Artificial Intelligence (AI) has been used in business applications since the early eighties. As with all technologies, AI initially generated much interest, but failed to live up to the hype. However, with the advent of web-enabled infrastructure and rapid strides made by the AI development community, the application of AI techniques in real-time business applications has picked up substantially in the recent past.
AI is a broad discipline that promises to simulate numerous innate human skills such as automatic programming, case-based reasoning, neural networks, decision-making, expert systems, fuzzy logic, natural language processing, pattern recognition and speech recognition etc. AI technologies bring more complex data-analysis features to existing applications. Business applications utilise the specific technologies mentioned earlier to try and make better sense of potentially enormous variability (for example, unknown patterns/relationships in sales data, customer buying habits, and so on).
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However, within the corporate world, AI is widely used for complex problem-solving and decision-support techniques (neural networks and expert systems) in real-time business applications. The business applicability of AI techniques is spread across functions ranging from finance management to forecasting and production. The proven success of Artificial Neural Networks (ANN) and expert systems has helped AI gain widespread adoption in enterprise business applications. In some instances, such as fraud detection, the use of AI has already become the most preferred method.
In addition, neural networks have become a well-established technique for pattern recognition, particularly of images, data streams and complex data sources and, in turn, have emerged as a modeling backbone for a majority of data-mining tools available in the market. Some of the key business applications of AI/ANN include fraud detection, cross-selling, customer relationship management analytics, demand prediction, failure prediction, and non-linear control. Numerous software vendors in the market, such as Ward Systems Group and Neural ware, provide off-the-shelf tools for ANN.
However, a majority of the enterprises adopt horizontal or vertical solutions that embed neural networks such as insurance risk assessment or fraud-detection tools from HNC, or data-mining tools that include neural networks (for instance, from SAS, IBM and SPSS) as one of the modeling options. Additionally, autonomic computing concepts derived from AI technologies, which facilitate self-healing systems, have generated a lot of hype in the recent past. Autonomic systems auto-configure for changing conditions, continuously monitor the constituent system parts and fine- tune workflow to achieve pre-determined system goals.
Of late, AI has found a home in financial services and is recognised as a valuable addition to numerous business applications. Sophisticated technologies encompassing neural networks and business rules along with AI-based techniques are yielding positive results in transaction-oriented scenarios for financial services. AI has been widely adopted in such areas of risk management, compliance, and securities trading and monitoring, with an extension into customer relationship management (CRM).
Tangible benefits of AI adoption include reduced risk of fraud, increased revenues from existing customers due to newer opportunities, avoidance of fines stemming from non-compliance and averted securities trade excetions that could result in delayed settlement, if not detected. It is also being widely adopted in diagnostics and testing. Diagnostic systems are used to examine networks, aircraft engines, manufacturng machinery and other types of equipment, energy pipelines, hazardous materials, and so on.
Similarly, in the transportation industry, which is also fast catching up with the trend, AI is being used for traffic management systems, aircraft maintenance operations, airport gate scheduling, railroad planning and forecasting assignments. In the fiercely competitive and dynamic market scenario, decision-making has become fairly complex and latency is inherent in many processes. In addition, the amount of data to be analysed has increased substantially. AI technologies help enterprises reduce latency in making business decisions, minimise fraud and enhance revenue opportunities.
Enterprises that utilise AI-enhanced applications are expected to become more diverse, as the needs for the ability to analyse data across multiple variables, fraud detection and customer relationship management emerge as key business drivers to gain competitive advantage. Though the financial services sector is an evangelist in the implementation of AI technologies, other sectors such as manufacturing, transportation, logistics, and healthcare have fast caught on. According to a report from a business research company, the total worldwide AI market was $11. 9 billion in 2002 and is expected to grow at an average annual growth rate of 12. per cent to reach $21. 2 billion by 2007. Isn’t this testimony to the fact that AI for business applications is truly back with a bang! Artificial Intelligence Plain and simple. Take me back to the main artificial intelligence page please! What is Artificial Intelligence? Definition… Artificial Intelligence is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way.
A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behaviour appears. AI is generally associated with Computer Science, but it has many important links with other fields such as Maths, Psychology, Cognition, Biology and Philosophy, among many others. Our ability to combine knowledge from all these fields will ultimately benefit our progress in the quest of creating an intelligent artificial being. Further details… Field of Artificial Intelligence Why Artificial Intelligence? Motivation…
Computers are fundamentally well suited to performing mechanical computations, using fixed programmed rules. This allows artificial machines to perform simple monotonous tasks efficiently and reliably, which humans are ill-suited to. For more complex problems, things get more difficult… Unlike humans, computers have trouble understanding specific situations, and adapting to new situations. Artificial Intelligence aims to improve machine behaviour in tackling such complex tasks. Together with this, much of AI research is allowing us to understand our intelligent behaviour.
Humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial Intelligence can help us understand this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities. Tell me more! Artificial Intelligence Discussion When will Computers become truly Intelligent? Limitations… To date, all the traits of human intelligence have not been captured and applied together to spawn an intelligent artificial creature.
Currently, Artificial Intelligence rather seems to focus on lucrative domain specific applications, which do not necessarily require the full extent of AI capabilities. This limit of machine intelligence is known to researchers as narrow intelligence. There is little doubt among the community that artificial machines will be capable of intelligent thought in the near future. It’s just a question of what and when… The machines may be pure silicon, quantum computers or hybrid combinations of manufactured components and neural tissue. As for the date, expect great things to happen within this century!
What about… The Future of Artificial Intelligence How does Artificial Intelligence work? Technology… There are many different approaches to Artificial Intelligence, none of which are either completely right or wrong. Some are obviously more suited than others in some cases, but any working alternative can be defended. Over the years, trends have emerged based on the state of mind of influencial researchers, funding opportunities as well as available computer hardware. Over the past five decades, AI research has mostly been focusing on solving specific problems.
Numerous solutions have been devised and improved to do so efficiently and reliably. This explains why the field of Artificial Intelligence is split into many branches, ranging from Pattern Recognition to Artificial Life, including Evolutionary Computation and Planning. Show me! Artificial Intelligence Knowledge Who uses Artificial Intelligence? Applications… The potential applications of Artificial Intelligence are abundant. They stretch from the military for autonomous control and target identification, to the entertainment industry for computer games and robotic pets.
Lets also not forget big establishments dealing with huge amounts of information such as hospitals, banks and insurances, who can use AI to predict customer behaviour and detect trends. As you may expect, the business of Artificial Intelligence is becoming one of the major driving forces for research. With an ever growing market to satisfy, there’s plenty of room for more personel. So if you know what you’re doing, there’s plenty of money to be made from interested big companies! More still… Artificial Intelligence Applications Where can I find out about Artificial Intelligence?
Information… If you’re interested in AI, you’ve come to the right place! The Artificial Intelligence Depot is a site purely dedicated to AI bringing you daily news and regular features, providing you with community interaction as well as an ever growing database of knowledge resources. Whether you are a complete beginner, experienced programmer, computer games hacker or academic researcher, you will find something to suit your needs here. Once you’ve finished reading this page, the first thing you should do is visit the Artificial Intelligence Depot’s main page.
This deals with the daily Artificial Intelligence business, and contains links to useful resources. From now onwards you will always be taken to this main page, but you can always come back to this introduction page via the menu. If you need a quick guide to the site before you start, refer to our introduction for the AI Depot. That said, you shoult not limit yourself to online information. Getting a good book on the subject is probably one of the smartest moves to make if you are really serious about Artificial Intelligence.
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.
Some applications, in fact only show a remote emergent possibility of intelligence, a fact covered up by the use of numerous marketing buzz-words. Admittedly, A. I. is a hot topic that has increasingly picked up popularity and interest, and is becoming an umbrella of coolness targeted by advertising. None the less, the many branches and applications of A. I. remain fascinating, and we’ll try to bring some of them together to analyse their common characteristics. The first step towards this goal will be taken in the next page of this essay by discussing the importance of the representation of a problem.
Then we’ll consider the two major approaches to solving problems, namely the classical approach and the statistical approach. The following section will show how these concepts are split into various branches, of which we’ll describe a few. With the theoretical background covered, we’ll finally mention some practical applications of artificial intelligence Representation Previous: Overview Definition: The Field of Artificial Intelligence Alex J. Champandard Most problems in Artificial Intelligence are solved by algorithms, which need to be given information in a way they understand.
This information represents the problem, whatever it may be: a simple virtual world, a collection of pictures, a list of paths between cities. Much of the performance of the algorithm, and therefore the quality of the results, depends directly on the representation chosen and how this information is passed to it. A lot of researchers have worked on such issues, and are still thinking about new potential solutions. Symbolic A symbolic representation means that each item or concept of the problem is represented as a symbol. For example, if my problem contained an animal, he might be represented as the symbol cat, snake or hippopotamus.
You can also declare simple truths about these symbols, using first order predicate logic (i. e. statements that give information about symbols). mammal(cat). reptile(snake). legs(cat,4). legs(snake,0). The statements mentioned are assumed to be true, and using a closed world assumption (i. e. the problem is finite and we know about it all), the statements not mentioned are assumed to be false. The main advantage of this scheme is simplicity, since the algorithms work with straightforward symbols, of which there aren’t usually very many. Also note that the numbers in the predicates are symbols too.
In fact it can be surprisingly tedious to solve simple arithmetic problems with such statements. This type of programming is known as declarative programming, and specifically Prolog in the examples above. More complex predicates exist, namely statements that give information about statements. These are called high-order functions, and can be defined as part applications of other functions. It all gets very tricky, although it generally remains very effortless: very complex programs can take about 10 lines in languages such as Haskell. This is known as functional programming.
In the early days, A. I. started with such symbolic representations of problems. Languages such as LISP were created specially for this. At the time, the precious memory meant that such simple representations were needed for programs to work at all. Today, research in this area continues, notably in attempts to merge functional programming and declarative programming in an elegant fashion. This is something to lookout for, as it will hopefully revive interest in this field, and increase its potential. Fuzzy By definition, fuzzy representations are not very clear!
Unlike with symbols, the information is vague and imprecise. This usually means that probabilities are used to express truths, and simple operators are needed to combine them: “80% of the people who read this page and the introduction will read the next page” The field dealing with such statements is called Fuzzy Logic. It is understandably slightly more challenging to extract meaningful information from such statements, and more elaborate techniques are required (see Dempster-Shafer Theory and Bayesian Networks). Another type of fuzzy information is stored in the famous Neural Networks.
This is known as a neuro-fuzziness. The information inside an artificial neural network is usually imprecise, due to the weighted connection between neurons (called synapses). Fuzzy representations have increased in popularity, due to the increased capabilities of computers: more processing power is usually required to create such rules, and interpreting them generally also requires a bit more time. The preferred languages for this type of representation are usually procedural like C, C++ or Pascal. Implicit Implicit representations do not store the information directly.
You need a simple algorithm to extract the information from the representation. This implies that very big chunks of information can be stored in a very compact fashion. Think of humans for example, given the DNA or a single person, you can build an entire body: a complex process called growth. In practice, you can for example build a full-size neural network based on simple rules. The process is important enough to warrant its own new terminology, but the following definitions can also be used in other cases. The encoded representation of the information is called genotype, and once decoded this forms a phenotype.
The structure (i. e. not the information, just the outline) of the representation is called the genome – like for human DNA, and the structure of the decoded information is called phenome. Ways of decoding the information include cellular automata (a very simple process is applied repeatedly to a short binary string – lots of 0’s and 1’s), grammar based (a set of rules is applied to existing symbols) or procedural (where the decoding method is also partly encoded in the representation! ). Generally, the algorithms do not know what the information is when they create the representation: i. e. he genotypes are created and optimised blindly, the phenotypes are only used to evaluate the quality of the solution. This is partly due to the fact that mappings from complex phenotypes to implicit genotypes are fairly rare. As you can understand, it takes a bit of effort to extract the information from the representation, which is why this technique has started to grow in popularity: the processing power is available for such costly operations. It also takes time to optimise the genotype, using techniques such as genetic algorithms 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) Approaches Previous: Representation Definition: The Field of Artificial Intelligence Alex J. Champandard Classical The classical approach to A. I. is deductive. Given a set of base rules, deduce what combination produces the desired result. In practice, this is usually done by a search of the possible combinations.
A particular kind of search called depth-first search, involves a minimal use of the memory. This was one of the initial motivations for such an approach. Typically, a symbolic representation is used in this case. As such the popular languages for such an approach are LISP and Prolog. The search mechanism is directly built into the core of Prolog. With a bit more hassle, the good old “C” can also be used to implement these searches, and is often used in logic games such as chess. A major problem with this approach resides in the initial creation of the rules.
This can be extremely tedious and time consuming, and many cases need to be taken into account explicitly. Special care is also needed to assure that the search is possible, and can be accomplished in a finite amount of time. Statistical The statistical approach to A. I. is based on inductive reasoning. Given a large set of data for the problem, we try an extract patterns from it, and induce the trend. This has become possible thanks to machine learning, which allows these patterns to be abstracted and generalised. Generally, a fuzzy representation is used to express the rules extracted from the data.
This is more practical since the large data sets may often have noise, and exact data would be impossible to determine. Probabilities allow this uncertainty to be expressed. This approach is more intense on the memory than the previous approach, since the large set of data needs to be accessed in an efficient fashion, requiring it to be in memory. Efficient, lower level languages are therefore preferred, such as C and possibly even C++. Theoretical Many leading researchers have done a fair amount of theoretical research. This usually involves the philosophical aspect of A. I.
Numerous books have been published on the theory of life, emotions, emergent systems (rule based groups of agents with a seemingly intelligent behaviour). This type of work is difficult to experiment with, but key concepts can thereby be picked out and researched further in isolation 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) Branches
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 modelled as a set of variables, which can be assigned particular values. Different types of constraints are set-up on these variables (equality, numerical constraints), in order to specify the requirements for the problem. A search is then performed on the variables, in order to find the potential solutions. There are many nifty tricks involved to partly resolve constraints in order to guide the search more efficiently (this is called a heuristic search). The problems solved can also be a combinatorial optimisation, where a particular solution has a better value than another, and the best needs to be found.
The class of problems usually solved is NP-complete, where the complexity increases exponentially as the problem size increases linearly. Function Optimisation This is the task of finding the optimal set of parameters of a function. There are many simple ways of doing this, including hill-climbing. Metaphorically, hill-climbing looks around the current position for a higher position, and moves to it. If there is no higher position, then the top is reached! This approach is fairly naive, and can lead to finding sub-optimal solutions (called local maxima).
Genetic Algorithms also provide optimisation capabilities, by mimicking the process of evolution (according to Darwin’s theory) and the survival of the fittest. The best solutions are mated together to create better offspring solutions. This approach has less problems with local maxima, but there are still no guarantees of finding the ideal solution. Planning Planning involves finding a sequence of actions that can lead from the current state, to the goal state. This is usually done in a hierarchical manner: overall plans are elaborated first, and the details are worked out later. This is a more efficient approach.
The major problem planning has to contend with is an imperfect world. With perfect environments, a simple search can be performed, and if a result is found, it will be possible in practice. Sometimes, however, the actions do not have the expected results, causing the plans not to work out. Machine Learning Machine learning is becoming increasingly popular, and equally important. People realise that it is theoretically much easier to get a machine to learn something from facts, rather than have to spend time teaching it explicitly. The quality of the learning algorithm is of course a major factor!
Neural Networks Artificial Neural Networks, often just called Neural Networks (NN), are modelled on the human brain. The internal structure of the network, composed of a small number of artificial neurons, implies that the information learnt is not perfect. There is, however, the advantage of being able to generalise, i. e. work with data that it didn’t come across during its training. How well it performs depends on how well it can generalise, which in turn depends on how well the network was designed and trained. As such, a lot of research is done on ways to assure good generalisation.
Inductive Programming Given only the results of a function (a limited amount of them), inductive programming tries to write the definition of the program that created those results. This is more or less successful depending on how many example results were given, and how complex the function is. Currently, some inductive programming algorithms can learn simple logic programs, even recursively defined. More complex programs will prove challenging to learn, as well as applying this process to real-life data rather than computer generated functions. 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
This is the process of extracting useful rules from very large sets of data. When trends are observed, their causes need to be identified, and a rule expressing their relationship needs to be established. In this field, the challenge is being able to process a lot of information very efficiently, and ignore the potential errors. Bayesian Networks Bayesian Networks models the relationship between variables. This is called conditional dependence: a the state of a variable may depend on many others. This can be represented as a graph, and there’s a clever algorithm to estimate the probability of unknown events given existing knowledge.
Admittedly, one common complaint against this approach relates to the design; it can be very tedious to model such networks. As such, 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 applications of A. I. are the driving force behind the research. There are many practical uses for such systems, and it is big business. You can expect a few million dollars/pounds income if you come up with something clever and create a nice product based on it. Funding is, therefore, less of a problem when the potential of the application is obvious. Pattern Recognition Pattern recognition involves determining the characteristics in specific samples and sorting them into classes; a process called classification.
This is usually done with Machine Learning techniques, allowing the system to adapt to the data given to it. It can be applied to detecting single words in speech, recognising voices, sorting scanned objects by type and filtering out unwanted pictures (among many others). In practice, a way of doing this is to represent the sample as a set of features (e. g. for a sound: pitch, volume, timbre, smoothness). A training set is then created: i. e. a set of samples where the result is known (e. g. for facial recognition: Fred has green eyes and brown hair, Henry has blue eyes and blond hair).
The learning mechanism can then learn to associate the features with the known types of sound or image. Depending on the representation, more or less samples are required. With symbolic representations, small numbers of examples are usually required, whereas for fuzzy learning (in neural networks for example) larger training sets are needed. Robotics The main aspect of robotics today is mobility. For example how can a mechanical device be controlled to move its body parts in a planned fashion, or navigate around a room? This can be done by learning the task in a virtual simulation, and then applying it to the real robot.
If specific conditions of training are respected, the problem has a high probability of working in real life, but this is no guarantee. In practice when moving robotic arms, the arm has a few movement possibilities: the shoulder allows rotations according to two axis, and the elbow also allow two basic rotations. Each of these possibilities is called one degree of freedom. Usually, one controller is assigned to provide movement for one DOF. The task at hand is to learn the optimal combination of controllers, where they can successfully cooperate to perform a given task. Natural Language Processing
This is the task of extracting meaning from text, also known as computational linguistics. Once this meaning is processed, it can also potentially be interpreted and understood, or at least the basics! One of the first approach was symbolic, assigning semantic meaning to each word (verb, noun, adjective). The basic structure of valid sentences would have to be defined manually, and a search would be performed to match the template with the current sentence. A lot of time needed to be spent resolving ambiguous sentences, and getting the person and tenses of the verbs to match.
If the programmer spends enough time creating the sentence templates, the results would be fairly encouraging. But this monotonous task needs to be repeated for new sentence constructs and new languages all together. A very recent approach is to use statistical analysis of the text. In essence, large parts of books are processed and learning algorithms attempt to extract the rules and patterns. This requires a smarter approach, taking more time to design, but it results in a more flexible program. Artificial Life This is a very popular aspect of Artificial Intelligence, which involves modelling and mimicking living systems.
This includes ant hills, wasp nests, larger forests, towns and cities. To date, very complex and interesting systems have been created by a multitude of very simple entities. For example many ants programmed by very small programs would potentially create an entire system with signs of emergent intelligence. We have yet to define systems that are based on complex individuals, capable of learning. This is a task that has captured the mind of many researchers and dreamers alike 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) Summary Previous: Applications Definition: The Field of Artificial Intelligence Alex J. Champandard In this essay, we first showed how important the representation is. It is one of the major aspects of Artificial Intelligence, and should not be neglected.
There are often many ways of representing a problem, and the most appropriate should be chosen only after carefully considering alternatives. As demonstrated, the change in the representations used over the decades also reflects the state of mind of the researchers, moving from certainty, to uncertainty, then to implicit methods. We also discussed the approaches of A. I. , showing how the field has slowly moved away from the classical approach. The information was modelled perfectly, and the programmer had to define rigid sets of rules and predicates to allow the computer to perform its task.
A lot of focus now is placed on statistical approaches, where the computers learn from large amounts of data. Although this requires better designed algorithms, and smarter solutions, this aspect of machine learning provides more flexibility in the solutions and allows the programmer not to have to deal with the tedious part of teaching the computer directly. The third section listed some of the major fields of Artificial Intelligence, and tried to class them into categories. A fair deal of current research is going into combining all these models in order to tackle larger problems.
Ultimate intelligence (whatever that may be 😉 is more of a long term objective than anything else, but in the short term many fields of AI are benefiting from using techniques in other fields. Thus, most domain specific applications can be tackled very successfully with such techniques, if a knowledge engineer spends enough time on the problem. In the preceding page, we considered some of the major applications of AI. This is where much of the money is, and if you’re interested in that sort of thing, that’s where your efforts should go!
There are a lot of tools and applications today just crying out for a bit of ‘cleverness’ that AI could provide. The field Artificial Intelligence is wide, and only a small part of it was mentioned here. Many new branches are been looked into as we speak. They all, however, involve one key factor: getting computers to perform tasks that humans don’t especially want! These algorithms do show some clever abilities (learning, abstraction, generalisation, deliberative planning), but most are restricted to the sand-box they were created in.
This is not really what non computer-scientist people expect when they think about artificial intelligence, and much work will have to be done to create entities capable of performing outside of their natal environment 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)