Category Archives: Artificial Intelligence

The phrase Artificial Intelligence was first coined by John McCarthy four decades ago. One representative definition is pivoted around comparing intelligent machines with human beings. Another definition is concerned with the performance of machines which historically have been judged to lie within the domain of intelligence.

Yet none of these definitions have been universally accepted, probably because the reference of the word intelligence which is an immeasurable quantity. A better definition of artificial intelligence, and probably the most accurate would be: An artificial system capable of planning and executing the right task at the right time rationally. Or far simpler: a machine that can act rationally.

With all this a common questions arises:

Does rational thinking and acting include all characteristics of an intelligent system?

If so, how does it represent behavioral intelligence such as learning, perception and planning?

If we think a little, a system capable of reasoning would be a successful planner. Moreover, a system can act rationally only after acquiring knowledge from the real world. So the property of perception is a perquisite of building up knowledge from the real world.

With all this we may conclude that a machine that lacks of perception cannot learn, therefore cannot acquire knowledge.

To understand the practical meaning or artificial intelligence we must illustrate some common problems. All problems that are dealt with artificial intelligence solutions use the common term state.

A state represents the status of a solution at a given step during the problem solving procedure. The solution of a problem is a collection of states. The problem solving procedure or algorithm applies an operator to a state to get the next state. Then, it applies another operator to the resulting state to derive a new state.

The process of applying operators to each state is continued until a desired goal is achieved.

Example : Consider a 4-puzzle problem, where in a 4-cell board there are 3 cells filled with digits and 1 blank cell. The initial state of the game represents a particular orientation of the digits in the cells and the final state to be achieved is another orientation supplied to the game player. The problem of the game is to reach from the given initial state to the goal (final) state, if possible, with a minimum of moves. Let the initial and the final state be as shown in figures 1(a) and (b) respectively.

We now define two operations, blank-up (BU) / blank-down (BD) and blank-left (BL) / blank-right (BR), and the state-space (tree) for the problem is presented below using these operators. The algorithm for the above kind of problems is straightforward. It consists of three steps, described by steps 1, 2(a) and 2(b) below.

Algorithm for solving state-space problems

Begin

It is clear that the main trick in solving problems by the state-space approach is to determine the set of operators and to use it at appropriate states of the problem.

Researchers in artificial intelligence have segregated the AI problems from the non-AI problems. Generally, problems, for which straightforward mathematical / logical algorithms are not readily available and which can be solved by intuitive approach only, are called AI problems.

The 4-puzzle problem, for instance, is an ideal AI Problem. There is no formal algorithm for its realization, i.e., given a starting and a goal state, one cannot say prior to execution of the tasks the sequence of steps required to get the goal from the starting state. Such problems are called the ideal AI problems.

The well known water-jug problem, the Traveling Salesperson Problem (TSP), and the n-Queen problem are typical examples of the classical AI problems.

Among the non-classical AI problems, the diagnosis problems and the pattern classification problem need special mention. For solving an AI problem, one may employ both artificial intelligence and non-AI algorithms. An obvious question is: what is an AI algorithm?

Formally speaking, an artificial intelligence algorithm generally means a non-conventional intuitive approach for problem solving. The key to artificial intelligence approach is intelligent search and matching. In an intelligent search problem / sub-problem, given a goal (or starting) state, one has to reach that state from one or more known starting (or goal) states.

For example, consider the 4-puzzle problem, where the goal state is known and one has to identify the moves for reaching the goal from a pre-defined starting state. Now, the less number of states one generates for reaching the goal, the better. That is the AI algorithm.

The question that then naturally arises is: how to control the generation of states?

This can be achieved by suitably designing control strategies, which would filter a few states only from a large number of legal states that could be generated from a given starting / intermediate state.

As an example, consider the problem of proving a trigonometric identity that children are used to doing during their schooldays. What would they do at the beginning? They would start with one side of the identity, and attempt to apply a number of formula there to find the possible resulting derivations.

But they wont really apply all the formula there. Rather, they identify the right candidate formula that fits there, such that the other side of the identity that seems to be closer in some sense (outlook). Ultimately, when the decision regarding the selection of the formula is over, they apply it to one side (say the L.H.S) of the identity and derive the new state.

Therefore, they continue the process and go on generating new intermediate states until the R.H.S (goal) is reached. But do they always select the right candidate formula at a given state? From our experience, we know the answer is not always. But what would we do if we find that after generation of a few states, the resulting expression seems to be far away from the R.H.S of the identity.

Perhaps we would prefer to move to some old state, which is more promising, i.e., closer to the R.H.S of the identity. The above line of thinking has been realized in many intelligent search problems of AI.

Some of these well-known search algorithms are:

a) Generate and Test Approach : This approach concerns the generation of the state-space from a known starting state (root) of the problem and continues expanding the reasoning space until the goal node or the terminal state is reached.

In fact after generating each and every state, the generated node is compared with the known goal state. When the goal is found, the algorithm terminates. In case there exist multiple paths leading to the goal, then the path having the smallest distance from the root is preferred. The basic strategy used in this search is only generation of states and their testing for goals but it does not allow filtering of states.

(b) Hill Climbing Approach : Under this approach, one has to first generate a starting state and measure the total cost for reaching the goal from the given starting state. Let this cost be f. While f = a predefined utility value and the goal is not reached, new nodes are generated as children of the current node. However, in case all neighborhood nodes (states) yield an identical value of f and the goal is not included in the set of these nodes, the search algorithm is trapped at a hillock or local extreme.

One way to overcome this problem is to select randomly a new starting state and then continue the above search process. While proving trigonometric identities, we often use Hill Climbing, perhaps unknowingly.

(c) Heuristic Search: Classically heuristics means rule of thumb. In heuristic search, we generally use one or more heuristic functions to determine the better candidate states among a set of legal states that could be generated from a known state.

The heuristic function, in other words, measures the fitness of the candidate states. The better the selection of the states, the fewer will be the number of intermediate states for reaching the goal.

However, the most difficult task in heuristic search problems is the selection of the heuristic functions. One has to select them intuitively, so that in most cases hopefully it would be able to prune the search space correctly.

(d) Means and Ends Analysis: This method of search attempts to reduce the gap between the current state and the goal state. One simple way to explore this method is to measure the distance between the current state and the goal, and then apply an operator to the current state, so that the distance between the resulting state and the goal is reduced. In many mathematical theorem- proving processes, we use Means and Ends Analysis.

The subject of artificial intelligence spans a wide horizon. It deals with various kinds of knowledge representation schemes, different techniques of intelligent search, various methods for resolving uncertainty of data and knowledge, different schemes for automated machine learning and many others.

Among the application areas of AI, we have Expert systems, Game-playing, and Theorem-proving, Natural language processing, Image recognition, Robotics and many others. The subject of artificial intelligence has been enriched with a wide discipline of knowledge from Philosophy, Psychology, Cognitive Science, Computer Science, Mathematics and Engineering. Thus has the figure shows, they have been referred to as the parent disciplines of AI. An at-a-glance look at fig. also reveals the subject area of AI and its application areas. Fig.: AI, its parent disciplines and application areas.

The subject of artificial intelligence was originated with game-playing and theorem-proving programs and was gradually enriched with theories from a number of parent disciplines. As a young discipline of science, the significance of the topics covered under the subject changes considerably with time. At present, the topics which we find significant and worthwhile to understand the subject are outlined below: FigA: Pronunciation learning of a child from his mother.

Learning Systems: Among the subject areas covered under artificial intelligence, learning systems needs special mention. The concept of learning is illustrated here with reference to a natural problem of learning of pronunciation by a child from his mother (vide figA). The hearing system of the child receives the pronunciation of the character A and the voice system attempts to imitate it. The difference of the mothers and the childs pronunciation, hereafter called the error signal, is received by the childs learning system auditory nerve, and an actuation signal is generated by the learning system through a motor nerve for adjustment of the pronunciation of the child. The adaptation of the childs voice system is continued until the amplitude of the error signal is insignificantly low. Each time the voice system passes through an adaptation cycle, the resulting tongue position of the child for speaking A is saved by the learning process. The learning problem discussed above is an example of the well-known parametric learning, where the adaptive learning process adjusts the parameters of the childs voice system autonomously to keep its response close enough to the sample training pattern. The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. Besides this type, the other common learning methods, which we do unknowingly, are inductive and analogy-based learning. In inductive learning, the learner makes generalizations from examples. For instance, noting that cuckoo flies, parrot flies and sparrow flies, the learner generalizes that birds fly. On the other hand, in analogy-based learning, the learner, for example, learns the motion of electrons in an atom analogously from his knowledge of planetary motion in solar systems.

Knowledge Representation and Reasoning: In a reasoning problem, one has to reach a pre-defined goal state from one or more given initial states. So, the lesser the number of transitions for reaching the goal state, the higher the efficiency of the reasoning system. Increasing the efficiency of a reasoning system thus requires minimization of intermediate states, which indirectly calls for an organized and complete knowledge base. A complete and organized storehouse of knowledge needs minimum search to identify the appropriate knowledge at a given problem state and thus yields the right next state on the leading edge of the problem-solving process. Organization of knowledge, therefore, is of paramount importance in knowledge engineering. A variety of knowledge representation techniques are in use in Artificial Intelligence. Production rules, semantic nets, frames, filler and slots, and predicate logic are only a few to mention. The selection of a particular type of representational scheme of knowledge depends both on the nature of applications and the choice of users.

Planning: Another significant area of artificial intelligence is planning. The problems of reasoning and planning share many common issues, but have a basic difference that originates from their definitions. The reasoning problem is mainly concerned with the testing of the satisfiability of a goal from a given set of data and knowledge. The planning problem, on the other hand, deals with the determination of the methodology by which a successful goal can be achieved from the known initial states. Automated planning finds extensive applications in robotics and navigational problems, some of which will be discussed shortly.

Knowledge Acquisition: Acquisition (Elicitation) of knowledge is equally hard for machines as it is for human beings. It includes generation of new pieces of knowledge from given knowledge base, setting dynamic data structures for existing knowledge, learning knowledge from the environment and refinement of knowledge. Automated acquisition of knowledge by machine learning approach is an active area of current research in Artificial Intelligence. Intelligent Search: Search problems, which we generally encounter in Computer Science, are of a deterministic nature, i.e., the order of visiting the elements of the search space is known. For example, in depth first and breadth first search algorithms, one knows the sequence of visiting the nodes in a tree. However, search problems, which we will come across in AI, are non-deterministic and the order of visiting the elements in the search space is completely dependent on data sets. The diversity of the intelligent search algorithms will be discussed in detail later.

Logic Programming: For more than a century, mathematicians and logicians were used to designing various tools to represent logical statements by symbolic operators. One outgrowth of such attempts is propositional logic, which deals with a set of binary statements (propositions) connected by Boolean operators. The logic of propositions, which was gradually enriched to handle more complex situations of the real world, is called predicate logic. One classical variety of predicate logic-based programs is Logic Program. PROLOG, which is an abbreviation for PROgramming in LOGic, is a typical language that supports logic programs. Logic Programming has recently been identified as one of the prime area of research in AI. The ultimate aim of this research is to extend the PROLOG compiler to handle spatio-temporal models and support a parallel programming environment. Building architecture for PROLOG machines was a hot topic of the last decade.

Soft Computing: Soft computing, according to Prof. Zadeh, is an emerging approach to computing, which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision . It, in general, is a collection of computing tools and techniques, shared by closely related disciplines that include fuzzy logic, artificial neural nets, genetic algorithms, belief calculus, and some aspects of machine learning like inductive logic programming. These tools are used independently as well as jointly depending on the type of the domain of applications.

Management of Imprecision and Uncertainty: Data and knowledgebases in many typical AI problems, such as reasoning and planning, are often contaminated with various forms of incompleteness. The incompleteness of data, hereafter called imprecision, generally appears in the database for i) lack of appropriate data, and ii) poor authenticity level of the sources. The incompleteness of knowledge, often referred to as uncertainty, originates in the knowledge base due to lack of certainty of the pieces of knowledge Reasoning in the presence of imprecision of data and uncertainty of knowledge is a complex problem. Various tools and techniques have been devised for reasoning under incomplete data and knowledge. Some of these techniques employ i) stochastic ii) fuzzy and iii) belief network models. In a stochastic reasoning model, the system can have transition from one given state to a number of states, such that the sum of the probability of transition to the next states from the given state is strictly unity. In a fuzzy reasoning system, on the other hand, the sum of the membership value of transition from the given state to the next state may be greater than or equal to one. The belief network model updates the stochastic / fuzzy belief assigned to the facts embedded in the network until a condition of equilibrium is reached, following which there would be no more change in beliefs. Recently, fuzzy tools and techniques have been applied in a specialized belief network, called a fuzzy Petri net, for handling both imprecision of data and uncertainty of knowledge by a unified approach.

Almost every branch of science and engineering currently shares the tools and techniques available in the domain of artificial intelligence. However, for the sake of the convenience of the readers, we mention here a few typical applications, where AI plays a significant and decisive role in engineering automation. Expert Systems: In this example, we illustrate the reasoning process involved in an expert system for a weather forecasting problem with special emphasis to its architecture. An expert system consists of a knowledge base, database and an inference engine for interpreting the database using the knowledge supplied in the knowledge base. The reasoning process of a typical illustrative expert system is described in Fig. PR 1 in Fig. represents i-th production rule. The inference engine attempts to match the antecedent clauses (IF parts) of the rules with the data stored in the database. When all the antecedent clauses of a rule are available in the database, the rule is fired, resulting in new inferences. The resulting inferences are added to the database for activating subsequent firing of other rules. In order to keep limited data in the database, a few rules that contain an explicit consequent (THEN) clause to delete specific data from the databases are employed in the knowledge base. On firing of such rules, the unwanted data clauses as suggested by the rule are deleted from the database. Here PR1 fires as both of its antecedent clauses are present in the database. On firing of PR1, the consequent clause it-will-rain will be added to the database for subsequent firing of PR2. Fig. Illustrative architecture of an expert system.

Image Understanding and Computer Vision: A digital image can be regarded as a two-dimensional array of pixels containing gray levels corresponding to the intensity of the reflected illumination received by a video camera. For interpretation of a scene, its image should be passed through three basic processes: low, medium and high level vision . Fig.: Basic steps in scene interpretation.

The importance of low level vision is to pre-process the image by filtering from noise. The medium level vision system deals with enhancement of details and segmentation (i.e., partitioning the image into objects of interest ). The high level vision system includes three steps: recognition of the objects from the segmented image, labeling of the image and interpretation of the scene. Most of the AI tools and techniques are required in high level vision systems. Recognition of objects from its image can be carried out through a process of pattern classification, which at present is realized by supervised learning algorithms. The interpretation process, on the other hand, requires knowledge-based computation.

Speech and Natural Language Understanding: Understanding of speech and natural languages is basically two class ical problems. In speech analysis, the main probl em is to separate the syllables of a spoken word and determine features like ampli tude, and fundamental and harmonic frequencies of each syllable. The words then could be ident ified from the extracted features by pattern class ification techniques. Recently, artificial neural networks have been employed to class ify words from their features. The probl em of understanding natural languages like English, on the other hand, includes syntactic and semantic interpretation of the words in a sentence, and sentences in a paragraph. The syntactic steps are required to analyze the sentences by its grammar and are similar with the steps of compilation. The semantic analysis, which is performed following the syntactic analysis, determines the meaning of the sentences from the association of the words and that of a paragraph from the closeness of the sentences. A robot capable of understanding speech in a natural language will be of immense importance, for it could execute any task verbally communicated to it. The phonetic typewriter, which prints the words pronounced by a person, is another recent invention where speech understanding is employed in a commercial application.

Scheduling: In a scheduling problem, one has to plan the time schedule of a set of events to improve the time efficiency of the solution. For instance in a class-routine scheduling problem, the teachers are allocated to different classrooms at different time slots, and we want most classrooms to be occupied most of the time. In a flowshop scheduling problem, a set of jobs J1 and J2 (say) are to be allocated to a set of machines M1, M2 and M3. (say). We assume that each job requires some operations to be done on all these machines in a fixed order say, M1, M2 and M3. Now, what should be the schedule of the jobs (J1-J2) or (J2 -J1), so that the completion time of both the jobs, called the make-span, is minimized? Let the processing time of jobs J1 and J2 on machines M1, M2 and M3 be (5, 8, 7) and (8, 2, 3) respectively. The gantt charts in fig. (a) and (b) describe the make-spans for the schedule of jobs J1 J2 and J2 J1 respectively. It is clear from these figures that J1-J2 schedule requires less make-span and is thus preferred. Fig.: The Gantt charts for the flowshop scheduling problem with 2 jobs and 3 machines.

Flowshop scheduling problems are a NP complete problem and determination of optimal scheduling (for minimizing the make-span) thus requires an exponential order of time with respect to both machine-size and job-size. Finding a sub-optimal solution is thus preferred for such scheduling problems. Recently, artificial neural nets and genetic algorithms have been employed to solve this problem. The heuristic search, to be discussed shortly, has also been used for handling this problem.

Intelligent Control: In process control, the controller is designed from the known models of the process and the required control objective. When the dynamics of the plant is not completely known, the existing techniques for controller design no longer remain valid. Rule-based control is appropriate in such situations. In a rule-based control system, the controller is realized by a set of production rules intuitively set by an expert control engineer. The antecedent (premise) part of the rules in a rule-based system is searched against the dynamic response of the plant parameters. The rule whose antecedent part matches with the plant response is selected and fired. When more than one rule is firable, the controller resolves the conflict by a set of strategies. On the other hand, there exist situations when the antecedent part of no rules exactly matches with the plant responses. Such situations are handled with fuzzy logic, which is capable of matching the antecedent parts of rules partially/ approximately with the dynamic plant responses. Fuzzy control has been successfully used in many industrial plants. One typical application is the power control in a nuclear reactor. Besides design of the controller, the other issue in process control is to design a plant (process) estimator, which attempts to follow the response of the actual plant, when both the plant and the estimator are jointly excited by a common input signal. The fuzzy and artificial neural network-based learning techniques have recently been identified as new tools for plant estimation.

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Artificial Intelligence | Neuro AI

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Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, youll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Note: Parts of this course are featured in the Machine Learning Engineer Nanodegree and the Data Analyst Nanodegree programs. If you are interested in AI, be sure to check out those programs as well!

Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives. It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the state-of-the-art research fields from robotics, medical diagnosis, and quantum science. In this course youll learn the basics and applications of AI, including: machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.

See the Technology Requirements for using Udacity.

Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

This class is self paced. You can begin whenever you like and then follow your own pace. Its a good idea to set goals for yourself to make sure you stick with the course.

This class will always be available!

Take a look at the Class Summary, What Should I Know, and What Will I Learn sections above. If you want to know more, just enroll in the course and start exploring.

Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that youre confused, you can always go back and watch something that you skipped.

Its completely free! If youre feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.

Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.

Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc... But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.

Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.

Nanodegree is a trademark of Udacity 20112016 Udacity, Inc.

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Artificial Intelligence - The New York Times

A.I. Artificial Intelligence, also known as A.I., is a 2001 American science fiction drama film directed by Steven Spielberg. The screenplay by Spielberg was based on a screen story by Ian Watson and the 1969 short story Super-Toys Last All Summer Long by Brian Aldiss. The film was produced by Kathleen Kennedy, Spielberg and Bonnie Curtis. It stars Haley Joel Osment, Jude Law, Frances O'Connor, Brendan Gleeson and William Hurt. Set in a futuristic post-climate change society, A.I. tells the story of David (Osment), a childlike android uniquely programmed with the ability to love.

Development of A.I. originally began with producer-director Stanley Kubrick in the early 1970s. Kubrick hired a series of writers until the mid-1990s, including Brian Aldiss, Bob Shaw, Ian Watson, and Sara Maitland. The film languished in protracted development for years, partly because Kubrick felt computer-generated imagery was not advanced enough to create the David character, whom he believed no child actor would convincingly portray. In 1995, Kubrick handed A.I. to Spielberg, but the film did not gain momentum until Kubrick's death in 1999. Spielberg remained close to Watson's film treatment for the screenplay. The film was greeted with generally positive reviews from critics, grossed approximately $235 million, and was nominated for two Academy Awards at the 74th Academy Awards for Best Visual Effects and Best Original Score (by John Williams). The film is dedicated to Stanley Kubrick.

In the late 21st century, global warming has flooded the coastlines, wiping out coastal cities (such as Amsterdam, Venice, and New York City) and drastically reducing the human population. There is a new class of robots called Mecha, advanced humanoids capable of emulating thoughts and emotions.

David (Haley Joel Osment), a prototype model created by Cybertronics of New Jersey, is designed to resemble a human child and to display love for its human owners. They test their creation with one of their employees, Henry Swinton (Sam Robards), and his wife Monica (Frances O'Connor). The Swintons' son, Martin (Jake Thomas), had been placed in suspended animation until a cure could be found for his rare disease. Initially frightened of David, Monica eventually warms up enough to him to activate his imprinting protocol, which irreversibly causes David to have an enduring childlike love for her. He is also befriended by Teddy (Jack Angel), a robotic teddy bear, who takes it upon himself to care for David's well-being.

A cure is found for Martin and he is brought home; as he recovers, it becomes clear he does not want a sibling and soon makes moves to cause issues for David. First, he attempts to make Teddy choose whom he likes more. He then makes David promise to do something and in return Martin will tell Monica that he loves his new "brother", making her love him more. The promise David makes is to go to Monica in the middle of the night and cut off a lock of her hair. This upsets the parents, particularly Henry, who fears that the scissors are a weapon, and warns Monica that a robot programmed to love may also be able to hate.

At a pool party, one of Martin's friends unintentionally activates David's self-protection programming by poking him with a knife. David grabs Martin, apparently for protection, but they both fall into the pool. David sinks to the bottom while still clinging to Martin. Martin is saved from drowning, but Henry mistakes David's fear during the pool incident as hate for Martin.

Henry persuades Monica to return David to Cybertronics, where he will be destroyed. However, Monica cannot bring herself to do this and, instead, tearfully abandons David in the forest (with Teddy) to hide as an unregistered Mecha.

David is captured for an anti-Mecha "Flesh Fair", an event where obsolete and unlicensed Mecha are destroyed in front of cheering crowds. David is nearly killed, but the crowd is swayed by his fear (since Mecha do not plea for their lives) into believing he is human and he escapes with Gigolo Joe (Jude Law), a male prostitute Mecha on the run after being framed for murder.

The two set out to find the Blue Fairy, who David remembers from the story The Adventures of Pinocchio. He is convinced that the Blue Fairy will transform him into a human boy, allowing Monica to love him and take him home.

Joe and David make their way to Rouge City, a Las Vegas-esque resort. Information from a holographic answer engine called "Dr. Know" (Robin Williams) eventually leads them to the top of Rockefeller Center in the flooded ruins of Manhattan. There, David meets an identical copy of himself and, believing he is not special, becomes filled with anger and destroys the copy Mecha. David then meets his human creator, Professor Allen Hobby (William Hurt), who excitedly tells David that finding him was a test, which has demonstrated the reality of his love and desire. However, David learns that he is the namesake and image of Professor Hobby's deceased son and that many copies of David, along with female versions, are already being manufactured.

Sadly realizing that he is not unique, a disheartened David attempts to commit suicide by falling from a ledge into the ocean, but Joe rescues him with their stolen amphibicopter. David tells Joe he saw the Blue Fairy underwater and wants to go down to her. At that moment, Joe is captured by the authorities with the use of an electromagnet, but he sets the amphibicopter on submerge. David and Teddy take it to the fairy, which turns out to be a statue from a submerged attraction at Coney Island. Teddy and David become trapped when the Wonder Wheel falls on their vehicle. Believing the Blue Fairy to be real, David asks to be turned into a real boy, repeating his wish without an end, until the ocean freezes in another ice age and his internal power source drains away.

Two thousand years later, humans are extinct and Manhattan is buried under several hundred feet of glacial ice. The now highly advanced Mecha have evolved into an intelligent, silicon-based form. On their project to study humans believing it was the key to understanding the meaning of existence they find David and Teddy and discover they are original Mecha who knew living humans, making the pair very special and unique.

David is revived and walks to the frozen Blue Fairy statue, which cracks and collapses as he touches it. Having downloaded and comprehended his memories, the advanced Mecha use these to reconstruct the Swinton home and explain to David via an interactive image of the Blue Fairy (Meryl Streep) that it is impossible to make him human. However, at David's insistence, they recreate Monica from DNA in the lock of her hair, which Teddy had saved. One of the Mecha warns David that the clone can live for only a single day and that the process cannot be repeated. The next morning, David is reunited with Monica and spends the happiest day of his life with her and Teddy. Monica tells David that she loves him and has always loved him as she drifts to sleep for the last time. David lies down next to her, closes his eyes and goes "to that place where dreams are born." Teddy climbs onto the bed and watches as David and Monica lie peacefully together.

Kubrick began development on an adaptation of Super-Toys Last All Summer Long in the early 1970s, hiring the short story's author, Brian Aldiss, to write a film treatment. In 1985, Kubrick brought longtime friend Steven Spielberg on board to produce the film,[5] along with Jan Harlan. Warner Bros. agreed to co-finance A.I. and cover distribution duties.[6] The
film labored in development hell, and Aldiss was fired by Kubrick over creative differences in 1989.[7]Bob Shaw served as writer very briefly, leaving after six weeks because of Kubrick's demanding work schedule, and Ian Watson was hired as the new writer in March 1990. Aldiss later remarked, "Not only did the bastard fire me, he hired my enemy [Watson] instead." Kubrick handed Watson The Adventures of Pinocchio for inspiration, calling A.I. "a picaresque robot version of Pinocchio".[6][8]

Three weeks later Watson gave Kubrick his first story treatment, and concluded his work on A.I. in May 1991 with another treatment, at 90 pages. Gigolo Joe was originally conceived as a GI Mecha, but Watson suggested changing him to a male prostitute. Kubrick joked, "I guess we lost the kiddie market."[6] In the meantime, Kubrick dropped A.I. to work on a film adaptation of Wartime Lies, feeling computer animation was not advanced enough to create the David character. However, after the release of Spielberg's Jurassic Park (with its innovative use of computer-generated imagery), it was announced in November 1993 that production would begin in 1994.[9]Dennis Muren and Ned Gorman, who worked on Jurassic Park, became visual effects supervisors,[7] but Kubrick was displeased with their previsualization, and with the expense of hiring Industrial Light & Magic.[10]

Stanley [Kubrick] showed Steven [Spielberg] 650 drawings which he had, and the script and the story, everything. Stanley said, "Look, why don't you direct it and I'll produce it." Steven was almost in shock.

In early 1994, the film was in pre-production with Christopher "Fangorn" Baker as concept artist, and Sara Maitland assisting on the story, which gave it "a feminist fairy-tale focus".[6] Maitland said that Kubrick never referred to the film as A.I., but as Pinocchio.[10]Chris Cunningham became the new visual effects supervisor. Some of his unproduced work for A.I. can be seen on the DVD, The Work of Director Chris Cunningham.[12] Aside from considering computer animation, Kubrick also had Joseph Mazzello do a screen test for the lead role.[10] Cunningham helped assemble a series of "little robot-type humans" for the David character. "We tried to construct a little boy with a movable rubber face to see whether we could make it look appealing," producer Jan Harlan reflected. "But it was a total failure, it looked awful." Hans Moravec was brought in as a technical consultant.[10] Meanwhile, Kubrick and Harlan thought A.I. would be closer to Steven Spielberg's sensibilities as director.[13][14] Kubrick handed the position to Spielberg in 1995, but Spielberg chose to direct other projects, and convinced Kubrick to remain as director.[11][15] The film was put on hold due to Kubrick's commitment to Eyes Wide Shut (1999).[16] After the filmmaker's death in March 1999, Harlan and Christiane Kubrick approached Spielberg to take over the director's position.[17][18] By November 1999, Spielberg was writing the screenplay based on Watson's 90-page story treatment. It was his first solo screenplay credit since Close Encounters of the Third Kind (1977).[19] Spielberg remained close to Watson's treatment, but removed various sex scenes with Gigolo Joe. Pre-production was briefly halted during February 2000, because Spielberg pondered directing other projects, which were Harry Potter and the Philosopher's Stone, Minority Report and Memoirs of a Geisha.[16][20] The following month Spielberg announced that A.I. would be his next project, with Minority Report as a follow-up.[21] When he decided to fast track A.I., Spielberg brought Chris Baker back as concept artist.[15]

The original start date was July 10, 2000,[14] but filming was delayed until August.[22] Aside from a couple of weeks shooting on location in Oxbow Regional Park in Oregon, A.I. was shot entirely using sound stages at Warner Bros. Studios and the Spruce Goose Dome in Long Beach, south LA.[23] The Swinton house was constructed on Stage 16, while Stage 20 was used for Rouge City and other sets.[24][25] Spielberg copied Kubrick's obsessively secretive approach to filmmaking by refusing to give the complete script to cast and crew, banning press from the set, and making actors sign confidentiality agreements. Social robotics expert Cynthia Breazeal served as technical consultant during production.[14][26] Haley Joel Osment and Jude Law applied prosthetic makeup daily in an attempt to look shinier and robotic.[3] Costume designer Bob Ringwood (Batman, Troy) studied pedestrians on the Las Vegas Strip for his influence on the Rouge City extras.[27] Spielberg found post-production on A.I. difficult because he was simultaneously preparing to shoot Minority Report.[28]

The film's soundtrack was released by Warner Bros. Records in 2001. The original score was composed by John Williams and featured singers Lara Fabian on two songs and Josh Groban on one. The film's score also had a limited release as an official "For your consideration Academy Promo", as well as a complete score issue by La-La Land Records in 2015. The band Ministry appears in the film playing the song "What About Us?" (but the song does not appear on the official soundtrack album).

Warner Bros. used an alternate reality game titled The Beast to promote the film. Over forty websites were created by Atomic Pictures in New York City (kept online at Cloudmakers.org) including the website for Cybertronics Corp. There were to be a series of video games for the Xbox video game console that followed the storyline of The Beast, but they went undeveloped. To avoid audiences mistaking A.I. for a family film, no action figures were created, although Hasbro released a talking Teddy following the film's release in June 2001.[14]

In November 2000, during production, a video-only webcam (dubbed the "Bagel Cam") was placed in the craft services truck on the film's set at the Queen Mary Dome in Long Beach, California. Steven Spielberg, producer Kathleen Kennedy and various other production personnel visited the camera and interacted with fans over the course of three days.[29][30]

A.I. had its premiere at the Venice Film Festival in 2001.[31]

The film opened in 3,242 theaters in the United States on June 29, 2001, earning $29,352,630 during its opening weekend. A.I went on to gross $78.62 million in US totals as well as $157.31 million in foreign countries, coming to a worldwide total of $235.93 million.[32]

The film received generally positive reviews. Based on 190 reviews collected by Rotten Tomatoes, 73% of the critics gave the film positive notices with a score of 6.6 out of 10. The website described the critical consensus perceiving the film as "a curious, not always seamless, amalgamation of Kubrick's chilly bleakness and Spielberg's warm-hearted optimism. [The film] is, in a word, fascinating."[33] By comparison, Metacritic collected an average score of 65, based on 32 reviews, which is considered favorable.[34]

Producer Jan Harlan stated that Kubrick "would have applauded" the final film, while Kubrick's widow Christiane also enjoyed A.I.[35] Brian Aldiss admired the film as well: "I thought what an inventive, intriguing, ingenious, involving film this was. There are flaws in it and I suppose I might have a personal quibble but it's so long since I wrote it." Of the film's ending, he wondered how it might have been had Kubrick directed the film: "That is one of the 'ifs' of film history - at least the ending indicates Spielberg adding some sugar to Kubrick's wine. The actual ending is overly sympathetic and moreover rather overt
ly engineered by a plot device that does not really bear credence. But it's a brilliant piece of film and of course it's a phenomenon because it contains the energies and talents of two brilliant filmmakers."[36]Richard Corliss heavily praised Spielberg's direction, as well as the cast and visual effects.[37]Roger Ebert awarded the film 3 out of 4 stars, saying that it was "Audacious, technically masterful, challenging, sometimes moving [and] ceaselessly watchable. [But] the movie's conclusion is too facile and sentimental, given what has gone before. It has mastered the artificial, but not the intelligence."[38] On July 8, 2011, Ebert reviewed A.I. again when he added it to his "Great Movies" pantheon.[39]Leonard Maltin gives the film a not-so-positive review in his Movie Guide, giving it two stars out of four, writing: "[The] intriguing story draws us in, thanks in part to Osment's exceptional performance, but takes several wrong turns; ultimately, it just doesn't work. Spielberg rewrote the adaptation Stanley Kubrick commissioned of the Brian Aldiss short story 'Super Toys Last All Summer Long'; [the] result is a curious and uncomfortable hybrid of Kubrick and Spielberg sensibilities." However, he calls John Williams' music score "striking". Jonathan Rosenbaum compared A.I. to Solaris (1972), and praised both "Kubrick for proposing that Spielberg direct the project and Spielberg for doing his utmost to respect Kubrick's intentions while making it a profoundly personal work."[40] Film critic Armond White, of the New York Press, praised the film noting that "each part of Davids journey through carnal and sexual universes into the final eschatological devastation becomes as profoundly philosophical and contemplative as anything by cinemas most thoughtful, speculative artists Borzage, Ozu, Demy, Tarkovsky."[41] Filmmaker Billy Wilder hailed A.I. as "the most underrated film of the past few years."[42] When British filmmaker Ken Russell saw the film, he wept during the ending.[43]

Mick LaSalle gave a largely negative review. "A.I. exhibits all its creators' bad traits and none of the good. So we end up with the structureless, meandering, slow-motion endlessness of Kubrick combined with the fuzzy, cuddly mindlessness of Spielberg." Dubbing it Spielberg's "first boring movie", LaSalle also believed the robots at the end of the film were aliens, and compared Gigolo Joe to the "useless" Jar Jar Binks, yet praised Robin Williams for his portrayal of a futuristic Albert Einstein.[44][not in citation given]Peter Travers gave a mixed review, concluding "Spielberg cannot live up to Kubrick's darker side of the future." But he still put the film on his top ten list that year for best movies.[45] David Denby in The New Yorker criticized A.I. for not adhering closely to his concept of the Pinocchio character. Spielberg responded to some of the criticisms of the film, stating that many of the "so called sentimental" elements of A.I., including the ending, were in fact Kubrick's and the darker elements were his own.[46] However, Sara Maitland, who worked on the project with Kubrick in the 1990s, claimed that one of the reasons Kubrick never started production on A.I. was because he had a hard time making the ending work.[47]James Berardinelli found the film "consistently involving, with moments of near-brilliance, but far from a masterpiece. In fact, as the long-awaited 'collaboration' of Kubrick and Spielberg, it ranks as something of a disappointment." Of the film's highly debated finale, he claimed, "There is no doubt that the concluding 30 minutes are all Spielberg; the outstanding question is where Kubrick's vision left off and Spielberg's began."[48]

Screenwriter Ian Watson has speculated, "Worldwide, A.I. was very successful (and the 4th highest earner of the year) but it didn't do quite so well in America, because the film, so I'm told, was too poetical and intellectual in general for American tastes. Plus, quite a few critics in America misunderstood the film, thinking for instance that the Giacometti-style beings in the final 20 minutes were aliens (whereas they were robots of the future who had evolved themselves from the robots in the earlier part of the film) and also thinking that the final 20 minutes were a sentimental addition by Spielberg, whereas those scenes were exactly what I wrote for Stanley and exactly what he wanted, filmed faithfully by Spielberg."[49]

In 2002, Spielberg told film critic Joe Leydon that "People pretend to think they know Stanley Kubrick, and think they know me, when most of them don't know either of us". "And what's really funny about that is, all the parts of A.I. that people assume were Stanley's were mine. And all the parts of A.I. that people accuse me of sweetening and softening and sentimentalizing were all Stanley's. The teddy bear was Stanley's. The whole last 20 minutes of the movie was completely Stanley's. The whole first 35, 40 minutes of the film all the stuff in the house was word for word, from Stanley's screenplay. This was Stanley's vision." "Eighty percent of the critics got it all mixed up. But I could see why. Because, obviously, I've done a lot of movies where people have cried and have been sentimental. And I've been accused of sentimentalizing hard-core material. But in fact it was Stanley who did the sweetest parts of A.I., not me. I'm the guy who did the dark center of the movie, with the Flesh Fair and everything else. That's why he wanted me to make the movie in the first place. He said, 'This is much closer to your sensibilities than my own.'"[50]

Upon rewatching the film many years after its release, BBC film critic Mark Kermode apologized to Spielberg in an interview in January 2013 for "getting it wrong" on the film when he first viewed it in 2001. He now believes the film to be Spielberg's "enduring masterpiece".[51]

Visual effects supervisors Dennis Muren, Stan Winston, Michael Lantieri and Scott Farrar were nominated for the Academy Award for Best Visual Effects, while John Williams was nominated for Best Original Music Score.[52] Steven Spielberg, Jude Law and Williams received nominations at the 59th Golden Globe Awards.[53] The visual effects department was once again nominated at the 55th British Academy Film Awards.[54]A.I. was successful at the Saturn Awards. Spielberg (for his screenplay), the visual effects department, Williams and Haley Joel Osment (Performance by a Younger Actor) won in their respective categories. The film also won Best Science Fiction Film and for its DVD release. Frances O'Connor and Spielberg (as director) were also nominated.[55]

American Film Institute lists

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A.I. Artificial Intelligence - Wikipedia, the free ...

Founded in 1979, the Association for the Advancement of Artificial Intelligence (AAAI) (formerly the American Association for Artificial Intelligence) is a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. AAAI aims to promote research in, and responsible use of, artificial intelligence. AAAI also aims to increase public understanding of artificial intelligence, improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions. More

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Dr. Tom Dietterich, President of the Association for the Advancement of Artificial Intelligence and Distinguished Professor of Computer Science at Oregon State University, delves into the challenges of ensuring that artificial intelligence performs safely and properly in the face of programming errors, cyberattacks and other risks. He spoke at DARPA's Wait, What? A Future Technology Forum on Sept. 10, 2015.

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Association for the Advancement of Artificial Intelligence

The phrase Artificial Intelligence was first coined by John McCarthy four decades ago. One representative definition is pivoted around comparing intelligent machines with human beings. Another definition is concerned with the performance of machines which historically have been judged to lie within the domain of intelligence.

Yet none of these definitions have been universally accepted, probably because the reference of the word intelligence which is an immeasurable quantity. A better definition of artificial intelligence, and probably the most accurate would be: An artificial system capable of planning and executing the right task at the right time rationally. Or far simpler: a machine that can act rationally.

With all this a common questions arises:

Does rational thinking and acting include all characteristics of an intelligent system?

If so, how does it represent behavioral intelligence such as learning, perception and planning?

If we think a little, a system capable of reasoning would be a successful planner. Moreover, a system can act rationally only after acquiring knowledge from the real world. So the property of perception is a perquisite of building up knowledge from the real world.

With all this we may conclude that a machine that lacks of perception cannot learn, therefore cannot acquire knowledge.

To understand the practical meaning or artificial intelligence we must illustrate some common problems. All problems that are dealt with artificial intelligence solutions use the common term state.

A state represents the status of a solution at a given step during the problem solving procedure. The solution of a problem is a collection of states. The problem solving procedure or algorithm applies an operator to a state to get the next state. Then, it applies another operator to the resulting state to derive a new state.

The process of applying operators to each state is continued until a desired goal is achieved.

Example : Consider a 4-puzzle problem, where in a 4-cell board there are 3 cells filled with digits and 1 blank cell. The initial state of the game represents a particular orientation of the digits in the cells and the final state to be achieved is another orientation supplied to the game player. The problem of the game is to reach from the given initial state to the goal (final) state, if possible, with a minimum of moves. Let the initial and the final state be as shown in figures 1(a) and (b) respectively.

We now define two operations, blank-up (BU) / blank-down (BD) and blank-left (BL) / blank-right (BR), and the state-space (tree) for the problem is presented below using these operators. The algorithm for the above kind of problems is straightforward. It consists of three steps, described by steps 1, 2(a) and 2(b) below.

Algorithm for solving state-space problems

Begin

It is clear that the main trick in solving problems by the state-space approach is to determine the set of operators and to use it at appropriate states of the problem.

Researchers in artificial intelligence have segregated the AI problems from the non-AI problems. Generally, problems, for which straightforward mathematical / logical algorithms are not readily available and which can be solved by intuitive approach only, are called AI problems.

The 4-puzzle problem, for instance, is an ideal AI Problem. There is no formal algorithm for its realization, i.e., given a starting and a goal state, one cannot say prior to execution of the tasks the sequence of steps required to get the goal from the starting state. Such problems are called the ideal AI problems.

The well known water-jug problem, the Traveling Salesperson Problem (TSP), and the n-Queen problem are typical examples of the classical AI problems.

Among the non-classical AI problems, the diagnosis problems and the pattern classification problem need special mention. For solving an AI problem, one may employ both artificial intelligence and non-AI algorithms. An obvious question is: what is an AI algorithm?

Formally speaking, an artificial intelligence algorithm generally means a non-conventional intuitive approach for problem solving. The key to artificial intelligence approach is intelligent search and matching. In an intelligent search problem / sub-problem, given a goal (or starting) state, one has to reach that state from one or more known starting (or goal) states.

For example, consider the 4-puzzle problem, where the goal state is known and one has to identify the moves for reaching the goal from a pre-defined starting state. Now, the less number of states one generates for reaching the goal, the better. That is the AI algorithm.

The question that then naturally arises is: how to control the generation of states?

This can be achieved by suitably designing control strategies, which would filter a few states only from a large number of legal states that could be generated from a given starting / intermediate state.

As an example, consider the problem of proving a trigonometric identity that children are used to doing during their schooldays. What would they do at the beginning? They would start with one side of the identity, and attempt to apply a number of formula there to find the possible resulting derivations.

But they wont really apply all the formula there. Rather, they identify the right candidate formula that fits there, such that the other side of the identity that seems to be closer in some sense (outlook). Ultimately, when the decision regarding the selection of the formula is over, they apply it to one side (say the L.H.S) of the identity and derive the new state.

Therefore, they continue the process and go on generating new intermediate states until the R.H.S (goal) is reached. But do they always select the right candidate formula at a given state? From our experience, we know the answer is not always. But what would we do if we find that after generation of a few states, the resulting expression seems to be far away from the R.H.S of the identity.

Perhaps we would prefer to move to some old state, which is more promising, i.e., closer to the R.H.S of the identity. The above line of thinking has been realized in many intelligent search problems of AI.

Some of these well-known search algorithms are:

a) Generate and Test Approach : This approach concerns the generation of the state-space from a known starting state (root) of the problem and continues expanding the reasoning space until the goal node or the terminal state is reached.

In fact after generating each and every state, the generated node is compared with the known goal state. When the goal is found, the algorithm terminates. In case there exist multiple paths leading to the goal, then the path having the smallest distance from the root is preferred. The basic strategy used in this search is only generation of states and their testing for goals but it does not allow filtering of states.

(b) Hill Climbing Approach : Under this approach, one has to first generate a starting state and measure the total cost for reaching the goal from the given starting state. Let this cost be f. While f = a predefined utility value and the goal is not reached, new nodes are generated as children of the current node. However, in case all neighborhood nodes (states) yield an identical value of f and the goal is not included in the set of these nodes, the search algorithm is trapped at a hillock or local extreme.

One way to
overcome this problem is to select randomly a new starting state and then continue the above search process. While proving trigonometric identities, we often use Hill Climbing, perhaps unknowingly.

(c) Heuristic Search: Classically heuristics means rule of thumb. In heuristic search, we generally use one or more heuristic functions to determine the better candidate states among a set of legal states that could be generated from a known state.

The heuristic function, in other words, measures the fitness of the candidate states. The better the selection of the states, the fewer will be the number of intermediate states for reaching the goal.

However, the most difficult task in heuristic search problems is the selection of the heuristic functions. One has to select them intuitively, so that in most cases hopefully it would be able to prune the search space correctly.

(d) Means and Ends Analysis: This method of search attempts to reduce the gap between the current state and the goal state. One simple way to explore this method is to measure the distance between the current state and the goal, and then apply an operator to the current state, so that the distance between the resulting state and the goal is reduced. In many mathematical theorem- proving processes, we use Means and Ends Analysis.

The subject of artificial intelligence spans a wide horizon. It deals with various kinds of knowledge representation schemes, different techniques of intelligent search, various methods for resolving uncertainty of data and knowledge, different schemes for automated machine learning and many others.

Among the application areas of AI, we have Expert systems, Game-playing, and Theorem-proving, Natural language processing, Image recognition, Robotics and many others. The subject of artificial intelligence has been enriched with a wide discipline of knowledge from Philosophy, Psychology, Cognitive Science, Computer Science, Mathematics and Engineering. Thus has the figure shows, they have been referred to as the parent disciplines of AI. An at-a-glance look at fig. also reveals the subject area of AI and its application areas. Fig.: AI, its parent disciplines and application areas.

The subject of artificial intelligence was originated with game-playing and theorem-proving programs and was gradually enriched with theories from a number of parent disciplines. As a young discipline of science, the significance of the topics covered under the subject changes considerably with time. At present, the topics which we find significant and worthwhile to understand the subject are outlined below: FigA: Pronunciation learning of a child from his mother.

Learning Systems: Among the subject areas covered under artificial intelligence, learning systems needs special mention. The concept of learning is illustrated here with reference to a natural problem of learning of pronunciation by a child from his mother (vide figA). The hearing system of the child receives the pronunciation of the character A and the voice system attempts to imitate it. The difference of the mothers and the childs pronunciation, hereafter called the error signal, is received by the childs learning system auditory nerve, and an actuation signal is generated by the learning system through a motor nerve for adjustment of the pronunciation of the child. The adaptation of the childs voice system is continued until the amplitude of the error signal is insignificantly low. Each time the voice system passes through an adaptation cycle, the resulting tongue position of the child for speaking A is saved by the learning process. The learning problem discussed above is an example of the well-known parametric learning, where the adaptive learning process adjusts the parameters of the childs voice system autonomously to keep its response close enough to the sample training pattern. The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. Besides this type, the other common learning methods, which we do unknowingly, are inductive and analogy-based learning. In inductive learning, the learner makes generalizations from examples. For instance, noting that cuckoo flies, parrot flies and sparrow flies, the learner generalizes that birds fly. On the other hand, in analogy-based learning, the learner, for example, learns the motion of electrons in an atom analogously from his knowledge of planetary motion in solar systems.

Knowledge Representation and Reasoning: In a reasoning problem, one has to reach a pre-defined goal state from one or more given initial states. So, the lesser the number of transitions for reaching the goal state, the higher the efficiency of the reasoning system. Increasing the efficiency of a reasoning system thus requires minimization of intermediate states, which indirectly calls for an organized and complete knowledge base. A complete and organized storehouse of knowledge needs minimum search to identify the appropriate knowledge at a given problem state and thus yields the right next state on the leading edge of the problem-solving process. Organization of knowledge, therefore, is of paramount importance in knowledge engineering. A variety of knowledge representation techniques are in use in Artificial Intelligence. Production rules, semantic nets, frames, filler and slots, and predicate logic are only a few to mention. The selection of a particular type of representational scheme of knowledge depends both on the nature of applications and the choice of users.

Planning: Another significant area of artificial intelligence is planning. The problems of reasoning and planning share many common issues, but have a basic difference that originates from their definitions. The reasoning problem is mainly concerned with the testing of the satisfiability of a goal from a given set of data and knowledge. The planning problem, on the other hand, deals with the determination of the methodology by which a successful goal can be achieved from the known initial states. Automated planning finds extensive applications in robotics and navigational problems, some of which will be discussed shortly.

Knowledge Acquisition: Acquisition (Elicitation) of knowledge is equally hard for machines as it is for human beings. It includes generation of new pieces of knowledge from given knowledge base, setting dynamic data structures for existing knowledge, learning knowledge from the environment and refinement of knowledge. Automated acquisition of knowledge by machine learning approach is an active area of current research in Artificial Intelligence. Intelligent Search: Search problems, which we generally encounter in Computer Science, are of a deterministic nature, i.e., the order of visiting the elements of the search space is known. For example, in depth first and breadth first search algorithms, one knows the sequence of visiting the nodes in a tree. However, search problems, which we will come across in AI, are non-deterministic and the order of visiting the elements in the search space is completely dependent on data sets. The diversity of the intelligent search algorithms will be discussed in detail later.

Logic Programming: For more than a century, mathematicians and logicians were used to designing various tools to represent logical statements by symbolic operators. One outgrowth of such attempts is propositional logic, which deals with a set of binary statements (propositions) connected by Boolean operators. The logic of propositions, which was gradual
ly enriched to handle more complex situations of the real world, is called predicate logic. One classical variety of predicate logic-based programs is Logic Program. PROLOG, which is an abbreviation for PROgramming in LOGic, is a typical language that supports logic programs. Logic Programming has recently been identified as one of the prime area of research in AI. The ultimate aim of this research is to extend the PROLOG compiler to handle spatio-temporal models and support a parallel programming environment. Building architecture for PROLOG machines was a hot topic of the last decade.

Soft Computing: Soft computing, according to Prof. Zadeh, is an emerging approach to computing, which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision . It, in general, is a collection of computing tools and techniques, shared by closely related disciplines that include fuzzy logic, artificial neural nets, genetic algorithms, belief calculus, and some aspects of machine learning like inductive logic programming. These tools are used independently as well as jointly depending on the type of the domain of applications.

Management of Imprecision and Uncertainty: Data and knowledgebases in many typical AI problems, such as reasoning and planning, are often contaminated with various forms of incompleteness. The incompleteness of data, hereafter called imprecision, generally appears in the database for i) lack of appropriate data, and ii) poor authenticity level of the sources. The incompleteness of knowledge, often referred to as uncertainty, originates in the knowledge base due to lack of certainty of the pieces of knowledge Reasoning in the presence of imprecision of data and uncertainty of knowledge is a complex problem. Various tools and techniques have been devised for reasoning under incomplete data and knowledge. Some of these techniques employ i) stochastic ii) fuzzy and iii) belief network models. In a stochastic reasoning model, the system can have transition from one given state to a number of states, such that the sum of the probability of transition to the next states from the given state is strictly unity. In a fuzzy reasoning system, on the other hand, the sum of the membership value of transition from the given state to the next state may be greater than or equal to one. The belief network model updates the stochastic / fuzzy belief assigned to the facts embedded in the network until a condition of equilibrium is reached, following which there would be no more change in beliefs. Recently, fuzzy tools and techniques have been applied in a specialized belief network, called a fuzzy Petri net, for handling both imprecision of data and uncertainty of knowledge by a unified approach.

Almost every branch of science and engineering currently shares the tools and techniques available in the domain of artificial intelligence. However, for the sake of the convenience of the readers, we mention here a few typical applications, where AI plays a significant and decisive role in engineering automation. Expert Systems: In this example, we illustrate the reasoning process involved in an expert system for a weather forecasting problem with special emphasis to its architecture. An expert system consists of a knowledge base, database and an inference engine for interpreting the database using the knowledge supplied in the knowledge base. The reasoning process of a typical illustrative expert system is described in Fig. PR 1 in Fig. represents i-th production rule. The inference engine attempts to match the antecedent clauses (IF parts) of the rules with the data stored in the database. When all the antecedent clauses of a rule are available in the database, the rule is fired, resulting in new inferences. The resulting inferences are added to the database for activating subsequent firing of other rules. In order to keep limited data in the database, a few rules that contain an explicit consequent (THEN) clause to delete specific data from the databases are employed in the knowledge base. On firing of such rules, the unwanted data clauses as suggested by the rule are deleted from the database. Here PR1 fires as both of its antecedent clauses are present in the database. On firing of PR1, the consequent clause it-will-rain will be added to the database for subsequent firing of PR2. Fig. Illustrative architecture of an expert system.

Image Understanding and Computer Vision: A digital image can be regarded as a two-dimensional array of pixels containing gray levels corresponding to the intensity of the reflected illumination received by a video camera. For interpretation of a scene, its image should be passed through three basic processes: low, medium and high level vision . Fig.: Basic steps in scene interpretation.

The importance of low level vision is to pre-process the image by filtering from noise. The medium level vision system deals with enhancement of details and segmentation (i.e., partitioning the image into objects of interest ). The high level vision system includes three steps: recognition of the objects from the segmented image, labeling of the image and interpretation of the scene. Most of the AI tools and techniques are required in high level vision systems. Recognition of objects from its image can be carried out through a process of pattern classification, which at present is realized by supervised learning algorithms. The interpretation process, on the other hand, requires knowledge-based computation.

Speech and Natural Language Understanding: Understanding of speech and natural languages is basically two class ical problems. In speech analysis, the main probl em is to separate the syllables of a spoken word and determine features like ampli tude, and fundamental and harmonic frequencies of each syllable. The words then could be ident ified from the extracted features by pattern class ification techniques. Recently, artificial neural networks have been employed to class ify words from their features. The probl em of understanding natural languages like English, on the other hand, includes syntactic and semantic interpretation of the words in a sentence, and sentences in a paragraph. The syntactic steps are required to analyze the sentences by its grammar and are similar with the steps of compilation. The semantic analysis, which is performed following the syntactic analysis, determines the meaning of the sentences from the association of the words and that of a paragraph from the closeness of the sentences. A robot capable of understanding speech in a natural language will be of immense importance, for it could execute any task verbally communicated to it. The phonetic typewriter, which prints the words pronounced by a person, is another recent invention where speech understanding is employed in a commercial application.

Scheduling: In a scheduling problem, one has to plan the time schedule of a set of events to improve the time efficiency of the solution. For instance in a class-routine scheduling problem, the teachers are allocated to different classrooms at different time slots, and we want most classrooms to be occupied most of the time. In a flowshop scheduling problem, a set of jobs J1 and J2 (say) are to be allocated to a set of machines M1, M2 and M3. (say). We assume that each job requires some operations to be done on all these machines in a fixed order say, M1, M2 and M3. Now, what should be the schedule of the jobs (J1-J2) or (J2 -J1), so that the completion time of both the jobs, called the make-span, is minimized? Let the processing time of jobs J1 and J2 on machines M1, M2 and M3 be (5, 8, 7) and (8, 2, 3) respe
ctively. The gantt charts in fig. (a) and (b) describe the make-spans for the schedule of jobs J1 J2 and J2 J1 respectively. It is clear from these figures that J1-J2 schedule requires less make-span and is thus preferred. Fig.: The Gantt charts for the flowshop scheduling problem with 2 jobs and 3 machines.

Flowshop scheduling problems are a NP complete problem and determination of optimal scheduling (for minimizing the make-span) thus requires an exponential order of time with respect to both machine-size and job-size. Finding a sub-optimal solution is thus preferred for such scheduling problems. Recently, artificial neural nets and genetic algorithms have been employed to solve this problem. The heuristic search, to be discussed shortly, has also been used for handling this problem.

Intelligent Control: In process control, the controller is designed from the known models of the process and the required control objective. When the dynamics of the plant is not completely known, the existing techniques for controller design no longer remain valid. Rule-based control is appropriate in such situations. In a rule-based control system, the controller is realized by a set of production rules intuitively set by an expert control engineer. The antecedent (premise) part of the rules in a rule-based system is searched against the dynamic response of the plant parameters. The rule whose antecedent part matches with the plant response is selected and fired. When more than one rule is firable, the controller resolves the conflict by a set of strategies. On the other hand, there exist situations when the antecedent part of no rules exactly matches with the plant responses. Such situations are handled with fuzzy logic, which is capable of matching the antecedent parts of rules partially/ approximately with the dynamic plant responses. Fuzzy control has been successfully used in many industrial plants. One typical application is the power control in a nuclear reactor. Besides design of the controller, the other issue in process control is to design a plant (process) estimator, which attempts to follow the response of the actual plant, when both the plant and the estimator are jointly excited by a common input signal. The fuzzy and artificial neural network-based learning techniques have recently been identified as new tools for plant estimation.

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Artificial Intelligence | Neuro AI

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.

Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with few sub-problems such as facial, object and speech recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? - Definition from ...