Artificial Intelligence 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.

When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most.

From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes.

Programs do that. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic. [Mit97] is a comprehensive undergraduate text on machine learning. Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.

Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations

AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program. Discoveries are continually made about how to do this more efficiently in various domains.

Jobs - Depending on the level and type of intelligence these machines receive in the future, it will obviously have an effect on the type of work they can do, and how well they can do it (they can become more efficient). As the level of AI increases so will their competency to deal with difficult, complex even dangerous tasks that are currently done by humans.

They don't stop - As they are machines there is no need for sleep, they don't get ill , there is no need for breaks or facebook, they are able to go, go, go! There obviously may be the need for them to be charged or refueled, however the point is they are definitely going to get a lot more work done than we can.

No risk of harm - When we are exploring new undiscovered land or even planets, when a machine gets broken or dies, there is no harm done as they don't feel, they don't have emotions. Where as going on the same type of expeditions a machine does, may simply not be possible or they are exposing themselves to high risk situations.

Act as aids - They can act as 24/7 aids to children with disabilities or the elderly, they could even act as a source for learning and teaching. They could even be part of security alerting you to possible fires that you are in threat of, or fending off crime.

Their functions are almost limitless - As the machines will be able to do everything (but just better) essentially their use, pretty much doesn't have any boundaries. They will make fewer mistakes, they are emotionless, they are more efficient, they are basically giving us more free time to do as we please.

•Over reliance on AI - As you may have seen in many films such as The Matrix, iRobot or even kids films such as WALL.E, if we rely on machines to do almost everything for us we become very dependent, so much so they have the potential to ruin our lives if something were to go wrong. Although the films are essentially just fiction, it wouldn't be too smart not to have some sort of back up plan to potential issues on our part.

•Human Feel - As they are machines they obviously can't provide you with that 'human touch and quality', the feeling of a togetherness and emotional understanding, that machines will lack the ability to sympathize and empathize with your situations, and may act irrationally as a consequence.

•Inferior - as machines will be able to perform almost every task better than us in practically all respects, they will take up many of our jobs, which will then result in masses of people who are then jobless and as a result feel essentially useless. This could then lead us to issues of mental illness and obesity problems etc.

•Misuse - there is no doubt that this level of technology in the wrong hands can cause mass destruction, where robot armies could be formed, or they could perhaps malfunction or be corrupted which then we could be facing a similar scene to that of terminator ( hey, you never know).

Neural network is a simplified model of biological nervous system and therefore has drawn its motivation from the king of computing performed by human brain. Generally, Neural network is a high interconnection of network elements called “neurons”; where each neuron has its own local memory. Neurons are connected by communication channels where each link has its associated weight which processes the information used by the network.

Biological Terms

Artificial Terms

Neuron Node / Unit / Cell

Synapses Connections / Links

Synaptic Efficiency Weight/ Connection Strength

Firing Frequency Node Output

Input Output

Input Output

Input Output

There are three basic models of neuron. They are-

Once an architecture has been selected and Input signals are prepared then the next step is to train the network. To start the training process initial weights are chosen randomly. Neural Network can be trained in two ways-

Learning is the process by which the free parameters of neural network are adapted and then simulated. There are five types of learning processes-

(a)Decide the network architecture according to the problem(b)Decide number of Input nodes(c)Decide number of Output nodes(d)Prepare training set. The training set must contain many

examples so that the network becomes familiarize with the given problem

(e)If network training is supervised provide the network with the desired output for input vectors

(f)Train the network using input vectors(g)Finally test the network; If the network fails to provide the

desired output, then, repeat the above procedure until optimal solution has been achieved

(1)Inheritably, massively parallel (Multi-process)(2)It is designed to be adaptive(3)It needs less effort for the characterization of the problems(4)Artificial neural network cannot be programmed. It learns by its

previous examples(5)Artificial neural network is robust in nature i.e. it can operate even if

the portions of the given problems are incorrect(6)Artificial neural network may be fault tolerant because of parallelism(7)Artificial neural network exhibits mapping capabilities i.e. they can

map their input patterns to their associated input patterns(8)Neural network has the capability to generalize the input(9)They can predict new outcomes from the past problems(10)The neural network can process information simultaneously at high

speed and in a distributed manner

(1)No clear rules or guidelines can be defined in designing artificial neural network

(2)There is no general way to access the internal operations of artificial neural network

(3)Training may be difficult in an artificial neural network but it is not impossible

It attempts to predict the movement of stock from the previous data by using linear model

It is used for comparing signature with the already stored signature like in banks

(i) Toys- With the ability of neural network chip the system is designed to recognize simple entities. For example, simple commands like stop and go

(ii) Pen PCs- By Pen PCs one can write on a tablet. The writing can be recognized and further translated into text with the help ASCII codes

To monitor the state of air-craft by monitoring vibration levels etc. Early warnings of the engine problem can be predicted previously

It is used to improve marketing mail shots. In this technique a test mail shot is run and it recognizes the pattern of these mail shots. Finally, predictive mapping of data is done and next mail shot is run

Bibliography-

>> Neural Network -S. Rajeshkaran >> www.wikipidea.com

>> Applications of AI -John McCarthy

>> www.google.com

>> www.cnn.com

>> Call Centers of the Future -L Venkata Subramaniam

>> www.infobarrel.com