Artificial Intelligence Short Questions.

The term artificial intelligence was first coined in 1956, at the Dartmouth conference, and since then Artificial Intelligence has expanded because of the theories and principles developed by its dedicated researchers.

Q. What is artificial intelligence?

A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Q. Yes, but what is intelligence?

A. Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

Q. Isn't there a solid definition of intelligence that doesn't depend on relating it

to human intelligence?

A. Not yet. The problem is that we cannot yet characterize in general what kinds of computational procedures we want to call intelligent. We understand some of the mechanisms of intelligence and not others.

Q. Is intelligence a single thing so that one can ask a yes or no question ``Is this machine intelligent or not?''?

A. No. Intelligence involves mechanisms, and AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered ``somewhat intelligent''.

Q. Isn't AI about simulating human intelligence?

A. Sometimes but not always or even usually. On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI researchers are free to use methods that are not observed in people or that involve much more computing than people can do.

Q. What about IQ? Do computer programs have IQs?

A. No. IQ is based on the rates at which intelligence develops in children. It is the ratio of the age at which a child normally makes a certain score to the child's age. The scale is extended to adults in a suitable way. IQ correlates well with various measures of success or failure in life, but making computers that can score high on IQ tests would be weakly correlated with their usefulness. For example, the ability of a child to repeat back a long sequence of digits correlates well with other intellectual abilities, perhaps because it measures how much information the child can compute with at once. However, ``digit span'' is trivial for even extremely limited computers.

However, some of the problems on IQ tests are useful challenges for AI.

Q. What about other comparisons between human and computer intelligence?

A.Arthur R. Jensen [Jen98], a leading researcher in human intelligence, suggests ``as a heuristic hypothesis'' that all normal humans have the same intellectual mechanisms and that differences in intelligence are related to ``quantitative biochemical and physiological conditions''. I see them as speed, short term memory, and the ability to form accurate and retrievable long term memories.

Whether or not Jensen is right about human intelligence, the situation in AI today is the reverse.

Computer programs have plenty of speed and memory but their abilities correspond to the intellectual mechanisms that program designers understand well enough to put in programs. Some abilities that children normally don't develop till they are teenagers may be in, and some abilities possessed by two year olds are still out. The matter is further complicated by the fact that the cognitive sciences still have not succeeded in determining exactly what the human abilities are. Very likely the organization of the intellectual mechanisms for AI can usefully be different from that in people.

Whenever people do better than computers on some task or computers use a lot of computation to do as well as people, this demonstrates that the program designers lack understanding of the intellectual mechanisms required to do the task efficiently.

Q. When did AI research start?

A. After WWII, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines. By the late 1950s, there were many researchers on AI, and most of them were basing their work on programming computers.

Q. Does AI aim to put the human mind into the computer?

A. Some researchers say they have that objective, but maybe they are using the phrase metaphorically. The human mind has a lot of peculiarities, and I'm not sure anyone is serious about imitating all of them.

Q. What is the Turing test?

A. Alan Turing's 1950 article Computing Machinery and Intelligence [Tur50] discussed conditions for considering a machine to be intelligent. He argued that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. This test would satisfy most people but not all philosophers. The observer could interact with the machine and a human by teletype (to avoid requiring that the machine imitate the appearance or voice of the person), and the human would try to persuade the observer that it was human and the machine would try to fool the observer.

The Turing test is a one-sided test. A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human.

Daniel Dennett's book Brainchildren has an excellent discussion of the Turing test and the various partial Turing tests that have been implemented, i.e. with restrictions on the observer's knowledge of AI and the subject matter of questioning. It turns out that some people are easily led into believing that a rather dumb program is intelligent.

Q. Does AI aim at human-level intelligence?

A. Yes. The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans. However, many people involved in particular research areas are much less ambitious.

Q. How far is AI from reaching human-level intelligence? When will it happen?

A. A few people think that human-level intelligence can be achieved by writing large numbers of programs of the kind people are now writing and assembling vast knowledge bases of facts in the languages now used for expressing knowledge.

However, most AI researchers believe that new fundamental ideas are required, and therefore it cannot be predicted when human-level intelligence will be achieved.

Q. Are computers the right kind of machine to be made intelligent?

A. Computers can be programmed to simulate any kind of machine.

Many researchers invented non-computer machines, hoping that they would be intelligent in different ways than the computer programs could be. However, they usually simulate their invented machines on a computer and come to doubt that the new machine is worth building. Because many billions of dollars that have been spent in making computers faster and faster, another kind of machine would have to be very fast to perform better than a program on a computer simulating the machine.

Q. Are computers fast enough to be intelligent?

A. Some people think much faster computers are required as well as new ideas. My own opinion is that the computers of 30 years ago were fast enough if only we knew how to program them. Of course, quite apart from the ambitions of AI researchers, computers will keep getting faster.

Q. What about parallel machines?

A. Machines with many processors are much faster than single processors can be. Parallelism itself presents no advantages, and parallel machines are somewhat awkward to program. When extreme speed is required, it is necessary to face this awkwardness.

Q. What about making a ``child machine'' that could improve by reading and by learning from experience?

A. This idea has been proposed many times, starting in the 1940s. Eventually, it will be made to work. However, AI programs haven't yet reached the level of being able to learn much of what a child learns from physical experience. Nor do present programs understand language well enough to learn much by reading.

Q. Might an AI system be able to bootstrap itself to higher and higher level intelligence by thinking about AI?

A. I think yes, but we aren't yet at a level of AI at which this process can begin.

Q. What about chess?

A. Alexander Kronrod, a Russian AI researcher, said ``Chess is the Drosophila of AI.'' He was making an analogy with geneticists' use of that fruit fly to study inheritance. Playing chess requires certain intellectual mechanisms and not others. Chess programs now play at grandmaster level, but they do it with limited intellectual mechanisms compared to those used by a human chess player, substituting large amounts of computation for understanding. Once we understand these mechanisms better, we can build human-level chess programs that do far less computation than do present programs.

Unfortunately, the competitive and commercial aspects of making computers play chess have taken precedence over using chess as a scientific domain. It is as if the geneticists after 1910 had organized fruit fly races and concentrated their efforts on breeding fruit flies that could win these races.

Q. What about Go?

A. The Chinese and Japanese game of Go is also a board game in which the players take turns moving. Go exposes the weakness of our present understanding of the intellectual mechanisms involved in human game playing. Go programs are very bad players, in spite of considerable effort (not as much as for chess). The problem seems to be that a position in Go has to be divided mentally into a collection of subpositions which are first analyzed separately followed by an analysis of their interaction. Humans use this in chess also, but chess programs consider the position as a whole. Chess programs compensate for the lack of this intellectual mechanism by doing thousands or, in the case of Deep Blue, many millions of times as much computation.

Sooner or later, AI research will overcome this scandalous weakness.

Q. Don't some people say that AI is a bad idea?

A. The philosopher John Searle says that the idea of a non-biological machine being intelligent is incoherent. He proposes the Chainees room agrement . The philosopher Hubert Dreyfus says that AI is impossible. The computer scientist Joseph Weizenbaum says the idea is obscene, anti-human and immoral. Various people have said that since artificial intelligence hasn't reached human level by now, it must be impossible. Still other people are disappointed that companies they invested in went bankrupt.

Perception and Learning.

PERCEPTION

Perception is an essential component of intelligent behavior. We perceive the world around us through five basic senses of sight, hearing , touch, smell, and taste., of these, sight and hearing have been the main areas of Artificial Intelligence research leading to speech understanding . when we perceive some signal . it may a be sound or light. We respond appropriately to that signal. To produce an appropriate response we must categorize or analyze that signal. For example to analyze a sentence we must first identify individual sounds, then combine these sounds into words, and then combine words into a meaningful sentence structure . but this is hard because dividing sounds into words needs additional knowledge or information about the situation. A series of sounds may be interpreted in many ways . For instance

“Tigers care their kids”

and “Tiger scare their kids”

might both have been the possible interpretations of the same series of sounds.

To overcome the perceptual problems in speech understanding , the process of analyzing a speech is divided into five stages.

1. Digitization : The continuous input is divided into discrete chunks . in speech the division is done on a time scale and in images, it may be based on

color or area or tint.

2. Smoothing: Since the real world is usually continuous , large spikes and variation in the input is avoided.

3. Segmentation: Group the smaller chunks produced by digitization into larger chunks corresponding to logic components of the signal. For speech understanding segments correspond to individual sounds called phonemes.

4. Labeling: Each segment is given a label.

5. Analysis : The labeled segments are put together to form a coherent object.

LEARNING

Learning is the improvement of performance with experience over time.

Learning element is the portion of a learning AI system that decides how to modify the performance element and implements those modifications.

We all learn new knowledge through different methods, depending on the type of material to be learned, the amount of relevant knowledge we already possess, and the environment in which the learning takes place. There are five methods of learning . They are,

1. Memorization (rote learning)

2. Direct instruction (by being told)

3. Analogy

4. Induction

5. Deduction

Learning by memorizations is the simplest from of le4arning. It requires the least amount of inference and is accomplished by simply copying the knowledge in the same form that it will be used directly into the knowledge base.

Example:- Memorizing multiplication tables, formulate , etc.

Direct instruction is a complex form of learning. This type of learning requires more inference than role learning since the knowledge must be transformed into an operational form before learning when a teacher presents a number of facts directly to us in a well organized manner.

Analogical learning is the process of learning a new concept or solution through the use of similar known concepts or solutions. We use this type of learning when solving problems on an exam where previously learned examples serve as a guide or when make frequent use of analogical learning. This form of learning requires still more inferring than either of the previous forms. Since difficult transformations must be made between the known and unknown situations.

Learning by induction is also one that is used frequently by humans . it is a powerful form of learning like analogical learning which also require s more inferring than the first two methods. This learning re quires the use of inductive inference, a form of invalid but useful inference. We use inductive learning of instances of examples of the concept. For example we learn the

concepts of color or sweet taste after experiencing the sensations associated with several examples of colored objects or sweet foods.

Deductive learning is accomplished through a sequence of deductive inference steps using known facts. From the known facts, new facts or relationships are logically derived. Deductive learning usually requires more inference than the other methods.

Matching and Learning.

MATCHING:So far, we have seen the process of using search to solve problems as the application of appropriate rules to individual problem states to generate new states to which the rules can then be applied, and so forth, until a solution is found. Clever search involves choosing from among the rules that can be applied at a particular point, the ones that are most likely to lead to a solution. We need to extract from the entire collection of rules, those that can be applied at a given point. To do so requires some kind of matching between the current state and the preconditions of the rules.

How should this be done?

One way to select applicable rules is to do a simple search through all the rules comparing one’s preconditions to the current state and extracting all the ones that match . this requires indexing of all the rules. But there are two problems with this simple solutions:

A. It requires the use of a large number of rules. Scanning through all of them would be hopelessly inefficeint.

B. It is not always immediately obvious whether a rule’s preconditions are satisfied by a particular state.

Sometimes , instead of searching through the rules, we can use the current state as an index into the rules and select the matching ones immediately. In spite of limitations, indexing in some form is very important in the efficient operation of rules based systems.

A more complex matching is required when the preconditions of rule specify required properties that are not stated explicitly in the description of the current state. In this case, a separate set of rules must be used to describe how some properties can be inferred from others. An even more complex matching process is required if rules should be applied and if their pre condition approximately match the current situation. This is often the case in situations involving physical descriptions of the world.

LEARNING

Learning is the improvement of performance with experience over time.

Learning element is the portion of a learning AI system that decides how to modify the performance element and implements those modifications.

We all learn new knowledge through different methods, depending on the type of material to be learned, the amount of relevant knowledge we already possess, and the environment in which the learning takes place. There are five methods of learning . They are,

1. Memorization (rote learning)

2. Direct instruction (by being told)

3. Analogy

4. Induction

5. Deduction

Learning by memorizations is the simplest from of le4arning. It requires the least amount of inference and is accomplished by simply copying the knowledge in the same form that it will be used directly into the knowledge base.

Example:- Memorizing multiplication tables, formulate , etc.

Direct instruction is a complex form of learning. This type of learning requires more inference than role learning since the knowledge must be transformed into an operational form before learning when a teacher presents a number of facts directly to us in a well organized manner.

Analogical learning is the process of learning a new concept or solution through the use of similar known concepts or solutions. We use this type of learning when solving problems on an exam where previously learned examples serve as a guide or when make frequent use of analogical learning. This form of learning requires still more inferring than either of the previous forms. Since difficult transformations must be made between the known and unknown situations.

Learning by induction is also one that is used frequently by humans . it is a powerful form of learning like analogical learning which also require s more inferring than the first two methods. This learning re quires the use of inductive inference, a form of invalid but useful inference. We use inductive learning of instances of examples of the concept. For example we learn the

concepts of color or sweet taste after experiencing the sensations associated with several examples of colored objects or sweet foods.

Deductive learning is accomplished through a sequence of deductive inference steps using known facts. From the known facts, new facts or relationships are logically derived. Deductive learning usually requires more inference than the other methods.

Review Questions:-

1. what is perception ?

2. How do we overcome the Perceptual Problems?

3. Explain in detail the constraint satisfaction waltz algorithm?

4. What is learning ?

5. What is Learning element ?

6. List and explain the methods of learning?

Types of learning:- Classification or taxonomy of learning types serves as a guide in studying or comparing a differences among them. One can develop learning taxonomies based on the type of knowledge representation used (predicate calculus , rules, frames), the type of knowledge learned (concepts, game playing, problem solving), or by the area of application(medical diagnosis , scheduling , prediction and so on).

The classification is intuitively more appealing and is one which has become popular among machine learning researchers . it is independent of the knowledge domain and the representation scheme is used. It is based on the type of inference strategy employed or the methods used in the learning process. The five different learning methods under this taxonomy are:

Memorization (rote learning)

Direct instruction(by being told)

Analogy

Induction

Deduction

Learning by memorization is the simplest form of learning. It requires the least5 amount of inference and is accomplished by simply copying the knowledge in the same form that it will be used directly into the knowledge base. We use this type of learning when we memorize multiplication tables ,

for example.

A slightly more complex form of learning is by direct instruction. This type of learning requires more understanding and inference than role learning since the knowledge must be transformed into an operational form before being integrated into the knowledge base. We use this type of learning when a teacher presents a number of facts directly to us in a well organized manner.

The third type listed, analogical learning, is the process of learning an ew concept or solution through the use of similar known concepts or solutions. We use this type of learning when solving problems on an examination where previously learned examples serve as a guide or when we learn to drive a truck using our knowledge of car driving. We make frewuence use of analogical learning. This form of learning requires still more inferring than either of the previous forms, since difficult transformations must be made between the known and unknown situations. This is a kind of application of knowledge in a new situation.

The fourth type of learning is also one that is used frequency by humans. It is a powerful form of learning which, like analogical learning, also requires more inferring than the first two methods. This form of learning requires the use of inductive inference, a form of invalid but useful inference. We use inductive learning when wed formulate a general concept after seeing a number of instance or examples of the concept. For example, we learn the concepts of color sweet taste after experiencing the sensation associated with several examples of colored objects or sweet foods.

The final type of acquisition is deductive learning. It is accomplished through a sequence of deductive inference steps using known facts. From the known facts, new facts or relationships are logically derived. Deductive learning usually requires more inference than the other methods. The inference method used is, of course , a deductive type, which is a valid from of inference.

In addition to the above classification, we will sometimes refer to learning methods as wither methods or knowledge-rich methods. Weak methods are general purpose methods in which little or no initial knowledge is available. These methods are more mechanical than the classical AI knowledge – rich methods. They often rely on a form of heuristics search in the learning process.

Waltz Algorithm.

CONSTRAINT SATISFACTION – WALTZ ALGORIHTM

Many perceptual tasks appear to be highly complex . this is because the number of interpretations that can be assigned to individual components of an input is large and the number of combinations of those components also appears to be enormous. But a clear analysis well reveal that many do the combinations can not actually occur. These natural constraints can be exploited in the understanding process to reduce the complexity from unmanageable to manageable.

There are two important steps in the use of constraints in problem solving:

1. Analyze the problem domain to determine the actual constraints.

2. Solve the problem by applying a constraint satisfaction algorithm.

Consider for example a three dimensional line drawing . The analysis process is to determine the object described by the lines. The geometric relationships between different types of line junctions helped to determine the object types.

Three Dimentioal Polyhedral junction types.

In waltz’s algorithm, labels are assigned to lines of various types-say concave edges are produced by two adjacent toching surfaces which duce a concave (less than 180 Degrees) depth change .

Conversely, convex edges produce a convexly viewed depth (greater than 180 degrees), and a boundary edge outlines a surface that obstracts other objects.

To label a concave edge, a mints sign is used. Convex edges one labeled with a plus sign, and a right or left arrow is used to label the boundary edges. By restricting vertices to be the intersection of three object faces, it is possible to reduce the number of basic vertex types to only four : the L, the T , the Fork and the Arrow.

The L types Fork Types T types

Valid junction labels for three-dimensional shapes.

When a three-dimensional object is viewed from all possible positions, the four junction types, together with the valid edge labels, give rise to eighteen different permissible junction configurations as shown in figurre

Geometric constraints, together with a consistent labeling scheme, can simplify the object identification process. A set of labeling rules which facilitates this process can be developed for different classes of objects. The following rules will apply for many polyhedral objects.

1. The arrow should be directed to mark boundaries by traversing the object in a clockwise direction.

2. Unbroken lines should have the same lable assigned at both ends.

3. When a fork is labeled with a+ edge, it must have all three edges label as + , and

4. Arrow junctions which have aà label on both bard edges must also have a + label on the shaft.

These rules can be applied to a polygonal object as given in figure

example of object labeling.

Starting with any edge having an object face on its right, the external boundary is labeled with the à in a clockwise direction. Interior lines are then labeled with + or _ consistent with the ot

her labeling rules.

To see how waltz constraint satisfaction algorithm works, consider the image dr

awing of a pyramid as given in figure3 At the right side of the pyramid are all po

ssible labelings for the four u\junctions A, b, C and D.

Using these labels as mutual constraints on connected junctions, permissible labels for the whole pyramid can be determined. The constraint satisfaction procedure works as follows.

Starting at an arbitrary junction, say A, a record of all permissible labels is made

for that junction. An adjacent junction is then chosen, say , B and labels which are

inconsistent with the line AB are then eliminated from the p

ermissible A and B lists. In this case, the line joining B can only be a +, - or an up

arrowà consequently, two of the possible

A labelings can be eliminated and the remainin

g are

Choosing junction c next, we find that t

he BC constraints are

satisfied by all of the B and C lableings, so on reduction

is possible with this step. On the otherhand, the line AC must be labled as – or as an up-

left-arrow ß to be consistent. Therefore, an additional label for A can be eliminated to reduce the remainder to the following.

This new restriction on a now permit the elimination of one B leabeling to maintain consistency. Thus, the permissible B leabelings remaining are now







This reduction in turn, places a new restriction on BC, permitting the elimination of one C label, since BC must now be labeled as a + only. This leaves the remaining C labels as show in side diagram.



4. Moving now to junction d, we see that of the six possible D leadings, only three satisfy the BD constraint of a up or a down arrow. Therefore, the remaining permissible leabelings for d are now


Continuing with the above procedure, we see that further label eliminations are not possible since all constraints have been satisfied. This process is completed by finding the different combinations of unique lableings that can be assigned to the figure. An enumerations of the remaining label shows that its is possible to find only three different lableings.

Artificial Intelligence role in e-Governance : e - village.

The meaning of getting connected for a village:

For many years, people working to enhance information & telecommunication infrastructure and applications have referred to rural communities as being at the “last mile of connectivity.” But they are still on “first mile of connectivity.”
For a rural person, getting connected is a means for sharing the wide range of options available to urbanites, a means for accessing the services (health, education, information, etc.) that enable urban people to improve their lives.

The Dream of E-Village:

To convert a village into E-village(Electronic village) using ICT, first we have to think, how to motivate the villagers to use the services of ICT. If most of them are living in BPL(Below poverty Line), first approach should be the economic development providing employment or earning modes to villagers. An awareness of good use of IT is necessary and training to use IT and IT services is also required.

ICT(Interactive Computer Training) for rural development

Rural people constitute the greater part of the population of developing countries and often lack access to basic needs such as water, food, education, health care, sanitation and security. Knowledge and information are basic ingredients for facilitating rural development and bringing about social and economic change. According to Albert Water son, as quoted by Cohen (1987), the purpose of rural development is “to improve the standard of living of the rural population is multi-dimensional including agriculture, industry, and social facilities”. Rural communities require information about market prices and their competitors.

ICT have played a major role in diffusing information to rural communities, and have much more potential. There is need to connect rural communities, research and extension networks and provides access to the much needed knowledge, technology and services.

Although ICT(Interactive Computer Training) or the Internet is not only to solve rural development problems, it can open new communication channels that bring new knowledge and information resources to rural communities. Radio and TV for example has been very effective for disseminating information to all types of audiences, but broadcasting times are sometimes not appropriate for most people. But radio could be linked to Internet, and a few initiatives have been started on this concept, such as the project Internet Radio was started in Sri Lanka.
Some examples of areas where ICT could play a catalytic role in economic development for a village in rural area include:-

Market Scenario

Farmers/Villagers could promote their products and handle simple transactions such as orders using ICT using PDA and Palmtop Computers (A low cost, battery powered small pc, having very simple user interface in local language), linked using Broadband radio link over the web. It has been shown to be cheaper and faster to trade online than on paper-based medium. E-commerce/V-commerce (Electronic/Voice-commerce) therefore, enable entrepreneurs to access global market information and open up new regional and global markets that fetch better prices and increase farmers’ earnings.

Benificious to rural communities

With new ICT, rural communities can acquire the capacity to improve their living conditions and become motivated through training and dialogue with others to a level where they make decisions for their own development. New ICT have the potential to penetrate under-serviced areas and enhance education through distance learning, facilitate development of relevant local content and faster delivery of information on technical assistance and basic human needs such as food, agriculture, health and water. Farmers can also interact with other farmers, their families, neighbors, suppliers, customers and intermediaries and this is a way of educating rural communities. The Internet can also enable the remotest village to access regular and reliable information from a global library (the web). Different media combinations may, however, be best in different cases – through radio, television, video cassettes, audio cassettes, video conferencing, CDs or the Internet.

Provides Employment Opportunities

ICT will create employment opportunities in rural areas by engaging subject matter specialists, information managers, translators and information technology technicians. Such centers help bridge the gap between urban and rural migration problem. The centers will also provide training and those trained may become small-scale entrepreneurs. That help in socio – economic development of a village. It is already suggested by NSSO(National Sample Survey Organization) to Govt. of India.

Improvements in Basic services like Education, Health, Agriculture

We should mobilize the power of the new media, like the Internet, as well as traditional media such as television, radio and the vernacular newspapers. The combination of the Broadband Internet and community (FM) radio can be particularly powerful to provide better education / awareness timely across to those who need them.
With training and technical help in local language, by using NLP Systems i.e local children, women and men will be able to get basic education that will help us to spread awareness of IT use and applications in rural areas.

Connectivity (Less cost options)

To get connected on High speed and in long range with in less cost the best option is use of radio frequency based broadband (Wifi), again it can be used easily with wireless devices like Mobile.
broadband system approach for remote and rural areas say our E-Village.
• Low price PC and Other Hardware in Local Language Interface.
• Information Technology awareness in rural areas with the help of students of near by technology institute as a part of their syllabus.
• Students of B.Tech,MCA,MBA in thousands of Institutes can be used as free available work force to train rural youth, to develop rural ICT infrastructure, low priced software’s and hardware for rural citizens.

Conclusion:

There is a great need to link rural India with urban and ICT can help, where there is less than 10% of Indian population is now well connected with ICT framework, we have to think how the rest part mainly rural India could also obtain the benefits of information and communication technologies (ICT). It will be a joint effort of Govt. of India, researchers, IT students, and state government. There is substantial demand for ICT services, primarily for e-Governance services, such as records like land records and birth certificates etc and entitlements like health information and social welfare services. However, existing services focus on email and Internet-based information and entertainment only, which have do not generally promote self-sufficiency. To accommodate ICT demand at the rural level in India.