Artificial Intelligence

Published on November 2016 | Categories: Documents | Downloads: 88 | Comments: 0 | Views: 736
of 14
Download PDF   Embed   Report



Artificial intelligence (AI) refers to computer software that exhibits intelligent behavior.. Intelligence includes the capacity to learn, maintain a large storehouse of knowledge, utilize commonsense reasoning, apply analytical abilities, discern relationships between facts, communicate ideas to others and understand communications from others, and perceive and make sense of the world around us. Thus, artificial intelligence systems are computer programs that exhibit one or more of these behaviors. AI systems can be divided into two broad categories:

Knowledge representation systems : Knowledge representation
systems, also known as expert systems, provide a structure for capturing and encoding the knowledge of a human expert in a particular domain. For example, the knowledge of medical doctors might be captured in a computerized model that can be used to help diagnose patient illnesses.

The second category of AI, machine learning systems, creates new knowledge by finding previously unknown patterns in data. In contrast to knowledge representation approaches, which model the problem-solving structure of human experts, machine learning systems derive solutions by "learning" patterns in data, with little or no intervention by an expert. There are three main machine learning techniques: neural networks, induction algorithms, and genetic algorithms.

Neural networks simulate the human nervous system. The concepts that guide neural network research and practice stem from studies of biological systems. These systems model the interaction between nerve cells. Components of a neural network include neurons (sometimes called "processing

elements"), input lines to the neurons (called dendrites), and output lines from the neurons (called axons).

Neural networks are composed of richly connected sets of neurons forming layers. The neural network architecture consists of an input layer, which inputs data to the network; an output layer, which produces the resulting guess of the network; and a series of one or more hidden layers, which assist in propagating. This is illustrated in Figure 1. During processing, each neuron performs a weighted sum of inputs from the neurons connecting to it; this is called activation. The neuron chooses to fire if the sum of inputs exceeds some previously set threshold value; this is called transfer. Inputs with high weights tend to give greater activation to a neuron than inputs with low weights. The weight of an input is analogous to the strength of a synapse in a biological system. In biological systems, learning occurs by strengthening or weakening the synaptic connections between nerve cells. An artificial neural network simulates synaptic connection strength by increasing or decreasing the weight of input lines into neurons. Neural networks are trained with a series of data points. The networks guess which response should be given, and the guess is compared against the correct answer for each data point. If errors occur, the weights into the neurons are adjusted and the process repeats itself. This learning approach is called backpropagation, and is similar to statistical regression. Neural networks are used in a wide variety of business problems, including optical character recognition, financial forecasting, market demographics trend assessment, and various robotics applications.

Figure 1

Induction algorithms form another approach to machine learning. In contrast to neural networks, which are highly mathematical in nature, induction approaches tend to involve symbolic data. As the name implies, these algorithms work by implementing inductive reasoning approaches. Induction is a reasoning method that can be characterized as "learning by example." Unlike rule-based deduction, induction begins with a set of observations and constructs rules to account for these observations. Inductive reasoning attempts to find general patterns that can fully explain the observations. The system is presented with a large set of data consisting of several input variables and one decision variable. The system constructs a decision tree by recursively partitioning data sets based on the variables that best distinguish between the data elements. That is, it attempts to partition the data so that each partition contains data with the same value for a decision variable. It does this by selecting the input variables that do the best job of dividing the data set into homogeneous partitions. For example,

consider Figure 2, which contains the data set pertaining to decisions that were made on credit loan applications.

Figure 2 Artificial Intelligence & Expert Systems An induction algorithm would infer the rules in Figure 3 to explain this data.

Figure 3 As this example illustrates, an induction algorithm is able to induce rules that identify the general patterns in data. In doing so, these algorithms can prune out irrelevant or unnecessary attributes. In the example above, salary was irrelevant in terms of explaining the loan decision of the data set. Induction algorithms are often used for data mining applications, such as marketing problems that help companies decide on the best market strategies for new product lines. Data mining is a common service included in data warehouses, which are frequently used as decision support tools.

Genetic algorithms use an evolutionary approach to solve optimization problems. These are based on Darwin's theory of evolution, and in particular the notion of survival of the fittest. Concepts such as reproduction, natural selection, mutation, chromosome, and gene are all included in the genetic algorithm approach.

Genetic algorithms are useful in optimization problems that must select from a very large number of possible solutions to a problem. A classic example of this is the traveling salesperson problem. Consider a salesman who must visitncities. The salesperson's problem is to find the shortest route by which to visit each of thesencities exactly once, so that the salesman will tour all the cities and return to the origin. For such a problem there are (ní 1)! possible solutions, or (ní 1) factorial. For six cities, this would mean 5 × 4 × 3 × 2 × 1 = 120 possible solutions. Suppose that the salesman must travel to 100 cities. This would involve 99! possible solutions. This is such an astronomical number that if the world's most powerful computer began solving such a problem at the time that the universe had begun and worked continuously on it since, it would be less than one percent complete today! Obviously, for this type of problem a brute strength method of exhaustively comparing all possible solutions will not work. This requires the use of heuristic methods, of which the genetic algorithm is a prime example. For the traveling salesperson problem, a chromosome would be one possible route through the cities, and a gene would be a city in a particular sequence on the chromosome. The genetic algorithm would start with an initial population of chromosomes (routes) and measure each according to a fitness function (the total distance traveled in the route). Those with the best fitness functions would be selected and those with the worst would be discarded. Then random pairs of surviving chromosomes would mate, a process called crossover. This involves swapping city positions between the pair of chromosomes, resulting in a pair of child chromosomes. In addition, some random subset of the population would be mutated, such that some portion of the sequence of cities would be altered. The process of selection, crossover, and mutation results in a new population for the next generation. This procedure is repeated through as many generations as necessary in order to obtain an optimal solution. Genetic algorithms are very effective at finding good solutions to optimization problems. Scheduling, configuration, and routing problems are good candidates for a genetic algorithm approach. Although genetic algorithms do not guarantee the absolute best solution, they do consistently arrive at very good solutions in a relatively short period of time.

Artificial intelligence systems provide a key component in many computer applications that serve the world of business. In fact, AI is so prevalent that many people encounter such applications on a daily basis without even being aware of it. One of the most ubiquitous uses of AI can be found in network servers that route electronic mail. Expert systems are routinely utilized in the medical field, where they take the place of doctors in assessing the results of tests like mammograms or electrocardiograms. Neural networks are commonly used by credit card companies, banks, and insurance firms to help detect fraud. These AI systems can, for example, monitor consumer spending habits, detect patterns in the data, and alert the company when uncharacteristic patterns arise. Genetic algorithms serve logistics planning functions in airports, factories, and even military operations, where they are used to help solve incredibly complex resource-allocation problems. And perhaps most familiar, many companies employ AI systems to help monitor calls in their customer service call centers. These systems can analyze the emotional tones of callers' voices or listen for specific words, and route those calls to human supervisors for follow-up attention. Although computer scientists have thus far failed to create machines that can function with the complex intelligence of human beings, they have succeeded in creating a wide range of AI applications that make people's lives simpler and more convenient.

What is Artificial Intelligence? Intelligence is the ability to think, to imagine, to create, memorize, understand, recognize patterns, make choices, adapt to change and learn from experience. Artificial intelligence is a human endeavor to create a non-organic machine-based entity, that has all the above abilities of natural organic intelligence. Hence it is called as 'Artificial Intelligence' (AI). It is the ultimate challenge for an intelligence, to create an equal, another intelligent being. It is the ultimate form of art, where the artist's creation, not only inherits the impressions of his thoughts, but also his ability to think! How will one recognize artificial intelligence? According to Alan Turing, if you question a human and an

artificially intelligent being and if by their answers, you can't recognize which is the artificial one, then you have succeeded in creating artificial intelligence. Initial hopes of computer scientists of creating an artificial intelligence, were dashed hopelessly as they realized how much they had underrated the human mind's capabilities! How do you teach a machine to imagine? They realized that they must understand what makes natural intelligence, the human mind, possible. Only then could they get any near to their goal. Approaches to AI Initially, researchers thought that creating an AI would be simply writing programs for each and every function an intelligence performs! As they went on with this task, they realized that this approach was too shallow. Even simple functions like face recognition, spacial sense, pattern recognition and language comprehension were beyond their programming skills! They understood that to create an AI, they must delve deeper into natural intelligence first. They tried to understand how cognition, comprehension, decision-making happen in the human mind. They had to understand what understanding really means! Some went into the study of the brain and tried to understand how the network of neurons creates the mind. Thus, researchers branched into different approaches, but they had the same goal of creating intelligent machines. Let us introduce ourselves to some of the main approaches to artificial intelligence. They are divided into two main lines of thought, the bottom up and the top down approach: Neural Networks: This is the bottom up approach. It basically aims at mimicking the structure and functioning of the human brain, to create intelligent behavior. Researchers are attempting to build a siliconbased electronic network that is modeled on the working and form of the human brain! Our brain is a network of billions of neurons, each connected with the other. At an individual level, a neuron has very little intelligence, in the sense that it operates by a simple set of rules, conducting electric signals through its network. However, the combined network of all these neurons creates intelligent behavior that is unrivaled and unsurpassed. So these researchers created network of electronic analogues of a neuron, based on Boolean logic. Memory was recognized to be an electronic signal pattern in a closed neural network. How the human brain works is, it learns to realize patterns and remembers them. Similarly, the neural networks developed have the ability to learn patterns and remember. This approach has its limitations due to the scale and complexity of developing an exact replica of a human brain, as the neurons number in billions! Currently, through simulation techniques, people create virtual neural networks. This approach has not been able to achieve the ultimate goal but there is a very positive progress in the field. The progress in the development of parallel computing will aid it in the future. Expert Systems: This is the top down approach. Instead of starting at the base level of neurons, by taking advantage of the phenomenal computational power of the modern computers, followers of the expert systems approach are designing intelligent machines that solve problems by deductive logic. It is like the dialectic approach in philosophy. This is an intensive approach as opposed to the extensive approach in neural networks. As the name expert systems suggest, these are machines devoted to solving problems in very specific niche areas.

They have total expertise in a specific domain of human thought. Their tools are like those of a detective or sleuth. They are programmed to use statistical analysis and data mining to solve problems. They arrive at a decision through a logical flow developed by answering yes-no questions. Chess computers like Fritz and its successors that beat chess grandmaster Kasparov are examples of expert systems. Chess is known as the drosophila or experimental specimen of artificial intelligence. Applications of AI Artificial Intelligence in the form of expert systems and neural networks have applications in every field of human endeavor. They combine precision and computational power with pure logic, to solve problems and reduce error in operation. Already, robot expert systems are taking over many jobs in industries that are dangerous for or beyond human ability. Some of the applications divided by domains are as follows: Heavy Industries and Space: Robotics and cybernetics have taken a leap combined with artificially intelligent expert systems. An entire manufacturing process is now totally automated, controlled and maintained by a computer system in car manufacture, machine tool production, computer chip production and almost every high-tech process. They carry out dangerous tasks like handling hazardous radioactive materials. Robotic pilots carry out complex maneuvering techniques of unmanned spacecrafts sent in space. Japan is the leading country in the world in terms of robotics research and use. Finance: Banks use intelligent software applications to screen and analyze financial data. Softwares that can predict trends in the stock market have been created which have been known to beat humans in predictive power. Computer Science: Researchers in quest of artificial intelligence have created spin offs like dynamic programming, object oriented programming, symbolic programming, intelligent storage management systems and many more such tools. The primary goal of creating an artificial intelligence still remains a distant dream but people are getting an idea of the ultimate path which could lead to it. Aviation: Air lines use expert systems in planes to monitor atmospheric conditions and system status. The plane can be put on auto pilot once a course is set for the destination. Weather Forecast: Neural networks are used for predicting weather conditions. Previous data is fed to a neural network which learns the pattern and uses that knowledge to predict weather patterns. Swarm Intelligence: This is an approach to, as well as application of artificial intelligence similar to a neural network. Here, programmers study how intelligence emerges in natural systems like swarms of bees even though on an individual level, a bee just follows simple rules. They study relationships in nature like the prey-predator relationships that give an insight into how intelligence emerges in a swarm or collection from simple rules at an individual level. They develop intelligent systems by creating agent programs that mimic the behavior of these natural systems! Is artificial Intelligence really possible? Can an intelligence like a human mind surpass itself and create its own image? The depth and the powers of the human mind are just being tapped. Who knows, it might be possible, only time can tell! Even if such an intelligence is created, will it share our sense of morals and justice, will it share our idiosyncrasies? This will be the next step in the evolution of intelligence. Hope I have succeeded in conveying to you the excitement and possibilities this subject holds!


Artificially intelligent computer applications are useful in many different areas of business, as well as for entertainment, health, the financial sector, and national defense. Due to the fact that AI is used for many different purposes, there are many different measures of success, depending on the type of AI. In the area of business intelligence, AI offers many benefits, and success is measured by profit and loss, rather than by the ability of the program to appear human. AI Business Intelligence Applications One area in which AI programming is highly useful is the area of business intelligence. This aspect of business planning refers to the ability to use information to gain a competitive edge over competitors. Data mining is an essential tool in business intelligence, due to the fact that trends are a very important aspect of improving market share.
Data mining analyzes trends, whether they are pricing trends, sales trends, or the number of accidents in a particular workplace. Any data gathered for the AI program can be used to predict future developments (For more information about the use of Data mining, read: Text Analytics in Data Mining Software). This can be of great benefit to a small company in need of a competitive edge, or a larger business with an edge to maintain. Computer science AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered a part of AI. According to Russell & Norvig, all of the following were originally developed in AI laboratories:

Time sharing Interactive interpreters Graphical user interfaces and the computer mouse Rapid development environments The linked list data type Automatic storage management Symbolic programming Functional programming


Dynamic programming

Object-oriented programming Finance Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition[4]. Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. Medicine A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information. Artificial neural networks are used as clinical decision support systems for medical diagnosis, such as in Concept Processing technology in EMR software). Heavy industry Robots have become common in many industries. They are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading. Japan is the leader in using and producing robots in the world. In 1999, 1,700,000 robots were in use worldwide. For more information, see survey about artificial intelligence in business. Transportation Fuzzy logic controllers have been developed for automatic gearboxes in automobiles (the 2006 Audi TT, VW Toureg and VW Caravell feature the DSP transmission which utilizes Fuzzy logic, a number of koda variants also currently include a Fuzzy Logic based controller). Telecommunications Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search in a scheduling application that provides the work schedules of 20,000 engineers. Toys and games

The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of AI, specifically in the form of Tamagotchis and Giga Pets, the Internet (example: basic search engine interfaces are one simple form), and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy. AI has also been applied to video games. Music The evolution of music has always been affected by technology. With AI, scientists are trying to make the computer emulate the activities of the skillful musician. Composition, performance, music theory, sound processing are some of the major areas on which research in Music and Artificial Intelligence are focusing. Aviation The Air Operations Division AOD, uses AI for the rule based expert systems. The AOD has use for artificial intelligence for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries. The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed, and strength of the forces and counter forces. Pilots may be given assistance in the air during combat by computers. The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a sentient being to perform. Multiple aircraft are needed to get good approximations for some calculations so computer simulated pilots are used to gather data. These computer simulated pilots are also used to train future air traffic controllers. The system used by the AOD in order to measure performance was the Interactive Fault Diagnosis and Isolation System, or IFDIS. It is a rule based expert system put together by collecting information from TF-30 documents and the expert advice from

mechanics that work on the TF-30. This system was designed to be used to for the development of the TF-30 for the RAAF F-111C. The performance system was also used to replace specialized workers. The system allowed the regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers. The AOD also uses artificial intelligence in speech recognition software. The air traffic controllers are giving directions to the artificial pilots and the AOD wants to the pilots to respond to the ATC¶s with simple responses. The programs that incorporate the speech software must be trained, which means they use neural networks. The program used, the Verbex 7000, is still a very early program that has plenty of room for improvement. The improvements are imperative because ATCs use very specific dialog and the software needs to be able to communicate correctly and promptly every time. The Artificial Intelligence supported Design of Aircraft [1], or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective. In 2003, NASA¶s Dryden Flight Research Center, and many other companies, created software that could enable a damaged aircraft to continue flight until a safe landing zone can be reached. . The software compensates for all the damaged components by relying on the undamaged components. The neural network used in the software proved to be effective and marked a triumph for artificial intelligence. The Integrated Vehicle Health Management system, also used by NASA, on board an aircraft must process and interpret data taken from the various sensors on the aircraft. The system needs to be able to determine the structural integrity of the aircraft. The system also needs to implement protocols in case of any damage taken the vehicle. Artificial intelligence marketing (AIM) is a form of direct marketing leveraging database marketing techniques as well as AI concept and model such as machine learning and Bayesian Network. The main difference resides in the reasoning part which suggests it is performed by computer and algorithm instead of human. Behavioral targeting

Artificial intelligence marketing provides a set of tools and techniques that enable behavioral targeting. Collect, reason, act Artificial intelligence marketing principle is based on the perception-reasoning-action cycle you find in cognitive science. In marketing context this cycle is adapted to form the collect, reason and act cycle. Collect This term relates to all activities which aims at capturing customer or prospect data. Whether taken online or offline these data are then saved into customer or prospect databases. Reason This is the part where data is transformed into information and eventually intelligence or insight. This is the section where artificial intelligence and machine learning in particular have a key role to play. Act With the intelligence gathered from the reason step above you can then act. In marketing context act would be some sort of communications that would attempt to influence a prospect or customer purchase decision using incentive driven message Again artificial intelligence has a role to play in this stage as well. Ultimately in an unsupervised model the machine would take the decision and act accordingly to the information it receives at thecollect stage. [edit]Machine Learning Machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". As defined above machine learning is one of the techniques that can be employed to enable more effective behavioral targeting Other Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering. Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition (, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of

emotion and non verbal queues. Other applications are robot navigation, obstacle avoidance, object recognition.

Disadvantages of Artificial Intelligence If robots start replacing human resources in every field, we will have to deal with serious issues like unemployment in turn leading to mental depression, poverty and crime in the society. Human beings deprived of their work life may not find any means to channelize their energies and harness their expertise. Human beings will be left with empty time. Secondly, replacing human beings with robots in every field may not be a right decision to make. There are many jobs that require the human touch. Intelligent machines will surely not be able to substitute for the caring behavior of hospital nurses or the promising voice of a doctor. Intelligent machines may not be the right choice for customer service. One of the major disadvantages of intelligent machines is that they cannot be µhuman¶. We might be able to make them think. But will we be able to make them feel? Intelligent machines will definitely be able to work for long hours. But will they do it with dedication? Will they work with devotion? How will intelligent machines work wholeheartedly when they don¶t have a heart? (!) Apart from these concerns, there are chances that intelligent machines overpower human beings. Machines may enslave human beings and start ruling the world. Imagine artificial intelligence taking over human intellect! The picture is definitely not rosy. Some thinkers consider it ethically wrong to create artificial intelligent machines. According to them, intelligence is God¶s gift to mankind. It is not correct to even try to recreate intelligence. It is against ethics to create replicas of human beings

Sponsor Documents

Or use your account on


Forgot your password?

Or register your new account on


Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in