Artificial Intelligence (AI) has been utilized in business applications since the early 1980s, initially generating interest but not meeting expectations. However, with the emergence of web-enabled infrastructure and advancements in the AI development community, there has been a significant rise in the utilization of AI methods in real-time business applications.
AI encompasses diverse technologies intended to mimic various human abilities, such as auto programming, case-based reasoning, neural networks, decision-making, expert systems, fuzzy logic, natural language processing, pattern recognition, and speech recognition. These AI technologies enhance existing applications by offering more advanced data analysis capabilities. In business settings, these specific technologies are employed to extract valuable insights from extensive and varied datasets. This entails identifying patterns or correlations in sales data and customer purchasing behavior.
AI is extensively used in the corporate world for complex problem-solving and decision-support techniques, such as neural networks and expert systems, in real-time business applications. Its applicability spans across various functions, including finance management, forecasting, and production. The demonstrated effectiveness of Artificial Neural Networks (ANN) and expert systems has led to the widespread adoption of AI in enterprise business applications. In certain cases, like fraud detection, AI has already emerged as the preferred approach.
In addition, neural networks have become a widely accepted method for pattern recognition, specifically in the areas of image analysis, data streaming, and complicated data sources. As a result, they have become the foundational modeling technique for many data-mining tools currently on the market. AI/ANN has various important business applications, including fraud detection, cross-selling, analytics for customer relationship management, predicting demand, forecasting failures, and implementing non-linear control. Several software vendors, like Ward Systems Group and Neural ware, offer ready-to-use ANN tools.
Many companies use horizontal or vertical solutions that incorporate neural networks. Examples include insurance risk assessment tools from HNC and data-mining tools with neural networks as a modeling option, such as those from SAS, IBM, and SPSS. Autonomic computing concepts derived from AI technologies have also gained attention. These concepts enable self-healing systems that automatically adjust to changing conditions, monitor system parts, and optimize workflow to meet system goals.
In recent times, artificial intelligence (AI) has become increasingly important in the financial services sector, proving its worth in various business applications. Combining advanced technologies like neural networks and business rules with AI techniques has proved to be successful in transaction-oriented situations within the financial services field. AI has been extensively implemented in risk management, compliance, securities trading, and monitoring. Its application has also expanded to encompass customer relationship management (CRM).
AI adoption offers several concrete advantages. These include a lower risk of fraud, a boost in revenue from current customers by tapping into new opportunities, avoidance of fines resulting from non-compliance, and prevention of securities trade exceptions that may cause delayed settlements if left undetected. AI is also being widely embraced in the fields of diagnostics and testing. Diagnostic systems are employed to inspect various equipment such as networks, aircraft engines, manufacturing machinery, energy pipelines, hazardous materials, and more.
AI is being increasingly used in the transportation industry for various purposes such as traffic management systems, aircraft maintenance operations, airport gate scheduling, and railroad planning and forecasting assignments. Decision-making in this industry has become more complex in the competitive market, leading to inherent latency in many processes. Additionally, the amount of data to be analyzed has significantly increased. By implementing AI technologies, enterprises can reduce latency in decision-making, combat fraud, and maximize revenue opportunities.
The need for data analysis, fraud detection, and customer relationship management is driving the increasing diversity of AI in enterprises. While the financial services sector leads in AI implementation, other sectors like manufacturing, transportation, logistics, and healthcare are also rapidly adopting it. According to a business research company’s report, the global AI market was $11.9 billion in 2002 and is projected to reach $21.2 billion by 2007 with an average annual growth rate of 12%. This demonstrates that AI for business applications is making a comeback!
AI can be defined as a branch of Science that enables machines to solve complex problems similarly to humans by incorporating human intelligence characteristics into computer-friendly algorithms.
A flexible or efficient approach can be taken depending on the established requirements, which affects the appearance of artificial intelligent behavior. AI is commonly associated with Computer Science, but it has significant connections to Maths, Psychology, Cognition, Biology and Philosophy. Combining knowledge from these fields will ultimately benefit our progress in creating an intelligent artificial being. Further details… Field of Artificial Intelligence Why Artificial Intelligence? Motivation…
Computers are inherently suitable for executing mechanical computations based on predetermined rules. They excel at efficiently and reliably performing simple, repetitive tasks that humans struggle with. However, when it comes to more intricate problems, computers face challenges. Unlike humans, they struggle to comprehend specific situations and adjust to new ones. Artificial Intelligence (AI) aims to enhance machine performance in handling complex tasks. Additionally, AI research provides insight into our own intelligent behavior.
Artificial Intelligence (AI) has the potential to understand and replicate humans’ distinctive problem-solving abilities, which involve abstract thought, high-level reasoning, and pattern recognition. This article discusses AI’s progress in surpassing our current capabilities and creating truly intelligent computers. However, it is important to acknowledge that AI has not fully replicated all aspects of human intelligence.
Currently, researchers refer to the limitation of machine intelligence as narrow intelligence, as Artificial Intelligence predominantly concentrates on profitable domain-specific applications that don’t necessarily demand the full potential of AI capabilities. There is a strong consensus within the community that machines will soon possess intelligent reasoning. The only uncertainty lies in the type of machines and the timeframe. Potentially, these machines could consist of pure silicon, quantum computers, or a hybrid mixture involving artificial components and neural tissue. Nevertheless, anticipate remarkable advancements to occur within this century!
Artificial Intelligence is a constantly changing area with diverse approaches and no conclusive solution. Various AI methods may be appropriate for different situations, but any valid option can be rationalized. Trends in AI have arisen from influential researchers’ viewpoints, funding possibilities, and computer hardware accessibility. For the past fifty years, AI research has mainly concentrated on resolving particular issues.
In the field of Artificial Intelligence, there are several efficient and reliable solutions that have been developed and improved. This has resulted in the subdivision of Artificial Intelligence into different branches, including Pattern Recognition, Artificial Life, Evolutionary Computation, and Planning.
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Who uses Artificial Intelligence? Applications…
The potential applications of Artificial Intelligence are extensive, spanning from autonomous control and target identification in the military to computer games and robotic pets in the entertainment industry.
Let’s not overlook large establishments, such as hospitals, banks, and insurance companies, which can utilize AI to anticipate customer behavior and identify patterns. It is no surprise that Artificial Intelligence has become a significant catalyst for research. With a continuously expanding market to cater to, there is ample opportunity for more personnel. Therefore, if you possess the necessary expertise, there is considerable money to be earned from prominent companies that are interested. Furthermore, where can I discover information concerning Artificial Intelligence Applications?
Information… If you’re interested in AI, you’ve come to the right place! The Artificial Intelligence Depot is a site solely focused on AI. It offers daily news and regular features, along with community interaction and an expanding database of knowledge resources. Whether you’re a beginner, experienced programmer, computer games hacker, or academic researcher, this site has something for you. After reading this page, visit the main page of the Artificial Intelligence Depot.
This page focuses on daily Artificial Intelligence business and provides links to valuable resources. You will always be redirected to this main page, but you can return to this introduction page through the menu. Refer to our introduction for the AI Depot if you need a quick guide to the site before starting. However, it is advised not to solely rely on online information. To truly delve into Artificial Intelligence, getting a reputable book on the subject is a wise decision.
A good starting point is the book called Artificial Intelligence: A Modern Approach, which covers important material from the ground upwards. Overview Version: Printer Friendly Definition: The Field of Artificial Intelligence Alex J. Champandard It seems rather ironical for a site dedicated to the field of Artificial Intelligence not to have even the simplest definition. So here it is, and as a consequence, it is also a partial list of the content you can expect from this site. Let’s get things started by stating a very important fact: This s not a definition of intelligence, human or otherwise, nor of the process of simulating it artificially. This essay discusses and describes the field of Artificial Intelligence, its branches, research openings and applications. There is an considerable difference between the two, as you will quickly notice. Indeed, the field of A. I. has grown to be so much more than attempts to simulate (human) intelligence. Many branches of Artificial Intelligence today set out to solve domain specific problems, by using algorithms that display single characteristics of intelligence, if any at all.
The use of marketing buzzwords often obscures the fact that some applications may only have a remote potential for intelligence. However, A.I. has become a popular and intriguing topic, embraced as a trendy concept in advertising. Nonetheless, the various branches and applications of A.I. still captivate our interest. In this essay, we will explore their shared traits, starting with an examination of the significance of problem representation on the following page.
In this section, we will cover the classical approach and the statistical approach to problem-solving in Artificial Intelligence. We will also delve into the various branches within these approaches. Furthermore, we will touch on some real-life applications of artificial intelligence. This section gives a summary of the field of artificial intelligence and its definition by Alex J. Champandard. Algorithms are used in AI to solve problems, but they need information to be presented in a format that they can understand.
The chosen representation and the way information is passed to it directly affect the performance and quality of results for various problems, such as a virtual world, a collection of pictures, or a list of paths between cities. Many researchers have dedicated their efforts to finding solutions for these issues, and continue to explore new possibilities. A symbolic representation involves assigning symbols to each item or concept in the problem. For instance, an animal could be represented by symbols like cat, snake, or hippopotamus.
First-order predicate logic allows for the expression of simple facts about symbols. These statements provide information about the symbols, such as declaring that a cat is a mammal and a snake is a reptile. They can also specify details about the number of legs each animal has; for example, cats have four legs while snakes have none. It’s important to note that these mentioned statements are considered true, while any unmentioned statements are assumed false based on the closed world assumption. This assumption relies on our complete knowledge of the finite nature of the problem. One advantage of this approach is its simplicity, as algorithms can work with clear-cut symbols that are typically limited in number. It should be noted that numbers used within predicates are also seen as symbols in this context.
Using Prolog for solving basic arithmetic problems can be unexpectedly laborious. Furthermore, high-order functions, which are more intricate predicates, can offer insights into statements and can be defined as partial applications of other functions. Although it can become quite challenging, functional programming in languages such as Haskell enables the creation of highly complex programs with just 10 lines.
In the early days, Artificial Intelligence (A.I.) started with symbolic representations of problems using specialized languages like LISP because of limited memory availability. Currently, research in this field continues to explore ways to integrate functional programming and declarative programming more effectively. This advancement is significant as it can renew interest in the field and expand its capabilities. It should be noted that fuzzy representations lack clarity.
Unlike symbols, vague and imprecise information is provided. This typically involves using probabilities to express truths and requiring simple operators to combine them, such as stating that “80% of the people who read this page and the introduction will read the next page.” This field is known as Fuzzy Logic. Extracting meaningful information from these statements is understandably more challenging, requiring more elaborate techniques like Dempster-Shafer Theory and Bayesian Networks. Another form of fuzzy information is stored in Neural Networks.
Neuro-fuzziness refers to the imprecise information inside an artificial neural network, which is caused by the weighted connections between neurons, known as synapses. Due to the increased computing capabilities, fuzzy representations have become more popular. Creating and interpreting these rules often require more processing power and time. Procedural languages like C, C++, or Pascal are commonly used for this type of representation. Implicit representations do not directly store the information.
In order to extract information from a representation, a simple algorithm is needed. This means that large amounts of information can be stored in a compact manner. Take humans for example, with just the DNA of one individual, it is possible to create a complete body through a process known as growth. Similarly, a full-size neural network can be built based on simple rules. This process is significant enough to have its own terminology, but the definitions provided can also be applied to other scenarios. The encoded form of the information is referred to as genotype, and once decoded, it becomes a phenotype.
The representation of information is divided into two parts: the structure, known as the genome, and the decoded information, called the phenome. There are different ways to decode the information, including cellular automata, grammar-based methods, and procedural methods. During the creation of the representation, the algorithms do not have knowledge of the information they are processing. The genotypes are blindly created and optimized, with the phenotypes used for evaluating the quality of the solution. Extracting information from the representation requires effort due to the rarity of mappings from complex phenotypes to implicit genotypes. This technique has grown in popularity due to the availability of processing power for such operations. Optimizing the genotype using techniques like genetic algorithms also takes time. Feel free to visit the Message Store to discuss this essay.
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A specific type of search known as depth-first search is characterized by its limited memory usage, which was one of the main reasons for adopting this approach initially. Typically, a symbolic representation is utilized for this purpose, making languages like LISP and Prolog popular choices. In fact, Prolog incorporates the search mechanism directly into its core. While it is more cumbersome, the traditional programming language “C” can also be employed to implement these searches and is frequently used in logic games like chess. However, a major drawback of this methodology lies in the initial formulation of the rules.
The process of extracting patterns from a large set of data for problem-solving in A.I. can be a tedious and time-consuming task. It requires careful consideration of various cases and ensuring that the search is feasible within a finite amount of time. The statistical approach to A.I. is based on inductive reasoning, where patterns are induced from the data through machine learning. This allows for the abstraction and generalization of these patterns, typically expressed through fuzzy representations of extracted rules.
The use of probabilities in dealing with large data sets is more practical because it accounts for the uncertainty and noise present in the data. While this approach requires more memory, it allows for efficient access to the large dataset. To achieve this efficiency, lower level languages like C and possibly even C++ are commonly used. Additionally, many leading researchers have conducted significant theoretical research in the philosophical aspects of A.I.
Several books have been published on the theory of life, emotions, and emergent systems, which are rule-based groups of agents with apparently intelligent behavior. While conducting experiments on this type of work is challenging, it is possible to identify and further research key concepts in isolation. Remember, you can visit the Message Store to discuss this essay.
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Previous: Approaches Definition: The Field of Artificial Intelligence Alex J. Champandard
The branches of Artificial Intelligence split off in many directions, and some overlap quite extensively. It is therefore difficult to classify them.
Also note that some fields initially started as distinct part of AI, but have grown to become only remotely related to AI.
Search and Optimisation
There are many kinds of searches, the simplest of which involve trying out all the solutions in a particular order. The entire set of possible solutions is called the search space.
Constraint Satisfaction
Here, the problem is modeled as a set of variables with assigned values. Various types of constraints (equality, numerical) are established on these variables to specify the problem’s requirements. A search is conducted on the variables to discover potential solutions. To guide the search more efficiently, clever methods are employed to partially resolve constraints (known as heuristic search). The problems that are tackled may involve combinatorial optimization, seeking the best solution among alternatives with varying values.
The typical class of problems solved is NP-complete, with complexity that grows exponentially as the problem size increases linearly. Function Optimization involves finding the best set of parameters for a function. There are several straightforward methods for accomplishing this, such as hill-climbing. In a metaphorical sense, hill-climbing examines the nearby surroundings from the current position and moves to a higher position if one is found. When there is no higher position, the top is reached! This approach is somewhat simplistic and may result in finding sub-optimal solutions (referred to as local maxima).
Genetic Algorithms mimic the process of evolution and survival of the fittest to provide optimization capabilities. By mating the best solutions, better offspring solutions are created. Although this approach has fewer issues with local maxima, there is no guarantee of finding the optimal solution.
Planning involves finding a sequence of actions that can lead from the current state to the goal state. Typically, this is done hierarchically, where overall plans are developed first and the details are refined later. This hierarchical approach is more efficient.
The main challenge faced by planning is dealing with an imperfect world. In ideal circumstances, a straightforward search can be conducted, and if a result is obtained, it can be practically implemented. However, there are instances when the actions do not yield the expected outcomes, leading to unsuccessful plans. Machine Learning is gaining popularity and is equally significant. People recognize that it is theoretically more convenient to allow a machine to learn from facts rather than having to invest time in teaching it explicitly. The quality of the learning algorithm is, of course, a crucial aspect!
Neural Networks, also known as Artificial Neural Networks or NN, are designed to mimic the structure and functionality of the human brain. Despite the imperfect information learned by a network composed of artificial neurons, it offers the advantage of generalization. This means that it can work with data that was not encountered during its training. The performance of a neural network heavily relies on its ability to generalize, which is influenced by its design and training. Therefore, extensive research is conducted to ensure optimal generalization.
Inductive Programming is a technique that attempts to write the definition of a program based on a limited number of function results. The success of this process depends on the number of example results and the complexity of the function. Presently, certain inductive programming algorithms can learn basic logic programs, including those with recursive definitions. However, learning more complex programs and applying this process to real-life data rather than computer generated functions proves to be challenging. This technique is related to Decision Tree Learning.
A decision tree is a structure that allows learning of opinions (e. g. good or bad) about objects based on their attributes (length, colour…). Given a series of examples, the learning algorithm can build a decision tree that will be able of classifying new examples. If the new examples are handled correctly, nothing is done. Otherwise, the structure of the tree is modified until the correct results are displayed. The challenge is getting the algorithm to perform well on very large sets of data, handling errors in values (noise), and determining the optimal fit of the tree to the training and test data. Data Mining
Extracting useful rules from large sets of data is the process of interest here. When trends are observed, it becomes crucial to identify their causes and establish a rule expressing the relationship between them. The challenge in this field lies in efficiently processing a large amount of information and disregarding any potential errors. Bayesian Networks, on the other hand, model the relationship between variables and refer to it as conditional dependence. This means that the state of one variable may depend on several others. Such relationships can be represented in a graph, and an algorithm exists to estimate the probability of unknown events based on existing knowledge.
One common complaint against this approach relates to the design. It can be very tedious to model such networks. Learning the structure and the inference between variables seems like an appealing option. Remember you can visit the Message Store to discuss this essay. Warning: mysql_connect() [function.mysql-connect]: Access denied for user ‘alexjc_r’@’209. 68. 1. 186’ (using password: YES) in /usr/home/alexjc/public_www/Include/Utils.php on line 5 Access denied for user ‘alexjc_r’@’209. 68. 1. 186’ (using password: YES) Applications Previous: Branches
Definition: The Field of Artificial Intelligence Alex J. Champandard The research on Artificial Intelligence (A.I.) is primarily driven by its applications. These applications have numerous practical uses and are highly profitable. If you come up with an innovative idea and develop a successful product based on it, you can expect to earn millions of dollars or pounds. Consequently, funding is not a major issue when the application’s potential is evident. Pattern recognition, also known as classification, involves identifying the unique features in particular samples and organizing them into categories.
Machine Learning techniques are commonly utilized to enable systems to adapt to given data. This can have various applications, such as identifying individual words in speech, distinguishing different voices, categorizing scanned objects, and eliminating unwanted images. The practical approach involves representing samples as a set of features, like pitch, volume, timbre, and smoothness for sound. Subsequently, a training set is established where the outcome is already known. For instance, in facial recognition, a training set may include descriptions of individuals like Fred having green eyes and brown hair, and Henry having blue eyes and blond hair.
The learning mechanism can learn to associate features with known types of sound or image by using different representations. Symbolic representations require a small number of examples, while fuzzy learning (such as neural networks) requires larger training sets. In robotics, the focus is currently on mobility, such as controlling a mechanical device to move or navigate. One approach is to learn the task in a virtual simulation and then apply it to the real robot.
The problem has a high chance of working in real life if specific training conditions are met, but this is not guaranteed. When moving robotic arms, there are limited movement options: the shoulder can rotate along two axes, and the elbow can rotate in two basic ways. Each of these options is referred to as one degree of freedom (DOF). Typically, one controller is used for each DOF to facilitate movement. The goal is to learn the best combination of controllers where they can effectively work together to accomplish a specific task. This task relates to Natural Language Processing.
The task at hand is the extraction of meaning from text, which is also referred to as computational linguistics. After processing this meaning, it has the potential to be interpreted and understood, or at least the fundamentals! Initially, a symbolic approach was taken wherein semantic meaning was assigned to individual words such as verbs, nouns, and adjectives. The manual definition of the basic structure of valid sentences was required, followed by a search process to match the template with the present sentence. Resolving ambiguous sentences and ensuring agreement between the person and tense of the verbs consumed a significant amount of time.
The results can be quite promising if the programmer dedicates enough time to creating sentence templates. However, this repetitive task needs to be done again for new sentence structures and different languages altogether. A more recent technique involves using statistical analysis of text. Essentially, extensive portions of books are processed, and learning algorithms strive to extract rules and patterns. Although this approach requires a more intelligent design and more time, it leads to a program that is more adaptable. Artificial Life is a widely popular aspect of Artificial Intelligence that entails modeling and imitating living systems.
This consists of ant hills, wasp nests, larger forests, towns, and cities. Thus far, numerous intricate and fascinating systems have been formed by multiple simple entities. For instance, a multitude of ants guided by minuscule programs could potentially construct a complete system exhibiting signs of emergent intelligence. However, we have not yet established systems founded on complex individuals that possess learning capabilities. Many researchers and dreamers alike have been captivated by this endeavor. Remember, you can visit the Message Store to discuss this essay.
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When choosing the best way to represent a problem, it is important to consider the alternatives. The change in representations used by researchers over time reflects their state of mind.
