows augmented reality by combining information with the real-time video feed from the camera of the device, transparent display eliminates the need of using camera. Current AR technology includes head-mounted displays and virtual retinal displays for visualizing the information. It is widely applied in various areas including entertainment, advertising, game industry, navigation, education, military applications, and information sharing.

While having larger, transparent, and flexible displays in different forms, one direction in display technologies is to reduce the size or even eliminate the display through projection and holograms. Current trends in projectors include 3D projection, synchronization of multiple projections, and pico projectors. The world’s smallest glass lens (1mm x 1mm) introduced in 2011 will help minimize some of the problems of projectors such as size, power and heat, and improve their integration in smartphones and tablets.

Recent prototypes of holographic displays progressed significantly demonstrating 3D and full color animated images, since its first introduction at the MIT Media Lab 1989 [6]. While developments in 3D displays are promising, holography provides the best 3-D experience since it is closest to how we see our environment. A hologram uses an optical effect called “diffraction” to produce the light that would have come from an object, and makes the image of the object appear in front of the viewer. It is possible to view objects from different angles in holographic display by walking around them. Unlike other 3D display systems, holographic displays do not require special glasses for viewing and allows multiple viewers to experience the view from different angles at the same time. Future applications of holography can be implemented in health, entertainment and communication sectors, from 3D movies to telepresence applications.

All of the above examples and trends in computers deal with the computing as a product. Cloud computing can be defined as the delivery of computing as a service where shared resources, software, and information are provided over a network. Cloud computing describes a new delivery and consumption model that allows dynamic scalability and virtualization of resources. Users can dynamically upgrade the storage and computing power from virtualized resources on demand without hardware changes on the base system. Organizations can save from investing on expensive hardware, and human capital.

Many technologies from coming centuries are featured in science fiction books and movies such as Minority Report, Star Trek, and Star Wars. While some of them are already available to consumers, others might require decades to come. Motivation for the advancement in computer technologies usually stems from our needs and desires. At the same time, new technologies significantly impact consumer behavior and increase our dependence on new technologies. Our economy is structured such that all citizens have to consume more and more, even if that means disposing of perfectly good technological devices. Do we really need a new computer or phone every year? Most of us don’t.

Computer technologies are a significant part of our daily lives, and the line between the products and services is becoming thinner with the dependency on computers increasing in every aspect of our life. While improving the quality of our lives by making our daily tasks easier, computers and Internet technologies can affect us in different ways. It has already started to change how we read, write and even communicate with others. Many concerns are raised about the negative effects of the use of technology including Internet addiction, privacy, attention span, concentration, time consumption, anxiety, isolation, depression, digital security, communication disorder and various health issues. The challenge for us is to understand the benefits of the technology, have a balance in dependence and its use, and protect ourselves from its adverse effects.

Acknowledgment: This article is produced at Mergeous [7], an online article and project development service for authors and publishers dedicated to the advancement of technologies in the merging realms of science and religion.

References

1. Kaku, Michio. 2011. Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100, Knopf Doubleday Publishing Group.

2. Demir, Halil I. 2011. Super Computers in a Cell, The Fountain, Issue 80, March – April.

3. Adleman, Leonard M. 1994. “Molecular Computation of Solutions to Combinatorial Problems,” Science, 266 (11), 1021–1024.

4. Demir, Halil I. 2011. Quantum Worlds from Entanglement to Telepathy, The Fountain, Issue 84, November – December.

5. Shadbolt, P. J. et al., Generating, manipulating and measuring entanglement and mixture with a reconfigurable photonic circuit, arXiv:1108.3309v1 [quant-ph].

6. Hilaire, P. St., S. A. Benton, M. Lucente, M. L. Jepsen, J. Kollin, H. Yoshikawa and J. Underkoffler. 1990. “Electronic display system for computational holography.”In Practical Holography IV, Proceedings of the SPIE, volume 1212-20, pp. 174-182, Bellingham, WA. 7. Mergeous, Online article and project development platform, http://www.mergeous.com

ENIAC had cost around $500.000 and was capable of 5000 simple operations per second. Today, basic personal computers (PC) cost around $500 with enough processing power to perform millions of operations per second. An average PC is enough in terms of computing power for everyday use like word processing, checking emails, and computer games. However, some areas in scientific research require computers at the frontline of current processing capacity, called Super Computers. Twice a year, the TOP500 project, which started in 1993, ranks and publishes details of the 500 most powerful super computers in the world. The IBM Roadrunner, located at Los Alamos National Laboratory, was announced as the fastest supercomputer in the world as of May 2008.

In computing, “flop” (Floating Point Operations Per Second) is a measure of a computer’s performance which is similar to calculations per second. The IBM Roadrunner had cost $133 million and had a peak performance of 1.7 petaflops, which is around 1.7×1015 operations per second. The Roadrunner was delivered on 21 tractor-trailer trucks to its current location. Supercomputers are an essential component of research in areas like computational biology, fluid dynamics, structural mechanics and cancer research, which requires high computing power.

While computers get faster every year, their computing power is way behind when compared to a human brain. They consume hundreds of times more energy than a brain. It is estimated that a computer will be able to simulate a human brain in seven years, yet we are decades away from expecting a computer that can think like a human and make decisions. The brain is one of the most miraculous parts of the human body, full of mysteries. It works more efficiently than any machine developed in the last 50 years of the computer history.

However, the human brain is not the only body part which has an incredible computing power. In 1994, Leonard M. Adleman, a professor at the University of Southern California, introduced the idea of using DNA (Deoxyribonucleic Acid) to solve computational problems [3]. This idea then led to a new field of science, called DNA Computing, which combines two disciplines, biology and computer science, to build the fastest and smallest computers ever. DNA is known as the blueprint of life, with unique properties such as self-assembly, molecular recognition, minute size and high information density.

Computationally challenging problems have known solutions, but enormous amounts of resources (time and/or cost) are required to find the optimum solution. Some problems, such as optimization, can be solved by generating many possible solutions, and then selecting the optimum one. Standard computing methods can generate or test a possible solution one at a time. On the other hand, parallel computing methods can carry out this process simultaneously for thousands of possible solutions.

Similarly, enzymes can work in parallel for replicating and repairing DNA strands. They can even work on the next strand before the first one is replicated. An enzyme can replicate a DNA strand 500 times in a second, which is equal to 0.001 MIPS (million instructions per second). The computations in DNA can reach to 1014 MIPS, while a modern computer runs at an average of 1000 MIPS. DNA computing is not only faster in processing, but also much more efficient in energy consumption. The energy consumption of a DNA operation (on one strand) is about 1010 times less than the energy consumption of an operation on modern computers.

The Traveling Salesman Problem (TSP) is one of the most studied problems in computational mathematics. Here is an example of the problem: a traveling salesman needs to visit 20 cities once, with predefined starting and ending locations, and certain rules. The complexity of the TSP problems increases exponentially with the number of cities, so problems with only hundreds of cities will take thousands of years to solve by modern computers. If there are 18 factorial possible paths in this problem, it will take 2 whole years for a computer with 100 MIPS of processing power to generate the possible paths and find the correct answer. However, all possible paths can be generated in a very short time by using DNA computing. A simplified version of the Traveling Salesman problem presented by Adleman involves the following scenario:

A salesman wants to visit the cities (Figure 3) Monroe, Gainesville, and Conyers, starting from Athens, and arriving at Atlanta last. Each city should be visited only once. The cities are not fully connected. While some cities are connected to another in one direction, others are connected in both directions. Our objective is to find the shortest route to visit all cities once. The solution for this problem is a travel from Athens -> Gainesville -> Monroe -> Conyers -> Atlanta.

When we convert the problem to a molecular language, each city is coded as a single-stranded DNA molecule with 8 nucleotides. We can think of nucleotides as bytes in computer programming, which will take the value 0 or 1. Nucleotides exist as four bases: adenine (A), thymine (T), guanine (G) and cytosine (C). All cities are coded with eight nucleotides as follows:

City Code

Athens ATGC CATG

Gainesville TCAG GTCA

Monroe GACT TGAC

Conyers CGTA ACGT

Atlanta AGCT TAGC

Connections between two cities are coded with the last 4 nucleotides of the departure city and the first 4 nucleotides of the arrival city. For example, the connection between Athens (ATGCCATG) and Monroe (GACTTGAC) is coded as CATGGACT. The complementary codes for connections (the Watson-Crick complements), where every C is replaced by a G, every G by a C, every A by a T, and every T by an A, and connection codes are given below:

Connection Code Complementary Code

Athens – Gainesville CATG TCAG GTAC AGTC

Athens – Monroe CATG GACT GTAC CTGA

Gainesville – Atlanta GTCA AGCT CAGT TCGA

Gainesville – Monroe GTCA GACT CAGT CTGA

Monroe – Conyers TGAC CGTA ACTG GCAT

Conyers – Atlanta ACGT AGCT TGCA TCGA

Conyers – Monroe ACGT GACT TGCA CTGA

The mixture for performing reactions will include DNA strands and their complements for 5 cities and 7 connections between cities in our example. If an Athens molecule (ATGC CATG) encounters the complement strand of an Athens-Monroe (GTAC CTGA) connection in the mixture, a hydrogen bond will be formed between strands (Figure 4). Other strands will continue forming bonds for valid connections between cities, building a complete travel path with the help of DNA ligase. There needs to be enough copies of each DNA strand to generate all possible travel paths.

ATGC – CATG

| | | |

GTAC – CTGA

Polymerase Chain Reaction (PCR) will be used to make multiple duplicates of DNA strands containing Athens (start) and Atlanta (end) cities. The result of PCR will be the amplification of correct travel from Athens to Atlanta, which makes it easy to separate. PCR is a method by which a few strands of DNA can be copied into millions in a very short amount of time. This also makes PCR a very important method to increase small amounts of DNA found in blood or hair samples, which could be enough to carry out analysis and reveal a person’s identity in forensic science.

Electrophoresis follows the PCR process to sort the resulting paths according to their sizes. Since every city is coded with 8 nucleotides, the correct path should include exactly 40 nucleotides representing a full path for 5 cities. The gel electrophoresis process uses an electric field to separate DNA strands by size as they travel through a gel matrix. The speed of DNA molecules differs by their size, which results in the sorting of the molecules by size. After this step, DNA strands starting with the code of Athens, ending with the code of Atlanta and with a size of 40 nucleotides are separated from the mixture.

The last step will be reading the code and removing the DNA strands that didn’t contain all the cities. Adleman used a common method known as affinity purification for the separation process. Finally, the mixture has the DNA strands with the correct travel path, starting from Athens and arriving to Atlanta, and traveling through every city once. All the laboratory work looks complex for this simple problem, but as a new concept for computing, it is revolutionary. In its ability to perform parallel computations, DNA computing shows great promise over traditional computing approaches.

Data density is another unique advantage of DNA. Billions of DNA strands can be stored in a regular laboratory tube. A DNA strand is composed of bases A, T, C and G spaced evenly, 0.35 nanometers apart from each other. The data density of DNA is around 106 GB (gigabytes) per square inch, which is 100,000 times larger than the data density of today’s storage technologies (7 GB per square inches). Moreover, DNA is a durable and strong molecule; the information stored within it can be kept for thousands of years in the right conditions. In 2008, 80% of the woolly mammoth genome, several thousand years old, has been identified from tufts of frozen woolly mammoth hair [4].

DNA is also created with remarkable mechanisms such as built-in error correction. The double stranded nature of DNA provides a double check on pairing. Error repairing enzymes are always ready to search for anomalies during the DNA replication process. It results ina ratio of one error per billion replications. DNA is located and protected at the center of each cell with a perfect balance. The miraculous architecture of DNA has waited for thousands of years to be understood by humans and be used for the benefit of the world. Further studies on DNA might open new opportunities to help researchers in solving technologically challenging problems.

Acknowledgment: This article was produced at MERGEOUS [5], an online article and project development service for authors and publishers dedicated to the advancement of technologies in the merging realms of science and religion.

Halil I. Demir is a postdoctoral scholar in the area of Informatics, and lives in Iowa.

References

[1] ENIAC, Image Credit: Wikimedia, http://upload.wikimedia.org/wikipedia/commons/4/4e/Eniac.jpg

[2] IBM Roadrunner, Image Credit: Wikimedia,

http://upload.wikimedia.org/wikipedia/commons/c/c7/Roadrunner_supercomputer_HiRes.jpg

[3] Leonard M. Adleman (1994-11-11). “Molecular Computation of Solutions to Combinatorial Problems.” Science, 266 (11): 1021–1024.

[4] Miller, W (et al). 2008. “Sequencing the nuclear genome of the extinct woolly mammoth”, November, Nature.

[5] Mergeous, http://www.mergeous.com

How about quantum computers? Quantum computers make use of a quantum mechanical phenomenon, so-called quantum superposition (being in different states simultaneously). Classically, voltage across a circuit element can be either positive or negative when measured by a voltmeter, but not simultaneously negative and positive. Could it somehow be possible to be in both states simultaneously?

Quantum superposition

For electrical circuits, the answer is obviously no. In microscopic scales of single atoms, or photons (i.e., single quantized packets that constitutes the light beam), however, the answer is yes. Consider an optical component, for instance, that splits an incoming light beam into two beams of equal intensity. In optics, such a device is called a 50/50-beam splitter. You can ask what happens when a single photon is sent to such a beam splitter. Since a single photon cannot be split in this simple experiment, you might expect that it would either be transmitted or reflected with equal probability . Experiments, however, show that this is not actually true in the single photon level. The single photon is indeed simultaneously reflected and transmitted.

Once microscopic quantum superposition is brought into our macroscopic world, we can imagine many interesting phenomena. Simultaneously occupying many different places and being dead and alive at the same time are only two of them. Of course such technology, especially applied to humans is highly science fiction, given current experimental and theoretical challenges. Nevertheless, quantum superposition has a strikingly interesting similarity with the spiritual states already achievable by saints, such that they can be available in more than one place at a given time or become dead and alive, in the sense that they live both in the future and in the past.

It is not known exactly why quantum superposition exists, but what we know is that it is a necessary ingredient for our complex universe to perform its vital functions in a finite amount of time. Quantum superposition principle reflects the great wisdom and power of the Omnipotent. Similar to the single photon example above, with this principle, God gives the underlying particles of the universe an immense power to achieve many tasks simultaneously. Otherwise, regarding the finite age of the universe (about 15 billion years), our physical universe and the events taking place all around us would not come into existence. The Quantum superposition principle has also inspired researchers to build unprecedentedly fast computers to solve the problems that are intractable with any classical computing method. In this article, we introduce this new strategy to computing.

Quantum computing with superposition

Having provided some background about the quantum superposition, we ask the question “How could we exploit quantum superposition for fast computing?” Below we will give a glimpse of that power. Consider a three-bit register. It can only store one out of eight numbers in the set, {0, 1, 2, 3, 4, 5, 6, 7}, in a given moment of time. For example, number 5 is stored in a three-bit register as “101.” Now suppose that these three bits are replaced by their quantum cousins, so-called qubits (short for quantum bit). You can imagine, for example, a quantum register consisting of three rubidium (Rb) atoms. These individual atoms can be prepared in the 0 and 1 logical states simultaneously by shining a laser beam for a certain amount of time. Then it is possible for three atoms combined to be prepared in a superposition of eight numbers, which is impossible classically. In other words all those eight guys physically exist in the same room, although it doesn’t allow more than one guy to fit classically. If we want to make operations on all of these numbers, we don’t need to perform serially; instead, we can achieve that in only one computational step on a single hardware. Thus, quantum superposition leads to a massive parallelism, which renders the computational complexity (i.e., a measure of how efficiently a given problem could be solved) highly reduced for various difficult problems in computer science.

For example, let’s consider RSA, a well-known algorithm (i.e., a set of instructions to solve a problem on a computer) for secure communication that was invented by Rivest, Shamir, and Adelman, hence the name, in 1977 at MIT. It is widely used in electronic commerce protocols. The details of RSA are out of scope in this article (See the FAQ section of the RSA Laboratories’ web site in Ref. [1] for a brief introduction to RSA). Here, we only want to mention its vulnerability to quantum computers if they were to exist. The security of the RSA cryptosystem relies on the difficulty of factoring large numbers, which is intractable with classical computers. Factorization for small numbers, say 15, is quite simple. When the number of digits increase up to a few 100s, for example, enormous computational resource is required. RSA Laboratories publish the RSA challenge numbers (see Ref. [2] for the list of challenge numbers and the prize) on their web site to test the security of their algorithm at various key lengths. The largest integer, RSA-640, which has 193 decimal digits (640 bits), was factorized recently by F. Bahr, et al. The next challenge number in turn is RSA-704, and the prize is $30,000. Imagine factorizing a 1000-digit number. You would probably be a considerably rich person in just a few minutes, if you had a moderate quantum computer and the RSA Laboratories kept feeding you with new challenge numbers, because the factorization of such a large number with current computational resources takes forever, perhaps even more than the estimated age of the universe. Of course, the RSA Laboratories will not let you be very rich, by simply quitting posting new challenge numbers. They would be interested in your quantum computer, though.

How does the quantum computer crack the world’s most secure cryptosystems with little effort? One can construct new algorithms for quantum computers based on above described principle of superposition. These algorithms can take the outcome of previous calculations and input them as a superposition to the next stage of the instructions, which results in a highly efficient form of computing (please consult Ref [3] to have for a simple explanation of quantum superposition for fast computation). In 1994, Peter Shor from AT&T’s Bell Labs in New Jersey just did that. He developed the world’s first quantum algorithm, which efficiently performs factorization. In 1996, Lov Grover also at Bell Labs invented the unstructured database (i.e., a disordered list such as a list of city names not in alphabetical order) search algorithm for quantum computers, so-called Grover’s algorithm.

Suppose that there is a basket with ten balls in it. You are now asked to find a specific one with your eyes closed, say red. It is known, however, beforehand that there is only one red ball in the basket. All you, or your smart digital friend, can do is just pick one randomly and see if it is red. If you are lucky enough, the first ball you pick might be red. In the worst case, however, you will be successful at your last choice. So, classically you have to repeat the process on average at half times the number of balls. If you made a quantum friend rather than classical, however, your life would be smoother. You would be able to find and manage your stuff easily, no matter how messy you are. Quantum computers speed up such unsorted database searches quadratically. You can find, say your favorite socks, in a number of trials that is about the square root of the total number of your stuff. You may think that you don’t have that much stuff. But consider identifying a specific element in a considerably large pool of unsorted data. As the number of elements in the set increases, it quickly becomes intractable to find what exactly you are looking for. In that case the significance of quadratic boost cannot be denied.

Motivated by the above mentioned factorization and unsorted database search algorithms, the power of quantum computing has inspired great attention, since their invention, among many disciplines including physicists, computer scientists, mathematicians, engineers, and material scientists.

Quantum computer today

Despite promising developments in theory, progress in the physical realization of quantum circuits, algorithms, and communication systems have been extremely challenging to date. There are many approaches for quantum information processing. Major model physical systems include nuclear spins, ions, neutral atoms, solid state nanostructures, superconductors, and optical circuits. In optics, for example, the qubit can be represented by the polarization (i.e., direction of oscillation of electric field) state of a single photon. So that the instructions described by the algorithm could be implemented by manipulating the polarization states of single photons. Unfortunately, all the models for quantum computing have their own drawbacks besides their advantages.

Given the trends, nobody knows whether or not a sufficiently scalable (i.e., large enough to harvest its potential power) quantum computer would be available in the decades to come. Nonetheless, D-Wave Systems, Inc., The Quantum Computing Company, was eager enough to unveil the “world’s first commercially viable quantum computer” (see Figure 1, and Ref [4] for the story.). D-Waves’ 16-qubit quantum computer makes use of superconducting element niobium, which operates at an extremely low temperature. It can search for molecular structures that match a target molecule, create a complicated seating plan, and fill in Sudoku puzzles. Although the device is very slow compared to an inexpensive PC, D-Wave intends to develop a 1000-qubit quantum computer.* The goal is to scale the quantum computer to about 10 thousand qubits to solve the most challenging problems outright, which are simply intractable with classical computers. The researchers, however, are not very optimistic. Prof. Lloyd of Massachusetts of Institute of Technology, a pioneering scientist in superconducting approach for quantum computing that underlies the D-Wave’s quantum computer, says “It’s too good to be true.”

Once quantum computers of reasonable power are built, the world will be unimaginably exciting and perhaps scary too. When the first commercial computer, Universal Atomic Computer I (UNIVAC I) (see Figure 2), was shipped to the United States Air Force in 1952, nobody was indeed aware of what this fat guy would lead to in our social, economical, political, and psychological life. Its descendants, however, are now inevitable parts of our lives. They are helping us in many aspects of daily life. Controlling machines, sending electronic mail, scheduling our plane tickets, communicating with our best friends, playing games, making our payments are only some of them.

In this article we only sketched the quantum superposition principle as an important ingredient for quantum computation. This is certainly not the whole story. “Entanglement” [6], for example, is another complementary resource for quantum computing and communications, as well as quantum mechanics to test its foundations.

Contrary to its classical counterparts, the power of quantum computers indeed comes directly from our granted capability of tailoring and mimicking the amazing design hidden in the microscopic world of atoms, photons or other quantum particles. Quantum computers sooner or later will bring the most science-fiction into reality. They will play a significant role especially in the development of ultra-intelligent machines and robots superior to classical ones, and communication systems whose ultimate security is guarantied by the nature’s architecture which was lay down by God. Quantum computers will reveal to us the deepest secrets of our Creator embedded in our universe, which cannot be explored using conventional computers. That day, the future will only be lacked by our limited imagination.

Acknowledgment

This article was produced in MERGEOUS [7], an online article and project development service for authors and publishers dedicated to the advancement of technologies in the merging realm of science and religion.

Omer D. Ikramoglu is a freelance writer in optics and quantum physics.

References

1. RSA Laboratories, http://www.rsa.com/rsalabs/

2. RSA Challenge Numbers, http://www.rsa.com/rsalabs/node.asp?id=2093

3. A short introduction to quantum computation by A. Barenco, A.Ekert, A. Sanpera and C.Machiavello from La Recherche, November 1996. http://cam.qubit.org/articles/intros/comp.php

4. J. R. Minkel, “First “Commercial” Quantum Computer Solves Sudoku Puzzles”, Scientific American, Feb 13 (2007).

5. UNIVAC I, http://en.wikipedia.org/wiki/UNIVAC_I

6. S. Candaroglu, “Quantum Entanglement: Illusion or Reality?” Fountain, Issue 61 (January-February 2008).

7. http://www.mergeous.com/

* At the time of writing D-Wave Systems had only 16-qubit quantum chip and they were intending to develop a 1000-qubit quantum computer by the end of 2008. Although they couldn’t meet their goal, they now have a design of a 128-qubit most powerful ever quantum chip which awaits the tests (see http://www.dwavesys.com for up to date information).

Such sceneries are very common now in many houses and internet cafes. Children and youth are spending most of their times playing computer games. While parents favor computers as necessities of modern age and as assistants for their children’s homework, children mostly consider them as toy boxes. They initially start with innocent intentions, but in time they lose their original purpose. “Uncontrolled use of computers,” the common problem of many parents, is also a matter very difficult to solve for the experts.

How do computer games affect the children’s subconscious and their future lives? Do games with positive effects have an appealing presentation?

Computer games can be grouped under two categories. First are the games that help developing talents, understanding the life experiments and ethical values by using an entertaining method; Second are the games that do not contribute anything to the child’s imagination and thinking abilities and inciting violent and immoral behavior. For the game to positively affect the child’s intellectual, physical and spiritual development depends upon both the content and the duration of the game. This period of playing time should neither be too short, nor too long. To be able to obtain a proper balance in this matter, we should first equip the child with an education of using the time wisely. Children wasting their time for useless activities, can also be insensitive to effective use of time when they grow up. Concisely, children must acquire the consciousness of valuing time. Wasted time in front of computers cause children and youth to get passive. This increases the stress experienced by the children. Especially in active boys, this inactiveness causes an energy accumulation which negatively affects the child’s behaviors. The use of excessive energy by ways of different sports is very useful for the physical and mental development and raising of a social conscious. Moreover, spending time in front of computers, deprives children and youth from cultural activities like playing group games, studying together and engaging with a sports activity.

Many of us complain that our children cannot express themselves properly and are not clear at communicating with their environment. When we ask a question to our child, either we get a very brief or a routine answer. Times elapsed aimlessly in front of computers have a significant effect on this.

We see that violence increase in each level of any new computer game. The game producers consider every type of method as permissible just to be able to attract children to the screen. Scenes which are not suitable for the age form the subconscious of a child. Children who have been exposed to several violent scenes are mostly inclined to aggression, anxiety and panic and they are generally furious and quick-tempered and consider violence as an ordinary matter. Reactions of children who watch violent films and play fierce and crimson computer games change in time and start applying more and more force to people and children around them. They tend to hit and kick others even after a slight disagreement. Being affected from destructive violent scenes, children reveal problems like over anxiety, sleep disturbances and behavioral disorders. Since the causes and effects of behaviors affected by the five senses are not questioned so much in pre-school children, abnormalities mentioned earlier can be seen more on them. We can give these as example: “A three year old child who watched violent films, applied what he watched on TV and stabbed and killed his little brother with a knife. Another child who thought himself to be a pokemon (a cartoon character), threw himself from the seventh floor with the intention of flying. And another child in France who could not win any computer games became epileptic because of having too frequent and excessive anger fits due to the unsuccessful playing sessions.” Unfortunately we often hear such news. In order to illuminate the subconscious of our children with beauties, favors, affection, and altruism, we should offer them alternative useful and educational games.

Harmful games also prevent children to learn their own culture, thus cause a cultural corrosion. Such games take our children to a world of weird clothes and hairstyles, weapons being the accessories. In this virtual world, no values exist except for “might is right.” Furthermore, in most of these games Islam is shown as a boogeyman and Muslims are shown as terrorists deserved to be killed. Children who are seized by the computer games become alienated to their environment. Having difficulty to make friends around them, children become enslaved by the computer and isolate themselves from the society. The only friend of the child is the computer, and in spite of elapsing the time, child still feels great loneliness inside. Hence, parents should provide suitable activities for their children and reasonably arrange the time spent for computer games.

Many bad characters shown on computer games also have perverse effects upon the child’s personal development. It is definite that messages given by these games like being strong and unchallengeable, inconsideration of other people’s lives, killing for the sake of living, disdaining ethical values, despising other people’s feelings, disrespecting the person addressed, categorizing the people, negatively affect the child’s personal development. Such games also impose unfavorable impacts upon learning abilities and awareness of the child. Scenes that are rapidly changing on computer screen distract the child greatly, make him confused and disturb his mind. This triggers a difficulty in concentrating when learning. Besides, excessive light changes may cause epileptic fits for some children.

Conclusively, computer games inspire children that the life is only a game and lead children to think too much about the characters in the game and mentally and physically to be with them all the time. Children playing too much computer games, carry the same concepts in the game to their relations with their family and friends, cause the child to confuse between the reality and imagination and eventually, the time spent together decreases and unsocial behaviors settle in the child’s personality.

Surely, we cannot underestimate the beneficial sides of the computer which is a must in modern age. But the ideal is to benefit from the positive services of the computer and to ensure a protection against its harmful usage. The playing time which is essential for the child’s healthy growth, should be distributed fairly between virtual games and real group games. For this, we must prefer games;

• in which they can play an active role,

• that are suggested by specialists and other experts,

• which can provide national awareness and cultural identity,

• which support comprehension and intelligence of the child,

• which contribute to virtues like sharing, team spirit, caring for others, honesty, and diligence,

• which instigate curiosity and make learning fun,

• which include parents into the game,

• which infuse consciousness of performing duties and responsibility,

• which help developing imagination, thinking ability, exploring and inventing capability.

If eyes, ears and other organs that are bestowed upon us by the All-Compassionate Creator are engaged with only entertaining computer games, then they are being used out of their innate purposes. Therefore, the vital responsibility of parents is to be very careful for guiding their children to use their sight, hearing, feeling, judging faculties in the right direction and to prepare an environment that provides the child a rich learning background and to bloom his mental abilities.

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

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

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

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

Figure 1 A simple illustration of a Turing Machine

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

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

Suppose we have any 10 numbers.

Problem: What is the sum of these numbers?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Another example:

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

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

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

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

References

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

Programs and documents on the computer are held in two areas: software and hardware. For scientists, one of many goals is to make the processors or chips, which are like the human brain that consists of nerves, much smaller, but still powerful enough to process many more calculations. There are many differences between the current chips and earlier ones. Chips which will be produced in the future will be smaller and probably process more calculations more quickly.

Programs, which can be seen as being the mechanical counterpart of the human mind, bring the above-mentioned improvements into daily life. New programs boost the capability of the computer in parallel with the capability of their chips. Without these programs, computers would be no more than ordinary electronic machines.

Developed technologies in fields like industry, communication, or the military bring us face to face with new developments and have made the computer an undeniable part of our lives. Mobile phones equipped with new features, medical machines which can easily make a large number of analyses and provide ease in diagnosis and treatment, robots that can operate with minimum error and have a low cost when used in production, and weapons that automatically focus on the target are all part of this progress.

With time, software and hi-tech sensors have enabled computers to communicate with people; there are now systems that are controlled by the voice, which are able to recognize a person from their iris or fingerprints, control systems which are carried out by touching a screen, etc. Such systems are operated with the help of special sensors or by some signals that carry messages from the person or the environment to the computer. The most important feature of these sensors is that the signals produced at the output are very weak and there are few differences between them. An ATM can recognize a particular person’s iris, thanks to the ability of its computer to compare the signals from the ATM’s iris scanner with previously recorded data. In this process, the computer uses the small differences that one person’s iris has to another’s. In this or similar systems, complicated programs are used, called “expert systems” or “artificial intelligence”. These programs imitate human senses, but they aim to operate with an even keener sensitivity and clearer criteria.

A question that is a subject of fiction comes to mind; “Will computers vie with or even fight with human beings?” In the mid-term, the rapid development of technology will create computers which can communicate with humans, which can understand them, and put forward ideas. A negative outcome of such a situation depends, once again, on man. Such a horrific situation could be the result of technology that can cause environmental disasters; this technology is almost identical to the one that we have described above. If we are able to establish an understanding of “civilization” which does not ignore human values for the sake of technological development, then such fears will be groundless.

Artificial Nervous Systems

As we all know, people have imitated nature in many of their inventions. In a way, artificial nervous systems imitate how a nerve cell learns and how it works. Fuzzy systems however imitate how the judgment of a human being works, rather than the nerve cells of the brain. In these systems people try to form a decision making criterion by assuming that there are endless grey tones between white and black or by assuming that there are infinite values between zero and one.

The purpose of the research on artificial nervous systems is to understand how the brain operates, then to make a system that imitates it and carries out the same operations. Artificial nervous systems are made of simple nerve cells which are bound in parallel, called process elements; these allow for real objects to be seen as if they were biological systems.

Here, the program that resembles the nerve cell operates in the same way as a nerve cell. The main part of a nerve cell is formed from the body, called a “soma”, an “axon” that is bound to the body and many “dendrites”. There are many “roots”, or synapses, on the dendrite of a cell which make contact with the dendrites of other cells. A nerve cell either transmits the electrical stimulus that comes through the axon to the other nerve cells through the synapse, or it does not transmit it, depending on whether or not the signal is over or below the threshold value. So a nerve works by itself, but its activity becomes meaningful when working as a part of a nervous system. It would be useful if we consider how the learning process occurs here. It is thought that the required data are stored in the memory center and this fact is taken as a model for some artificial nervous system software that has been successfully developed to date.

A nerve cell and the process of transporting signals from one cell to another can be written as software. It is clear that a natural nerve cell is more complex and that it is bound to more cells than an artificial one can be. The number of communication ports (synapses) of a natural nerve can vary from between 1,000 to 10,000.

An artificial cell produces output if the input value is over the cell’s threshold value; if this is not the case then there is no production. If there is output – as in natural cells – then this output is transported to the next cell group. Each cell produces its output as an input for the next cell.

A cell is separated into three groups: input, the hidden layer and output. Each group is considered to be made up of one layer, while the hidden layer can consist of more than one, according to the complexity of the job. As can be seen, the placement of the layers is similar in the process of the human body. We can compare the cells on the input layer with human senses. In this way we can teach a robot to avoid heat and cold, we can make them see and act according to this information. (Do not forget that a robot is in fact a computer.) It is natural that some sensors must be bound to the cells on the input layer. For instance, a sensor which is sensitive to heat can make the robot react to heat when the temperature is over the limit value or when the temperature is dramatically low it can move closer to a heat source. Or if pictures received from a video-camera are similar to an object that has been fed into the robot such data input can cause the robot to move to that object.

Artificial nervous systems are not only used in robot applications. They are commonly used in making clinical diagnoses, determining market-customer profiles, recognizing voices or pictures, classifications such as determining micro-structures, like germs and cell materials, economic profiles, energy sources, the futures of market shares, some predictive sciences, such as weather forecast, zipping data for computers, process control in industry, checking resources and some other matters in technological areas. As can be seen, there are many application areas for artificial nervous systems, all of which differ from one another.

The Basic Features of Artificial Nervous Systems

The features of artificial nervous systems can be simplified as follows: firstly, they can learn how to solve problems. In order to do this they use sample data and learning styles and while doing this they do not require any special help. Secondly, they can recognize important features and relations to help them distinguish different data forms.

When an artificial nervous system is operated, the first thing to be carried out is the training process. In order to do this, the program needs to have two alternating operations. It may obtain information concerning some results to be achieved, using results that come from the user, or the program is itself asked to produce some results. These two types of learning are not very different from how a human learns. One shows a young child an animal, and repeats the name. Now the child has learned the name of the animal and correlates it with the picture of the same. If no one teaches a child what a bird is, the child will all the same classify all animals that have wings and beaks and that have a certain physical shape, maybe even creating a name for the animal by him/herself. The difference between the computer and the human in this process is that a human has the ability to judge, while computers classify the animals according to their shapes and groups them thus. Naming and giving a naming feature to the computer is again a decision that a human will make. When the training process is finished, the data can be entered into the computer and the desired results can be attained.

Artificial nervous systems are changing and developing day by day. With each new development they become closer to the human nervous system; they are able to recognize different characteristics of different people and they are learning to make sorting decisions, even limited judgments. Whether or not these machines may one day enact a nightmare scenario, taking over from us is not a great threat, as whatever they are capable of doing is up to us to decide, as their “masters”. We should not fear these systems, but try to develop more of them; such systems help us in every day tasks, from drawing money out of the bank to our annual check-up at the doctor’s.

The brain is probably the most complex organized biological structure in existence. We think, learn, compute, memorize, feel, show emotion, and love. The brain is the center of all these activities, and of many other mental and physical functions as well. Each human being has a unique personality, and each individual behaves in a certain way. Our behavior reflects how our brain thinks.

Throughout one’s life, a person’s brain constantly learns. Each input, such as events that affect us, leaves its traces in the brain. We recollect these events later. But how do we learn? How do we remember things? What is the physical dimension of learning, feeling, and remembering? What is the physical significance of brain dynamics? Some of these questions are probably the most difficult questions for neuroscientists and other interdisciplinary brain researchers to answer.

Studies of the brain are as old as the practice of medicine. Although advancements in medicine, with the help of engineering and computer technologies, have been significant in recent years, brain research progresses much slower. Brain research has been a focus of such interdisciplinary sciences as neuroscience, biomedical engineering, electrical engineering, medicine, artificial intelligence, and psychology. However, an exact and detailed understanding of the brain’s dynamics and associating its neuronal activities with certain physical phenomenon remains largely beyond our grasp. The fact that the human brain cannot be used for experimental purposes is another factor in brain research.

THE BRAIN’S STRUCTURE

The brain is considered the human body’s central commanding unit. Along with the spinal cord, it forms the human being’s central nervous system. It poses a modular structure, each module of which is known to be responsible for certain functions, and possesses its own respective complexity. Readers wanting to know more about the brain’s structures should check the literature produced by specialists in the field of neuroanatomy.

Figure 1 illustrates the brain’s structure. The cerebral cortex, essentially a biological sheet of tissue covering the brain, is about 0.08 inches (2 mm) to 0.24 inches (6 mm) thick, and gives a geometrical representation of the brain’s shape. The brain’s stem (not shown) is the area between the thalamus and the spinal cord. It is the center of the most of the brain’s basic functions, such as breathing and the heart rate. The area behind the brain stem is the cerebellum. Located at the brain’s base is the hypothalamus, which, among other things, controls the body’s temperature. It reacts to hot and cold temperatures by sending out signals to adjust the body’s temperature. The thalamus serves as a sink for sensory information, and communicates the received information to the cerebral cortex. Although not proven in human beings, the thalamus serves as the center of sleep spindles, sinusoidal signals emitted by animals while they sleep.

NEURONS

Neurons, the brain’s building blocks, are the only cells that do not renew themselves (all other cells die and are replaced). Each human being is born with approximately 100 billion neurons in his or her brain. Thus, a certain neuron in the brain of a newborn human being is the same neuron when he or she is old. A normal brain loses 3 to 5 neurons each second. Stress, drug and alcohol consumption, and aging may cause more neurons to be lost. For an ordinary human being, however, the total number of neurons lost during an average lifetime is very negligible.

Neurons are probably the most complex and intelligent communication networking ever created. The brain contains billions of cells, each one of which is connected to another. All of them share and transmit and, more importantly, process the information. This feature introduces the intelligence of neurons, the nature of which is not yet completely known to scientists, who remain fascinated by the engineering behind this intelligent networking.

To better understand neurons’ functionality, imagine yourself cruising in your convertible on a two-lane road. As you start to pass the car in front of you, you suddenly notice a car coming toward you. You have no more than 2 or 3 seconds to evaluate the options and respond accordingly: you either accelerate and complete the pass, or slow down and get behind the car you were passing. In either case, you have to consider the speed of the oncoming car, its distance, and some other safety parameters. You eventually evaluate your options and reach the safest decision in less than 2 seconds.

What is the big deal? This is just another ordinary event that we are used to experiencing every day. Behind this seemingly ordinary event, however, a tremendous amount of communication and computation is taking place among the neurons. Stimuli invoked by visual information (the oncoming car) observed by the eyes make their way through the central nervous system to the brain. Neurons receive the stimuli, evaluate them, and pass their response to nearby neurons by electrochemical polarization. Billions of neurons are involved in the process.

This information flow among neurons depends on learning (one’s driving experience). The response, the final decision of neuronal computation combined with learning and consciousness, is delivered to central nervous system so that it can act. The nature of consciousness and how it is linked to neuronal computation remain unknown.

These processes are so automated that we do not consciously realize that each physical, mental, and emotional function is governed by our brain, which, in turn, is governed by its tiny component neurons. These neurons enable us to make such judgments every day, and to communicate with the surrounding environment through sound, sight, touch, smell, taste, emotion, feeling, thinking, and so on.

THE HUMAN BRAIN AND COMPUTERS

The human brain and computers are two different things. One is a living, thinking, learning, feeling, crying, and loving organism. Happiness and sadness, in the form of marginal emotions, are reactions of the brain. Such terminology makes no sense to computers. In that sense, it might be misleading to compare the human brain and computers. However, there are some common functionalities that make such a comparison logical.

Both the human brain and computers have memory. Memory in human brains is defined as “stronger synaptic connections,” whereas computer memories are formed by semiconductor chips. Both can adapt and learn. The human brain can learn easier and faster than a computer, which can only “learn” certain tasks by being programmed with special algorithms. The nature of such “learning” is very limited.

On the other hand, computers can perform many complex tasks much faster than human brains. For example, try multiplying two numbers, dividing the result by 7, and then subtracting 9 from that result. The computational speed of a human brain is much slower than that of a computer.

Due to their high speed, computers perform parallel jobs relatively faster. The human brain also can perform parallel tasks at the same time. For example, it controls the heart rate and blood pressure while performing computational tasks. In addition, the human brain is better at interfacing with the outside world and coming up with new ideas; computers only do what they are instructed to do, regardless of the task’s simplicity or complexity. The human brain distinguishes itself from computers by its extraordinary capability in the areas of imagination and innovation.

Another common functionality is that both transmit information. Computers use semiconductor switches that are either on or off. Everything inside of a computer is represented by either a one (1) or a zero (0). Although neurons in the human brain are either on or off, meaning that they are or are not firing an action potential at a particular point in time, an accumulated charge that activates neurons gives the human brain more flexibility. Neurons are more than just on or off, for their excitability is always changing as they constantly receive information from other cells through synaptic contacts. As stated earlier, this information is carried through electrochemical polarization. Although this electrochemical process does not always result in an action potential, it may alter the chance that an action potential will be produced by raising or lowering the neuron’s threshold.

Another important distinction between computers and the human brain is that the human brain never rests, while computers do after they have been turned off. Even during sleep, the human brain continues to work dynamically. Indeed, it produces distinct signals, called sleep spindles, that may be observed externally while the person is asleep. While an individual’s body rests during sleep, his or her brain recollectively refreshes itself.

UNDERSTANDING THE BRAIN’S DYNAMICS

All activity inside the human brain is conducted through electrochemical polarization, a process that can be observed by placing electrodes on an individual’s scalp. The brain’s dynamics can be observed in the form of an electroencephalograph (EEG) or a magnetoencephalograph (MEG). An EEG, which is relatively less sophisticated than a MEG, maps the brain’s dynamics into electrically recorded brain waves. Multiple electrodes are systematically placed on the scalp, and potential differences are measured with respect to a reference point. In the case of a multichannel EEG, the number of electrodes may be as high as 64 or even 128. Figure 2 shows a single-channel recorded EEG. Potential differences measured through electrodes are sampled and stored in a computer for analysis.

The challenge presented to researchers is how to read multichannel EEGs and extract the information that really reflects neuronal activity. If the patient is epileptic, brain abnormalities may be easily distinguished in a multichannel EEG. From the location of electrodes, it may be possible to identify the general part of the brain giving rise to epileptic EEGs. In clinics, neurosurgeons usually open the patient’s scalp and measure the EEG directly by placing electrode grids over the cortical tissue. Even then, it is a real challenge to identify the defective region and proceed accordingly.

The EEGs of epileptic patients distinguish themselves from other brain activities by their relatively high amplitude. But what about other physical and mental tasks? Can we detect and identify those EEGs that reflect a certain mental task? Scientists from many disciplines are focusing on such questions. Many researchers are combining EEGs, MEGs, magnetic resonance imaging (MRI), and such engineering tools and algorithms as digital signal processing and spectral analysis to identify and understand the brain’s dynamics. The clinical need for such solutions are in high demand.

THE HUMAN BRAIN AND INTERDISCIPLINARY SCIENCE

The human brain has been a research focus of scientists from many disciplines. Scientists in medicine, neuroscience, engineering (electrical engineering and biomedical engineering), mathematics, physics, physiology, and computer science have been conducting either sole or interdisciplinary research for many years. The brain has so many dimensions that no single discipline can cover all of its aspects. Some of these disciplines are described below:

• Artificial intelligence attempts to build knowledge representation on the hypothesis that intelligent systems act intelligently. Hence, if the human brain’s intelligence were represented in a finite domain, this domain could be used by computers to mimic human intelligence. This approach faces a major challenge: human intelligence cannot be represented to the degree that artificial intelligence requires to mimic human intelligence.

• Computational intelligence, on the other hand, approaches the problem from the perspective of such engineering tools and algorithms as neural networks, fuzzy logic, and genetic algorithms. Neural networks and genetic algorithms can learn an underlying task to some degree, whereas fuzzy logic relaxes information representation by providing one more degree of freedom to the binary representation of information: a membership function concept. In this concept, the information has a probability of being a member of a certain class. The human brain’s electrochemical process may not always result in an action potential for a certain neuron(s). The binary concept cannot represent this phenomenon, whereas fuzzy logic may be helpful in modeling the chance of a neuron to produce action potential.

• Engineering provides technical tools and algorithms for conducting research on the human brain. Electrical engineering provides signal processing tools and algorithms for filtering and imaging, and other tools to process EEGs. Many scientists use these tools and algorithms to understand and localize EEGs. It would be very effective to localize human brain abnormalities with the help of engineering tools and algorithms.

Each science and method mentioned above has its own limitations. Combined interdisciplinary research provides more promising results for understanding the brain’s dynamics. Many other methods not mentioned in this article also are being used to study the human brain.

SOME FACTS

An average adult human brain weighs about 3 pounds (1.36 kilograms). A stegosaurus weighed about 3,528 pounds (1,600 kilograms) but had a brain that weighed only about 0.15 pounds (70 grams), or just 0.004 percent of its total body weight. In contrast, an adult human being weighs about 154 pounds (70 kilograms) and has a brain that weighs about 3.1 pounds (1.4 kilograms), or about 2 percent of his or her total body weight. That makes a human being’s brain-to-body ratio 500 times greater than that of the stegosaurus.

DISCUSSION

This is only a very brief description of the human brain and its functionality. As scientists and researchers learn more about the human brain, they realize that what they know is very small when compared with how much they still do not know. All scientific efforts undertaken thus far have opened only a small window on a large universe: our own galaxy, located inside our brain, with the neurons as its stars. Let each neuron be a moon. How much do we know about the moon compared with the universe in which it resides? The answer is the same for the following question: How much we know about the human brain’s neurons and the universe in which they reside?

REFERENCES

• Chudler, E. H., S. Pretel, and D. R. Kenshalo, Jr. “Distribution of GAD-like immunoreactive neurons in the first (SI) and second (SII) somatosensory cortex of the monkey.” Brain Research (1988) 456:57-63.
• Nunez, P. L. “Neurocortical Dynamics and Human EEG Rhythms.” New York: Oxford University Press, 1995.
• Figures 1 and 2 are courtesy of Eric H. Chudler, Research Associate Professor, University of Washington.

At the end of the 1950s, Richard Feynman (1961, pp.282-96) described the possibility of building computers that were ‘sub-microscopic’. More recently, several people have advocated the realization of massively parallel computation using the techniques and chemistry of molecular biology.

At the end of 1994 Leonard Adleman published a paper on ‘Molecular Computation of Solutions of Combinatorial Problems’ (Science, vol.266, pp.1021 – 24). He explained how a problem could be set up by synthesizing DNA molecules with a particular sequence, and solved by letting the DNA molecules react in a test tube, producing a molecule whose sequence is the answer. In the same paper he recounted how he had put this theory into practice by solving a standard problem with a DNA reaction system. Adleman called his DNA computer the TT-100, for test tube filled with 100 microlitres of fluid, which is all it took for the reactions to occur.

Since then, many advances have been proposed to refine the protocol for programming a DNA computer to reduce the complexity of the operations and eliminate errors (see Lipton, n.d.; and Boneh and Lipton, nd.). Despite their respective complexities, biological and mathematical operations have some similarities:

The very complex structure of a living being is the result of applying simple operations to initial information encoded in a DNA sequence.

The result f(w) of applying a computable function to an argument can be obtained by applying a combination of basic simple functions to w.

For the same reasons that DNA was probably selected for living organisms as a genetic material, its stability and predictability in reactions, DNA strings can also be used to encode information for mathematical systems.

Conventional computers represent information in terms of 0’s and l’s, physically expressed in terms of the flow of electrons through logical circuits. Builders of DNA computers represent information in terms of the chemical units of DNA. Calculating with an ordinary computer is done with a program that instructs electrons to travel on particular paths; with a DNA computer, calculation requires synthesizing particular sequences of DNA and letting them react in a test tube. In a scheme devised by Lipton (n.d.), the logical command AND is performed by separating DNA strands according to their sequences, and the command OR is done by pouring together DNA solutions containing specific sequences.

‘It will fill a bathtub, not the universe,’ says Lipton, ‘and it will be incredibly cheap to build.’ A pound of DNA in 1,000 quarts of fluid, about three-feet square, will hold more memory than all the computers ever made. The chemicals are inexpensive; DNA runs virtually on its own power, and the soup, with a little splicing, can be re-used from one experiment to the next. Lipton estimates that a superparallel DNA computer, offering trillions of processors working simultaneously, could be built for $100,000.

The fastest supercomputers can currently perform 1000 million instructions per second (MIPS); a single DNA molecule requires approximately 1000 seconds to perform an instruction (.001 MIPS). Obviously, if you want to perform one calculation at a time (serial logic), DNA computers are not a viable option. However, if one wanted to perform many calculations simultaneously (parallel logic), a computer such as the one described above can easily perform 1014 MIPS. DNA computers also require less energy and space. While existing supercomputers operate 109 operations per joule, a DNA computer could perform 2 x 1019 operations per joule (many times more efficient). Data can be stored on DNA at a density of approximately 1 bit per cubic nm, while existing storage media require 1012 cubic nm to store 1 bit (Adleman, 1995).

Thus, the potential of molecular computation is impressive. However, it is too early for either great optimism or great pessimism. It is possible that DNA computers will become more common for solving very complex problems and DNA computers may also become automated. In addition to the direct benefits of using DNA computers for performing complex computations, some of the operations of DNA computers already have (Adleman, 1995), and more could be, used in molecular and biochemical research.

References

• Adleman,L.(1994).’Moleculer computation of solutions to combinatorial problems’,Science,vol.266,pp.1021-24.
• Adleman,L.(1995 ‘On constructing a moleculer computer’:ftp://usc.edu/pub/csinfo/papers/adleman/molecular_coputer.ps
• Boneh,D.&Lipton,R.J.’Making DNA computers error resitant’.(Unpublished manuscript.)
• Feynman,R.P. (1961)’Minaturization’,in D.H.Gilbert (ed.)Reinhold,New York.
• Lipton,R.J.(n.d.)’Speeding up computations via molecular biology’:ftp://ftp.cs.princeton.edu/pub/people/rjl/bio.ps

The personal computer industry has created an enthusiastic, ever-growing market for multimedia technology for use in homes and businesses, the high profile of Internet (‘the data superhighway’) and continuing pressure to work and learn more efficiently have added to the demand for new concepts in education and training. The characteristics in demand are flexibility, user- friendliness and lower costs.

Ten years ago computers were command-driven and mostly used for simple tasks such as word-processing. These days graphical interfaces and user-friendly programming have revolutionized the old ‘cold face’ of computer working. Applications such as Windows, OS/2, and Mac Systems are now mouse-driven, capable of multi-tasking, and less intimidating for the novice. Computers are now used for CAD (Computer Aided Design), CAM (Computer Aided Manufacturing), graphics, communication, automation, simulation, animation, and much more. Complex tasks are easier then ever before and have replaced human labour in many areas.

Development of new and better computers, local area networks (LANs). remote access software (especially ATM (Asynchronous Transfer Mode) and ISDN (Integrated System Digital Network)), using the latest in leased and digital telephone lines, have opened up new horizons in working, teaching and learning practices. These developments in remote communication systems have greatly reduced the need to travel. Wide availability of this technology has changed the working practices of some companies fundamentally. Teleworking is recognized as a practical alternative to conventional office working. Video-conferencing facilities enable direct visual and audio contact between emote offices, and almost instantaneous information/document transfer. Working from home means not having to be in the office, more control over daily schedules, the freedom to live further from the workplace, and relief from the stresses (and costs) of commuting. The company benefits from reduced office rental and personnel costs: expertise and skills can he dialled up as needed,

Reduced hardware and software costs and increased computing power have extended and popularized teleworking applications. Medical specialists can now examine patients on a screen and give advice down a phone line: just such a link-up exists between some hospitals as far apart as the USA and Saudi Arabia. It is possible to attend a lecture without leaving one’s favourite chair, while still having the opportunity to ask questions and have them answered. ‘Distance learning’ technology has especially important implications for the disabled who can access education without the stress of getting to a class or (unless they want to) facing class-mates. New York University’s School of Continuing Education is building its own Virtual College. Other institutions, such as California Polytechnic and the New Jersey Institute of Technology, are exploring similar projects.

Interactive distance learning is not just a future project. In many isolated places in the USA school children already enjoy such facilities via dial-tip modems: they do not have to travel through rain and snow any more. And getting on to the school communications network does not cost the user any more than an ordinary telephone call.

In the past, computers in schools ate into school budgets without providing any appreciable return. In companies, investments in information technology were used mostly to automate old learning processes instead of to enable new ones. All that is starting to change. Enormous growth in CD-ROM drives, LANs and Internet connections, multimedia, and collaborative software environments, is fuelling a wave of new, better teaching tools. Networking enables virtual workgroups to be set up almost instantly. There are dial-in services that permit anytime/anyplace access to course materials and fellow students.

The technology promises more than just an improvement in educational productivity: it may deliver a qualitative change in the nature of learning itself. Because computers have no feelings, they make very patient teachers, incapable of exasperation or anger: and they can work 24 hours a day. Also, because they have enormous memory capacity and don’t make choices about what to remember, they can be responsive to questioning in many disciplines. The information they can offer can therefore be multi-disciplinary, multilevelled. Multimedia interactive packages exploit this to the benefit of system users. The consequence is a dramatic change in educational methods. Instead of a one-way information flow to the learner – typified by TV broadcast, video cassette or a teacher addressing a class of students passively taking notes – the new teaching techniques are two-way, collaborative, interactive, and interdisciplinary. Multimedia enables teaching material to change from a flat, one-dimensional text to something that moves, speaks, sings, has innumerable examples and references and lines of inquiry on hand if the individual user feels inclined to follow them up. Cut- and-paste features, now more or less standard, mean the user can compose notes, essays and reports which are just as lively and varied as the source he or she is accessing.

Studies on multimedia argue in favour of its effectiveness as a learning tool. The Software Publishers Association’s 1994 report on technology effectiveness cites accounts of measurable improvements from the use of animation, video, laser discs, CD-ROM books, and hypermedia. Cognitive studies show thaw people get 80 percent of their information visually but retain only 11 percent of that. They acquire significantly less through hearing but then remember a much higher proportion of it. A combination of the two methods is, unsurprisingly, the most effective of all boosting retention rates by as much as 50 percent. Therefore, the new system of ‘Education on demand’, in homes and on-the-job, is likely to become part of everyday learning activities.

Producing educational systems and materials has long been very big business in the world. The US spends $300 billion yearly on education from nursery through high school. More than half the schools in the country now use computers in almost every discipline, and 99 percent of schools have at least one computer. According to a report from IBM Academic Consulting, American institutions of higher education have spent an estimated $70 billion on computer-related goods and services over the past 15 years; of that amount, as much as $20 billion was for teaching and learning technology. Training magazine, in its annual industry survey, estimates that US corporations with more than 100 employees budgeted $51 billion for training in 1994. Some estimates put the total spending per year by all companies and their employees at $90 to $100 billion.

The Software Publishers Association’s Report on the Effectiveness of Technology in Schools, 1990-1994, a summary of 133 studies, found that educational technology clearly boosted student achievement, improved student attitudes and self-concept, and enhanced the quality of student-teacher relationships. In this area of learning, the most promising technologies are interactive video networking and collaboration tools. Computers are amazingly diligent teachers, they can stimulate creative thinking, promote enterprise, and sharpen curiosity. Of course, availability of hardware and software is not by itself the solution to current educational problems. Reaping the benefits of available technology first requires extensive teacher training, adapted or new curricula, and, most important, changes to educational models. Modern educational concepts emphasize individualized, hands-on learning: teamwork; and guided discovery of information. All of these require technological assistance, and they are almost impossible to achieve without the help of computers. It is right to note that for teacher training and for the preparation of individually designed course materials, the latest software packages, even with relatively little preparatory training, provide the appropriate tools.

The interactive technology has made education more accessible and more enjoyable. With the help of computers educators can tailor tutoring to the individuals own needs. There is more information about any topic these days than anybody can handle. Therefore, storing and retrieving readily available study materials on computers mean teachers can focus on explaining in form at ion instead ol conveying information. The implications of this transformation affect both students and teachers. Teachers become more like coaches, while students are free to discover knowledge on their own – they can browse, pick and choose what and how, and how fast, they want to learn.

Multimedia applications include analogue and digital video, 2-D and 3- D animation, audio, and even hyperlinks and digital links: CD-ROM discs and drives, graphics display hardware and sound cards. Digital signal processors for speech recognition and signal processing are starting to appear in desktop systems and will play an increasing role in learning systems. Given the enormous growth of CDROM-equipped PCs in homes, multimedia could soon become the key ‘crossover’ application linking the home and school markets.

Instead of the conventional broadcast model of distance learning, which requires participating students to watch a live video transmission via cable or satellite links or wait for days to receive a videotape in the mail, new schemes allow students to dial in at their convenience and participate in a class asynchronously. While it isn’t in real time, the opportunity for feedback and participation is enhanced by rich two-way communications channels.

The new employee-training concept of training on demand, learning while working. promises to bring information to employees at their workstations. Training as a separate centralized department in a company may soon be a thing of the past. The changing nature and growing diversity of the work force require new kinds of training in cultural sensitivity, communications skills, and problem solving. Employees are more geographically dispersed than in the past, and staff turnover is higher because companies and employees are less loyal to each other. Hardly anyone holds a job for life. Technology is evolving so quickly that skills require frequent refreshing. Taking workers to traditional classrooms means losing man-hours, heavier training costs and less hands-on training. Also, employee-training is a risky investment, given that the employee is free, after training, to go elsewhere. Companies are therefore trying to link learning to the job itself. This can take the form of expert systems integrated into the work area or even hand-held computers connected via wireless communications to a constantly updated information base. This kind of distant’ and ‘on-the-job’ learning is significantly less expensive than transporting employees to a central location, putting them up in hotels, and forfeiting their lost productivity. If the training material is distributed throughout the Local Area Network, trainees can pick it up for themselves. This increases retention of information and decreases learning time.

The single recent advance that is making the biggest difference in training and education is the Internet. It gives everybody easy access to information they cannot find locally or even, sometimes, nationally. Via Internet anybody can access almost any library resources in the world, talk to any interest groups or obtain any information from more than 30 million users in the Internet ‘community’. Thousands of Gopher, WWW, FTP, WAIS, ARCHIE servers offer terabytes of information on every subject imaginable. One can visit virtual galleries, virtual museums, virtual schools, virtual libraries, virtual communities, shop at virtual malls – and play games.

But, but….

Clearly the new technology facilitates access to information and in doing so makes many tasks easier, more agreeable, often more exciting. However, as we are all social beings, the importance of direct contact between people in the creation of the more subtle human skills should not be forgotten. Being paid for the efficient performance of tasks is not the only reason people work; they may also need to be out at work, away from their home-base, for purposes other than recreation, and to he physically part of a team. The sharing and exchange of experience between people. both in the context of the family and the work-place, is certainly instrumental in the creation of sound, responsible human character. That in turn is relevant to the capacity people have for making good’ decisions relating to their own and other people’s lives.

Information is not quite the same thing as knowledge, still less is knowledge to be confused with wisdom. Few parents can equal the capacity of television programmes, for example, to convey information to their children. Nevertheless, the role of parents in conveying (however indirectly and inexpertly) their experience of life to their children is the more decisive influence in moulding their children’s characters. It is certainly true that, in some families, children have more contact with the ‘present, immediate’ world of TV images than with the ‘past’ of their parents’ experience of life, but in these cases the TV is being abused, which is not the fault of the technology. Similarly, the new interactive multimedia information technologies should not be rejected because they might be abused. Rather, the dangers, the possible abuses, should be pondered and understood and proper regimes introduced to avoid the dangers. These new technologies are there and make available a vastly increased potential to get at and handle information. They cannot be a substitute for human relations, nor can they replace the learning and understanding that come through the experience of human relations.

WHY OPTICAL?

Despite the many benefits that classical computers (‘classical’ here means computers in which the signals are carried electrically) have brought to our lives, they have some limitations which prevent any improvement in the speed or volume of signals carried. These limitations are inherent to the way these computers work.

For example, classic electric circuits carry information units serially, one by one, and there are some lower limits beyond which such circuits cannot be built-below that limit they simply cannot process the information reliably. Another handicap is that electrons floating in circuits can interfere with each other-and this interference, incidentally, is one reason why engineers cannot produce smaller circuits. By contrast, photons, light particles, which are the main signal or information carrying agent simply do not interact with each other because they do not carry a charge.

An optical computer could be run faster than one running electrons, theoretically at the speed of light, along optical fibres which are specifically designed guide-wires to transfer light-photons in and out between chips in an optical computer without distortion.

One of the main advantages of optical computers is their capability of processing more than one piece of information at the same moment. That means multi-beams can be processed in one chip. This would allow engineers to use parallel processing which greatly enhances the speed of the computer.

THE DIFFICULTIES

Lasers would, naturally, be the source of light in this new generation of computers. Scientists and engineers all over the world are trying to build appropriately tiny lasers emitting precise frequencies of infrared light. But they face a number of practical hurdles. One has to do with making lasers of appropriate size and efficiency. Current technology does not have the means to build optical chips comparable in size to ‘classical’ ones. The efficiency of the lasers is not high enough for the specifications required. Most of the energy to run these lasers escapes as heat and is not used. Since one or at most two percent of this energy can be transformed into the useful form of light, the rest can generate a lot of heat which is dangerous to the condition of the chips.

Making the right lasers is not the only problem on the way to fully optical computers. Switches are at the heart of optical computers, but as photons do not interact with each other, there are substaintial difficulties in building switches.

SOME PROPOSED SOLUTIONS

One solution to this problem is to build computers which are part electrical, part optical. Many scientists now believe that the most viable use for optical technology is in this type of hybrid system combining optics and electronics. Researchers are now focusing their work on optical interconnections between chips, which could be a reality in as little as one or two years. This type of connection can vastly increase the amount of data moving in and out of chips.

Such a machine would have to contain prisms, mirrors, and lasers to channel the light, as well as gallium arsenide chips that convert pulses of laser light into electrons so as to function as switches. If all this does happen, there will be a need for new computer architectures, that is, new computer structures.

However, there are some scientists following a different route. They are trying to find ways to use current transistor technology so as to detect laser beams in the information processing. NPN type transistors without a metal cover would be appropriate because they are faster. This approach also allows for adaptation of existing designs, with all the advantages in time and savings that brings.

FUTURE

The first optical processor developed at AT&T Bell Labs measured about two feet by two feet. Scientists hope some day to fit it all into three square inches. A fully optical computer is more than five years away.

Scientists have set themselves a target for the year 2000: 1,000 I/O (input and output) channels running at 1 giga-bit/sec. That is a thousand times faster than current modern computers.

It is a pity that we must wait for a decade, while scientists and engineers try to accomplish this difficult task. But what an exciting wait!

•  FURTHER READING
• ‘Bright future’, Scientific American, May 1990.
• ‘Now easier optical’, Electronics, May 1990.
• ‘Slacken lights up’, Scientific American, July 1991.
• ‘Optical computer no longer lighters away’, Byte, April 1992.
• ‘Optical computing sheds ‘blue sky’ image’ Electronics, April 1990.